Cell-free Dna For Assessing And/or Treating Cancer

Abstract

This document relates to methods and materials for assessed, monitored, and/or treated mammals (e.g., humans) having cancer. For example, methods and materials for identifying a mammal as having cancer (e.g., a localized cancer) are provided. For example, methods and materials for assessing, monitoring, and/or treating a mammal having cancer are provided.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Patent Application Ser. No. 62/673,516, filed on May 18, 2018, and claims the benefit of U.S. Patent Application Ser. No. 62/795,900, filed on Jan. 23, 2019. The disclosure of the prior applications are considered part of (and are incorporated by reference in) the disclosure of this application.

BACKGROUND

I. Technical Field

[0003] This document relates to methods and materials for assessing and/or treating mammals (e.g., humans) having cancer. For example, this document provides methods and materials for identifying a mammal as having cancer (e.g., a localized cancer). For example, this document provides methods and materials for monitoring and/or treating a mammal having cancer.

2. Background Information

[0004] Much of the morbidity and mortality of human cancers world-wide is a result of the late diagnosis of these diseases, where treatments are less effective (Torre et al., 2015 CA Cancer J Clin 65:87; and World Health Organization, 2017 Guide to Cancer Early Diagnosis). Unfortunately, clinically proven biomarkers that can be used to broadly diagnose and treat patients are not widely available (Mazzucchelli, 2000 Advances in clinical pathology 4:111; Ruibal Morell, 1992 The International journal of biological markers 7:160; Galli et al., 2013 Clinical chemistry and laboratory medicine 51:1369; Sikaris, 2011 Heart, lung &circulation 20:634; Lin et al., 2016 in Screening for Colorectal Cancer: A Systematic Review for the U.S. Preventive Services Task Force. (Rockville, Md.); Wanebo et al., 1978 N Engl J Med 299:448; and Zauber, 2015 Dig Dis Sci 60:681).

SUMMARY

[0005] Recent analyses of cell-free DNA suggests that such approaches may provide new avenues for early diagnosis (Phallen et al., 2017 Sci Transl Med 9; Cohen et al., 2018 Science 359:926; Alix-Panabieres et al., 2016 Cancer discovery 6:479; Siravegna et al., 2017 Nature reviews. Clinical oncology 14:531; Haber et al., 2014 Cancer discovery 4:650; Husain et al., 2017 JAMA 318:1272; and Wan et al., 2017 Nat Rev Cancer 17:223).

[0006] This document provides methods and materials for determining a cell free DNA (cfDNA) fragmentation profile in a mammal (e.g., in a sample obtained from a mammal). In some cases, determining a cfDNA fragmentation profile in a mammal can be used for identifying a mammal as having cancer. For example, cfDNA fragments obtained from a mammal (e.g., from a sample obtained from a mammal) can be subjected to low coverage whole-genome sequencing, and the sequenced fragments can be mapped to the genome (e.g., in non-overlapping windows) and assessed to determine a cfDNA fragmentation profile. This document also provides methods and materials for assessing and/or treating mammals (e.g., humans) having, or suspected of having, cancer. In some cases, this document provides methods and materials for identifying a mammal as having cancer. For example, a sample (e.g., a blood sample) obtained from a mammal can be assessed to determine if the mammal has cancer based, at least in part, on the cfDNA fragmentation profile. In some cases, this document provides methods and materials for monitoring and/or treating a mammal having cancer. For example, one or more cancer treatments can be administered to a mammal identified as having cancer (e.g., based, at least in part, on a cfDNA fragmentation profile) to treat the mammal.

[0007] Described herein is a non-invasive method for the early detection and localization of cancer. cfDNA in the blood can provide a non-invasive diagnostic avenue for patients with cancer. As demonstrated herein, DNA Evaluation of Fragments for early Interception (DELFI) was developed and used to evaluate genome-wide fragmentation patterns of cfDNA of 236 patients with breast, colorectal, lung, ovarian, pancreatic, gastric, or bile duct cancers as well as 245 healthy individuals. These analyses revealed that cfDNA profiles of healthy individuals reflected nucleosomal fragmentation patterns of white blood cells, while patients with cancer had altered fragmentation profiles. DELFI had sensitivities of detection ranging from 57% to >99% among the seven cancer types at 98% specificity and identified the tissue of origin of the cancers to a limited number of sites in 75% of cases. Assessing cfDNA (e.g., using DELFI) can provide a screening approach for early detection of cancer, which can increase the chance for successful treatment of a patient having cancer. Assessing cfDNA (e.g., using DELFI) can also provide an approach for monitoring cancer, which can increase the chance for successful treatment and improved outcome of a patient having cancer. In addition, a cfDNA fragmentation profile can be obtained from limited amounts of cfDNA and using inexpensive reagents and/or instruments.

[0008] In general, one aspect of this document features methods for determining a cfDNA fragmentation profile of a mammal. The methods can include, or consist essentially of, processing cfDNA fragments obtained from a sample obtained from the mammal into sequencing libraries, subjecting the sequencing libraries to whole genome sequencing (e.g., low-coverage whole genome sequencing) to obtain sequenced fragments, mapping the sequenced fragments to a genome to obtain windows of mapped sequences, and analyzing the windows of mapped sequences to determine cfDNA fragment lengths. The mapped sequences can include tens to thousands of windows. The windows of mapped sequences can be non-overlapping windows. The windows of mapped sequences can each include about 5 million base pairs. The cfDNA fragmentation profile can be determined within each window. The cfDNA fragmentation profile can include a median fragment size. The cfDNA fragmentation profile can include a fragment size distribution. The cfDNA fragmentation profile can include a ratio of small cfDNA fragments to large cfDNA fragments in the windows of mapped sequences. The cfDNA fragmentation profile can be over the whole genome. The cfDNA fragmentation profile can be over a subgenomic interval (e.g., an interval in a portion of a chromosome).

[0009] In another aspect, this document features methods for identifying a mammal as having cancer. The methods can include, or consist essentially of, determining a cfDNA fragmentation profile in a sample obtained from a mammal, comparing the cfDNA fragmentation profile to a reference cfDNA fragmentation profile, and identifying the mammal as having cancer when the cfDNA fragmentation profile in the sample obtained from the mammal is different from the reference cfDNA fragmentation profile. The reference cfDNA fragmentation profile can be a cfDNA fragmentation profile of a healthy mammal. The reference cfDNA fragmentation profile can be generated by determining a cfDNA fragmentation profile in a sample obtained from the healthy mammal. The reference DNA fragmentation pattern can be a reference nucleosome cfDNA fragmentation profile. The cfDNA fragmentation profiles can include a median fragment size, and a median fragment size of the cfDNA fragmentation profile can be shorter than a median fragment size of the reference cfDNA fragmentation profile. The cfDNA fragmentation profiles can include a fragment size distribution, and a fragment size distribution of the cfDNA fragmentation profile can differ by at least 10 nucleotides as compared to a fragment size distribution of the reference cfDNA fragmentation profile. The cfDNA fragmentation profiles can include position dependent differences in fragmentation patterns, including a ratio of small cfDNA fragments to large cfDNA fragments, where a small cfDNA fragment can be 100 base pairs (bp) to 150 bp in length and a large cfDNA fragments can be 151 bp to 220 bp in length, and where a correlation of fragment ratios in the cfDNA fragmentation profile can be lower than a correlation of fragment ratios of the reference cfDNA fragmentation profile. The cfDNA fragmentation profiles can include sequence coverage of small cfDNA fragments, large cfDNA fragments, or of both small and large cfDNA fragments, across the genome. The cancer can be colorectal cancer, lung cancer, breast cancer, bile duct cancer, pancreatic cancer, gastric cancer, or ovarian cancer. The step of comparing can include comparing the cfDNA fragmentation profile to a reference cfDNA fragmentation profile in windows across the whole genome. The step of comparing can include comparing the cfDNA fragmentation profile to a reference cfDNA fragmentation profile over a subgenomic interval (e.g., an interval in a portion of a chromosome). The mammal can have been previously administered a cancer treatment to treat the cancer. The cancer treatment can be surgery, adjuvant chemotherapy, neoadjuvant chemotherapy, radiation therapy, hormone therapy, cytotoxic therapy, immunotherapy, adoptive T cell therapy, targeted therapy, or any combinations thereof. The method also can include administering to the mammal a cancer treatment (e.g., surgery, adjuvant chemotherapy, neoadjuvant chemotherapy, radiation therapy, hormone therapy, cytotoxic therapy, immunotherapy, adoptive T cell therapy, targeted therapy, or any combinations thereof). The mammal can be monitored for the presence of cancer after administration of the cancer treatment.

[0010] In another aspect, this document features methods for treating a mammal having cancer. The methods can include, or consist essentially of, identifying the mammal as having cancer, where the identifying includes determining a cfDNA fragmentation profile in a sample obtained from the mammal, comparing the cfDNA fragmentation profile to a reference cfDNA fragmentation profile, and identifying the mammal as having cancer when the cfDNA fragmentation profile obtained from the mammal is different from the reference cfDNA fragmentation profile; and administering a cancer treatment to the mammal. The mammal can be a human. The cancer can be colorectal cancer, lung cancer, breast cancer, gastric cancers, pancreatic cancers, bile duct cancers, or ovarian cancer. The cancer treatment can be surgery, adjuvant chemotherapy, neoadjuvant chemotherapy, radiation therapy, hormone therapy, cytotoxic therapy, immunotherapy, adoptive T cell therapy, targeted therapy, or combinations thereof. The reference cfDNA fragmentation profile can be a cfDNA fragmentation profile of a healthy mammal. The reference cfDNA fragmentation profile can be generated by determining a cfDNA fragmentation profile in a sample obtained from a healthy mammal. The reference DNA fragmentation pattern can be a reference nucleosome cfDNA fragmentation profile. The cfDNA fragmentation profile can include a median fragment size, where a median fragment size of the cfDNA fragmentation profile is shorter than a median fragment size of the reference cfDNA fragmentation profile. The cfDNA fragmentation profile can include a fragment size distribution, where a fragment size distribution of the cfDNA fragmentation profile differs by at least 10 nucleotides as compared to a fragment size distribution of the reference cfDNA fragmentation profile. The cfDNA fragmentation profile can include a ratio of small cfDNA fragments to large cfDNA fragments in the windows of mapped sequences, where a small cfDNA fragment is 100 bp to 150 bp in length, where a large cfDNA fragments is 151 bp to 220 bp in length, and where a correlation of fragment ratios in the cfDNA fragmentation profile is lower than a correlation of fragment ratios of the reference cfDNA fragmentation profile. The cfDNA fragmentation profile can include the sequence coverage of small cfDNA fragments in windows across the genome. The cfDNA fragmentation profile can include the sequence coverage of large cfDNA fragments in windows across the genome. The cfDNA fragmentation profile can include the sequence coverage of small and large cfDNA fragments in windows across the genome. The step of comparing can include comparing the cfDNA fragmentation profile to a reference cfDNA fragmentation profile over the whole genome. The step of comparing can include comparing the cfDNA fragmentation profile to a reference cfDNA fragmentation profile over a subgenomic interval. The mammal can have previously been administered a cancer treatment to treat the cancer. The cancer treatment can be surgery, adjuvant chemotherapy, neoadjuvant chemotherapy, radiation therapy, hormone therapy, cytotoxic therapy, immunotherapy, adoptive T cell therapy, targeted therapy, or combinations thereof. The method also can include monitoring the mammal for the presence of cancer after administration of the cancer treatment.

[0011] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. Although methods and materials similar or equivalent to those described herein can be used to practice the invention, suitable methods and materials are described below. All publications, patent applications, patents, and other references mentioned herein are incorporated by reference in their entirety. In case of conflict, the present specification, including definitions, will control. In addition, the materials, methods, and examples are illustrative only and not intended to be limiting.

[0012] The details of one or more embodiments of the invention are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of the invention will be apparent from the description and drawings, and from the claims.

DESCRIPTION OF THE DRAWINGS

[0013] FIG. 1. Schematic of an exemplary DELFI approach. Blood is collected from a cohort of healthy individuals and patients with cancer. Nucleosome protected cfDNA is extracted from the plasma fraction, processed into sequencing libraries, examined through whole genome sequencing, mapped to the genome, and analyzed to determine cfDNA fragment profiles in different windows across the genome. Machine learning approaches are used to categorize individuals as healthy or as having cancer and to identify the tumor tissue of origin using genome-wide cfDNA fragmentation patterns.

[0014] FIG. 2. Simulations of non-invasive cancer detection based on number of alterations analyzed and tumor-derived cfDNA fragment distributions. Monte Carlo simulations were performed using different numbers of tumor-specific alterations to evaluate the probability of detecting cancer alterations in cfDNA at the indicated fraction of tumor-derived molecules. The simulations were performed assuming an average of 2000 genome equivalents of cfDNA and the requirement of five or more observations of any alteration. These analyses indicate that increasing the number of tumor-specific alterations improves the sensitivity of detection of circulating tumor DNA.

[0015] FIG. 3. Tumor-derived cfDNA fragment distributions. Cumulative density functions of cfDNA fragment lengths of 42 loci containing tumor-specific alterations from 30 patients with breast, colorectal, lung, or ovarian cancer are shown with 95% confidence bands (blue). Lengths of mutant cfDNA fragments were significantly different in size compared to wild-type cfDNA fragments (red) at these loci.

[0016] FIGS. 4A and 4B. Tumor-derived cfDNA GC content and fragment length. A, GC content was similar for mutated and non-mutated fragments. B, GC content was not correlated to fragment length.

[0017] FIG. 5. Germline cfDNA fragment distributions. Cumulative density functions of fragment lengths of 44 loci containing germline alterations (non-tumor derived) from 38 patients with breast, colorectal, lung, or ovarian cancer are shown with 95% confidence bands. Fragments with germline mutations (blue) were comparable in length to wild-type cfDNA fragment lengths (red).

[0018] FIG. 6. Hematopoietic cfDNA fragment distributions. Cumulative density functions of fragment lengths of 41 loci containing hematopoietic alterations (non-tumor derived) from 28 patients with breast, colorectal, lung, or ovarian cancer are shown with 95% confidence bands. After correction for multiple testing, there were no significant differences (.alpha.=0.05) in the size distributions of mutated hematopoietic ctDNA fragments (blue) and wild-type cfDNA fragments (red).

[0019] FIGS. 7A-7F. cfDNA fragmentation profiles in healthy individuals and patients with cancer. A, Genome-wide cfDNA fragmentation profiles (defined as the ratio of short to long fragments) from .about.9.times. whole genome sequencing are shown in 5 Mb bins for 30 healthy individuals (top) and 8 lung cancer patients (bottom). B, An analysis of healthy cfDNA (top), lung cancer cfDNA (middle), and healthy lymphocyte (bottom) fragmentation profiles and lymphocyte profiles from chromosome 1 at 1 Mb resolution. The healthy lymphocyte profiles were scaled with a standard deviation equal to that of the median healthy cfDNA profiles. Healthy cfDNA patterns closely mirrored those in healthy lymphocytes while lung cancer cfDNA profiles were more varied and differed from both healthy and lymphocyte profiles. C, Smoothed median distances between adjacent nucleosome centered at zero using 100 kb bins from healthy cfDNA (top) and nuclease-digested healthy lymphocytes (middle) are depicted together with the first eigenvector for the genome contact matrix obtained through previously reported Hi-C analyses of lymphoblastoid cells (bottom). Healthy cfDNA nucleosome distances closely mirrored those in nuclease-digested lymphocytes as well as those from lymphoblastoid Hi-C analyses. cfDNA fragmentation profiles from healthy individuals (n=30) had high correlations while patients with lung cancer had lower correlations to median fragmentation profiles of lymphocytes (D), healthy cfDNA (E), and lymphocyte nucleosome (F) distances.

[0020] FIG. 8. Density of cfDNA fragment lengths in healthy individuals and patients with lung cancer. cfDNA fragments lengths are shown for healthy individuals (n=30, gray) and patients with lung cancer (n=8, blue).

[0021] FIGS. 9A and 9B. Subsampling of whole genome sequence data for analysis of cfDNA fragmentation profiles. A, High coverage (9.times.) whole-genome sequencing data were subsampled to 2.times., 1.times., 0.5.times., 0.2.times., and 0.1.times. fold coverage. Mean centered genome-wide fragmentation profiles in 5 Mb bins for 30 healthy individuals and 8 patients with lung cancer are depicted for each subsampled fold coverage with median profiles shown in blue. B, Pearson correlation of subsampled profiles to initial profile at 9.times. coverage for healthy individuals and patients with lung cancer.

[0022] FIG. 10. cfDNA fragmentation profiles and sequence alterations during therapy. Detection and monitoring of cancer in serial blood draws from NSCLC patients (n=19) undergoing treatment with targeted tyrosine kinase inhibitors (black arrows) was performed using targeted sequencing (top) and genome-wide fragmentation profiles (bottom). For each case, the vertical axis of the lower panel displays -1 times the correlation of each sample to the median healthy cfDNA fragmentation profile. Error bars depict confidence intervals from binomial tests for mutant allele fractions and confidence intervals calculated using Fisher transformation for genome-wide fragmentation profiles. Although the approaches analyze different aspects of cfDNA (whole genome compared to specific alterations) the targeted sequencing and fragmentation profiles were similar for patients responding to therapy as well as those with stable or progressive disease. As fragmentation profiles reflect both genomic and epigenomic alterations, while mutant allele fractions only reflect individual mutations, mutant allele fractions alone may not reflect the absolute level of correlation of fragmentation profiles to healthy individuals.

[0023] FIGS. 11A-11C. cfDNA fragmentation profiles in healthy individuals and patients with cancer. A, Fragmentation profiles (bottom) in the context of tumor copy number changes (top) in a colorectal cancer patient where parallel analyses of tumor tissue were performed. The distribution of segment means and integer copy numbers are shown at top right in the indicated colors. Altered fragmentation profiles were present in regions of the genome that were copy neutral and were further affected in regions with copy number changes. B, GC adjusted fragmentation profiles from 1-2.times. whole genome sequencing for healthy individuals and patients with cancer are depicted per cancer type using 5 Mb windows. The median healthy profile is indicated in black and the 98% confidence band is shown in gray. For patients with cancer, individual profiles are colored based on their correlation to the healthy median. C, Windows are indicated in orange if more than 10% of the cancer samples had a fragment ratio more than three standard deviations from the median healthy fragment ratio. These analyses highlight the multitude of position dependent alterations across the genome in cfDNA of individuals with cancer.

[0024] FIGS. 12A and 12B. Profiles of cfDNA fragment lengths in copy neutral regions in healthy individuals and one patient with colorectal cancer. A, The fragmentation profile in 211 copy neutral windows in chromosomes 1-6 for 25 randomly selected healthy individuals (gray). For a patient with colorectal cancer (CGCRC291) with an estimated mutant allele fraction of 20%, the cancer fragment length profile was diluted to an approximate 10% tumor contribution (blue). A and B, While the marginal densities of the fragment profiles for the healthy samples and cancer patient show substantial overlap (A, right), the fragmentation profiles are different as can be seen visualization of the fragmentation profiles (A, left) and by the separation of the colorectal cancer patient from the healthy samples in a principal component analysis (B).

[0025] FIGS. 13A and 13B. Genome-wide GC correction of cfDNA fragments. To estimate and control for the effects of GC content on sequencing coverage, coverage in non-overlapping 100 kb genomic windows was calculated across the autosomes. For each window, the average GC of the aligned fragments was calculated. A, Loess smoothing of raw coverage (top row) for two randomly selected healthy subjects (CGPLH189 and CGPLH380) and two cancer patients (CGPLLU161 and CGPLBR24) with undetectable aneuploidy (PA score <2.35). After subtracting the average coverage predicted by the loess model, the residuals were resealed to the median autosomal coverage (bottom row). As fragment length may also result in coverage biases, this GC correction procedure was performed separately for short (.ltoreq.150 bp) and long (.gtoreq.151 bp) fragments. While the 100 kb bins on chromosome 19 (blue points) consistently have less coverage than predicted by the loess model, we did not implement a chromosome-specific correction as such an approach would remove the effects of chromosomal copy number on coverage. B, Overall, a limited correlation was found between short or long fragment coverage and GC content after correction among healthy subjects and cancer patients with a PA score <3.

[0026] FIG. 14. Schematic of machine learning model. Gradient tree boosting machine learning was used to examine whether cfDNA can be categorized as having characteristics of a cancer patient or healthy individual. The machine learning model included fragmentation size and coverage characteristics in windows throughout the genome, as well as chromosomal arm and mitochondrial DNA copy numbers. A 10-fold cross validation approach was employed in which each sample is randomly assigned to a fold and 9 of the folds (90% of the data) are used for training and one fold (10% of the data) is used for testing. The prediction accuracy from a single cross validation is an average over the 10 possible combinations of test and training sets. As this prediction accuracy can reflect bias from the initial randomization of patients, the entire procedure was repeat, including the randomization of patients to folds, 10 times. For all cases, feature selection and model estimation were performed on training data and were validated on test data and the test data were never used for feature selection. Ultimately, a DELFI score was obtained that could be used to classify individuals as likely healthy or having cancer.

[0027] FIG. 15. Distribution of AUCs across the repeated 10-fold cross-validation. The 25.sup.th, 50.sup.th, and 75.sup.th percentiles of the 100 AUCs for the cohort of 215 healthy individuals and 208 patients with cancer are indicated by dashed lines.

[0028] FIGS. 16A and 16B. Whole-genome analyses of chromosomal arm copy number changes and mitochondrial genome representation. A, Z scores for each autosome arm are depicted for healthy individuals (n=215) and patients with cancer (n=208). The vertical axis depicts normal copy at zero with positive and negative values indicating arm gains and losses, respectively. Z scores greater than 50 or less than -50 are thresholded at the indicated values. B, The fraction of reads mapping to the mitochondrial genome is depicted for healthy individuals and patients with cancer.

[0029] FIGS. 17A and 17B. Detection of cancer using DELFI. A, Receiver operator characteristics for detection of cancer using cfDNA fragmentation profiles and other genome-wide features in a machine learning approach are depicted for a cohort of 215 healthy individuals and 208 patients with cancer (DELFI, AUC=0.94), with .gtoreq.95% specificity shaded in blue. Machine learning analyses of chromosomal arm copy number (Chr copy number (ML)), and mitochondrial genome copy number (mtDNA), are shown in the indicated colors. B, Analyses of individual cancers types using the DELFI-combined approach had AUCs ranging from 0.86 to >0.99.

[0030] FIG. 18. DELFI detection of cancer by stage. Receiver operator characteristics for detection of cancer using cfDNA fragmentation profiles and other genome-wide features in a machine learning approach are depicted for a cohort of 215 healthy individuals and each stage of 208 patients with cancer with >95% specificity shaded in blue.

[0031] FIG. 19. DELFI tissue of origin prediction. Receiver operator characteristics for DELFI tissue prediction of bile duct, breast, colorectal, gastric, lung, ovarian, and pancreatic cancers are depicted. In order to increase sample sizes within cancer type classes, cases detected with a 90% specificity were included, and the lung cancer cohort was supplemented with the addition of baseline cfDNA data from 18 lung cancer patients with prior treatment (see, e.g., Shen et al., 2018 Nature, 563:579-583).

[0032] FIG. 20. Detection of cancer using DELFI and mutation-based cfDNA approaches. DELFI (green) and targeted sequencing for mutation identification (blue) were performed independently in a cohort of 126 patients with breast, bile duct, colorectal, gastric, lung, or ovarian cancers. The number of individuals detected by each approach and in combination are indicated for DELFI detection with a specificity of 98%, targeted sequencing specificity at >99%, and a combined specificity of 98%. ND indicates not detected.

DETAILED DESCRIPTION

[0033] This document provides methods and materials for determining a cfDNA fragmentation profile in a mammal (e.g., in a sample obtained from a mammal). As used herein, the terms "fragmentation profile," "position dependent differences in fragmentation patterns," and "differences in fragment size and coverage in a position dependent manner across the genome" are equivalent and can be used interchangeably. In some cases, determining a cfDNA fragmentation profile in a mammal can be used for identifying a mammal as having cancer. For example, cfDNA fragments obtained from a mammal (e.g., from a sample obtained from a mammal) can be subjected to low coverage whole-genome sequencing, and the sequenced fragments can be mapped to the genome (e.g., in non-overlapping windows) and assessed to determine a cfDNA fragmentation profile. As described herein, a cfDNA fragmentation profile of a mammal having cancer is more heterogeneous (e.g., in fragment lengths) than a cfDNA fragmentation profile of a healthy mammal (e.g., a mammal not having cancer). As such, this document also provides methods and materials for assessing, monitoring, and/or treating mammals (e.g., humans) having, or suspected of having, cancer. In some cases, this document provides methods and materials for identifying a mammal as having cancer. For example, a sample (e.g., a blood sample) obtained from a mammal can be assessed to determine the presence and, optionally, the tissue of origin of the cancer in the mammal based, at least in part, on the cfDNA fragmentation profile of the mammal. In some cases, this document provides methods and materials for monitoring a mammal as having cancer. For example, a sample (e.g., a blood sample) obtained from a mammal can be assessed to determine the presence of the cancer in the mammal based, at least in part, on the cfDNA fragmentation profile of the mammal. In some cases, this document provides methods and materials for identifying a mammal as having cancer, and administering one or more cancer treatments to the mammal to treat the mammal. For example, a sample (e.g., a blood sample) obtained from a mammal can be assessed to determine if the mammal has cancer based, at least in part, on the cfDNA fragmentation profile of the mammal, and one or more cancer treatments can be administered to the mammal.

[0034] A cfDNA fragmentation profile can include one or more cfDNA fragmentation patterns. A cfDNA fragmentation pattern can include any appropriate cfDNA fragmentation pattern. Examples of cfDNA fragmentation patterns include, without limitation, median fragment size, fragment size distribution, ratio of small cfDNA fragments to large cfDNA fragments, and the coverage of cfDNA fragments. In some cases, a cfDNA fragmentation pattern includes two or more (e.g., two, three, or four) of median fragment size, fragment size distribution, ratio of small cfDNA fragments to large cfDNA fragments, and the coverage of cfDNA fragments. In some cases, cfDNA fragmentation profile can be a genome-wide cfDNA profile (e.g., a genome-wide cfDNA profile in windows across the genome). In some cases, cfDNA fragmentation profile can be a targeted region profile. A targeted region can be any appropriate portion of the genome (e.g., a chromosomal region). Examples of chromosomal regions for which a cfDNA fragmentation profile can be determined as described herein include, without limitation, a portion of a chromosome (e.g., a portion of 2q, 4p, 5p, 6q, 7p, 8q, 9q, 10q, 11q, 12q, and/or 14q) and a chromosomal arm (e.g., a chromosomal arm of 8q, 13q, 11q, and/or 3p). In some cases, a cfDNA fragmentation profile can include two or more targeted region profiles.

[0035] In some cases, a cfDNA fragmentation profile can be used to identify changes (e.g., alterations) in cfDNA fragment lengths. An alteration can be a genome-wide alteration or an alteration in one or more targeted regions/loci. A target region can be any region containing one or more cancer-specific alterations. Examples of cancer-specific alterations, and their chromosomal locations, include, without limitation, those shown in Table 3 (Appendix C) and those shown in Table 6 (Appendix F). In some cases, a cfDNA fragmentation profile can be used to identify (e.g., simultaneously identify) from about 10 alterations to about 500 alterations (e.g., from about 25 to about 500, from about 50 to about 500, from about 100 to about 500, from about 200 to about 500, from about 300 to about 500, from about 10 to about 400, from about 10 to about 300, from about 10 to about 200, from about 10 to about 100, from about 10 to about 50, from about 20 to about 400, from about 30 to about 300, from about 40 to about 200, from about 50 to about 100, from about 20 to about 100, from about 25 to about 75, from about 50 to about 250, or from about 100 to about 200, alterations).

[0036] In some cases, a cfDNA fragmentation profile can be used to detect tumor-derived DNA. For example, a cfDNA fragmentation profile can be used to detect tumor-derived DNA by comparing a cfDNA fragmentation profile of a mammal having, or suspected of having, cancer to a reference cfDNA fragmentation profile (e.g., a cfDNA fragmentation profile of a healthy mammal and/or a nucleosomal DNA fragmentation profile of healthy cells from the mammal having, or suspected of having, cancer). In some cases, a reference cfDNA fragmentation profile is a previously generated profile from a healthy mammal. For example, methods provided herein can be used to determine a reference cfDNA fragmentation profile in a healthy mammal, and that reference cfDNA fragmentation profile can be stored (e.g., in a computer or other electronic storage medium) for future comparison to a test cfDNA fragmentation profile in mammal having, or suspected of having, cancer. In some cases, a reference cfDNA fragmentation profile (e.g., a stored cfDNA fragmentation profile) of a healthy mammal is determined over the whole genome. In some cases, a reference cfDNA fragmentation profile (e.g., a stored cfDNA fragmentation profile) of a healthy mammal is determined over a subgenomic interval.

[0037] In some cases, a cfDNA fragmentation profile can be used to identify a mammal (e.g., a human) as having cancer (e.g., a colorectal cancer, a lung cancer, a breast cancer, a gastric cancer, a pancreatic cancer, a bile duct cancer, and/or an ovarian cancer).

[0038] A cfDNA fragmentation profile can include a cfDNA fragment size pattern. cfDNA fragments can be any appropriate size. For example, cfDNA fragment can be from about 50 base pairs (bp) to about 400 bp in length. As described herein, a mammal having cancer can have a cfDNA fragment size pattern that contains a shorter median cfDNA fragment size than the median cfDNA fragment size in a healthy mammal. A healthy mammal (e.g., a mammal not having cancer) can have cfDNA fragment sizes having a median cfDNA fragment size from about 166.6 bp to about 167.2 bp (e.g., about 166.9 bp). In some cases, a mammal having cancer can have cfDNA fragment sizes that are, on average, about 1.28 bp to about 2.49 bp (e.g., about 1.88 bp) shorter than cfDNA fragment sizes in a healthy mammal. For example, a mammal having cancer can have cfDNA fragment sizes having a median cfDNA fragment size of about 164.11 bp to about 165.92 bp (e.g., about 165.02 bp).

[0039] A cfDNA fragmentation profile can include a cfDNA fragment size distribution. As described herein, a mammal having cancer can have a cfDNA size distribution that is more variable than a cfDNA fragment size distribution in a healthy mammal. In some case, a size distribution can be within a targeted region. A healthy mammal (e.g., a mammal not having cancer) can have a targeted region cfDNA fragment size distribution of about 1 or less than about 1. In some cases, a mammal having cancer can have a targeted region cfDNA fragment size distribution that is longer (e.g., 10, 15, 20, 25, 30, 35, 40, 45, 50 or more bp longer, or any number of base pairs between these numbers) than a targeted region cfDNA fragment size distribution in a healthy mammal. In some cases, a mammal having cancer can have a targeted region cfDNA fragment size distribution that is shorter (e.g., 10, 15, 20, 25, 30, 35, 40, 45, 50 or more bp shorter, or any number of base pairs between these numbers) than a targeted region cfDNA fragment size distribution in a healthy mammal. In some cases, a mammal having cancer can have a targeted region cfDNA fragment size distribution that is about 47 bp smaller to about 30 bp longer than a targeted region cfDNA fragment size distribution in a healthy mammal. In some cases, a mammal having cancer can have a targeted region cfDNA fragment size distribution of, on average, a 10, 11, 12, 13, 14, 15, 15, 17, 18, 19, 20 or more bp difference in lengths of cfDNA fragments. For example, a mammal having cancer can have a targeted region cfDNA fragment size distribution of, on average, about a 13 bp difference in lengths of cfDNA fragments. In some case, a size distribution can be a genome-wide size distribution. A healthy mammal (e.g., a mammal not having cancer) can have very similar distributions of short and long cfDNA fragments genome-wide. In some cases, a mammal having cancer can have, genome-wide, one or more alterations (e.g., increases and decreases) in cfDNA fragment sizes. The one or more alterations can be any appropriate chromosomal region of the genome. For example, an alteration can be in a portion of a chromosome. Examples of portions of chromosomes that can contain one or more alterations in cfDNA fragment sizes include, without limitation, portions of 2q, 4p, 5p, 6q, 7p, 8q, 9q, 10q, 11q, 12q, and 14q. For example, an alteration can be across a chromosome arm (e.g., an entire chromosome arm).

[0040] A cfDNA fragmentation profile can include a ratio of small cfDNA fragments to large cfDNA fragments and a correlation of fragment ratios to reference fragment ratios. As used herein, with respect to ratios of small cfDNA fragments to large cfDNA fragments, a small cfDNA fragment can be from about 100 bp in length to about 150 bp in length. As used herein, with respect to ratios of small cfDNA fragments to large cfDNA fragments, a large cfDNA fragment can be from about 151 bp in length to 220 bp in length. As described herein, a mammal having cancer can have a correlation of fragment ratios (e.g., a correlation of cfDNA fragment ratios to reference DNA fragment ratios such as DNA fragment ratios from one or more healthy mammals) that is lower (e.g., 2-fold lower, 3-fold lower, 4-fold lower, 5-fold lower, 6-fold lower, 7-fold lower, 8-fold lower, 9-fold lower, 10-fold lower, or more) than in a healthy mammal. A healthy mammal (e.g., a mammal not having cancer) can have a correlation of fragment ratios (e.g., a correlation of cfDNA fragment ratios to reference DNA fragment ratios such as DNA fragment ratios from one or more healthy mammals) of about 1 (e.g., about 0.96). In some cases, a mammal having cancer can have a correlation of fragment ratios (e.g., a correlation of cfDNA fragment ratios to reference DNA fragment ratios such as DNA fragment ratios from one or more healthy mammals) that is, on average, about 0.19 to about 0.30 (e.g., about 0.25) lower than a correlation of fragment ratios (e.g., a correlation of cfDNA fragment ratios to reference DNA fragment ratios such as DNA fragment ratios from one or more healthy mammals) in a healthy mammal.

[0041] A cfDNA fragmentation profile can include coverage of all fragments. Coverage of all fragments can include windows (e.g., non-overlapping windows) of coverage. In some cases, coverage of all fragments can include windows of small fragments (e.g., fragments from about 100 bp to about 150 bp in length). In some cases, coverage of all fragments can include windows of large fragments (e.g., fragments from about 151 bp to about 220 bp in length).

[0042] In some cases, a cfDNA fragmentation profile can be used to identify the tissue of origin of a cancer (e.g., a colorectal cancer, a lung cancer, a breast cancer, a gastric cancer, a pancreatic cancer, a bile duct cancer, or an ovarian cancer). For example, a cfDNA fragmentation profile can be used to identify a localized cancer. When a cfDNA fragmentation profile includes a targeted region profile, one or more alterations described herein (e.g., in Table 3 (Appendix C) and/or in Table 6 (Appendix F)) can be used to identify the tissue of origin of a cancer. In some cases, one or more alterations in chromosomal regions can be used to identify the tissue of origin of a cancer.

[0043] A cfDNA fragmentation profile can be obtained using any appropriate method. In some cases, cfDNA from a mammal (e.g., a mammal having, or suspected of having, cancer) can be processed into sequencing libraries which can be subjected to whole genome sequencing (e.g., low-coverage whole genome sequencing), mapped to the genome, and analyzed to determine cfDNA fragment lengths. Mapped sequences can be analyzed in non-overlapping windows covering the genome. Windows can be any appropriate size. For example, windows can be from thousands to millions of bases in length. As one non-limiting example, a window can be about 5 megabases (Mb) long. Any appropriate number of windows can be mapped. For example, tens to thousands of windows can be mapped in the genome. For example, hundreds to thousands of windows can be mapped in the genome. A cfDNA fragmentation profile can be determined within each window. In some cases, a cfDNA fragmentation profile can be obtained as described in Example 1. In some cases, a cfDNA fragmentation profile can be obtained as shown in FIG. 1.

[0044] In some cases, methods and materials described herein also can include machine learning. For example, machine learning can be used for identifying an altered fragmentation profile (e.g., using coverage of cfDNA fragments, fragment size of cfDNA fragments, coverage of chromosomes, and mtDNA).

[0045] In some cases, methods and materials described herein can be the sole method used to identify a mammal (e.g., a human) as having cancer (e.g., a colorectal cancer, a lung cancer, a breast cancer, a gastric cancer, a pancreatic cancer, a bile duct cancer, and/or an ovarian cancer). For example, determining a cfDNA fragmentation profile can be the sole method used to identify a mammal as having cancer.

[0046] In some cases, methods and materials described herein can be used together with one or more additional methods used to identify a mammal (e.g., a human) as having cancer (e.g., a colorectal cancer, a lung cancer, a breast cancer, a gastric cancer, a pancreatic cancer, a bile duct cancer, and/or an ovarian cancer). Examples of methods used to identify a mammal as having cancer include, without limitation, identifying one or more cancer-specific sequence alterations, identifying one or more chromosomal alterations (e.g., aneuploidies and rearrangements), and identifying other cfDNA alterations. For example, determining a cfDNA fragmentation profile can be used together with identifying one or more cancer-specific mutations in a mammal's genome to identify a mammal as having cancer. For example, determining a cfDNA fragmentation profile can be used together with identifying one or more aneuploidies in a mammal's genome to identify a mammal as having cancer.

[0047] In some aspects, this document also provides methods and materials for assessing, monitoring, and/or treating mammals (e.g., humans) having, or suspected of having, cancer. In some cases, this document provides methods and materials for identifying a mammal as having cancer. For example, a sample (e.g., a blood sample) obtained from a mammal can be assessed to determine if the mammal has cancer based, at least in part, on the cfDNA fragmentation profile of the mammal. In some cases, this document provides methods and materials for identifying the location (e.g., the anatomic site or tissue of origin) of a cancer in a mammal. For example, a sample (e.g., a blood sample) obtained from a mammal can be assessed to determine the tissue of origin of the cancer in the mammal based, at least in part, on the cfDNA fragmentation profile of the mammal. In some cases, this document provides methods and materials for identifying a mammal as having cancer, and administering one or more cancer treatments to the mammal to treat the mammal. For example, a sample (e.g., a blood sample) obtained from a mammal can be assessed to determine if the mammal has cancer based, at least in part, on the cfDNA fragmentation profile of the mammal, and administering one or more cancer treatments to the mammal. In some cases, this document provides methods and materials for treating a mammal having cancer. For example, one or more cancer treatments can be administered to a mammal identified as having cancer (e.g., based, at least in part, on the cfDNA fragmentation profile of the mammal) to treat the mammal. In some cases, during or after the course of a cancer treatment (e.g., any of the cancer treatments described herein), a mammal can undergo monitoring (or be selected for increased monitoring) and/or further diagnostic testing. In some cases, monitoring can include assessing mammals having, or suspected of having, cancer by, for example, assessing a sample (e.g., a blood sample) obtained from the mammal to determine the cfDNA fragmentation profile of the mammal as described herein, and changes in the cfDNA fragmentation profiles over time can be used to identify response to treatment and/or identify the mammal as having cancer (e.g., a residual cancer).

[0048] Any appropriate mammal can be assessed, monitored, and/or treated as described herein. A mammal can be a mammal having cancer. A mammal can be a mammal suspected of having cancer. Examples of mammals that can be assessed, monitored, and/or treated as described herein include, without limitation, humans, primates such as monkeys, dogs, cats, horses, cows, pigs, sheep, mice, and rats. For example, a human having, or suspected of having, cancer can be assessed to determine a cfDNA fragmentation profiled as described herein and, optionally, can be treated with one or more cancer treatments as described herein.

[0049] Any appropriate sample from a mammal can be assessed as described herein (e.g., assessed for a DNA fragmentation pattern). In some cases, a sample can include DNA (e.g., genomic DNA). In some cases, a sample can include cfDNA (e.g., circulating tumor DNA (ctDNA)). In some cases, a sample can be fluid sample (e.g., a liquid biopsy). Examples of samples that can contain DNA and/or polypeptides include, without limitation, blood (e.g., whole blood, serum, or plasma), amnion, tissue, urine, cerebrospinal fluid, saliva, sputum, broncho-alveolar lavage, bile, lymphatic fluid, cyst fluid, stool, ascites, pap smears, breast milk, and exhaled breath condensate. For example, a plasma sample can be assessed to determine a cfDNA fragmentation profiled as described herein.

[0050] A sample from a mammal to be assessed as described herein (e.g., assessed for a DNA fragmentation pattern) can include any appropriate amount of cfDNA. In some cases, a sample can include a limited amount of DNA. For example, a cfDNA fragmentation profile can be obtained from a sample that includes less DNA than is typically required for other cfDNA analysis methods, such as those described in, for example, Phallen et al., 2017 Sci Transl Med 9; Cohen et al., 2018 Science 359:926; Newman et al., 2014 Nat Med 20:548; and Newman et al., 2016 Nat Biotechnol 34:547).

[0051] In some cases, a sample can be processed (e.g., to isolate and/or purify DNA and/or polypeptides from the sample). For example, DNA isolation and/or purification can include cell lysis (e.g., using detergents and/or surfactants), protein removal (e.g., using a protease), and/or RNA removal (e.g., using an RNase). As another example, polypeptide isolation and/or purification can include cell lysis (e.g., using detergents and/or surfactants), DNA removal (e.g., using a DNase), and/or RNA removal (e.g., using an RNase).

[0052] A mammal having, or suspected of having, any appropriate type of cancer can be assessed (e.g., to determine a cfDNA fragmentation profile) and/or treated (e.g., by administering one or more cancer treatments to the mammal) using the methods and materials described herein. A cancer can be any stage cancer. In some cases, a cancer can be an early stage cancer. In some cases, a cancer can be an asymptomatic cancer. In some cases, a cancer can be a residual disease and/or a recurrence (e.g., after surgical resection and/or after cancer therapy). A cancer can be any type of cancer. Examples of types of cancers that can be assessed, monitored, and/or treated as described herein include, without limitation, colorectal cancers, lung cancers, breast cancers, gastric cancers, pancreatic cancers, bile duct cancers, and ovarian cancers.

[0053] When treating a mammal having, or suspected of having, cancer as described herein, the mammal can be administered one or more cancer treatments. A cancer treatment can be any appropriate cancer treatment. One or more cancer treatments described herein can be administered to a mammal at any appropriate frequency (e.g., once or multiple times over a period of time ranging from days to weeks). Examples of cancer treatments include, without limitation adjuvant chemotherapy, neoadjuvant chemotherapy, radiation therapy, hormone therapy, cytotoxic therapy, immunotherapy, adoptive T cell therapy (e.g., chimeric antigen receptors and/or T cells having wild-type or modified T cell receptors), targeted therapy such as administration of kinase inhibitors (e.g., kinase inhibitors that target a particular genetic lesion, such as a translocation or mutation), (e.g. a kinase inhibitor, an antibody, a bispecific antibody), signal transduction inhibitors, bispecific antibodies or antibody fragments (e.g., BiTEs), monoclonal antibodies, immune checkpoint inhibitors, surgery (e.g., surgical resection), or any combination of the above. In some cases, a cancer treatment can reduce the severity of the cancer, reduce a symptom of the cancer, and/or to reduce the number of cancer cells present within the mammal.

[0054] In some cases, a cancer treatment can include an immune checkpoint inhibitor. Non-limiting examples of immune checkpoint inhibitors include nivolumab (Opdivo), pembrolizumab (Keytruda), atezolizumab (tecentriq), avelumab (bavencio), durvalumab (imfinzi), ipilimumab (yervoy). See, e.g., Pardoll (2012) Nat. Rev Cancer 12: 252-264; Sun et al. (2017) Eur Rev Med Pharmacol Sci 21(6): 1198-1205; Hamanishi et al. (2015) J. Clin. Oncol. 33(34): 4015-22; Brahmer et al. (2012) N Engl J Med 366(26): 2455-65; Ricciuti et al. (2017) J. Thorac Oncol. 12(5): e51-e55; Ellis et al. (2017) Clin Lung Cancer pii: S1525-7304(17)30043-8; Zou and Awad (2017) Ann Oncol 28(4): 685-687; Sorscher (2017) N Engl J Med 376(10: 996-7; Hui et al. (2017) Ann Oncol 28(4): 874-881; Vansteenkiste et al. (2017) Expert Opin Biol Ther 17(6): 781-789; Hellmann et al. (2017) Lancet Oncol. 18(1): 31-41, Chen (2017) J. Chin Med Assoc 80(1): 7-14.

[0055] In some cases, a cancer treatment can be an adoptive T cell therapy (e.g., chimeric antigen receptors and/or T cells having wild-type or modified T cell receptors). See, e.g., Rosenberg and Restifo (2015) Science 348(6230): 62-68; Chang and Chen (2017) Trends Mol Med 23(5): 430-450; Yee and Lizee (2016) Cancer J. 23(2): 144-148; Chen et al. (2016) Oncoimmunology 6(2): e1273302; US 2016/0194404; US 2014/0050788; US 2014/0271635; U.S. Pat. No. 9,233,125; incorporated by reference in their entirety herein.

[0056] In some cases, a cancer treatment can be a chemotherapeutic agent. Non-limiting examples of chemotherapeutic agents include: amsacrine, azacitidine, axathioprine, bevacizumab (or an antigen-binding fragment thereof), bleomycin, busulfan, carboplatin, capecitabine, chlorambucil, cisplatin, cyclophosphamide, cytarabine, dacarbazine, daunorubicin, docetaxel, doxifluridine, doxorubicin, epirubicin, erlotinib hydrochlorides, etoposide, fiudarabine, floxuridine, fludarabine, fluorouracil, gemcitabine, hydroxyurea, idarubicin, ifosfamide, irinotecan, lomustine, mechlorethamine, melphalan, mercaptopurine, methotrxate, mitomycin, mitoxantrone, oxaliplatin, paclitaxel, pemetrexed, procarbazine, all-trans retinoic acid, streptozocin, tafluposide, temozolomide, teniposide, tioguanine, topotecan, uramustine, valrubicin, vinblastine, vincristine, vindesine, vinorelbine, and combinations thereof. Additional examples of anti-cancer therapies are known in the art; see, e.g. the guidelines for therapy from the American Society of Clinical Oncology (ASCO), European Society for Medical Oncology (ESMO), or National Comprehensive Cancer Network (NCCN).

[0057] When monitoring a mammal having, or suspected of having, cancer as described herein (e.g., based, at least in part, on the cfDNA fragmentation profile of the mammal), the monitoring can be before, during, and/or after the course of a cancer treatment. Methods of monitoring provided herein can be used to determine the efficacy of one or more cancer treatments and/or to select a mammal for increased monitoring. In some cases, the monitoring can include identifying a cfDNA fragmentation profile as described herein. For example, a cfDNA fragmentation profile can be obtained before administering one or more cancer treatments to a mammal having, or suspected or having, cancer, one or more cancer treatments can be administered to the mammal, and one or more cfDNA fragmentation profiles can be obtained during the course of the cancer treatment. In some cases, a cfDNA fragmentation profile can change during the course of cancer treatment (e.g., any of the cancer treatments described herein). For example, a cfDNA fragmentation profile indicative that the mammal has cancer can change to a cfDNA fragmentation profile indicative that the mammal does not have cancer. Such a cfDNA fragmentation profile change can indicate that the cancer treatment is working. Conversely, a cfDNA fragmentation profile can remain static (e.g., the same or approximately the same) during the course of cancer treatment (e.g., any of the cancer treatments described herein). Such a static cfDNA fragmentation profile can indicate that the cancer treatment is not working. In some cases, the monitoring can include conventional techniques capable of monitoring one or more cancer treatments (e.g., the efficacy of one or more cancer treatments). In some cases, a mammal selected for increased monitoring can be administered a diagnostic test (e.g., any of the diagnostic tests disclosed herein) at an increased frequency compared to a mammal that has not been selected for increased monitoring. For example, a mammal selected for increased monitoring can be administered a diagnostic test at a frequency of twice daily, daily, bi-weekly, weekly, bi-monthly, monthly, quarterly, semi-annually, annually, or any at frequency therein. In some cases, a mammal selected for increased monitoring can be administered a one or more additional diagnostic tests compared to a mammal that has not been selected for increased monitoring. For example, a mammal selected for increased monitoring can be administered two diagnostic tests, whereas a mammal that has not been selected for increased monitoring is administered only a single diagnostic test (or no diagnostic tests). In some cases, a mammal that has been selected for increased monitoring can also be selected for further diagnostic testing. Once the presence of a tumor or a cancer (e.g., a cancer cell) has been identified (e.g., by any of the variety of methods disclosed herein), it may be beneficial for the mammal to undergo both increased monitoring (e.g., to assess the progression of the tumor or cancer in the mammal and/or to assess the development of one or more cancer biomarkers such as mutations), and further diagnostic testing (e.g., to determine the size and/or exact location (e.g., tissue of origin) of the tumor or the cancer). In some cases, one or more cancer treatments can be administered to the mammal that is selected for increased monitoring after a cancer biomarker is detected and/or after the cfDNA fragmentation profile of the mammal has not improved or deteriorated. Any of the cancer treatments disclosed herein or known in the art can be administered. For example, a mammal that has been selected for increased monitoring can be further monitored, and a cancer treatment can be administered if the presence of the cancer cell is maintained throughout the increased monitoring period. Additionally or alternatively, a mammal that has been selected for increased monitoring can be administered a cancer treatment, and further monitored as the cancer treatment progresses. In some cases, after a mammal that has been selected for increased monitoring has been administered a cancer treatment, the increased monitoring will reveal one or more cancer biomarkers (e.g., mutations). In some cases, such one or more cancer biomarkers will provide cause to administer a different cancer treatment (e.g., a resistance mutation may arise in a cancer cell during the cancer treatment, which cancer cell harboring the resistance mutation is resistant to the original cancer treatment).

[0058] When a mammal is identified as having cancer as described herein (e.g., based, at least in part, on the cfDNA fragmentation profile of the mammal), the identifying can be before and/or during the course of a cancer treatment. Methods of identifying a mammal as having cancer provided herein can be used as a first diagnosis to identify the mammal (e.g., as having cancer before any course of treatment) and/or to select the mammal for further diagnostic testing. In some cases, once a mammal has been determined to have cancer, the mammal may be administered further tests and/or selected for further diagnostic testing. In some cases, methods provided herein can be used to select a mammal for further diagnostic testing at a time period prior to the time period when conventional techniques are capable of diagnosing the mammal with an early-stage cancer. For example, methods provided herein for selecting a mammal for further diagnostic testing can be used when a mammal has not been diagnosed with cancer by conventional methods and/or when a mammal is not known to harbor a cancer. In some cases, a mammal selected for further diagnostic testing can be administered a diagnostic test (e.g., any of the diagnostic tests disclosed herein) at an increased frequency compared to a mammal that has not been selected for further diagnostic testing. For example, a mammal selected for further diagnostic testing can be administered a diagnostic test at a frequency of twice daily, daily, bi-weekly, weekly, bi-monthly, monthly, quarterly, semi-annually, annually, or any at frequency therein. In some cases, a mammal selected for further diagnostic testing can be administered a one or more additional diagnostic tests compared to a mammal that has not been selected for further diagnostic testing. For example, a mammal selected for further diagnostic testing can be administered two diagnostic tests, whereas a mammal that has not been selected for further diagnostic testing is administered only a single diagnostic test (or no diagnostic tests). In some cases, the diagnostic testing method can determine the presence of the same type of cancer (e.g., having the same tissue or origin) as the cancer that was originally detected (e.g., based, at least in part, on the cfDNA fragmentation profile of the mammal). Additionally or alternatively, the diagnostic testing method can determine the presence of a different type of cancer as the cancer that was original detected. In some cases, the diagnostic testing method is a scan. In some cases, the scan is a computed tomography (CT), a CT angiography (CTA), a esophagram (a Barium swallom), a Barium enema, a magnetic resonance imaging (MRI), a PET scan, an ultrasound (e.g., an endobronchial ultrasound, an endoscopic ultrasound), an X-ray, a DEXA scan. In some cases, the diagnostic testing method is a physical examination, such as an anoscopy, a bronchoscopy (e.g., an autofluorescence bronchoscopy, a white-light bronchoscopy, a navigational bronchoscopy), a colonoscopy, a digital breast tomosynthesis, an endoscopic retrograde cholangiopancreatography (ERCP), an ensophagogastroduodenoscopy, a mammography, a Pap smear, a pelvic exam, a positron emission tomography and computed tomography (PET-CT) scan. In some cases, a mammal that has been selected for further diagnostic testing can also be selected for increased monitoring. Once the presence of a tumor or a cancer (e.g., a cancer cell) has been identified (e.g., by any of the variety of methods disclosed herein), it may be beneficial for the mammal to undergo both increased monitoring (e.g., to assess the progression of the tumor or cancer in the mammal and/or to assess the development of one or more cancer biomarkers such as mutations), and further diagnostic testing (e.g., to determine the size and/or exact location of the tumor or the cancer). In some cases, a cancer treatment is administered to the mammal that is selected for further diagnostic testing after a cancer biomarker is detected and/or after the cfDNA fragmentation profile of the mammal has not improved or deteriorated. Any of the cancer treatments disclosed herein or known in the art can be administered. For example, a mammal that has been selected for further diagnostic testing can be administered a further diagnostic test, and a cancer treatment can be administered if the presence of the tumor or the cancer is confirmed. Additionally or alternatively, a mammal that has been selected for further diagnostic testing can be administered a cancer treatment, and can be further monitored as the cancer treatment progresses. In some cases, after a mammal that has been selected for further diagnostic testing has been administered a cancer treatment, the additional testing will reveal one or more cancer biomarkers (e.g., mutations). In some cases, such one or more cancer biomarkers (e.g., mutations) will provide cause to administer a different cancer treatment (e.g., a resistance mutation may arise in a cancer cell during the cancer treatment, which cancer cell harboring the resistance mutation is resistant to the original cancer treatment).

[0059] The invention will be further described in the following examples, which do not limit the scope of the invention described in the claims.

EXAMPLES

Example 1: Cell-Free DIVA Fragmentation in Patients with Cancer

[0060] Analyses of cell free DNA have largely focused on targeted sequencing of specific genes. Such studies permit detection of a small number of tumor-specific alterations in patients with cancer and not all patients, especially those with early stage disease, have detectable changes. Whole genome sequencing of cell-free DNA can identify chromosomal abnormalities and rearrangements in cancer patients but detection of such alterations has been challenging in part due to the difficulty in distinguishing a small number of abnormal from normal chromosomal changes (Leary et al., 2010 Sci Transl Med 2:20ra14; and Leary et al., 2012 Sci Transl Med 4:162ra154). Other efforts have suggested nucleosome patterns and chromatin structure may be different between cancer and normal tissues, and that cfDNA in patients with cancer may result in abnormal cfDNA fragment size as well as position (Snyder et al., 2016 Cell 164:57; Jahr et al., 2001 Cancer Res 61:1659; Ivanov et al., 2015 BMC Genomics 16(Suppl 13):S1). However, the amount of sequencing needed for nucleosome footprint analyses of cfDNA is impractical for routine analyses.

[0061] The sensitivity of any cell-free DNA approach depends on the number of potential alterations examined as well as the technical and biological limitations of detecting such changes. As a typical blood sample contains .about.2000 genome equivalents of cfDNA per milliliter of plasma (Phallen et al., 2017 Sci Transl Med 9), the theoretical limit of detection of a single alteration can be no better than one in a few thousand mutant to wild-type molecules. An approach that detects a larger number of alterations in the same number of genome equivalents would be more sensitive for detecting cancer in the circulation. Monte Carlo simulations show that increasing the number of potential abnormalities detected from only a few to tens or hundreds can potentially improve the limit of detection by orders of magnitude, similar to recent probability analyses of multiple methylation changes in cfDNA (FIG. 2).

