U.S. patent application number 17/182044 was filed with the patent office on 2021-08-19 for gene expression-based biomarker for the detection and monitoring of bronchial premalignant lesions.
The applicant listed for this patent is Trustees of Boston University. Invention is credited to Jennifer E. Beane-Ebel, Marc E. Lenburg, Avrum Spira, Anna Tassinari.
Application Number | 20210254171 17/182044 |
Document ID | / |
Family ID | 1000005553134 |
Filed Date | 2021-08-19 |
United States Patent
Application |
20210254171 |
Kind Code |
A1 |
Beane-Ebel; Jennifer E. ; et
al. |
August 19, 2021 |
GENE EXPRESSION-BASED BIOMARKER FOR THE DETECTION AND MONITORING OF
BRONCHIAL PREMALIGNANT LESIONS
Abstract
Disclosed herein are assays and methods for the identification
of premalignant lesions, as well as methods of determining the
likelihood that such premalignant lesions will progress to lung
cancer. Also disclosed are methods and assays that are useful for
monitoring the progression of premalignant lesions to lung cancer.
The assays and methods disclosed herein provide minimally invasive
means of accurately detecting and monitoring the presence or
absence of premalignant lesions, thus providing novel insights into
the earliest stages of lung cancer and facilitating early detection
and early intervention.
Inventors: |
Beane-Ebel; Jennifer E.;
(Fort Collins, CO) ; Tassinari; Anna; (Boston,
MA) ; Spira; Avrum; (Newton, MA) ; Lenburg;
Marc E.; (Brookline, MA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Trustees of Boston University |
Boston |
MA |
US |
|
|
Family ID: |
1000005553134 |
Appl. No.: |
17/182044 |
Filed: |
February 22, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15644721 |
Jul 7, 2017 |
10927417 |
|
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17182044 |
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62360218 |
Jul 8, 2016 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
C12Q 2600/158 20130101;
C12Q 1/6886 20130101; C12Q 2600/112 20130101; C12Q 2600/118
20130101 |
International
Class: |
C12Q 1/6886 20060101
C12Q001/6886 |
Claims
1.-18. (canceled)
19. A method of processing a sample from a subject suspected of
having a premalignant bronchial lesion comprising the steps of: (a)
providing a biological sample from the mouth or nose of the subject
or from a brushing of the bronchi walls of the subject; and (b)
measuring the expression of two or more genes in the sample by
northern-blot hybridization, a ribonuclease protection assay, or a
reverse transcriptase polymerase chain reaction (RT-PCR) method,
wherein the two or more genes are genes involved in an oxidative
phosphorylation (OXPHOS), electron transport chain (ETC), or
mitochondrial protein transport pathway.
20. The method of claim 19, wherein the expression of at least five
genes involved in an oxidative phosphorylation (OXPHOS), electron
transport chain (ETC), or mitochondrial protein transport pathway
are measured.
21. The method of claim 19, wherein the expression in the sample of
at least twenty genes are measured.
22. The method of claim 19, wherein the two or more genes comprise
cDNA.
23. The method of claim 19, wherein the expression of two or more
genes in the sample is measured by an RT-PCR method.
24. The method of claim 19, wherein the biological sample is
obtained from the mouth of the subject.
25. The method of claim 19, wherein the subject has a positive
result in an imaging study of a premalignant bronchial lesion.
26. The method of claim 19, wherein the subject has previously been
diagnosed with a lung, bronchus, head/neck, and/or esophagus cancer
but has no current evidence of the cancer.
27. The method of claim 19, wherein the subject is a current smoker
or a former smoker with 20+ pack years.
28. The method of claim 27, wherein the subject is at least 50
years old.
29. The method of claim 19, wherein the subject has emphysema,
chronic bronchitis, chronic obstructive pulmonary disease, an
occupationally related asbestos disease, or a family history of
lung cancer in a first degree relative.
30. The method of claim 29, wherein the subject is at least 50
years old.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of U.S. application Ser.
No. 15/644,721, filed Jul. 7, 2017, which claims the benefit of
U.S. Provisional Application No. 62/360,218, filed on Jul. 8, 2016,
the contents of which are hereby incorporated by reference in its
entirety.
BACKGROUND OF THE INVENTION
[0002] Lung cancer (LC) is the leading cause of cancer death in the
United States. The molecular events preceding the onset of LC and
the progression of premalignant lesions (PMLs) to lung cancer are
poorly understood. This is due in part to the lack of reliable
biomarkers which complicates the study of such lesions. Currently
there are no molecular tests to identify PMLs or describe their
changes over time. The only technology that is able to visualize
and sample premalignant lesions is auto-fluorescent bronchoscopy,
which is limited in sensitivity and is not in widespread clinical
use.
[0003] Needed are novel biomarkers, methods and assays that are
capable of facilitating the evaluation of PMLs. Suspicious lesions
on chest computed tomography (CT) scans typically prompt
bronchoscopic evaluation, which is also limited by varying
diagnostic yields. Moreover, negative bronchoscopies prove a
clinical dilemma, whereby the need to provide a diagnostic answer
is countered by the invasiveness of follow-up studies.
[0004] A previously reported biomarker, PERCEPTA.RTM. (Veracyte
Inc.), has demonstrated the potential benefit of employing a
bronchial gene expression-based classifier on a sub-set of patients
with non-diagnostic bronchoscopies, through modifying risk
stratification of patients. However, this biomarker has
demonstrated greatest benefit amongst those with a moderate
pre-test probability with modest overall sensitivities. The
employment of a novel pre-malignancy marker would complement the
PERCEPTA.RTM. biomarker in this sub-set of patients, facilitating
the identification of those patients that would be at high risk for
PML progression.
[0005] Also needed are new biomarkers, methods and assays for use
in lung cancer screening assays and the early detection of PMLs. A
recent large randomized controlled trial has led to the recent
endorsement of annual lung cancer screening with low dose CT for
asymptomatic patients that are at higher lung cancer risk. This has
created a large volume of chest CTs, whose performance is marred by
the high rate of false positive results. It is anticipated that
this will lead to a large need for invasive procedures for benign
disease. A pre-malignancy biomarker could complement the diagnostic
work up of lesions identified through screening, which are
typically more complicated since such lesions identified on
screening are usually smaller and more complex. Additionally,
patient screening eligibility is based solely on epidemiological
and demographic considerations, which still vary between different
proposed guidelines. This leads to varying referral patterns and
missed opportunities to screen a large proportion of those patients
with high risk that do not meet dictated criteria. The availability
of biomarkers, methods and assays for the detection of PMLs would
overcome this challenge by facilitating the identification of
pre-malignancy-associated changes and risk of progression, would
provide a first step to identifying molecular risk factors for lung
cancer, and would identify those patients who would benefit from CT
screening. Such biomarkers would also be useful for patent risk
stratification, which would assist in the identification of those
patients that may benefit from additional screening of those
patients harboring premalignant molecular alterations, which could
in turn inform future decision making.
[0006] The limited understanding of the mechanisms involved in
transforming PMLs into LC has restricted the ability to intervene
in these processes, making the identification of chemoprevention
agents difficult in view of the challenges involved in discerning
premalignant phenotypes through currently available means.
Furthermore, clinical trials in this space are exceedingly
difficult given the long duration required to detect significant
outcome benefits. Accordingly, biomarkers, assays and methods that
are reflective of pre-malignancy would facilitate "smart" patient
enrollment for trials and would allow accounting for molecular
heterogeneity involved in random patient recruitment in such
trials.
SUMMARY OF THE INVENTION
[0007] The present inventions provide insight into the mechanisms
that are involved in the transformation or progression of
premalignant bronchial lesions into lung cancer. Provided herein
are novel biomarkers, methods and assays that are useful in lung
cancer screening and the early detection of premalignant lesions
(PMLs). The biomarkers, methods and assays of the present invention
also facilitate the monitoring of PMLs and their progression or
regression over time. Advantageously, the assays and methods
disclosed herein may be rapidly performed in a non-invasive or
minimally-invasive manner, providing objective results,
contributing to the identification and monitoring of subjects that
are suspected of having PMLs, facilitating the clinical decision
making of the treatment of such subjects and informing clinical
trial recruitment efforts.
[0008] In certain aspects, the biomarkers, methods and assays
disclosed herein may be assessed or performed on a biological
sample that is obtained from a subject at a site that is distal to
the suspected site of the premalignant bronchial lesion. For
example, in certain embodiments, the assays and methods of
determining the presence of PMLs or cancer in the lungs may be
performed by determining the expression of one or more genes in
nasal or buccal epithelial cells and/or tissues. Similarly, such
assays and methods may be performed by determining the expression
of one or more genes in the subject's peripheral blood cells. In
certain aspects, the biomarkers, methods and assays disclosed
herein may be assessed or performed on, or additionally include, a
biological sample that is obtained from a subject with a positive
result in an imaging study (e.g., chest X-ray, CT scan, etc.). In
some aspects, the methods and assays disclosed herein can comprise
a step of performing an imaging study. In certain aspects, the
biomarkers, methods and assays disclosed herein may be assessed or
performed on, or additionally include, a biological sample that is
obtained from a subject with a positive result in an imaging study
(e.g., chest X-ray, CT scan, etc.) to confirm or rule out the
positive result. In some aspects, the methods or assays disclosed
herein are used to determine whether a positive result in an
imaging study warrants a further invasive procedure (e.g.,
bronchoscopy), chemoprophylaxis, and/or chemotherapy.
[0009] In some embodiments, methods and assays disclosed herein may
be assessed or performed on a biological sample that is obtained
from a subject at a suspected site of a PML (e.g., premalignant
bronchial lesion). In some embodiments, the suspected site is
identified as having abnormal fluorescent during auto-fluorescence
bronchoscopy, although the method of identifying the suspected site
is not limited. In some embodiments, the methods and assays
disclosed herein may be performed on a biopsy of a suspected PML as
an alternative to, or in addition to, a histological examination of
the biopsy.
[0010] In certain aspects, disclosed herein are methods of
determining the presence or absence of a premalignant lesion in a
subject. Such methods comprise the steps of: (a) measuring a
biological sample comprising airway epithelial cells of the subject
for expression of one or more genes; and (b) comparing the
expression of the one or more genes to a control sample of those
genes from individuals without premalignant lesions; wherein the
one or more genes are selected from the group consisting of genes
in Table 3, and wherein differential expression of the subject's
one or more genes relative to the control sample is indicative of
the presence of a premalignant lesion in the subject. Similarly, in
certain embodiments, non-differential expression of the subject's
one or more genes relative to the control sample is indicative of
the absence of a premalignant lesion in the subject.
[0011] Also disclosed herein are methods of determining the
likelihood that a premalignant lesion in a subject will progress to
lung cancer. In certain aspects, such methods comprise the steps
of: (a) measuring a biological sample comprising airway epithelial
cells of the subject for expression of one or more genes; and (b)
comparing the expression of the one or more genes to a control
sample of those genes from individuals with lung cancer; wherein
the one or more genes are selected from the group consisting of
genes in Table 3, and wherein differential expression of the
subject's one or more genes relative to the control sample is
indicative of a low likelihood of the premalignant lesion
progressing to lung cancer. In some embodiments, non-differential
expression of the subject's one or more genes relative to the
control sample is indicative of a high likelihood of the
premalignant lesion progressing to lung cancer.
[0012] In certain embodiments, also disclosed herein are methods of
monitoring whether a premalignant lesion will progress to lung
cancer in a subject. Such methods comprise subjecting a biological
sample comprising airway epithelial cells of the subject to a gene
expression analysis, wherein the gene expression analysis comprises
comparing gene expression levels of one or more genes selected from
the group of genes in Table 3 to the expression levels of a control
sample of those genes from individuals with cancer, and wherein
differential expression of the subject's one or more genes relative
to the control sample is indicative of a lack of progression of the
premalignant lesion to lung cancer. Similarly, in certain aspects
non-differential expression of the subject's one or more genes
relative to the control sample is indicative of progression of the
premalignant lesion to lung cancer.
[0013] In yet other embodiments, also disclosed herein are methods
of determining the presence of a premalignant lesion in a subject
comprising the steps of: (a) measuring a biological sample
comprising airway epithelial cells of the subject for expression of
one or more genes; and (b) comparing the expression of the one or
more genes to a control sample of those genes obtained from
individuals without premalignant lesions; wherein the one or more
genes are selected from the group of genes in at least one pathway
in Dataset 2, and wherein differential expression of the subject's
one or more genes relative to the control sample is indicative of
the presence of a premalignant lesion in the subject. In some
embodiments, non-differential expression of the subject's one or
more genes relative to the control sample is indicative of the
absence of a premalignant lesion in the subject.
[0014] In certain aspects of any of the foregoing methods, at least
two genes, at least five genes, at least ten genes, at least twenty
genes, at least thirty genes, at least forty genes, at least fifty
genes, at least one hundred genes, at least two hundred genes or at
least two hundred and eighty genes are measured. In some
embodiments of the foregoing methods, the one or more genes
comprise those genes associated with a pathway identified in
Dataset 2.
[0015] In some embodiments of any of the foregoing methods the
airway epithelial cells comprise bronchial epithelial cells. In
certain aspects, such bronchial epithelial cells are obtained by
brushing the bronchi walls of the subject. In certain aspects of
any of the foregoing methods, the airway epithelial cells comprise
nasal epithelial cells. In certain aspects of any of the foregoing
methods, the airway epithelial cells comprise buccal epithelial
cells. In still other embodiments of the present inventions, the
airway epithelial cells do not comprise bronchial epithelial cells.
In some embodiments, the airway epithelial cells are obtained from
a suspected PML site (e.g., abnormal fluorescing areas during
auto-fluorescence bronchoscopy).
[0016] In certain aspects, the methods disclosed herein are
performed with, or further comprise assessing or determining one or
more of the subject's secondary factors that affect the subject's
risk for having or developing lung cancer. For example, in some
embodiments, one or more secondary factors are selected from the
group consisting of advanced age, smoking status, the presence of a
lung nodule greater than 3 cm on CT scan and time since quitting
smoking. In certain embodiments of the foregoing methods,
expression of the one or more genes is determined using a
quantitative reverse transcription polymerase chain reaction, a
bead-based nucleic acid detection assay or an oligonucleotide array
assay.
[0017] The foregoing methods are useful for predicting or
monitoring the progression of PMLs to lung cancer. For example, a
lung cancer selected from the group consisting of adenocarcinoma,
squamous cell carcinoma, small cell cancer or non-small cell
cancer.
[0018] In some embodiments, the one or more genes comprise mRNA
and/or microRNA. In some embodiments, the differential expression
is determined by reverse transcribing one or more RNAs of the one
or more genes into cDNA in vitro. In some aspects, the one or more
genes comprise cDNA. In yet other embodiments, the one or more
genes are labeled prior to the measuring.
[0019] The above discussed, and many other features and attendant
advantages of the present inventions will become better understood
by reference to the following detailed description of the
invention.
BRIEF DESCRIPTION OF THE DRAWINGS
[0020] The patent or application file contains at least one drawing
executed in color. Copies of this patent or patent application
publication with color drawings will be provided by the Office upon
request and payment of the necessary fee.
[0021] FIG. 1 represents a flow diagram depicting the design of the
study used in the Examples. Depicted is the use of bronchial
brushings collected from subjects with (red, n=50) and without
(gray, n=25) PMLs from the BCCA as part of the BC-LHS for
differential gene expression/pathway analysis and for biomarker
development. Independent human and mouse bronchial biopsies and
biopsy cell cultures were used to validate these findings via
mitochondrial enumeration, bioenergetics, and immunohistochemistry
(left panel). Biomarker development was conducted by splitting
samples from the BC-LHS into a discovery (n=58) and a validation
set (Validation 1, n=17) (right panel). The discovery set was used
to create the gene expression-based biomarker to detect the
presence of PMLs in the airway field of injury. The biomarker was
tested on the BC-LHS validation set and an external validation set
(bottom) from RPCI (Validation 2, n=28 matched time point pairs,
stable/progressing pairs in yellow and regressing pairs in
blue).
[0022] FIG. 2 shows an unsupervised hierarchal clustering of genes
associated with the presence of premalignant lesions. Residual gene
expression of the 280 genes differentially expressed between
subjects with PMLs (red) and without PMLs (gray). Top color bars
represent the worst biopsy histological grade observed during
bronchoscopy and genomically-derived smoking status of the
subjects. The 14 genes in the KEGG oxidative phosphorylation
pathway are indicated in cyan. The residual values after adjusting
for the 7 surrogate variables were z-score normalized prior to Ward
hierarchal clustering.