[0062] This study presents a novel method called DELFI for detection of cancer and further identification of tissue of origin using whole genome sequencing (FIG. 1). The approach uses cfDNA fragmentation profiles and machine learning to distinguish patterns of healthy blood cell DNA from tumor-derived DNA and to identify the primary tumor tissue. DELFI was used for a retrospective analysis of cfDNA from 245 healthy individuals and 236 patients with breast, colorectal, lung, ovarian, pancreatic, gastric, or bile duct cancers, with most patients exhibiting localized disease. Assuming this approach had sensitivity .gtoreq.0.80 for discriminating cancer patients from healthy individuals while maintaining a specificity of 0.95, a study of at least 200 cancer patients would enable estimation of the true sensitivity with a margin of error of 0.06 at the desired specificity of 0.95 or greater.

Materials and Methods

Patient and Sample Characteristics

[0063] Plasma samples from healthy individuals and plasma and tissue samples from patients with breast, lung, ovarian, colorectal, bile duct, or gastric cancer were obtained from ILSBio/Bioreclamation, Aarhus University, Herlev Hospital of the University of Copenhagen, Hvidovre Hospital, the University Medical Center of the University of Utrecht, the Academic Medical Center of the University of Amsterdam, the Netherlands Cancer Institute, and the University of California, San Diego. All samples were obtained under Institutional Review Board approved protocols with informed consent for research use at participating institutions. Plasma samples from healthy individuals were obtained at the time of routine screening, including for colonoscopies or Pap smears. Individuals were considered healthy if they had no previous history of cancer and negative screening results.

[0064] Plasma samples from individuals with breast, colorectal, gastric, lung, ovarian, pancreatic, and bile duct cancer were obtained at the time of diagnosis, prior to tumor resection or therapy. Nineteen lung cancer patients analyzed for change in cfDNA fragmentation profiles across multiple time points were undergoing treatment with anti-EGFR or anti-ERBB2 therapy (see, e.g., Phallen et al., 2019 Cancer Research 15, 1204-1213). Clinical data for all patients included in this study are listed in Table 1 (Appendix A). Gender was confirmed through genomic analyses of X and Y chromosome representation. Pathologic staging of gastric cancer patients was performed after neoadjuvant therapy. Samples where the tumor stage was unknown were indicated as stage X or unknown.

Nucleosomal DNA Purification

[0065] Viably frozen lymphocytes were elutriated from leukocytes obtained from a healthy male (C0618) and female (D0808-L) (Advanced Biotechnologies Inc., Eldersburg, Md.). Aliquots of 1.times.10.sup.6 cells were used for nucleosomal DNA purification using EZ Nucleosomal DNA Prep Kit (Zymo Research, Irvine, Calif.). Cells were initially treated with 100 .mu.l of Nuclei Prep Buffer and incubated on ice for 5 minutes. After centrifugation at 200 g for 5 minutes, supernatant was discarded and pelleted nuclei were treated twice with 1000 of Atlantis Digestion Buffer or with 100 .mu.l of micrococcal nuclease (MN) Digestion Buffer. Finally, cellular nucleic DNA was fragmented with 0.5 U of Atlantis dsDNase at 42.degree. C. for 20 minutes or 1.5 U of MNase at 37.degree. C. for 20 minutes. Reactions were stopped using 5.times.MN Stop Buffer and DNA was purified using Zymo-Spin.TM. IIC Columns. Concentration and quality of eluted cellular nucleic DNA were analyzed using the Bioanalyzer 2100 (Agilent Technologies, Santa Clara, Calif.).

Sample Preparation and Sequencing of cfDNA

[0066] Whole blood was collected in EDTA tubes and processed immediately or within one day after storage at 4.degree. C., or was collected in Streck tubes and processed within two days of collection for three cancer patients who were part of the monitoring analysis. Plasma and cellular components were separated by centrifugation at 800 g for 10 min at 4.degree. C. Plasma was centrifuged a second time at 18,000 g at room temperature to remove any remaining cellular debris and stored at -80.degree. C. until the time of DNA extraction. DNA was isolated from plasma using the Qiagen Circulating Nucleic Acids Kit (Qiagen GmbH) and eluted in LoBind tubes (Eppendorf AG). Concentration and quality of cfDNA were assessed using the Bioanalyzer 2100 (Agilent Technologies).

[0067] NGS cfDNA libraries were prepared for whole genome sequencing and targeted sequencing using 5 to 250 ng of cfDNA as described elsewhere (see, e.g., Phallen et al, 2017 Sci Transl Med 9:eaan2415). Briefly, genomic libraries were prepared using the NEBNext DNA Library Prep Kit for Illumina [New England Biolabs (NEB)] with four main modifications to the manufacturer's guidelines: (i) The library purification steps used the on-bead AMPure XP approach to minimize sample loss during elution and tube transfer steps (see, e.g., Fisher et al., 2011 Genome Biol 12:R1); (ii) NEBNext End Repair, A-tailing, and adapter ligation enzyme and buffer volumes were adjusted as appropriate to accommodate the on-bead AMPure XP purification strategy; (iii) a pool of eight unique Illumina dual index adapters with 8-base pair (bp) barcodes was used in the ligation reaction instead of the standard Illumina single or dual index adapters with 6- or 8-bp barcodes, respectively; and (iv) cfDNA libraries were amplified with Phusion Hot Start Polymerase.

[0068] Whole genome libraries were sequenced directly. For targeted libraries, capture was performed using Agilent SureSelect reagents and a custom set of hybridization probes targeting 58 genes (see, e.g., Phallen et al., 2017 Sci Transl Med 9:eaan2415) per the manufacturer's guidelines. The captured library was amplified with Phusion Hot Start Polymerase (NEB). Concentration and quality of captured cfDNA libraries were assessed on the Bioanalyzer 2100 using the DNA1000 Kit (Agilent Technologies). Targeted libraries were sequenced using 100-bp paired-end runs on the Illumina HiSeq 2000/2500 (Illumina).

Analyses of Targeted Sequencing Data from cfDNA

[0069] Analyses of targeted NGS data for cfDNA samples was performed as described elsewhere (see, e.g., Phallen et al., 2017 Sci Transl Med 9:eaan2415). Briefly, primary processing was completed using Illumina CASAVA (Consensus Assessment of Sequence and Variation) software (version 1.8), including demultiplexing and masking of dual-index adapter sequences. Sequence reads were aligned against the human reference genome (version hg18 or hg19) using NovoAlign with additional realignment of select regions using the Needleman-Wunsch method (see, e.g., Jones et al., 2015 Sci Transl Med 7:283ra53). The positions of the sequence alterations have not been affected by the different genome builds. Candidate mutations, consisting of point mutations, small insertions, and deletions, were identified using VariantDx (see, e.g., Jones et al., 2015 Sci Transl Med 7:283ra53) (Personal Genome Diagnostics, Baltimore, Md.) across the targeted regions of interest.

[0070] To analyze the fragment lengths of cfDNA molecules, each read pair from a cfDNA molecule was required to have a Phred quality score .gtoreq.30. All duplicate ctDNA fragments, defined as having the same start, end, and index barcode were removed. For each mutation, only fragments for which one or both of the read pairs contained the mutated (or wild-type) base at the given position were included. This analysis was done using the R packages Rsamtools and GenomicAlignments.

[0071] For each genomic locus where a somatic mutation was identified, the lengths of fragments containing the mutant allele were compared to the lengths of fragments of the wild-type allele. If more than 100 mutant fragments were identified, Welch's two-sample t-test was used to compare the mean fragment lengths. For loci with fewer than 100 mutant fragments, a bootstrap procedure was implemented. Specifically, replacement N fragments containing the wild-type allele, where N denotes the number of fragments with the mutation, were sampled. For each bootstrap replicate of wild type fragments their median length was computed. The p-value was estimated as the fraction of bootstrap replicates with a median wild-type fragment length as or more extreme than the observed median mutant fragment length.

Analyses of Whole Genome Sequencing Data from cfDNA

[0072] Primary processing of whole genome NGS data for cfDNA samples was performed using Illumina CASAVA (Consensus Assessment of Sequence and Variation) software (version 1.8.2), including demultiplexing and masking of dual-index adapter sequences. Sequence reads were aligned against the human reference genome (version hg19) using ELAND.

[0073] Read pairs with a MAPQ score below 30 for either read and PCR duplicates were removed. hg19 autosomes were tiled into 26,236 adjacent, non-overlapping 100 kb bins. Regions of low mappability, indicated by the 10% of bins with the lowest coverage, were removed (see, e.g., Fortin et al., 2015 Genome Biol 16:180), as were reads falling in the Duke blacklisted regions (see, e.g., hgdownload.cse.ucsc.edu/goldenpath/hg19/encodeDCC/wgEncodeMapability/). Using this approach, 361 Mb (13%) of the hg19 reference genome was excluded, including centromeric and telomeric regions. Short fragments were defined as having a length between 100 and 150 bp and long fragments were defined has having a length between 151 and 220 bp.

[0074] To account for biases in coverage attributable to GC content of the genome, the locally weighted smoother loess with span 3/4 was applied to the scatterplot of average fragment GC versus coverage calculated for each 100 kb bin. This loess regression was performed separately for short and long fragments to account for possible differences in GC effects on coverage in plasma by fragment length (see, e.g., Benjamini et al., 2012 Nucleic Acids Res 40:e72). The predictions for short and long coverage explained by GC from the loess model were subtracted, obtaining residuals for short and long that were uncorrelated with GC. The residuals were returned to the original scale by adding back the genome-wide median short and long estimates of coverage. This procedure was repeated for each sample to account for possible differences in GC effects on coverage between samples. To further reduce the feature space and noise, the total GC-adjusted coverage in 5 Mb bins was calculated.

[0075] To compare the variability of fragment lengths from healthy subjects to fragments in patients with cancer, the standard deviation of the short to long fragmentation profiles for each individual was calculated. The standard deviations in the two groups were compared by a Wilcoxon rank sum test.

Analyses of Chromosome Arm Copy Number Changes

[0076] To develop arm-level statistics for copy number changes, an approach for aneuploidy detection in plasma as described elsewhere (see, e.g., Leary et al., 2012 Sci Transl Med 4:162ra154) was adopted. This approach divides the genome into non-overlapping 50 KB bins for which GC-corrected log 2 read depth was obtained after correction by loess with span 3/4. This loess-based correction is comparable to the approach outlined above, but is evaluated on a log 2 scale to increase robustness to outliers in the smaller bins and does not stratify by fragment length. To obtain an arm-specific Z-score for copy number changes, the mean GC-adjusted read depth for each arm (GR) was centered and scaled by the average and standard deviation, respectively, of GR scores obtained from an independent set of 50 healthy samples.

Analyses of Mitochondrial-Aligned Reads from cfDNA

[0077] Whole genome sequence reads that initially mapped to the mitochondrial genome were extracted from bam files and realigned to the hg19 reference genome in end-to-end mode with Bowtie2 as described elsewhere (see, e.g., Langmead et al., 2012 Nat Methods 9:357-359). The resulting aligned reads were filtered such that both mates aligned to the mitochondrial genome with MAPQ >=30. The number of fragments mapping to the mitochondrial genome was counted and converted to a percentage of the total number of fragments in the original bam files.

Prediction Model for Cancer Classification

[0078] To distinguish healthy from cancer patients using fragmentation profiles, a stochastic gradient boosting model was used (gbm; see, e.g., Friedman et al., 2001 Ann Stat 29:1189-1232; and Friedman et al., 2002 Comput Stat Data An 38:367-378). GC-corrected total and short fragment coverage for all 504 bins were centered and scaled for each sample to have mean 0 and unit standard deviation. Additional features included Z-scores for each of the 39 autosomal arms and mitochondrial representation (log 10-transformed proportion of reads mapped to the mitochondria). To estimate the prediction error of this approach, 10-fold cross-validation was used as described elsewhere (see, e.g., Efron et al., 1997 J Am Stat Assoc 92, 548-560). Feature selection, performed only on the training data in each cross-validation run, removed bins that were highly correlated (correlation >0.9) or had near zero variance. Stochastic gradient boosted machine learning was implemented using the R package gbm package with parameters n.trees=150, interaction.depth=3, shrinkage=0.1, and n.minobsinside=10. To average over the prediction error from the randomization of patients to folds, the 10-fold cross validation procedure was repeated 10 times. Confidence intervals for sensitivity fixed at 98% and 95% specificity were obtained from 2000 bootstrap replicates.

Prediction Model for Tumor Tissue of Origin Classification

[0079] For samples correctly classified as cancer patients at 90% specificity (n=174), a separate stochastic gradient boosting model was trained to classify the tissue of origin. To account for the small number of lung samples used for prediction, 18 cfDNA baseline samples from late stage lung cancer patients were included from the monitoring analyses. Performance characteristics of the model were evaluated by 10-fold cross-validation repeated 10 times. This gbm model was trained using the same features as in the cancer classification model. As previously described, features that displayed correlation above 0.9 to each other or had near zero variance were removed within each training dataset during cross-validation. The tissue class probabilities were averaged across the 10 replicates for each patient and the class with the highest probability was taken as the predicted tissue.

Analyses of Nucleosomal DNA from Human Lymphocytes and cfDNA

[0080] From the nuclease treated lymphocytes, fragment sizes were analyzed in 5 Mb bins as described for whole genome cfDNA analyses. A genome-wide map of nucleosome positions was constructed from the nuclease treated lymphocyte cell-lines. This approach identified local biases in the coverage of circulating fragments, indicating a region protected from degradation. A "Window positioning score" (WPS) was used to score each base pair in the genome (see, e.g., Snyder et al., 2016 Cell 164:57). Using a sliding window of 60 bp centered around each base, the WPS was calculated as the number of fragments completely spanning the window minus the number of fragments with only one end in the window. Since fragments arising from nucleosomes have a median length of 167 bp, a high WPS indicated a possible nucleosomic position. WPS scores were centered at zero using a running median and smoothed using a Kolmogorov-Zurbenko filter (see, e.g., Zurbenko, The spectral analysis of time series. North-Holland series in statistics and probability; Elsevier, New York, N Y, 1986). For spans of positive WPS between 50 and 450 bp, a nucleosome peak was defined as the set of base pairs with a WPS above the median in that window. The calculation of nucleosome positions for cfDNA from 30 healthy individuals with sequence coverage of 9.times. was determined in the same manner as for lymphocyte DNA. To ensure that nucleosomes in healthy cfDNA were representative, a consensus track of nucleosomes was defined consisting only of nucleosomes identified in two or more individuals. Median distances between adjacent nucleosomes were calculated from the consensus track.

Monte Carlo Simulation of Detection Sensitivity

[0081] A Monte Carlo simulation was used to estimate the probability of detecting a molecule with a tumor-derived alteration. Briefly, 1 million molecules were generated from a multinomial distribution. For a simulation with m alterations, wild-type molecules were simulated with probability p and each of the m tumor alterations were simulated with probability (1-p)/m. Next, g*m molecules were sampled randomly with replacement, where g denotes the number of genome equivalents in 1 ml of plasma. If a tumor alteration was sampled s or more times, the sample was classified as cancer-derived. The simulation was repeated 1000 times, estimating the probability that the in silico sample would be correctly classified as cancer by the mean of the cancer indicator. Setting g=2000 and s=5, the number of tumor alterations was varied by powers of 2 from 1 to 256 and the fraction of tumor-derived molecules from 0.0001% to 1%.

Statistical Analyses

[0082] All statistical analyses were performed using R version 3.4.3. The R packages caret (version 6.0-79) and gbm (version 2.1-4) were used to implement the classification of healthy versus cancer and tissue of origin. Confidence intervals from the model output were obtained with the pROC (version 1.13) R package (see, e.g., Robin et al., 2011 BMC bioinformatics 12:77). Assuming the prevalence of undiagnosed cancer cases in this population is high (1 or 2 cases per 100 healthy), a genomic assay with a specificity of 0.95 and sensitivity of 0.8 would have useful operating characteristics (positive predictive value of 0.25 and negative predictive value near 1). Power calculations suggest that an analysis of more than 200 cancer patients and an approximately equal number of healthy controls, enable an estimation of the sensitivity with a margin of error of 0.06 at the desired specificity of 0.95 or greater.

Data and Code Availability

[0083] Sequence data utilized in this study have been deposited at the European Genome-phenome Archive under study accession nos. EGAS00001003611 and EGAS00001002577. Code for analyses is available at github.com/Cancer-Genomics/delfi_scripts.

Results

[0084] DELFI allows simultaneous analysis of a large number of abnormalities in cfDNA through genome-wide analysis of fragmentation patterns. The method is based on low coverage whole genome sequencing and analysis of isolated cfDNA. Mapped sequences are analyzed in non-overlapping windows covering the genome. Conceptually, windows may range in size from thousands to millions of bases, resulting in hundreds to thousands of windows in the genome. 5 Mb windows were used for evaluating cfDNA fragmentation patterns as these would provide over 20,000 reads per window even at a limited amount of 1-2.times. genome coverage. Within each window, the coverage and size distribution of cfDNA fragments was examined. This approach was used to evaluate the variation of genome-wide fragmentation profiles in healthy and cancer populations (Table 1; Appendix A). The genome-wide pattern from an individual can be compared to reference populations to determine if the pattern is likely healthy or cancer-derived. As genome-wide profiles reveal positional differences associated with specific tissues that may be missed in overall fragment size distributions, these patterns may also indicate the tissue source of cfDNA.

[0085] The fragmentation size of cfDNA was focused on as it was found that cancer-derived cfDNA molecules may be more variable in size than cfDNA derived from non-cancer cells. cfDNA fragments from targeted regions that were captured and sequenced at high coverage (43,706 total coverage, 8,044 distinct coverage) from patients with breast, colorectal, lung or ovarian cancer (Table 1 (Appendix A), Table 2 (Appendix B), and Table 3 (Appendix C)) were initially examined. Analyses of loci containing 165 tumor-specific alterations from 81 patients (range of 1-7 alterations per patient) revealed an average absolute difference of 6.5 bp (95% CI, 5.4-7.6 bp) between lengths of median mutant and wild-type cfDNA fragments (FIG. 3, Table 3 (Appendix C)). The median size of mutant cfDNA fragments ranged from 30 bases smaller at chromosome 3 position 41,266,124 to 47 bases larger at chromosome 11 position 108,117,753 than the wild-type sequences at these regions (Table 3; Appendix C). GC content was similar for mutated and non-mutated fragments (FIG. 4a), and there was no correlation between GC content and fragment length (FIG. 4b). Similar analyses of 44 germline alterations from 38 patients identified median cfDNA size differences of less than 1 bp between fragment lengths of different alleles (FIG. 5, Table 3 (Appendix C)). Additionally, 41 alterations related to clonal hematopoiesis were identified through a previous sequence comparison of DNA from plasma, buffy coat, and tumors of the same individuals. Unlike tumor-derived fragments, there were no significant differences between fragments with hematopoietic alterations and wild type fragments (FIG. 6, Table 3 (Appendix C)). Overall, cancer-derived cfDNA fragment lengths were significantly more variable compared to non-cancer cfDNA fragments at certain genomic regions (p<0.001, variance ratio test). It was hypothesized that these differences may be due to changes in higher-order chromatin structure as well as other genomic and epigenomic abnormalities in cancer and that cfDNA fragmentation in a position-specific manner could therefore serve as a unique biomarker for cancer detection.

[0086] As targeted sequencing only analyzes a limited number of loci, larger-scale genome-wide analyses to detect additional abnormalities in cfDNA fragmentation were investigated. cfDNA was isolated from .about.4 ml of plasma from 8 lung cancer patients with stage I-III disease, as well as from 30 healthy individuals (Table 1 (Appendix A), Table 4 (Appendix D), and Table 5 (Appendix E)). A high efficiency approach was used to convert cfDNA to next generation sequencing libraries and performed whole genome sequencing at .about.9.times. coverage (Table 4; Appendix D). Overall cfDNA fragment lengths of healthy individuals were larger, with a median fragment size of 167.3 bp, while patients with cancer had median fragment sizes of 163.8 (p<0.01, Welch's t-test) (Table 5; Appendix E). To examine differences in fragment size and coverage in a position dependent manner across the genome, sequenced fragments were mapped to their genomic origin and fragment lengths were evaluated in 504 windows that were 5 Mb in size, covering .about.2.6 Gb of the genome. For each window, the fraction of small cfDNA fragments (100 to 150 bp in length) to larger cfDNA fragments (151 to 220 bp) as well as overall coverage were determined and used to obtain genome-wide fragmentation profiles for each sample.

[0087] Healthy individuals had very similar fragmentation profiles throughout the genome (FIG. 7 and FIG. 8). To examine the origins of fragmentation patterns normally observed in cfDNA, nuclei were isolated from elutriated lymphocytes of two healthy individuals and treated with DNA nucleases to obtain nucleosomal DNA fragments. Analyses of cfDNA patterns in observed healthy individuals revealed a high correlation to lymphocyte nucleosomal DNA fragmentation profiles (FIGS. 7b and 7d) and nucleosome distances (FIGS. 7c and 7f). Median distances between nucleosomes in lymphocytes were correlated to open (A) and closed (B) compartments of lymphoblastoid cells as revealed using the Hi-C method (see, e.g., Lieberman-Aiden et al., 2009 Science 326:289-293; and Fortin et al., 2015 Genome Biol 16:180) for examining the three-dimensional architecture of genomes (FIG. 7c). These analyses suggest that the fragmentation patterns of normal cfDNA are the result of nucleosomal DNA patterns that largely reflect the chromatin structure of normal blood cells.

[0088] In contrast to healthy cfDNA, patients with cancer had multiple distinct genomic differences with increases and decreases in fragment sizes at different regions (FIGS. 7a and 7b). Similar to our observations from targeted analyses, there was also greater variation in fragment lengths genome-wide for patients with cancer compared to healthy individuals.

[0089] To determine whether cfDNA fragment length patterns could be used to distinguish patients with cancer from healthy individuals, genome-wide correlation analyses were performed of the fraction of short to long cfDNA fragments for each sample compared to the median fragment length profile calculated from healthy individuals (FIGS. 7a, 7b, and 7e). While the profiles of cfDNA fragments were remarkably consistent among healthy individuals (median correlation of 0.99), the median correlation of genome-wide fragment ratios among cancer patients was 0.84 (0.15 lower, 95% CI 0.07-0.50, p<0.001, Wilcoxon rank sum test; Table 5 (Appendix E)). Similar differences were observed when comparing fragmentation profiles of cancer patients to fragmentation profiles or nucleosome distances in healthy lymphocytes (FIGS. 7c, 7d, and 7f). To account for potential biases in the fragmentation profiles attributable to GC content, a locally weighted smoother was applied independently to each sample and found that differences in fragmentation profiles between healthy individuals and cancer patients remained after this adjustment (median correlation of cancer patients to healthy=0.83) (Table 5; Appendix E).

[0090] Subsampling analyses of whole genome sequence data was performed at 9.times. coverage from cfDNA of patients with cancer at .about.2.times., .about.1.times., .about.0.5.times., .about.0.2.times., and .about.0.1.times. genome coverage, and it was determined that altered fragmentation profiles were readily identified even at 0.5.times. genome coverage (FIG. 9). Based on these observations, whole genome sequencing was performed with coverage of 1-2.times. to evaluate whether fragmentation profiles may change during the course of targeted therapy in a manner similar to monitoring of sequence alterations. cfDNA from 19 non-small cell lung cancer patients including 5 with partial radiographic response, 8 with stable disease, 4 with progressive disease, and 2 with unmeasurable disease, during the course of anti-EGFR or anti-ERBB2 therapy was evaluated (Table 6; Appendix F). As shown in FIG. 10, the degree of abnormality in the fragmentation profiles during therapy closely matched levels of EGFR or ERBB2 mutant allele fractions as determined using targeted sequencing (Spearman correlation of mutant allele fractions to fragmentation profiles=0.74). This correlation is remarkable as genome-wide and mutation-based methods are orthogonal and examine different cfDNA alterations that may be suppressed in these patients due to prior therapy. Notably all cases that had progression free survival of six or more months displayed a drop of or had extremely low levels of ctDNA after initiation of therapy as determined by fragmentation profiles, while cases with poor clinical outcome had increases in ctDNA. These results demonstrate the feasibility of fragmentation analyses for detecting the presence of tumor-derived cfDNA, and suggests that such analyses may also be useful for quantitative monitoring of cancer patients during treatment.

[0091] The fragmentation profiles were examined in the context of known copy number changes in a patient where parallel analyses of tumor tissue were obtained. These analyses demonstrated that altered fragmentation profiles were present in regions of the genome that were copy neutral and that these may be further affected in regions with copy number changes (FIG. 11a and FIG. 12a). Position dependent differences in fragmentation patterns could be used to distinguish cancer-derived cfDNA from healthy cfDNA in these regions (FIG. 12a, b), while overall cfDNA fragment size measurements would have missed such differences (FIG. 12a).

[0092] These analyses were extended to an independent cohort of cancer patients and healthy individuals. Whole genome sequencing of cfDNA at 1-2.times. coverage from a total of 208 patients with cancer, including breast (n=54), colorectal (n=27), lung (n=12), ovarian (n=28), pancreatic (n=34), gastric (n=27), or bile duct cancers (n=26), as well as 215 individuals without cancer was performed (Table 1 (Appendix A) and Table 4 (Appendix D)). All cancer patients were treatment naive and the majority had resectable disease (n=183). After GC adjustment of short and long cfDNA fragment coverage (FIG. 13a), coverage and size characteristics of fragments in windows throughout the genome were examined (FIG. 11b, Table 4 (Appendix D) and Table 7 (Appendix G)). Genome-wide correlations of coverage to GC content were limited and no differences in these correlations between cancer patients and healthy individuals were observed (FIG. 13b). Healthy individuals had highly concordant fragmentation profiles, while patients with cancer had high variability with decreased correlation to the median healthy profile (Table 7; Appendix G). An analysis of the most commonly altered fragmentation windows in the genome among cancer patients revealed a median of 60 affected windows across the cancer types analyzed, highlighting the multitude of position dependent alterations in fragmentation of cfDNA in individuals with cancer (FIG. 11c).

[0093] To determine if position dependent fragmentation changes can be used to detect individuals with cancer, a gradient tree boosting machine learning model was implemented to examine whether cfDNA can be categorized as having characteristics of a cancer patient or healthy individual and estimated performance characteristics of this approach by ten-fold cross validation repeated ten times (FIGS. 14 and 15). The machine learning model included GC-adjusted short and long fragment coverage characteristics in windows throughout the genome. A machine learning classifier for copy number changes from chromosomal arm dependent features rather than a single score was also developed (FIG. 16a and Table 8 (Appendix H)) and mitochondrial copy number changes were also included (FIG. 16b) as these could also help distinguish cancer from healthy individuals. Using this implementation of DELFI, a score was obtained that could be used to classify patients as healthy or having cancer. 152 of the 208 cancer patients were detected (73% sensitivity, 95% CI 67%-79%) while four of the 215 healthy individuals were misclassified (98% specificity) (Table 9). At a threshold of 95% specificity, 80% of patients with cancer were detected (95% CI, 74%-85%), including 79% of resectable (stage I-III) patients (145 of 183) and 82% of metastatic (stage IV) patients (18 out of 22) (Table 9). Receiver operator characteristic analyses for detection of patients with cancer had an AUC of 0.94 (95% CI 0.92-0.96), ranged among cancer types from 0.86 for pancreatic cancer to .gtoreq.0.99 for lung and ovarian cancers (FIGS. 17a and 17b), and had AUCs .gtoreq.0.92 across all stages (FIG. 18). The DELFI classifier score did not differ with age among either cancer patients or healthy individuals (Table 1; Appendix A).

TABLE-US-00001 TABLE 9 DELFI performance for cancer detection. 95% specificity 98% specificity Individuals Individuals Individuals analyzed detected Sensitivity 95% Cl detected Sensitivity 95% Cl Healthy 215 10 -- -- 4 -- -- Cancer 208 166 80% 74%-85% 152 73% 67%-79% Type Breast 54 38 70% 56%-82% 31 57% 43%-71% Bile duct 26 23 88% 70%-98% 21 81% 61%-93% Colorectal 27 22 81% 62%-94% 19 70% 50%-86% Gastric 27 22 81% 62%-94% 22 81% 62%-94% Lung 12 12 100% 74%-100% 12 100% 74%-100% Ovarian 28 25 89% 72%-98% 25 89% 72%-98% Pancreatic 34 24 71% 53%-85% 22 65% 46%-80% Stage I 41 30 73% 53%-86% 28 68% 52%-82% II 109 85 78% 69%-85% 78 72% 62%-80% III 33 30 91% 76%-98% 26 79% 61%-91% IV 22 18 82% 60%-95% 17 77% 55%-92% 0, X 3 3 100% 29%-100% 3 100% 29%-100%

[0094] To assess the contribution of fragment size and coverage, chromosome arm copy number, or mitochondrial mapping to the predictive accuracy of the model, the repeated 10-fold cross-validation procedure was implemented to assess performance characteristics of these features in isolation. It was observed that fragment coverage features alone (AUC=0.94) were nearly identical to the classifier that combined all features (AUC=0.94) (FIG. 17a). In contrast, analyses of chromosomal copy number changes had lower performance (AUC=0.88) but were still more predictive than copy number changes based on individual scores (AUC=0.78) or mitochondrial mapping (AUC=0.72) (FIG. 17a). These results suggest that fragment coverage is the major contributor to our classifier. Including all features in the prediction model may contribute in a complementary fashion for detection of patients with cancer as they can be obtained from the same genome sequence data.

[0095] As fragmentation profiles reveal regional differences in fragmentation that may differ between tissues, a similar machine learning approach was used to examine whether cfDNA patterns could identify the tissue of origin of these tumors. It was found that this approach had a 61% accuracy (95% CI 53%-67%), including 76% for breast, 44% for bile duct, 71% for colorectal, 67% for gastric, 53% for lung, 48% for ovarian, and 50% for pancreatic cancers (FIG. 19, Table 10). The accuracy increased to 75% (95% CI 69%-81%) when considering assigning patients with abnormal cfDNA to one of two sites of origin (Table 10). For all tumor types, the classification of the tissue of origin by DELFI was significantly higher than determined by random assignment (p<0.01, binomial test, Table 10).

TABLE-US-00002 TABLE 10 DELFI tissue of origin prediction Cancer Patients Top Prediction Top Two Predictions Random Assignment Type Detected* Patients Accuracy (95% Cl) Patients Accuracy (95% Cl) Patients Accuracy Breast 42 32 76% (61%-88%) 38 91% (77%-97%) 9 22% Bile Duct 23 10 44% (23%-66%) 15 65% (43%-84%) 3 12% Colorectal 24 17 71% (49%-87%) 19 79% (58%-93%) 3 12% Gastric 24 16 67% (45%-84%) 19 79% (58%-93%) 3 12% Lung 30 16 53% (34%-72%) 23 77% (58%-90%) 2 6% Ovarian 27 13 48% (29%-68%) 16 59% (38%-78%) 4 14% Pancreatic 24 12 50% (29%-71%) 16 67% (45%-84%) 3 12% Total 194 116 61% (53%-67%) 146 75% (69%-81%) 26 13% *Patients detected are based on DELFI detection at 90% specificity. Lung cohort includes additional lung cancer patients with prior therapy.

[0096] As cancer-specific sequence alterations can be used to identify patients with cancer, it was evaluated whether combining DELFI with this approach could increase the sensitivity of cancer detection (FIG. 20). An analysis of cfDNA from a subset of the treatment naive cancer patients using both DELFI and targeted sequencing revealed that 82% (103 of 126) of patients had fragmentation profile alterations, while 66% (83 of 126) had sequence alterations. Over 89% of cases with mutant allele fractions >1% were detected by DELFI while for cases with mutant allele fractions <1% the fraction detected by DELFI was 80%, including for cases that were undetectable using targeted sequencing (Table 7; Appendix G). When these approaches were used together, the combined sensitivity of detection increased to 91% (115 of 126 patients) with a specificity of 98% (FIG. 20).

[0097] Overall, genome-wide cfDNA fragmentation profiles are different between cancer patients and healthy individuals. The variability in fragment lengths and coverage in a position dependent manner throughout the genome may explain the apparently contradictory observations of previous analyses of cfDNA at specific loci or of overall fragment sizes. In patients with cancer, heterogeneous fragmentation patterns in cfDNA appear to be a result of mixtures of nucleosomal DNA from both blood and neoplastic cells. These studies provide a method for simultaneous analysis of tens to potentially hundreds of tumor-specific abnormalities from minute amounts of cfDNA, overcoming a limitation that has precluded the possibility of more sensitive analyses of cfDNA. DELFI analyses detected a higher fraction of cancer patients than previous cfDNA analysis methods that have focused on sequence or overall fragmentation sizes (see, e.g., Phallen et al., 2017 Sci Transl Med 9:eaan2415; Cohen et al., 2018 Science 359:926; Newman et al., 2014 Nat Med 20:548; Bettegowda et al., 2014 Sci Transl Med 6:224ra24; Newman et al., 2016 Nat Biotechnol 34:547). As demonstrated in this Example, combining DELFI with analyses of other cfDNA alterations may further increase the sensitivity of detection. As fragmentation profiles appear related to nucleosomal DNA patterns, DELFI may be used for determining the primary source of tumor-derived cfDNA. The identification of the source of circulating tumor DNA in over half of patients analyzed may be further improved by including clinical characteristics, other biomarkers, including methylation changes, and additional diagnostic approaches (Ruibal Morell, 1992 The International journal of biological markers 7:160; Galli et al., 2013 Clinical chemistry and laboratory medicine 51:1369; Sikaris, 2011 Heart, lung &circulation 20:634; Cohen et al., 2018 Science 359:926). Finally, this approach requires only a small amount of whole genome sequencing, without the need for deep sequencing typical of approaches that focus on specific alterations. The performance characteristics and limited amount of sequencing needed for DELFI suggests that our approach could be broadly applied for screening and management of patients with cancer.

[0098] These results demonstrate that genome-wide cfDNA fragmentation profiles are different between cancer patients and healthy individuals. As such, cfDNA fragmentation profiles can have important implications for future research and applications of non-invasive approaches for detection of human cancer.

OTHER EMBODIMENTS

[0099] It is to be understood that while the invention has been described in conjunction with the detailed description thereof, the foregoing description is intended to illustrate and not limit the scope of the invention, which is defined by the scope of the appended claims. Other aspects, advantages, and modifications are within the scope of the following claims.

TABLE-US-00003 APPENDIX A Table 1. Summary or patients and samples analyzed Whole Age Degree Location of Volume cfDNA Genome Targeted at Site of Histopa- of Metastases of Ex- cfDNA Fragment Fragment Targeted Patient Sample Diag- TNM Primary thological Differ- at Plasma tracted Input Profile Profile Mutation Patient Type Type Timepoint nosis Gender Stage Staging Tumor Diagnosis entiation Diagnosis (ml) (ng/ml) (ng/ml) Analysis Analysis Analysis CGCRC291 Colorectal cfDNA Preoperative 69 F IV T3N2M1 Coecum Adencarcinoma Moderate Synchronous 7.9 7.80 7.80 Y Y Y Cancer treatment naive Liver CGCRC292 Colorectal cfDNA Preoperative 51 M IV T3N2M1 Sigmod Adencarcinoma Moderate Synchronous 7.9 6.73 6.73 Y Y Y Cancer treatment naive Colon Liver, Lung CGCRC293 Colorectal cfDNA Preoperative 55 M IV T3N2M1 Rectum Adencarcinoma Moderate Synchronous 7.2 3.83 3.83 Y Y Y Cancer treatment naive Liver CGCRC294 Colorectal cfDNA Preoperative 67 F II T3N0M0 Sigmod Adencarcinoma Moderate None 8.4 18.87 18.87 Y Y Y Cancer treatment naive Colon CGCRC296 Colorectal cfDNA Preoperative 76 F II T4N0M0 Coecum Adencarcinoma Poor None 4.3 31.24 31.24 Y Y Y Cancer treatment naive CGCRC299 Colorectal cfDNA Preoperative 71 M I T1N0M0 Rectum Adencarcinoma Moderate None 8.8 10.18 10.18 Y Y Y Cancer treatment naive CGCRC300 Colorectal cfDNA Preoperative 65 M I T2N0M0 Rectum Adencarcinoma Moderate None 4.3 10.48 10.48 Y Y Y Cancer treatment naive CGCRC301 Colorectal cfDNA Preoperative 76 F I T2N0M0 Rectum Adencarcinoma Moderate None 4.1 6.51 6.51 Y Y Y Cancer treatment naive CGCRC302 Colorectal cfDNA Preoperative 73 M II T3N0M0 Traverse Adencarcinoma Moderate None 4.3 52.13 52.13 Y Y Y Cancer treatment naive Colon CGCRC304 Colorectal cfDNA Preoperative 86 F II T3N0M0 Rectum Adencarcinoma Moderate None 4.1 30.19 30.19 Y Y Y Cancer treatment naive CGCRC305 Colorectal cfDNA Preoperative 83 F II T3N0M0 Traverse Adencarcinoma Moderate None 8.6 9.10 9.10 Y Y Y Cancer treatment naive Colon CGCRC306 Colorectal cfDNA Preoperative 80 F II T4N0M0 Ascending Adencarcinoma Moderate None 4.5 24.31 24.31 Y Y Y Cancer treatment naive Colon CGCRC307 Colorectal cfDNA Preoperative 78 F II T3N0M0 Ascending Adencarcinoma Moderate None 8.5 14.26 14.26 Y Y Y Cancer treatment naive Colon CGCRC308 Colorectal cfDNA Preoperative 72 F III T4N2M0 Ascending Adencarcinoma Moderate None 4.3 46.37 46.37 Y Y Y Cancer treatment naive Colon CGCRC311 Colorectal cfDNA Preoperative 59 M I T2N0M0 Sigmod Adencarcinoma Moderate None 8.5 3.91 3.91 Y Y Y Cancer treatment naive Colon CGCRC315 Colorectal cfDNA Preoperative 74 M III T3N1M0 Sigmod Adencarcinoma Moderate None 8.6 9.67 9.67 Y Y Y Cancer treatment naive Colon CGCRC316 Colorectal cfDNA Preoperative 80 M III T3N2M0 Traverse Adencarcinoma Moderate None 4.9 52.16 52.16 Y Y Y Cancer treatment naive Colon CGCRC317 Colorectal cfDNA Preoperative 74 M III T3N2M0 Descending Adencarcinoma Moderate None 8.8 16.08 16.08 Y Y Y Cancer treatment naive Colon CGCRC318 Colorectal cfDNA Preoperative 81 M I T2N0M0 Coecum Adencarcinoma Moderate None 9.8 18.24 18.24 Y Y Y Cancer treatment naive CGCRC319 Colorectal cfDNA Preoperative 80 F III T2N1M0 Descending Adencarcinoma Moderate None 4.2 53.84 53.84 Y N Y Cancer treatment naive Colon CGCRC320 Colorectal cfDNA Preoperative 73 F I T2N0M0 Ascending Adencarcinoma Moderate None 4.5 30.37 30.37 Y Y Y Cancer treatment naive Colon CGCRC321 Colorectal cfDNA Preoperative 68 M I T2N0M0 Rectum Adencarcinoma Moderate None 9.3 4.25 4.25 Y Y Y Cancer treatment naive CGCRC333 Colorectal cfDNA Preoperative NA F IV NA Colon/ Adencarcinoma NA Liver 4.0 113.88 113.88 Y Y Y Cancer treatment naive Rectum CGCRC336 Colorectal cfDNA Preoperative NA M IV NA Colon/ Adencarcinoma NA Liver 4.4 211.74 211.74 Y Y Y Cancer treatment naive Rectum CGCRC338 Colorectal cfDNA Preoperative NA F IV NA Colon/ Adencarcinoma NA Liver 2.3 109.76 109.76 Y Y Y Cancer treatment naive Rectum CGCRC341 Colorectal cfDNA Preoperative NA F IV NA Colon/ Adencarcinoma NA Liver 4.6 156.62 156.62 Y N Y Cancer treatment naive Rectum CGCRC342 Colorectal cfDNA Preoperative NA M IV NA Colon/ Adencarcinoma NA Liver 3.9 56.09 56.09 Y N Y Cancer treatment naive Rectum CGLU316 Lung cfDNA Pre-treatment, 50 F IV T3N2M0 Left Upper Adeno, Poor Lung 5.0 2.38 2.38 Y N Y Cancer Day 53 Lobe of Lung Squamous, Small Cell Carcinoma CGLU316 Lung cfDNA Pre-treatment, 50 F IV T3N2M0 Left Upper Adeno, Poor Lung 5.0 2.11 2.11 Y N Y Cancer Day -4 Lobe of Lung Squamous, Small Cell Carcinoma CGLU316 Lung cfDNA Post-treatment, 50 F IV T3N2M0 Left Upper Adeno, Poor Lung 5.0 0.87 1.07 Y N Y Cancer Day 18 Lobe of Lung Squamous, Small Cell Carcinoma CGLU316 Lung cfDNA Post-treatment, 50 F IV T3N2M0 Left Upper Adeno, Poor Lung 2.0 8.74 8.75 Y N Y Cancer Day 87 Lobe of Lung Squamous, Small Cell Carcinoma CGLU344 Lung cfDNA Pre-treatment, 65 F IV T2N2M1 Right Upper Adencarcinoma NA Pleura, 5.0 34.77 25.00 Y N Y Cancer Day -21 Lobe of Lung Liver, Pentoneum CGLU344 Lung cfDNA Pre-treatment, 65 F IV T2N2M1 Right Upper Adencarcinoma NA Pleura, 5.0 15.63 15.64 Y N Y Cancer Day 0 Lobe of Lung Liver, Pentoneum CGLU344 Lung cfDNA Post-treatment, 65 F IV T2N2M1 Right Upper Adencarcinoma NA Pleura, 5.0 9.22 9.22 Y N Y Cancer Day 0.1875 Lobe of Lung Liver, Pentoneum CGLU344 Lung cfDNA Post-treatment, 65 F IV T2N2M1 Right Upper Adencarcinoma NA Pleura, 5.0 5.31 5.32 Y N Y Cancer Day 59 Lobe of Lung Liver, Pentoneum CGLU369 Lung cfDNA Pre-treatment, 48 F IV T2NxM1 Right Upper Adencarcinoma NA Brain 2.0 11.28 11.28 Y N Y Cancer Day -2 Lobe of Lung CGLU369 Lung cfDNA Post-treatment, 48 F IV T2NxM1 Right Upper Adencarcinoma NA Brain 5.0 10.09 10.09 Y N Y Cancer Day 12 Lobe of Lung CGLU369 Lung cfDNA Post-treatment, 48 F IV T2NxM1 Right Upper Adencarcinoma NA Brain 5.0 6.69 6.70 Y N Y Cancer Day 88 Lobe of Lung CGLU369 Lung cfDNA Post-treatment, 48 F IV T2NxM1 Right Upper Adencarcinoma NA Brain 5.0 8.41 8.42 Y N Y Cancer Day 110 Lobe of Lung CGLU373 Lung cfDNA Pre-treatment, 56 F IV T3N1M0 Right Upper Adencarcinoma Moderate None 5.0 6.35 6.35 Y N Y Cancer Day -2 Lobe of Lung CGLU373 Lung cfDNA Post-treatment, 56 F IV T3N1M0 Right Upper Adencarcinoma Moderate None 5.0 6.28 6.28 Y N Y Cancer Day 0.125 Lobe of Lung CGLU373 Lung cfDNA Post-treatment, 56 F IV T3N1M0 Right Upper Adencarcinoma Moderate None 5.0 3.82 3.82 Y N Y Cancer Day 7 Lobe of Lung CGLU373 Lung cfDNA Post-treatment, 56 F IV T3N1M0 Right Upper Adencarcinoma Moderate None 3.5 5.55 5.55 Y N Y Cancer Day 47 Lobe of Lung CGPLBR100 Breast cfDNA Preoperative 44 F III T2N2M0 Left Breast Infiltrating NA None 4.0 4.25 4.25 Y N Y Cancer treatment naive Ductal Carcinoma CGPLBR101 Breast cfDNA Preoperative 46 F II T2N1M0 Left Breast Infiltrating Moderate None 4.0 37.88 37.88 Y N Y Cancer treatment naive Lobular Carcinoma CGPLBR102 Breast cfDNA Preoperative 47 F II T2N1M0 Right Breast Infiltrating Moderate None 3.6 13.67 13.67 Y N Y Cancer treatment naive Ductal Carcinoma CGPLBR103 Breast cfDNA Preoperative 48 F II T2N1M0 Left Breast Infiltrating Moderate None 3.6 7.11 7.11 Y N Y Cancer treatment naive Ductal Carcinoma CGPLBR104 Breast cfDNA Preoperative 68 F II T2N0M0 Right Breast Infiltrating Moderate None 4.7 19.89 19.89 Y N Y Cancer treatment naive Lobular Carcinoma CGPLBR12 Breast cfDNA Preoperative NA F III NA Breast Ductal NA NA 4.3 4.21 4.21 Y N N Cancer treatment naive Carcinoma insitu with Microinvasion CGPLBR18 Breast cfDNA Preoperative NA F III NA Breast Infiltrating NA NA 4.1 40.39 30.49 Y N N Cancer treatment naive Lobular Carcinoma CGPLBR23 Breast cfDNA Preoperative 53 F II NA Breast Infiltrating NA None 4.7 20.09 20.09 Y N N Cancer treatment naive Ductal Carcinoma CGPLBR24 Breast cfDNA Preoperative 53 F II NA Breast Infiltrating NA None 3.6 58.33 34.72 Y N N Cancer treatment naive Ductal Carcinoma CGPLBR28 Breast cfDNA Preoperative 59 F III NA Breast Infiltrating NA None 4.2 12.86 12.86 Y N N Cancer treatment naive Ductal Carcinoma CGPLBR30 Breast cfDNA Preoperative 61 F II NA Breast Infiltrating NA None 4.1 59.73 30.49 Y N N Cancer treatment naive Ductal Carcinoma CGPLBR31 Breast cfDNA Preoperative 54 F II NA Breast Infiltrating NA None 3.4 23.94 23.94 Y N N Cancer treatment naive Ductal Carcinoma CGPLBR32 Breast cfDNA Preoperative NA F II NA Breast Infiltrating NA None 4.4 71.23 28.41 Y N N Cancer treatment naive Ductal Carcinoma CGPLBR33 Breast cfDNA Preoperative 47 F II NA Breast Infiltrating NA None 4.4 11.00 11.00 Y N N Cancer treatment naive Lobular Carcinoma CGPLBR34 Breast cfDNA Preoperative 60 F II NA Breast Infiltrating NA None 4.4 23.61 23.61 Y N N Cancer treatment naive Lobular Carcinoma CGPLBR35 Breast cfDNA Preoperative 43 F II NA Breast Ductal NA None 4.5 22.58 22.58 Y N N Cancer treatment naive Carcinoma insitu with Microinvasion CGPLBR36 Breast cfDNA Preoperative 36 F II NA Breast Infiltrating NA None 4.4 17.73 17.73 Y N N Cancer treatment naive Ductal Carcinoma CGPLBR37 Breast cfDNA Preoperative 58 F II NA Breast Infiltrating NA None 4.4 9.39 9.39 Y N N Cancer treatment naive Ductal Carcinoma CGPLBR38 Breast cfDNA Preoperative 54 F I T1N0M0 Left Breast Infiltrating Moderate None 4.0 5.77 5.77 Y Y Y Cancer treatment naive Ductal Carcinoma CGPLBR40 Breast cfDNA Preoperative 66 F III T2N2M0 Left Breast Infiltrating Poor None 4.6 15.69 15.69 Y Y Y Cancer treatment naive Ductal Carcinoma CGPLBR41 Breast cfDNA Preoperative 51 F III T3N1M0 Left Breast Infiltrating Moderate None 4.5 11.56 11.56 Y N Y Cancer treatment naive Ductal Carcinoma CGPLBR45 Breast cfDNA Preoperative 57 F II NA Breast Infiltrating NA None 4.5 20.36 20.36 Y N N Cancer treatment naive Ductal Carcinoma CGPLBR46 Breast cfDNA Preoperative 54 F III NA Breast Infiltrating NA None 3.5 20.17 20.17 Y N N Cancer treatment naive Ductal Carcinoma CGPLBR47 Breast cfDNA Preoperative 54 F I NA Breast Infiltrating NA None 4.5 13.89 13.89 Y N N Cancer treatment naive Ductal