[0023] FIGS. 3A-3E illustrate OXPHOS up-regulation in premalignant
lesion biopsies. FIG. 3A shows the mean baseline OCR/ECAR ratio
measured in human bronchial biopsies cultures from PMLs (pink, n=6)
was 2.5 fold higher than the biopsies of normal airway epithelium
(gray n=6) (p=0.035). Error bars represent standard error of the
mean. FIG. 3B shows bioenergetic studies testing mitochondrial
function demonstrate PMLs (pink) have a significantly (.about.1.5
fold) higher maximal respiration (p=0.022). Error bars represent
standard error of the mean. FIG. 3C and FIG. 3D show mitochondrial
enumeration by FACS analysis of MitoTraker GFP suggests increased
OCR is not reliant on increase mitochondria as the difference in
GFP per cell was not significant (p=0.150). FIG. 3E shows
representative images of TOMM22 and COX IV staining in which
expression of both proteins is increased in low and moderate
dysplastic lesions in both human and NTCU-mouse PMLs.
(Magnification 400.times.).
[0024] FIGS. 4A-4C shows that PML-associated gene expression
alterations in the field are concordant with SCC-related datasets.
The genes up-regulated in the field of subjects with PMLs are red
and genes down regulated in blue. GSEA identified the significant
enrichment of the lung cancer-related gene expression signatures
shown in this ranked list. The black vertical lines represent the
position of the genes in the gene set in the ranked list and the
height corresponds to the magnitude of the running enrichment score
from GSEA. FIG. 4A shows top differentially expressed genes from
analysis of TCGA RNA-Seq data comparing lung SCC and matched
adjacent normal tumor tissue. FIG. 4B shows Ooi et al. gene sets
for early gene expression changes defined by genes altered between
premalignant and normal tissue and between tumor and normal tissue
(p<0.05) using laser capture microdissected (LCM) epithelium
from the margins of resected SCC tumors. FIG. 4C shows top
differentially expressed genes from analysis of cytologically
normal bronchial epithelial cells from smokers with and without
lung cancer (GSE4115).
[0025] FIGS. 5A-5B show performance of an airway biomarker in
detecting the presence and progression of premalignant lesions. The
ROC curves demonstrate the biomarker performance. FIG. 5A is a ROC
curve (AUC=0.92) showing biomarker performance based on predictions
of the presence of PMLs in the validation samples (n=17), red line.
Shuffling of class labels (n=100 permutations) produced an average
ROC curve (black line) with a significantly lower AUC
(p<<0.001). FIG. 5B is a ROC curve (AUC=0.75) showing
biomarker performance based on changes in biomarker score over time
in detecting PML regression or stable/progression.
[0026] FIG. 6 shows unsupervised hierarchal clustering of genes
associated with smoking status. The weighted voting algorithm was
trained on z-score normalized microarray data (GSE7895) across 94
genes differentially expressed between current and never smokers
and used to predict smoking status in log 2-transformed counts per
million (cpm) that were z-score normalized from the 82 mRNA-Seq
samples. The heatmap shows the results of unsupervised Ward
hierarchal clustering across the 82 mRNA-Seq samples and the 94
genes. The row color label indicates if genes were up-regulated
(red) or down-regulated (green) in current smokers compared to
never smokers in GSE7895. The lower column color labels indicate
the smoking status in the clinical annotation (self-report) with
light gray indicating former smokers and dark gray indicating
current smokers. The upper column color labels indicate the
predicted class of the samples based on the 94 genes with white
indicating former smokers and black indicating current smokers. Log
2-cpm mRNA-Seq data was z-score normalized prior to clustering.
[0027] FIGS. 7A-7H show cellular metabolism in cancer cell lines
and in the airway field associated with premalignant lesions FIG.
7A shows GSVA scores were calculated based on genes in KEGG OXPHOS
pathway and KEGG, Biocarta, and Reactome Glycolysis pathways in the
CCLE cell lines highlighting the H1229 (green) (high OXPHOS and
moderate glycolysis), SW900 (red) (moderate OXPHOS and low
glycolysis) and H2805 (blue) ((low OXPHOS and moderate glycolysis).
FIG. 7B shows baseline OCR/ECAR ratio values for the cancer cells
lines demonstrating the relationship between elevated OXPHOS GSVA
scores and oxygen consumption. FIG. 7C shows elevation of
respiratory capacity associated with high OXPHOS gene score in
response to mitochondrial perturbation. FIG. 7D shows elevated ECAR
response in the H1299 and H205 is associated with the moderate
glycolysis GSVA score, however, although the SW900 glycolysis GSVA
scores agree with baseline ECAR, in the state of repressed OXPHOS,
glycolysis is activated. FIG. 7E shows enumeration of mitochondria
within each cancer cell suggests that increased GSVA scores for
OXPHOS or glycolysis did not correlate with mitochondrial number.
H2085 cells had the lowest OXPHOS GSVA score, the lowest basal OCR,
and the lowest respiratory capacity, but their mitochondrial
content was significantly greater that H1299 and SW900 (p=0.03).
FIG. 7F shows cell area (FSC-A) is correlated with mitochondrial
number (fluorescence of MitoTracker Green FM). FIG. 7G shows GSVA
scores were calculated based on genes in KEGG OXPHOS pathway. The
GSVA scores for OXPHOS activity were significantly elevated in the
airway field of subjects with PMLs compared to subjects without
PMLs (p<0.01). FIG. 7H shows GSVA scores were calculated based
on genes in the KEGG, Biocarta, and Reactome Glycolysis pathways.
The mean GSVA scores were moderately elevated in the airway field
of subjects with PMLs compared to subjects without PMLs.
[0028] FIG. 8 shows a biomarker discovery flowchart. Samples (n=75)
were split into a discovery set (n=58) and a validation set (n=17).
The pipeline was run 500 times, and each time the discovery set was
randomly split into training (80% of samples, n=46) and test (20%
of samples, n=12) sets. The training set samples were used to train
the biomarker using all combinations of pipeline parameters,
including: 1. Up-/down-regulation ratio: TRUE or FALSE; 2. Data
type: raw counts, RPKM or CPM; 3. Gene filter: genes with signal in
at least 1%, 5%, 10%, or 15% of samples; 4. Feature selection:
edgeR, edgeR correcting for gb-ratio, limma, limma correcting for
gb-ratio, glmnet, random forest, DESeq, SVA, or partial AUC; 5.
Gene number: 10, 20, 40, 60, 80, 100, or 200 genes (see Biomarker
size); and 6. Prediction method: weighted voting, random forest,
SVM, naive bayes, or glmnet.
[0029] FIG. 9 shows that biomarker predicts dysplasia status in
bronchial biopsies. ROC curve demonstrates the performance of the
biomarker in distinguishing between premalignant lesion biopsies
(severe=8, moderate=25, and mild dysplasia=14) and biopsies with
normal histology (normal=24 and hyperplasia=20). Biomarker achieved
AUC of 72% (with a 62%-83% confidence interval), sensitivity of 81%
(38 of 47 dysplastic biopsies predicted correctly), and specificity
of 66% (29 of 44 normal biopsies predicted correctly).
DETAILED DESCRIPTION OF THE INVENTION
[0030] Lung cancer develops in a sequenced manner Patches of lung
cells gain the ability to multiply faster than their neighboring
normal cells by acquiring mutations and these patches of cells are
called "premalignant lesions" or "PMLs." Some of these PMLs may
progress to lung cancer. The inventions disclosed herein are based
upon a biomarker that is capable of identifying and distinguishing
epithelial cells from a person with lung cancer from normal
epithelial cells. In particular, the inventions disclosed herein
are based on the findings that exposure to carcinogens such as
cigarette smoke induces smoking-related mRNA and microRNA
expression alterations in the cytologically normal epithelium that
lines the respiratory tract, creating an airway field of injury
(1-8). Such gene expression alterations that were observed in the
airway field of injury were used to develop a diagnostic test to
facilitate early lung cancer lung cancer detection (9-12).
Examination of gene signatures for p63 and the phosphatidylinositol
3-kinase (PI3K) pathway, revealed increased PI3K activation in the
airway field of smokers with lung cancer or bronchial premalignant
lesions (PMLs) (13). These results suggest the airway field of
injury reflects processes associated with a precancerous disease
state; however, the molecular changes have not been adequately
characterized.
[0031] This is an important shortcoming because bronchial PMLs are
precursors of squamous cell lung carcinoma, yet effective tools to
identify smokers with PMLs at highest risk of progression to
invasive cancer are lacking. Several studies report loss of
heterozygosity, chromosomal aneusomy, and aberrant methylation and
protein expression in bronchial PMLs (14-23). These molecular
events can give rise to histological changes that can be
reproducibly graded by a pathologist prior to the development of
invasive carcinoma. Autofluorescence bronchoscopy can be used to
detect and sample PMLs, which have a prevalence of approximately 9%
for moderate dysplasia and 0.8% for carcinoma in situ (CIS)
(24-26). The presence of high grade PMLs (severe dysplasia or CIS)
is a marker of increased lung cancer risk in both the central and
peripheral airways indicating the presence of changes throughout
the airway field (27, 28).