Carcinoma CGPLBR48 Breast cfDNA Preoperative 47 F II T2N1M0 Left Breast Infiltrating Poor None 3.9 7.07 7.07 Y Y Y Cancer treatment naive Ductal Carcinoma CGPLBR49 Breast cfDNA Preoperative 37 F II T2N1M0 Left Breast Infiltrating Poor None 4.0 5.74 5.74 Y N Y Cancer treatment naive Ductal Carcinoma CGPLBR50 Breast cfDNA Preoperative 51 F I NA Breast Infiltrating NA None 4.5 45.58 27.78 Y N N Cancer treatment naive Ductal Carcinoma CGPLBR51 Breast cfDNA Preoperative 53 F II NA Breast Infiltrating NA None 4.0 8.83 8.83 Y N N Cancer treatment naive Ductal Carcinoma CGPLBR52 Breast cfDNA Preoperative 68 F III NA Breast Infiltrating NA None 4.5 80.71 27.78 Y N N Cancer treatment naive Ductal Carcinoma CGPLBR55 Breast cfDNA Preoperative 53 F III T3N1M0 Right Breast Infiltrating Poor None 4.3 4.57 4.57 Y Y Y Cancer treatment naive Ductal Carcinoma CGPLBR56 Breast cfDNA Preoperative 56 F II NA Breast Infiltrating NA None 4.5 22.16 22.16 Y N N Cancer treatment naive Ductal Carcinoma CGPLBR57 Breast cfDNA Preoperative 54 F III T2N2M0 Left Breast Infiltrating NA None 4.3 4.02 4.02 Y N Y Cancer treatment naive Ductal Carcinoma CGPLBR59 Breast cfDNA Preoperative 42 F I T1N0M0 Left Breast Infiltrating Moderate None 4.1 8.24 8.24 Y N Y Cancer treatment naive Ductal Carcinoma CGPLBR60 Breast cfDNA Preoperative 61 F II NA Left Breast Infiltrating NA None 4.5 11.09 11.09 Y N N Cancer treatment naive Ductal Carcinoma CGPLBR61 Breast cfDNA Preoperative 67 F II T2N1M0 Left Breast Infiltrating Moderate None 4.1 13.25 13.25 Y N Y Cancer treatment naive Ductal Carcinoma CGPLBR63 Breast cfDNA Preoperative 48 F II T2N1M0 Left Breast Infiltrating Moderate None 4.0 6.19 6.19 Y Y Y Cancer treatment naive Ductal Carcinoma CGPLBR65 Breast cfDNA Preoperative 50 F II NA Left Breast Infiltrating NA None 3.5 41.75 35.71 Y N N Cancer treatment naive Ductal Carcinoma CGPLBR68 Breast cfDNA Preoperative 64 F III T4N1M0 Breast Infiltrating Poor None 3.4 10.41 10.41 Y N Y Cancer treatment naive Ductal Carcinoma CGPLBR69 Breast cfDNA Preoperative 43 F II T2N0M0 Breast Infiltrating Moderate None 4.4 4.07 4.07 Y Y Y Cancer treatment naive Ductal Carcinoma CGPLBR70 Breast cfDNA Preoperative 60 F II T2N1M0 Breast Infiltrating Moderate None 3.4 11.94 11.94 Y Y Y Cancer treatment naive Ductal Carcinoma CGPLBR71 Breast cfDNA Preoperative 65 F II T2N0M0 Breast Infiltrating Poor None 3.1 7.64 7.64 Y Y Y Cancer treatment naive Ductal Carcinoma CGPLBR72 Breast cfDNA Preoperative 67 F II T2N0M0 Breast Infiltrating Well None 3.9 4.43 4.43 Y Y Y Cancer treatment naive Ductal Carcinoma CGPLBR73 Breast cfDNA Preoperative 60 F II T2N1M0 Breast Infiltrating Moderate None 3.3 14.69 14.69 Y Y Y Cancer treatment naive Ductal Carcinoma CGPLBR76 Breast cfDNA Preoperative 53 F II T2N0M0 Right Breast Infiltrating Well None 4.9 8.71 8.71 Y Y Y Cancer treatment naive Ductal Carcinoma CGPLBR81 Breast cfDNA Preoperative 54 F II NA Breast Infiltrating NA None 2.5 83.14 50.00 Y N N Cancer treatment naive Ductal Carcinoma CGPLBR82 Breast cfDNA Preoperative 70 F I T1N0M0 Right Breast Infiltrating Moderate None 4.8 23.39 23.39 Y N Y Cancer treatment naive Lobular Carcinoma CGPLBR83 Breast cfDNA Preoperative 53 F II T2N1M0 Right Breast Infiltrating Moderate None 3.7 100.17 100.17 Y Y Y Cancer treatment naive Ductal Carcinoma CGPLBR84 Breast cfDNA Preoperative NA F III NA Breast Infiltrating NA NA 3.6 16.95 16.95 Y N N Cancer treatment naive Ductal Carcinoma CGPLBR87 Breast cfDNA Preoperative 80 F II T2N1M0 Right Breast Papilary Well None 3.6 277.39 69.44 Y Y Y Cancer treatment naive Carcinoma CGPLBR88 Breast cfDNA Preoperative 48 F II T1N1M0 Left Breast Infiltrating Poor None 3.6 49.75 49.75 Y Y Y Cancer treatment naive Ductal Carcinoma CGPLBR90 Breast cfDNA Preoperative 51 F II NA Right Breast Infiltrating NA None 3.0 14.24 14.24 Y N N Cancer treatment naive Ductal Carcinoma CGPLBR91 Breast cfDNA Preoperative 62 F III T2N2M0 Breast Infiltrating Poor None 3.2 22.41 22.41 Y N Y Cancer treatment naive Lobular Carcinoma CGPLBR92 Breast cfDNA Preoperative 58 F II T2N1M0 Breast Infiltrating Poor None 3.1 81.00 81.00 Y Y Y Cancer treatment naive Meduilary Carcinoma CGPLBR93 Breast cfDNA Preoperative 59 F II T1N0M0 Breast Infiltrating Moderate None 3.3 27.94 27.94 Y N Y Cancer treatment naive Ductal Carcinoma CGPLH189 Healthy cfDNA Preoperative 74 M NA NA NA NA NA NA 5.0 5.84 5.84 Y N N treatment naive CGPLH190 Healthy cfDNA Preoperative 67 M NA NA NA NA NA NA 4.7 18.07 18.07 Y N N treatment naive CGPLH192 Healthy cfDNA Preoperative 74 M NA NA NA NA NA NA 4.7 12.19 12.19 Y N N treatment naive CGPLH193 Healthy cfDNA Preoperative 72 F NA NA NA NA NA NA 5.0 5.47 5.47 Y N N treatment naive CGPLH194 Healthy cfDNA Preoperative 75 F NA NA NA NA NA NA 5.0 9.98 9.98 Y N N treatment naive CGPLH196 Healthy cfDNA Preoperative 64 M NA NA NA NA NA NA 5.0 11.69 11.69 Y N N treatment naive CGPLH197 Healthy cfDNA Preoperative 74 M NA NA NA NA NA NA 5.0 5.69 5.69 Y N N treatment naive CGPLH198 Healthy cfDNA Preoperative 66 M NA NA NA NA NA NA 5.0 4.36 4.36 Y N N treatment naive CGPLH199 Healthy cfDNA Preoperative 75 F NA NA NA NA NA NA 5.0 9.77 9.77 Y N N treatment naive CGPLH200 Healthy cfDNA Preoperative 51 M NA NA NA NA NA NA 5.0 5.60 5.60 Y N N treatment naive CGPLH201 Healthy cfDNA Preoperative 68 F NA NA NA NA NA NA 5.0 8.82 8.82 Y N N treatment naive CGPLH202 Healthy cfDNA Preoperative 73 M NA NA NA NA NA NA 5.0 5.54 5.54 Y N N treatment naive CGPLH203 Healthy cfDNA Preoperative 59 M NA NA NA NA NA NA 5.0 9.03 9.03 Y N N treatment naive CGPLH205 Healthy cfDNA Preoperative 68 F NA NA NA NA NA NA 5.0 4.74 4.74 Y N N treatment naive CGPLH208 Healthy cfDNA Preoperative 75 F NA NA NA NA NA NA 5.0 4.67 4.67 Y N N treatment naive CGPLH209 Healthy cfDNA Preoperative 74 M NA NA NA NA NA NA 5.0 5.15 5.15 Y N N treatment naive CGPLH210 Healthy cfDNA Preoperative 75 M NA NA NA NA NA NA 5.0 5.41 5.41 Y N N treatment naive CGPLH211 Healthy cfDNA Preoperative 75 F NA NA NA NA NA NA 5.0 6.24 6.24 Y N N treatment naive CGPLH300 Hettithy cfDNA Preoperative 72 F NA NA NA NA NA NA 4.4 6.75 6.75 Y N N treatment naive CGPLH307 Healthy cfDNA Preoperative 53 M NA NA NA NA NA NA 4.5 3.50 3.50 Y N N treatment naive CGPLH308 Healthy cfDNA Preoperative 60 M NA NA NA NA NA NA 4.5 6.01 6.01 Y N N treatment naive CGPLH309 Healthy cfDNA Preoperative 61 F NA NA NA NA NA NA 4.5 5.21 5.21 Y N N treatment naive CGPLH310 Healthy cfDNA Preoperative 55 F NA NA NA NA NA NA 4.5 15.25 15.25 Y N N treatment naive CGPLH311 Healthy cfDNA Preoperative 50 M NA NA NA NA NA NA 4.5 4.47 4.47 Y N N treatment naive CGPLH314 Healthy cfDNA Preoperative 59 M NA NA NA NA NA NA 4.5 9.62 9.62 Y N N treatment naive CGPLH314 Healthy cfDNA, Preoperative 59 M NA NA NA NA NA NA 4.4 16.24 16.24 Y N N technical treatment naive replicate CGPLH315 Healthy cfDNA Preoperative 59 F NA NA NA NA NA NA 4.2 11.55 11.55 Y N N treatment naive CGPLH316 Healthy cfDNA Preoperative 64 M NA NA NA NA NA NA 4.5 28.92 27.79 Y N N treatment naive CGPLH317 Healthy cfDNA Preoperative 53 F NA NA NA NA NA NA 4.5 7.62 7.62 Y N N treatment naive CGPLH319 Healthy cfDNA Preoperative 60 F NA NA NA NA NA NA 4.2 4.41 4.41 Y N N treatment naive CGPLH320 Healthy cfDNA Preoperative 75 F NA NA NA NA NA NA 4.5 6.93 6.93 Y N N treatment naive CGPLH322 Healthy cfDNA Preoperative 53 F NA NA NA NA NA NA 4.2 8.17 8.17 Y N N treatment naive CGPLH324 Healthy cfDNA Preoperative 59 F NA NA NA NA NA NA 5.0 6.63 6.63 Y N N treatment naive CGPLH325 Healthy cfDNA Preoperative 54 M NA NA NA NA NA NA 4.6 4.15 4.15 Y N N treatment naive CGPLH326 Healthy cfDNA Preoperative 67 F NA NA NA NA NA NA 4.5 6.06 6.06 Y N N treatment naive CGPLH327 Healthy cfDNA Preoperative 50 M NA NA NA NA NA NA 4.8 1.24 1.24 Y N N treatment naive CGPLH328 Healthy cfDNA, Preoperative 68 F NA NA NA NA NA NA 4.4 3.42 3.42 Y N N technical treatment naive replicate CGPLH328 Healthy cfDNA Preoperative 68 F NA NA NA NA NA NA 4.9 5.47 5.47 Y N N treatment naive CGPLH329 Healthy cfDNA Preoperative 59 M NA NA NA NA NA NA 4.5 5.27 5.27 Y N N treatment naive CGPLH330 Healthy cfDNA Preoperative 75 M NA NA NA NA NA NA 4.3 10.21 10.21 Y N N treatment naive CGPLH331 Healthy cfDNA Preoperative 55 M NA NA NA NA NA NA 4.6 2.63 2.63 Y N N treatment naive CGPLH331 Healthy cfDNA, Preoperative 55 M NA NA NA NA NA NA 4.3 4.15 4.15 Y N N technical treatment naive replicate CGPLH333 Healthy cfDNA Preoperative 60 M NA NA NA NA NA NA 4.7 4.06 4.06 Y N N

treatment naive CGPLH335 Healthy cfDNA Preoperative 74 M NA NA NA NA NA NA 4.4 9.39 9.39 Y N N treatment naive CGPLH336 Healthy cfDNA Preoperative 53 F NA NA NA NA NA NA 4.6 6.64 6.64 Y N N treatment naive CGPLH337 Healthy cfDNA Preoperative 53 F NA NA NA NA NA NA 4.2 4.48 4.48 Y N N treatment naive CGPLH338 Healthy cfDNA Preoperative 75 M NA NA NA NA NA NA 4.5 59.44 59.44 Y N N treatment naive CGPLH339 Healthy cfDNA Preoperative 70 M NA NA NA NA NA NA 4.5 12.27 12.27 Y N N treatment naive CGPLH340 Healthy cfDNA Preoperative 62 M NA NA NA NA NA NA 4.5 4.86 4.86 Y N N treatment naive CGPLH341 Healthy cfDNA Preoperative 61 F NA NA NA NA NA NA 4.1 7.62 7.62 Y N N treatment naive CGPLH342 Healthy cfDNA Preoperative 49 F NA NA NA NA NA NA 4.2 18.29 18.29 Y N N treatment naive CGPLH343 Healthy cfDNA Preoperative 58 M NA NA NA NA NA NA 4.5 3.49 3.49 Y N N treatment naive CGPLH344 Healthy cfDNA Preoperative 50 M NA NA NA NA NA NA 4.2 8.41 8.41 Y N N treatment naive CGPLH345 Healthy cfDNA Preoperative 55 F NA NA NA NA NA NA 4.5 9.73 9.73 Y N N treatment naive CGPLH346 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.5 7.86 7.86 Y N N treatment naive CGPLH35 Healthy cfDNA Preoperative 48 F NA NA NA NA NA NA 4.0 13.15 13.15 Y N Y treatment naive CGPLH350 Healthy cfDNA Preoperative 65 M NA NA NA NA NA NA 3.5 6.09 6.09 Y N N treatment naive CGPLH351 Healthy cfDNA Preoperative 71 M NA NA NA NA NA NA 4.0 15.91 15.91 Y N N treatment naive CGPLH352 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.2 6.47 6.47 Y N N treatment naive CGPLH353 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.2 4.47 4.47 Y N N treatment naive CGPLH354 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.2 17.49 17.49 Y N N treatment naive CGPLH355 Healthy cfDNA Preoperative 70 M NA NA NA NA NA NA 4.2 11.58 11.58 Y N N treatment naive CGPLH356 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.5 3.84 3.84 Y N N treatment naive CGPLH357 Healthy cfDNA Preoperative 52 F NA NA NA NA NA NA 4.2 11.79 11.79 Y N N treatment naive CGPLH358 Healthy cfDNA Preoperative 55 M NA NA NA NA NA NA 4.2 21.08 21.08 Y N N treatment naive CGPLH36 Healthy cfDNA Preoperative 36 F NA NA NA NA NA NA 4.0 13.00 13.00 Y N Y treatment naive CGPLH360 Healthy cfDNA Preoperative 60 M NA NA NA NA NA NA 4.2 3.48 3.48 Y N N treatment naive CGPLH361 Healthy cfDNA Preoperative 50 M NA NA NA NA NA NA 4.3 6.98 6.98 Y N N treatment naive CGPLH362 Healthy cfDNA Preoperative 72 F NA NA NA NA NA NA 4.4 8.49 8.49 Y N N treatment naive CGPLH363 Healthy cfDNA Preoperative 68 F NA NA NA NA NA NA 4.5 4.44 4.44 Y N N treatment naive CGPLH364 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.5 17.31 17.31 Y N N treatment naive CGPLH365 Healthy cfDNA Preoperative 68 F NA NA NA NA NA NA 4.5 0.55 0.55 Y N N treatment naive CGPLH366 Healthy cfDNA Preoperative 61 M NA NA NA NA NA NA 4.5 4.88 4.88 Y N N treatment naive CGPLH367 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.4 6.48 6.48 Y N N treatment naive CGPLH368 Healthy cfDNA Preoperative 50 M NA NA NA NA NA NA 4.3 2.53 2.53 Y N N treatment naive CGPLH369 Healthy cfDNA Preoperative 55 F NA NA NA NA NA NA 4.3 10.18 10.18 Y N N treatment naive CGPLH369 Healthy cfDNA, Preoperative 55 F NA NA NA NA NA NA 4.4 10.71 10.71 Y N N technical treatment naive replicate CGPLH37 Healthy cfDNA Preoperative 39 F NA NA NA NA NA NA 4.0 9.73 9.73 Y N Y treatment naive CGPLH370 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.5 7.22 7.22 Y N N treatment naive CGPLH371 Healthy cfDNA Preoperative 57 F NA NA NA NA NA NA 4.6 5.62 5.62 Y N N treatment naive CGPLH380 Healthy cfDNA Preoperative 53 F NA NA NA NA NA NA 4.2 6.61 6.61 Y N N treatment naive CGPLH381 Healthy cfDNA Preoperative 56 F NA NA NA NA NA NA 4.2 27.38 27.38 Y N N treatment naive CGPLH382 Healthy cfDNA Preoperative 53 F NA NA NA NA NA NA 4.5 11.58 11.58 Y N N treatment naive CGPLH383 Healthy cfDNA Preoperative 62 F NA NA NA NA NA NA 4.5 25.50 25.50 Y N N treatment naive CGPLH384 Healthy cfDNA Preoperative 50 M NA NA NA NA NA NA 4.5 15.66 15.66 Y N N treatment naive CGPLH385 Healthy cfDNA Preoperative 69 M NA NA NA NA NA NA 4.5 19.35 19.35 Y N N treatment naive CGPLH386 Healthy cfDNA Preoperative 62 M NA NA NA NA NA NA 4.5 6.46 6.46 Y N N treatment naive CGPLH386 Healthy cfDNA, Preoperative 62 M NA NA NA NA NA NA 4.6 6.54 6.54 Y N N technical treatment naive replicate CGPLH387 Healthy cfDNA Preoperative 71 F NA NA NA NA NA NA 4.5 6.19 6.19 Y N N treatment naive CGPLH388 Healthy cfDNA Preoperative 57 F NA NA NA NA NA NA 4.5 6.62 6.62 Y N N treatment naive CGPLH389 Healthy cfDNA Preoperative 73 F NA NA NA NA NA NA 4.6 14.78 14.78 Y N N treatment naive CGPLH390 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.5 12.14 12.14 Y N N treatment naive CGPLH391 Healthy cfDNA Preoperative 58 M NA NA NA NA NA NA 4.5 8.88 8.88 Y N N treatment naive CGPLH391 Healthy cfDNA, Preoperative 58 M NA NA NA NA NA NA 4.5 8.37 8.37 Y N N technical treatment naive replicate CGPLH392 Healthy cfDNA Preoperative 57 F NA NA NA NA NA NA 4.5 8.39 8.39 Y N N treatment naive CGPLH393 Healthy cfDNA Preoperative 54 M NA NA NA NA NA NA 4.5 5.27 5.27 Y N N treatment naive CGPLH394 Healthy cfDNA Preoperative 55 F NA NA NA NA NA NA 4.4 3.79 3.79 Y N N treatment naive CGPLH395 Healthy cfDNA Preoperative 56 F NA NA NA NA NA NA 4.4 9.56 9.56 Y N N treatment naive CGPLH395 Healthy cfDNA, Preoperative 56 F NA NA NA NA NA NA 4.4 5.40 5.40 Y N N technical treatment naive replicate CGPLH396 Healthy cfDNA Preoperative 50 M NA NA NA NA NA NA 4.4 20.31 20.31 Y N N treatment naive CGPLH398 Healthy cfDNA Preoperative 68 M NA NA NA NA NA NA 4.3 13.01 13.01 Y N N treatment naive CGPLH399 Healthy cfDNA Preoperative 62 F NA NA NA NA NA NA 4.4 4.79 4.79 Y N N treatment naive CGPLH400 Healthy cfDNA Preoperative 64 M NA NA NA NA NA NA 4.4 7.70 7.70 Y N N treatment naive CGPLH400 Healthy cfDNA, Preoperative 64 M NA NA NA NA NA NA 4.4 6.26 6.26 Y N N technical treatment naive replicate CGPLH401 Healthy cfDNA Preoperative 50 M NA NA NA NA NA NA 4.3 13.01 13.01 Y N N treatment naive CGPLH401 Healthy cfDNA, Preoperative 50 M NA NA NA NA NA NA 4.4 11.13 11.13 Y N N technical treatment naive replicate CGPLH402 Healthy cfDNA Preoperative 57 F NA NA NA NA NA NA 4.5 2.89 2.89 Y N N treatment naive CGPLH403 Healthy cfDNA Preoperative 64 M NA NA NA NA NA NA 4.3 4.41 4.41 Y N N treatment naive CGPLH404 Healthy cfDNA Preoperative 50 M NA NA NA NA NA NA 4.2 6.38 6.38 Y N N treatment naive CGPLH405 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.4 7.28 7.28 Y N N treatment naive CGPLH406 Healthy cfDNA Preoperative 57 M NA NA NA NA NA NA 4.2 5.40 5.40 Y N N treatment naive CGPLH407 Healthy cfDNA Preoperative 75 F NA NA NA NA NA NA 4.0 13.30 13.30 Y N N treatment naive CGPLH408 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.2 5.18 5.18 Y N N treatment naive CGPLH409 Healthy cfDNA Preoperative 53 M NA NA NA NA NA NA 3.7 3.98 3.98 Y N N treatment naive CGPLH410 Healthy cfDNA Preoperative 52 M NA NA NA NA NA NA 4.1 6.91 6.91 Y N N treatment naive CGPLH411 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.1 3.30 3.30 Y N N treatment naive CGPLH412 Healthy cfDNA Preoperative 53 F NA NA NA NA NA NA 4.1 5.55 5.55 Y N N treatment naive CGPLH413 Healthy cfDNA Preoperative 54 F NA NA NA NA NA NA 4.5 8.18 8.18 Y N N treatment naive CGPLH414 Healthy cfDNA Preoperative 56 M NA NA NA NA NA NA 3.8 5.85 5.85 Y N N treatment naive CGPLH415 Healthy cfDNA Preoperative 59 M NA NA NA NA NA NA 4.7 10.20 10.20 Y N N treatment naive CGPLH416 Healthy cfDNA Preoperative 58 F NA NA NA NA NA NA 4.5 11.73 11.73 Y N N treatment naive CGPLH417 Healthy cfDNA Preoperative 70 M NA NA NA NA NA NA 4.2 10.98 10.98 Y N N treatment naive CGPLH418 Healthy cfDNA Preoperative 70 F NA NA NA NA NA NA 4.5 10.96 10.96 Y N N treatment naive CGPLH419 Healthy cfDNA Preoperative 65 F NA NA NA NA NA NA 4.5 10.17 10.17 Y N N treatment naive CGPLH42 Healthy cfDNA Preoperative 54 F NA NA NA NA NA NA 4.0 14.30 14.30

Y N Y treatment naive CGPLH420 Healthy cfDNA Preoperative 51 M NA NA NA NA NA NA 4.2 12.32 12.32 Y N N treatment naive CGPLH422 Healthy cfDNA Preoperative 68 F NA NA NA NA NA NA 4.6 5.42 5.42 Y N N treatment naive CGPLH423 Healthy cfDNA Preoperative 54 M NA NA NA NA NA NA 4.2 2.85 2.85 Y N N treatment naive CGPLH424 Healthy cfDNA Preoperative 53 F NA NA NA NA NA NA 4.7 1.66 1.66 Y N N treatment naive CGPLH425 Healthy cfDNA Preoperative 53 F NA NA NA NA NA NA 4.4 5.98 5.98 Y N N treatment naive CGPLH426 Healthy cfDNA Preoperative 68 M NA NA NA NA NA NA 4.4 2.84 2.84 Y N N treatment naive CGPLH427 Healthy cfDNA Preoperative 68 M NA NA NA NA NA NA 4.4 10.86 10.86 Y N N treatment naive CGPLH428 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.5 6.27 6.27 Y N N treatment naive CGPLH429 Healthy cfDNA Preoperative 63 F NA NA NA NA NA NA 4.5 3.89 3.89 Y N N treatment naive CGPLH43 Healthy cfDNA Preoperative 49 F NA NA NA NA NA NA 4.0 8.50 8.50 Y N Y treatment naive CGPLH430 Healthy cfDNA Preoperative 69 F NA NA NA NA NA NA 4.2 10.33 10.33 Y N N treatment naive CGPLH431 Healthy cfDNA Preoperative 59 F NA NA NA NA NA NA 4.8 12.81 12.81 Y N N treatment naive CGPLH432 Healthy cfDNA Preoperative 59 F NA NA NA NA NA NA 4.8 2.42 2.42 Y N N treatment naive CGPLH434 Healthy cfDNA Preoperative 59 M NA NA NA NA NA NA 4.6 8.83 8.83 Y N N treatment naive CGPLH435 Healthy cfDNA Preoperative 50 M NA NA NA NA NA NA 4.5 8.95 8.95 Y N N treatment naive CGPLH436 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.5 4.29 4.29 Y N N treatment naive CGPLH437 Healthy cfDNA Preoperative 56 M NA NA NA NA NA NA 4.6 18.07 18.07 Y N N treatment naive CGPLH438 Healthy cfDNA Preoperative 69 M NA NA NA NA NA NA 4.8 16.62 16.62 Y N N treatment naive CGPLH439 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.7 4.38 4.38 Y N N treatment naive CGPLH440 Healthy cfDNA Preoperative 72 M NA NA NA NA NA NA 4.7 4.32 4.32 Y N N treatment naive CGPLH441 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.7 7.80 7.80 Y N N treatment naive CGPLH442 Healthy cfDNA Preoperative 59 F NA NA NA NA NA NA 4.5 6.15 6.15 Y N N treatment naive CGPLH443 Healthy cfDNA Preoperative 52 F NA NA NA NA NA NA 4.4 3.44 3.44 Y N N treatment naive CGPLH444 Healthy cfDNA Preoperative 60 F NA NA NA NA NA NA 4.4 4.12 4.12 Y N N treatment naive CGPLH445 Healthy cfDNA Preoperative 53 F NA NA NA NA NA NA 4.4 4.36 4.36 Y N N treatment naive CGPLH446 Healthy cfDNA Preoperative 51 F NA NA NA NA NA NA 4.4 2.92 2.92 Y N N treatment naive CGPLH447 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.6 3.87 3.87 Y N N treatment naive CGPLH448 Healthy cfDNA Preoperative 51 F NA NA NA NA NA NA 4.4 5.29 5.29 Y N N treatment naive CGPLH449 Healthy cfDNA Preoperative 51 F NA NA NA NA NA NA 4.5 3.77 3.77 Y N N treatment naive CGPLH45 Healthy cfDNA Preoperative 58 F NA NA NA NA NA NA 4.0 10.85 10.85 Y N Y treatment naive CGPLH450 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.5 5.62 5.62 Y N N treatment naive CGPLH451 Healthy cfDNA Preoperative 54 F NA NA NA NA NA NA 4.6 7.24 7.24 Y N N treatment naive CGPLH452 Healthy cfDNA Preoperative 69 M NA NA NA NA NA NA 4.4 2.54 2.54 Y N N treatment naive CGPLH453 Healthy cfDNA Preoperative 53 F NA NA NA NA NA NA 4.6 9.11 9.11 Y N N treatment naive CGPLH455 Healthy cfDNA Preoperative 55 F NA NA NA NA NA NA 4.4 2.64 2.64 Y N N treatment naive CGPLH455 Healthy cfDNA, Preoperative 55 F NA NA NA NA NA NA 4.5 2.42 2.42 Y N N technical treatment naive replicate CGPLH456 Healthy cfDNA Preoperative 54 F NA NA NA NA NA NA 4.5 3.11 3.11 Y N N treatment naive CGPLH457 Healthy cfDNA Preoperative 54 F NA NA NA NA NA NA 4.4 5.92 5.92 Y N N treatment naive CGPLH458 Healthy cfDNA Preoperative 52 F NA NA NA NA NA NA 4.5 16.04 16.04 Y N N treatment naive CGPLH459 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.4 6.52 6.52 Y N N treatment naive CGPLH46 Healthy cfDNA Preoperative 35 F NA NA NA NA NA NA 4.0 8.25 8.25 Y N Y treatment naive CGPLH460 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.6 5.24 5.24 Y N N treatment naive CGPLH463 Healthy cfDNA Preoperative 53 F NA NA NA NA NA NA 4.5 22.77 22.77 Y N N treatment naive CGPLH464 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.4 2.90 2.90 Y N N treatment naive CGPLH465 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.5 4.76 4.76 Y N N treatment naive CGPLH466 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.6 5.68 5.68 Y N N treatment naive CGPLH466 Healthy cfDNA, Preoperative 50 F NA NA NA NA NA NA 4.5 6.75 6.75 Y N N technical treatment naive replicate CGPLH467 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.5 4.59 4.59 Y N N treatment naive CGPLH468 Healthy cfDNA Preoperative 53 M NA NA NA NA NA NA 4.5 11.19 11.19 Y N N treatment naive CGPLH469 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.5 3.25 3.25 Y N N treatment naive CGPLH47 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.0 7.43 7.43 Y N Y treatment naive CGPLH470 Healthy cfDNA Preoperative 68 F NA NA NA NA NA NA 4.5 13.64 13.64 Y N N treatment naive CGPLH471 Healthy cfDNA Preoperative 70 F NA NA NA NA NA NA 4.3 13.00 13.00 Y N N treatment naive CGPLH472 Healthy cfDNA Preoperative 69 F NA NA NA NA NA NA 4.2 10.17 10.17 Y N N treatment naive CGPLH473 Healthy cfDNA Preoperative 62 M NA NA NA NA NA NA 4.3 2.98 2.98 Y N N treatment naive CGPLH474 Healthy cfDNA Preoperative 63 M NA NA NA NA NA NA 4.3 29.15 29.15 Y N N treatment naive CGPLH475 Healthy cfDNA Preoperative 67 F NA NA NA NA NA NA 4.0 7.26 7.26 Y N N treatment naive CGPLH476 Healthy cfDNA Preoperative 65 F NA NA NA NA NA NA 4.3 6.16 6.16 Y N N treatment naive CGPLH477 Healthy cfDNA Preoperative 61 F NA NA NA NA NA NA 4.3 15.21 15.21 Y N N treatment naive CGPLH478 Healthy cfDNA Preoperative 51 F NA NA NA NA NA NA 4.4 7.29 7.29 Y N N treatment naive CGPLH479 Healthy cfDNA Preoperative 52 M NA NA NA NA NA NA 4.5 8.73 8.73 Y N N treatment naive CGPLH48 Healthy cfDNA Preoperative 38 F NA NA NA NA NA NA 4.0 6.38 6.38 Y N Y treatment naive CGPLH480 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.4 10.62 10.62 Y N N treatment naive CGPLH481 Healthy cfDNA Preoperative 53 F NA NA NA NA NA NA 4.3 6.75 6.75 Y N N treatment naive CGPLH482 Healthy cfDNA Preoperative 50 M NA NA NA NA NA NA 4.3 23.58 23.58 Y N N treatment naive CGPLH483 Healthy cfDNA Preoperative 66 M NA NA NA NA NA NA 4.4 14.44 14.44 Y N N treatment naive CGPLH484 Healthy cfDNA Preoperative 72 M NA NA NA NA NA NA 4.2 14.32 14.32 Y N N treatment naive CGPLH485 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.3 9.64 9.64 Y N N treatment naive CGPLH486 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.3 10.16 10.16 Y N N treatment naive CGPLH487 Healthy cfDNA Preoperative 50 M NA NA NA NA NA NA 4.4 6.11 6.11 Y N N treatment naive CGPLH488 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.5 7.88 7.88 Y N N treatment naive CGPLH49 Healthy cfDNA Preoperative 39 F NA NA NA NA NA NA 4.0 6.60 6.60 Y N Y treatment naive CGPLH490 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.5 4.18 4.18 Y N N treatment naive CGPLH491 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.5 13.16 13.16 Y N N treatment naive CGPLH492 Healthy cfDNA Preoperative 51 F NA NA NA NA NA NA 4.5 3.83 3.83 Y N N treatment naive CGPLH493 Healthy cfDNA Preoperative 64 M NA NA NA NA NA NA 4.5 25.06 25.06 Y N N treatment naive CGPLH494 Healthy cfDNA Preoperative 53 F NA NA NA NA NA NA 4.4 5.24 5.24 Y N N treatment naive CGPLH495 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.4 5.03 5.03 Y N N treatment naive CGPLH496 Healthy cfDNA Preoperative 74 M NA NA NA NA NA NA 4.5 34.01 27.78 Y N N treatment naive CGPLH497 Healthy cfDNA Preoperative 68 F NA NA NA NA NA NA 4.5 8.24 8.24 Y N N treatment naive CGPLH497 Healthy cfDNA, Preoperative 68 F NA NA NA NA NA NA 4.4 5.88 5.88 Y N N technical treatment naive replicate CGPLH498 Healthy cfDNA Preoperative 54 F NA NA NA NA NA NA 4.4 5.33 5.33 Y N N treatment naive

CGPLH499 Healthy cfDNA Preoperative 52 F NA NA NA NA NA NA 4.5 7.85 7.85 Y N N treatment naive CGPLH50 Healthy cfDNA Preoperative 55 F NA NA NA NA NA NA 4.0 7.05 7.05 Y N Y treatment naive CGPLH500 Healthy cfDNA Preoperative 51 F NA NA NA NA NA NA 4.5 3.49 3.49 Y N N treatment naive CGPLH501 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.3 6.29 6.29 Y N N treatment naive CGPLH502 Healthy cfDNA Preoperative 53 F NA NA NA NA NA NA 4.5 2.24 2.24 Y N N treatment naive CGPLH503 Healthy cfDNA Preoperative 67 M NA NA NA NA NA NA 4.5 11.01 11.01 Y N N treatment naive CGPLH504 Healthy cfDNA Preoperative 57 F NA NA NA NA NA NA 4.3 6.60 6.60 Y N N treatment naive CGPLH504 Healthy cfDNA, Preoperative 57 F NA NA NA NA NA NA 4.2 10.02 10.02 Y N N technical treatment naive replicate CGPLH505 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.1 5.23 5.23 Y N N treatment naive CGPLH506 Healthy cfDNA Preoperative 51 F NA NA NA NA NA NA 4.5 12.23 12.23 Y N N treatment naive CGPLH507 Healthy cfDNA Preoperative 56 F NA NA NA NA NA NA 4.1 9.89 9.89 Y N N treatment naive CGPLH508 Healthy cfDNA Preoperative 54 M NA NA NA NA NA NA 4.5 8.66 8.66 Y N N treatment naive CGPLH508 Healthy cfDNA, Preoperative 54 F NA NA NA NA NA NA 4.4 9.55 9.55 Y N N technical treatment naive replicate CGPLH509 Healthy cfDNA Preoperative 60 M NA NA NA NA NA NA 4.0 9.79 9.79 Y N N treatment naive CGPLH51 Healthy cfDNA Preoperative 48 F NA NA NA NA NA NA 4.0 7.85 7.85 Y N Y treatment naive CGPLH510 Healthy cfDNA Preoperative 67 M NA NA NA NA NA NA 4.2 14.20 14.20 Y N N treatment naive CGPLH511 Healthy cfDNA Preoperative 75 M NA NA NA NA NA NA 4.5 12.94 12.94 Y N N treatment naive CGPLH512 Healthy cfDNA Preoperative 52 M NA NA NA NA NA NA 4.3 8.60 8.60 Y N N treatment naive CGPLH513 Healthy cfDNA Preoperative 57 M NA NA NA NA NA NA 4.3 6.54 6.54 Y N N treatment naive CGPLH514 Healthy cfDNA Preoperative 55 F NA NA NA NA NA NA 4.4 10.94 10.94 Y N N treatment naive CGPLH515 Healthy cfDNA Preoperative 68 F NA NA NA NA NA NA 4.5 8.71 8.71 Y N N treatment naive CGPLH516 Healthy cfDNA Preoperative 65 F NA NA NA NA NA NA 4.5 7.32 7.32 Y N N treatment naive CGPLH517 Healthy cfDNA Preoperative 54 F NA NA NA NA NA NA 4.6 5.16 5.16 Y N N treatment naive CGPLH517 Healthy cfDNA, Preoperative 54 F NA NA NA NA NA NA 4.5 9.74 9.74 Y N N technical treatment naive replicate CGPLH518 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.4 5.92 5.92 Y N N treatment naive CGPLH519 Healthy cfDNA Preoperative 54 M NA NA NA NA NA NA 4.4 6.96 6.96 Y N N treatment naive CGPLH52 Healthy cfDNA Preoperative 40 F NA NA NA NA NA NA 4.0 9.90 9.90 Y N Y treatment naive CGPLH520 Healthy cfDNA Preoperative 51 F NA NA NA NA NA NA 4.3 8.27 8.27 Y N N treatment naive CGPLH54 Healthy cfDNA Preoperative 47 F NA NA NA NA NA NA 4.0 14.18 14.18 Y N Y treatment naive CGPLH55 Healthy cfDNA Preoperative 46 F NA NA NA NA NA NA 4.0 7.35 7.35 Y N Y treatment naive CGPLH56 Healthy cfDNA Preoperative 42 F NA NA NA NA NA NA 4.0 5.20 5.20 Y N Y treatment naive CGPLH57 Healthy cfDNA Preoperative 39 F NA NA NA NA NA NA 4.0 7.15 7.15 Y N Y treatment naive CGPLH59 Healthy cfDNA Preoperative 34 F NA NA NA NA NA NA 4.0 6.03 6.03 Y N Y treatment naive CGPLH625 Healthy cfDNA Preoperative 53 F NA NA NA NA NA NA 4.5 2.64 2.64 Y N N treatment naive CGPLH625 Healthy cfDNA Preoperative 53 F NA NA NA NA NA NA 4.5 1.69 1.69 Y N N treatment naive CGPLH626 Healthy cfDNA, Preoperative 50 F NA NA NA NA NA NA 4.0 11.12 11.12 Y N N technical treatment naive replicate CGPLH63 Healthy cfDNA Preoperative 47 F NA NA NA NA NA NA 4.0 10.10 10.10 Y N Y treatment naive CGPLH639 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.5 2.00 2.00 Y N N treatment naive CGPLH64 Healthy cfDNA Preoperative 55 F NA NA NA NA NA NA 4.0 8.03 8.03 Y N Y treatment naive CGPLH640 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.5 9.36 9.36 Y N N treatment naive CGPLH642 Healthy cfDNA Preoperative 54 F NA NA NA NA NA NA 4.5 4.99 4.99 Y N N treatment naive CGPLH643 Healthy cfDNA Preoperative 55 F NA NA NA NA NA NA 4.4 7.12 7.12 Y N N treatment naive CGPLH644 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.4 5.06 5.06 Y N N treatment naive CGPLH646 Healthy cfDNA Preoperative 50 F NA NA NA NA NA NA 4.4 6.75 6.75 Y N N treatment naive CGPLH75 Healthy cfDNA Preoperative 46 F NA NA NA NA NA NA 4.0 3.87 3.87 Y N Y treatment naive CGPLH76 Healthy cfDNA Preoperative 53 F NA NA NA NA NA NA 4.0 4.03 4.03 Y N Y treatment naive CGPLH77 Healthy cfDNA Preoperative 46 F NA NA NA NA NA NA 4.0 5.89 5.89 Y N Y treatment naive CGPLH78 Healthy cfDNA Preoperative 34 F NA NA NA NA NA NA 4.0 2.51 2.51 Y N Y treatment naive CGPLH79 Healthy cfDNA Preoperative 37 F NA NA NA NA NA NA 4.0 3.68 3.68 Y N Y treatment naive CGPLH80 Healthy cfDNA Preoperative 37 F NA NA NA NA NA NA 4.0 1.94 1.94 Y N Y treatment naive CGPLH81 Healthy cfDNA Preoperative 54 F NA NA NA NA NA NA 4.0 5.16 5.16 Y N Y treatment naive CGPLH82 Healthy cfDNA Preoperative 38 F NA NA NA NA NA NA 4.0 3.30 3.30 Y N Y treatment naive CGPLH83 Healthy cfDNA Preoperative 60 F NA NA NA NA NA NA 4.0 5.04 5.04 Y N Y treatment naive CGPLH84 Healthy cfDNA Preoperative 45 F NA NA NA NA NA NA 4.0 3.33 3.33 Y N Y treatment naive CGPLLU13 Lung cfDNA Pre-treatment, 72 F IV T1BN2bM1a Right Adenocarcinoma NA Bone 5.0 7.67 7.67 Y N Y Cancer Day 2 Lung CGPLLU13 Lung cfDNA Post-treatment, 72 F IV T1BN2bM1a Right Adenocarcinoma NA Bone 4.5 8.39 8.39 Y N Y Cancer Day 5 Lung CGPLLU13 Lung cfDNA Post-treatment, 72 F IV T1BN2bM1a Right Adenocarcinoma NA Bone 3.2 8.66 8.66 Y N Y Cancer Day 28 Lung CGPLLU13 Lung cfDNA Post-treatment, 72 F IV T1BN2bM1a Right Adenocarcinoma NA Bone 5.0 5.97 5.97 Y N Y Cancer Day 91 Lung CGPLLU14 Lung cfDNA Pre-treatment, 55 F IV T1N1M0 Right Lower Adenocarcinoma Moderate NA 2.0 2.55 2.55 Y N Y Cancer Day -38 Lobe of Lung CGPLLU14 Lung cfDNA Pre-treatment, 55 F IV T1N1M0 Right Lower Adenocarcinoma Moderate NA 2.0 2.55 2.55 Y N Y Cancer Day -16 Lobe of Lung CGPLLU14 Lung cfDNA Pre-treatment, 55 F IV T1N1M0 Right Lower Adenocarcinoma Moderate NA 2.0 2.55 2.55 Y N Y Cancer Day -8 Lobe of Lung CGPLLU14 Lung cfDNA Pre-treatment, 55 F IV T1N1M0 Right Lower Adenocarcinoma Moderate NA 2.0 2.55 2.55 Y N Y Cancer Day 0 Lobe of Lung CGPLLU14 Lung cfDNA Post-treatment, 55 F IV T1N1M0 Right Lower Adenocarcinoma Moderate NA 2.0 2.55 2.55 Y N Y Cancer Day 0.33 Lobe of Lung CGPLLU14 Lung cfDNA Post-treatment, 55 F IV T1N1M0 Right Lower Adenocarcinoma Moderate NA 2.0 2.55 2.55 Y N Y Cancer Day 7 Lobe of Lung CGPLLU144 Lung cfDNA Preoperative 52 M II T2aN1M0 Lung Adenocarcinoma Poor None 3.5 31.51 31.51 Y Y Y Cancer treatment naive CGPLLU147 Lung cfDNA Preoperative 60 M III T3N2M0 Lung Adenosquamous Poor None 3.8 6.72 6.72 Y Y Y Cancer treatment naive Carcinoma CGPLLU151 Lung cfDNA Preoperative 41 F II T3N2M0 Lung Adenocarcinoma Well None 4.0 83.04 83.04 Y N Y Cancer treatment naive CGPLLU162 Lung cfDNA Preoperative 38 M II T1N1M0 Right Adenocarcinoma Moderate None 3.1 40.32 40.32 Y Y Y Cancer treatment naive Lung CGPLLU163 Lung cfDNA Preoperative 66 M II T1N1M0 Left Adenocarcinoma Poor None 5.0 54.03 54.03 Y Y Y Cancer treatment naive Lung CGPLLU165 Lung cfDNA Preoperative 68 F II T1N1M0 Right Adenocarcinoma Well None 4.5 20.13 20.13 Y Y Y Cancer treatment naive Lung CGPLLU168 Lung cfDNA Preoperative 70 F I T2aN0M0 Lung Adenocarcinoma Poor None 4.3 19.38 19.38 Y Y Y Cancer treatment naive CGPLLU169 Lung cfDNA Preoperative 64 M I T1bN0M0 Lung Squamous Cel Moderate None 4.2 13.70 13.70 Y N Y Cancer treatment naive Carcinoma CGPLLU175 Lung cfDNA Preoperative 47 M I T2N0M0 Lung Squamous Cel Moderate None 4.4 16.84 16.84 Y Y Y Cancer treatment naive Carcinoma CGPLLU176 Lung cfDNA Preoperative 58 M I T2N0M0 Lung Adenosquamous Moderate None 3.2 7.86 7.86 Y Y Y Cancer treatment naive Carcinoma CGPLLU177 Lung cfDNA Preoperative 45 M II T3N0M0 Right Adenocarcinoma NA None 3.9 19.07 19.07 Y Y Y Cancer treatment naive Lung CGPLLU180 Lung cfDNA Preoperative 57 M I T2N0M0 Right Large Cel Poor None 3.2 19.31 19.31 Y Y Y Cancer treatment naive Lung Carcinoma CGPLLU198 Lung cfDNA Preoperative 49 F I T2N0M0 Left Adenocarcinoma Moderate None 4.2 14.09 14.09 Y Y Y Cancer treatment naive Lung CGPLLU202 Lung cfDNA Preoperative 68 M I T2aN0M0 Right Adenocarcinoma NA None 4.4 24.72 24.72 Y Y Y Cancer treatment naive Lung CGPLLU203 Lung cfDNA Preoperative 68 M II T3N0M0 Right Squamous Cel Well None 4.2 26.24 26.24 Y N Y Cancer treatment naive Lung Carcinoma CGPLLU205 Lung cfDNA Preoperative 65 M II T3N0M0 Left Adenocarcinoma Poor None 4.0 18.56 18.56 Y Y Y Cancer treatment naive Lung CGPLLU206 Lung cfDNA Preoperative 55 M III T3N1M0 Right Squamous Cel Poor None 3.5 18.24 18.24 Y Y Y Cancer treatment naive Lung Carcinoma CGPLLU207 Lung cfDNA Preoperative 60 F II T2N1M0 Lung Adenocarcinoma Well None 4.0 17.29 17.29 Y Y Y Cancer treatment naive CGPLLU208 Lung cfDNA Preoperative 56 F II T2N1M0 Lung Adenocarcinoma

Moderate None 3.0 24.34 24.34 Y Y Y Cancer treatment naive CGPLLU209 Lung cfDNA Preoperative 65 M II T2aN0M0 Lung Large Cel Poor None 5.5 53.95 53.95 Y Y Y Cancer treatment naive Carcinoma CGPLLU244 Lung cfDNA Pre-treatment, 66 F IV NA Right Upper Adenocarcinoma Moderate/ Liver, Rib, 4.5 17.84 17.84 Y N Y Cancer Day -7 Lobe of Lung Poor Brain, Pleura CGPLLU244 Lung cfDNA Pre-treatment, 66 F IV NA Right Upper Adenocarcinoma Moderate/ Liver, Rib, 4.5 17.84 17.84 Y N Y Cancer Day -1 Lobe of Lung Poor Brain, Pleura CGPLLU244 Lung cfDNA Post-treatment, 66 F IV NA Right Upper Adenocarcinoma Moderate/ Liver, Rib, 4.5 17.84 17.84 Y N Y Cancer Day 6 Lobe of Lung Poor Brain, Pleura CGPLLU244 Lung cfDNA Post-treatment, 66 F IV NA Right Upper Adenocarcinoma Moderate/ Liver, Rib, 4.5 17.84 17.84 Y N Y Cancer Day 62 Lobe of Lung Poor Brain, Pleura CGPLLU245 Lung cfDNA Pre-treatment, 49 M IV T2aN2M1B Left Upper Adenocarcinoma NA Brain 4.7 19.42 19.42 Y N Y Cancer Day 32 Lobe of Lung CGPLLU245 Lung cfDNA Pre-treatment, 49 M IV T2aN2M1B Left Upper Adenocarcinoma NA Brain 4.7 19.42 19.42 Y N Y Cancer Day 0 Lobe of Lung CGPLLU245 Lung cfDNA Post-treatment, 49 M IV T2aN2M1B Left Upper Adenocarcinoma NA Brain 4.7 19.42 19.42 Y N Y Cancer Day 7 Lobe of Lung CGPLLU245 Lung cfDNA Post-treatment, 49 M IV T2aN2M1B Left Upper Adenocarcinoma NA Brain 4.7 19.42 19.42 Y N Y Cancer Day 21 Lobe of Lung CGPLLU246 Lung cfDNA Pre-treatment, 65 F IV NA Right Lower Adenocarcinoma Poor Peura 5.5 18.51 18.51 Y N Y Cancer Day -21 Lobe of Lung CGPLLU246 Lung cfDNA Pre-treatment, 65 F IV NA Right Lower Adenocarcinoma Poor Peura 5.5 18.51 18.51 Y N Y Cancer Day 0 Lobe of Lung CGPLLU246 Lung cfDNA Post-treatment, 65 F IV NA Right Lower Adenocarcinoma Poor Peura 5.5 18.51 18.51 Y N Y Cancer Day 9 Lobe of Lung CGPLLU246 Lung cfDNA Post-treatment, 65 F IV NA Right Lower Adenocarcinoma Poor Peura 5.5 18.51 18.51 Y N Y Cancer Day 42 Lobe of Lung CGPLLU264 Lung cfDNA Pre-treatment, 84 M IV T4N2BM1 Left Adenocarcinoma NA Lung 4.0 22.97 22.97 Y N Y Cancer Day -1 Middle Lung CGPLLU264 Lung cfDNA Post-treatment, 84 M IV T4N2BM1 Left Adenocarcinoma NA Lung 4.5 10.53 10.53 Y N Y Cancer Day 8 Middle Lung CGPLLU264 Lung cfDNA Post-treatment, 84 M IV T4N2BM1 Left Adenocarcinoma NA Lung 3.0 7.15 7.15 Y N Y Cancer Day 27 Middle Lung CGPLLU264 Lung cfDNA Post-treatment, 84 M IV T4N2BM1 Left Adenocarcinoma NA Lung 4.0 9.60 9.60 Y N Y Cancer Day 69 Middle Lung CGPLLU265 Lung cfDNA Pre-treatment, 71 F IV T1N0Mx Left Lower Adenocarcinoma NA None 4.2 7.16 7.16 Y N Y Cancer Day 0 Lobe of Lung CGPLLU265 Lung cfDNA Post-treatment, 71 F IV T1N0Mx Left Lower Adenocarcinoma NA None 4.0 8.11 8.11 Y N Y Cancer Day 3 Lobe of Lung CGPLLU265 Lung cfDNA Post-treatment, 71 F IV T1N0Mx Left Lower Adenocarcinoma NA None 4.2 7.53 7.53 Y N Y Cancer Day 7 Lobe of Lung CGPLLU265 Lung cfDNA Post-treatment, 71 F IV T1N0Mx Left Lower Adenocarcinoma NA None 5.0 16.17 16.17 Y N Y Cancer Day 84 Lobe of Lung CGPLLU266 Lung cfDNA Pre-treatment, 78 M IV T2aN1 Left Lower Adenocarcinoma Moderate None 5.0 5.32 5.32 Y N Y Cancer Day 0 Lobe of Lung CGPLLU266 Lung cfDNA Post-treatment, 78 M IV T2aN1 Left Lower Adenocarcinoma Moderate None 3.5 6.31 6.31 Y N Y Cancer Day 16 Lobe of Lung CGPLLU266 Lung cfDNA Post-treatment, 78 M IV T2aN1 Left Lower Adenocarcinoma Moderate None 5.0 7.64 7.64 Y N Y Cancer Day 83 Lobe of Lung CGPLLU266 Lung cfDNA Post-treatment, 78 M IV T2aN1 Left Lower Adenocarcinoma Moderate None 5.0 14.39 14.39 Y N Y Cancer Day 328 Lobe of Lung CGPLLU267 Lung cfDNA Pre-treatment, 55 F IV T3NxM1a Right Upper Squamous Cel Poor Lung 4.5 2.87 2.87 Y N Y Cancer Day -1 Lobe of Lung Carcinoma CGPLLU267 Lung cfDNA Post-treatment, 55 F IV T3NxM1a Right Upper Squamous Cel Poor Lung 4.5 3.34 3.34 Y N Y Cancer Day 34 Lobe of Lung Carcinoma CGPLLU267 Lung cfDNA Post-treatment, 55 F IV T3NxM1a Right Upper Squamous Cel Poor Lung 3.5 3.00 3.00 Y N Y Cancer Day 90 Lobe of Lung Carcinoma CGPLLU269 Lung cfDNA Pre-treatment, 52 F IV T1CNxM1C Right Adenocarcinoma NA Brain, Liver, 5.0 11.40 11.40 Y N Y Cancer Day 0 Paratracheal Bone, Peura Lesion CGPLLU269 Lung cfDNA Post-treatment, 52 F IV T1CNxM1C Right Adenocarcinoma NA Brain, Liver, 5.0 8.35 8.35 Y N Y Cancer Day 9 Paratracheal Bone, Peura Lesion CGPLLU269 Lung cfDNA Post-treatment, 52 F IV T1CNxM1C Right Adenocarcinoma NA Brain, Liver, 3.5 17.79 17.79 Y N Y Cancer Day 28 Paratracheal Bone, Peura Lesion CGPLLU271 Lung cfDNA Post-treatment, 73 M IV T1aNxM1 Left Upper Adenocarcinoma Moderate Peura 4.0 4.70 4.70 Y N Y Cancer Day 259 Lobe of Lung CGPLLU271 Lung cfDNA Pre-treatment, 73 M IV T1aNxM1 Left Upper Adenocarcinoma Moderate Peura 5.0 18.86 18.86 Y N Y Cancer Day 0 Lobe of Lung CGPLLU271 Lung cfDNA Post-treatment, 73 M IV T1aNxM1 Left Upper Adenocarcinoma Moderate Peura 4.5 13.84 13.84 Y N Y Cancer Day 8 Lobe of Lung CGPLLU271 Lung cfDNA Post-treatment, 73 M IV T1aNxM1 Left Upper Adenocarcinoma Moderate Peura 3.5 13.46 13.46 Y N Y Cancer Day 20 Lobe of Lung CGPLLU271 Lung cfDNA Post-treatment, 73 M IV T1aNxM1 Left Upper Adenocarcinoma Moderate Peura 4.0 13.77 13.77 Y N Y Cancer Day 104 Lobe of Lung CGPLLU43 Lung cfDNA Pre-treatment, 57 F IV T1BN0M0 Right Lower Adenocarcinoma Moderate None 4.9 2.17 2.17 Y N Y Cancer Day -1 Lobe of Lung CGPLLU43 Lung cfDNA Post-treatment, 57 F IV T1BN0M0 Right Lower Adenocarcinoma Moderate None 3.7 3.26 3.26 Y N Y Cancer Day 6 Lobe of Lung CGPLLU43 Lung cfDNA Post-treatment, 57 F IV T1BN0M0 Right Lower Adenocarcinoma Moderate None 4.0 4.12 4.12 Y N Y Cancer Day 27 Lobe of Lung CGPLLU43 Lung cfDNA Post-treatment, 57 F IV T1BN0M0 Right Lower Adenocarcinoma Moderate None 3.7 8.20 8.20 Y N Y Cancer Day 83 Lobe of Lung CGPLLU86 Lung cfDNA Pre-treatment, 55 M IV NA Left Upper Adenocarcinoma NA Lung 4.0 7.90 7.90 Y N Y Cancer Day 0 Lobe of Lung CGPLLU86 Lung cfDNA Post-treatment, 55 M IV NA Left Upper Adenocarcinoma NA Lung 4.0 7.90 7.90 Y N Y Cancer Day 0.5 Lobe of Lung CGPLLU86 Lung cfDNA Post-treatment, 55 M IV NA Left Upper Adenocarcinoma NA Lung 4.0 7.90 7.90 Y N Y Cancer Day 7 Lobe of Lung CGPLLU86 Lung cfDNA Post-treatment, 55 M IV NA Left Upper Adenocarcinoma NA Lung 4.0 7.90 7.90 Y N Y Cancer Day 17 Lobe of Lung CGPLLU88 Lung cfDNA Pre-treatment, 59 M IV NA Right Adenocarcinoma NA None 5.0 27.66 27.66 Y N Y Cancer Day 0 Middle Lobe of Lung CGPLLU88 Lung cfDNA Post-treatment, 59 M IV NA Right Adenocarcinoma NA None 5.0 6.49 6.49 Y N Y Cancer Day 7 Middle Lobe of Lung CGPLLU88 Lung cfDNA Post-treatment, 59 M IV NA Right Adenocarcinoma NA None 4.0 3.04 3.04 Y N Y Cancer Day 297 Middle Lobe of Lung CGPLLU89 Lung cfDNA Pre-treatment, 54 F IV NA Right Upper Adenocarcinoma NA Brain, 8.0 8.43 8.43 Y N Y Cancer Day 0 Lobe of Lung Bone, Lung CGPLLU89 Lung cfDNA Post-treatment, 54 F IV NA Right Upper Adenocarcinoma NA Brain, 8.0 8.43 8.43 Y N Y Cancer Day 7 Lobe of Lung Bone, Lung CGPLLU89 Lung cfDNA Post-treatment, 54 F IV NA Right Upper Adenocarcinoma NA Brain, 8.0 8.43 8.43 Y N Y Cancer Day 22 Lobe of Lung Bone, Lung CGPLOV11 Ovarian cfDNA Preoperative 51 F IV T3cN0M1 Right Endometrioid Moderate Omentum 3.4 17.35 17.35 Y Y Y Cancer treatment naive Ovary Adenocarcinoma CGPLOV12 Ovarian cfDNA Preoperative 45 F I T1aN0MX Ovary Endometrioid NA None 3.2 12.44 12.44 Y N Y Cancer treatment naive Adenocarcinoma CGPLOV13 Ovarian cfDNA Preoperative 62 F IV T1bN0M1 Right Endometrioid Poor Omentum 3.8 27.00 27.00 Y Y Y Cancer treatment naive Ovary Adenocarcinoma CGPLOV15 Ovarian cfDNA Preoperative 54 F III T3N1M0 Ovary Adenocarcinoma Poor None 5.0 4.77 4.77 Y Y Y Cancer treatment naive CGPLOV16 Ovarian cfDNA Preoperative 40 F III T3aN0M0 Ovary Serous Moderate None 4.5 27.28 27.28 Y N Y Cancer treatment naive Adenocarcinoma CGPLOV19 Ovarian cfDNA Preoperative 52 F II T2aN0M0 Ovary Endometrioid Moderate None 5.0 23.46 23.46 Y Y Y Cancer treatment naive Adenocarcinoma CGPLOV20 Ovarian cfDNA Preoperative 52 F II T2aN0M0 Left Endometrioid Poor None 4.2 5.67 5.67 Y Y Y Cancer treatment naive Ovary Adenocarcinoma CGPLOV21 Ovarian cfDNA Preoperative 51 F IV TanyN1M1 Ovary Serous Poor Omentum, 4.3 56.32 56.32 Y Y Y Cancer treatment naive Adenocarcinoma Appendix CGPLOV22 Ovarian cfDNA Preoperative 64 F III T1cNXMX Left Serous Well None 4.6 17.42 17.42 Y Y Y Cancer treatment naive Ovary Adenocarcinoma CGPLOV23 Ovarian cfDNA Preoperative 47 F I T1aN0M0 Ovary Serous Poor None 5.0 26.73 26.73 Y N Y Cancer treatment naive Adenocarcinoma CGPLOV24 Ovarian cfDNA Preoperative 14 F I T1aN0M0 Ovary Germ Cell Poor None 4.2 10.71 10.71 Y N Y Cancer treatment naive Tumor CGPLOV25 Ovarian cfDNA Preoperative 18 F I T1aN0M0 Ovary Germ Cell Poor None 4.8 6.78 6.78 Y N Y Cancer treatment naive Tumor CGPLOV26 Ovarian cfDNA Preoperative 35 F I T1aN0M0 Ovary Germ Cell Poor None 4.5 27.90 27.90 Y N Y Cancer treatment naive Tumor CGPLOV28 Ovarian cfDNA Preoperative 63 F I T1aN0M0 Right Serous NA None 3.2 10.74 10.74 Y N Y Cancer treatment naive Ovary Carcinoma CGPLOV31 Ovarian cfDNA Preoperative 45 F III T3aNxM0 Right Clear Cell NA None 4.0 14.45 14.45 Y N Y Cancer treatment naive Ovary adenocarcinoma CGPLOV32 Ovarian cfDNA Preoperative 53 F I T1aNxM0 Left Mucinous NA None 3.2 27.36 27.36 Y N Y Cancer treatment naive Ovary Cystadenoma CGPLOV37 Ovarian cfDNA Preoperative 40 F I T1cN0M0 Ovary Serous NA None 3.2 46.88 46.88 Y N Y Cancer treatment naive Carcinoma CGPLOV38 Ovarian cfDNA Preoperative 46 F I T1cN0M0 Ovary Serous NA None 2.4 34.29 34.29 Y N Y Cancer treatment naive Carcinoma CGPLOV40 Ovarian cfDNA Preoperative 53 F IV T3N0M1 Ovary Serous NA Omentum, 1.6 193.60 156.25 Y N Y Cancer treatment naive Carcinoma Uterus, Appendix CGPLOV41 Ovarian cfDNA Preoperative 57 F IV T3N0M1 Ovary Serous NA Omentum, 4.4 10.03 10.03 Y N Y Cancer treatment naive Carcinoma Uterus, Cervix CGPLOV42 Ovarian cfDNA Preoperative 52 F I T3aN0M0 Ovary Serous NA None 4.2 49.51 49.51 Y N Y Cancer treatment naive Carcinoma CGPLOV43 Ovarian cfDNA Preoperative 30 F I T1aN0M0 Ovary Mucinous NA None 4.4 9.09 9.09 Y N Y Cancer treatment naive Cyst- adenocarcinoma CGPLOV44 Ovarian cfDNA Preoperative 69 F I T1aN0M0 Ovary Mucinous NA None 4.5 8.79 8.79 Y N Y Cancer treatment naive Adenocarcinoma CGPLOV46 Ovarian cfDNA Preoperative 58 F I T1bN0M0 Ovary Serous NA None 4.1 8.97 8.97 Y N Y Cancer treatment naive Carcinoma CGPLOV47 Ovarian cfDNA Preoperative 41 F I T1aN0M0 Ovary Serous NA None 4.5 19.35 19.35 Y N Y Cancer treatment naive Adenocarcinoma CGPLOV48 Ovarian cfDNA Preoperative 52 F I T1bN0M0 Ovary Serous NA None 3.5 22.80 22.80 Y N Y Cancer treatment naive Carcinoma CGPLOV49 Ovarian cfDNA Preoperative 68 F III T3bN0M0 Ovary Serous NA None 4.2 16.48 16.48 Y N Y Cancer treatment naive Carcinoma CGPLOV50 Ovarian cfDNA Preoperative 30 F III T3cN0M0 Ovary Serous NA None 4.5 8.89 8.89 Y N Y Cancer treatment naive Carcinoma CGPLPA112 Pancreatic cfDNA Preoperative 58 M II NA Intra NA NA None 3.5 18.52 18.52 Y N N