[0032] The molecular characterization of the airway field of injury
in smokers with PMLs disclosed herein provides novel insights into
the earliest stages of lung carcinogenesis and identifies
relatively accessible biomarkers to guide early lung cancer
detection and early intervention. Accordingly, disclosed herein are
novel biomarkers and gene expression signatures and related assays
and methods that are able to provide information about the
precancerous disease state and if this pre-cancerous disease state
is progressing and/or regressing. Such biomarkers and the related
assays and methods are useful for monitoring the progression of
premalignant or pre-cancerous conditions in a subject by obtaining
(e.g., non-invasively obtaining) a biological sample of epithelial
cells from the respiratory tract of the subject (e.g., bronchial or
nasal epithelial cells). In certain aspects, alterations in gene
expression observed in epithelial cells that are distal to the lung
tissues (e.g., nasal or buccal epithelial cells) are concordant
with changes in the bronchial epithelium.
[0033] The present inventions represent a significant advance in
the detection and monitoring of individuals with premalignant
lesions (PMLs), particularly in comparison to the standard of care
auto-fluorescence bronchoscopy techniques which are less sensitive.
In addition to detecting and monitoring of PMLs, the present
inventions provide means of advancing the identification of
chemoprevention agents, which historically has been bounded by the
difficulty of discerning premalignant phenotypes through currently
available means. The present inventions further provide means of
using gene expression profiling as a surrogate end point that
complements both histological and marker end points used today,
such as Ki67.
[0034] The biomarkers and related methods and assays disclosed
herein are based in part upon the finding of a strong correlation
between PMLs and the alterations in gene expression in tissues that
are physically distant from the site of disease (e.g., the nasal
epithelium). It has further been found that these biomarkers
strongly predict whether a suspected PML is pre-malignant. The
biomarkers, assays and methods disclosed herein are characterized
by the accuracy with which they can detect and monitor lung cancer
and their non-invasive or minimally-invasive nature. In some
aspects, the assays and methods disclosed herein are based on
detecting differential expression of one or more genes in airway
epithelial cells and such assays and methods are based on the
discovery that such differential expression in airway epithelial
cells are useful for identifying and monitoring PMLs in the distant
lung tissue. Accordingly, the inventions disclosed herein provide a
substantially less invasive method for diagnosis, prognosis and
monitoring of lung cancer using gene expression analysis of
biological samples comprising airway epithelial cells.
[0035] In contrast to conventional invasive methods, such as
auto-fluorescence bronchoscopy, the assays and methods disclosed
herein rely on expression of certain genes in a biological sample
obtained from a subject. As the phrase is used herein, "biological
sample" means any sample taken or derived from a subject comprising
one or more airway epithelial cells. As used herein, the phrase
"obtaining a biological sample" refers to any process for directly
or indirectly acquiring a biological sample from a subject. For
example, a biological sample may be obtained (e.g., at a
point-of-care facility, a physician's office, a hospital) by
procuring a tissue or fluid sample from a subject. Alternatively, a
biological sample may be obtained by receiving the sample (e.g., at
a laboratory facility) from one or more persons who procured the
sample directly from the subject.
[0036] Such biological samples comprising airway epithelial cells
may be obtained from a subject (e.g., a subject suspected of having
one or more PMLs or that is otherwise at risk for developing lung
cancer) using a brush or a swab. The biological sample comprising
airway epithelial cells may be collected by any means known to one
skilled in the art and, in certain embodiments, is obtained in a
non-invasive or minimally-invasive manner. For example, in certain
embodiments, a biological sample comprising airway epithelial cells
(e.g., nasal epithelial cells) may be collected from a subject by
nasal brushing. Similarly, nasal epithelial cells may be collected
by brushing the inferior turbinate and/or the adjacent lateral
nasal wall. For example, following local anesthesia with 2%
lidocaine solution, a CYROBRUSH.RTM. (MedScand Medical,
Malmo.delta., Sweden) or a similar device, is inserted into the
nare of the subject, for example the right nare, and under the
inferior turbinate using a nasal speculum for visualization. The
brush is turned (e.g., turned 1, 2, 3, 4, 5 times or more) to
collect the nasal epithelial cells, which may then be subjected to
analysis in accordance with the assays and methods disclosed
herein.
[0037] In some embodiments, methods and assays disclosed herein may
be assessed or performed on a biological sample that is obtained
from a subject at a suspected site of a PML (e.g., premalignant
bronchial lesion). In some embodiments, the suspected site is
identified as having abnormal fluorescent during auto-fluorescence
bronchoscopy, although the method of identifying the suspected site
is not limited. In some embodiments, the methods and assays
disclosed herein may be performed on a biopsy of a suspected PML as
an alternative to, or in addition to, a histological examination of
the biopsy.
[0038] In certain embodiments, the biological sample does not
include or comprise bronchial airway epithelial cells. For example,
in certain embodiments, the biological sample does not include
epithelial cells from the mainstem bronchus. In certain aspects,
the biological sample does not include cells or tissue collected
from bronchoscopy. In some embodiments, the biological sample does
not include cells or tissue isolated from a pulmonary lesion. In
some embodiments, the biological sample does not include cells or
tissue isolated from a PML.
[0039] To isolate nucleic acids from the biological sample, the
airway epithelial cells can be placed immediately into a solution
that prevents nucleic acids from degradation. For example, if the
nasal epithelial cells are collected using the CYTOBRUSH, and one
wishes to isolate RNA, the brush is placed immediately into an RNA
stabilizer solution, such as RNALATER.RTM., AMBION.RTM., Inc. One
can also isolate DNA. After brushing, the device can be placed in a
buffer, such as phosphate buffered saline (PBS) for DNA
isolation.
[0040] The nucleic acids (e.g., mRNA) are then subjected to gene
expression analysis. Preferably, the nucleic acids are isolated and
purified. However, if techniques such as microfluidic devices are
used, cells may be placed into such device as whole cells without
substantial purification. In one embodiment, airway epithelial cell
gene expression is analyzed using gene/transcript groups and
methods of using the expression profile of these gene/transcript
groups in diagnosis and prognosis of lung diseases. In some
embodiments, differential expression of the one or more genes
determined with reference to the one or more of the 280 genes set
forth in Table 3.
[0041] As used herein, the term "differential expression" refers to
any qualitative or quantitative differences in the expression of
the gene or differences in the expressed gene product (e.g., mRNA
or microRNA) in the airway epithelial cells of the subject. A
differentially expressed gene may qualitatively have its expression
altered, including an activation or inactivation, in, for example,
the presence of absence of cancer and, by comparing such expression
in airway epithelial cell to the expression in a control sample in
accordance with the methods and assays disclosed herein, and the
presence or absence of PMLs may be determined and their progression
or regression monitored.
[0042] In certain embodiments, the methods and assays disclosed
herein are characterized as being much less invasive relative to,
for example, bronchoscopy. The methods provided herein not only
significantly increase the sensitivity or diagnostic accuracy of
detecting and monitoring PMLs, but in certain aspects also make the
analysis faster, much less invasive and thus much easier for the
clinician to perform. In some embodiments, the likelihood that the
subject has a PML or the likelihood that such a PML will progress
to lung cancer is also determined based on the presence or absence
of one or more secondary factors or diagnostic indicia of lung
cancer, such as the subject's smoking history or status, or the
results of previously performed imaging studies (e.g., chest CT
scans). When the biomarkers, assays and methods of the present
invention are combined with, for example, one or more relevant
secondary factors (e.g., a subject's smoking history), the
sensitivity and accuracy of detecting PMLs or their progression to
lung cancer may be dramatically enhanced, enabling the detection of
PMLs or their progression to lung cancer at an earlier stage, and
by providing far fewer false negatives and/or false positives. As
used herein, the phrase "secondary factors" refers broadly to any
diagnostic indicia that would be relevant for determining a
subject's risk of having or developing lung cancer. Exemplary
secondary factors that may be used in combination with the methods
or assays disclosed herein include, for example, imaging studies
(e.g., chest X-ray, CT scan, etc.), the subject's smoking status or
smoking history, the subject's family history and/or the subject's
age. In certain aspects, when such secondary factors are combined
with the methods and assays disclosed herein, the sensitivity,
accuracy and/or predictive power of such methods and assays may be
further enhanced. In some aspects, the methods and assays described
herein are performed on a patient with a positive result in an
imaging study (e.g., chest X-ray, CT scan, etc.). In some aspects,
the methods or assays disclosed herein are used to confirm or rule
out a positive result in an imaging study (e.g., chest X-ray, CT
scan, etc.). In some aspects, the methods or assays disclosed
herein are used to determine whether a positive result in an
imaging study warrants a further invasive procedure (e.g.,
bronchoscopy), chemoprophylaxis, and/or chemotherapy.
[0043] The present inventors have discovered that PMLs and normal
lung cells use different pathways to produce energy and survive and
have harnessed this difference to develop the biomarker and related
assays and methods disclosed herein. In some embodiments, the
biological sample comprising the subject's airway epithelial cells
(e.g., nasal or buccal epithelial cells) are analyzed for the
expression of certain genes or gene transcripts corresponding to
such metabolic pathways, either individually or in groups or
subsets. In one embodiment, the inventions disclosed herein provide
a group of genes corresponding to one or more pathways (e.g.,
metabolic pathways) that are significantly enriched in genes that
are up- or down-regulated in the presence of PMLs (e.g., one or
more pathways identified in Dataset 2) and that may be analyzed to
determine the presence or absence of PMLs and/or their progression
to lung cancer (e.g., adenocarcinoma, squamous cell carcinoma,
small cell cancer and/or non-small cell cancer) from a biological
sample comprising the subject's airway epithelial cells. For
example, in certain aspects the biological sample may be analyzed
to determine the differential expression of one or more genes from
pathways involved in oxidative phosphorylation (OXPHOS), the
electron transport chain (ETC), and mitochondrial protein transport
to determine whether the subject has PMLs or is at risk of
developing lung cancer. Other up-regulated pathways included DNA
repair and the HIF1A pathway. Down-regulated pathways included the
STATS pathway, the JAK/STAT pathway, IL-4 signaling, RAC1
regulatory pathway, NCAM1 interactions, collagen formation, and
extracellular matrix organization.
[0044] In certain embodiments, the airway epithelial cells are
analyzed using at least one and no more than 280 of the genes
listed in Table 3. For example, about 1, about 2, about 3, about 4,
about 5, about 6, about 7, about 8, about 9, about 10, about 10-15,
about 15-20, about 20-30, about 30-40, about 40-50, at least about
10, at least about 20, at least about 30, at least about 40, at
least about 50, at least about 60, at least about 70, at least
about 80, at least about 90, at least about 100, at least about
110, at least about 120, at least about 130, at least about 140, at
least about 150, at least about 160, at least about 170, at least
about 180, at least about 190, at least about 200, 210, 220, 230,
240, 250, 260, 270 or 275 or a maximum of the 280 genes as listed
on Table 3.
[0045] Examples of the gene transcript groups useful in the
diagnostic and prognostic assays and methods of the invention are
set forth in Table 3. The present inventors have determined that
taking any group that has at least about 5, 10, 15, 20, 25, 30, 40,
50, 60, 70, 80, 90, 100, 125, 150, 175, 200, 225, 250, 275 or more
of the Table 3 genes provides a much greater PML detection
sensitivity than chance alone. Preferably one would analyze the
airway epithelial cells using more than about 20 of these genes,
for example about 20-280 and any combination between, for example,
21, 22, 23, 24, 25, 26, 27, 28, 29, 30, and so on. In some
instances, the present inventors have determined that one can
enhance the sensitivity or diagnostic accuracy of the methods and
assays disclosed herein by adding additional genes to any of these
specific groups. For example, in certain aspects, the accuracy of
such methods may approach about 70%, about 75%, about 80%, about
82.5%, about 85%, about 87.5%, about 88%, about 90%, about 92.5%,
about 95%, about 97.5%, about 98%, about 99% or more by evaluating
the differential expression of more genes from the set (e.g., the
set of genes set forth in Table 3).
[0046] In some embodiments, the presence of PMLs or their
progression/regression is made by comparing the expression of the
genes or groups of genes set forth in, for example Table 3, by the
subject's airway epithelial cells to a control subject or a control
group (e.g., a positive control with confirmed PMLs or a confirmed
diagnosis of lung cancer). In certain embodiments, an appropriate
control is an expression level (or range of expression levels) of a
particular gene that is indicative of the known presence of PMLs or
a known lung cancer status. An appropriate reference can be
determined experimentally by a practitioner of the methods
disclosed herein or may be a pre-existing expression value or range
of values. When an appropriate control is indicative of lung
cancer, a lack of a detectable difference (e.g., lack of a
statistically significant difference) between an expression level
determined from a subject in need of characterization or diagnosis
of lung cancer and the appropriate control may be indicative of
lung cancer in the subject. When an appropriate control is
indicative of the presence of PMLs or lung cancer, a difference
between an expression level determined from a subject in need of
characterization or determination of PMLs or diagnosis of lung
cancer and the appropriate reference may be indicative of the
subject being free of PMLs or lung cancer.
[0047] Alternatively, an appropriate control may be an expression
level (or range of expression levels) of one or more genes that is
indicative of a subject being free of PMLs or lung cancer. For
example, an appropriate control may be representative of the
expression level of a particular set of genes in a reference
(control) biological sample obtained from a subject who is known to
be free of PMLs or lung cancer. When an appropriate control is
indicative of a subject being free of PMLs or lung cancer, a
difference between an expression level determined from a subject in
need of detection of PMLs or the diagnosis of lung cancer and the
appropriate reference may be indicative of the presence of PMLs
and/or lung cancer in the subject. Alternatively, when an
appropriate reference is indicative of the subject being free of
PMLs or lung cancer, a lack of a detectable difference (e.g., lack
of a statistically significant difference) between an expression
level determined from a subject in need of detection of PMLs or
diagnosis of lung cancer and the appropriate reference level may be
indicative of the subject being free of PMLs and/or lung
cancer.
[0048] The control groups can be or comprise one or more subjects
with a confirmed presence of PMLs, positive lung cancer diagnosis,
a confirmed absence of PMLs or a negative lung cancer diagnosis.
Preferably, the genes or their expression products in the airway
epithelial cell sample of the subject are compared relative to a
similar group, except that the members of the control groups may
not have PMLs and/or lung cancer. For example, such a comparison
may be performed in the airway epithelial cell sample from a smoker
relative to a control group of smokers who do not have PMLs or lung
cancer. The transcripts or expression products are then compared
against the control to determine whether increased expression or
decreased expression can be observed, which depends upon the
particular gene or groups of genes being analyzed, as set forth,
for example, in Table 3. In certain embodiments, at least 50% of
the gene or groups of genes subjected to expression analysis must
provide the described pattern. Greater reliability is obtained as
the percent approaches 100%. Thus, in one embodiment, at least
about 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 98%, 99% of the
one or more genes subjected to expression analysis demonstrate an
altered expression pattern that is indicative of the presence or
absence of PMLs or lung cancer, as set forth in, for example, Table
3. Similarly, in one embodiment, at least about 55%, 60%, 65%, 70%,
75%, 80%, 85%, 90%, 95%, 98%, 99% of the one or more genes involved
in a pathways set forth in Dataset 2 are subjected to expression
analysis and demonstrate an altered expression pattern that is
indicative of the subject's cancer status.
[0049] Any combination of the genes and/or transcripts of Table 3
can be used in connection with the assays and methods disclosed
herein. In one embodiment, any combination of at least 5-10, 10-20,
20-30, 30-40, 40-50, 50-60, 60-70, 70-80, 80, 80-90, 90-100,
100-120, 120-140, 140-150, 150-160, 160-170, 170-180, 180-190,
190-200, 200-210, 210-220, 220-230, 230-240, 240-250, 250-260,
260-270 or 270-280 genes selected from the group consisting of
genes or transcripts as shown in the Table 3.
[0050] The analysis of the gene expression of one or more genes may
be performed using any gene expression methods known to one skilled
in the art. Such methods include, but are not limited to expression
analysis using nucleic acid chips (e.g. Affymetrix chips) and
quantitative RT-PCR based methods using, for example real-time
detection of the transcripts. Analysis of transcript levels
according to the present invention can be made using total or
messenger RNA or proteins encoded by the genes identified in the
diagnostic gene groups of the present invention as a starting
material. In certain aspects, analysis of transcript levels
according to the present invention can be made using micronRNA. In
the preferred embodiment the analysis is an immunohistochemical
analysis with an antibody directed against proteins comprising at
least about 10-20, 20-30, preferably at least 36, at least 36-50,
50, about 50-60, 60-70, 70-80, 80-90, 96, 100-180, 180-200, 200-250
or 250-280 of the proteins encoded by the genes and/or transcripts
as shown in Table 3.
[0051] The methods of analyzing expression and/or determining an
expression profile of the one or more genes include, for example,
Northern-blot hybridization, ribonuclease protection assay, and
reverse transcriptase polymerase chain reaction (RT-PCR) based
methods. In certain aspects, the different RT-PCR based techniques
are a suitable quantification method for diagnostic purposes of the
present invention, because they are very sensitive and thus require
only a small sample size which is desirable for a diagnostic test.