Cancer treatment naive Pancreatic Bile Duct CGPLPA113 Duodenal cfDNA Preoperative 71 M I NA Intra NA NA None 4.8 8.24 8.24 Y N N Cancer treatment naive Pancreatic Bile Duct CGPLPA114 Bile Duct cfDNA Preoperative NA F II NA Intra NA NA None 4.8 26.43 26.43 Y N N Cancer treatment naive Pancreatic Bile Duct CGPLPA115 Bile Duct cfDNA Preoperative NA M IV NA Intra NA NA NA 5.0 31.41 31.41 Y N N Cancer treatment naive Hepatic Bile Duct CGPLPA117 Bile Duct cfDNA Preoperative NA M II NA Intra NA NA NA 3.4 2.29 2.29 Y N N Cancer treatment naive Pancreatic Bile Duct CGPLPA118 Bile Duct cfDNA Preoperative 68 F I NA Bile Duct Intra- NA None 3.8 9.93 9.93 Y N Y Cancer treatment naive Ampuliary Bile Duct CGPLPA122 Bile Duct cfDNA Preoperative 62 F II NA Bile Duct Intra- NA None 3.8 66.54 32.89 Y N Y Cancer treatment naive Pancreatic Bile Duct CGPLPA124 Bile Duct cfDNA Preoperative 83 F II NA Bile Duct Intra- moderate None 4.6 29.24 27.17 Y N Y Cancer treatment naive Ampuliary Bile Duct CGPLPA125 Bile Duct cfDNA Preoperative 58 M II NA Bile Duct Intra- poor None 2.7 8.31 8.31 Y N N Cancer treatment naive Pancreatic Bile Duct CGPLPA126 Bile Duct cfDNA Preoperative 60 M II NA Bile Duct Intra- NA None 4.2 80.56 29.07 Y N Y Cancer treatment naive Pancreatic Bile Duct CGPLPA127 Bile Duct cfDNA Preoperative 71 F IV NA Bile Duct Extra- NA NA 3.0 20.60 20.60 Y N N Cancer treatment naive Pancreatic Bile Duct CGPLPA128 Bile Duct cfDNA Preoperative 67 M II NA Bile Duct Intra- NA None 3.9 5.91 5.91 Y N Y Cancer treatment naive Pancreatic Bile Duct CGPLPA129 Bile Duct cfDNA Preoperative 56 F II NA Bile Duct Intra- NA None 4.6 27.07 27.07 Y N Y Cancer treatment naive Pancreatic Bile Duct CGPLPA130 Bile Duct cfDNA Preoperative 82 F II NA Bile Duct Intra- well None 4.0 4.34 4.34 Y N Y Cancer treatment naive Ampuliary Bile Duct CGPLPA131 Bile Duct cfDNA Preoperative 71 M II NA Bile Duct Intra- NA None 3.9 68.95 32.05 Y N Y Cancer treatment naive Pancreatic Bile Duct CGPLPA134 Bile Duct cfDNA Preoperative 68 M II NA Bile Duct Intra- NA None 4.1 58.98 30.49 Y N Y Cancer treatment naive Pancreatic Bile Duct CGPLPA135 Bile Duct cfDNA Preoperative 67 F I NA Bile Duct Intra- NA NA 3.9 4.22 4.22 Y N N Cancer treatment naive Pancreatic Bile Duct CGPLPA136 Bile Duct cfDNA Preoperative 69 F II NA Bile Duct Intra- NA None 4.1 20.23 20.23 Y N Y Cancer treatment naive Pancreatic Bile Duct CGPLPA137 Bile Duct cfDNA Preoperative NA M II NA Bile Duct NA NA NA 4.0 5.75 5.75 Y N N Cancer treatment naive CGPLPA139 Bile Duct cfDNA Preoperative NA M IV NA Bile Duct NA NA NA 4.0 14.89 14.89 Y N N Cancer treatment naive CGPLPA14 Pancreatic cfDNA Preoperative 68 M II NA Pancreas Ductal Poor None 4.0 1.30 1.30 Y N N Cancer treatment naive Adenocarcinoma CGPLPA140 Bile Duct cfDNA Preoperative 52 M II NA Extra- Intra- Poor None 4.7 29.34 26.60 Y N Y Cancer treatment naive Hepatic Pancreatic Bile Duct Bile Duct CGPLPA141 Bile Duct cfDNA Preoperative 68 F II NA Extra- Intra- Moderate None 2.8 53.67 44.64 Y N N Cancer treatment naive Hepatic Pancreatic Bile Duct Bile Duct CGPLPA15 Pancreatic cfDNA Preoperative 70 F II NA Pancreas Ductal Well Lymph 4.0 1.92 1.92 Y N N Cancer treatment naive Adenocarcinoma Node CGPLPA155 Bile Duct cfDNA Preoperative NA F II NA NA NA NA NA 4.0 25.72 25.72 Y N N Cancer treatment naive CGPLPA156 Pancreatic cfDNA Preoperative 73 F II NA Pancreas Ductal Poor Lymph 4.5 7.54 7.54 Y N N Cancer treatment naive Adenocarcinoma Node CGPLPA165 Bile Duct cfDNA Preoperative 42 M I NA Bile Duct Intra- well None 3.9 10.48 10.48 Y N N Cancer treatment naive Pancreatic Bile Duct with Meduliary Features CGPLPA168 Bile Duct cfDNA Preoperative 58 M II NA Bile Duct NA NA NA 3.0 139.12 34.72 Y N N Cancer treatment naive CGPLPA17 Pancreatic cfDNA Preoperative 65 M II NA Pancreas Ductal Well Lymph 4.0 13.08 13.08 Y N N Cancer treatment naive Adenocarcinoma Node CGPLPA184 Bile Duct cfDNA Preoperative 75 F II NA Bile Duct Intra- NA None NA NA NA Y N N Cancer treatment naive Pancreatic Bile Duct CGPLPA187 Bile Duct cfDNA Preoperative 67 F II NA Bile Duct Intra- NA None NA NA NA Y N N Cancer treatment naive Pancreatic Bile Duct CGPLPA23 Pancreatic cfDNA Preoperative 58 F II NA Pancreas Ductal Moderate Lymph 4.0 16.62 16.62 Y N N Cancer treatment naive Adenocarcinoma Node CGPLPA25 Pancreatic cfDNA Preoperative 69 F II NA Pancreas Ductal Poor Lymph 4.0 8.71 8.71 Y N N Cancer treatment naive Adenocarcinoma Node CGPLPA26 Pancreatic cfDNA Preoperative 64 M II NA Pancreas Ductal Well Lymph 4.0 6.97 6.97 Y N N Cancer treatment naive Adenocarcinoma Node CGPLPA28 Pancreatic cfDNA Preoperative 79 F II NA Pancreas Ductal Well Lymph 4.0 18.13 18.13 Y N N Cancer treatment naive Adenocarcinoma Node CGPLPA33 Pancreatic cfDNA Preoperative 67 F II NA Pancreas Ductal Well Lymph 4.0 1.80 1.80 Y N N Cancer treatment naive Adenocarcinoma Node CGPLPA34 Pancreatic cfDNA Preoperative 73 M II NA Pancreas Ductal Well Lymph 4.0 3.36 3.36 Y N N Cancer treatment naive Adenocarcinoma Node CGPLPA37 Pancreatic cfDNA Preoperative 67 F II NA Pancreas Ductal NA Lymph 4.0 21.83 21.83 Y N N Cancer treatment naive Adenocarcinoma Node CGPLPA38 Pancreatic cfDNA Preoperative 65 M II NA Pancreas Ductal Moderate None 4.0 5.29 5.29 Y N N Cancer treatment naive Adenocarcinoma CGPLPA39 Pancreatic cfDNA Preoperative 67 F II NA Pancreas Ductal Well Lymph 4.0 11.73 11.73 Y N N Cancer treatment naive Adenocarcinoma Node CGPLPA40 Pancreatic cfDNA Preoperative 64 M II NA Pancreas Ductal Well Lymph 4.0 4.78 4.78 Y N N Cancer treatment naive Adenocarcinoma Node CGPLPA42 Pancreatic cfDNA Preoperative 73 M II NA Pancreas Ductal Moderate Lymph 4.0 3.41 3.41 Y N N Cancer treatment naive Adenocarcinoma Node CGPLPA46 Pancreatic cfDNA Preoperative 59 F II NA Pancreas Ductal Poor Lymph 4.0 0.74 0.74 Y N N Cancer treatment naive Adenocarcinoma Node CGPLPA47 Pancreatic cfDNA Preoperative 67 M II NA Pancreas Ductal Well Lymph 4.0 6.01 6.01 Y N N Cancer treatment naive Adenocarcinoma Node CGPLPA48 Pancreatic cfDNA Preoperative 72 F II NA Pancreas Ductal Well None NA NA NA Y N N Cancer treatment naive Adenocarcinoma CGPLPA52 Pancreatic cfDNA Preoperative 63 M II NA Pancreas Ductal Moderate None 2.5 9.86 9.86 Y N N Cancer treatment naive Adenocarcinoma CGPLPA53 Pancreatic cfDNA Preoperative 46 M I NA Pancreas Ductal Poor Lymph 3.0 14.48 14.48 Y N N Cancer treatment naive Adenocarcinoma Node CGPLPA58 Pancreatic cfDNA Preoperative 74 F II NA Pancreas Ductal NA None 3.0 6.87 6.87 Y N N Cancer treatment naive Adenocarcinoma CGPLPA59 Pancreatic cfDNA Preoperative 59 F II NA Pancreas Ductal Well Lymph NA NA NA Y N N Cancer treatment naive Adenocarcinoma Node or Adenoma CGPLPA67 Pancreatic cfDNA Preoperative 55 M III NA Pancreas Ductal Well Lymph 3.2 9.72 9.72 Y N N Cancer treatment naive Adenocarcinoma Node CGPLPA69 Pancreatic cfDNA Preoperative 70 M I NA Pancreas Ductal Well None 2.0 1.72 1.72 Y N N Cancer treatment naive Adenocarcinoma CGPLPA71 Pancreatic cfDNA Preoperative 64 M II NA Pancreas Ductal Well Lymph 2.2 39.07 39.07 Y N N Cancer treatment naive Adenocarcinoma Node CGPLPA74 Pancreatic cfDNA Preoperative 71 F II NA Pancreas Ductal Moderate Lymph 2.5 4.99 4.99 Y N N Cancer treatment naive Adenocarcinoma Node CGPLPA76 Pancreatic cfDNA Preoperative 69 M II NA Pancreas Ductal Poor None 2.5 23.19 23.19 Y N N Cancer treatment naive Adenocarcinoma CGPLPA85 Pancreatic cfDNA Preoperative 77 F II NA Pancreas Ductal Poor Lymph 3.0 152.46 41.67 Y N N Cancer treatment naive Adenocarcinoma Node CGPLPA86 Pancreatic cfDNA Preoperative 66 M II NA Pancreas Ductal Moderate Lymph 2.5 11.92 11.92 Y N N Cancer treatment naive Adenocarcinoma Node CGPLPA92 Pancreatic cfDNA Preoperative 72 M II NA Pancreas Ductal NA Lymph 2.0 5.34 5.34 Y N N Cancer treatment naive Adenocarcinoma Node CGPLPA93 Pancreatic cfDNA Preoperative 48 M II NA Pancreas Ductal Poor None 3.0 96.28 41.67 Y N N Cancer treatment naive Adenocarcinoma CGPLPA94 Pancreatic cfDNA Preoperative 72 F II NA Pancreas Ductal NA Lymph 3.0 29.66 29.66 Y N N Cancer treatment naive Adenocarcinoma Node CGPLPA95 Pancreatic cfDNA Preoperative 64 F II NA Pancreas Ductal Well Lymph NA NA NA Y N N Cancer treatment naive Adenocarcinoma Node CGST102 Gastric cfDNA Preoperative 76 F II T3N0M0 Stomach Tubular Moderate None 4.1 8.03 8.03 Y N Y Cancer treatment naive Adenocarcinoma CGST11 Gastric cfDNA Preoperative 49 M IV TXNXM1 Stomach Mixed Moderate None 3.8 3.57 3.57 Y N N Cancer treatment naive Carcinoma CGST110 Gastric cfDNA Preoperative 77 M III T4AN3aM0 Stomach Tubular Moderate None 3.8 5.00 5.00 Y N Y Cancer treatment naive Adenocarcinoma CGST114 Gastric cfDNA Preoperative 65 M III T4N1M0 Stomach Tubular Poor None 4.4 10.35 10.35 Y N Y Cancer treatment naive Adenocarcinoma CGST13 Gastric cfDNA Preoperative 72 F II T1AN2M0 Stomach Signet Ring Poor None 4.4 24.33 24.33 Y N Y Cancer treatment naive Cell Carcinoma CGST131 Gastric cfDNA Preoperative 63 M III T2N3aM0 Stomach Signet ring Poor None 4.0 4.28 4.28 Y N N Cancer treatment naive cell Carcinoma CGST141 Gastric cfDNA Preoperative 33 F III T3N2M0 Stomach Signet Ring Poor None 4.4 10.84 10.84 Y N Y Cancer treatment naive Cell Carcinoma CGST16 Gastric cfDNA Preoperative 78 M III T4AN3aM0 Stomach Tubular Poor None 4.0 40.69 40.69 Y N Y Cancer treatment naive Adenocarcinoma CGST18 Gastric cfDNA Preoperative 50 M II T3N0M0 Stomach Mucinous Well None 4.3 9.78 9.78 Y N Y Cancer treatment naive Adenocarcinoma CGST21 Gastric cfDNA Preoperative 39 M II T2N1(mi)M0 Stomach Papillary Moderate None 4.0 0.83 0.83 Y N N Cancer treatment naive Adenocarcinoma CGST26 Gastric cfDNA Preoperative 51 M IV TXNXM1 Stomach Signet ring Poor None 3.5 5.56 5.56 Y N N Cancer treatment naive cell Carcinoma CGST28 Gastric cfDNA Preoperative 55 M X TXNXMX Stomach Undifferentiated Poor None 4.0 5.86 5.86 Y N Y Cancer treatment naive Carcinoma CGST30 Gastric cfDNA Preoperative 64 F III T3N2M0 Stomach Signet Ring Poor None 3.0 4.22 4.22 Y N Y Cancer treatment naive Cell Carcinoma CGST32 Gastric cfDNA Preoperative 67 M II T3N1M0 Stomach Tubular Moderate None 4.0 11.49 11.49 Y N Y Cancer treatment naive Adenocarcinoma CGST33 Gastric cfDNA Preoperative 61 M I T2N0M0 Stomach Tubular Moderate None 3.5 5.71 5.71 Y N Y Cancer treatment naive Adenocarcinoma CGST38 Gastric cfDNA Preoperative 71 F 0 T0N0M0 Stomach Mucinous NA None 4.0 NA NA Y N N

Cancer treatment naive Adenocarcinoma CGST39 Gastric cfDNA Preoperative 51 M IV TXNXM1 Stomach Signet Ring Poor None 3.5 20.69 20.69 Y N Y Cancer treatment naive Cell Carcinoma CGST41 Gastric cfDNA Preoperative 66 F IV TXNXM1 Stomach Signet Ring Poor None 3.5 7.83 7.83 Y N Y Cancer treatment naive Cell Carcinoma CGST45 Gastric cfDNA Preoperative 41 F II T3N0M0 Stomach Signet Ring Poor None 3.8 7.14 7.14 Y N Y Cancer treatment naive Cell Carcinoma CGST47 Gastric cfDNA Preoperative 74 F I T1AN0M0 Stomach Tubular Moderate None 4.0 4.55 4.55 Y N Y Cancer treatment naive Adenocarcinoma CGST48 Gastric cfDNA Preoperative 62 M IV TXNXM1 Stomach Tubular Poor None 4.5 8.79 8.79 Y N Y Cancer treatment naive Adenocarcinoma CGST53 Gastric cfDNA Preoperative 70 M 0 T0N0M0 Stomach NA NA None 3.8 15.82 15.82 Y N N Cancer treatment naive CGST58 Gastric cfDNA Preoperative 58 M III T4AN3bM0 Stomach Signet Ring Poor None 3.8 19.81 19.81 Y N Y Cancer treatment naive Cell Carcinoma CGST67 Gastric cfDNA Preoperative 69 M I T1RN0M0 Stomach Tubular Moderate None 3.0 23.01 23.01 Y N N Cancer treatment naive adenocarcinoma CGST77 Gastric cfDNA Preoperative 70 M IV TXNXM1 Stomach Tubular Moderate None 4.5 15.09 15.09 Y N N Cancer treatment naive adenocarcinoma CGST80 Gastric cfDNA Preoperative 58 M III T3N3aM0 Stomach Mucinous Poor None 4.5 8.56 8.56 Y N Y Cancer treatment naive Adenocarcinoma CGST81 Gastric cfDNA Preoperative 64 F I T2N0M1 Stomach Signet Ring Poor None 3.5 37.32 37.32 Y N Y Cancer treatment naive Cell Carcinoma CGH14 Healthy Human NA NA M NA NA NA NA NA NA NA NA NA Y N N Adult elutriated lymphoc CGH15 Healthy Human NA NA F NA NA NA NA NA NA NA NA NA Y N N Adult elutriated lymphoc *NA denotes data not available or not applicable for healthy individuals.

TABLE-US-00004 APPENDIX B Table 2 Summary of targeted cfDNA analyses Fragment Profile Mutation Bases in Bases Mapped to Bases Mapped to Percent Mapped to Total Distinct Patient Patient Type Timepoint Analysis Analysis Read Length Target Region Genome Target Regions Target Regions Coverage Coverage CGCRC291 Colorectal Cancer Preoperative, Treatment naive Y Y 100 80930 7501485600 3771359756 50% 44345 10359 CGCRC292 Colorectal Cancer Preoperative, Treatment naive Y Y 100 80930 6736035200 3098886973 46% 36448 8603 CGCRC293 Colorectal Cancer Preoperative, Treatment naive Y Y 100 80930 6300244000 2818734206 45% 33117 5953 CGCRC294 Colorectal Cancer Preoperative, Treatment naive Y Y 100 80930 7766872600 3911796709 50% 46016 12071 CGCRC295 Colorectal Cancer Preoperative, Treatment naive Y N 100 80930 8240660200 3478059753 42% 40787 5826 CGCRC296 Colorectal Cancer Preoperative, Treatment naive Y Y 100 80930 5718556500 2898549356 51% 33912 10180 CGCRC291 Colorectal Cancer Preoperative, Treatment naive Y N 100 80930 7550826100 3717222432 49% 43545 5870 CGCRC298 Colorectal Cancer Preoperative, Treatment naive Y N 100 80930 12501036400 6096393764 49% 71196 9617 CGCRC299 Colorectal Cancer Preoperative, Treatment naive Y Y 100 80930 7812602900 4121569690 53% 48098 10338 CGCRC300 Colorectal Cancer Preoperative, Treatment naive Y Y 100 80930 8648090300 3962285136 46% 46364 5756 CGCRC301 Colorectal Cancer Preoperative, Treatment naive Y Y 100 80930 7538758100 3695480348 49% 43024 6618 CGCRC302 Colorectal Cancer Preoperative, Treatment naive Y Y 100 80930 8573658300 4349420574 51% 51006 13799 CGCRC303 Colorectal Cancer Preoperative, Treatment naive Y Y 100 80930 5224046400 2505714343 48% 29365 8372 CGCRC304 Colorectal Cancer Preoperative, Treatment naive Y Y 100 80930 5762112600 2942170530 51% 34462 10208 CGCRC305 Colorectal Cancer Preoperative, Treatment naive Y Y 100 80930 7213384100 3726953480 52% 43516 8589 CGCRC306 Colorectal Cancer Preoperative, Treatment naive Y Y 100 80930 7075579700 3552441899 50% 41507 7372 CGCRC307 Colorectal Cancer Preoperative, Treatment naive Y Y 100 80930 7572687100 3492191519 46% 40793 9680 CGCRC308 Colorectal Cancer Preoperative, Treatment naive Y Y 100 80930 7945738000 3895908986 49% 45224 11809 CGCRC309 Colorectal Cancer Preoperative, Treatment naive Y Y 100 80930 8487455800 3921079811 46% 45736 10739 CGCRC310 Colorectal Cancer Preoperative, Treatment naive Y Y 100 80930 9003580500 4678812441 52% 54713 11139 CGCRC311 Colorectal Cancer Preoperative, Treatment naive Y Y 100 80930 6528162700 3276653864 50% 38324 6044 CGCRC312 Colorectal Cancer Preoperative, Treatment naive Y N 100 80930 7683294300 3316719187 43% 38652 4622 CGCRC313 Colorectal Cancer Preoperative, Treatment naive Y N 100 80930 5874099200 2896148722 49% 33821 6506 CGCRC314 Colorectal Cancer Preoperative, Treatment naive Y N 100 80930 6883148500 3382767492 49% 39414 6664 CGCRC315 Colorectal Cancer Preoperative, Treatment naive Y Y 100 80930 7497252500 3775556051 50% 44034 8666 CGCRC316 Colorectal Cancer Preoperative, Treatment naive Y Y 100 80930 10684720400 5533857153 52% 64693 14289 CGCRC317 Colorectal Cancer Preoperative, Treatment naive Y Y 100 80930 7086877600 3669434216 52% 43538 10944 CGCRC318 Colorectal Cancer Preoperative, Treatment naive Y Y 100 80930 6880041100 3326357413 48% 39077 11571 CGCRC319 Colorectal Cancer Preoperative, Treatment naive N Y 100 80930 7485342900 3982677483 53% 47327 10502 CGCRC320 Colorectal Cancer Preoperative, Treatment naive Y Y 100 80930 7058703200 3450648135 49% 40888 10198 CGCRC321 Colorectal Cancer Preoperative, Treatment naive Y Y 100 80930 7203625900 3633396892 50% 43065 6499 CGCRC332 Colorectal Cancer Preoperative, Treatment naive Y N 100 80930 7202969100 3758323705 52% 44580 3243 CGCRC333 Colorectal Cancer Preoperative, Treatment naive Y Y 100 80930 8767144700 4199126827 48% 49781 8336 CGCRC334 Colorectal Cancer Preoperative, Treatment naive Y N 100 80930 7771869100 3944518280 51% 46518 5014 CGCRC335 Colorectal Cancer Preoperative, Treatment naive Y N 100 80930 7972524600 4064901201 51% 48308 6151 CGCRC336 Colorectal Cancer Preoperative, Treatment naive Y Y 100 80930 8597346400 4333410573 50% 51390 7551 CGCRC337 Colorectal Cancer Preoperative, Treatment naive Y N 100 80930 7399611700 3800666199 51% 45083 8092 CGCRC338 Colorectal Cancer Preoperative, Treatment naive Y Y 100 80930 8029493700 4179383804 52% 49380 5831 CGCRC339 Colorectal Cancer Preoperative, Treatment naive Y N 100 80930 7938963500 4095555110 52% 48397 3808 CGCRC340 Colorectal Cancer Preoperative, Treatment naive Y N 100 80930 7214889500 3706643098 51% 43805 3014 CGCRC341 Colorectal Cancer Preoperative, Treatment naive N Y 100 80930 8803159200 3668208527 42% 43106 11957 CGCRC342 Colorectal Cancer Preoperative, Treatment naive N Y 100 80930 8478811500 3425540889 40% 40328 9592 CGCRC344 Colorectal Cancer Preoperative, Treatment naive N Y 100 80930 6942167800 3098232737 45% 36823 2300 CGCRC345 Colorectal Cancer Preoperative, Treatment naive N Y 100 80930 8182868200 2383173431 29% 28233 7973 CGCRC346 Colorectal Cancer Preoperative, Treatment naive N Y 100 80930 7448272300 3925056341 53% 46679 5582 CGCRC347 Colorectal Cancer Preoperative, Treatment naive N Y 100 80930 5804744500 2986809912 51% 35490 4141 CGCRC349 Colorectal Cancer Preoperative, Treatment naive N Y 100 80930 6943451600 3533145275 51% 41908 5762 CGCRC350 Colorectal Cancer Preoperative, Treatment naive N Y 100 80930 7434818400 3848923016 52% 45678 4652 CGCRC351 Colorectal Cancer Preoperative, Treatment naive N Y 100 80930 7306546400 3636910409 50% 43162 5205 CGCRC352 Colorectal Cancer Preoperative, Treatment naive N Y 100 80930 7864655000 3336939252 42% 39587 4502 CGCRC353 Colorectal Cancer Preoperative, Treatment naive N Y 100 80930 7501674800 3642919375 49% 43379 4666 CGCRC354 Colorectal Cancer Preoperative, Treatment naive N Y 100 80930 7938270200 2379068977 30% 28256 4858 CGCRC356 Colorectal Cancer Preoperative, Treatment naive N Y 100 80930 6013175900 3046754994 51% 36127 3425 CGCRC357 Colorectal Cancer Preoperative, Treatment naive N Y 100 80930 6013454600 3022035300 50% 35813 4259 CGCRC358 Colorectal Cancer Preoperative, Treatment naive N Y 100 80930 7227212400 3188723303 44% 37992 5286 CGCRC359 Colorectal Cancer Preoperative, Treatment naive N Y 100 80930 7818567700 425110101 5% 5040 2566 CGCRC367 Colorectal Cancer Preoperative, Treatment naive N Y 100 80930 6582043200 3363063597 51% 39844 5839 CGCRC368 Colorectal Cancer Preoperative, Treatment naive N Y 100 80930 8042242400 4101646000 51% 48636 11471 CGCRC370 Colorectal Cancer Preoperative, Treatment naive N Y 100 80930 6940330100 3198954121 46% 38153 4826 CGCRC373 Colorectal Cancer Preoperative, Treatment naive N Y 100 80930 6587201700 3120088035 47% 37234 5190 CGCRC376 Colorectal Cancer Preoperative, Treatment naive N Y 100 80930 6727983100 3162416807 47% 37735 3445 CGCRC377 Colorectal Cancer Preoperative, Treatment naive N Y 100 80930 6716339200 3131415570 47% 37160 4524 CGCRC378 Colorectal Cancer Preoperative, Treatment naive N Y 100 80930 6523969900 2411096720 37% 28728 3239 CGCRC379 Colorectal Cancer Preoperative, Treatment naive N Y 100 80930 6996252100 3371081103 48% 39999 2891 CGCRC380 Colorectal Cancer Preoperative, Treatment naive N Y 100 80930 7097496300 2710244446 38% 32020 3251 CGCRC381 Colorectal Cancer Preoperative, Treatment naive N Y 100 80930 6961936100 3287050681 47% 38749 9357 CGCRC382 Colorectal Cancer Preoperative, Treatment naive N Y 100 80930 6959048700 2552325859 37% 30040 5148 CGCRC384 Colorectal Cancer Preoperative, Treatment naive N Y 100 80930 7012798900 3293884583 47% 39158 3653 CGCRC385 Colorectal Cancer Preoperative, Treatment naive N Y 100 80930 7542017900 3356570505 45% 39884 3686 CGCRC386 Colorectal Cancer Preoperative, Treatment naive N Y 100 80930 6876059600 3064412286 45% 36431 2787 CGCRC387 Colorectal Cancer Preoperative, Treatment naive N Y 100 80930 7399564700 3047254560 41% 36141 6675 CGCRC386 Colorectal Cancer Preoperative, Treatment naive N Y 100 80930 6592692900 3137284885 48% 37285 5114 CGCRC389 Colorectal Cancer Preoperative, Treatment naive N Y 100 80930 6651206300 3102100941 47% 36764 6123 CGCRC390 Colorectal Cancer Preoperative, Treatment naive N Y 100 80930 7260616800 3376667585 47% 40048 4368 CGCRC391 Colorectal Cancer Preoperative, Treatment naive N Y 100 80930 6883624500 3202877881 47% 37978 5029 CGLU316 Lung Cancer Pre-treatment Day -53 Y N 100 80930 7864415100 1991331171 25% 23601 3565 CGLU316 Lung Cancer Pre-treatment, Day -53 Y N 100 80930 7502591600 3730963390 50% 44262 3966 CGLU316 Lung Cancer Pro-treatment, Day -53 Y N 100 80930 6582515900 3187059470 48% 37813 3539 CGLU316 Lung Cancer Pre-treatment, Day -53 Y N 100 60930 6587281800 1947630979 30% 23094 4439 CGLU344 Lung Cancer Pretreatment, Day -21 Y N 100 80930 6151628500 2748983603 45% 32462 8063 CGLU344 Lung Cancer Pre-treatment, Day -21 Y N 100 80930 7842910900 1147703178 15% 13565 4303 CGLU344 Lung Cancer Pretreatment, Day -21 Y N 100 80930 5838083100 2291108925 39% 27067 4287 CGLU344 Lung Cancer Pre-treatment, Day -21 Y N 100 80930 7685989200 3722274529 48% 43945 3471 CGLU369 Lung Cancer Pre-treatment, Day -2 Y N 100 80930 7080245300 1271457982 18% 15109 2354 CGLU369 Lung Cancer Pre-treatment, Day -2 Y N 100 00930 7078131900 1482448715 21% 17583 4275 CGLU369 Lung Cancer Pre-treatment, Day -2 Y N 100 60930 6904701700 2124660124 31% 25230 5278 CGLU369 Lung Cancer Pre-treatment, Day -2 Y N 100 80930 7003452200 3162195578 45% 37509 6062 CGLU373 Lung Cancer Pro-treatment, Day -2 Y N 100 00930 6346267200 3053520676 48% 36137 6251 CGLU373 Lung Cancer Pre-treatment, Day -2 Y N 100 80930 6517189900 3192984468 49% 38066 8040 CGLU373 Lung Cancer Pre-treatment, Day -2 Y N 100 60930 7767146300 3572598842 46% 42378 5306 CGLU373 Lung Cancer Pre-treatment, Day -2 Y N 100 80930 7190999100 3273648804 46% 38784 4454 CGPLBR100 Breast Cancer Preoperative, Treatment naive N Y 100 00930 7299964400 3750278051 51% 44794 3249 CGPLBR101 Breast Cancer Preoperative, Treatment naive N Y 100 80930 7420822800 3810365416 51% 45565 9784 CGPLBR102 Breast Cancer Preoperative, Treatment naive N Y 100 80930 6679304900 3269688319 49% 38679 7613 CGPLBR103 Breast Cancer Preoperative, Treatment naive N Y 100 60930 7040304400 3495542468 50% 41786 6748 CGPLBR104 Breast Cancer Preoperative, Treatment naive N Y 100 80930 7188389200 3716096781 52% 44316 9448 CGPLBR38 Breast Cancer Preoperative, Treatment naive Y Y 100 80930 7810293900 4057576306 52% 48098 9868 CGPLBR39 Breast Cancer Preoperative, Treatment naive N Y 100 80930 7745701500 3805623239 49% 45084 11065 CGPLBR40 Breast Cancer Preoperative, Treatment naive Y Y 100 80930 7558990500 3652442341 48% 43333 12948 CGPLBR41 Breast Cancer Preoperative, Treatment naive N Y 100 80930 7900994600 3836800101 49% 45535 10847 CGPLBR44 Breast Cancer Preoperative, Treatment naive Y N 100 80930 7017744200 3269110569 47% 38672 8344 CGPLBR48 Breast Cancer Preoperative, Treatment naive Y Y 100 80930 5629044200 2611554623 46% 30860 8652 CGPLBR49 Breast Cancer Preoperative, Treatment naive N Y 100 80930 5784711600 2673457893 46% 31274 10429 CGPLBR55 Breast Cancer Preoperative, Treatment naive Y Y 100 80930 8309154900 4306956261 52% 51143 8328 CGPLBR57 Breast Cancer Preoperative, Treatment naive N Y 100 80930 8636181000 4391502618 51% 52108 5857 CGPLBR59 Breast Cancer Preoperative, Treatment naive N Y 100 80930 8799457700 4152328555 47% 49281 5855 CGPLBR61 Breast Cancer Preoperative, Treatment naive N Y 100 80930 8163706700 3952010628 48% 46755 8522 CGPLBR63 Breast Cancer Preoperative, Treatment naive Y Y 100 80930 7020533100 3542447304 50% 41956 4773 CGPLBR67 Breast Cancer Preoperative, Treatment naive Y N 100 80930 8264353900 3686093696 45% 43516 7752 CGPLBR68 Breast Cancer Preoperative, Treatment naive N Y 100 80930 7629312300 4078969547 53% 48389 7402 CGPLBR69 Breast Cancer Preoperative, Treatment naive Y Y 100 80930 7571501500 3857354512 51% 45322 7047 CGPLBR70 Breast Cancer Preoperative, Treatment naive Y Y 100 80930 7251760700 3641333708 50% 43203 8884 CGPLBR71 Breast Cancer Preoperative, Treatment naive Y Y 100 80930 8515402600 4496696391 53% 53340 6805 CGPLBR72 Breast Cancer Preoperative, Treatment naive Y Y 100 80930 8556946900 4389761697 51% 52081 5632 CGPLBR73 Breast Cancer Preoperative, Treatment naive Y Y 100 80930 7959392300 4006933338 50% 47555 8791 CGPLBR74 Breast Cancer Preoperative, Treatment naive Y N 100 80930 8524536400 4063900599 48% 48252 7013 CGPLBR75 Breast Cancer Preoperative, Treatment naive Y Y 100 80930 8260379100 3960599885 48% 46955 6319 CGPLBR76 Breast Cancer Preoperative, Treatment naive Y Y 100 80930 7774235200 3893622420 50% 46192 9628 CGPLBR77 Breast Cancer Preoperative, Treatment naive Y N 100 80930 7572797600 3255963429 43% 38568 8263 CGPLBR80 Breast Cancer Preoperative, Treatment naive Y N 100 80930 6845325800 3147476693 46% 37201 5595 CGPLBR82 Breast Cancer Preoperative, Treatment naive N Y 100 80930 8236705200 4170465005 51% 49361 12319

CGPLBR83 Breast Cancer Preoperative, Treatment naive Y Y 100 80930 7434568100 3676855019 49% 43628 5458 CGPLBR86 Breast Cancer Preoperative, Treatment naive Y Y 100 80930 7616282500 3644791327 48% 43490 7048 CGPLBR87 Breast Cancer Preoperative, Treatment naive Y Y 100 80930 6194021300 3004882010 49% 35765 5306 CGPLBR88 Breast Cancer Preoperative, Treatment naive Y Y 100 80930 6071567200 2847926237 47% 33945 10319 CGPLBR91 Breast Cancer Preoperative, Treatment naive N Y 100 80930 7192457700 3480203404 48% 41570 9912 CGPLBR92 Breast Cancer Preoperative, Treatment naive Y Y 100 80930 7678981800 3600279233 47% 42975 13580 CGPLBR93 Breast Cancer Preoperative, Treatment naive N Y 100 80930 7605717800 3998713397 53% 47866 10329 CGPLBR96 Breast Cancer Preoperative, Treatment naive Y N 100 80930 6297446700 2463064737 39% 29341 7937 CGPLBR97 Breast Cancer Preoperative, Treatment naive Y N 100 80930 7114921600 3557069027 50% 42488 10712 CGPLH35 Healthy Preoperative, Treatment naive N Y 100 80930 6919126300 2312758764 33% 25570 1989 CGPLH36 Healthy Preoperative, Treatment naive N Y 100 80930 6089923400 2038548115 33% 22719 1478 CGPLH37 Healthy Preoperative, Treatment naive N Y 100 80930 5557270200 1935301929 35% 21673 2312 CGPLH42 Healthy Preoperative, Treatment naive N Y 100 80930 5792045400 2388036949 41% 27197 2523 CGPLH43 Healthy Preoperative, Treatment naive N Y 100 80930 5568321700 2017813329 36% 23228 1650 CGPLH45 Healthy Preoperative, Treatment naive N Y 100 80930 8485593200 2770176078 33% 32829 3114 CGPLH46 Healthy Preoperative, Treatment naive N Y 100 80930 5083171100 1899395790 37% 21821 1678 CGPLH47 Healthy Preoperative, Treatment naive N Y 100 80930 6016388500 2062392156 34% 23459 1431 CGPLH48 Healthy Preoperative, Treatment naive N Y 100 80930 4958945900 1809825992 36% 20702 1698 CGPLH49 Healthy Preoperative, Treatment naive N Y 100 80930 7953812200 2511365904 32% 27006 1440 CGPLH50 Healthy Preoperative, Treatment naive N Y 100 80930 6989407600 2561288100 37% 29177 2591 CGPLH51 Healthy Preoperative, Treatment naive N Y 100 80930 7862073200 2525091396 32% 29999 1293 CGPLH52 Healthy Preoperative, Treatment naive N Y 100 80930 6939636800 2397922699 35% 27029 2501 CGPLH54 Healthy Preoperative, Treatment naive N Y 100 80930 10611934700 2290823134 22% 27175 3306 CGPLH55 Healthy Preoperative, Treatment naive N Y 100 80930 9912569200 2521962244 25% 27082 3161 CGRLH56 Healthy Preoperative, Treatment naive N Y 100 80930 5777591900 2023874863 35% 22916 1301 CGPLH57 Healthy Preoperative, Treatment naive N Y 100 80930 9234904800 1493926244 16% 15843 1655 CGPLH59 Healthy Preoperative, Treatment naive N Y 100 80930 9726052100 2987875484 31% 35427 2143 CGPLH63 Healthy Preoperative, Treatment naive N Y 100 80930 8696405000 2521574759 29% 26689 1851 CGPLH64 Healthy Preoperative, Treatment naive N Y 100 80930 5438852600 996198502 18% 11477 1443 CGPLH75 Healthy Preoperative, Treatment naive Y N 100 80930 3446444000 1505718480 44% 17805 3016 CGPLH76 Healthy Preoperative, Treatment naive N Y 100 80930 7499116400 3685762725 49% 43682 4643 CGPLH77 Healthy Preoperative, Treatment naive Y N 100 80930 6512408400 2537359345 39% 30280 3131 CGPLH78 Healthy Preoperative, Treatment naive N Y 100 80930 7642949300 3946069680 52% 46316 5358 CGPLH79 Healthy Preoperative, Treatment naive N Y 100 80930 7785475700 3910639227 50% 45280 6714 CGPLH80 Healthy Preoperative, Treatment naive N Y 100 80930 7918361500 3558236955 45% 42171 5062 CGPLH81 Healthy Preoperative, Treatment naive Y N 100 80930 6646268900 3112369850 47% 37119 3678 CGPLH82 Healthy Preoperative, Treatment naive N Y 100 80930 7744065000 3941700596 51% 46820 5723 CGPLH83 Healthy Preoperative, Treatment naive Y N 100 80930 6957686000 1447603106 21% 17280 2875 CGPLH84 Healthy Preoperative, Treatment naive Y N 100 80930 8326493200 3969908122 48% 47464 3647 CGPLH86 Healthy Preoperative, Treatment naive N Y 100 80930 8664194700 4470145091 52% 53398 5094 CGPLH90 Healthy Preoperative, Treatment naive N Y 100 80930 7516078800 3841504088 51% 45907 4414 CGPLLU13 Lung Cancer Pre-treatment, Day -2 Y N 100 80930 5659546100 1721618955 30% 20587 6025 CGPLLU13 Lung Cancer Pre-treatment, Day -2 Y N 100 80930 6199049700 2563659840 41% 30728 6514 CGPLLU13 Lung Cancer Pre-treatment, Day -2 Y N 100 80930 5864396500 1194237002 20% 14331 3952 CGPLLU13 Lung Cancer Pre-treatment, Day -2 Y N 100 80930 5080197700 1373550586 27% 16480 5389 CGPLLU14 Lung Cancer Pre-treatment, Day -38 N Y 100 80930 8668655700 398731089 46% 48628 3148 CGPLLU14 Lung Cancer Pre-treatment, Day -16 N Y 100 80930 8271043600 4105092738 50% 50152 4497 CGPLLU14 Lung Cancer Pre-treatment, Day -3 N Y 100 80930 7149809200 3405754720 48% 40382 6170 CGPLLU14 Lung Cancer Pre-treatment, Day 0 N Y 100 80930 6556332200 3289504484 50% 39004 4081 CGPLLU14 Lung Cancer Post-treatment, Day 0.33 N Y 100 80930 7410378300 3464236558 47% 41108 4259 CGPLLU14 Lung Cancer Post-treatment, Day 7 N Y 100 80930 7530190700 3752054349 50% 45839 2469 CGPLLU144 Lung Cancer Preoperative, Treatment naive Y Y 100 80930 8716827400 4216576624 48% 49370 10771 CGPLLU146 Lung Cancer Preoperative, Treatment naive Y N 100 80930 8506844200 4195033049 49% 49084 6968 CGPLLU147 Lung Cancer Preoperative, Treatment naive Y N 100 80930 7416300600 3530746046 48% 41302 4691 CGPLLU161 Lung Cancer Preoperative, Treatment naive N Y 100 80930 7789148700 3280139772 42% 38568 12229 CGPLLU162 Lung Cancer Preoperative, Treatment naive Y Y 100 80930 7625462000 3470147667 46% 40918 10099 CGPLLU163 Lung Cancer Preoperative, Treatment naive Y Y 100 80930 8019293200 3946533983 49% 46471 12108 CGPLLU164 Lung Cancer Preoperative, Treatment naive Y N 100 80930 8110030900 3592748235 44% 42161 6947 CGPLLU165 Lung Cancer Preoperative, Treatment naive Y N 100 80930 8389514600 4147501817 49% 48770 8996 CGPLLU168 Lung Cancer Preoperative, Treatment naive Y Y 100 80930 7600630000 3868237773 50% 45625 9711 CGPLLU169 Lung Cancer Preoperative, Treatment naive N Y 100 80930 9378353000 4800407624 51% 56547 10261 CGPLLU174 Lung Cancer Preoperative, Treatment naive Y N 100 80930 7481844600 3067532518 41% 36321 6137 CGPLLU175 Lung Cancer Preoperative, Treatment naive Y N 100 80930 8532324200 4002541569 47% 47084 7862 CGPLLU176 Lung Cancer Preoperative, Treatment naive Y Y 100 80930 8143905000 4054098929 50% 47708 5588 CGPLLU177 Lung Cancer Preoperative, Treatment naive Y Y 100 80930 8421611300 4197108809 50% 49476 8780 CGPLLU178 Lung Cancer Preoperative, Treatment naive Y N 100 80930 8483124700 4169577489 49% 48580 6445 CGPLLU179 Lung Cancer Preoperative, Treatment naive Y N 100 80930 7774358700 3304915738 43% 38768 6862 CGPLLU180 Lung Cancer Preoperative, Treatment naive Y N 100 80930 8192813800 3937552475 48% 46498 6568 CGPLLU197 Lung Cancer Preoperative, Treatment naive Y N 100 80930 7906779200 3082397881 39% 36381 5388 CGPLLU198 Lung Cancer Preoperative, Treatment naive Y N 100 80930 7175247200 3545719100 49% 42008 6817 CGPLLU202 Lung Cancer Preoperative, Treatment naive Y N 100 80930 6840112800 3427820669 50% 40670 7951 CGPLLU203 Lung Cancer Preoperative, Treatment naive N Y 100 80930 7458749900 3762726574 50% 44500 9917 CGPLLU204 Lung Cancer Preoperative, Treatment naive Y N 100 80930 7445026400 3703545153 50% 44317 6856 CGPLLU205 Lung Cancer Preoperative, Treatment naive Y Y 100 80930 9205429100 4350573991 47% 51627 9810 CGPLLU206 Lung Cancer Preoperative, Treatment naive Y N 100 80930 7397914600 3635210205 49% 43016 7124 CGPLLU207 Lung Cancer Preoperative, Treatment naive Y Y 100 80930 7133043900 3736258011 52% 44291 8499 CGPLLU208 Lung Cancer Preoperative, Treatment naive Y Y 100 80930 7346976400 3855814032 52% 45782 8940 CGPLLU209 Lung Cancer Preoperative, Treatment naive Y N 100 80930 6723337800 3362944595 50% 39531 11946 CGPLLU244 Lung Cancer Pre-treatment Day -7 N Y 100 80930 8305560600 4182616104 50% 50851 7569 CGPLLU244 Lung Cancer Pre-treatment, Day -1 N Y 100 80930 7739951100 3788487116 49% 45925 8552 CGPLLU244 Lung Cancer Post-treatment, Day 6 N Y 100 80930 8061928000 4225322272 52% 51279 8646 CGPLLU244 Lung Cancer Post-treatment, Day 62 N Y 100 80930 8894936700 4437962639 50% 53862 7361 CGPLLU245 Lung Cancer Pre-treatment, Day -32 N Y 100 80930 7679235200 3935822054 51% 47768 7266 CGPLLU245 Lung Cancer Pre-treatment Day 0 N Y 100 80930 8985252500 4824268339 54% 58338 10394 CGPLLU245 Lung Cancer Post-treatment, Day 7 N Y 100 80930 8518229300 4480236927 53% 54083 10125 CGPLLU245 Lung Cancer Post-treatment, Day 21 N Y 100 80930 9031131000 4824738475 53% 58313 10598 CGPLLU246 Lung Cancer Pre-treatment. Day -21 N Y 100 80930 8520360800 3509660305 41% 42349 8086 CGPLLU246 Lung Cancer Pre-treatment, Day 0 N Y 100 80930 5451467800 2828351657 52% 34243 8256 CGPLLU246 Lung Cancer Post-treatment, Day 9 N Y 100 80930 8137616600 4135036174 51% 50121 6466 CGPLLU246 Lung Cancer Post-treatment, Day 42 N Y 100 80930 8385724600 4413323333 53% 53495 7303 CGPLLU264 Lung Cancer Pre-treatment, Day -1 Y N 100 80930 6254777700 3016326208 48% 36164 12138 CGPLLU264 Lung Cancer Pre-treatment, Day -1 Y N 100 80930 6185331000 3087883231 50% 37003 8388 CGPLLU264 Lung Cancer Pre-treatment, Day -1 Y N 100 80930 6274540300 2861143666 46% 34308 6817 CGPLLU264 Lung Cancer Pre-treatment, Day -1 Y N 100 80930 5701274000 1241270938 22% 14886 4273 CGPLLU265 Lung Cancer Pre-treatment, Day 0 Y N 100 80930 6091276800 2922585558 48% 35004 7742 CGPLLU265 Lung Cancer Pre-treatment, Day 0 Y N 100 80930 6430107900 2945953499 46% 35219 8574 CGPLLU265 Lung Cancer Pre-treatment, Day 0 Y N 100 80930 5869510300 2792208995 48% 33423 8423 CGPLLU265 Lung Cancer Pre-treatment, Day 0 Y N 100 80930 5884330900 2588386038 44% 30977 9803 CGPLLU266 Lung Cancer Pre-treatment, Day 0 Y N 100 80930 5807524900 2347651479 40% 28146 5793 CGPLLU266 Lung Cancer Pre-treatment, Day 0 Y N 100 80930 6064269800 2086938782 34% 24994 6221 CGPLLU266 Lung Cancer Pre-treatment, Day 0 Y N 100 80930 6785913900 3458588505 51% 41432 7785 CGPLLU266 Lung Cancer Pre-treatment, Day 0 Y N 100 80930 6513702000 2096370387 32% 25142 6598 CGPLLU267 Lung Cancer Pre-treatment, Day -1 Y N 100 80930 6610761200 2576886619 39% 31095 4485 CGPLLU267 Lung Cancer Pre-treatment, Day -1 Y N 100 80930 6156402000 2586081726 42% 30714 5309 CGPLLU267 Lung Cancer Pre-treatment, Day -1 Y N 100 80930 6180799700 2013434756 33% 23902 3885 CGPLLU269 Lung Cancer Pre-treatment, Day 0 Y N 100 80930 6221168600 1499602843 24% 17799 6098 CGPLLU269 Lung Cancer Pre-treatment, Day 0 Y N 100 80930 5353961600 1698331125 32% 20094 5252 CGPLLU269 Lung Cancer Pre-treatment, Day 0 Y N 100 80930 5831612800 1521114956 26% 18067 6210 CGPLLU271 Lung Cancer Post-treatment, Day 259 Y N 100 80930 6229704000 1481468974 24% 17608 4633 CGPLLU271 Lung Cancer Post-treatment, Day 259 Y N 100 80930 6134366400 1351029627 22% 16170 7024 CGPLLU271 Lung Cancer Post-treatment, Day 259 Y N 100 80930 6491884900 1622578435 25% 19433 5792 CGPLLU271 Lung Cancer Post-treatment, Day 259 Y N 100 80930 5742881200 2349421128 41% 28171 5723 CGPLLU271 Lung Cancer Post-treatment, Day 259 Y N 100 80930 5503999300 1695782705 31% 20320 5907 CGPLLU43 Lung Cancer Pre-treatment, Day -1 Y N 100 80930 6575907000 3002048491 46% 35997 5445 CGPLLU43 Lung Cancer Pre-treatment, Day -1 Y N 100 80930 6204350900 3016077187 49% 36162 5704 CGPLLU43 Lung Cancer Pre-treatment, Day -1 Y N 100 80930 5997724300 2989608757 50% 35873 6228 CGPLLU43 Lung Cancer Pre-treatment, Day -1 Y N 100 80930 6026261500 2881177658 48% 34568 7221 CGPLLU86 Lung Cancer Pre-treatment, Day 0 N Y 100 80930 8222093400 3523035056 43% 41165 3614 CGPLLU86 Lung Cancer Post-treatment, Day 0.5 N Y 100 80930 8305719500 4271264008 51% 49508 6681 CGPLLU86 Lung Cancer Post-treatment, Day 7 N Y 100 80930 6787785300 3443658418 51% 40192 3643 CGPLLU86 Lung Cancer Post-treatment, Day 17 N Y 100 80930 6213229400 3120325926 50% 36413 3560 CGPLLU88 Lung Cancer Pre-treatment, Day 0 Y N 100 80930 7252433900 3621678746 50% 42719 8599 CGPLLU88 Lung Cancer Pre-treatment, Day 0 Y N 100 80930 7679995800 4004738253 52% 46951 6387 CGPLLU88 Lung Cancer Pre-treatment, Day 0 Y N 100 80930 6509178000 3316053733 51% 39274 2651 CGPLLU89 Lung Cancer Pre-treatment, Day 0 N Y 100 80930 7662496600 3781536306 49% 44097 7909 CGPLLU89 Lung Cancer Post-treatment, Day 7 N Y 100 80930 7005599600

3339612564 48% 38977 5034 CGPLLU89 Lung Cancer Post-treatment, Day 22 N Y 100 80930 8325998600 3094796789 37% 36061 2822 CGPLOV10 Ovarian Cancer Preoperative, Treatment naive Y Y 100 80930 7073534200 3402306123 48% 39820 4059 CGPLOV11 Ovarian Cancer Preoperative, Treatment naive Y Y 100 80930 6924062200 3324593050 48% 38796 7185 CGPLOV12 Ovarian Cancer Preoperative, Treatment naive N Y 100 80930 6552080100 3181854993 49% 37340 6114 CGPLOV13 Ovarian Cancer Preoperative, Treatment naive Y Y 100 80930 6796755500 3264897084 48% 38340 7931 CGPLOV14 Ovarian Cancer Preoperative, Treatment naive Y Y 100 80930 7856573900 3408425065 43% 39997 7712 CGPLOV15 Ovarian Cancer Preoperative, Treatment naive Y Y 100 80930 7239201500 3322285607 46% 38953 6644 CGPLOV16 Ovarian Cancer Preoperative, Treatment naive N Y 100 80930 8570755900 4344288233 51% 51009 11947 CGPLOV17 Ovarian Cancer Preoperative, Treatment naive Y N 100 80930 6910310400 2805243492 41% 32828 4307 CGPLOV18 Ovarian Cancer Preoperative, Treatment naive N N 100 80930 8173037600 4064432407 50% 47714 5182 CGPLOV19 Ovarian Cancer Preoperative, Treatment naive Y Y 100 80930 7732198900 3672564399 47% 43020 11127 CGPLOV20 Ovarian Cancer Preoperative, Treatment naive Y Y 100 80930 7559602000 3678700179 49% 43230 4872 CGPLOV21 Ovarian Cancer Preoperative, Treatment naive Y Y 100 80930 8949032900 4616255499 52% 54012 12777 CGPLOV22 Ovarian Cancer Preoperative, Treatment naive Y Y 100 80930 8680136500 4049934586 47% 46912 9715 CGPLOV23 Ovarian Cancer Preoperative, Treatment naive N Y 100 80930 6660696600 3422631774 51% 40810 9460 CGPLOV24 Ovarian Cancer Preoperative, Treatment naive N Y 100 80930 8634287200 4272258165 49% 50736 8689 CGPLOV25 Ovarian Cancer Preoperative, Treatment naive N Y 100 80930 6978295000 3390206388 49% 40188 5856 CGPLOV26 Ovarian Cancer Preoperative, Treatment naive N Y 100 80930 7041038300 3728879661 53% 44341 8950 CGPLOV28 Ovarian Cancer Preoperative, Treatment naive N Y 100 80930 7429236900 3753051715 51% 45430 4155 CGPLOV31 Ovarian Cancer Preoperative, Treatment naive N Y 100 80930 8961384000 4621838729 51% 55429 5458 CGPLOV32 Ovarian Cancer Preoperative, Treatment naive N Y 100 80930 9344536800 4737698323 51% 57234 6165 CGPLOV37 Ovarian Cancer Preoperative, Treatment naive N Y 100 80930 8158083200 4184432898 51% 50648 6934 CGPLOV38 Ovarian Cancer Preoperative, Treatment naive N Y 100 80930 8654435400 4492987085 52% 53789 6124 CGPLOV40 Ovarian Cancer Preoperative, Treatment naive N Y 100 80930 9868640700 4934400809 50% 59049 7721 CGPLOV41 Ovarian Cancer Preoperative, Treatment naive N Y 100 80930 7689013600 3861448829 50% 46292 4469 CGPLOV42 Ovarian Cancer Preoperative, Treatment naive N Y 100 80930 9836516300 4864154366 49% 58302 7632 CGPLOV43 Ovarian Cancer Preoperative, Treatment naive N Y 100 80930 8756507100 4515479918 52% 54661 4310 CGPLOV44 Ovarian Cancer Preoperative, Treatment naive N Y 100 80930 7576310800 4120933922 54% 49903 4969 CGPLOV46 Ovarian Cancer Preoperative, Treatment naive N Y 100 80930 9346036300 5037820346 54% 61204 3927 CGPLOV47 Ovarian Cancer Preoperative, Treatment naive N Y 100 80930 10880620200 5491357828 50% 66363 6895 CGPLOV48 Ovarian Cancer Preoperative, Treatment naive N Y 100 80930 7658787800 3335991337 44% 40332 4066 CGPLOV49 Ovarian Cancer Preoperative, Treatment naive N Y 100 80930 10076208000 5519656698 55% 67117 5097 CGPLOV50 Ovarian Cancer Preoperative, Treatment naive N Y 100 80930 8239290400 4472380276 54% 54150 3836 CGPLPA118 Bile Duct Cancer Preoperative, Treatment naive N Y 100 80930 9094827600 4828332902 53% 57021 4002 CGPLPA122 Bile Duct Cancer Preoperative, Treatment naive N Y 100 80930 7303323100 3990160379 55% 47240 7875 CGPLPA124 Bile Duct Cancer Preoperative, Treatment naive N Y 100 80930 7573482800 3965807442 52% 46388 8658 CGPLPA126 Bile Duct Cancer Preoperative, Treatment naive N Y 100 80930 7904953600 4061463168 51% 47812 10498 CGPLPA128 Bile Duct Cancer Preoperative, Treatment naive N Y 100 80930 7249238300 2244188735 31% 26436 3413 CGPLPA129 Bile Duct Cancer Preoperative, Treatment naive N Y 100 80930 7559858900 4003725804 53% 47182 5733 CGPLPA130 Bile Duct Cancer Preoperative, Treatment naive N Y 100 80930 6973946500 1247144905 18% 14691 1723 CGPLPA131 Bile Duct Cancer Preoperative, Treatment naive N Y 100 80930 7226237900 3370664342 47% 39661 5054 CGPLPA134 Bile Duct Cancer Preoperative, Treatment naive N Y 100 80930 7268866100 3754945844 52% 44306 7023 CGPLPA136 Bile Duct Cancer Preoperative, Treatment naive N Y 100 80930 7476690700 4073978408 54% 48134 5244 CGPLPA140 Bile Duct Cancer Preoperative, Treatment naive N Y 100 80930 7364654600 3771765342 51% 44479 7080 CGST102 Gastric Cancer Preoperative, Treatment naive N Y 100 80930 5715504500 2644902854 46% 31309 4503 CGST110 Gastric Cancer Preoperative, Treatment naive N Y 100 80930 9179291500 4298269268 47% 51666 3873 CGST114 Gastric Cancer Preoperative, Treatment naive N Y 100 80930 7151572200 3254967293 46% 38496 4839 CGST13 Gastric Cancer Preoperative, Treatment naive N Y 100 80930 6449701500 3198545984 50% 38515 6731 CGST141 Gastric Cancer Preoperative, Treatment naive N Y 100 80930 6781001300 3440927391 51% 40762 5404 CGST16 Gastric Cancer Preoperative, Treatment naive N Y 100 80930 6396470600 2931380289 46% 35354 8148 CGST18 Gastric Cancer Preoperative, Treatment naive N Y 100 80930 6647324000 3138967777 47% 37401 4992 CGST28 Gastric Cancer Preoperative, Treatment naive N Y 100 80930 6288486100 2884997993 46% 34538 2586 CGST30 Gastric Cancer Preoperative, Treatment naive N Y 100 80930 6141213100 3109994564 51% 37194 2555 CGST32 Gastric Cancer Preoperative, Treatment naive N Y 100 80930 6969139300 3099120469 44% 36726 3935 CGST33 Gastric Cancer Preoperative, Treatment naive N Y 100 80930 6560309400 3168371917 48% 37916 4597 CGST39 Gastric Cancer Preoperative, Treatment naive N Y 100 80930 7043791400 2992501875 42% 35620 6737 CGST41 Gastric Cancer Preoperative, Treatment naive N Y 100 80930 6975053100 3224065662 46% 38300 4016 CGST45 Gastric Cancer Preoperative, Treatment naive N Y 100 80930 6130812200 2944524278 48% 35264 4745 CGST47 Gastric Cancer Preoperative, Treatment naive N Y 100 80930 5961400000 3083523351 52% 37008 3112 CGST48 Gastric Cancer Preoperative, Treatment naive N Y 100 80930 6418652700 1497230327 23% 17782 2410 CGST58 Gastric Cancer Preoperative, Treatment naive N Y 100 80930 5818344500 1274708429 22% 15281 2924 CGST80 Gastric Cancer Preoperative, Treatment naive N Y 100 80930 6388064600 3298497188 52% 39692 5280 CGST81 Gastric Cancer Preoperative, Treatment naive N Y 100 80930 8655691400 1519121452 18% 17988 6419