A number of quantitative RT-PCR based methods have been described
and are useful in measuring the amount of transcripts according to
the present invention. These methods include RNA quantification
using PCR and complementary DNA (cDNA) arrays (Shalon, et al.,
Genome Research 6(7):639-45, 1996; Bernard, et al., Nucleic Acids
Research 24(8): 1435-42, 1996), real competitive PCR using a
MALDI-TOF Mass spectrometry based approach (Ding, et al., PNAS,
100: 3059-64, 2003), solid-phase mini-sequencing technique, which
is based upon a primer extension reaction (U.S. Pat. No. 6,013,431,
Suomalainen, et al., Mol. Biotechnol. June; 15(2): 123-31, 2000),
ion-pair high-performance liquid chromatography (Doris, et al., J.
Chromatogr. A May 8; 806(1):47-60, 1998), and 5' nuclease assay or
real-time RT-PCR (Holland, et al., Proc Natl Acad Sci USA 88:
7276-7280, 1991).
[0052] The presently described gene expression profile can also be
used to screen for subjects with confirmed PMLs to determine
whether such subject are susceptible to or otherwise at risk for
developing lung cancer. For example, a current smoker of advanced
age (e.g., 70 years old) with PMLs may be at an increased risk for
developing lung cancer and may represent an ideal candidate for the
assays and methods disclosed herein. Moreover, the early detection
of lung cancer in such a subject may improve the subject's overall
survival. Accordingly, in certain aspects, the assays and methods
disclosed herein are performed or otherwise comprise an analysis of
the subject's secondary risk factors for developing cancer. For
example, one or more secondary factors selected from the group
consisting of advanced age (e.g., age greater than about 40 years,
50 years, 55 years, 60 years, 65 years, 70 years, 75 years, 80
years, 85 years, 90 years or more), smoking status, the presence of
a lung nodule greater than 3 cm on CT scan and the time since the
subject quit smoking. In certain embodiments, the assays and
methods disclosed herein further comprise a step of considering the
presence of any such secondary factors to inform the determination
of whether the subject has PMLs or whether such PMLs are likely to
progress to lung cancer.
[0053] As used herein, a "subject" means a human or animal Usually
the animal is a vertebrate such as a primate, rodent, domestic
animal or game animal. In certain embodiments, the subject is a
mammal (e.g., a primate or a human). The subject may be an infant,
a toddler, a child, a young adult, an adult or a geriatric. The
subject may be a smoker, a former smoker or a non-smoker. The
subject may have a personal or family history of cancer. The
subject may have a cancer-free personal or family history. The
subject may exhibit one or more symptoms of lung cancer or other
lung disorder (e.g., emphysema, COPD). For example, the subject may
have a new or persistent cough, worsening of an existing chronic
cough, blood in the sputum, persistent bronchitis or repeated
respiratory infections, chest pain, unexplained weight loss and/or
fatigue, or breathing difficulties such as shortness of breath or
wheezing. The subject may have a lesion, which may be observable by
computer-aided tomography or chest X-ray. The subject may be an
individual who has undergone a bronchoscopy or who has been
identified as a candidate for bronchoscopy (e.g., because of the
presence of a detectable lesion or suspicious imaging result). The
terms, "patient" and "subject" are used interchangeably herein. In
some embodiments, the subject is at risk for developing lung
cancer. In some embodiments, the subject has PMLs or lung cancer
and the assays and methods disclosed herein may be used to monitor
the progression of the subject's disease or to monitor the efficacy
of one or more treatment regimens.
[0054] In some embodiments, the methods and assays disclosed herein
are useful for identifying subjects that are candidates for
enrollment in a clinical trial to assess the efficacy of one or
more chemotherapeutic agents. In certain aspects, the methods and
assays disclosed herein are useful for determining a treatment
course for a subject. For example, such methods and assays may
involve determining the expression levels of one or more genes
(e.g., one or more of the genes set forth in Table 3) in a
biological sample obtained from the subject, and determining a
treatment course for the subject based on the expression profile of
such one or more genes. In some embodiments, the treatment course
is determined based on a risk-score derived from the expression
levels of the one or more genes analyzed. The subject may be
identified as a candidate for a particular intervention or
treatment based on an expression profile that indicates the
subject's likelihood of having PMLs that will progress lung cancer.
Similarly, the subject may be identified as a candidate for an
invasive lung procedure (e.g., transthoracic needle aspiration,
mediastinoscopy, lobectomy, or thoracotomy) based on an expression
profile that indicates the subject has a relatively high likelihood
of having PMLs or a high likelihood that such PMLs will progress to
lung cancer (e.g., greater than 60%, greater than 70%, greater than
80%, greater than 90%). Conversely, the subject may be identified
as not being a candidate for interventional therapy or an invasive
lung procedure based on an expression profile that indicates the
subject has a relatively low likelihood (e.g., less than 50%, less
than 40%, less than 30%, less than 20%) of having PMLs or a low
likelihood that such PMLs will progress to lung cancer. In some
embodiments, a health care provider may elect to monitor the
subject using the assays and methods disclosed herein and/or repeat
the assays or methods at one or more later points in time, or
undertake further diagnostics procedures to rule out PMLs or lung
cancer. Also contemplated herein is the inclusion of one or more of
the genes and/or transcripts presented in, for example, Table 3
into a composition or a system for detecting lung cancer in a
subject. For example, any one or more genes and or gene transcripts
from Table 3 may be added as a PML marker or lung cancer marker for
a gene expression analysis. In some aspects, the present inventions
relate to compositions that may be used to determine the expression
profile of one or more genes from a subject's biological sample
comprising airway epithelial cells. For example, compositions are
provided that consist essentially of nucleic acid probes that
specifically hybridize with one or more genes set forth in Table 3.
These compositions may also include probes that specifically
hybridize with one or more control genes and may further comprise
appropriate buffers, salts or detection reagents. In certain
embodiments, such probes may be fixed directly or indirectly to a
solid support (e.g., a glass, plastic or silicon chip) or a bead
(e.g., a magnetic bead).
[0055] The compositions described herein may be assembled into
diagnostic or research kits to facilitate their use in one or more
diagnostic or research applications. In some embodiments, such kits
and diagnostic compositions are provided that comprise one or more
probes capable of specifically hybridizing to up to 5, up to 10, up
to 25, up to 50, up to 100, up to 200, up to 225, up to 250 or up
to 280 genes set forth in Table 3 or their expression products
(e.g., mRNA or microRNA). In some embodiments, each of the nucleic
acid probes specifically hybridizes with one or more genes selected
from those genes set forth in Table 3, or with a nucleic acid
having a sequence complementary to such genes. A kit may include
one or more containers housing one or more of the components
provided in this disclosure and instructions for use. Specifically,
such kits may include one or more compositions described herein,
along with instructions describing the intended application and the
proper use and/or disposition of these compositions. Kits may
contain the components in appropriate concentrations or quantities
for running various experiments.
[0056] The articles "a" and "an" as used herein in the
specification and in the claims, unless clearly indicated to the
contrary, should be understood to include the plural referents.
Claims or descriptions that include "or" between one or more
members of a group are considered satisfied if one, more than one,
or all of the group members are present in, employed in, or
otherwise relevant to a given product or process unless indicated
to the contrary or otherwise evident from the context. The
invention includes embodiments in which exactly one member of the
group is present in, employed in, or otherwise relevant to a given
product or process. The invention also includes embodiments in
which more than one, or the entire group members are present in,
employed in, or otherwise relevant to a given product or process.
Furthermore, it is to be understood that the invention encompasses
all variations, combinations, and permutations in which one or more
limitations, elements, clauses, descriptive terms, etc., from one
or more of the listed claims is introduced into another claim
dependent on the same base claim (or, as relevant, any other claim)
unless otherwise indicated or unless it would be evident to one of
ordinary skill in the art that a contradiction or inconsistency
would arise. Where elements are presented as lists, (e.g., in
Markush group or similar format) it is to be understood that each
subgroup of the elements is also disclosed, and any element(s) can
be removed from the group. It should be understood that, in
general, where the invention, or aspects of the invention, is/are
referred to as comprising particular elements, features, etc.,
certain embodiments of the invention or aspects of the invention
consist, or consist essentially of, such elements, features, etc.
For purposes of simplicity those embodiments have not in every case
been specifically set forth in so many words herein. It should also
be understood that any embodiment or aspect of the invention can be
explicitly excluded from the claims, regardless of whether the
specific exclusion is recited in the specification. The
publications and other reference materials referenced herein to
describe the background of the invention and to provide additional
detail regarding its practice are hereby incorporated by
reference.
EXAMPLES
Example 1
Patient Population
[0057] Bronchial airway brushings were obtained during
autofluorescence bronchoscopy procedures between June 2000 and
March 2011 from subjects in the British Columbia Lung Health Study
at the British Columbia Cancer Agency (BCCA) (Vancouver, BC) (29)
and between December 2009 and March 2013 from subjects in the
High-Risk Lung Cancer-Screening Program at Roswell Park Cancer
Institute (RPCI) (Buffalo, N.Y.) (detailed cohort information in
the Methods section below). Premalignant Lesions were sampled (if
present) using endobronchial biopsy, graded by a team of
pathologists at BCCA or RPCI, and the worst histology observed was
recorded. Bronchial brushes of normal-appearing epithelium from 84
BCCA subjects (1 brush per subject) with and without PMLs were
selected to undergo mRNA-Seq while ensuring balanced clinical
covariates. Fifty-one bronchial brushes of normal-appearing
epithelium from 23 RPCI subjects were also profiled by mRNA-Seq (18
subjects had 2 procedures, and 5 subjects had 3 procedures). The
RPCI samples were utilized in biomarker validation to calculate
changes in the biomarker score between sequential procedures. Sets
of samples were classified as stable/progressive if the worst
histological grade at the second time point for a given patient
remained the same or worsened, and regressive if the worst
histological grade at the second time point improved. The
Institutional Review Boards (IRBs) of all participating
institutions approved the study and all subjects provided written
informed consent.
RNA-Seq Library Preparation, Sequencing and Data Processing
[0058] Total RNA was extracted from bronchial brushings using
miRNeasy Mini Kit (Qiagen). Sequencing libraries were prepared from
total RNA samples using Illumina.RTM. TruSeq.RTM. RNA Kit v2 and
multiplexed in groups of four using Illumina.RTM. TruSeq.RTM.
Paired-End Cluster Kit. Each sample was sequenced on the
Illumina.RTM. HiSeq.RTM. 2500 to generate paired-end 100 nucleotide
reads. Demultiplexing and creation of FASTQ files were performed
using Illumina CASAVA v1.8.2. For the BCCA samples, reads were
aligned to hg19 using TopHat v2.0.4. The insert size mean and
standard deviation were determined using the alignments and MISO
(32). Reads were realigned using TopHat and the insert size
parameters. Alignment and quality metrics were calculated using
RSeQC v2.3.3. Gene count estimates were derived using HTSeq-count
v0.5.4 (33) and the Ensembl v64 GTF file. Gene filtering was
conducted on normalized counts per million (cpm) calculated using R
v3.0.0 and edgeR v3.4.2 using a modified version of the mixture
model in the SCAN. UPC Bioconductor package (34). A gene was
included in downstream analyses if the mixture model classified it
as "on" (i.e. "signal") in at least 15% of the samples. For the
RPCI samples, gene counts were computed using RSEM (v1.2.1) (30)
and Bowtie (v1.0.0) (31) with Ensembl 74 annotation. The data is
available from NCBI's Gene Expression Omnibus (GEO) using the
accession ID GSE79315.
Data Analysis for the BCCA Samples
[0059] Sample and gene filtering yielded 13,870 out of 51,979 genes
and 82 samples (n=2 excluded due to quality or sex annotation
mismatches) for analysis. Data from Beane et al. (3) was used to
predict the smoking status of the 82 samples (Dataset 1, FIG. 6 and
Methods) used in all further analysis. Airway brushings were
dichotomized into two groups: samples with no evidence of PMLs
(samples with no abnormal fluorescing areas or biopsies having
normal or hyperplasia histology, n=25); and samples with evidence
of PMLs (biopsies having mild, moderate, or severe dysplasia,
n=50). Brushes with a worst histology of metaplasia (n=7) were
excluded from the dichotomized groups. The limma (35), edgeR (36)
and sva packages (37) were used to identify differentially
expressed genes associated with presence of PMLs using normalized
voom-tranformed (38) data and surrogate variable analysis using the
first 7 surrogate variables (Table 51). Gene set enrichment
analyses were conducted using ROAST (39) and GSEA (40), and GSVA
(41). The Molecular Signatures Database (MSigDb) v4 Entrez ID Gene
Sets were converted to Ensembl IDs using BioMart. Additional gene
sets were created from CEL files or RNA-Seq counts from The Cancer
Cell Line Compendium (CCLE), SCC tumor and adjacent normal tissue
from TCGA, GSE19188, GSE18842, and GSE4115 (Supplemental
Methods).
Cell Culture
[0060] The human bronchial epithelial biopsy cell cultures (Table
S2) were obtained from the Colorado Lung SPORE Tissue Bank and
cultured in Bronchial Epithelial Growth Media (BEGM). Human
non-small cell lung cancer (NSCLC) cell lines were purchased from
ATCC and short tandem repeat (STR) profiles were verified at the
time of use by the Promega Gene Print.RTM. 10 system at the Dana
Faber Cancer Institute. H1299, H2085 and SW900 cells were cultured
in RPMI supplemented with 10% fetal bovine serum and 1%
penicillin/streptomycin, and H2085 cells were cultured in ALC-4
media. All cells were grown in a 37.degree. C. humidified incubator
with 5% CO.sub.2.
Bioenergetics Studies
[0061] Oxygen consumption rates (OCR) and extracellular
acidification rates (ECAR) were measured using the XF96
Extracellular Flux Analyzer instrument (Seahorse Bioscience Inc).
Briefly, approximately 30,000 cancer cells/well or approximately
40,000 bronchial epithelial biopsy cells/well (higher numbers due
to slow growth rate) were seeded on XF96 cell culture plates and
grown overnight. Prior to running the assay, media was replaced
with Seahorse base media (2 mM (milimole/L) L-glutamine) and placed
at 37.degree. C. and 0% CO.sub.2 for approximately 30 minutes. The
XF Cell Mito Stress Test kit and protocol were utilized to examine
mitochondrial function. Measurements were taken every 5 minutes
over 80 minutes. To modulate mitochondrial respiration, 504
oligomycin, 1 .mu.M FCCP and 504 antimycin A were used. Prism
software v6 was used to calculate t-statistics for baseline OCR
comparisons and a 2-way ANOVA was conducted to compare OCR and ECAR
measurements.
Mitochondrial Enumeration Using Flow Cytometry
[0062] Using an established protocol (40), cell cultures
(5.times.10.sup.5 cells/10 cc dish of bronchial biopsy cultures and
cancer cell cultures) were grown overnight and exposed to 120 uM
MitoTracker Green FM in media free of FBS for 30 min at 37.degree.
C. humidified incubator with 5% CO.sub.2. Cells were subsequently
collected, washed in PBS and resuspended in 0.5 mL PBS-EDTA and 1
uL of propidium iodide (PI) was added to distinguish live/dead
cells. MitoTracker FM and PI were measured using a BD LSRII flow
cytometer and BD FACS Diva software (6.2.1). Data was analyzed
using FlowJo (10.2), gating out doublets and dead cells, and
normalizing mean fluorescence to the number of cell counts.
Immunohistochemistry
[0063] Formalin-fixed, paraffin-embedded (FFPE) sections of human
PMLs sampled from high-risk subjects undergoing screening for lung
cancer were provided by RPCI as part of an IRB-approved study
detailed below (Table S3). Dr. Candace Johnson at RPCI provided the
PIPE lung sections from the N-nitroso-tris-chloroethylurea (NTCU)
mouse model of lung SCC, from mice treated with 25 ml of 40 mmol/L
NTCU for 25 weeks in accordance with the Institutional Animal Care
and Use Committee approved protocol (42). Antibody dilutions and
immunohistochemistry methods were detailed in the Supplemental
Methods. Briefly, slides were de-paraffinized and rehydrated. For
antigen retrieval, slides were heated in citrate buffer. Slides
were subsequently incubated in primary antibody (Translocase of the
Outer Mitochondrial Membrane 22 (TOMM22): mouse tissue 1:300 and
human 1:1,200 (Abcam), and Cytochrome C Oxidase subunit IV
(COX4I1): mouse tissue 1:500 and human 1:5,000 (Abcam)) diluted in
1% Bovine Serum Albumin (BSA). Signal was amplified using an ABC
kit (Vector Labs). To reveal endogenous peroxidase activity, slides
were incubated in a 3,3'-Diaminobenzidine (DAB) solution. Slides
were rinsed, counterstained with hematoxylin, dehydrated in graded
alcohol followed by xylene and cover slipped.