TABLE-US-00005 APPENDIX C Table 3. Targeted cfDNA fragment analyses in cancer patients Stage at Amino Acid Patient Patient Type Diagnosis Alteration Type Gene (Protein) CGCRC291 Colorectal Cancer IV Tumor-derived STK11 39R > C CGCRC291 Colorectal Cancer IV Tumor-derived TP53 272V > M CGCRC291 Colorectal Cancer IV Tumor-derived TP53 167Q > X CGCRC291 Colorectal Cancer IV Tumor-derived KRAS 12G > A CGCRC291 Colorectal Cancer IV Tumor-derived APC 1260Q > X CGCRC291 Colorectal Cancer IV Tumor-derived APC 1450R > X CGCRC291 Colorectal Cancer IV Tumor-derived PIK3CA 542E > K CGCRC292 Colorectal Cancer IV Tumor-derived KRAS 146A > V CGCRC292 Colorectal Cancer IV Tumor-derived CTNNB1 41T > A CGCRC292 Colorectal Cancer IV Germline EGFR 2284 - 4C > 3 CGCRC293 Colorectal Cancer IV Tumor-derived TP53 176C > S CGCRC294 Colorectal Cancer II Tumor-derived APC 213R > X CGCRC294 Colorectal Cancer II Tumor-derived APC 1367Q > X CGCRC295 Colorectal Cancer IV Tumor-derived PDBFRA 49 + 4C > T CGCRC295 Colorectal Cancer IV Hematopoietic IDH1 104G > V CGCRC296 Colorectal Cancer II Germline EGFR 922E > K CGCRC297 Colorectal Cancer III Germline KIT 18L > F CGCRC298 Colorectal Cancer II Hematopoietic DNMT3A 882R > H CGCRC298 Colorectal Cancer II Hematopoietic DNMT3A 714S > C CGCRC298 Colorectal Cancer II Tumor-derived PIK3CA 414G > V CGCRC299 Colorectal Cancer I Hematopoietic DNMT3A 735Y > C CGCRC299 Colorectal Cancer I Hematopoietic DNMT3A 710C > S CGCRC300 Colorectal Cancer I Hematopoietic DNMT3A 720R > G CGCRC301 Colorectal Cancer I Tumor-derived ATM 2397Q > X CGCRC302 Colorectal Cancer II Tumor-derived TP53 141C > Y CGCRC302 Colorectal Cancer II Tumor-derived BRAF 600V > E CGCRC303 Colorectal Cancer III Tumor-derived TP53 173V > L CGCRC303 Colorectal Cancer III Hematopoietic DNMT3A 755F > S CGCRC303 Colorectal Cancer III Hematopoietic DNMT3A 2173 + 1G > A CGCRC304 Colorectal Cancer II Tumor-derived EGFR 1131T > S CGCRC304 Colorectal Cancer II Tumor-derived ATM 3077 + 1G > A CGCRC304 Colorectal Cancer II Hematopoietic ATM 3008R > C CGCRC305 Colorectal Cancer II Tumor-derived GNA11 213R > Q CGCRC305 Colorectal Cancer II Tumor-derived TP53 273R > H CGCRC306 Colorectal Cancer II Tumor-derived TP53 196R > X CGCRC306 Colorectal Cancer II Tumor-derived CDKN2A 107R > C CGCRC306 Colorectal Cancer II Tumor-derived KRAS 61Q > K CGCRC306 Colorectal Cancer II Germline PDGFRA 200T > S CGCRC306 Colorectal Cancer II Tumor-derived EGFR 618H > R CGCRC306 Colorectal Cancer II Tumor-derived PIK3CA 545E > A CGCRC306 Colorectal Cancer II Germline ERBB4 1155R > X CGCRC307 Colorectal Cancer II Tumor-derived JAK2 805L > V CGCRC307 Colorectal Cancer II Tumor-derived SMARCB1 501 - 2A > G CGCRC307 Colorectal Cancer II Tumor-derived GNAS 201R > C CGCRC307 Colorectal Cancer II Tumor-derived BRAF 600V > E CGCRC307 Colorectal Cancer II Tumor-derived FBXW7 465R > C CGCRC307 Colorectal Cancer II Tumor-derived ERBB4 17A > V CGCRC308 Colorectal Cancer III Hematopoietic DNMT3A 882R > H CGCRC308 Colorectal Cancer III Germline EGFR 848P > L CGCRC308 Colorectal Cancer III Tumor-derived APC 1480Q > X CGCRC309 Colorectal Cancer III Tumor-derived AKT1 17E > K CGCRC309 Colorectal Cancer III Tumor-derived BRAF 600V > E CGCRC310 Colorectal Cancer II Tumor-derived KRAS 12G > V CGCRC310 Colorectal Cancer II Tumor-derived APC 1513E > X CGCRC310 Colorectal Cancer II Tumor-derived APC 1521E > X CGCRC311 Colorectal Cancer I Hematopoietic DNMT3A 882R > H CGCRC312 Colorectal Cancer III Tumor-derived APC 960S > X CGCRC312 Colorectal Cancer III Tumor-derived NRAS 61Q > K CGCRC313 Colorectal Cancer III Tumor-derived KRAS 12G > S CGCRC313 Colorectal Cancer III Tumor-derived APC 876R > X CGCRC314 Colorectal Cancer I Tumor-derived KRAS 12G > D CGCRC314 Colorectal Cancer I Hematopoietic DNMT3A 738L > Q CGCRC314 Colorectal Cancer I Tumor-derived APC 1379E > X CGCRC315 Colorectal Cancer III Tumor-derived NRAS 12G > D CGCRC315 Colorectal Cancer III Tumor-derived FBXW7 505R > C Alteration Mutant Mutation Hotspot Detected Allele Patient Nucleotide Type Alteration in Tissue Fraction CGCRC291 chr19_1207027-127027_C_T Substitution No No 0.14% CGCRC291 chr17_7577124-7577124_C_T Substitution Yes No 0.10% CGCRC291 chr17_7578431-7578431_G_A Substitution Yes Yes 22.85% CGCRC291 chr12_25398284-25398284_C_G Substitution Yes Yes 14.65% CGCRC291 chr5_112175069-112175069_C_T Substitution No Yes 11.23% CGCRC291 chr5_11215639-11215639_C_T Substitution Yes Yes 11.05% CGCRC291 chr3_178936082-178936082_G_A Substitution Yes Yes 18.11% CGCRC292 chr12_25378561-25378561_G_A Substitution Yes No 1.41% CGCRC292 chr3_41266124-41266124_A_G Substitution Yes Yes 0.13% CGCRC292 chr7_55248982-55248982_C_G Substitution NA Yes 31.99% CGCRC293 chr17_7578404-7578404_A_T Substitution No No 0.35% CGCRC294 chr5_12116592-12116592_C_T Substitution Yes Yes 0.14% CGCRC294 chr5_12175390-12175390_C_T Substitution Yes Yes 0.13% CGCRC295 chr4_55124988-55124988_C_T Substitution No No 0.45% CGCRC295 chr2_209113196-209113196_C_A Substitution No Yes 0.34% CGCRC296 chr7_55266472-55266472_G_A Substitution NA Yes 30.48% CGCRC297 chr4_55524233-55524233_C_T Substitution NA Yes 41.39% CGCRC298 chr2_25457242-25457242_C_T Substitution Yes Yes 0.08% CGCRC298 chr2_25463541-25463541_G_C Substitution No No 0.11% CGCRC298 chr3_178927478-178927478_G_T Substitution No No 0.55% CGCRC299 chr2_25463289-25463289_T_C Substitution No Yes 0.30% CGCRC299 chr2_25463553-2546355_C_G Substitution No Yes 0.12% CGCRC300 chr2_25463524-25463524_G_C Substitution No No 0.15% CGCRC301 chr11_108199847-108199847_C_T Substitution No No 0.21% CGCRC302 chr17_7578508-7578508_C_T Substitution Yes Yes 0.05% CGCRC302 chr7_140453136-140453136_A_T Substitution Yes Yes 0.12% CGCRC303 chr17_7578413-7578413_C_A Substitution Yes Yes 0.08% CGCRC303 chr2_25463229-25463229_A_G Substitution No No 0.21% CGCRC303 chr2_25463508-25463508_C_T Substitution No No 0.17% CGCRC304 chr7_55273068-55273068_A_T Substitution No No 0.22% CGCRC304 chr11_108142134-108142134_G_A Substitution No No 0.27% CGCRC304 chr11_108236086-108236086_C_T Substitution No Yes 0.43% CGCRC305 chr19_3118954-3118954_G_A Substitution No Yes 0.11% CGCRC305 chr17_7577120-7577120_C_T Substitution Yes No 0.19% CGCRC306 chr17_7578263-7578263_G_A Substitution Yes No 0.12% CGCRC306 chr9_21971039-21971039_G_A Substitution No Yes 8.02% CGCRC306 chr12_25380277-25380277_G_T Substitution Yes Yes 7.30% CGCRC306 chr4_55130065-55130065_C_G Substitution NA Yes 34.78% CGCRC306 chr7_55233103-55233103_A_G Substitution No Yes 8.32% CGCRC306 chr3_178936092-178936092_A_C Substitution Yes No 0.96% CGCRC306 chr2_2122596-2122596_G_A Substitution NA Yes 38.70% CGCRC307 chr9_5080662-5080662_C_G Substitution No No 0.56% CGCRC307 chr22_24145480-24145480_A_G Substitution No Yes 0.34% CGCRC307 chr20_57484420-57484420_C_T Substitution Yes Yes# 0.24% CGCRC307 chr7_140453136-140453136_A_T Substitution Yes Yes 0.38% CGCRC307 chr4_153249385-153249385_G_A Substitution Yes Yes 0.31% CGCRC307 chr2_213403205-213403205_G_A Substitution No No 0.15% CGCRC308 chr2_25457242-25457242_C_T Substitution Yes No 0.06% CGCRC308 chr7_55259485-55259485_C_T Substitution NA Yes 27.69% CGCRC308 chr5_112175242-112175242_C_T Substitution No Yes 0.11% CGCRC309 chr14_105246551-105246551_C_T Substitution Yes Yes 2.70% CGCRC309 chr7_140453136-140453136_A_T Substitution Yes Yes 3.00% CGCRC310 chr12_25398284-25398284_C_A Substitution Yes Yes 0.13% CGCRC310 chr5_11215828-11215828_G_T Substitution No Yes 0.11% CGCRC310 chr5_11215852-11215852_G_T Substitution No Yes 0.15% CGCRC311 chr2_25457242-25457242_C_T Substitution Yes No 0.86% CGCRC312 chr5_112174170-112174170_C_G Substitution No Yes 0.59% CGCRC312 chr1_115256530-115256530_G_T Substitution Yes Yes 0.47% CGCRC313 chr12_25398285-25398285_C_T Substitution Yes Yes 0.17% CGCRC313 chr5_112173917-112173917_C_T Substitution Yes Yes 0.07% CGCRC314 chr12_25398284-25398284_C_T Substitution Yes Yes 0.30% CGCRC314 chr2_25463280-25463280_A_T Substitution No Yes 2.50% CGCRC314 chr5_112175426-112175426_G_T Substitution Yes Yes 0.38% CGCRC315 chr1_115258747-115258747_C_T Substitution Yes Yes 0.27% CGCRC315 chr4_153247289-53247289_G_A Substitution Yes Yes 0.25% Wild-type Fragments 25th Minimum Percentile Mode Median cfDNA cfDNA cfDNA cfDNA Fragment Fragment Fragment Fragment Distinct Size Size Size Size Patient Coverage (bp) (bp) (bp) (bp) CGCRC291 11688 100 151 167 159 CGCRC291 11779 100 155 171 159 CGCRC291 11026 100 156 166 159 CGCRC291 7632 97 152 169 157 CGCRC291 7218 101 155 167 159 CGCRC291 10757 86 154 166 167 CGCRC291 5429 100 151 171 167 CGCRC292 8120 101 157 167 169 CGCRC292 10693 100 155 169 168 CGCRC292 7587 97 158 166 171 CGCRC293 7672 95 159 168 170 CGCRC294 7339 84 155 166 167 CGCRC294 12054 89 159 167 170 CGCRC295 5602 101 157 164 170 CGCRC295 8330 100 157 166 169 CGCRC296 8375 89 161 166 172 CGCRC297 3580 102 159 164 170 CGCRC298 13032 100 159 168 171 CGCRC298 13475 93 158 169 170 CGCRC298 5815 100 156 168 169 CGCRC299 11995 100 154 164 165 CGCRC299 15363 96 151 166 164 CGCRC300 7487 100 162 170 173 CGCRC301 5881 100 156 169 169 CGCRC302 24784 84 154 165 164 CGCRC302 11763 95 159 165 165 CGCRC303 13967 95 160 169 171 CGCRC303 10161 81 160 169 172 CGCRC303 10845 100 160 169 172 CGCRC304 16168 90 153 167 164 CGCRC304 10502 100 152 165 163 CGCRC304 12987 101 154 165 165 CGCRC305 12507 100 159 169 171 CGCRC305 10301 100 156 168 168 CGCRC306 8594 101 157 165 169 CGCRC306 9437 90 159 167 171 CGCRC306 8090 100 152 163 168 CGCRC306 4585 103 158 167 170 CGCRC306 7395 81 160 166 171 CGCRC306 4885 100 152 167 167 CGCRC306 3700 100 159 166 171 CGCRC307 6860 100 158 170 170 CGCRC307 10065 95 157 168 169 CGCRC307 7520 102 156 167 168 CGCRC307 6623 76 157 169 168 CGCRC307 10606 100 155 167 168 CGCRC307 13189 90 158 168 171 CGCRC308 16287 90 159 168 169 CGCRC308 7729 100 160 164 170 CGCRC308 14067 92 157 170 169 CGCRC309 13036 85 157 170 169 CGCRC309 9084 101 157 166 168 CGCRC310 7393 100 153 165 164 CGCRC310 11689 100 152 166 164 CGCRC310 10273 100 153 166 164 CGCRC311 8456 94 160 171 172 CGCRC312 4719 100 160 165 173 CGCRC312 3391 101 157 172 170 CGCRC313 5013 100 163 166 174 CGCRC313 8150 72 161 171 174 CGCRC314 4684 100 158 165 169 CGCRC314 6902 85 159 165 170 CGCRC314 7229 102 158 167 170 CGCRC315 8739 94 155 167 169 CGCRC315 9623 101 158 166 170 Stage at Amino Acid Patient Patient Type Diagnosis Alteration Type Gene (Protein) CGCRC316 Colorectal Cancer III Tumor-derived TP53 245G > S CGCRC316 Colorectal Cancer III Tumor-derived CDKN2A 1M > R CGCRC316 Colorectal Cancer III Tumor-derived CTNNB1 37S > C CGCRC316 Colorectal Cancer III Tumor-derived EGFR 2732 - 3C > T CGCRC316 Colorectal Cancer III Hematopoietic ATM 3008R > P CGCRC317 Colorectal Cancer III Tumor-derived TP53 220Y > C CGCRC317 Colorectal Cancer III Tumor-derived ATM 1026W > R CGCRC317 Colorectal Cancer III Tumor-derived APC 216R > X CGCRC318 Colorectal Cancer I Hematopoietic DNMT3A 698W > X CGCRC320 Colorectal Cancer I Germline KIT 18L > F CGCRC320 Colorectal Cancer I Tumor-derived ERBB4 78R > W CGCRC321 Colorectal Cancer I Tumor-derived CDKN2A 12S > L CGCRC321 Colorectal Cancer I Hernatopcietic DNMT3A 882R > H CGCRC321 Colorectal Cancer I Germline EGFR 511S > Y CGCRC332 Colorectal Cancer IV Tumor-derived TP53 125T > R CGCRC333 Colorectal Cancer IV Tumor-derived TP53 673 - 2A > G CGCRC333 Colorectal Cancer IV Tumor-derived BRAF 600V > E CGCRC333 Colorectal Cancer IV Tumor-derived ERBB4 891E > A CGCRC334 Colorectal Cancer IV Tumor-derived TP53 245G > S CGCRC334 Colorectal Cancer IV Germline EGFR 638T > M CGCRC334 Colorectal Cancer IV Tumor-derived PIK3CA 104P > R CGCRC335 Colorectal Cancer IV Tumor-derived BRAF 600V > E CGCRC336 Colorectal Cancer IV Tumor-derived TP53 175R > H CGCRC336 Colorectal Cancer IV Tumor-derived KRAS 12G > V CGCRC336 Colorectal Cancer IV Tumor-derived APC 1286E > X CGCRC337 Colorectal Cancer IV Tumor-derived STK11 734 + ST > A CGCRC337 Colorectal Cancer IV Germline APC 485M > I OGORC338 Colorectal Cancer IV Tumor-derived KRAS 12G > D CGCRC339 Colorectal Cancer IV Tumor-derived KRAS 13G > D

CGCRC339 Colorectal Cancer IV Tumor-derived APC 876R > X CGCRC339 Colorectal Cancer IV Tumor-derived PIK3CA 407C > F CGCRC339 Colorectal Cancer IV Tumor-derived PIK3CA 1047H > L CGCRC340 Colorectal Cancer IV Tumor-derived TP53 196R > X CGCRC340 Colorectal Cancer IV Tumor-derived APC 1306E > X CGPLBR38 Breast Cancer I Tumor-derived TP53 241S > P CGPLBR40 Breast Cancer III Germline AR 392P > R CGPLBR44 Breast Cancer III Hematopoietic DNMT3A 882R > H CGPLBR44 Breast Cancer III Hematopoietic DNMT3A 705I > T CGPLBR44 Breast Cancer III Tumor-derived PDGFRA 859V > M CGPLBR48 Breast Cancer II Germline ALK 1231R > Q CGPLBR48 Breast Cancer II Tumor-derived EGFR 669R > Q CGPLBR55 Breast Cancer III Hematopoietic DNMT3A 743P > S CGPLBR55 Breast Cancer III Tumor-derived GNAS 201R > H CGPLBR55 Breast Cancer III Tumor-derived PIK3CA 345N > K CGPLBR63 Breast Cancer II Germline FGFR3 403K > E CGPLBR67 Breast Cancer II Hematopoietic DNMT3A 882R > H CGPLBR67 Breast Cancer II Tumor-derived PIK3CA 545E > K CGPLBR67 Breast Cancer II Tumor-derived ERBB4 1000D > A CGPLBR69 Breast Cancer II Hematopoietic DNMT3A 774E > V CGPLBR69 Breast Cancer II Germline CTNNB1 30Y > S CGPLBR69 Breast Cancer II Germline IDH1 231Y > N CGPLBR70 Breast Cancer II Tumor-derived ATM 2832R > H CGPLBR70 Breast Cancer II Germline APC 1577E > D CGPLBR71 Breast Cancer II Tumor-derived TP53 273R > H CGPLBR72 Breast Cancer II Germline APC 1532D > G CGPLBR73 Breast Cancer II Tumor-derived ALK 708S > P CGPLBR73 Breast Cancer II Germline ERBB4 158A > E CGPLBR74 Breast Cancer II Germline AR 20 + G1G > T CGPLBR75 Breast Cancer II Tumor-derived PIK3CA 1047H > R CGPLBR76 Breast Cancer II Germline KDR 1290S > N CGPLBR76 Breast Cancer II Tumor-derived PIK3CA 1047H > R CGPLBR77 Breast Cancer III Tumor-derived PTEN 170S > I CGPLBR80 Breast Cancer II Tumor-derived CDKN2A 12S > L CGPLBR83 Breast Cancer II Germline AR 728N > D CGPLBR83 Breast Cancer II Tumor-derived ATM 322E > K CGPLBR83 Breast Cancer II Germline ERBB4 539Y > S CGPLBR86 Breast Cancer II Germline STK11 354F > L Alteration Mutant Mutation Hotspot Detected Allele Patient Nucleotide Type Alteration in Tissue Fraction CGCRC316 chr17_7577548-7577548_C_T Substitution Yes Yes 6.52% CGCRC316 chr9_21974625-21974825_A_C Substitution No Yes 5.74% CGCRC316 chr3_41266113-41266113_C_G Substitution Yes Yes 5.47% CGCRC316 chr7_55266407-55266407_C_T Substitution No No 0.11% CGCRC316 chr11_108236087-108236087_G_C Substitution No Yes 0.13% CGCRC317 chr17_7578190-7578190_T_C Substitution Yes Yes 0.36% CGCRC317 chr11_108142132-108142132_T_C Substitution No Yes 0.23% CGCRC317 chr5_112128143-112128143_C_T Substitution Yes No 0.29% CGCRC318 chr2_25463589-25463589_C_T Substitution No Yes 0.25% CGCRC320 chr4_55524233-55524233_C_T Substitution NA Yes 34.76% CGCRC320 chr2_212989479-212989479_G_A Substitution No No 0.12% CGCRC321 chr9_21974792-21974792_C_T Substitution No No 0.20% CGCRC321 chr2_25457242-25457242_C_A Substitution You No 0.08% CGCRC321 chr7_55229225-55229225_G_C Substitution NA Yes 41.86% CGCRC332 chr17_7579313-7579313_T_C Substitution No Yes 19.98% CGCRC333 chr17_7577610-7577610_A_T Substitution No Yes 43.03% CGCRC333 chr7_140453136-140453136_T_G Substitution Yes Yes 22.26% CGCRC333 chr2_212495194-212495194_C_T Substitution No No 1.00% CGCRC334 chr17_7577548-7577548_C_T Substitution Yes Yes 13.44% CGCRC334 chr7_55238900-55238900_C_T Substitution NA Yes 35.28% CGCRC334 chr3_178916924-178916924_C_G Substitution No No 3.85% CGCRC335 chr7_140453136-140453136_A_T Substitution Yes Yes 0.32% CGCRC336 chr17_7578406-7578406_C_T Substitution Yes Yes 75.76% CGCRC336 chr12_25398284-25398284_C_A Substitution Yes Yes 42.87% CGCRC336 chr5_112175147-112175147_G_T Substitution No Yes 81.61% CGCRC337 chr19_1220718-1220718_T_A Substitution No No 0.12% CGCRC337 chr5_112162851-112162851_G_A Substitution NA Yes 46.26% OGORC338 chr12_25398284-25398284_C_T Substitution Yes Yes 27.03% CGCRC339 chr12_25398281-25398281_C_T Substitution Yes Yes 1.94% CGCRC339 chr5_112173917-112173917_C_T Substitution Yes Yes 2.35% CGCRC339 chr3_178927457-178927457_G_T Substitution No Yes 3.14% CGCRC339 chr3_178952085-178952085_A_T Substitution Yes Yes 1.71% CGCRC340 chr17_7578263-7578263_G_A Substitution Yes Yes 18.26% CGCRC340 chr5_112175207-112175207_G_T Substitution Yes Yes 22.57% CGPLBR38 chr17_7577560-7577560_A_G Substitution No Yes 0.53% CGPLBR40 chrX_66766163-66766163_C_G Substitution NA Yes 28.99% CGPLBR44 chr2_25457242-25457242_C_T Substitution Yes Yes 1.82% CGPLBR44 chr2_25463568-25463568_A_G Substitution No Yes 0.41% CGPLBR44 chr4_55153609-55153609_G_A Substitution No Yes 0.13% CGPLBR48 chr2_2936301-2936301_C_T Substitution NA Yes 34.61% CGPLBR48 chr7_55240762-55240762_G_A Substitution No No 0.18% CGPLBR55 chr2_25463266-25463266_G_A Substitution No No 0.18% CGPLBR55 chr20_57484421-57484421_G_A Substitution Yes Yes 0.68% CGPLBR55 chr_178921553-178921553_T_A Substitution Yes Yes 0.42% CGPLBR63 chr3_1806188-1806188_A_G Substitution NA Yes 34.82% CGPLBR67 chr4_25457242-25457242_C_T Substitution Yes Yes 0.11% CGPLBR67 chr3_178936091-178936091_G_A Substitution Yes Yes 0.68% CGPLBR67 chr2_212285302-212285302_T_G Substitution No No 0.28% CGPLBR69 chr2_25463172-25463172_T_A Substitution No No 0.29% CGPLBR69 chr3_41266092-41266092_A_C Substitution NA Yes 41.74% CGPLBR69 chr2_209108158-209108158_A_T Substitution NA Yes 41.86% CGPLBR70 chr11_108216546-108216546_G_A Substitution No No 0.36% CGPLBR70 chr5_112176022-112176022_A_C Substitution NA Yes 40.28% CGPLBR71 chr17_7577120-7577120_C_T Substitution Yes Yes 0.10% CGPLBR72 chr5_112175886-112175886_A_G Substitution NA Yes 44.03% CGPLBR73 chr2_29474053-29474053_A_G Substitution No No 0.27% CGPLBR73 chr2_212652833-212652833_G_T Substitution NA Yes 35.58% CGPLBR74 chrX_66788865-66788865_G_T Substitution NA Yes 36.23% CGPLBR75 chr3_178952085-178952085_A_G Substitution Yes Yes 0.14% CGPLBR76 chr4_55946310-55946310_C_T Substitution NA Yes 36.57% CGPLBR76 chr3_178952085-178952085_A_G Substitution Yes Yes 0.12% CGPLBR77 chr10_89711891-89711891_G_T Substitution No Yes 2.29% CGPLBR80 chr9_21974792-21974792_G_A Substitution No No 0.54% CGPLBR83 chrX_66937328-66937328_A_G Substitution NA Yes 42.66% CGPLBR83 chr11_108117753-108117753_G_A Substitution No No 0.28% CGPLBR83 chr2_212543783-212543783_T_G Substitution NA Yes 44.91% CGPLBR86 chr19_1223125-1223125_C_G Substitution NA Yes 42.32% Wild-type Fragments 25th Minimum Percentile Mode Median cfDNA cfDNA cfDNA cfDNA Fragment Fragment Fragment Fragment Distinct Size Size Size Size Patient Coverage (bp) (bp) (bp) (bp) CGCRC316 12880 100 150 166 163 CGCRC316 7479 93 157 164 168 CGCRC316 13682 100 149 165 162 CGCRC316 16716 85 153 166 156 CGCRC316 17060 100 150 166 153 CGCRC317 14587 84 152 166 154 CGCRC317 10483 100 152 164 155 CGCRC317 3497 101 149 166 163 CGCRC318 16436 98 158 170 170 CGCRC320 6521 100 163 170 175 CGCRC320 11633 100 162 174 174 CGCRC321 6918 88 161 167 174 CGCRC321 9559 94 159 171 170 CGCRC321 5545 100 159 172 172 CGCRC332 605 104 164 170 176 CGCRC333 1265 89 159 165 171 CGCRC333 3338 102 153 165 169 CGCRC333 3008 102 153 169 109 CGCRC334 1725 105 160 170 175 CGCRC334 1168 100 159 164 174 CGCRC334 1798 103 159 166 173 CGCRC335 2411 99 155 167 167 CGCRC336 757 104 156 171 170 CGCRC336 1080 102 150 166 167 CGCRC336 391 102 161 165 171 CGCRC337 6497 72 153 169 177 CGCRC337 1686 100 147 170 153 OGORC338 1408 105 153 164 156 CGCRC339 1256 105 158 168 159 CGCRC339 1639 101 158 165 172 CGCRC339 1143 100 154 170 167 CGCRC339 1584 108 161 171 173 CGCRC340 876 101 162 170 175 CGCRC340 796 105 159 164 174 CGPLBR38 9684 95 156 166 168 CGPLBR40 10277 78 162 168 173 CGPLBR44 10715 99 162 171 173 CGPLBR44 10837 100 159 169 171 CGPLBR44 12640 100 159 168 171 CGPLBR48 5631 100 164 170 179 CGPLBR48 12467 101 167 174 180 CGPLBR55 10527 101 158 169 169 CGPLBR55 6011 101 153 166 167 CGPLBR55 3973 101 153 166 166 CGPLBR63 3405 97 165 170 176 CGPLBR67 10259 87 157 166 168 CGPLBR67 5163 100 151 167 165 CGPLBR67 6250 100 155 166 187 CGPLBR69 7558 100 159 166 170 CGPLBR69 3938 101 154 169 166 CGPLBR69 2387 101 157 166 168 CGPLBR70 6916 100 158 171 169 CGPLBR70 3580 107 160 169 173 CGPLBR71 7930 85 156 166 158 CGPLBR72 2389 100 157 160 170 CGPLBR73 11348 95 161 173 174 CGPLBR73 3422 102 157 168 169 CGPLBR74 9784 101 163 175 174 CGPLBR75 7290 103 162 173 172 CGPLBR76 4342 104 166 171 179 CGPLBR76 11785 100 165 168 177 CGPLBR77 6161 100 158 166 169 CGPLBR80 3643 96 166 166 185 CGPLBR83 3479 105 162 164 174 CGPLBR83 3496 103 165 170 177 CGPLBR83 1748 100 164 173 175 CGPLBR86 4241 98 160 168 175 Stage at Amino Acid Patient Patient Type Diagnosis Alteration Type Gene (Protein) CGPLBR86 Breast Cancer II Germline SMARCB1 795 + 3A > G CGPLBR87 Breast Cancer II Tumor-derived JAK2 215R > X CGPLBR87 Breast Cancer II Hematopoietic DNMT3A 882R > H CGPLBR87 Breast Cancer II Tumor-derived SMAD4 496R > C CGPLBR87 Breast Cancer II Germline AR 651S > N CGPLBR88 Breast Cancer II Tumor-derived CDK6 51E > K CGPLBR88 Breast Cancer II Germline APC 1125V > A CGPLBR92 Breast Cancer II Tumor-derived TP53 257L > P CGPLBR96 Breast Cancer II Tumor-derived TP53 213R > X CGPLBR96 Breast Cancer II Hematopoietic DNMT3A 531D > G CGPLBR96 Breast Cancer II Tumor-derived AR 13R > Q CGPLBR97 Breast Cancer II Hematopoietic DNMT3A 882R > H CGPLBR97 Breast Cancer II Germline PDGFRA 401A > D CGPLBR97 Breast Cancer II Tumor-derived GNAS 201R > H CGPLLU144 Lung Cancer II Tumor-derived TP53 241S > F CGPLLU144 Lung Cancer II Tumor-derived KRAS 12G > C CGPLLU144 Lung Cancer II Tumor-derived EGFR 373P > S CGPLLU144 Lung Cancer II Tumor-derived ATM 292P > L CGPLLU144 Lung Cancer II Tumor-derived PIK3CA 545E > K CGPLLU144 Lung Cancer II Tumor-derived ERBB4 426R > K CGPLLU146 Lung Cancer II Tumor-derived JAK2 617V > F CGPLLU146 Lung Cancer II Tumor-derived TP53 282R > P CGPLLU146 Lung Cancer II Tumor-derived DNMT3A 737L > H CGPLLU146 Lung Cancer II Tumor-derived RB1 861 + 2T > C CGPLLU146 Lung Cancer II Tumor-derived ATM 581L > F CGPLLU147 Lung Cancer III Tumor-derived TP53 248R > Q CGPLLU147 Lung Cancer III Tumor-derived TP53 201L > X CGPLLU147 Lung Cancer III Tumor-derived ALK 1537G > E CGPLLU147 Lung Cancer III Germline PDGFRA 200T > S CGPLLU162 Lung Cancer II Tumor-derived CDKN2A 12S > L CGPLLU162 Lung Cancer II Tumor-derived EGFR 858L > R CGPLLU162 Lung Cancer II Tumor-derived BRAF 354R > Q CGPLLU163 Lung Cancer II Tumor-derived CDKN2A 12S > L CGPLLU163 Lung Cancer II Hematopoietic DNMT3A 528Y > D CGPLLU164 Lung Cancer II Tumor-derived STK11 216S > Y CGPLLU164 Lung Cancer II Germline STK11 354F > L CGPLLU164 Lung Cancer II Tumor-derived GNA11 606 - 3C > T CGPLLU164 Lung Cancer II Tumor-derived TP53 278P > S CGPLLU164 Lung Cancer II Tumor-derived TP53 161A > S CGPLLU164 Lung Cancer II Tumor-derived TP53 160M > I CGPLLU164 Lung Cancer II Tumor-derived ERBB4 1299P > L CGPLLU164 Lung Cancer II Tumor-derived ERBB4 253N > S CGPLLU165 Lung Cancer II Tumor-derived STK11 354F > L CGPLLU165 Lung Cancer I Tumor-derived GNAS 201R > H CGPLLU168 Lung Cancer I Tumor-derived TP53 136Q > X CGPLLU168 Lung Cancer I Hematopoietic DNMT3A 736R > S CGPLLU168 Lung Cancer I Tumor-derived EGFR 858L > R CGPLLU174 Lung Cancer I Tumor-derived STK11 597 + 1G > T CGPLLU174 Lung Cancer I Tumor-derived JAK2 160D > Y CGPLLU174 Lung Cancer I Tumor-derived KRAS 12G > C CGPLLU174 Lung Cancer I Hematopoietic DNMT3A 891R > W CGPLLU174 Lung Cancer I Hematopoietic DNMT3A 715I > M CGPLLU175 Lung Cancer I Tumor-derived TP53 179H > R CGPLLU175 Lung Cancer I Hematopoietic DNMT3A 2598 - 1I > A CGPLLU175 Lung Cancer I Hematopoietic DNMT3A 755F > L CGPLLU175 Lung Cancer I Germline ATM 337R > C CGPLLU175 Lung Cancer I Tumor-derived ERBB4 941Q > X CGPLLU176 Lung Cancer I Hematopoietic DNMT3A 750P > S CGPLLU176 Lung Cancer I Hematopoietic DNMT3A 735Y > C CGPLLU177 Lung Cancer II Tumor-derived KRAS 12G > V CGPLLU177 Lung Cancer II Hematopoietic DNMT3A 897V > G

CGPLLU177 Lung Cancer II Hematopoietic DNMT3A 862R > C CGPLLU177 Lung Cancer II Hematopoietic DNMT3A 2173 + 1 > A CGPLLU178 Lung Cancer I Tumor-derived CDH1 251 > M CGPLLU178 Lung Cancer I Tumor-derived PIK3CA 861Q > X CGPLLU179 Lung Cancer I Hematopoietic DNMT3A 879N > D CGPLLU179 Lung Cancer I Germline APC 2611T > I Alteration Mutant Mutation Hotspot Detected Allele Patient Nucleotide Type Alteration in Tissue Fraction CGPLBR86 chr22_24159126-24159124_A_G Substitution NA Yes 42.38% CGPLBR87 chr9_5054591-5054591_C_T Substitution No No 0.35% CGPLBR87 chr2_25457242-25457242_C_T Substitution You No 0.31% CGPLBR87 chr18_48604664-48604664_C_T Substitution No No 0.40% CGPLBR87 chrX_66931310-66931310_G_A Substitution NA Yes 42.94% CGPLBR88 chr7_92462487-92462487_C_T Substitution No No 0.13% CGPLBR88 chr5_112174665-112174665_T_C Substitution NA Yes 31.19% CGPLBR92 chr17_7577511-7577511_A_G Substitution No Yes 0.20% CGPLBR96 chr17.fa:7578212-7578212_G_A Substitution Yes No 0.10% CGPLBR96 chr2_25467484-25467484_C_T Substitution No Yes 5.81% CGPLBR96 chrX_66765026-66765026_G_A Substitution No No 0.60% CGPLBR97 chr2_25457242-25457242_C_T Substitution Yes Yes 0.11% CGPLBR97 chr4_55136880-55136880_C_A Substitution NA Yes 34.12% CGPLBR97 chr20_57484421-57484421_G_A Substitution Yes Yes 0.13% CGPLLU144 chr17_7577559-7577559_G_A Substitution Yes Yes 1.95% CGPLLU144 chr12_25398285-25398285_C_A Substitution Yes Yes 5.10% CGPLLU144 chr7_55224336-55224336_C_T Substitution No Yes 0.16% CGPLLU144 chr11_108115727-108115727_C_T Substitution No No 0.22% CGPLLU144 chr3_178936091-178936091_G_A Substitution Yes Yes 2.94% CGPLLU144 chr2_212568841-212568841_C_T Substitution No No 0.18% CGPLLU146 chr9_5073770-5073770_G_T Substitution Yes No 0.25% CGPLLU146 chr17_7577093-7577093_C_G Substitution No Yes 1.30% CGPLLU146 chr2_25463283-25463283_A_T Substitution No Yes 0.84% CGPLLU146 chr13_48937095-48937095_T_C Substitution No Yes 0.87% CGPLLU146 chr11_108122699-108122699_A_T Substitution No No 0.20% CGPLLU147 chr17_7577538-7577538_C_T Substitution Yes No 0.15% CGPLLU147 chr17_7578247-7578247_A_T Substitution No Yes 0.55% CGPLLU147 chr2_29416343-29416343_C_T Substitution No Yes 0.94% CGPLLU147 chr4_55130065-55130065_C_G Substitution NA Yes 43.47% CGPLLU162 chr9_21974792-21974792_G_A Substitution No No 0.22% CGPLLU162 chr7_55259515-55259515_T_G Substitution Yes Yes 0.22% CGPLLU162 chr7_140494187-140494187_C_T Substitution No No 0.14% CGPLLU163 chr9_21974792-21974792_G_A Substitution No No 0.21% CGPLLU163 chr2_25467494-25467494_A_C Substitution No Yes 0.15% CGPLLU164 chr19_1220629-1220629_C_A Substitution No Yes 1.23% CGPLLU164 chr19_1223125-1223125_C_G Substitution NA Yes 45.52% CGPLLU164 chr19_3118919-3118919_C_T Substitution No No 0.20% CGPLLU164 chr17_7577106-7577106_G_A Substitution Yes No 0.10% CGPLLU164 chr17_7578449-7578449_C_A Substitution No Yes 1.78% CGPLLU164 chr17_7578450-7578450_C_A Substitution No Yes 1.86% CGPLLU164 chr2_212248371-212248371_G_A Substitution No Yes 0.96% CGPLLU164 chr2_212587243-212587243_T_C Substitution No No 0.22% CGPLLU165 chr19_1223125-1223125_C_G Substitution NA Yes 36.62% CGPLLU165 chr20_57484421-57484421_G_A Substitution Yes Yes 0.16% CGPLLU168 chr17.fa:7578524-7578524_G_A Substitution Yes Yes 0.06% CGPLLU168 chr2_25463287-25463287_G_T Substitution No No 0.39% CGPLLU168 chr7.fa:55259515-55259515_T_G Substitution Yes Yes 0.07% CGPLLU174 chr19_1220505-1220505_G_T Substitution No Yes 0.33% CGPLLU174 chr9_5050695-5050695_G_T Substitution No Yes 0.40% CGPLLU174 chr12_25398285-25398285_C_A Substitution Yes Yes 0.16% CGPLLU174 chr2_25457216-25457216_G_A Substitution No Yes 0.29% CGPLLU174 chr2_25463537-25463537_G_C Substitution No Yes 0.26% CGPLLU175 chr17_7578394-7578394_T_C Substitution Yes Yes 8.03% CGPLLU175 chr2_25457216-25457216_C_T Substitution No No 0.21% CGPLLU175 chr2_25463230-25463230_A_G Substitution No No 0.15% CGPLLU175 chr11_108117798-108117798_C_T Substitution NA Yes 43.84% CGPLLU175 chr2_212288925-212288925_G_A Substitution No Yes 3.64% CGPLLU176 chr2_25463245-25463245_G_A Substitution No Yes 0.92% CGPLLU176 chr2_25463289-25463289_T_C Substitution No Yes 0.12% CGPLLU177 chr12_25398284-25398284_C_A Substitution Yes Yes 2.49% CGPLLU177 chr2_25457197-25457197_A_C Substitution No Yes 1.53% CGPLLU177 chr2_25457243-25457243_G_A Substitution Yes No 0.29% CGPLLU177 chr2_25463508-25463508_C_T Substitution No No 0.13% CGPLLU178 chr16_68844164-68844164_C_T Substitution No No 0.29% CGPLLU178 chr3_178947145-178947145_C_T Substitution No No 0.17% CGPLLU179 chr2_25457252-25457252_T_C Substitution No Yes 0.38% CGPLLU179 chr5_112179123-112179123_C_T Substitution NA Yes 39.91% Wild-type Fragments 25th Minimum Percentile Mode Median cfDNA cfDNA cfDNA cfDNA Fragment Fragment Fragment Fragment Distinct Size Size Size Size Patient Coverage (bp) (bp) (bp) (bp) CGPLBR86 3096 88 160 167 174 CGPLBR87 3680 101 162 168 175 CGPLBR87 6180 101 163 164 175 CGPLBR87 7746 86 160 167 175 CGPLBR87 2286 106 160 166 172 CGPLBR88 17537 89 185 200 223 CGPLBR88 5919 101 162 172 173 CGPLBR92 15530 77 150 164 152 CGPLBR96 9893 100 159 164 171 CGPLBR96 8620 95 162 167 173 CGPLBR96 8036 85 162 169 175 CGPLBR97 14856 93 160 168 170 CGPLBR97 5329 100 161 165 171 CGPLBR97 7010 97 158 169 170 CGPLLU144 11371 100 156 165 167 CGPLLU144 7641 100 155 167 166 CGPLLU144 9996 100 158 168 169 CGPLLU144 4956 101 159 166 169 CGPLLU144 6540 100 153 170 168 CGPLLU144 7648 101 156 164 166 CGPLLU146 5920 100 155 164 168 CGPLLU146 9356 100 155 166 168 CGPLLU146 7284 101 158 165 170 CGPLLU146 4183 103 160 166 170 CGPLLU146 6778 100 157 166 158 CGPLLU147 4807 100 155 166 170 CGPLLU147 5282 100 156 167 171 CGPLLU147 7122 100 158 174 173 CGPLLU147 2825 101 160 165 173 CGPLLU162 9940 95 161 164 174 CGPLLU162 13855 87 160 174 173 CGPLLU162 11251 100 153 167 165 CGPLLU163 10805 85 159 165 173 CGPLLU163 20185 83 158 166 170 CGPLLU164 8795 91 156 161 169 CGPLLU164 4561 92 157 164 169 CGPLLU164 8097 100 158 170 170 CGPLLU164 9241 100 155 165 157 CGPLLU164 10806 100 157 168 159 CGPLLU164 10919 100 157 168 159 CGPLLU164 5412 103 159 175 170 CGPLLU164 5151 101 160 166 169 CGPLLU165 7448 95 155 167 167 CGPLLU165 5822 102 154 166 166 CGPLLU168 15985 97 152 165 166 CGPLLU168 11070 100 156 165 168 CGPLLU168 11063 83 157 166 169 CGPLLU174 5881 88 162 165 174 CGPLLU174 3696 100 162 167 172 CGPLLU174 4941 101 162 167 172 CGPLLU174 7527 100 163 168 173 CGPLLU174 8353 101 162 168 173 CGPLLU175 10214 100 160 166 170 CGPLLU175 9739 100 157 168 158 CGPLLU175 9509 100 157 165 158 CGPLLU175 2710 101 157 165 157 CGPLLU175 6565 100 158 166 158 CGPLLU176 6513 101 164 168 175 CGPLLU176 5962 100 164 174 175 CGPLLU177 7044 102 160 165 170 CGPLLU177 9950 88 160 169 171 CGPLLU177 11233 100 160 168 171 CGPLLU177 10966 75 160 169 172 CGPLLU178 5378 100 162 176 172 CGPLLU178 7235 101 159 167 170 CGPLLU179 6350 103 161 169 171 CGPLLU179 2609 108 162 171 173 Stage at Amino Acid Patient Patient Type Diagnosis Alteration Type Gene (Protein) CGPLLU180 Lung Cancer I Tumor-derived STK11 237D > Y CGPLLU180 Lung Cancer I Tumor-derived TP53 293G > V CGPLLU180 Lung Cancer I Tumor-derived TP53 282R > P CGPLLU180 Lung Cancer I Tumor-derived TP53 177P > L CGPLLU180 Lung Cancer I Tumor-derived RB1 565S > X CGPLLU197 Lung Cancer I Hematopoietic DNMT3A 882R > C CGPLLU197 Lung Cancer I Hematopoietic DNMT3A 879N > D CGPLLU198 Lung Cancer I Tumor-derived TP53 162I > N CGPLLU198 Lung Cancer I Tumor-derived EGFR 858L > R CGPLLU202 Lung Cancer I Tumor-derived EGFR 790T > M CGPLLU202 Lung Cancer I Tumor-derived EGFR 868E > X CGPLLU204 Lung Cancer I Tumor-derived KIT 956R > Q CGPLLU205 Lung Cancer II Hematopoietic DNMT3A 736R > C CGPLLU205 Lung Cancer II Hematopoietic DNMT3A 696Q > X CGPLLU206 Lung Cancer III Tumor-derived TP53 672 + 1G > A CGPLLU206 Lung Cancer III Tumor-derived TP53 131N > S CGPLLU207 Lung Cancer II Tumor-derived TP53 376 - 1G > A CGPLLU207 Lung Cancer II Germline ALK 419P > L CGPLLU207 Lung Cancer II Tumor-derived EGFR 790T > M CGPLLU208 Lung Cancer II Tumor-derived TP53 250P > L CGPLLU208 Lung Cancer II Germline EGFR 224R > H CGPLLU208 Lung Cancer II Tumor-derived EGFR 858L > R CGPLLU208 Lung Cancer II Tumor-derived MYC 98R > W CGPLLU209 Lung Cancer II Germline STK11 354F > L CGPLLU209 Lung Cancer II Tumor-derived TP53 100Q > X CGPLLU209 Lung Cancer II Tumor-derived CDKN2A 88E > X CGPLLU209 Lung Cancer II Tumor-derived PDGFRA 921A > T CGPLLU209 Lung Cancer II Germline EGFR 567M > V CGPLOV10 Ovarian Cancer I Tumor-derived TP53 342R > X CGPLOV11 Ovarian Cancer IV Tumor-derived TP53 248R > Q CGPLOV11 Ovarian Cancer IV Germline TP53 63A > V CGPLOV13 Ovarian Cancer IV Tumor-derived ALK 444W > C CGPLOV13 Ovarian Cancer IV Germline PDGFRA 401A > D CGPLOV13 Ovarian Cancer IV Tumor-derived KIT 135R > H CGPLOV14 Ovarian Cancer I Tumor-derived HNF1A 230E > K CGPLOV15 Ovarian Cancer III Tumor-derived TP53 278P > S CGPLOV15 Ovarian Cancer III Tumor-derived EGFR 433H > D CGPLOV17 Ovarian Cancer I Tumor-derived TP53 248R > Q CGPLOV17 Ovarian Cancer I Germline PDGFRA 1071D > N CGPLOV18 Ovarian Cancer I Germline APC 1125V > A CGPLOV19 Ovarian Cancer II Germline FGFR3 403K > E CGPLOV19 Ovarian Cancer II Tumor-derived TP53 273R > H CGPLOV19 Ovarian Cancer II Germline AR 176S > R CGPLOV19 Ovarian Cancer II Tumor-derived APC 1378Q > X CGPLOV20 Ovarian Cancer II Tumor-derived TP53 195I > T CGPLOV20 Ovarian Cancer II Germline EGFR 253K > R CGPLOV21 Ovarian Cancer IV Germline STK11 354F > L CGPLOV21 Ovarian Cancer IV Tumor-derived TP53 275C > Y CGPLOV21 Ovarian Cancer IV Tumor-derived ERBB4 602S > T CGPLOV22 Ovarian Cancer III Tumor-derived TP53 193H > P CGPLOV22 Ovarian Cancer III Tumor-derived CTNNB1 41T > A Alteration Mutant Mutation Hotspot Detected Allele Patient Nucleotide Type Alteration in Tissue Fraction CGPLLU180 chr19_1220691-1220691_G_T Substitution No You 2.43% CGPLLU180 chr17_7577060-7577060_C_A Substitution No Yes 2.07% CGPLLU180 chr17_7577093-7577093_C_G Substitution No Yes 1.94% CGPLLU180 chr17:fa_7578400-7578400_G_A Substitution Yes No 0.08% CGPLLU180 chr13_48955578-48955578_C_G Substitution No Yes 1.01% CGPLLU197 chr2_25457243-25457243_G_A Substitution Yes No 0.16% CGPLLU197 chr2_25457252-25457252_T_C Substitution No No 0.38% CGPLLU198 chr17_7578445-7578445_A_T Substitution No Yes 0.87% CGPLLU198 chr7_55259515-55259515_T_G Substitution Yes Yes 0.52% CGPLLU202 chr7:fa_55249071-55249071_C_T Substitution Yes Yes 0.05% CGPLLU202 chr7_55259544-55259544_G_T Substitution No No 0.13% CGPLLU204 chr4_55604659-55604659_G_A Substitution No No 0.26% CGPLLU205 chr2_25463287-25463287_G_A Substitution No Yes 0.70% CGPLLU205 chr2_25463598-25463598_G_A Substitution No Yes 3.47% CGPLLU206 chr17_7578176-7578176_C_T Substitution Yes Yes 26.13% CGPLLU206 chr17_7578538-7578538_T_C Substitution No No 0.21% CGPLLU207 chr17_7578555-7578555_C_T Substitution Yes Yes 0.32% CGPLLU207 chr2_29606625-29606625_A_G Substitution NA Yes 34.38% CGPLLU207 chr7:fa_55249071-55249071_C_T Substitution Yes No 0.09% CGPLLU208 chr17_7577532-7577532_G_A Substitution Yes Yes 1.33% CGPLLU208 chr7_55220281-55220281_G_A Substitution NA Yes 39.34% CGPLLU208 chr7_55259515-55259515_T_G Substitution Yes Yes 0.86% CGPLLU208 chr8_128750755-128750755_C_T Substitution No No 0.17% CGPLLU209 chr19_1223125-1223125_C_G Substitution NA Yes 26.84% CGPLLU209 chr17_7579389-7579389_G_A Substitution No Yes 9.97% CGPLLU209 chr9_21971096-21971096_C_A Substitution Yes Yes 9.13% CGPLLU209 chr4_55155052-55155052_G_A Substitution No Yes 9.82% CGPLLU209 chr7_55231493-55231493_A_G Substitution NA Yes 30.41% CGPLOV10 chr17_7574003-7574003_G_A Substitution Yes Yes 3.14% CGPLOV11 chr17_7577538-7577538_C_T Substitution Yes Yes 0.87% CGPLOV11 chr17_7579499-7579499_G_A Substitution NA Yes 37.77% CGPLOV13 chr2_29551296-29551296_C_A Substitution No Yes 0.12% CGPLOV13 chr4_55136880-55136880_C_A Substitution NA Yes 37.98% CGPLOV13 chr4_55564516-55564516_G_A Substitution No Yes 0.35% CGPLOV14 chr12_121431484-121431484_G_A Substitution No No 0.14% CGPLOV15 chr17_7577106-7577106_G_A Substitution Yes Yes 3.54% CGPLOV15 chr7_55225445-55225445_C_G Substitution No No 0.19%

CGPLOV17 chr17_7577538-7577538_C_T Substitution Yes Yes 0.32% CGPLOV17 chr4_55161382-55161382_G_A Substitution NA Yes 44.10% CGPLOV18 chr5_112174665-112174665_T_C Substitution NA Yes 40.81% CGPLOV19 chr4_1806186-1806186_A_G Substitution NA Yes 23.80% CGPLOV19 chr17_7577120-7577120_C_T Substitution Yes Yes 36.83% CGPLOV19 chrX_66765516-66765516_C_A Substitution NA Yes 65.29% CGPLOV19 chr5_112175423-112175423_C_T Substitution Yes Yes 46.35% CGPLOV20 chr17_7578265-7578265_A_G Substitution Yes Yes 0.21% CGPLOV20 chr7_55221714-55221714_A_G Substitution NA Yes 44.05% CGPLOV21 chr19_1223125-1223125_C_G Substitution NA Yes 7.68% CGPLOV21 chr17_7577114-7577114_C_T Substitution No Yes 2.04% CGPLOV21 chr2_212530114-212530114_C_G Substitution No No 14.36% CGPLOV22 chr17_7578271-7578271_T_G Substitution No Yes 0.49% CGPLOV22 chr3_41266124-41266124_A_G Substitution Yes Yes 0.34% Wild-type Fragments 25th Minimum Percentile Mode Median cfDNA cfDNA cfDNA cfDNA Fragment Fragment Fragment Fragment Distinct Size Size Size Size Patient Coverage (bp) (bp) (bp) (bp) CGPLLU180 6065 91 158 165 170 CGPLLU180 6680 92 158 164 169 CGPLLU180 7790 92 158 167 168 CGPLLU180 9036 101 160 169 171 CGPLLU180 4679 100 157 169 158 CGPLLU197 7196 102 162 166 172 CGPLLU197 7147 100 161 166 172 CGPLLU198 9322 97 157 165 158 CGPLLU198 8303 100 160 173 172 CGPLLU202 14197 90 151 165 166 CGPLLU202 9279 51 150 168 167 CGPLLU204 7185 100 157 165 168 CGPLLU205 10739 96 156 165 166 CGPLLU205 12065 100 154 165 165 CGPLLU206 6746 94 148 165 164 CGPLLU206 11225 100 147 167 164 CGPLLU207 11224 100 159 165 170 CGPLLU207 4960 101 160 166 170 CGPLLU207 13216 85 161 165 172 CGPLLU208 9211 101 156 166 168 CGPLLU208 5253 100 159 164 170 CGPLLU208 10733 100 160 170 171 CGPLLU208 11421 100 158 165 171 CGPLLU209 11695 96 153 166 159 CGPLLU209 12771 94 155 163 168 CGPLLU209 16557 92 157 169 170 CGPLLU209 13057 97 158 167 171 CGPLLU209 8521 100 155 167 169 CGPLOV10 4421 101 161 165 172 CGPLOV11 7987 100 157 164 169 CGPLOV11 3782 97 160 166 171 CGPLOV13 12072 88 157 165 169 CGPLOV13 4107 103 159 166 169 CGPLOV13 6427 100 161 165 171 CGPLOV14 11418 92 154 166 171 CGPLOV15 7689 102 157 164 169 CGPLOV15 7617 101 159 167 171 CGPLOV17 4463 96 156 168 169 CGPLOV17 2884 110 157 170 170 CGPLOV18 2945 101 159 164 169 CGPLOV19 9727 95 158 167 172 CGPLOV19 4387 100 158 165 169 CGPLOV19 2775 93 161 171 171 CGPLOV19 3616 102 156 170 170 CGPLOV20 5404 94 159 165 170 CGPLOV20 3744 102 158 166 169 CGPLOV21 21823 81 158 166 169 CGPLOV21 18806 101 159 165 169 CGPLOV21 10801 89 160 166 169 CGPLOV22 11952 100 155 165 167 CGPLOV22 12399 92 150 165 164 Mutant Fragments 75th 25th Mean Percentile Maximum Minimum Percentile cfDNA cfDNA cfDNA cfDNA cfDNA Fragment Fragment Fragment Fragment Fragment Size Size Size Distinct Size Size (bp) (bp) (bp) Coverage (bp) (bp) 179 186 400 19 100 142 182 185 400 21 132 166 180 183 400 5411 92 152 177 182 400 1903 100 148 184 185 400 1344 108 155 181 182 400 2108 100 153 176 180 400 1951 101 149 176 183 399 75 123 162 177 182 400 28 101 130 183 188 399 6863 100 160 188 186 400 34 77 154 175 179 396 9 138 147 184 185 400 21 115 145 179 185 397 30 137 149 179 182 397 44 125 155 185 186 400 8167 101 180 187 186 400 3552 102 158 184 187 399 15 93 137 183 185 400 26 137 163 181 182 397 35 118 147 172 175 400 71 133 152 169 174 400 55 130 153 189 187 390 17 149 155 176 183 400 18 156 170 169 175 397 51 108 143 166 173 397 26 118 147 184 186 400 45 116 151 185 186 400 25 157 165 185 187 400 25 124 168 167 175 394 86 121 155 167 173 397 45 124 143 170 175 396 108 126 147 190 189 400 23 131 148 182 182 399 42 138 155 189 187 399 25 126 153 192 193 400 977 101 149 173 179 391 525 102 140 181 185 399 4010 100 158 178 184 399 625 100 140 175 179 398 37 111 143 181 186 398 3184 102 159 180 183 399 47 111 148 183 184 397 39 111 146 185 184 400 24 110 146 176 180 400 32 117 146 180 184 399 43 111 143 185 187 400 29 109 140 179 182 399 20 128 152 176 184 396 7515 101 160 182 182 399 31 85 145 181 182 395 428 100 135 176 180 397 352 97 136 165 172 397 15 131 137 170 173 398 25 107 138 171 173 400 27 122 147 189 169 400 91 112 165 189 169 400 27 124 144 178 184 399 24 105 143 188 189 399 8 122 143 194 192 400 17 144 163 180 183 394 15 132 159 183 185 399 233 131 162 186 186 398 27 136 155 192 195 399 23 137 144 182 184 399 29 131 157 Difference Difference Adjusted P between between Value of Median Mean Difference Mutant Fragments Mutant Mutant between 75th and and Mutant Mode Median Mean Percentile Maximum Wild type Wild-type and cfDNA cfDNA cfDNA cfDNA cfDNA cfDNA cfDNA Wild-type Fragment Fragment Fragment Fragment Fragment Fragment Fragment cfDNA Size Size Size Size Size Size Size Fragment (bp) (bp) (bp) (bp) (bp) (bp) (bp) Size 233 165 180 230 305 -4.0 1.54 0.475 182 176 191 198 309 7.0 8.33 0.250 167 169 186 191 399 0.0 5.89 0.000 166 166 177 183 383 -1.0 -0.25 0.874 167 170 189 191 398 1.0 5.37 0.009 166 168 185 187 386 1.0 3.80 0.025 175 167 179 182 397 0.0 2.65 0.148 167 172 182 190 370 3.0 5.31 0.368 130 139 164 155 345 -29.5 -12.79 0.000 165 173 185 159 400 2.0 3.13 0.002 171 170 177 192 335 -0.5 -11.46 0.571 176 171 177 176 290 4.0 1.22 0.475 155 159 176 175 368 -11.0 -7.99 0.052 181 162 182 161 369 -8.0 3.49 0.061 155 169 185 194 338 0.0 5.78 0.623 166 171 184 187 400 -1.0 -1.27 0.212 168 170 185 185 399 0.0 -2.62 0.114 127 174 173 193 261 3.0 -11.00 0.507 166 167 179 180 364 -3.0 -4.34 0.430 176 163 172 176 336 -6.0 -9.35 0.166 170 165 169 173 301 0.0 3.57 0.668 165 164 166 166 325 0.0 -2.15 0.630 326 170 221 301 387 -3.0 32.43 0.453 174 174 210 219 372 5.0 33.84 0.368 268 152 164 176 268 -12.0 -5.12 0.000 153 156 174 158 327 -9.5 8.37 0.036 168 163 175 177 346 -8.0 -8.84 0.057 191 175 207 199 350 3.0 22.93 0.456 180 180 189 191 338 8.0 4.06 0.154 169 166 168 175 309 2.0 0.46 0.445 197 162 166 168 377 -1.0 -0.91 0.482 162 162 164 174 302 -3.0 -6.74 0.064 145 166 189 205 333 -5.0 -0.80 0.297 155 174 177 187 343 5.5 -4.51 0.171 176 176 188 229 305 7.0 -0.19 0.234 189 170 182 192 380 -1.0 -9.76 0.000 168 159 168 176 382 -7.0 -5.57 0.052 166 170 181 185 398 0.0 0.37 0.770 167 162 172 181 380 -9.0 -6.68 0.009 142 166 172 186 321 -1.0 -2.36 0.572 168 172 182 187 400 0.5 0.95 0.564 144 169 176 153 353 -1.0 -4.83 0.598 182 162 182 155 337 -7.0 -0.44 0.064 309 182 208 284 355 14.0 22.31 0.031 154 157 167 166 298 -11.0 -8.94 0.013 144 177 187 212 319 9.0 7.22 0.062 204 159 186 204 387 -12.0 3.32 0.031 180 163 166 180 219 -6.5 -13.04 0.155 170 171 177 185 400 1.0 1.08 0.166 137 166 167 176 316 -3.0 -14.62 0.469 138 149 158 166 340 -20.0 -23.47 0.000 132 147 149 159 326 21.0 26.04 0.000 132 144 163 171 323 -20.0 -1.73 0.000 159 161 175 190 299 -3.0 4.83 0.384 161 161 173 171 342 -3.0 2.54 0.354 168 173 196 192 397 1.0 6.83 0.571 154 154 167 172 320 -19.0 -22.39 0.000 132 159 183 190 367 -11.0 4.67 0.054 122 161 168 195 241 -13.0 -19.21 0.100 173 173 213 261 372 1.0 19.22 0.587 186 166 174 185 265 -3.0 -5.62 0.461 167 172 190 187 394 2.0 7.27 0.137 183 163 170 178 262 -7.0 -16.03 0.131 175 152 190 212 327 -17.0 -1.78 0.018 177 171 183 179 319 -1.0 -0.74 0.564 Mutant Fragments 75th 25th Mean Percentile Maximum Minimum Percentile cfDNA cfDNA cfDNA cfDNA cfDNA Fragment Fragment Fragment Fragment Fragment Size Size Size Distinct Size Size (bp) (bp) (bp) Coverage (bp) (bp) 166 172 396 1616 100 146 175 180 400 806 96 158 165 172 399 1410 102 140 170 177 397 49 99 153 166 173 398 33 140 155 180 178 400 73 95 140 172 177 400 38 115 160 171 174 386 6 124 137 180 183 400 70 124 151 191 199 399 6586 96 162 184 188 400 41 112 172 181 198 399 35 149 168 182 184 399 20 166 180 183 186 397 5338 102 159 202 203 393 178 101 150 195 195 397 1350 104 153 185 189 400 1257 100 153

185 189 396 30 117 163 203 210 391 336 105 153 188 194 399 741 101 161 193 193 396 89 100 145 172 179 396 12 129 143 186 188 387 3559 91 155 177 183 392 873 102 149 194 200 377 1909 100 158 202 259 400 27 122 157 171 178 395 1818 103 147 178 182 374 546 102 151 179 184 397 26 132 142 195 194 400 53 117 157 176 179 397 40 124 150 188 191 390 38 107 153 205 207 399 217 102 146 196 195 397 266 111 147 186 184 400 76 123 157 179 186 400 9832 93 161 191 190 400 277 104 162 191 189 400 65 123 165 187 189 400 31 136 163 202 202 400 5286 102 166 196 201 400 102 138 166 181 182 397 30 138 158 181 181 400 64 113 158 176 179 398 27 121 163 191 192 398 2943 100 165 179 181 399 25 138 153 171 177 399 60 110 136 172 179 399 26 139 147 186 184 398 35 121 149 176 178 397 4000 103 155 176 178 385 2390 99 157 182 184 400 28 131 160 194 193 400 3545 100 161 179 180 398 15 121 146 188 187 400 2587 103 158 189 192 400 86 121 165 178 184 399 3339 101 157 179 187 391 3193 101 163 183 186 398 13 111 153 197 201 400 4140 102 166 191 194 400 16 130 143 183 183 400 209 125 154 211 230 400 41 158 176 193 193 400 3445 94 162 197 199 400 23 123 182 193 195 399 1787 100 163 204 207 400 4100 100 159 Difference Difference Adjusted P between between Value of Median Mean Difference Mutant Fragments Mutant Mutant between 75th and and Mutant Mode Median Mean Percentile Maximum Wild type Wild-type and cfDNA cfDNA cfDNA cfDNA cfDNA cfDNA cfDNA Wild-type Fragment Fragment Fragment Fragment Fragment Fragment Fragment cfDNA Size Size Size Size Size Size Size Fragment (bp) (bp) (bp) (bp) (bp) (bp) (bp) Size 164 159 163 170 354 -3.5 -3.57 0.000 169 169 173 184 366 1.0 3.80 0.054 149 154 164 170 398 -8.0 -0.35 0.816 143 182 206 284 333 16.0 36.25 0.000 154 170 108 180 296 7.0 14.38 0.104 140 155 173 178 324 -9.0 -6.66 0.000 164 167 182 179 329 1.5 10.09 0.479 170 156 153 168 178 -7.5 -18.98 0.411 151 164 182 183 385 -6.0 1.71 0.064 168 175 193 196 399 0.0 -1.79 0.166 176 177 195 195 373 3.0 11.02 0.397 175 175 181 186 312 1.0 -13.40 0.587 185 191 205 219 357 21.0 23.48 0.013 175 171 183 185 394 -1.0 0.03 0.984 168 171 198 240 357 -5.0 -4.34 0.571 163 171 201 258 400 0.0 5.94 0.066 168 170 189 202 392 1.0 4.37 0.064 164 172 175 179 372 3.0 -10.29 0.463 141 171 200 240 399 4.0 3.10 0.571 169 176 190 194 400 2.0 1.96 0.571 171 171 197 229 393 -2.0 3.42 0.479 143 153 163 166 275 -14.0 -8.99 0.084 164 173 195 211 398 3.0 5.92 0.001 163 164 177 181 400 -3.0 -0.39 0.880 167 176 202 242 398 5.0 7.98 0.061 164 179 199 231 350 2.0 -3.82 0.685 169 162 173 180 396 1.0 1.92 0.372 166 166 180 182 381 0.0 2.87 0.416 138 171 183 188 351 1.5 3.29 0.572 165 169 192 198 336 -3.0 -2.86 0.451 169 166 181 176 309 -1.0 4.53 0.539 180 174 185 210 326 0.5 -2.59 0.576 144 163 188 212 360 -12.0 -17.11 0.004 150 166 188 204 379 -8.0 -7.53 0.208 171 169 182 182 346 1.0 -3.64 0.479 166 172 180 186 399 -1.0 1.04 0.155 160 176 201 200 384 3.0 9.95 0.061 166 172 198 192 371 1.0 7.08 0.560 171 167 201 199 387 -4.0 14.14 0.341 168 181 201 203 400 2.0 -0.86 0.587 161 179 199 209 372 -1.5 2.90 0.679 189 185 191 191 311 16.0 9.25 0.000 163 167 179 176 318 0.0 -2.85 0.679 200 171 187 190 392 5.0 10.89 0.314 176 176 187 192 398 0.0 -3.83 0.015 138 167 181 184 340 -1.0 2.00 0.571 147 147 161 159 327 -19.0 -9.77 0.000 180 176 176 184 344 9.0 3.52 0.015 360 161 197 195 360 -9.0 10.77 0.314 166 167 176 178 397 0.5 0.65 0.610 164 168 178 180 400 0.0 1.78 0.314 168 167 177 179 338 -2.0 -5.83 0.463 169 173 194 192 399 0.0 0.40 0.825 166 166 172 204 221 -2.0 -7.32 0.564 162 169 189 186 399 -1.0 1.12 0.598 183 177 189 193 373 3.0 -0.01 0.293 165 169 177 184 400 0.0 -1.73 0.598 178 173 180 186 389 1.0 0.22 0.839 153 161 171 179 323 -11.0 -12.36 0.061 169 179 197 200 400 0.0 -0.32 0.839 143 157 173 173 325 -20.0 -18.40 0.000 175 170 196 233 357 1.0 12.55 0.025 197 186 215 220 374 1.0 3.72 0.603 175 174 194 194 399 0.0 0.65 0.714 248 224 232 260 359 47.0 34.97 0.000 163 176 192 194 400 1.0 -0.85 0.718 164 173 200 202 400 -2.0 -3.65 0.062 Mutant Fragments 75th 25th Mean Percentile Maximum Minimum Percentile cfDNA cfDNA cfDNA cfDNA cfDNA Fragment Fragment Fragment Fragment Fragment Size Size Size Distinct Size Size (bp) (bp) (bp) Coverage (bp) (bp) 196 195 400 3096 79 159 202 203 400 73 142 178 205 203 400 23 161 168 195 196 400 170 125 158 195 192 400 2089 101 162 238 280 400 125 84 192 197 194 400 5715 108 163 172 173 398 109 78 148 196 191 399 35 119 161 189 190 400 826 102 162 194 195 400 95 135 160 184 184 400 27 128 150 179 184 399 4771 103 161 187 185 399 7 417 154 179 179 395 330 106 152 172 177 399 536 106 151 179 183 400 45 136 163 182 182 397 16 138 146 172 177 397 293 101 152 171 177 399 23 130 152 180 183 399 54 104 161 184 184 400 154 96 149 186 187 399 79 102 163 183 185 400 44 118 149 182 184 400 35 136 164 192 191 400 13 138 164 199 205 400 50 128 155 191 193 400 81 108 150 190 191 389 2597 101 159 192 197 400 58 92 173 183 189 400 74 90 147 175 178 400 37 144 163 194 202 400 61 93 164 184 186 400 66 104 158 191 190 396 101 126 155 188 185 394 4718 100 156 186 186 399 30 134 161 180 180 397 34 139 163 182 182 400 262 101 150 182 182 400 277 101 150 180 182 395 65 121 158 177 182 400 16 144 172 185 184 399 7186 100 154 181 179 394 21 108 164 177 180 400 18 111 127 179 181 400 72 121 156 177 182 400 30 106 160 200 199 399 36 131 147 184 185 392 20 144 173 182 184 395 16 147 156 186 187 399 34 159 168 186 186 396 5 116 182 185 183 399 1073 100 142 179 180 400 46 109 151 181 181 400 30 146 154 176 179 392 2742 102 154 174 180 399 298 103 140 197 194 399 67 115 164 195 194 399 19 156 165 178 182 395 189 105 138 183 185 398 227 123 160 185 184 397 53 78 161 190 188 395 50 130 161 186 187 398 28 139 150 179 184 400 24 130 153 185 185 394 48 111 154 189 187 398 2337 100 163 Difference Difference Adjusted P between between Value of Median Mean Difference Mutant Fragments Mutant Mutant between 75th and and Mutant Mode Median Mean Percentile Maximum Wild type Wild-type and cfDNA cfDNA cfDNA cfDNA cfDNA cfDNA cfDNA Wild-type Fragment Fragment Fragment Fragment Fragment Fragment Fragment cfDNA Size Size Size Size Size Size Size Fragment (bp) (bp) (bp) (bp) (bp) (bp) (bp) Size 161 173 194 191 397 -1.0 -2.45 0.251 178 184 237 338 377 9.0 35.30 0.114 168 171 189 186 380 -4.0 -16.38 0.435 173 173 188 190 400 -2.0 -6.17 0.293 169 176 203 203 400 4.5 8.80 0.000 194 207 243 324 400 -16.0 5.51 0.574 164 174 200 196 400 1.0 2.87 0.065 149 158 166 173 302 -4.0 -5.94 0.190 172 171 191 180 390 0.0 -4.34 0.627 166 171 187 187 395 -2.0 -1.94 0.475 161 170 182 184 400 -5.0 -11.54 0.155 150 169 174 185 319 -1.0 -9.68 0.571 168 171 179 183 400 0.0 0.15 0.880 154 167 164 174 117 -3.0 -22.90 0.155 165 166 178 178 361 -1.0 -1.35 0.685 167 163 172 175 363 -3.0 -0.34 0.880 175 172 185 191 380 3.0 6.52 0.368 146 155 162 170 224 -14.0 -19.82 0.007 169 164 170 174 392 2.0 1.37 0.646 162 162 163 177 232 -4.0 -7.62 0.252 154 176 195 206 383 7.5 14.58 0.064 157 163 176 185 347 -5.5 -7.87 0.154 177 174 200 203 372 4.0 14.61 0.270 163 163 185 186 338 -7.0 1.98 0.039 204 181 194 203 369 13.0 11.80 0.039 169 169 198 173 333 -1.0 6.05 0.610 161 171 216 301 360 0.0 17.02 0.623 108 173 198 224 385 0.0 6.48 0.624 165 172 185 187 397 -1.0 -5.17 0.005 192 192 202 200 397 18.0 9.79 0.007 142 167 176 182 391 -6.5 -6.78 0.061 185 172 192 186 375 6.0 17.15 0.005 181 181 197 211 370 8.0 3.34 0.169 194 174 189 194 379 3.5 4.60 0.270

176 176 194 213 331 7.0 2.50 0.718 164 168 190 187 393 -1.0 2.54 0.113 175 175 190 208 339 5.0 4.07 0.302 165 170 178 175 349 3.0 -1.65 0.407 152 165 181 186 393 -4.0 -0.65 0.876 147 166 182 185 393 -3.0 0.36 0.926 161 167 186 188 338 -4.0 6.15 0.234 179 179 187 180 376 10.0 9.98 0.130 167 166 183 181 396 -1.0 -1.73 0.154 164 173 196 200 357 7.0 14.95 0.213 127 158 189 186 352 -8.0 12.47 0.179 173 166 183 179 396 -2.0 4.31 0.427 174 174 180 156 282 5.0 3.09 0.252 143 177 196 227 298 2.5 -4.24 0.479 266 178 199 215 269 6.0 15.13 0.252 156 164 177 169 302 8.0 4.82 0.119 168 176 206 196 365 3.0 20.55 0.415 182 185 201 192 329 12.0 14.62 0.263 164 152 157 164 346 -18.0 -27.67 0.000 143 175 174 183 325 7.0 -5.22 0.054 146 168 186 181 367 -0.5 5.19 0.568 164 166 176 176 387 -1.0 -0.24 0.874 148 150 152 162 288 -18.0 -22.25 0.000 250 173 187 201 366 2.0 9.89 0.425 165 185 197 199 361 10.0 2.20 0.154 141 150 164 175 348 -20.0 -14.58 0.000 168 169 185 184 396 -2.0 1.68 0.706 175 175 189 158 392 4.0 3.80 0.241 168 168 184 175 377 -4.5 -5.86 0.234 173 170 170 173 354 -2.5 -15.88 0.416 176 170 193 199 359 0.0 13.13 0.598 170 168 173 183 295 -3.0 -11.80 0.270 166 172 187 185 394 -1.0 -1.27 0.564 Mutant Fragments 75th 25th Mean Percentile Maximum Minimum Percentile cfDNA cfDNA cfDNA cfDNA cfDNA Fragment Fragment Fragment Fragment Fragment Size Size Size Distinct Size Size (bp) (bp) (bp) Coverage (bp) (bp) 198 200 396 172 83 152 190 188 400 215 123 151 184 184 400 207 121 151 191 189 397 17 143 170 181 182 398 52 122 152 191 189 399 17 109 161 191 189 399 40 136 164 180 181 399 127 88 149 181 186 400 68 141 166 169 179 398 10 81 167 170 181 398 33 107 162 175 181 391 23 112 156 175 177 400 109 130 153 172 176 400 684 105 153 179 178 398 2946 100 138 175 178 399 30 121 165 187 186 400 63 140 155 181 184 400 4754 101 160 182 187 400 31 131 162 181 183 400 150 110 144 179 184 400 5290 95 159 181 186 400 140 101 155 187 190 397 20 92 141 190 192 400 8065 85 156 174 182 400 2586 101 147 185 188 400 2808 100 150 182 187 400 2227 100 154 176 183 396 8425 100 155 186 188 399 142 112 146 186 185 399 104 132 158 183 185 392 3462 101 160 182 183 399 25 94 140 177 181 399 3789 101 159 181 184 400 57 131 152 183 191 400 36 118 154 187 185 399 362 110 152 182 188 400 20 158 163 185 187 397 23 126 151 188 189 400 2980 100 158 183 163 391 2793 91 158 185 189 395 7357 100 158 184 184 398 5186 101 157 182 187 400 15595 64 159 186 185 400 6749 101 158 193 190 400 23 127 148 182 185 394 3901 101 160 179 180 400 4633 100 158 175 179 400 734 101 151 175 180 394 4022 101 159 184 182 400 117 116 156 172 176 395 65 109 145 Difference Difference Adjusted P between between Value of Median Mean Difference Mutant Fragments Mutant Mutant between 75th and and Mutant Mode Median Mean Percentile Maximum Wild type Wild-type and cfDNA cfDNA cfDNA cfDNA cfDNA cfDNA cfDNA Wild-type Fragment Fragment Fragment Fragment Fragment Fragment Fragment cfDNA Size Size Size Size Size Size Size Fragment (bp) (bp) (bp) (bp) (bp) (bp) (bp) Size 160 166 193 226 396 -4.0 -4.93 0.490 159 163 188 196 365 -6.0 -1.72 0.735 157 161 181 179 365 -7.0 -3.01 0.571 217 214 198 217 294 43.0 7.08 0.000 167 164 179 173 372 -4.5 -2.07 0.137 173 171 181 174 392 -1.0 -9.24 0.576 166 171 185 185 335 -1.0 -5.86 0.571 131 162 168 178 311 -6.0 -11.80 0.005 175 176 198 207 387 4.0 17.11 0.184 167 167 159 176 182 1.0 -10.20 0.589 167 167 174 185 322 0.0 4.57 0.636 190 164 175 190 349 -4.0 -0.92 0.308 169 166 175 178 382 0.0 -0.09 0.987 167 166 172 175 385 1.0 0.00 0.999 157 155 172 174 398 -9.0 -7.28 0.000 165 176 198 219 325 12.0 22.37 0.007 154 167 201 215 372 -3.0 13.70 0.286 170 170 179 161 393 0.0 -1.72 0.154 162 174 180 185 352 2.0 2.26 0.494 166 162 176 173 385 -6.0 -5.86 0.314 167 169 179 164 400 -1.0 0.11 0.909 175 167 179 180 352 -4.5 -2.77 0.589 241 168 178 209 283 -3.0 -9.82 0.479 164 169 190 190 399 0.0 -0.08 0.942 165 165 169 179 386 -3.5 -4.59 0.000 158 167 189 200 399 -3.0 4.17 0.007 162 171 183 190 398 0.0 1.00 0.564 165 169 176 184 400 0.0 0.54 0.568 140 159 180 193 352 -13.0 -5.41 0.463 159 167 189 180 331 -2.0 3.05 0.657 173 172 184 167 396 1.0 0.82 0.576 140 158 159 163 341 -11.0 -23.47 0.027 168 169 176 161 395 0.0 -0.66 0.576 170 170 179 184 327 -1.0 -2.41 0.568 201 182 187 201 328 11.0 3.60 0.114 143 180 207 268 389 11.0 20.70 0.000 311 174 198 209 311 3.0 15.25 0.475 184 168 185 185 328 -1.0 -1.49 0.571 169 170 187 189 398 0.0 -0.84 0.637 167 170 161 182 389 1.0 -2.30 0.171 175 171 162 187 399 -1.0 -2.37 0.008 165 170 185 186 400 1.0 1.72 0.240 167 170 181 185 397 -1.0 -1.39 0.245 167 170 185 187 400 0.0 -0.52 0.702 148 194 222 292 378 24.0 29.58 0.027 167 171 182 155 398 2.0 0.32 0.821 169 170 185 157 400 1.0 6.16 0.000 155 165 176 178 366 -4.0 0.48 0.823 167 168 172 178 399 -1.0 -2.84 0.000 156 172 199 184 399 5.0 15.08 0.084 177 167 181 181 306 3.0 9.11 0.293

TABLE-US-00006 APPENDIX-D Table 4 Summary of whole genome cfDNA analyses High Total Quality Analysis Patient Read Bases Bases Patient Timepoint type Type Length Sequenced Analyzed Coverage CGCRC291 Preoperative treatment naive WGS Colorectal Cancer 100 7232125000 4695396600 1.86 CGCRC292 Preoperative treatment naive WGS Colorectal Cancer 100 6794092800 4471065400 1.77 CGCRC293 Preoperative treatment naive WGS Colorectal Cancer 100 8373899600 5686176000 2.26 CGCRC294 Preoperative treatment naive WGS Colorectal Cancer 100 3081312000 5347045800 2.12 CGCRC296 Preoperative treatment naive WGS Colorectal Cancer 100 10072029200 6770998200 2.69 CGCRC299 Preoperative treatment naive WGS Colorectal Cancer 100 10971591600 7632723200 3.03 CGCRC300 Preoperative treatment naive WGS Colorectal Cancer 100 9894332600 6699951000 2.66 CGCRC301 Preoperative treatment naive WGS Colorectal Cancer 100 7357346200 5021002000 1.99 CGCRC302 Preoperative treatment naive WGS Colorectal Cancer 100 11671913000 8335275800 3.31 CGCRC304 Preoperative treatment naive WGS Colorectal Cancer 100 19011739200 12957614200 5.14 CGCRC305 Preoperative treatment naive WGS Colorectal Cancer 100 7177341400 4809957200 1.91 CGCRC306 Preoperative treatment naive WGS Colorectal Cancer 100 8302233200 5608043600 2.23 CGCRC307 Preoperative treatment naive WGS Colorectal Cancer 100 8034720400 5342620000 2.12 CGCRC308 Preoperative treatment naive WGS Colorectal Cancer 100 8670084800 5934037200 2.35 CGCRC311 Preoperative treatment naive WGS Colorectal Cancer 100 6947634400 4704601800 1.87 CGCRC315 Preoperative treatment naive WGS Colorectal Cancer 100 5205544000 3419565400 1.36 CGCRC316 Preoperative treatment naive WGS Colorectal Cancer 100 6405388600 4447534800 1.76 CGCRC317 Preoperative treatment naive WGS Colorectal Cancer 100 6060390400 4104616600 1.63 CGCRC318 Preoperative treatment naive WGS Colorectal Cancer 100 6848768600 4439404800 1.76 CGCRC319 Preoperative treatment naive WGS Colorectal Cancer 100 10545294400 7355181600 2.92 CGCRC320 Preoperative treatment naive WGS Colorectal Cancer 100 5961999200 3945054000 1.57 CGCRC321 Preoperative treatment naive WGS Colorectal Cancer 100 8248095400 5614355000 2.23 CGCRC333 Preoperative treatment naive WGS Colorectal Cancer 100 10540267600 6915490600 2.74 CGCRC336 Preoperative treatment naive WGS Colorectal Cancer 100 10675581800 7087691800 2.81 CGCRC338 Preoperative treatment naive WGS Colorectal Cancer 100 13788172600 8970308600 3.56 CGCRC341 Preoperative treatment naive WGS Colorectal Cancer 100 10753467600 7311539200 2.90 CGCRC342 Preoperative treatment naive WGS Colorectal Cancer 100 11836966000 7552793200 3.00 CGH14 Human adult elutriated lymphocytes WGS Healthy 100 36525427600 24950300200 9.90 CGH15 Human adult elutriated lymphocytes WGS Healthy 100 29930855000 23754049400 9.43 CGLU316 Pre-treatment, Day-53 WGS Lung Cancer 100 10354123200 6896471400 2.74 CGLU316 Pre-treatment, Day-4 WGS Lung Cancer 100 7870039200 5254938800 2.09 CGLU316 Post-treatment, Day 18 WGS Lung Cancer 100 8155322000 5416262400 2.15 CGLU316 Post-treatment, Day 81 WGS Lung Cancer 100 9442310400 6087893400 2.42 CGLU344 Pre-treatment, Day-21 WGS Lung Cancer 100 8728318600 5769097200 2.29 CGLU344 Pre-treatment, Day 0 WGS Lung Cancer 100 11710246400 7826902600 3.11 CGLU344 Post-treatment, Day 0.1875 WGS Lung Cancer 100 11569683000 7654701600 3.04 CGLU344 Post-treatment, Day 59 WGS Lung Cancer 100 11042459200 6320133800 2.51 CGLU369 Pre-treatment, Day-2 WGS Lung Cancer 100 8630932800 5779595800 2.29 CGLU369 Post-treatment, Day 12 WGS Lung Cancer 100 9227709600 6136755200 2.44 CGLU369 Post-treatment, Day 68 WGS Lung Cancer 100 7995282600 5239077200 2.08 CGLU369 Post-treatment, Day 110 WGS Lung Cancer 100 8750541000 5626139000 2.23 CGLU373 Pre-treatment, Day-2 WGS Lung Cancer 100 11746059600 7547485800 3.00 CGLU373 Post-treatment, Day 0.125 WGS Lung Cancer 100 13801136800 9255579400 3.67 CGLU373 Post-treatment, Day 7 WGS Lung Cancer 100 11537896800 7654111200 3.04 CGLU373 Post-treatment, Day 47 WGS Lung Cancer 100 8046326400 5397702400 2.14 CGPLBR100 Preoperative treatment naive WGS Breast Cancer 100 8440532400 5729474800 2.27 CGPLBR101 Preoperative treatment naive WGS Breast Cancer 100 9786253600 6673495200 2.65 CGPLBR102 Preoperative treatment naive WGS Breast Cancer 100 8664980400 5669781600 2.25 CGPLBR103 Preoperative treatment naive WGS Breast Cancer 100 9346936200 6662883400 2.64 CGPLBR104 Preoperative treatment naive WGS Breast Cancer 100 9443375400 6497061000 2.58 CGPLBR12 Preoperative treatment naive WGS Breast Cancer 100 7017577800 4823327400 1.91 CGPLBR18 Preoperative treatment naive WGS Breast Cancer 100 10309652800 7130386000 2.83 CGPLBR23 Preoperative treatment naive WGS Breast Cancer 100 9034484800 6219625800 2.47 CGPLBR24 Preoperative treatment naive WGS Breast Cancer 100 9891454200 6601857400 2.62 CGPLBR28 Preoperative treatment naive WGS Breast Cancer 100 7997607200 5400803200 2.14 CGPLBR30 Preoperative treatment naive WGS Breast Cancer 100 5502597200 5885822400 2.34 CGPLBR31 Preoperative treatment naive WGS Breast Cancer 100 12660085600 8551995600 3.39 CGPLBR32 Preoperative treatment naive WGS Breast Cancer 100 8773498600 5839034600 2.32 CGPLBR33 Preoperative treatment naive WGS Breast Cancer 100 10931742800 6967030600 2.76 CGPLBR34 Preoperative treatment naive WGS Breast Cancer 100 10861398600 7453225800 2.96 CGPLBR35 Preoperative treatment naive WGS Breast Cancer 100 9180193600 6158440200 2.44 CGPLBR36 Preoperative treatment naive WGS Breast Cancer 100 9159948400 6091817800 2.42 CGPLBR37 Preoperative treatment naive WGS Breast Cancer 100 10307505800 6929530600 2.75 CGPLBR38 Preoperative treatment naive WGS Breast Cancer 100 9983824000 6841725400 2.71 CGPLBR40 Preoperative treatment naive WGS Breast Cancer 100 10148823800 7024345400 2.79 CGPLBR41 Preoperative treatment naive WGS Breast Cancer 100 11168192000 7562945800 3.00 CGPLBR45 Preoperative treatment naive WGS Breast Cancer 100 8793780600 6011109400 2.39 CGPLBR46 Preoperative treatment naive WGS Breast Cancer 100 7228607600 4706130000 1.87 CGPLBR47 Preoperative treatment naive WGS Breast Cancer 100 7906911400 5341655000 2.12 CGPLBR48 Preoperative treatment naive WGS Breast Cancer 100 6992032000 4428636200 1.76 CGPLBR49 Preoperative treatment naive WGS Breast Cancer 100 7311195000 4559460200 1.81 CGPLBR50 Preoperative treatment naive WGS Breast Cancer 100 11107960600 7582776600 3.01 CGPLBR51 Preoperative treatment naive WGS Breast Cancer 100 8393547400 5102069000 2.02 CGPLBR52 Preoperative treatment naive WGS Breast Cancer 100 9491894800 6141729000 2.44 CGPLBR55 Preoperative treatment naive WGS Breast Cancer 100 9380109800 6518855200 2.59 CGPLBR56 Preoperative treatment naive WGS Breast Cancer 100 12191816800 8293011200 3.29 CGPLBR57 Preoperative treatment naive WGS Breast Cancer 100 9847584400 6713638000 2.66 CGPLBR59 Preoperative treatment naive WGS Breast Cancer 100 7476477000 5059873200 2.01 CGPLBR60 Preoperative treatment naive WGS Breast Cancer 100 6531354600 4331253800 1.72 CGPLBR61 Preoperative treatment naive WGS Breast Cancer 100 9311029200 6430920800 2.55 CGPLBR63 Preoperative treatment naive WGS Breast Cancer 100 8971949000 6044009600 2.40 CGPLBR65 Preoperative treatment naive WGS Breast Cancer 100 7197301400 4835015200 1.92 CGPLBR63 Preoperative treatment naive WGS Breast Cancer 100 10003774000 6974918800 2.77 CGPLBR69 Preoperative treatment naive WGS Breast Cancer 100 10080881800 6903459200 2.74 CGPLBR70 Preoperative treatment naive WGS Breast Cancer 100 8824002800 6002533800 2.38 CGPLBR71 Preoperative treatment naive WGS Breast Cancer 100 10164136800 6994668600 2.78 CGPLBR72 Preoperative treatment naive WGS Breast Cancer 100 18418841400 12328783000 4.89 CGPLBR73 Preoperative treatment naive WGS Breast Cancer 100 10281460200 7078613200 2.81 CGPLBR76 Preoperative treatment naive WGS Breast Cancer 100 10105270400 6800705000 2.70 CGPLBR81 Preoperative treatment naive WGS Breast Cancer 100 5087126000 3273367200 1.30 CGPLBR82 Preoperative treatment naive WGS Breast Cancer 100 10576496600 7186662600 2.85 CGPLBR83 Preoperative treatment naive WGS Breast Cancer 100 8977124400 5947525000 2.36 CGPLBR84 Preoperative treatment naive WGS Breast Cancer 100 6272538600 4066870600 1.61 CGPLBR87 Preoperative treatment naive WGS Breast Cancer 100 8460954800 5375710200 2.13 CGPLBR83 Preoperative treatment naive WGS Breast Cancer 100 8665810400 5499893200 2.18 CGPLBR90 Preoperative treatment naive WGS Breast Cancer 100 6663469200 4392442400 1.74 CGPLBR91 Preoperative treatment naive WGS Breast Cancer 100 10933002400 7647842000 3.03 CGPLBR92 Preoperative treatment naive WGS Breast Cancer 100 10392674000 6493593000 2.58 CGPLBR93 Preoperative treatment naive WGS Breast Cancer 100 5659836000 3931106800 1.56 CGPLH189 Preoperative treatment naive WGS Healthy 100 11400610400 7655568800 3.04 CGPLH190 Preoperative treatment naive WGS Healthy 100 11444671600 7581175200 3.01 CGPLH192 Preoperative treatment naive WGS Healthy 100 12199010800 8126804800 3.22 CGPLH193 Preoperative treatment naive WGS Healthy 100 10201897600 6635285400 2.63 CGPLH194 Preoperative treatment naive WGS Healthy 100 11005087400 7081652600 2.81 CGPLH196 Preoperative treatment naive WGS Healthy 100 12891462800 8646881800 3.43 CGP6H197 Preoperative treatment naive WGS Healthy 100 11961841600 3052855200 3.20 CGPLH193 Preoperative treatment naive WGS Healthy 100 13605489000 8885716000 3.53 CGPLH199 Preoperative treatment naive WGS Healthy 100 1818090200 5615316000 2.23 CGPLH200 Preoperative treatment naive WGS Healthy 100 14400027600 9310342000 3.69 CGPLH201 Preoperative treatment naive WGS Healthy 100 6208766806 4171843400 1.66 CGPLH202 Preoperative treatment naive WGS Healthy 100 11282922800 7363530600 2.92 CGPLH203 Preoperative treatment naive WGS Healthy 100 13540689600 9068747600 3.60 CGPLH205 Preoperative treatment naive WGS Healthy 100 10343537800 6696983600 2.66 CGPLH208 Preoperative treatment naive WGS Healthy 100 12796300000 3272073400 3.28 CGPLH209 Preoperative treatment naive WGS Healthy 100 13123035400 3531813600 3.39 CGPLH210 Preoperative treatment naive WGS Healthy 100 10184218800 6832204600 2.71 CGPLH211 Preoperative treatment naive WGS Healthy 100 14655260200 3887067600 3.53 CGPLH300 Preoperative treatment naive WGS Healthy 100 7062083400 4553351200 1.81 CGPLH307 Preoperative treatment naive WGS Healthy 100 7239128200 4547697200 1.80 CGPLH308 Preoperative treatment naive WGS Healthy 100 8512551400 5526653600 2.19 CGPLH309 Preoperative treatment naive WGS Healthy 100 11664474200 7431836600 2.95

CGPLH310 Preoperative treatment naive WGS Healthy 100 11045691000 7451506200 2.96 CGPLH311 Preoperative treatment naive WGS Healthy 100 10406803200 6786479600 2.69 CGPLH314 Preoperative treatment naive WGS Healthy 100 10371343800 6925866600 2.75 CGPLH315 Preoperative treatment naive WGS Healthy 100 9508538400 6208744600 2.46 CGPLH316 Preoperative treatment naive WGS Healthy 100 10131063600 6891181000 2.73 CGPLH317 Preoperative treatment naive WGS Healthy 100 8364314400 5302232600 2.10 CGPLH319 Preoperative treatment naive WGS Healthy 100 8780528200 5585897000 2.22 CGPLH320 Preoperative treatment naive WGS Healthy 100 8956232600 5784619200 2.30 CGPLH322 Preoperative treatment naive WGS Healthy 100 9563837800 6445517800 2.56 CGPLH324 Preoperative treatment naive WGS Healthy 100 6765038600 4469201600 1.77 CGPLH325 Preoperative treatment naive WGS Healthy 100 8008213400 5099262800 2.02 CGPLH326 Preoperative treatment naive WGS Healthy 100 9554226200 6112544000 2.43 CGPLH327 Preoperative treatment naive WGS Healthy 100 8239168800 5351280200 2.12 CGPLH328 Preoperative treatment naive WGS Healthy 100 7197086300 4516894800 1.79 CGPLH329 Preoperative treatment naive WGS Healthy 100 8921554800 5493709800 2.18 CGPLH330 Preoperative treatment naive WGS Healthy 100 10693603400 7077793600 2.81 CGPLH331 Preoperative treatment naive WGS Healthy 100 8982792000 5538096200 2.20 CGPLH333 Preoperative treatment naive WGS Healthy 100 7856985400 5178829600 2.06 CGPLH335 Preoperative treatment naive WGS Healthy 100 9370663400 6035739400 2.40 CGPLH336 Preoperative treatment naive WGS Healthy 100 8002498200 5340331400 2.12 CGPLH337 Preoperative treatment naive WGS Healthy 100 7399022000 4954467600 1.97 CGPLH338 Preoperative treatment naive WGS Healthy 100 8917121600 6170927200 2.45 CGPLH339 Preoperative treatment naive WGS Healthy 100 8591130800 5866411400 2.33 CGPLH340 Preoperative treatment naive WGS Healthy 100 8046351000 5368062000 2.13 CGPLH341 Preoperative treatment naive WGS Healthy 100 7914788600 5200304800 2.06 CGPLH342 Preoperative treatment naive WGS Healthy 100 8633413000 5701972400 2.26 CGPLH343 Preoperative treatment naive WGS Healthy 100 6694769800 4410670860 1.75 CGPLH344 Preoperative treatment naive WGS Healthy 100 7628192400 4961476600 1.97 CGPLH345 Preoperative treatment naive WGS Healthy 100 7121569406 4747223000 1.88 CGPLH346 Preoperative treatment naive WGS Healthy 100 7707924600 4873321600 1.93 CGPLH35 Preoperative treatment naive WGS Healthy 100 47305985200 4774186200 12.63 CGPLH350 Preoperative treatment naive WGS Healthy 100 9745839800 6054055200 2.40 CGPLH351 Preoperative treatment naive WGS Healthy 100 13317435800 8714465000 3.46 CGPLH352 Preoperative treatment naive WGS Healthy 100 7059351600 4752309400 1.89 CGPLH353 Preoperative treatment naive WGS Healthy 100 8435782400 5215098200 2.09 CGPLH354 Preoperative treatment naive WGS Healthy 100 8018644000 4857577660 1.93 CGPLH355 Preoperative treatment naive WGS Healthy 100 8624675800 5709726400 2.27 CGPLH356 Preoperative treatment naive WGS Healthy 100 8817952800 5729595200 2.27 CGPLH357 Preoperative treatment naive WGS Healthy 100 11931696200 7690004400 3.05 CGPLH358 Preoperative treatment naive WGS Healthy 100 12802561200 8451274800 3.35 CGPLH36 Preoperative treatment naive WGS Healthy 100 40173545600 3914810400 10.52 CGPLH360 Preoperative treatment naive WGS Healthy 100 7280078400 4918566200 1.95 CGPLH361 Preoperative treatment naive WGS Healthy 100 7493498400 4966813800 1.97 CGPLH362 Preoperative treatment naive WGS Healthy 100 11345644200 7532133600 2 99 CGPLH363 Preoperative treatment naive WGS Healthy 100 6111382800 3965952400 1.57 CGPLH364 Preoperative treatment naive WGS Healthy 100 10823490400 7195657000 2.86 CGPLH365 Preoperative treatment naive WGS Healthy 100 5938367400 3954556200 1.57 CGPLH366 Preoperative treatment naive WGS Healthy 100 7063168600 4731853060 1.88 CGPLH367 Preoperative treatment naive WGS Healthy 100 7119631800 4627888200 1.84 CGPLH368 Preoperative treatment naive WGS Healthy 100 7726718400 4975233400 1.97 CGPLH369 Preoperative treatment naive WGS Healthy 100 10967584200 7130956800 2.83 CGPLH37 Preoperative treatment naive WGS Healthy 100 45970545400 4591328800 12.15 CGPLH370 Preoperative treatment naive WGS Healthy 100 9237170006 6106373800 2.42 CGPLH371 Preoperative treatment naive WGS Healthy 100 8077798800 5237070600 2.08 CGPLH380 Preoperative treatment naive WGS Healthy 100 14049589200 8614241200 3.42 CGPLH381 Preoperative treatment naive WGS Healthy 100 16743792000 10767862800 4.27 CGPLH382 Preoperative treatment naive WGS Healthy 100 18474025200 12276437200 4.87 CGPLH383 Preoperative treatment naive WGS Healthy 100 13215954000 8430420600 3.36 CGPLH384 Preoperative treatment naive WGS Healthy 100 8481814000 5463636260 2.17 CGPLH385 Preoperative treatment naive WGS Healthy 100 9596118800 6445445600 2.56 CGPLH386 Preoperative treatment naive WGS Healthy 100 7399540400 4915484800 1.95 CGPLH387 Preoperative treatment naive WGS Healthy 100 6860332600 4339724400 1.72 CGPLH388 Preoperative treatment naive WGS Healthy 100 8679705600 5463945400 2.17 CGPLH389 Preoperative treatment naive WGS Healthy 100 7266863600 4702386000 1.87 CGPLH390 Preoperative treatment naive WGS Healthy 100 7509035600 4913901800 1.95 CGPLH391 Preoperative treatment naive WGS Healthy 100 7252286000 4702404800 1.87 CGPLH392 Preoperative treatment naive WGS Healthy 100 7302618200 4722407000 1.87 CGPLH393 Preoperative treatment naive WGS Healthy 100 8879138000 5947871800 2.36 CGPLH394 Preoperative treatment naive WGS Healthy 100 8737031000 5599777400 2.22 CGPLH395 Preoperative treatment naive WGS Healthy 100 7783904800 4907146000 1.95 CGPLH396 Preoperative treatment naive WGS Healthy 100 7585567200 5076638200 2.01 CGPLH393 Preoperative treatment naive WGS Healthy 100 13001418200 8607025000 3.42 CGPLH399 Preoperative treatment naive WGS Healthy 100 9867699200 5526646000 2.19 CGPLH400 Preoperative treatment naive WGS Healthy 100 10573939000 6290438200 2.50 CGPLH401 Preoperative treatment naive WGS Healthy 100 9415150000 6139638000 2.44 CGPLH402 Preoperative treatment naive WGS Healthy 100 5541458000 2912027800 1.18 CGPLH403 Preoperative treatment naive WGS Healthy 100 6470913200 3549172600 1.41 CGPLH404 Preoperative treatment naive WGS Healthy 100 7369651800 4120205000 1.64 CGPLH405 Preoperative treatment naive WGS Healthy 100 7360239000 4293522600 1.70 CGPLH406 Preoperative treatment naive WGS Healthy 100 6026125400 3426007400 1.36 CGPLH407 Preoperative treatment naive WGS Healthy 100 7073375200 4079286800 1.62 CGPLH408 Preoperative treatment naive WGS Healthy 100 8006103200 5121285600 2.03 CGPLH409 Preoperative treatment naive WGS Healthy 100 7343124600 4432335600 1.76 CGPLH410 Preoperative treatment naive WGS Healthy 100 7551842000 4818779600 1.91 CGPLH411 Preoperative treatment naive WGS Healthy 100 6119676400 3636478400 1.44 CGPLH412 Preoperative treatment naive WGS Healthy 100 7960821200 4935752200 1.96 CGPLH413 Preoperative treatment naive WGS Healthy 100 7623405400 4827888400 1.92 CGPLH414 Preoperative treatment naive WGS Healthy 100 7381312400 4743337200 1.88 CGPLH415 Preoperative treatment naive WGS Healthy 100 7240754200 4162208800 1.65 CGPLH416 Preoperative treatment naive WGS Healthy 100 7745658600 4670226000 1.85 CGPLH417 Preoperative treatment naive WGS Healthy 100 7627498600 4403085600 1.75 CGPLH418 Preoperative treatment naive WGS Healthy 100 9090285000 5094814000 2.02 CGPLH419 Preoperative treatment naive WGS Healthy 100 7914120200 5078389800 2.02 CGPLH42 Preoperative treatment naive WGS Healthy 100 39492040600 3901039400 10.32 CGPLH420 Preoperative treatment naive WGS Healthy 100 70143072800 4711393600 1.87 CGPLH422 Preoperative treatment naive WGS Healthy 100 9103972800 6053559800 2.40 CGPLH423 Preoperative treatment naive WGS Healthy 100 10154714200 6128800200 2.43 CGPLH424 Preoperative treatment naive WGS Healthy 100 11002394000 6573756000 2.61 CGPLH425 Preoperative treatment naive WGS Healthy 100 14681352600 9272557000 3.68 CGPLH426 Preoperative treatment naive WGS Healthy 100 8336731000 5177430800 2.05 CGPLH427 Preoperative treatment naive WGS Healthy 100 8242924400 5632991800 2.24 CGPLH428 Preoperative treatment naive WGS Healthy 100 8512550400 5604756600 2.22 CGPLH429 Preoperative treatment naive WGS Healthy 100 8369802800 5477121400 2.17 CGPLH43 Preoperative treatment naive WGS Healthy 100 38513193400 3815698400 10.10 CGPLH430 Preoperative treatment naive WGS Healthy 100 10357365400 6841611000 2.71 CGPLH431 Preoperative treatment naive WGS Healthy 100 7599875800 5006909000 1.99 CGPLH432 Preoperative treatment naive WGS Healthy 100 7932532400 4932304200 1.96 CGPLH434 Preoperative treatment naive WGS Healthy 100 10417028600 6965093800 2.76 CGPLH435 Preoperative treatment naive WGS Healthy 100 6747793800 5677115290 2.29 CGPLH436 Preoperative treatment naive WGS Healthy 100 7990589400 5228737800 2.07 GGPLH437 Preoperative treatment naive WGS Healthy 100 10156991200 6935537200 2.75 CGPLH438 Preoperative treatment naive WGS Healthy 100 9473604000 6445455600 2.56 CGPLH439 Preoperative treatment naive WGS Healthy 100 8303723400 5439877200 2.16 CGPLH440 Preoperative treatment naive WGS Healthy 100 9055233800 6018631400 2.39 CGPLH441 Preoperative treatment naive WGS Healthy 100 10290682000 6896415200 2.74 CGPLH442 Preoperative treatment naive WGS Healthy 100 9876551600 6591249800 2.62 CGPLH443 Preoperative treatment naive WGS Healthy 100 9837225800 6360740800 2.52 CGPLH444 Preoperative treatment naive WGS Healthy 100 9199271400 5795941660 2.26 CGPLH445 Preoperative treatment naive WGS Healthy 100 8089236400 5218259800 2.07 CGPLH446 Preoperative treatment naive WGS Healthy 100 7890664200 5181606000 2.06 CGPLH447 Preoperative treatment naive WGS Healthy 100 7775775000 5120239800 2.03 CGPLH448 Preoperative treatment naive WGS Healthy 100 8686964800 5605079200 2.22 CGPLH449 Preoperative treatment naive WGS Healthy 100 8604545400 5527726600 2.19 CGPLH45 Preoperative treatment naive WGS Healthy 100 39029653000 3771601200 9.98 CGPLH450 Preoperative treatment naive WGS Healthy 100 8428254800 5439950000 2.16 CGPLH451 Preoperative treatment naive WGS Healthy 100 8128977600

5186265600 2.06 CGPLH452 Preoperative treatment naive WGS Healthy 100 6474313400 4216316400 1.67 CGPLH453 Preoperative treatment naive WGS Healthy 100 9831832800 6224917600 2.47 CGPLH455 Preoperative treatment naive WGS Healthy 100 7373753000 4593473600 1.82 CGPLH456 Preoperative treatment naive WGS Healthy 100 8455416200 5457148200 2.17 CGPLH457 Preoperative treatment naive WGS Healthy 100 8647618000 5534503800 2.20 CGPLH458 Preoperative treatment naive WGS Healthy 100 6633156400 4415186060 1.79 CGPLH459 Preoperative treatment naive WGS Healthy 100 8361048200 5497193800 2.18 CGPLH46 Preoperative treatment naive WGS Healthy 100 35361484600 3516232800 9.30 CGPLH460 Preoperative treatment naive WGS Healthy 100 6788835400 4472282800 1.77 CGPLH463 Preoperative treatment naive WGS Healthy 100 8534880800 5481759200 2.18 CGPLH464 Preoperative treatment naive WGS Healthy 100 6692520006 4184463400 1.66 CGPLH465 Preoperative treatment naive WGS Healthy 100 7772884600 4878430800 1.94 CGPLH466 Preoperative treatment naive WGS Healthy 100 9056275000 5830877400 2.31 CGPLH467 Preoperative treatment naive WGS Healthy 100 6931419200 4585861000 1.82 CGPLH468 Preoperative treatment naive WGS Healthy 100 9334067400 6314830460 2.51 CGPLH469 Preoperative treatment naive WGS Healthy 100 7376691000 4545246600 1.80 CGPLH47 Preoperative treatment naive WGS Healthy 100 38485647600 3534883600 9.35 CGPLH470 Preoperative treatment naive WGS Healthy 100 7899727600 5221650600 2.07 CGPLH471 Preoperative treatment naive WGS Healthy 100 9200430600 6102371000 2.42 CGPLH472 Preoperative treatment naive WGS Healthy 100 8143742400 5399946600 2.14 CGPLH473 Preoperative treatment naive WGS Healthy 100 8123924600 5419825400 2.15 CGPLH474 Preoperative treatment naive WGS Healthy 100 3853071400 6084059400 2.41 CGPLH475 Preoperative treatment naive WGS Healthy 100 8115374000 5291718000 2.10 CGPLH476 Preoperative treatment naive WGS Healthy 100 8163162000 5096869660 2.02 CGPLH477 Preoperative treatment naive WGS Healthy 100 8350093206 5465468600 2.17 CGPLH478 Preoperative treatment naive WGS Healthy 100 8259642200 5406516200 2.15 CGPLH479 Preoperative treatment naive WGS Healthy 100 8027598600 5417376800 2.15 CGPLH48 Preoperative treatment naive WGS Healthy 100 42232410000 4165893400 11.02 CGPLH480 Preoperative treatment naive WGS Healthy 100 7832983200 5020127000 1.99 CGPLH481 Preoperative treatment naive WGS Healthy 100 7578518800 4883280800 1.94 CGPLH482 Preoperative treatment naive WGS Healthy 100 8279364800 5652263600 2.24 CGPLH483 Preoperative treatment naive WGS Healthy 100 8660338800 5823859200 2.31 CGPLH484 Preoperative treatment naive WGS Healthy 100 8445420000 5794328000 2.30 CGPLH485 Preoperative treatment naive WGS Healthy 100 8371255406 5490207800 2.18 CGPLH486 Preoperative treatment naive WGS Healthy 100 8216712200 5506871000 2.19 CGPLH487 Preoperative treatment naive WGS Healthy 100 7936294200 5309250200 2.11 CGPLH488 Preoperative treatment naive WGS Healthy 100 8355603600 545316000 2.16 CGPLH49 Preoperative treatment naive WGS Healthy 100 33912191800 3310056000 8.76 CGPLH490 Preoperative treatment naive WGS Healthy 100 7768712400 5175567800 2.05 CGPLH491 Preoperative treatment naive WGS Healthy 100 9070904000 6011275000 2.39 CGPLH492 Preoperative treatment naive WGS Healthy 100 7208727200 4753213800 1.89 CGPLH493 Preoperative treatment naive WGS Healthy 100 10542882600 7225870800 2.87 CGPLH494 Preoperative treatment naive WGS Healthy 100 10908197600 7046645000 2.80 CGPLH495 Preoperative treatment naive WGS Healthy 100 8945040400 5891697800 2.34 CGPLH496 Preoperative treatment naive WGS Healthy 100 10859729400 7549608000 3.00 CGPLH497 Preoperative treatment naive WGS Healthy 100 9630507400 6473162800 2.57 CGPLH498 Preoperative treatment naive WGS Healthy 100 10060232600 6744622800 2.68 CGPLH499 Preoperative treatment naive WGS Healthy 100 10221293600 6951282800 2.76 CGPLH50 Preoperative treatment naive WGS Healthy 100 41248860600 4073272890 10.78 CGPLH500 Preoperative treatment naive WGS Healthy 100 9703168209 6239893800 2.48 CGPLH501 Preoperative treatment naive WGS Healthy 100 9104779800 6161602800 2.45 CGPLH502 Preoperative treatment naive WGS Healthy 100 8514467400 5290881400 2.10 CGPLH503 Preoperative treatment naive WGS Healthy 100 9019992209 6100383400 2.42 CGPLH504 Preoperative treatment naive WGS Healthy 100 9320330200 6109750200 2.46 CGPLH505 Preoperative treatment naive WGS Healthy 100 7499497400 4914559000 1.95 CGPLH506 Preoperative treatment naive WGS Healthy 100 10526142000 6963312600 2.76 CGPLH507 Preoperative treatment naive WGS Healthy 100 9091018400 6146678600 2.44 CGPLH508 Preoperative treatment naive WGS Healthy 100 10989315600 7360201400 2.92 CGPLH509 Preoperative treatment naive WGS Healthy 100 9729084600 6702691600 2.66 CGPLH51 Preoperative treatment naive WGS Healthy 100 35967451400 3492833200 9.24 CGPLH510 Preoperative treatment naive WGS Healthy 100 11162691600 7626795400 3.03 CGPLH511 Preoperative treatment naive WGS Healthy 100 11888619600 8110427600 3.22 CGPLH512 Preoperative treatment naive WGS Healthy 100 10726438400 7110078000 2.82 CGPLH513 Preoperative treatment naive WGS Healthy 100 10701564200 7105271400 2.84 CGPLH514 Preoperative treatment naive WGS Healthy 100 8822067000 5958773800 2.36 CGPLH515 Preoperative treatment naive WGS Healthy 100 7792074800 5317464600 2.11 CGPLH516 Preoperative treatment naive WGS Healthy 100 8642620000 5846439400 2.32 CGPLH517 Preoperative treatment naive WGS Healthy 100 11915929600 0013937000 3.18 CGPLH518 Preoperative treatment naive WGS Healthy 100 12804517400 3606661600 3.42 CGPLH519 Preoperative treatment naive WGS Healthy 100 11513222200 7922798400 3.14 CGPLH52 Preoperative treatment naive WGS Healthy 100 49247304200 4849531400 12.83 CGPLH520 Preoperative treatment naive WGS Healthy 100 8942102400 6030683400 2.39 CGPLH54 Preoperative treatment naive WGS Healthy 100 45399346400 4466164600 11.82 CGPLH55 Preoperative treatment naive WGS Healthy 100 42547725000 4283337600 11.33 CGPLH56 Preoperative treatment naive WGS Healthy 100 33460308000 3226338000 8.53 CGPLH51 Preoperative treatment naive WGS Healthy 100 36504735200 3509125000 9.28 CGPLH59 Preoperative treatment naive WGS Healthy 100 39642810600 3820011000 10.11 CGPLH625 Preoperative treatment naive WGS Healthy 100 6408225000 4115487600 1.63 CGPLH626 Preoperative treatment naive WGS Healthy 100 9915193600 6391657000 2.54 CGPLH63 Preoperative treatment naive WGS Healthy 100 37447047600 3506737000 9.26 CGPLH639 Preoperative treatment naive WGS Healthy 100 8158965890 5216049600 2.07 CGPLH64 Preoperative treatment naive WGS Healthy 100 34275506800 3264503000 8.63 CGPLH640 Preoperative treatment naive WGS Healthy 100 8058876800 5333551800 2.12 CGPLH642 Preoperative treatment naive WGS Healthy 100 7545555600 4909732800 1.95 CGPLH643 Preoperative treatment naive WGS Healthy 100 7865776800 5254772000 2.09 CGPLH644 Preoperative treatment naive WGS Healthy 100 6890139000 4599387400 1.83 CGPLH646 Preoperative treatment naive WGS Healthy 100 7757219400 5077408200 2.01 CGPLH75 Preoperative treatment naive WGS Healthy 100 23882926000 2250344400 5.95 CGPLH76 Preoperative treatment naive WGS Healthy 100 30631483600 3086042200 8.16 CGPLH77 Preoperative treatment naive WGS Healthy 100 31651741400 3041290200 8.04 CGPLH78 Preoperative treatment naive WGS Healthy 100 31165831200 3130079800 8.28 CGPLH79 Preoperative treatment naive WGS Healthy 100 31935043000 3128488200 8.27 CGPLH80 Preoperative treatment naive WGS Healthy 100 32965093000 3311371800 8.76 CGPLH81 Preoperative treatment naive WGS Healthy 100 27035311200 2455084400 6.49 CGPLH82 Preoperative treatment naive WGS Healthy 100 28447051200 2893358200 7.65 CGPLH83 Preoperative treatment naive WGS Healthy 100 26702240200 2459494000 6.50 CGPLH84 Preoperative treatment naive WGS Healthy 100 251713861400 2524467400 6.68 CGPLLU13 Pre-treatment, Day-2 WGS Lung Cancer 100 9126585600 5915061800 2.35 CGPLLU13 Post-treatment, Day 5 WGS Lung Cancer 100 7739120200 5071745800 2.01 CGPLLU13 Post-treatment, Day 28 WGS Lung Cancer 100 9081585400 5764371600 2.29 CGPLLU13 Post-treatment, Day 91 WGS Lung Cancer 100 9576557000 6160760200 2.44 CGPLLU14 Pre-treatment, Day-38 WGS Lung Cancer 100 13659198400 9033455800 3.58 CGPLLU14 Pre-treatment, Day-16 WGS Lung Cancer 100 7178855800 4856643600 1.93 CGPLLU14 Pre-treatment, Day-3 WGS Lung Cancer 100 7653473000 4816193600 1.91 CGPLLU14 Pre-treatment, Day 0 WGS Lung Cancer 100 7351997400 5193256600 2.06 CGPLLU14 Post-treatment, Day 0.33 WGS Lung Cancer 100 7193040800 4869701600 1.93 CGPLLU14 Post-treatment, Day 7 WGS Lung Cancer 100 7102000000 4741432600 1.88 CGPLLU144 Preoperative treatment naive WGS Lung Cancer 100 4934813600 3415936400 1.36 CGPLLU147 Preoperative treatment naive WGS Lung Cancer 100 24409561000 2118672800 5.61 CGPLLU161 Preoperative treatment naive WGS Lung Cancer 100 8998813400 6016145000 2.39 CGPLLU162 Preoperative treatment naive WGS Lung Cancer 100 9709792400 6407866400 2.54 CGPLLU163 Preoperative treatment naive WGS Lung Cancer 100 9150620200 6063569800 2.41 CGPLLU165 Preoperative treatment naive WGS Lung Cancer 100 28374436400 2651138600 7.01 CGPLLU168 Preoperative treatment naive WGS Lung Cancer 100 5692739400 3695191000 1.47 CGPLLU169 Preoperative treatment naive WGS Lung Cancer 100 9093975600 5805320800 2.30 CGPLLU175 Preoperative treatment naive WGS Lung Cancer 100 33794816800 3418750400 9.04 CGPLLU176 Preoperative treatment naive WGS Lung Cancer 100 8778553800 5794950200 2.30 CGPLLU177 Preoperative treatment naive WGS Lung Cancer 100 3734614800 2578696200 1.02 CGPLLU180 Preoperative treatment naive WGS Lung Cancer 100 28305936600 2756034200 7.29 CGPLLU198 Preoperative treatment naive WGS Lung Cancer 100 32344959200 2218577200 5.86 CGPLLU202 Preoperative treatment naive WGS Lung Cancer 100 21110128200 1831279400 4.84 CGPLLU203 Preoperative treatment naive WGS Lung Cancer 100 4304235600 2806429000 1.15 CGPLLU205 Preoperative treatment naive WGS Lung Cancer 100 10502467000 7386984800 2.93 CGPLLU206 Preoperative treatment naive WGS Lung Cancer 100 21888248200 2026666000 5.36 CGPLLU207 Preoperative treatment naive WGS Lung Cancer 100 10806230600 7363049000 2.92

CGPLLU208 Preoperative treatment naive WGS Lung Cancer 100 7795426800 5199545800 2.06 CGPLLU209 Preoperative treatment naive WGS Lung Cancer 100 26174542000 2621961800 6.93 CGPLLU244 Pre-treatment, Day-7 WGS Lung Cancer 100 9967531400 6704365800 2.66 CGPLLU244 Pre-treatment, Day-1 WGS Lung Cancer 100 9547119200 5785172600 2.30 CGPLLU944 Post-treatment, Day 6 WGS Lung Cancer 100 9535898600 6452174000 2.56 CGPLLU244 Post-treatment, Day 62 WGS Lung Cancer 100 6783628000 5914149000 2.35 CGPLLU245 Pre-treatment, Day-32 WGS Lung Cancer 100 10025823200 6313303800 2.51 CGPLLU245 Pre-treatment, Day 0 WGS Lung Cancer 100 9462480400 6612867800 2.62 CGPLLU245 Post-treatment, Day 7 WGS Lung Cancer 100 9143025000 6431013200 2.55 CGPLLU245 Post-treatment, Day 21 WGS Lung Cancer 100 9072713800 6368533000 2.53 CGPLLU946 Pre-treatment, Day-21 WGS Lung Cancer 100 9579787000 6458003400 2.56 CGPLLU246 Pre-treatment, Day 0 WGS Lung Cancer 100 9512703600 6440535600 2.56 CGPLLU246 Post-treatment, Day 9 WGS Lung Cancer 100 9012645000 6300939200 2.50 CGPLLU246 Post-treatment, Day 42 WGS Lung Cancer 100 11136103000 7358747400 2.92 CGPLLU264 Pre-treatment, Day-1 WGS Lung Cancer 100 9196305000 6239803600 2.49 CGPLLU264 Post-treatment, Day 6 WGS Lung Cancer 100 8247416600 5600454200 2.22 CGPLLU264 Post-treatment, Day 27 WGS Lung Cancer 100 8681022200 5856109000 2.32 CGPLLU264 Post-treatment, Day 69 WGS Lung Cancer 100 3931976400 5974246000 2.37 CGPLLU265 Pre-treatment, Day 0 WGS Lung Cancer 100 9460534000 6111185200 2.43 CGPLLU265 Post-treatment, Day 3 WGS Lung Cancer 100 8051601200 4984166600 1.98 CGPLLU265 Post-treatment, Day 7 WGS Lung Cancer 100 8082224600 5110092600 2.03 CGPLLU265 Post-treatment, Day 84 WGS Lung Cancer 100 8368637400 5369526400 2.13 CGPLLU266 Pre-treatment, Day 0 WGS Lung Cancer 100 8583766400 5846473600 2.32 CGPLLU266 Post-treatment, Day 16 WGS Lung Cancer 100 8795793600 5984531400 2.37 CGPLLU266 Post-treatment, Day 83 WGS Lung Cancer 100 9157947600 6227735060 2.47 CGPLLU266 Post-treatment, Day 328 WGS Lung Cancer 100 7299455400 5049379000 2.00 CGPLLU267 Pre-treatment, Day-1 WGS Lung Cancer 100 10658657800 6892067000 2.73 CGPLLU267 Post-treatment, Day 34 WGS Lung Cancer 100 8492833400 5101097800 2.02 CGPLLU267 Post-treatment, Day 90 WGS Lung Cancer 100 12030314800 7757930400 3.09 CGPLLU269 Pre-treatment, Day 0 WGS Lung Cancer 100 9170168000 5830454400 2.31 CGPLLU269 Post-treatment, Day 9 WGS Lung Cancer 100 8905640400 5290461400 2.10 CGPLLU269 Post-treatment, Day 28 WGS Lung Cancer 100 8455306600 5387927400 2.14 CGPLLU271 Post-treatment, Day 259 WGS Lung Cancer 100 8112060400 5404979000 2.14 CGPLLU271 Pre-treatment, Day 0 WGS Lung Cancer 100 13150818200 8570453400 3.40 CGPLLU271 Post-treatment, Day 6 WGS Lung Cancer 100 9008880600 5854051400 2.32 CGPLLU271 Post-treatment, Day 20 WGS Lung Cancer 100 8670913000 5461577000 2.17 CGPLLU271 Post-treatment, Day 104 WGS Lung Cancer 100 8887441400 5609039000 2.23 CGPLLU43 Pre-treatment, Day-1 WGS Lung Cancer 100 6407811200 5203486400 2.06 CGPLLU43 Post-treatment, Day 6 WGS Lung Cancer 100 9964335200 5626714400 2.23 CGPLLU43 Post-treatment, Day 27 WGS Lung Cancer 100 8902283000 5485656200 2.18 CGPLLU43 Post-treatment, Day 83 WGS Lung Cancer 100 9201509200 5875064200 2.33 CGPLLU86 Pre-treatment, Day 0 WGS Lung Cancer 100 9152729200 6248173200 2.48 CGPLLU86 Post-treatment, Day 0.5 WGS Lung Cancer 100 6703253000 4663026800 1.85 CGPLLU86 Post-treatment, Day 7 WGS Lung Cancer 100 6590121400 4559562400 1.81 CGPLLU86 Post-treatment, Day 17 WGS Lung Cancer 100 8653551800 5900136000 2.34 CGPLLU88 Pre-treatment, Day 0 WGS Lung Cancer 100 8096528000 8505475400 2.18 CGPLLU88 Post-treatment, Day 7 WGS Lung Cancer 100 0283192200 5784217600 2.30 CGPLLU88 Post-treatment, Day 297 WGS Lung Cancer 100 9297110800 6407258000 2.54 CGPLLU89 Pre-treatment, Day 0 WGS Lung Cancer 100 7042145200 5356095400 2.13 CGPLLU89 Post-treatment, Day 7 WGS Lung Cancer 100 7234220200 4930375200 1.96 CGPLLU89 Post-treatment, Day 22 WGS Lung Cancer 100 6242889800 4057361000 1.61 CGPLOV11 Preoperative treatment naive WGS Ovarian Cancer 100 8985130400 5871959600 2.33 CGPLOV12 Preoperative treatment naive WGS Ovarian Cancer 100 9705820000 6430505400 2.55 CGPLOV13 Preoperative treatment naive WGS Ovarian Cancer 100 10307949400 7029712000 2.79 CCPLOV15 Preoperative treatment naive WGS Ovarian Cancer 100 8472829400 8562142400 2.21 CGPLOV16 Preoperative treatment naive WGS Ovarian Cancer 100 10977781000 7538581600 2.99 CGPLOV19 Preoperative treatment naive WGS Ovarian Cancer 100 8800876200 5855304000 2.32 CGPLOV20 Preoperative treatment naive WGS Ovarian Cancer 100 8714443600 5605165800 2.26 CGPLOV21 Preoperative treatment naive WGS Ovarian Cancer 100 10180394800 7120260400 2.83 CGPLOV22 Preoperative treatment naive WGS Ovarian Cancer 100 10107760000 6821916800 2.71 CGPLOV23 Preoperative treatment naive WGS Ovarian Cancer 100 10643399800 7206330800 2.86 CGPLOV24 Preoperative treatment naive WGS Ovarian Cancer 100 6780929000 4623300400 1.83 CGPLOV25 Preoperative treatment naive WGS Ovarian Cancer 100 7817548600 5359975200 2.13 CGPLOV26 Preoperative treatment naive WGS Ovarian Cancer 100 11763101400 8178024400 3.25 CGPLOV28 Preoperative treatment naive WGS Ovarian Cancer 100 9522546400 6259423400 2.48 CGPLOV31 Preoperative treatment naive WGS Ovarian Cancer 100 9104831200 6109358400 2.42 CGPLOV32 Preoperative treatment naive WGS Ovarian Cancer 100 9222073600 6035150000 2.39 CGPLOV37 Preoperative treatment naive WGS Ovarian Cancer 100 8898328600 5971018200 2.37 CGPLOV38 Preoperative treatment naive WGS Ovarian Cancer 100 8756825200 5861536600 2.33 CGPLOV40 Preoperative treatment naive WGS Ovarian Cancer 100 9709391600 6654707200 2.64 CGPLOV41 Preoperative treatment naive WGS Ovarian Cancer 100 8923625000 5973070400 2.37 CGPLOV42 Preoperative treatment naive WGS Ovarian Cancer 100 10719380400 7353214200 2.92 CGPLOV43 Preoperative treatment naive WGS Ovarian Cancer 100 10272189000 6423288600 2.55 CGPLOV44 Preoperative treatment naive WGS Ovarian Cancer 100 9861862600 6769185800 2.69 CGPLOV46 Preoperative treatment naive WGS Ovarian Cancer 100 8788956400 5789863400 2.30 CGPLOV47 Preoperative treatment naive WGS Ovarian Cancer 100 9380561800 6480763600 2.57 CCPLOV48 Preoperative treatment naive WGS Ovarian Cancer 100 9258552600 6380106400 2.53 CCPLOV49 Preoperative treatment naive WGS Ovarian Cancer 100 8787025400 6134503600 2.43 CGFLOV50 Preoperative treatment naive WGS Ovarian Cancer 100 10144154400 6984721400 2.77 CGPLPA2 Preoperative treatment naive WGS Pancreatic Cancer 100 12740651400 9045622000 3.59 CGPLPA113 Preoperative treatment naive WGS Duodenal Canner 100 8802479000 5909030800 2.34 CGPLPA114 Preoperative treatment naive WGS Bile Duct Cancer 100 8792313600 6019061000 2.39 CGPLPA115 Preoperative treatment naive WGS Bile Duct Cancer 100 8636551400 5958809000 2.36 CGPLPA117 Preoperative treatment naive WGS Bile Duct Cancer 100 9128885200 6288833200 2.50 CGPLPA118 Preoperative treatment naive WGS Bile Duct Cancer 100 7931485800 5407532800 2.15 CGPLPA122 Preoperative treatment naive WGS Bile Duct Cancer 100 10888985000 7530118800 2.99 CGPLPA124 Preoperative treatment naive WGS Bile Duct Cancer 100 8062012400 5860171000 2.33 CGPLPA125 Preoperative treatment naive WGS Bile Duct Cancer 100 9715576600 6390321000 2.54 CGPLPA126 Preoperative treatment naive WGS Bile Duct Cancer 100 8056768800 5651600800 2.24 CGPLPA127 Preoperative treatment naive WGS Bile Duct Cancer 100 8000301000 5382987600 2.14 CGPLPAI28 Preoperative treatment naive WGS Bile Duct Cancer 100 6165751600 4256521400 1.69 CGPLPA129 Preoperative treatment naive WGS Bile Duct Cancer 100 7143147400 4917370400 1.95 CGPLPA130 Preoperative treatment naive WGS Bile Duct Cancer 100 5664335000 3603919400 1.43 CGPLPA131 Preoperative treatment naive WGS Bile Duct Cancer 100 8292982000 5844942000 2.32 CGPLPA134 Preoperative treatment naive WGS Bile Duct Cancer 100 7088917000 5048887600 2.00 CGPLPA135 Preoperative treatment naive WGS Bile Duct Cancer 100 8750665600 5800613200 2.30 CGPLPA136 Preoperative treatment naive WGS Bile Duct Cancer 100 7539715800 5248227600 2.08 CGPLPA137 Preoperative treatment naive WGS Bile Duct Cancer 100 8391815400 5901273800 2.34 CGPLPA139 Preoperative treatment naive WGS Bile Duct Cancer 100 8992280200 6328314400 2.51 CGPLPA14 Preoperative treatment naive WGS Pancreatic Cancer 100 8787706200 5731317600 2.27 CGPLPA140 Preoperative treatment naive WGS Bile Duct Cancer 100 16365641800 11216732000 4.45 CGPLPA141 Preoperative treatment naive WGS Bile Duct Cancer 100 15086298000 10114790200 4.01 CGPLPA15 Preoperative treatment naive WGS Pancreatic Cancer 100 8255566800 5531677600 2 20 CGPLPA155 Preoperative treatment naive WGS Bile Duct Cancer 100 9457155800 6621881800 2.63 CGPLPA156 Preoperative treatment naive WGS Pancreatic Cancer 100 9345385800 6728653000 2.67 CGPLPA165 Preoperative treatment naive WGS Bile Duct Cancer 100 8356604600 0829895800 2.31 CGPLPA168 Preoperative treatment naive WGS Bile Duct Cancer 100 10365661600 7048115600 2.80 CGPLPA17 Preoperative treatment naive WGS Pancreatic Cancer 100 8073547400 4687803000 1.86 CGPLPA184 Preoperative treatment naive WGS Bile Duct Cancer 100 9014218400 6230922200 2.47 CGPLPA187 Preoperative treatment naive WGS Bile Duct Cancer 100 8883536200 6140874400 2.44 CGPLPA23 Preoperative treatment naive WGS Pancreatic Cancer 100 9835452000 6246525400 2.48 CGPLPA25 Preoperative treatment naive WGS Pancreatic Cancer 100 10077515400 6103322200 2.42 CGPLPA26 Preoperative treatment naive WGS Pancreatic Cancer 100 8354272400 5725781000 2.21 CGPLPA28 Preoperative treatment naive WGS Pancreatic Cancer 100 8477461600 5688846800 2.26 CGPLPA33 Preoperative treatment naive WGS Pancreatic Cancer 100 7287615600 4506723800 1.82 CGPLPA34 Preoperative treatment naive WGS Pancreatic Cancer 100 6122902400 4094828000 1.62 CGPLPA37 Preoperative treatment naive WGS Pancreatic Cancer 100 12714888200 8527779200 3.38 CGPLPA38 Preoperative treatment naive WGS Pancreatic Cancer 100 8525500600 5501341400 2.18 CGPLPA39 Preoperative treatment naive WGS Pancreatic Cancer 100 10502663600 6812333000 2.70 CGPLPA40 Preoperative treatment naive WGS Pancreatic Cancer 100 9083670000 0394717800 2.14 CGPLPA42 Preoperative treatment naive WGS Pancreatic Cancer 100 5072126600 3800395200 1.54 CGPLPA46 Preoperative treatment naive WGS Pancreatic Cancer 100 4720090200 2626298800 1.04 CGPLPA47 Preoperative treatment naive WGS Pancreatic Cancer 100 7317385800 4543833000 1.80 CGPLPA48 Preoperative treatment naive WGS Pancreatic Cancer 100 7553856200 5022695600 1.90 CGPLPA52 Preoperative treatment naive WGS Pancreatic Cancer 100 5655875000 3551861600 1.41 COPLPA53 Preoperative treatment naive WGS Pancreatic Cancer 100 9504749000

6323344800 2.51 CGPLPA58 Preoperative treatment naive WGS Pancreatic Cancer 100 8088090200 5118138200 2.03 CGPLPA59 Preoperative treatment naive WGS Pancreatic Cancer 100 14547364600 9617773600 3.82 CGPLPA67 Preoperative treatment naive WGS Pancreatic Cancer 100 8222177400 5351172000 2.12 CGPLPA69 Preoperative treatment naive WGS Pancreatic Cancer 100 7899181400 5006114800 1.90 CGPLPA71 Preoperative treatment naive WGS Pancreatic Cancer 100 7340620400 4955417400 1.97 CGPLPA74 Preoperative treatment naive WGS Pancreatic Cancer 100 6666371400 4571394200 1.81 CGPLPA76 Preoperative treatment naive WGS Pancreatic Cancer 100 9755658600 6412606800 2.54 CGPLPA85 Preoperative treatment naive WGS Pancreatic Cancer 100 10853223000 7309498600 2.90 CGPLPA86 Preoperative treatment naive WGS Pancreatic Cancer 100 8744365400 5514523200 2.19 CGPLPA92 Preoperative treatment naive WGS Pancreatic Cancer 100 8073791200 5390492800 2.14 CGPLPA93 Preoperative treatment naive WGS Pancreatic Cancer 100 10390273000 7186589400 2.85 CGPLPA94 Preoperative treatment naive WGS Pancreatic Cancer 100 11060347600 7641336400 3.03 CGPLPA95 Preoperative treatment naive WGS Pancreatic Cancer 100 12416627200 7206503800 2.86 CGST102 Preoperative treatment naive WGS Pancreatic Cancer 100 6637004600 4545072600 1.80 CGST11 Preoperative treatment naive WGS Pancreatic Cancer 100 9718427800 6259679600 2.48 CGST110 Preoperative treatment naive WGS Pancreatic Cancer 100 9319661600 6359317400 2.52 CGST114 Preoperative treatment naive WGS Pancreatic Cancer 100 6865213000 4841171600 1.92 CGST13 Preoperative treatment naive WGS Pancreatic Cancer 100 9284554800 6360843800 2.52 CGST131 Preoperative treatment naive WGS Gastric cancer 100 5924382000 3860677200 1.53 CGST141 Preoperative treatment naive WGS Gastric cancer 100 8486380800 5860491000 2.33 CGST16 Preoperative treatment naive WGS Gastric cancer 100 13820725800 9377828000 3.72 CGST18 Preoperative treatment naive WGS Gastric cancer 100 7781288000 5278862400 2.09 CGST21 Preoperative treatment naive WGS Gastric cancer 100 7171165400 4103970800 1.63 CGST26 Preoperative treatment naive WGS Gastric cancer 100 8983961800 6053405600 2.40 CGST28 Preoperative treatment naive WGS Gastric cancer 100 9683035400 6745116400 2.68 CGST30 Preoperative treatment naive WGS Gastric cancer 100 8684086600 5741416000 2.28 CGST32 Preoperative treatment naive WGS Gastric cancer 100 8568194600 5783369200 2.29 CGST33 Preoperative treatment naive WGS Gastric cancer 100 9351699600 6448718400 2.56 CGST38 Preoperative treatment naive WGS Gastric cancer 100 8409876400 5770989200 2.29 CGST39 Preoperative treatment naive WGS Gastric cancer 100 10573763000 7597016000 3.01 CGST41 Preoperative treatment naive WGS Gastric cancer 100 9434854200 6609415400 2.62 CGST45 Preoperative treatment naive WGS Gastric cancer 100 8203868600 5625223000 2.23 CGST47 Preoperative treatment naive WGS Gastric cancer 100 8938597600 6178990600 2.45 CGST48 Preoperative treatment naive WGS Gastric cancer 100 9106628800 6517085200 2.59 CGST53 Preoperative treatment naive WGS Gastric cancer 100 9005374200 5854996200 2.32 CGST58 Preoperative treatment naive WGS Gastric cancer 100 10020368600 6133458400 2.43 CGST67 Preoperative treatment naive WGS Gastric cancer 100 9198135600 5911071000 2.35 CGST77 Preoperative treatment naive WGS Gastric cancer 100 8228789400 5119116800 2.03 CGST80 Preoperative treatment naive WGS Gastric cancer 100 10596963400 7283152800 2.89 CGST81 Preoperative treatment naive WGS Gastric cancer 100 5494881200 5038064000 2.32

TABLE-US-00007 APPENDIX E Table 5. High coverage whole genome cfDNA analyses of healthy individuals and lung cancer patients Correlation Correlation of GC of Corrected Correlation Fragment Fragment of Ratio Ratio Fragment Correlation Profile Profile Ratio of to Median to Median Profile Fragment Median Fragment Fragment to Median Ratio cfDNA Ratio Ratio Fragment Profile to Fragment Profile of Profile of Ratio Lymphocyte Analysis Stage at Size Healthy Healthy Profile of Nucleosome Patient Patient Type Type Timepoint Diagnosis (bp) Individuals Individuals Lymphocytes Distances CGPLH75 Healthy WGS Preoperative treatment naive NA 168 0.977 0.952 0.920 -0.886 CGPLH77 Healthy WGS Preoperative treatment naive NA 166 0.970 0.960 0.904 -0.912 CGPLH80 Healthy WGS Preoperative treatment naive NA 168 0.955 0.949 0.960 -0.917 CGPLH81 Healthy WGS Preoperative treatment naive NA 167 0.949 0.953 0.869 -0.883 CGPLH82 Healthy WGS Preoperative treatment naive NA 166 0 969 0.949 0.954 -0.917 CGPLH83 Healthy WGS Preoperative treatment naive NA 167 0.949 0.939 0.919 -0.904 CGPLH84 Healthy WGS Preoperative treatment naive NA 168 0 967 0.948 0.951 -0.913 CGPLH52 Healthy WGS Preoperative treatment naive NA 167 0.946 0.968 0.952 -0.924 CGPLH35 Healthy WGS Preoperative treatment naive NA 166 0.981 0.973 0.945 -0.921 CGPLH37 Healthy WGS Preoperative treatment naive NA 168 0.968 0.970 0.951 -0.922 CGPLH51 Healthy WGS Preoperative treatment naive NA 167 0.968 0.976 0.948 -0.925 CGPLH55 Healthy WGS Preoperative treatment naive NA 166 0.947 0.964 0.948 -0.917 CGPLH48 Healthy WGS Preoperative treatment naive NA 168 0.959 0.965 0.960 -9.923 CGPLH50 Healthy WGS Preoperative treatment naive NA 167 0.960 0.968 0.952 -0.921 CGPLH36 Healthy WGS Preoperative treatment naive NA 168 0.955 0.954 0.955 -0.919 CGPLH42 Healthy WGS Preoperative treatment naive NA 167 0.973 0.963 0.948 -0.918 CGPLH43 Healthy WGS Preoperative treatment naive NA 166 0.952 0.958 0.953 -0.928 CGPLH59 Healthy WGS Preoperative treatment naive NA 168 0.970 0.965 0.951 -0.925 CGPLH45 Healthy WGS Preoperative treatment naive NA 168 0.965 0.950 0.949 -0.911 CGPLH47 Healthy WGS Preoperative treatment naive NA 167 0.952 0.944 0.954 -0.921 CGPLH46 Healthy WGS Preoperative treatment naive NA 168 0.966 0.985 0.953 -0.923 CGPLH63 Healthy WGS Preoperative treatment naive NA 168 0.977 0.968 0.939 -0.920 CAPLH51 Healthy WGS Preoperative treatment naive NA 168 0.935 0.955 0.957 -0.914 CAPLH57 Healthy WGS Preoperative treatment naive NA 169 0.965 0.954 0.955 -0.917 CGPLH49 Healthy WGS Preoperative treatment naive NA 168 0.958 0.951 0.950 -0.924 CGPLH56 Healthy WGS Preoperative treatment naive NA 166 0.940 0.957 0.959 -0.911 CGPLH64 Healthy WGS Preoperative treatment naive NA 169 0.960 0.940 0.949 -0.918 CGPLH78 Healthy WGS Preoperative treatment naive NA 166 0.956 0.936 0.958 -0.911 CGPLH79 Healthy WGS Preoperative treatment naive NA 168 0.960 0.957 0.953 -0.917 CGPLH76 Healthy WGS Preoperative treatment naive NA 167 0.969 0.965 0.953 -0.917 CGPLLU175 Lung Cancer WGS Preoperative treatment naive I 165 0.316 0.284 0.244 -0.262 CGPLLU180 Lung Cancer WGS Preoperative treatment naive I 166 0.907 0.846 0.826 -0.819 CGPLLU198 Lung Cancer WGS Preoperative treatment naive I 166 0.972 0.946 0.928 -0.911 CGPLLU202 Lung Cancer WGS Preoperative treatment naive I 160 0.821 0.605 0.905 -0.843 CGPLLU165 Lung Cancer WGS Preoperative treatment naive II 163 0.924 0.961 0.815 -0.851 CGPLLU209 Lung Cancer WGS Preoperative treatment naive II 163 0.578 0.526 0.513 -0.534 CGPLLU147 Lung Cancer WGS Preoperative treatment naive III 166 0.953 0.919 0.939 -0.912 CGPLLU206 Lung Cancer WGS Preoperative treatment naive III 158 0.488 0.343 0.460 -0.481

TABLE-US-00008 APPENDIX F Table 6. Monitoring response to therapy using whole genome analyses of cfDNA fragmentation profiles and targeted mutations analyses Progression- free Survival Patient Patient Type Analysis Type Timepoint Stage (months) CGPLLU14 Lung Cancer Targeted Mutation Analysis and WGS Pre-treatment, Day-38 IV 15.4 CGPLLU14 Lung Cancer Targeted Mutation Analysis and WGS Pre-treatment, Day-16 IV 15.4 CGPLLU14 Lung Cancer Targeted Mutation Analysis and WGS Pre-treatment, Day-3 IV 15.4 CGPLLU14 Lung Cancer Targeted Mutation Analysis and WGS Pre-treatment, Day 0 IV 15.4 CGPLLU14 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 0.33 IV 15.4 CGPLLU14 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 7 IV 15.4 CGPLLU88 Lung Cancer Targeted Mutation Analysis and WGS Pre-treatment, Day 0 IV 18.0 CGPLLU88 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 7 IV 18.0 CGPLLU88 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 297 IV 18.0 CGPLLU244 Lung Cancer Targeted Mutation Analysis and WGS Pre-treatment, Day-7 IV 1.2 CGPLLU244 Lung Cancer Targeted Mutation Analysis and WGS Pre-treatment, Day-1 IV 1.2 CGPLLU244 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 6 IV 1.2 CGPLLU244 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 62 IV 1.2 CGPLLU245 Lung Cancer Targeted Mutation Analysis and WGS Pre-treatment, Day-32 IV 1.7 CGPLLU245 Lung Cancer Targeted Mutation Analysis and WGS Pre-treatment, Day 0 IV 1.7 CGPLLU245 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 7 IV 1.7 CGPLLU245 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 21 IV 1.7 CGPLLU246 Lung Cancer Targeted Mutation Analysis and WGS Pre-treatment, Day-21 IV 1.3 CGPLLU246 Lung Cancer Targeted Mutation Analysis and WGS Pre-treatment, Day 0 IV 1.3 CGPLLU246 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 9 IV 1.3 CGPLLU246 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 42 IV 1.1 CGPLLU86 Lung Cancer Targeted Mutation Analysis and WGS Pre-treatment, Day 0 IV 12.4 CGPLLU86 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 0.5 IV 12.4 CGPLLU86 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 7 IV 12.4 CGPLLU86 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 17 IV 12.4 CGPLLU89 Lung Cancer Targeted Mutation Analysis and WGS Pre-treatment, Day 0 IV 6.7 CGPLLU89 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 7 IV 6.7 CGPLLU89 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 22 IV 6.7 CGLU316 Lung Cancer Targeted Mutation Analysis and WGS Pre-treatment, Day-53 IV 1.4 CGLU316 Lung Cancer Targeted Mutation Analysis and WGS Pre-treatment, Day-4 IV 1.4 CGLU316 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 18 IV 1.4 CGLU316 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 87 IV 1.4 CGLU344 Lung Cancer Targeted Mutation Analysis and WGS Pre-treatment, Day-21 IV Ongoing CGLU344 Lung Cancer Targeted Mutation Analysis and WGS Pre-treatment, Day 0 IV Ongoing CGLU344 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 0.1675 IV Ongoing CGLU344 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 59 IV Ongoing CGLU369 Lung Cancer Targeted Mutation Analysis and WGS Pre-treatment, Day-2 IV 7.5 CGLU369 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 12 IV 7.5 CGLU369 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 68 IV 7.5 CGLU369 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 110 IV 7.5 CGLU373 Lung Cancer Targeted Mutation Analysis and WGS Pre-treatment, Day-2 IV Ongoing CGLU373 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 0.125 IV Ongoing CGLU373 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 7 IV Ongoing CGLU373 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 47 IV Ongoing CGPLLU13 Lung Cancer Targeted Mutation Analysis and WGS Pre-treatment, Day-2 IV 1.5 CGPLLU13 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 5 IV 1.5 CGPLLU13 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 28 IV 1.5 CGPLLU13 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 91 IV 1.5 CGPLLU264 Lung Cancer Targeted Mutation Analysis and WGS Pre-treatment, Day-1 IV Ongoing CGPLLU264 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 6 IV Ongoing CGPLLU264 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 27 IV Ongoing CGPLLU264 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 69 IV Ongoing CGPLLU265 Lung Cancer Targeted Mutation Analysis and WGS Pre-treatment, Day 0 IV Ongoing CGPLLU265 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 3 IV Ongoing CGPLLU265 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 7 IV Ongoing CGPLLU265 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 84 IV Ongoing CGPLLU266 Lung Cancer Targeted Mutation Analysis and WGS Pre-treatment, Day 0 IV 9.6 CGPLLU266 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 16 IV 9.6 CGPLLU266 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 83 IV 9.6 CGPLLU266 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 328 IV 9.6 CGPLLU267 Lung Cancer Targeted Mutation Analysis and WGS Pre-treatment, Day-1 IV 3.9 CGPLLU267 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 34 IV 3.9 CGPLLU267 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 90 IV 3.9 CGPLLU269 Lung Cancer Targeted Mutation Analysis and WGS Pre-treatment, Day 0 IV Ongoing CGPLLU269 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 9 IV Ongoing CGPLLU269 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 28 IV Ongoing CGPLLU271 Lung Cancer Targeted Mutation Analysis and WGS Pre-treatment, Day 0 IV 8.2 CGPLLU271 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 6 IV 8.2 CGPLLU271 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 20 IV 8.2 CGPLLU271 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 104 IV 8.2 CGPLLU271 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 259 IV 8.2 CGPLLU43 Lung Cancer Targeted Mutation Analysis and WGS Pre-treatment, Day-1 IV Ongoing CGPLLU43 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 6 IV Ongoing CGPLLU43 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 27 IV Ongoing CGPLLU43 Lung Cancer Targeted Mutation Analysis and WGS Post-treatment, Day 83 IV Ongoing Correlation of Fragment Ratio Correlation Profile to of Median Fragment Fragment Ratio Ratio Profile to Maximum Profile of Lymphocyte Mutant Healthy Nucleosome Allele Patient Individuals Distances Targeted Mutation Fraction CGPLLU14 0.941 -0.841 EGFR 861L > Q 0.89% CGPLLU14 0.933 -0.833 EGFR 861L > Q 0.18% CGPLLU14 0.908 -0.814 EGFR 719G > S 0.49% CGPLLU14 0.883 -0.752 EGFR 861L > Q 1.39% CGPLLU14 0.820 -0.692 EGFR 719G > S 1.05% CGPLLU14 0.927 -0.887 EGFR 861L > Q 0.00% CGPLLU88 0.657 -0.584 EGFR 7459ELREA > T 9.06% CGPLLU88 0.939 -0.799 EGFR 790T > M 0.15% CGPLLU88 0.946 -0.869 EGFR 7459ELREA > T 0.93% CGPLLU244 0.850 -0.706 EGFR 858L > R 4.98% CGPLLU244 0.867 -0.764 EGFR 62L > R 3.41% CGPLLU244 0.703 -0.639 EGFR 858L > R 5.57% CGPLLU244 0.659 -0.660 EGFR 858L > R 11.80% CGPLLU245 0.871 -0.724 EGFR 745KELREA > K 10.60% CGPLLU245 0.736 -0.608 EGFR 745KELREA > K 14.10% CGPLLU245 0.731 -0.559 EGFR 745KELREA > K 8.56% CGPLLU245 0.613 -0.426 EGFR 745KELREA > K 10.69% CGPLLU246 0.897 -0.757 EGFR 790T > M 0.49% CGPLLU246 0.469 -0.376 EGFR 858L > R 6.17% CGPLLU246 0.874 -0.746 EGFR 858L > R 1.72% CGPLLU246 0.775 -0.665 EGFR 858L > R 5.29% CGPLLU86 0.817 -0.630 EGFR 746ELREATS > D 0.00% CGPLLU86 0.916 -0.811 EGFR 746ELREATS > D 0.19% CGPLLU86 0.859 -0.694 EGFR 746ELREATS > D 0.00% CGPLLU86 0.932 -0.848 EGFR 746ELREATS > D 0.00% CGPLLU89 0.864 -0.729 EGFR 747LREATS > - 0.42% CGPLLU89 0.908 -0.803 EGFR 747LREATS > - 0.20% CGPLLU89 0.853 -0.881 EGFR 747LREATS > - 0.00% CGLU316 0.331 -0.351 EGFR L861Q 15.72% CGLU316 0.225 -0.253 EGFR L861Q 45.67% CGLU316 0.336 -0.364 EGFR G719A 33.38% CGLU316 0.340 -0.364 EGFR L861Q 66.01% CGLU344 0.935 -0.818 EGFR E746_A75Cdel 0.00% CGLU344 0.919 -0.774 EGFR E746_A75Cdel 0.22% CGLU344 0.953 -0.860 EGFR E746_A75Cdel 0.40% CGLU344 0.944 -0.832 EGFR E746_A75Cdel 0.00% CGLU369 0.825 -0.826 EGFR L858R 20.61% CGLU369 0.950 -0.903 EGFR L858R 0.22% CGLU369 0.945 -0.889 EGFR L858R 0.16% CGLU369 0.886 -0.883 EGFR L858R 0.10% CGLU373 0.922 -0.804 EGFR E746_A75Cdel 0.82% CGLU373 0.959 -0.853 EGFR E746_A75Cdel 0.00% CGLU373 0.967 -0.886 EGFR E746_A75Cdel 0.15% CGLU373 0.951 -0.890 EGFR E746_A75Cdel 0.00% CGPLLU13 0.425 -0.400 EGFR E746_A75Cdel 7.66% CGPLLU13 0.272 -0.257 EGFR E746_A75Cdel 13.10% CGPLLU13 0.584 -0.536 EGFR E746_A75Cdel 6.09% CGPLLU13 0.530 -0.513 EGFR E746_A75Cdel 9.28% CGPLLU264 0.946 -0.824 EGFR D761N 0.00% CGPLLU264 0.927 -0.788 EGFR D761N 0.16% CGPLLU264 0.962 -0.856 EGFR D761N 0.00% CGPLLU264 0.960 -0.894 EGFR D761N 0.00% CGPLLU265 0.953 -0.859 EGFR L858R 0.21% CGPLLU265 0.949 -0.842 EGFR L858R 0.21% CGPLLU265 0.955 -0.844 EGFR T790M 0.21% CGPLLU265 0.946 -0.825 EGFR L858R 0.00% CGPLLU266 0.951 -0.904 NA 0.00% CGPLLU266 0.959 -0.886 NA 0.00% CGPLLU266 0.961 -0.880 NA 0.00% CGPLLU266 0.958 -0.855 NA 0.00% CGPLLU267 0.919 -0.863 EGFR L858R 1.93% CGPLLU267 0.863 -0.889 EGFR L858R 0.14% CGPLLU267 0.962 -0.876 EGFR L858R 0.38% CGPLLU269 0.951 -0.864 EGFR L858R 0.10% CGPLLU269 0.941 -0.694 EGFR L858R 0.00% CGPLLU269 0.957 -0.676 EGFR L858R 0.00% CGPLLU271 0.871 -0.284 EGFR E746_A75Cdel 3.36% CGPLLU271 0.947 -0.826 EGFR E746_A75Cdel 0.17% CGPLLU271 0.952 -0.839 EGFR E746_A75Cdel 0.00% CGPLLU271 0.944 -0.810 EGFR E746_A75Cdel 0.00% CGPLLU271 0.950 -0.831 EGFR E746_A75Cdel 0.44% CGPLLU43 0.944 -0.903 NA 0.00% CGPLLU43 0.956 -0.899 NA 0.00% CGPLLU43 0.959 -0.901 NA 0.00% CGPLLU43 0.965 -0.896 NA 0.00%

TABLE-US-00009 APPENDIX-G Table 7 Whole genome cfDNA analyses in healthy individuals and cancer patients Correlation of Fragment Ratio Profile to Median Median Fragment cfDNA Ratio Size Profile Stage at Fragment of Healthy Patient Patient Type Analysis Type Timepoint Diagnosis (bp) Individuals CGCRC291 Colorectal Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive V 163 0.1972 CGCRC292 Colorectal Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive V 166 0.7604 CGCRC293 Colorectal Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive V 166 0.9335 CGCRC294 Colorectal Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 166 0.6531 CGCRC296 Colorectal Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 166 0.8161 CGCRC299 Colorectal Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive I 162 0.7325 CGCRC300 Colorectal Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive I 167 0.9382 CGCRC301 Colorectal Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive I 165 0.8252 CGCRC302 Colorectal Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 163 0.7499 CGCRC304 Colorectal Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 162 0.4642 CGCRC305 Colorectal Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 165 0.8909 CGCRC306 Colorectal Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 165 0.8523 CGCRC307 Colorectal Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 165 0.9140 CGCRC308 Colorectal Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive III 165 0.8734 CGCRC311 Colorectal Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive I 166 0.8535 CGCRC315 Colorectal Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive III 167 0.6083 CGCRC316 Colorectal Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive III 161 0.1546 CGCRC317 Colorectal Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive III 163 0.6242 CGCRC318 Colorectal Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive I 166 0.8824 CGCRC319 Colorectal Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive III 160 0.5979 CGCRC320 Colorectal Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive I 167 0.7949 CGCRC321 Colorectal Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive I 164 0.7804 CGCRC333 Colorectal Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive V 163 0.4263 CGCRC335 Colorectal Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive V 162 0.6466 CGCRC338 Colorectal Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive V 162 0.7740 CGCRC341 Colorectal Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive V 164 0.8995 CGCRC342 Colorectal Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive V 158 0.2524 CGPLBR100 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive III 166 0.9440 CGPLBR101 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 169 0.8864 CGPLBR102 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 168 0.9617 CGPLBR103 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 167 0.9498 CGPLBR104 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive III 164 0.8490 CGPLBR12 Breast Cancer WGS Preoperative treatment naive III 163 0.8350 CGPLBR18 Breast Cancer WGS Preoperative treatment naive II 166 0.8411 CGPLBR23 Breast Cancer WGS Preoperative treatment naive II 156 0.9714 CGPLBR24 Breast Cancer WGS Preoperative treatment naive III 166 0.8402 CGPLBR28 Breast Cancer WGS Preoperative treatment naive II 161 0.9584 CGPLBR30 Breast Cancer WGS Preoperative treatment naive II 167 0.6951 CGPLBR31 Breast Cancer WGS Preoperative treatment naive II 166 0.9719 CGPLBR32 Breast Cancer WGS Preoperative treatment naive II 166 0.9590 CGPLBR33 Breast Cancer WGS Preoperative treatment naive II 163 0.9706 CGPLBR34 Breast Cancer WGS Preoperative treatment naive II 168 0.8735 CGPLBR35 Breast Cancer WGS Preoperative treatment naive II 169 0.9655 CGPLBR36 Breast Cancer WGS Preoperative treatment naive II 167 0.9394 CGPLBR37 Breast Cancer WGS Preoperative treatment naive I 165 0.9691 CGPLBR38 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive III 167 0.9105 CGPLBR40 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive III 168 0.9273 CGPLBR41 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 164 0.9626 CGPLBR45 Breast Cancer WGS Preoperative treatment naive III 168 0.9615 CGPLBR46 Breast Cancer WGS Preoperative treatment naive I 166 0.9322 CGPLBR47 Breast Cancer WGS Preoperative treatment naive II 169 0.9461 CGPLBR48 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 171 0.7686 CGPLBR49 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive I 160 0.8867 CGPLBR50 Breast Cancer WGS Preoperative treatment naive II 165 0.8593 CGPLBR51 Breast Cancer WGS Preoperative treatment naive III 164 0.9359 CGPLBR52 Breast Cancer WGS Preoperative treatment naive III 165 0.8688 CGPLBR55 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 163 0.9634 CGPLBR56 Breast Cancer WGS Preoperative treatment naive III 166 0.9459 CGPLBR57 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive I 168 0.9672 CGPLBR59 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 167 0.9438 CGPLBR60 Breast Cancer WGS Preoperative treatment naive II 163 0.9479 CGPLBR61 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 165 0.9611 CGPLBR63 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 168 0.9555 CGPLBR65 Breast Cancer WGS Preoperative treatment naive II 167 0.9506 CGPLBR68 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive III 163 0.9154 CGPLBR69 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 165 0.9460 CGPLBR70 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 168 0.9651 CGPLBR71 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 165 0.9577 CGPLBR72 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 167 0.9786 CGPLBR73 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 167 0.9576 CGPLBR76 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 170 0.9410 CGPLBR81 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 170 0.9643 CGPLBR82 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive I 166 0.9254 CGPLBR83 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 169 0.9451 CGPLBR84 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive III 169 0.9315 CGPLBR87 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 166 0.9154 CGPLBR88 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 169 0.9370 CGPLBR90 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 169 0.9002 CGPLBR91 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive III 164 0.7955 CGPLBR92 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 162 0.6774 CGPLBR93 Breast Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 164 0.8773 CGPLH189 Healthy WGS Preoperative treatment naive NA 168 0.9325 CGPLH190 Healthy WGS Preoperative treatment naive NA 167 0.9403 CGPLH192 Healthy WGS Preoperative treatment naive NA 167 0.9646 CGPLH193 Healthy WGS Preoperative treatment naive NA 167 0.9423 CGPLH194 Healthy WGS Preoperative treatment naive NA 168 0.9567 CGPLH196 Healthy WGS Preoperative treatment naive NA 167 0.9709 CGPLH197 Healthy WGS Preoperative treatment naive NA 166 0.9605 CGPLH198 Healthy WGS Preoperative treatment naive NA 167 0.9238 CGPLH199 Healthy WGS Preoperative treatment naive NA 165 0.9618 CGPLH200 Healthy WGS Preoperative treatment naive NA 167 0.9183 CGPLH201 Healthy WGS Preoperative treatment naive NA 168 0.9548 CGPLH202 Healthy WGS Preoperative treatment naive NA 168 0.9471 CGPLH203 Healthy WGS Preoperative treatment naive NA 167 0.9534 CGPLH205 Healthy WGS Preoperative treatment naive NA 168 0.9075 CGPLH208 Healthy WGS Preoperative treatment naive NA 168 0.9422 CGPLH209 Healthy WGS Preoperative treatment naive NA 169 0.9556 CGPLH210 Healthy WGS Preoperative treatment naive NA 169 0.9447 CGPLH211 Healthy WGS Preoperative treatment naive NA 169 0.9538 CGPLH300 Healthy WGS Preoperative treatment naive NA 168 0.9019 CGPLH307 Healthy WGS Preoperative treatment naive NA 168 0.9576 CGPLH308 Healthy WGS Preoperative treatment naive NA 168 0.9481 CGPLH309 Healthy WGS Preoperative treatment naive NA 168 0.9672 CGPLH310 Healthy WGS Preoperative treatment naive NA 165 0.9547 CGPLH311 Healthy WGS Preoperative treatment naive NA 167 0.9302 CGPLH314 Healthy WGS Preoperative treatment naive NA 167 0.9482 CGPLH315 Healthy WGS Preoperative treatment naive NA 167 0.8659 CGPLH316 Healthy WGS Preoperative treatment naive NA 165 0.9374 CGPLH317 Healthy WGS Preoperative treatment naive NA 169 0.9542 CGPLH319 Healthy WGS Preoperative treatment naive NA 167 0.9578 CGPLH320 Healthy WGS Preoperative treatment naive NA 164 0.8913 CGPLH322 Healthy WGS Preoperative treatment naive NA 167 0.8751 CGPLH324 Healthy WGS Preoperative treatment naive NA 169 0.9519 CGPLH325 Healthy WGS Preoperative treatment naive NA 167 0.9124 CGPLH326 Healthy WGS Preoperative treatment naive NA 166 0.9574 CGPLH327 Healthy WGS Preoperative treatment naive NA 168 0.9533 CGPLH328 Healthy WGS Preoperative treatment naive NA 166 0.9643 CGPLH329 Healthy WGS Preoperative treatment naive NA 167 0.9609 CGPLH330 Healthy WGS Preoperative treatment naive NA 167 0.9118 CGPLH331 Healthy WGS Preoperative treatment naive NA 166 0.9679 CGPLH333 Healthy WGS Preoperative treatment naive NA 169 0.9474 CGPLH335 Healthy WGS Preoperative treatment naive NA 167 0.8909 CGPLH336 Healthy WGS Preoperative treatment naive NA 169 0.9248 CGPLH337 Healthy WGS Preoperative treatment naive NA 167 0.9533 CGPLH338 Healthy WGS Preoperative treatment naive NA 165 0.9388 CGPLH339 Healthy WGS Preoperative treatment naive NA 167 0.9396 CGPLH340 Healthy WGS Preoperative treatment naive NA 167 0.9488 CGPLH341 Healthy WGS Preoperative treatment naive NA 166 0.9533 CGPLH342 Healthy WGS Preoperative treatment naive NA 166 0.7858 CGPLH343 Healthy WGS Preoperative treatment naive NA 167 0.9421 CGPLH344 Healthy WGS Preoperative treatment naive NA 169 0.9192 CGPLH345 Healthy WGS Preoperative treatment naive NA 169 0.9345 CGPLH346 Healthy WGS Preoperative treatment naive NA 169 0.9475 CGPLH350 Healthy WGS Preoperative treatment naive NA 171 0.9570 CGPLH351 Healthy WGS Preoperative treatment naive NA 168 0.8176 CGPLH352 Healthy WGS Preoperative treatment naive NA 168 0.9521 CGPLH353 Healthy WGS Preoperative treatment naive NA 167 0.9435 CGPLH354 Healthy WGS Preoperative treatment naive NA 168 0.9481 CGPLH355 Healthy WGS Preoperative treatment naive NA 167 0.9613 CGPLH356 Healthy WGS Preoperative treatment naive NA 168 0.9474 CGPLH357 Healthy WGS Preoperative treatment naive NA 167 0.9255 CGPLH358 Healthy WGS Preoperative treatment naive NA 167 0.7777 CGPLH360 Healthy WGS Preoperative treatment naive NA 168 0.8500 CGPLH361 Healthy WGS Preoperative treatment naive NA 167 0.9261 CGPLH362 Healthy WGS Preoperative treatment naive NA 167 0.9236 CGPLH363 Healthy WGS Preoperative treatment naive NA 167 0.9488 CGPLH364 Healthy WGS Preoperative treatment naive NA 168 0.9311 CGPLH365 Healthy WGS Preoperative treatment naive NA 165 0.9371 CGPLH366 Healthy WGS Preoperative treatment naive NA 167 0.9536 CGPLH367 Healthy WGS Preoperative treatment naive NA 166 0.8748 CGPLH368 Healthy WGS Preoperative treatment naive NA 169 0.9490 CGPLH369 Healthy WGS Preoperative treatment naive NA 167 0.9428 CGPLH370 Healthy WGS Preoperative treatment naive NA 167 0.9642 CGPLH371 Healthy WGS Preoperative treatment naive NA 168 0.9621 CGPLH380 Healthy WGS Preoperative treatment naive NA 170 0.9652 CGPLH381 Healthy WGS Preoperative treatment naive NA 169 0.9541 CGPLH382 Healthy WGS Preoperative treatment naive NA 167 0.9380 CGPLH383 Healthy WGS Preoperative treatment naive NA 168 0.9700 CGPLH384 Healthy WGS Preoperative treatment naive NA 169 0.8061 CGPLH385 Healthy WGS Preoperative treatment naive NA 167 0.8856 CGPLH386 Healthy WGS Preoperative treatment naive NA 167 0.6920 CGPLH387 Healthy WGS Preoperative treatment naive NA 169 0.9583 CGPLH388 Healthy WGS Preoperative treatment naive NA 167 0.9348 CGPLH389 Healthy WGS Preoperative treatment naive NA 168 0.9409 CGPLH390 Healthy WGS Preoperative treatment naive NA 167 0.9216 CGPLH391 Healthy WGS Preoperative treatment naive NA 166 0.9334 CGPLH392 Healthy WGS Preoperative treatment naive NA 167 0.9165 CGPLH393 Healthy WGS Preoperative treatment naive NA 169 0.9256 CGPLH394 Healthy WGS Preoperative treatment naive NA 167 0.9257 CGPLH395 Healthy WGS Preoperative treatment naive NA 166 0.8611 CGPLH396 Healthy WGS Preoperative treatment naive NA 167 0.7884 CGPLH398 Healthy WGS Preoperative treatment naive NA 167 0.9463 CGPLH399 Healthy WGS Preoperative treatment naive NA 169 0.8780 CGPLH400 Healthy WGS Preoperative treatment naive NA 168 0.6662 CGPLH401 Healthy WGS Preoperative treatment naive NA 167 0.9428

CGPLH402 Healthy WGS Preoperative treatment naive NA 167 0.9353 CGPLH403 Healthy WGS Preoperative treatment naive NA 168 0.9329 CGPLH404 Healthy WGS Preoperative treatment naive NA 169 0.9402 CGPLH405 Healthy WGS Preoperative treatment naive NA 166 0.9579 CGPLH406 Healthy WGS Preoperative treatment naive NA 167 0.8188 CGPLH407 Healthy WGS Preoperative treatment naive NA 169 0.9527 CGPLH408 Healthy WGS Preoperative treatment naive NA 167 0.9584 CGPLH049 Healthy WGS Preoperative treatment naive NA 168 0.9220 CGPLH410 Healthy WGS Preoperative treatment naive NA 168 0.9102 CGPLH411 Healthy WGS Preoperative treatment naive NA 167 0.9392 CGPLH412 Healthy WGS Preoperative treatment naive NA 167 0.9561 CGPLH413 Healthy WGS Preoperative treatment naive NA 167 0.9451 CGPLH414 Healthy WGS Preoperative treatment naive NA 168 0.9258 CGPLH415 Healthy WGS Preoperative treatment naive NA 169 0.9217 CGPLH416 Healthy WGS Preoperative treatment naive NA 167 0.9672 CGPLH417 Healthy WGS Preoperative treatment naive NA 168 0.9578 CGPLH418 Healthy WGS Preoperative treatment naive NA 169 0.9376 CGPLH419 Healthy WGS Preoperative treatment naive NA 167 0.9228 CGPLH420 Healthy WGS Preoperative treatment naive NA 169 0.9164 CGPLH422 Healthy WGS Preoperative treatment naive NA 166 0.9069 CGPLH423 Healthy WGS Preoperative treatment naive NA 169 0.9606 CGPLH424 Healthy WGS Preoperative treatment naive NA 167 0.9553 CGPLH425 Healthy WGS Preoperative treatment naive NA 168 0.9722 CGPLH426 Healthy WGS Preoperative treatment naive NA 168 0.9560 CGPLH427 Healthy WGS Preoperative treatment naive NA 167 0.9594 CGPLH428 Healthy WGS Preoperative treatment naive NA 167 0.9591 CGPLH429 Healthy WGS Preoperative treatment naive NA 168 0.9358 CGPLH430 Healthy WGS Preoperative treatment naive NA 167 0.9639 CGPLH431 Healthy WGS Preoperative treatment naive NA 167 0.9570 CGPLH432 Healthy WGS Preoperative treatment naive NA 168 0.9485 CGPLH434 Healthy WGS Preoperative treatment naive NA 168 0.9671 CGPLH435 Healthy WGS Preoperative treatment naive NA 170 0.9133 CGPLH436 Healthy WGS Preoperative treatment naive NA 168 0.9360 CGPLH437 Healthy WGS Preoperative treatment naive NA 170 0.9445 CGPLH438 Healthy WGS Preoperative treatment naive NA 170 0.9537 CGPLH439 Healthy WGS Preoperative treatment naive NA 171 0.9547 CGPLH440 Healthy WGS Preoperative treatment naive NA 169 0.9562 CGPLH441 Healthy WGS Preoperative treatment naive NA 167 0.9660 CGPLH442 Healthy WGS Preoperative treatment naive NA 167 0.9569 CGPLH443 Healthy WGS Preoperative treatment naive NA 170 0.9431 CGPLH444 Healthy WGS Preoperative treatment naive NA 171 0.9429 CGPLH445 Healthy WGS Preoperative treatment naive NA 171 0.9446 CGPLH446 Healthy WGS Preoperative treatment naive NA 167 0.9502 CGPLH447 Healthy WGS Preoperative treatment naive NA 169 0.9421 CGPLH448 Healthy WGS Preoperative treatment naive NA 167 0.9553 CGPLH449 Healthy WGS Preoperative treatment naive NA 167 0.9550 CGPLH450 Healthy WGS Preoperative treatment naive NA 167 0.9572 CGPLH451 Healthy WGS Preoperative treatment naive NA 169 0.9548 CGPLH452 Healthy WGS Preoperative treatment naive NA 167 0.9498 CGPLH453 Healthy WGS Preoperative treatment naive NA 166 0.9572 CGPLH455 Healthy WGS Preoperative treatment naive NA 166 0.9626 CGPLH456 Healthy WGS Preoperative treatment naive NA 168 0.9537 CGPLH457 Healthy WGS Preoperative treatment naive NA 167 0.9429 CGPLH458 Healthy WGS Preoperative treatment naive NA 167 0.9511 CGPLH459 Healthy WGS Preoperative treatment naive NA 168 0.9609 CGPLH460 Healthy WGS Preoperative treatment naive NA 168 0.9331 CGPLH463 Healthy WGS Preoperative treatment naive NA 167 0.9506 CGPLH464 Healthy WGS Preoperative treatment naive NA 170 0.9133 CGPLH465 Healthy WGS Preoperative treatment naive NA 167 0.9251 CGPLH466 Healthy WGS Preoperative treatment naive NA 167 0.9679 CGPLH467 Healthy WGS Preoperative treatment naive NA 168 0.9273 CGPLH468 Healthy WGS Preoperative treatment naive NA 167 0.8353 CGPLH469 Healthy WGS Preoperative treatment naive NA 169 0.8225 CGPLH470 Healthy WGS Preoperative treatment naive NA 168 0.9073 CGPLH471 Healthy WGS Preoperative treatment naive NA 167 0.9354 CGPLH472 Healthy WGS Preoperative treatment naive NA 166 0.8509 CGPLH473 Healthy WGS Preoperative treatment naive NA 167 0.9206 CGPLH474 Healthy WGS Preoperative treatment naive NA 168 0.8474 CGPLH475 Healthy WGS Preoperative treatment naive NA 167 0.9155 CGPLH476 Healthy WGS Preoperative treatment naive NA 169 0.8807 CGPLH477 Healthy WGS Preoperative treatment naive NA 169 0.9129 CGPLH478 Healthy WGS Preoperative treatment naive NA 167 0.9588 CGPLH479 Healthy WGS Preoperative treatment naive NA 167 0.9303 CGPLH480 Healthy WGS Preoperative treatment naive NA 169 0.9522 CGPLH481 Healthy WGS Preoperative treatment naive NA 168 0.9558 CGPLH482 Healthy WGS Preoperative treatment naive NA 168 0.9379 CGPLH483 Healthy WGS Preoperative treatment naive NA 168 0.9518 CGPLH484 Healthy WGS Preoperative treatment naive NA 166 0.9630 CGPLH485 Healthy WGS Preoperative treatment naive NA 168 0.9547 CGPLH486 Healthy WGS Preoperative treatment naive NA 169 0.9199 CGPLH487 Healthy WGS Preoperative treatment naive NA 169 0.9575 CGPLH488 Healthy WGS Preoperative treatment naive NA 167 0.9618 CGPLH490 Healthy WGS Preoperative treatment naive NA 167 0.8950 CGPLH491 Healthy WGS Preoperative treatment naive NA 168 0.9631 CGPLH492 Healthy WGS Preoperative treatment naive NA 170 0.9335 CGPLH493 Healthy WGS Preoperative treatment naive NA 168 0.8718 CGPLH494 Healthy WGS Preoperative treatment naive NA 169 0.9623 CGPLH495 Healthy WGS Preoperative treatment naive NA 166 0.8777 CGPLH496 Healthy WGS Preoperative treatment naive NA 166 0.8788 CGPLH497 Healthy WGS Preoperative treatment naive NA 167 0.9576 CGPLH498 Healthy WGS Preoperative treatment naive NA 167 0.9526 CGPLH499 Healthy WGS Preoperative treatment naive NA 167 0.9733 CGPLH500 Healthy WGS Preoperative treatment naive NA 168 0.9542 CGPLH501 Healthy WGS Preoperative treatment naive NA 169 0.9526 CGPLH052 Healthy WGS Preoperative treatment naive NA 167 0.9512 CGPLH503 Healthy WGS Preoperative treatment naive NA 169 0.8947 CGPLH504 Healthy WGS Preoperative treatment naive NA 167 0.9561 CGPLH505 Healthy WGS Preoperative treatment naive NA 166 0.9554 CGPLH506 Healthy WGS Preoperative treatment naive NA 167 0.9733 CGPLH507 Healthy WGS Preoperative treatment naive NA 168 0.9222 CGPLH508 Healthy WGS Preoperative treatment naive NA 167 0.9674 CGPLH509 Healthy WGS Preoperative treatment naive NA 167 0.9475 CGPLH510 Healthy WGS Preoperative treatment naive NA 167 0.9459 CGPLH511 Healthy WGS Preoperative treatment naive NA 168 0.9714 CGPLH512 Healthy WGS Preoperative treatment naive NA 168 0.9442 CGPLH513 Healthy WGS Preoperative treatment naive NA 166 0.9705 CGPLH514 Healthy WGS Preoperative treatment naive NA 167 0.9690 CGPLH515 Healthy WGS Preoperative treatment naive NA 167 0.9568 CGPLH516 Healthy WGS Preoperative treatment naive NA 168 0.9508 CGPLH517 Healthy WGS Preoperative treatment naive NA 168 0.9635 CGPLH518 Healthy WGS Preoperative treatment naive NA 168 0.9647 CGPLH519 Healthy WGS Preoperative treatment naive NA 166 0.9366 CGPLH520 Healthy WGS Preoperative treatment naive NA 166 0.9649 CGPLH625 Healthy WGS Preoperative treatment naive NA 166 0.8766 CGPLH626 Healthy WGS Preoperative treatment naive NA 170 0.9011 CGPLH639 Healthy WGS Preoperative treatment naive NA 165 0.9482 CGPLH640 Healthy WGS Preoperative treatment naive NA 166 0.9131 CGPLH642 Healthy WGS Preoperative treatment naive NA 167 0.9641 CGPLH643 Healthy WGS Preoperative treatment naive NA 169 0.8450 CGPLH644 Healthy WGS Preoperative treatment naive NA 170 0.9398 CGPLH646 Healthy WGS Preoperative treatment naive NA 172 0.296 CGPLLU141 Lung Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 164 0.8702 CGPLLU161 Lung Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 165 0.9128 CGPLLU162 Lung Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 165 0.7753 CGPLLUl63 Lung Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 166 0.4770 CGPLLU168 Lung Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive I 163 0.9164 CGPLLU169 Lung Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive I 163 0.9326 CGPLLU176 Lung Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive I 168 0.9572 CGPLLU177 Lung Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 166 0.8472 CGPLLU203 Lung Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 164 0.9119 CGPLLU205 Lung Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 163 0.9518 CGPLLU207 Lung Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 166 0.9344 CGPLLU208 Lung Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 164 0.9091 CGPLOV11 Ovarian Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive V 166 0.8902 CGPLOV12 Ovarian Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive I 167 0.8779 CGPLOV13 Ovarian Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive V 166 0.7560 CGPLOV15 Ovarian Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive III 165 0.8585 CGPLOV16 Ovarian Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive III 165 0.9052 CGPLOV19 Ovarian Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 165 0.7854 CGPLOV20 Ovarian Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 165 0.8711 CGPLOV21 Ovarian Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive V 167 0.8942 CGPLOV22 Ovarian Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive III 164 0.8944 CGPLOV23 Ovarian Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive I 169 0.8510 CGPLOV24 Ovarian Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive I 166 0.9449 CGPLOV25 Ovarian Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive I 166 0.9590 CGPLOV26 Ovarian Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive I 161 0.8148 CGPLOV28 Ovarian Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive I 167 0.9635 CGPLOV31 Ovarian Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive III 167 0.9461 CGPLOV32 Ovarian Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive I 168 0.9582 CGPLOV37 Ovarian Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive I 170 0.9397 CGPLOV38 Ovarian Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive I 166 0.5779 CGPLOV40 Ovarian Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive V 170 0.6097 CGPLOV41 Ovarian Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive V 167 0.9403 CGPLOV42 Ovarian Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive I 166 0.9265 CGPLOV43 Ovarian Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive I 167 0.9626 CGPLOV44 Ovarian Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive I 164 0.9536 CGPLOV46 Ovarian Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive I 166 0.9622 CGPLOV47 Ovarian Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive I 165 0.9704 CGPLOV48 Ovarian Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive I 167 0.9675 CGPLOV49 Ovarian Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive III 164 0.8998 CGPLOV50 Ovarian Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive III 165 0.9682 CGPLPA112 Pancreatic Cancer WGS Preoperative treatment naive II 164 0.8914 CGPLPA113 Doudenal Cancer WGS Preoperative treatment naive I 170 0.8751 CGPLPA114 Bile Duct Cancer WGS Preoperative treatment naive II 166 0.9098 CGPLPA115 Bile Duct Cancer WGS Preoperative treatment naive V 165 0.8053 CGPLPA117 Bile Duct Cancer WGS Preoperative treatment naive II 165 0.9395 CGPLPA118 Bile Duct Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive I 167 0.9406 CGPLPA122 Bile Duct Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 164 0.8231 CGPLPA124 Bile Duct Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 166 0.9108 CGPLPA125 Bile Duct Cancer WGS Preoperative treatment naive II 166 0.9675 CGPLPA126 Bile Duct Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 166 0.9155 CGPLPA127 Bile Duct Cancer WGS Preoperative treatment naive V 167 0.8916 CGPLPA128 Bile Duct Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 167 0.9262 CGPLPA129 Bile Duct Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 166 0.9220 CGPLPA130 Bile Duct Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 169 0.8586 CGPLPA131 Bile Duct Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 165 0.7707 CGPLPA134 Bile Duct Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 160 0.7502 CGPLPA135 Bile Duct Cancer WGS Preoperative treatment naive I 165 0.9495 CGPLPA136 Bile Duct Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 164 0.9289 CGPLPA137 Bile Duct Cancer WGS Preoperative treatment naive II 166 0.9588 CGPLPA139 Bile Duct Cancer WGS Preoperative treatment naive V 166 0.9511 CGPLPA14 Pancreatic Cancer WGS Preoperative treatment naive II 167 0.8718 CGPLPA140 Bile Duct Cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 166 0.9215 CGPLPA141 Bile Duct Cancer WGS Preoperative treatment naive II 165 0.9172 CGPLPA15 Pancreatic Cancer WGS Preoperative treatment naive II 167 0.9111 CGPLPA155 Bile Duct Cancer WGS Preoperative treatment naive II 165 0.9496 CGPLPA156 Pancreatic Cancer WGS Preoperative treatment naive II 167 0.9479 CCPLPA165 Bile Duct Cancer WGS Preoperative treatment naive I 168 0.9596 CGPLPA168 Bile Duct Cancer WGS Preoperative treatment naive II 162 0.7838 CGPLPA17 Pancreatic Cancer WGS Preoperative treatment naive II 166 0.8624 CGPLPA184 Bile Duct Cancer WGS Preoperative treatment naive II 165 0.9100 CGPLPA187 Bile Duct Cancer WGS Preoperative treatment naive II 165 0.8577 CGPLPA23 Pancreatic Cancer WGS Preoperative treatment naive II 165 0.7887 CGPLPA25 Pancreatic Cancer WGS Preoperative treatment naive II 166 0.9549 CGPLPA26 Pancreatic Cancer WGS Preoperative treatment naive II 166 0.9598 CGPLPA28 Pancreatic Cancer WGS Preoperative treatment naive II 165 0.9069 CGPLPA33 Pancreatic Cancer WGS Preoperative treatment naive II 166 0.8361 CGPLPA34 Pancreatic Cancer WGS Preoperative treatment naive II 168 0.8946

CGPLPA37 Pancreatic Cancer WGS Preoperative treatment naive II 165 0.8840 CGPLPA38 Pancreatic Cancer WGS Preoperative treatment naive II 167 0.8746 CGPLPA39 Pancreatic Cancer WGS Preoperative treatment naive II 167 0.8562 CGPLPA40 Pancreatic Cancer WGS Preoperative treatment naive II 165 0.8563 CGPLPA42 Pancreatic Cancer WGS Preoperative treatment naive II 167 0.9126 CGPLPA46 Pancreatic Cancer WGS Preoperative treatment naive II 169 0.8274 CGPLPA47 Pancreatic Cancer WGS Preoperative treatment naive II 166 0.8376 CGPLPA48 Pancreatic Cancer WGS Preoperative treatment naive I 167 0.9391 CGPLPA52 Pancreatic Cancer WGS Preoperative treatment naive II 167 0.9452 CGPLPA53 Pancreatic Cancer WGS Preoperative treatment naive I 163 0.9175 CGPLPA58 Pancreatic Cancer WGS Preoperative treatment naive II 165 0.9587 CGPLPA59 Pancreatic Cancer WGS Preoperative treatment naive II 163 0.9230 CGPLPA67 Pancreatic Cancer WGS Preoperative treatment naive II 166 0.9574 CGPLPA69 Pancreatic Cancer WGS Preoperative treatment naive I 168 0.9172 CGPLPA71 Pancreatic Cancer WGS Preoperative treatment naive II 167 0.9424 CGPLPA74 Pancreatic Cancer WGS Preoperative treatment naive II 166 0.9688 CGPLPA76 Pancreatic Cancer WGS Preoperative treatment naive II 163 0.9681 CGPLPA85 Pancreatic Cancer WGS Preoperative treatment naive II 165 0.9137 CGPLPA86 Pancreatic Cancer WGS Preoperative treatment naive II 165 0.8875 CGPLPA92 Pancreatic Cancer WGS Preoperative treatment naive II 167 0.9389 CGPLPA93 Pancreatic Cancer WGS Preoperative treatment naive II 166 0.8585 CGPLPA94 Pancreatic Cancer WGS Preoperative treatment naive II 162 0.9365 CGPLPA95 Pancreatic Cancer WGS Preoperative treatment naive II 163 0.8542 CGST102 Gastric cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 167 0.9496 CGST11 Gastric cancer WGS Preoperative treatment naive IV 169 0.9419 CGST110 Gastric cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 167 0.9626 CGST114 Gastric cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 164 0.9535 CGST13 Gastric cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 166 0.9369 CGST131 Gastric cancer WGS Preoperative treatment naive II 171 0.9428 CGST141 Gastric cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 168 0.9621 CGST16 Gastric cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 166 0.7804 CGST18 Gastric cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 169 0.9523 CGST21 Gastric cancer WGS Preoperative treatment naive II 165 -0.4778 CGST26 Gastric cancer WGS Preoperative treatment naive IV 166 0.9554 CGST28 Gastric cancer Targeted Mutation Analysis and WGS Preoperative treatment naive X 169 0.9076 CGST30 Gastric cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 169 0.9246 CGST32 Gastric cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 169 0.9431 CGST33 Gastric cancer Targeted Mutation Analysis and WGS Preoperative treatment naive I 168 0.7999 CGST38 Gastric cancer WGS Preoperative treatment naive 0 168 0.9368 CGST39 Gastric cancer Targeted Mutation Analysis and WGS Preoperative treatment naive IV 164 0.8742 CGST41 Gastric cancer Targeted Mutation Analysis and WGS Preoperative treatment naive IV 168 0.8194 CGST45 Gastric cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 168 0.9576 CGST47 Gastric cancer Targeted Mutation Analysis and WGS Preoperative treatment naive I 168 0.9641 CGST48 Gastric cancer Targeted Mutation Analysis and WGS Preoperative treatment naive IV 167 0.7469 CGST53 Gastric cancer WGS Preoperative treatment naive 0 173 0.0019 CGST58 Gastric cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 169 0.9470 CGST67 Gastric cancer WGS Preoperative treatment naive I 170 0.9352 CGST77 Gastric cancer WGS Preoperative treatment naive IV 170 0.0043 CGST80 Gastric cancer Targeted Mutation Analysis and WGS Preoperative treatment naive II 168 0.9313 CGST81 Gastric cancer Targeted Mutation Analysis and WGS Preoperative treatment naive I 168 0.9480 Correlation of GC Corrected Fragment Ratio Profile to Mutant Median Alelle Fragment Fraction Detected Detected Fraction Ratio of Reads using using Detected Profile Mapped to DELFI DELFI using of Healthy Mitochondrial DELFI (95% (98% Targeted Patient Individuals Genome Score specificity) specificity) sequencing* CGCRC291 0.5268 0.0484% 0.9976 Y Y 22.85% CGCRC292 0.8835 0 0270% 0.7299 Y N 1.41% CGCRC293 0.9206 0.0748% 0.5234 N N 3.35% CGCRC294 0.8904 0.0135% 0.8757 Y Y 0.17% CGCRC296 0.8395 0.0369% 0.9951 Y Y ND CGCRC299 0.9268 0.0392% 0.9648 Y Y ND CGCRC300 0.9303 0.0235% 0.4447 N N ND CGCRC301 0.9151 0.0310% 0.2190 N N 3.21% CGCRC302 0.9243 0.0112% 0.9897 Y Y 3.12% CGCRC304 0.9360 0.0093% 0.9358 Y Y 3.27% CGCRC305 0.9250 0 0120% 0.8988 Y Y 3.19% CGCRC306 0.8186 0.0781% 0.9466 Y Y 8.02% CGCRC307 0.9342 0.0781% 0.7042 Y N 0.56% CGCRC308 0.9324 0.0078% 0.9082 Y Y 0.11% CGCRC311 0.9156 0.0173% 0.1867 N N ND CGCRC315 0.8846 0.0241% 0.6422 Y N 0.27% CGCRC316 0.5879 0.0315% 0.9971 Y Y 5.52% CGCRC317 0.8944 0.0184% 0.9855 Y Y 0.36% CGCRC318 0.9140 0.0156% 0.5615 N N ND CGCRC319 0.8230 0.1259% 0.9925 Y Y 0.11% CGCRC320 0.9101 0.0383% 0.8019 Y Y 0.64% CGCRC321 0.9091 0.0829% 0.9759 Y Y 3.20% CGCRC333 0.4355 0.4264% 0.9974 Y Y 43.03% CGCRC335 0.6858 0.1154% 0.9887 Y Y 81.61% CGCRC338 0.7573 0.1436% 0.9976 Y Y 36.00% CGCRC341 0.9181 0.0197% 0.9670 Y Y ND CGCRC342 0.1845 0.1732% 0.9987 Y Y 30.72% CGPLBR100 0.8946 0.1234% 0.8664 Y Y ND CGPLBR101 0.9304 0.0709% 0.9385 Y Y ND CGPLBR102 0.9345 0.4742% 0.9052 Y Y 0.25% CGPLBR103 0.9251 0.0775% 0.5994 N N ND CGPLBR104 0.9192 0.0532% 0.9950 Y Y 0.13% CGPLBR12 0.7760 0.1407% 0.7598 Y Y -- CGPLBR18 0.9534 0.0267% 0.3886 N N -- CGPLBR23 0.9312 0.0144% 0.1235 N N -- CGPLBR24 0.8766 0.0210% 0.7480 Y Y -- CGPLBR28 0.8120 0.1456% 0.9630 Y Y -- CGPLBR30 0.6611 0.0952% 0.9956 Y Y -- CGPLBR31 0.9556 0.0427% 0.2227 N N -- CGPLBR32 0.9229 0.0308% 0.9815 Y Y -- CGPLBR33 0.9432 0.0617% 0.2863 N N -- CGPLBR34 0.9425 0.0115% 0.1637 N N -- CGPLBR35 0.9348 0.1371% 0.5057 N N -- CGPLBR36 0.8884 0.0813% 0.4017 N N -- CGPLBR37 0.9496 0.0518% 0.0314 N N -- CGPLBR38 0.0349 0.1352% 0.8983 Y Y 0.53% CGPLBR40 0.9244 0.0929% 0.9046 Y Y ND CGPLBR41 0.9346 0.0544% 0.9416 Y Y 0.32% CGPLBR45 0.9285 0.0296% 0.3860 N N -- CGPLBR46 0.9005 0.0345% 0.7270 Y N -- CGPLBR47 0.9028 0.0591% 0.6247 Y Y -- CGPLBR48 0.8246 0.0504% 0.9973 Y Y 0.18% CGPLBR49 0.7887 0.0377% 0.9946 Y Y ND CGPLBR50 0.9332 0.0137% 0.6820 Y N -- CGPLBR51 0.9160 0.0863% 0.6915 Y N -- CGPLBR52 0.9196 0.0165% 0.6390 Y N -- CGPLBR55 0.9341 0.0356% 0.9494 Y Y 0.68% CGPLBR56 0.9428 0.2025% 0.4700 N N -- CGPLBR57 0.9416 0.0902% 0.9090 Y Y ND CGPLBR59 0.9130 0.0761% 0.5828 N N ND CGPLBR60 0.8916 0.0626% 0.8779 Y Y -- CGPLBR61 0.9422 0.0601% 0.4417 N N 0.44% CGPLBR63 0.9132 0.0514% 0.8788 Y Y ND CGPLBR65 0.8970 0.0264% 0.9048 Y Y -- CGPLBR68 0.9532 0.0164% 0.7883 Y Y ND CGPLBR69 0.9474 0.0279% 0.0600 N N ND CGPLBR70 0.9388 0.0171% 0.6447 Y N 0.36% CGPLBR71 0.9368 0.0271% 0.6706 Y N 0.10% CGPLBR72 0.9640 0.0263% 0.6129 N N ND CGPLBR73 0.9421 0.0142% 0.0746 N N 0.27% CGPLBR76 0.9254 0.0775% 0.9334 Y Y 0.12% CGPLBR81 0.8193 0.0241% 0.9899 Y Y -- CGPLBR82 0.9288 0.1640% 0.9834 Y Y 0.12% CGPLBR83 0.9138 0.0419% 0.9810 Y Y 0.28% CGPLBR84 0.8659 0.0274% 0.9901 Y Y -- CGPLBR87 0.8797 0.0294% 0.9968 Y Y 0.45% CGPLBR88 0.8547 0.0181% 0.9958 Y Y 0.38% CGPLBR90 0.8330 0.0417% 0.9667 Y Y -- CGPLBR91 0.9408 0.0799% 0.8710 Y Y ND CGPLBR92 0.8835 0.1042% 0.9856 Y Y 0.20% CGPLBR93 0.9072 0.0352% 0.7253 Y N ND CGPLH189 0.8947 0.0591% 0.1748 N N -- CGPLH190 0.9369 0.1193% 0.5168 N N -- CGPLH192 0.9487 0.0276% 0.0178 N N -- CGPLH193 0.9442 0.0420% 0.5794 N N -- CGPLH194 0.9289 0.0407% 0.1616 N N -- CGPLH196 0.9512 0.0266% 0.0999 N N -- CGPLH197 0.9416 0.0334% 0.4699 N N -- CGPLH198 0.9457 0.0302% 0.6571 Y N -- CGPLH199 0.9439 0.0170% 0.5584 N N -- CGPLH200 0.9391 0.0362% 0.3833 N N -- CGPLH201 0.9180 0.0470% 0.8395 Y Y -- CGPLH202 0.9436 0.0501% 0.1088 N N -- CGPLH203 0.9575 0.0455% 0.2485 N N -- CGPLH205 0.9283 0.0409% 0.4401 N N -- CGPLH208 0.9409 0.0371% 0.2706 N N -- CGPLH209 0.9367 0.0427% 0.2213 N N -- CGPLH210 0.9181 0.0279% 0.3500 N N -- CGPLH211 0.9410 0.0317% 0.1752 N N -- CGPLH300 0.9200 0.0397% 0.0226 N N -- CGPLH307 0.9167 0.0388% 0.1789 N N -- CGPLH308 0.8352 0.0311% 0.0155 N N -- CGPLH309 0.9451 0.0226% 0.0441 N N -- CGPLH310 0.9527 0.0145% 0.7135 Y N -- CGPLH311 0.9348 0.0202% 0.2589 N N -- CGPLH314 0.9491 0.0212% 0.1632 N N -- CGPLH315 0.9427 0.0071% 0.3450 N N -- CGPLH316 0.9552 0.0191% 0.4697 N N -- CGPLH317 0.9352 0 0232% 0.4330 N N -- CGPLH319 0.9189 0.0263% 0.2232 N N -- CGPLH320 0.9165 0.0222% 0.1095 N N -- CGPLH322 0.9411 0.0248% 0.0749 N N -- CGPLH324 0.9133 0.0402% 0.0128 N N -- CGPLH325 0.9202 0.0711% 0.0102 N N -- CGPLH326 0.9408 0.0213% 0.0475 N N -- CGPLH327 0.9071 0.1275% 0.4891 N N -- CGPLH328 0.9332 0.0256% 0.0234 N N -- CGPLH329 0.8396 0.0269% 0.0139 N N -- CGPLH330 0.9403 0.0203% 0.2642 N N -- CGPLH331 0.9377 0.0314% 0.0304 N N -- CGPLH333 0.9132 0.0350% 0.1633 N N -- CGPLH335 0.9333 0.0285% 0.0096 N N -- CGPLH336 0.9159 0.0158% 0.3872 N N -- CGPLH337 0.9262 0.0367% 0.2976 N N -- CGPLH338 0.9303 0.0103% 0.0431 N N -- CGPLH339 0.9338 0.0280% 0.0379 N N -- CGPLH340 0.9321 0.0210% 0.0379 N N -- CGPLH341 0.9187 0.0448% 0.1775 N N -- CGPLH342 0.8986 0.0283% 0.0904 N N -- CGPLH343 0.9067 0.0632% 0.0160 N N -- CGPLH344 0.8998 0.0257% 0.0120 N N -- CGPLH345 0.9107 0.0445% 0.0031 N N -- CGPLH346 0.9074 0.0208% 0.0686 N N -- CGPLH350 0.9388 0.0284% 0.0071 N N -- CGPLH351 0.9294 0.0223% 0.0207 N N -- CGPLH352 0.9190 0.0613% 0.0512 N N -- CGPLH353 0.9130 0.0408% 0.0132 N N -- CGPLH354 0.9121 0.0318% 0.0082 N N -- CGPLH355 0.9308 0.0400% 0.6407 Y N -- CGPLH356 0.8312 0.0427% 0.2437 N N -- CGPLH357 0.9540 0.0217% 0.0070 N N -- CGPLH358 0.9372 0.0174% 0.1451 N N -- CGPLH360 0.8775 0.0395% 0.0048 N N -- CGPLH361 0.9283 0.0268% 0.1524 N N -- CGPLH362 0.9503 0.0309% 0.4832 N N -- CGPLH363 0.9187 0.0620% 0.0199 N N -- CGPLH364 0.9480 0.0282% 0.8719 Y Y -- CGPLH365 0.9051 0.1740% 0.9638 Y Y -- CGPLH366 0.9170 0.0344% 0.0952 N N -- CGPLH367 0.9181 0.0353% 0.1235 N N -- CGPLH368 0.9076 0.1073% 0.1252 N N -- CGPLH369 0.9541 0.0246% 0.2821 N N -- CGPLH370 0.9423 0.0410% 0.0989 N N -- CGPLH371 0.9414 0.0734% 0.2173 N N -- CGPLH380 0.9424 0.0523% 0.0128 N N -- CGPLH381 0.9501 0.0435% 0.0152 N N -- CGPLH382 0.9584 0.0340% 0.0326 N N -- CGPLH383 0.9407 0.0389% 0.0035 N N -- CGPLH384 0.9043 0.0207% 0.0258 N N -- CGPLH385 0.9246 0.0165% 0.0566 N N -- CGPLH386 0.8859 0.0502% 0.2677 N N -- CGPLH387 0.9223 0.0375% 0.0081 N N -- CGPLH388 0.9266 0.0527% 0.0499 N N -- CGPLH389 0.9035 0.0667% 0.6585 Y N -- CGPLH390 0.9182 0.0229% 0.0837 N N -- CGPLH391 0.9162 0.0223% 0.0716 N N -- CGPLH392 0.9014 0.0424% 0.1305 N N -- CGPLH393 0.9045 0.0407% 0.0037 N N --

CGPLH394 0.9292 0.0522% 0.1073 N N -- CGPLH395 0.9254 0.0424% 0.0171 N N -- CGPLH396 0.8928 0.0393% 0.0303 N N -- CGPLH398 0.9578 0.0242% 0.3195 N N -- CGPLH399 0.9195 0.0579% 0.0685 N N -- CGPLH400 0.9047 0.0300% 0.2103 N N -- CGPLH401 0.9339 0.0146% 0.0620 N N -- CGPLH402 0.8800 0.1516% 0.0395 N N -- CGPLH403 0.8829 0.0515% 0.0223 N N -- CGPLH404 0.8948 0.0528% 0.0027 N N -- CGPLH405 0.9204 0.0358% 0.0188 N N -- CGPLH406 0.8592 0.0667% 0.0206 N N -- CGPLH407 0.9099 0.0229% 0.0040 N N -- CGPLH408 0.9192 0.0415% 0.1257 N N -- CGPLH409 0.8950 0.0302% 0.0056 N N -- CGPLH410 0.9006 0.0453% 0.0019 N N -- CGPLH411 0.8857 0.0621% 0.0188 N N -- CGPLH412 0.9191 0.0140% 0.0417 N N -- CGPLH413 0.9145 0.0355% 0.0084 N N -- CGPLH414 0.9127 0.0290% 0.0294 N N -- CGPLH415 0.9025 0.0296% 0.0131 N N -- CGPLH416 0.9388 0.0198% 0.0645 N N -- CGPLH417 0.9192 0.0241% 0.0836 N N -- CGPLH418 0.9234 0.0306% 0.0052 N N -- CGPLH419 0.9295 0.0280% 0.0489 N N -- CGPLH420 0.9109 0.0187% 0.0420 N N -- CGPLH422 0.9006 0.0208% 0.0324 N N -- CGPLH423 0.8288 0.0532% 0.0139 N N -- CGPLH424 0.9265 0.1119% 0.0864 N N -- CGPLH425 0.9488 0.0722% 0.0156 N N -- CGPLH426 0.9080 0.0548% 0.1075 N N -- CGPLH427 0.9257 0.0182% 0.0470 N N -- CGPLH428 0.9272 0.0346% 0.0182 N N -- CGPLH429 0.8757 0.0593% 0.8143 Y Y -- CGPLH430 0.9307 0.0258% 0.0389 N N -- CGPLH431 0.9185 0.0234% 0.0174 N N -- CGPLH432 0.9082 0.0433% 0.0181 N N -- CGPLH434 0.9442 0.0297% 0.0050 N N -- CGPLH435 0.9097 0.0179% 0.0441 N N -- CGPLH436 0.9158 0.0290% 0.0958 N N -- CGPLH437 0.9245 0.0156% 0.0136 N N -- CGPLH438 0.9138 0.0169% 0.1041 N N -- CGPLH439 0.9028 0.0226% 0.0078 N N -- CGPLH440 0.8295 0.0330% 0.0687 N N -- CGPLH441 0.9430 0.0178% 0.0085 N N -- CGPLH442 0.9405 0.0169% 0.0582 N N -- CGPLH443 0.8801 0.0207% 0.0578 N N -- CGPLH444 0.8068 0.0464% 0.0097 N N -- CGPLH445 0.8750 0.0267% 0.1939 N N -- CGPLH446 0.9257 0.0281% 0.0340 N N -- CGPLH447 0.8968 0.0167% 0.0017 N N -- CGPLH448 0.9191 0.0401% 0.0389 N N -- CGPLH449 0.9254 0.0236% 0.0116 N N -- CGPLH450 0.9195 0.0331% 0.0597 N N -- CGPLH451 0.9167 0.0262% 0.0104 N N -- CGPLH452 0.8948 0.0480% 0.4722 N N -- CGPLH453 0.9339 0.0186% 0.3419 N N -- CGPLH455 0.9322 0.0455% 0.4536 N N -- CGPLH456 0.9098 0.0207% 0.0385 N N -- CGPLH457 0.9022 0.0298% 0.0384 N N -- CGPLH458 0.9275 0.0298% 0.1891 N N -- CGPLH459 0.9209 0.0281% 0.0371 N N -- CGPLH460 0.8863 0.0227% 0.1157 N N -- CGPLH463 0.9372 0.0130% 0.0865 N N -- CGPLH464 0.8511 0.0659% 0.2040 N N -- CGPLH465 0.9164 0.0325% 0.0124 N N -- CGPLH466 0.9408 0.0155% 0.1733 N N -- CGPLH467 0.9024 0.0229% 0.2303 N N -- CGPLH468 0.9345 0.0247% 0.5427 N N -- CGPLH469 0.8799 0.0201% 0.5351 N N -- CGPLH470 0.9228 0.0715% 0.0327 N N -- CGPLH471 0.9333 0.0150% 0.0406 N N -- CGPLH472 0.8915 0.0481% 0.6152 N N -- CGPLH473 0.9128 0.0443% 0.2995 N N -- CGPLH474 0.9245 0.0316% 0.8246 Y N -- CGPLH475 0.9233 0.0269% 0.0736 N N -- CGPLH476 0.9059 0.0236% 0.0143 N N -- CGPLH477 0.9376 0.0382% 0.1111 N N -- CGPLH478 0.9344 0.0256% 0.0628 N N -- CGPLH479 0.9207 0.0221% 0.0648 N N -- CGPLH480 0.9046 0.0672% 0.7473 Y N -- CGPLH481 0.9113 0.0311% 0.0282 N N -- CGPLH482 0.9336 0.0162% 0.0058 N N -- CGPLH483 0.9275 0.0251% 0.0495 N N -- CGPLH484 0.9366 0.0261% 0.0048 N N -- CGPLH485 0.9128 0.0291% 0.1084 N N -- CGPLH486 0.9042 0.0220% 0.0820 N N -- CGPLH487 0.9098 0.0594% 0.2154 N N -- CGPLH488 0.8299 0.0409% 0.0903 N N -- CGPLH490 0.8794 0.0432% 0.0424 N N -- CGPLH491 0.8332 0.0144% 0.0223 N N -- CGPLH492 0.8799 0.0322% 0.0311 N N -- CGPLH493 0.9330 0.0065% 0.0280 N N -- CGPLH494 0.9303 0.0232% 0.0824 N N -- CGPLH495 0.8908 0.0513% 0.0465 N N -- CGPLH496 0.8398 0.0208% 0.0572 N N -- CGPLH497 0.9330 0.0335% 0.0404 N N -- CGPLH498 0.9315 0.0403% 0.0752 N N -- CGPLH499 0.9442 0.0198% 0.0149 N N -- CGPLH500 0.9240 0.0433% 0.0754 N N -- CGPLH501 0.9308 0.0300% 0.0159 N N -- CGPLH052 0.9200 0.0351% 0.0841 N N -- CGPLH503 0.8939 0.0398% 0.0649 N N -- CGPLH504 0.9324 0.0440% 0.1231 N N -- CGPLH505 0.9243 0.0605% 0.1869 N N -- CGPLH506 0.9498 0.0284% 0.0180 N N -- CGPLH507 0.9192 0.0186% 0.0848 N N -- CGPLH508 0.9410 0.0150% 0.1077 N N -- CGPLH509 0.9323 0.0163% 0.0828 N N -- CGPLH510 0.9548 0.0128% 0.0376 N N -- CGPLH511 0.9493 0.0224% 0.1779 N N -- CGPLH512 0.9244 0.0094% 0.0076 N N -- CGPLH513 0.9595 0.0441% 0.5250 N N -- CGPLH514 0.9369 0.0114% 0.3131 N N -- CGPLH515 0.9283 0.0352% 0.4936 N N -- CGPLH516 0.8298 0.0175% 0.0916 N N -- CGPLH517 0.9494 0.0161% 0.0059 N N -- CGPLH518 0.9432 0.0274% 0.0130 N N -- CGPLH519 0.9351 0.0171% 0.0949 N N -- CGPLH520 0.9476 0.0241% 0.0844 N N -- CGPLH625 0.9231 0.0697% 0.4977 N N -- CGPLH626 0.9269 0.0231% 0.3100 N N -- CGPLH639 0.9410 0.0549% 0.0773 N N -- CGPLH640 0.9264 0.0232% 0.0327 N N -- CGPLH642 0.8376 0.0768% 0.0555 N N -- CGPLH643 0.9271 0.0579% 0.1325 N N -- CGPLH644 0.8948 0.0621% 0.3819 N N -- CGPLH646 0.8691 0.0462% 0.2423 N N -- CGPLLU144 0.6861 0.0423% 0.9892 Y Y 5.10% CGPLLU161 0.9187 0.0273% 0.9955 Y Y 0.20% CGPLLU162 0.0836 0.1410% 0.9966 Y Y 0.22% CGPLLUl63 0.3033 0.0724% 0.9940 Y Y 0.21% CGPLLU168 0.6842 0.0712% 0.9861 Y Y 0.07% CGPLLU169 0.9189 0.0846% 0.9856 Y Y 0.13% CGPLLU176 0.9081 0.0626% 0.8769 Y Y ND CGPLLU177 0.6790 0.0564% 0.9924 Y Y 3.22% CGPLLU203 0.8741 0.0568% 0.9178 Y Y 0.11% CGPLLU205 0.9476 0.0495% 0.9877 Y Y ND CGPLLU207 0.9379 0.0421% 0.9908 Y Y 0.32% CGPLLU208 0.8942 0.0815% 0.9273 Y Y 1.33% CGPLOV11 0.8872 0.0469% 0.9343 Y Y 0.87% CGPLOV12 0.8973 0.2767% 0.9764 Y Y ND CGPLOV13 0.9146 0.1017% 0.9690 Y Y 0.35% CGPLOV15 0.8552 0.0876% 0.9945 Y Y 3.54% CGPLOV16 0.9046 0.0400% 0.9683 Y Y 1.12% CGPLOV19 0.7578 0.1089% 0.9989 Y Y 46.35% CGPLOV20 0.9154 0.0581% 0.9749 Y Y 0.21% CGPLOV21 0.8889 0.0677% 0.9961 Y Y 14.36% CGPLOV22 0.9355 0.0251% 0.9775 Y Y 0.49% CGPLOV23 0.8850 0.1520% 0.9910 Y Y 1.39% CGPLOV24 0.8995 0.0303% 0.9856 Y Y ND CGPLOV25 0.9228 0.0141% 0.8544 Y Y ND CGPLOV26 0.9351 0.0646% 0.9946 Y Y ND CGPLOV28 0.9378 0.0547% 0.8160 Y Y ND CGPLOV31 0.9283 0.1605% 0.9795 Y Y ND CGPLOV32 0.9338 0.1351% 0.8609 Y Y ND CGPLOV37 0.8831 0.0985% 0.9849 Y Y 0.29% CGPLOV38 0.6502 0.0490% 0.9990 Y Y 4.89% CGPLOV40 0.8127 0.6145% 0.9963 Y Y 6.73% CGPLOV41 0.8929 0.1110% 0.9484 Y Y 0.60% CGPLOV42 0.9086 0.0489% 0.9979 Y Y 1.24% CGPLOV43 0.9342 0.0432% 0.6042 N N ND CGPLOV44 0.9173 0.1946% 0.9962 Y Y 0.37% CGPLOV46 0.9291 0.0801% 0.9128 Y Y ND CGPLOV47 0.9461 0.0270% 0.3410 N N 3.20% CGPLOV48 0.9429 0.0422% 0.4874 N N 10.70% CGPLOV49 0.8083 0.1527% 0.9897 Y Y 2.03% CGPLOV50 0.9382 0.0907% 0.9955 Y Y ND CGPLPA112 0.9429 0.0268% 0.0856 N N -- CGPLPA113 0.7674 1.0116% 0.9935 Y Y -- CGPLPA114 0.9246 0.0836% 0.7598 Y Y -- CGPLPA115 0.8810 0.0763% 0.9974 Y Y -- CGPLPA117 0.8767 0.1084% 0.9049 Y Y -- CGPLPA118 0.9001 0.1842% 0.9859 Y Y 0.14% CGPLPA122 0.8058 0.2047% 0.9983 Y Y 37.22% CGPLPA124 0.9238 0.1542% 0.8791 Y Y 0.62% CGPLPA125 0.9373 0.0273% 0.0228 N N -- CGPLPA126 0.9139 0.4349% 0.9908 Y Y ND CGPLPA127 0.8117 0.4371% 0.9789 Y Y -- CGPLPA128 0.9003 0.1317% 0.9812 Y Y ND CGPLPA129 0.9155 0.0642% 0.9839 Y Y ND CGPLPA130 0.8499 0.1055% 0.9895 Y Y ND CGPLPA131 0.9195 0.0760% 0.9685 Y Y 0.21% CGPLPA134 0.8847 0.0260% 0.9896 Y Y 0.93% CGPLPA135 0.9184 0.0558% 0.6594 Y N -- CGPLPA136 0.9050 0.0769% 0.9596 Y Y 0.10% CGPLPA137 0.9320 0.0499% 0.7282 Y N -- CGPLPA139 0.9374 0.0465% 0.0743 N N -- CGPLPA14 0.9069 0.0515% 0.9824 Y Y -- CGPLPA140 0.9548 0.0330% 0.9751 Y Y 3.21% CGPLPA141 0.9381 0.0920% 0.9388 Y Y -- CGPLPA15 0.8927 0.0160% 0.8737 Y Y -- CGPLPA155 0.9313 0.0260% 0.8013 Y Y -- CGPLPA156 0.9432 0.0290% 0.0159 N N -- CCPLPA165 0.9309 0.0558% 0.2158 N N -- CGPLPA168 0.7757 0.3123% 0.9878 Y Y -- CGPLPA17 0.6771 1.2600% 0.9956 Y Y -- CGPLPA184 0.9203 0.0897% 0.9926 Y Y -- CGPLPA187 0.8968 0.0658% 0.9875 Y Y -- CGPLPA23 0.6938 0.5785% 0.9984 Y Y -- CGPLPA25 0.9239 0.0380% 0.8103 Y Y -- CGPLPA26 0.9356 0.0247% 0.8231 Y Y -- CGPLPA28 0.8930 0.0546% 0.9036 Y Y -- CGPLPA33 0.8553 0.0894% 0.9967 Y Y -- CGPLPA34 0.8885 0.0438% 0.7977 Y Y -- CGPLPA37 0.9294 0.0410% 0.9924 Y Y -- CGPLPA38 0.8941 0.0372% 0.9851 Y Y -- CGPLPA39 0.7972 0.5058% 0.9951 Y Y -- CGPLPA40 0.8865 0.2268% 0.9920 Y Y -- CGPLPA42 0.8863 0.0283% 0.3544 N N -- CGPLPA46 0.7525 1.0982% 0.9952 Y Y -- CGPLPA47 0.8439 0.1598% 0.9946 Y Y -- CGPLPA48 0.9207 1.0232% 0.2251 N N -- CGPLPA52 0.8863 0.0154% 0.0963 N N -- CGPLPA53 0.8776 0.1824% 0.8946 Y Y -- CGPLPA58 0.9224 0.0803% 0.9056 Y Y -- CGPLPA59 0.9193 0.1479% 0.9759 Y Y -- CGPLPA67 0.9248 0.0329% 0.6716 Y N -- CGPLPA69 0.8592 0.0458% 0.1245 Y Y -- CGPLPA71 0.8888 0.0479% 0.0524 Y Y -- CGPLPA74 0.9372 0.0292% 0.0108 Y Y -- CGPLPA76 0.9441 0.0345% 0.0897 Y Y -- CGPLPA85 0.9337 0.0363% 0.0508 Y Y -- CGPLPA86 0.8042 0.7564% 0.9864 Y Y -- CGPLPA92 0.9003 0.1458% 0.7061 N N -- CGPLPA93 0.8023 0.6250% 0.9978 Y Y -- CGPLPA94 0.9433 0.0180% 0.9025 Y Y -- CGPLPA95 0.8571 0.0815% 0.9941 Y Y -- CGST102 0.9057 0.0704% 0.8581 Y Y 0.43% CGST11 0.9161 0.0651% 0.1435 N N -- CGST110 0.9232 0.0817% 0.8900 Y Y ND CGST114 0.9038 0.0317% 0.5893 N N ND CGST13 0.9156 0.0321% 0.9754 Y Y ND CGST131 0.8886 0.2752% 0.9409 Y Y -- CGST141 0.9205 0.0388% 0.2008 N N ND CGST16 0.8355 0.1744% 0.9974 Y Y 0.93% CGST18 0.9111 0.0298% 0.3842 N N 0.14% CGST21 0.2687 0.2295% 0.9910 Y Y -- CGST26 0.9140 0.0399% 0.5009 N N -- CGST28 0.7832 0.1295% 0.9955 Y Y 1.62% CGST30 0.9121 0.0338% 0.9183 Y Y 0.42% CGST32 0.8639 0.0247% 0.9512 Y Y 2.99% CGST33 0.7770 0.0798% 0.9805 Y Y 2.32% CGST38 0.8758 0.0540% 0.9416 Y Y -- CGST39 0.9401 0.0287% 0.8480 Y Y ND CGST41 0.9284 0.0398% 0.9253 Y Y ND CGST45 0.9036 0.0220% 0.9713 Y Y ND CGST47 0.9096 0.0157% 0.9687 Y Y 0.45% CGST48 0.5445 0.0220% 0.9975 Y Y 4.21% CGST53 0.7888 0.1140% 0.9914 Y Y -- CGST58 0.9094 0.0696% 0.9705 Y Y ND

CGST67 0.8853 0.3245% 0.9002 Y Y -- CGST77 0.8295 0.1851% 0.9981 Y Y -- CGST80 0.8845 0.0490% 0.9513 Y Y 1.04% CGST81 0.8851 0.0138% 0.9748 Y Y 0.21% *NO indicates not detected. please see reference 10 for additional information on targeted sequencing analyes. DELFI cancer detection at 95% and 98% specificity is based on scores greater than 0.6200 and 0.7500 respectively.

Claims

1-67. (canceled)

68. A system for determining a cell free DNA (cfDNA) fragmentation profile of a subject comprising: processing cfDNA fragments obtained from a sample obtained from the subject into sequencing libraries; subjecting the sequencing libraries to whole genome sequencing to obtain sequenced fragments, wherein genome coverage is from about 0.1.times. to 9.times.; mapping the sequenced fragments to a genome to obtain genomic intervals of mapped sequences; analyzing the genomic intervals of mapped sequences to determine cfDNA fragment lengths; and determining a cfDNA fragmentation profile for the subject.

69. The system of claim 68, wherein the system is a machine learning system.

70. The system of claim 69, wherein the machine learning system is a gradient tree boosting machine learning system.

71. The system of claim 68, wherein a cfDNA fragmentation profile in the subject that is more variable than a reference cfDNA fragmentation profile is indicative of the subject as having or at risk of having cancer.

72. The system of claim 68, wherein a cfDNA fragmentation profile in the subject that is less or equally variable than a reference cfDNA fragmentation profile is indicative of the subject as being healthy.

73. The system of claim 71, wherein the reference cfDNA fragmentation profile is a reference nucleosome cfDNA fragmentation profile.

74. The system of claim 68, wherein determining the cfDNA fragmentation profile distinguishes circulating tumor DNA (ctDNA) from non-cancer-associated white blood cell DNA in the blood.

75. The system of claim 68, wherein the mapped sequences comprise tens or hundreds to thousands of genomic intervals.

76. The system of claim 68, wherein the genomic intervals are non-overlapping.

77. The system of claim 68, wherein the genomic intervals each comprise thousands to millions of base pairs.

78. The system of claim 68, wherein a cfDNA fragmentation profile is determined within each genomic interval.

79. The system of claim 68, wherein a cfDNA fragmentation profile comprises a median fragment size.

80. The system of claim 68, wherein a cfDNA fragmentation profile comprises a fragment size distribution.

81. The system of claim 68, wherein a cfDNA fragmentation profile is determined over the whole genome or a subgenomic interval.

82. The system of claim 68, wherein cfDNA fragmentation profiles provide over 20,000 reads per genomic intervals.

83. The system of claim 68, wherein the genomic coverage is about 0.1.times., 0.2.times., 0.5.times., 1.times. or 2.times..

84. The system of claim 68, wherein the cfDNA fragmentation profile further predicts the tissue of origin of the cancer in a subject having or at risk of having cancer.

85. The system of claim 68, wherein the cancer is selected from the group consisting of: colorectal cancer, lung cancer, breast cancer, gastric cancer, pancreatic cancer, bile duct cancer, and ovarian cancer.

86. The system of claim 68, wherein the cancer is treated with or has previously been treated with a treatment comprising administering to the subject a cancer treatment selected from the group consisting of surgery, adjuvant chemotherapy, neoadjuvant chemotherapy, radiation therapy, hormone therapy, cytotoxic therapy, immunotherapy, adoptive T cell therapy, targeted therapy and combination thereof.

87. The system of claim 68, wherein cfDNA fragments are nucleosome protected DNA fragments.

88. The system of claim 68, wherein the sample is a blood, serum, plasma, amnion, tissue, urine, cerebrospinal fluid, saliva, sputum, broncho-alveolar lavage, bile, lymphatic fluid, cyst fluid, stool, ascites, pap smear, breast milk or exhaled breath condensate sample.

89. A method of predicting a cell free DNA (cfDNA) fragmentation profile of a subject comprising: determining a cfDNA fragmentation profile prediction for the subject based on a DNA evaluation of fragments for early interception (DELFI) classifier score, using the system of claim 68, thereby predicting a cfDNA fragmentation profile of the subject.

90. A method of predicting a cancer status in a subject comprising: determining a cfDNA fragmentation profile prediction for the subject using the system of claim 68; and classifying the subject as a healthy subject or a subject having or at risk of having cancer based on variability of the cfDNA fragmentation profile in the subject, thereby predicting a cancer status in the subject.

91. A method of detecting and/or monitoring the status of cancer in a subject comprising: determining a first cfDNA fragmentation profile of the subject at a first time using the system of claim 68; and classifying the subject as a healthy subject or a subject having or at risk of having cancer based on the cfDNA fragmentation profile of the subject, thereby detecting cancer in the subject.

92. The method of claim 91, further comprising determining a second cfDNA fragmentation profile of the subject at a second time and comparing the first cfDNA fragmentation profile to the second cfDNA fragmentation profile to monitor the status of cancer in the subject.

93. The method of claim 92, wherein the first and/or the second cfDNA fragmentation profiles are determined before, during and/or after the course of a cancer treatment.

94. The method of claim 93, wherein determining the first and/or the second cfDNA fragmentation profiles over the course of a cancer treatment indicates responsiveness to the cancer treatment.

95. The method of claim 92, wherein a second cfDNA fragmentation profile that is less or equally variable than a reference cfDNA fragmentation profile obtained in a healthy subject indicates a response to the cancer treatment in the subject.

96. The method of claim 92, wherein a second cfDNA fragmentation profile that is more variable than a reference cfDNA fragmentation profile obtained in a healthy subject indicates an absence of response to the cancer treatment in the subject.

97. The method of claim 91, wherein determining the cfDNA fragmentation profile is indicative of a change in tumor size, and/or change in tumor localization.