Biomarker Development and Validation
[0064] A gene expression biomarker discovery pipeline was developed
to test thousands of parameter combinations (6,160 predictive
models) to identify a biomarker capable of distinguishing between
samples from subjects with and without PMLs. Samples were first
assigned by batch (sequencing lane) to either a discovery set
(n=58) or a validation set (n=17), and the validation set was
excluded from biomarker development (FIG. S2 and Supplemental
Methods). The biomarker was developed using subsets of the
discovery set established by randomly splitting the samples into
training (80%, n=46) and test (20%, n=12) sets 500 times. Model
performance was assessed using standard metrics for both the
training and test sets (Supplemental Methods). The biomarker
pipeline was also used to develop biomarkers for sex and smoking
status as well as randomized class labels for all phenotypes
(serving as positive and negative controls, respectively). A final
model (biomarker) was selected (Supplemental Methods) and its
ability to distinguish between samples with and without PMLs was
tested in a validation set (n=17). In addition, using the bronchial
brushings collected longitudinally from subjects at RPCI, we tested
whether or not differences in biomarker scores over time were
reflective of progression of PMLs (n=28 matched time point pairs)
(Supplemental Methods).
Example 2
Results
Subject Population
[0065] The study design used 126 bronchial brushings obtained via
autofluorescence bronchoscopy at the BCCA and RPCI for differential
gene expression and pathway analysis, as well as for biomarker
development and validation (FIG. 1). A dataset consisting of
samples collected from BCCA subjects with (n=50) and without (n=25)
PMLs (n=25) was used to derive a gene expression signature
associated with the presence of dysplastic PMLs. Important clinical
covariates such as COPD and reported smoking history as well as
alignment statistics from the mRNA-Seq data were not significantly
different between the two groups (Table 1 and Table 2). For
biomarker development, the 75 BCCA samples were split by batch and
used in biomarker discovery (n=58) and validation (n=17) (Tables S4
and S5). The change in biomarker score as a predictor of
progression of PMLs was then tested in the 51 RPCI samples (Tables
S5 and S6).
Transcriptomic Alterations in the Airway Field of Injury Associated
with the Presence of PMLs
[0066] The present inventors identified 280 genes significantly
differentially expressed between subjects with and without PMLs
(FDR<0.002, FIG. 2). Utilizing the Molecular Signatures Database
v4 (MSigDB) canonical pathways, the present inventors identified
170 pathways significantly enriched in genes up- or down-regulated
in the presence of PMLs using ROAST (39) (FDR<0.05, Dataset 2).
Pathways involved in oxidative phosphorylation (OXPHOS), the
electron transport chain (ETC), and mitochondrial protein transport
were strongly enriched among genes up-regulated in the airways of
subjects with PMLs. Other up-regulated pathways included DNA repair
and the HIF1A pathway. Down-regulated pathways included the STATS
pathway, the JAK/STAT pathway, IL4 signaling, RAC1 regulatory
pathway, NCAM1 interactions, collagen formation, and extracellular
matrix organization.
OXPHOS is Increased in PML Cell Cultures and Biopsies of Increasing
Severity
[0067] The ETC and OXPHOS pathways, which involve genes distributed
between the complexes I-IV of the ETC and ATP synthase, were highly
activated in the airway field in the presence of PMLs. The present
inventors wanted to determine if the functional activity of these
pathways was similarly altered in PMLs compared to normal tissue.
Cellular bioenergetics were conducted by measuring oxygen
consumption rate (OCR) as a measure of ETC/OXPHOS and extracellular
acidification rate (ECAR) as a measure of glycolysis (anerobic
respiration) and MitoTraker Green FM as a measure of mitochondrial
content in primary cell cultures derived from bronchial biopsies.
Additionally, the present inventors performed immunohistochemistry
of select OXPHOS-related genes in mouse and human dysplastic
lesions and normal tissue to measure protein levels.
[0068] The present inventors established a significant concordance
between ETC/OXPHOS gene expression and cellular bioenergetics in
NSCLC cell lines (FIGS. 7A-7F). Next, using primary cell cultures
derived from normal to severe dysplastic tissue (Table S2), the
present inventors observed that the mean baseline OCR values were
2.5 fold higher in the cultures from PMLs compared to controls
(p<0.001, FIG. 3A). Baseline ECAR values were also higher in PML
cultures compared to controls, but to a lesser extent (1.5 fold,
p<0.001), reflecting predictions based on mRNA-Seq field data
(FIGS. 7G-7H). There was a greater reduction in OCR in PMLs
immediately following oligomycin treatment (p<0.001) suggesting
an increased dependence on OXPHOS for ATP production to meet
energetic demands. In addition, the mean spare respiratory capacity
following the release of the proton gradient was elevated by
approximately 1.5 fold in the PML cultures compared to controls
indicating increased ability to respond to energy demands (43).
Lastly, treatment with antimycin A resulted in a greater reduction
of OCR in PML cultures (p<0.001, FIG. 3B), suggesting that
oxygen consumption in the lesions is dependent on increased ETC
components in complex III. No significant changes to ECAR were
detected in response to mitochondrial perturbations. Furthermore to
examine if the increased OXPHOS was a result of increased
mitochondrial biogenesis in PML cultures, cells were incubated with
MitoTraker FM to stain for mitochondria content and fluorescence
enumerated using flow cytometry revealed no significant difference
between PML and controls (p=0.15, FIG. 3C-D).
[0069] Additionally, the present inventors found elevated protein
levels of Translocase of the Outer Mitochondrial Membrane 22
(TOMM22) and Cytochrome C Oxidase subunit IV (COX4I1) in
low/moderate grade dysplastic lesions compared to normal tissue
(FIG. 3C) using tissues from human bronchial biopsy FFPE sections
(Table S3) and whole lung sections from the NTCU mouse model of
SCC. The results suggest that PMLs are more ETC- and
OXPHOS-dependent and express OXPHOS-related proteins at higher
levels compared to normal tissue.
PML-Associated Gene Expression Alterations in the Airway Field are
Involved in Lung Squamous Cell Carcinogenesis
[0070] To further extend the connection between the airway field
and PMLs, the present inventors examined the relationship between
PML-associated genes in the airway field and other lung
cancer-related datasets. The present inventors identified genes
differentially expressed between lung tumor tissue (primarily
squamous) and normal lung tissue in three different datasets (TCGA,
GSE19188, and GSE18842). Genes associated with lung cancer in all
datasets were significantly (FDR<0.05) enriched by GSEA,
concordantly with gene expression changes associated with the
presence of PMLs in the field (FIG. 4A and Dataset 3). Extending
beyond the lung tumor, similar enrichment (FDR<0.05) was found
using early, stepwise, and late gene expression changes in SCC
identified by Ooi et al. (44) (FIG. 4B and Dataset 3) and among
genes associated with lung cancer in the airway field of injury
(GSE4115, FIG. 4C and Dataset 3). These results support the concept
that early events in lung carcinogenesis can be observed throughout
the respiratory tract, even in cells that appear cytologically
normal.
Development and Validation of a Biomarker for PML Detection and
Monitoring
[0071] The airway brushings from BCCA subjects with and without
PMLs were leveraged to build a biomarker predictive of the presence
of PMLs. The biomarker consisted of 200 genes (of which 91
overlapped with the gene signature in FIG. 2) and achieved a
ROC-curve AUC of 0.92, sensitivity of 0.75 (9/12 samples with PMLs
predicted correctly), and specificity of 1.00 (5/5 samples without
PMLs predicted correctly) in independent validation samples (n=17,
FIG. 5A). In addition, the biomarker was used to score an
independent set of longitudinally collected bronchial brushings
from RPCI subjects (FIG. 1). Biomarker scores were calculated for
each sample, and the difference in biomarker scores between
sequential procedures (n=28 time point pairs, Supplemental Methods)
was predictive of whether the worst PML histology observed during
the baseline procedure regressed or whether it was stable or
progressed with an AUC of 0.75 (FIG. 5B).
Biomarker Predicts Dysplasia Status in Bronchial Biopsies
[0072] Abnormal fluorescing areas were biopsied during
auto-fluorescence bronchoscopy of 91 subjects. Biopsies from 47 of
the subjects were determined to be premalignant legions (severe,
moderate or mild dysplasia) via histology. Biopsies from 44 of the
subjects were determined to be normal (normal or hyperplasia) via
histology. The ability of the biomarker to predict dysplasia status
was assessed. FIG. 9 shows an ROC curve demonstrating the
performance of the biomarker in distinguishing between premalignant
lesion biopsies (severe=8, moderate=25, and mild dysplasia=14) and
biopsies with normal histology (normal=24 and hyperplasia=20).
Biomarker achieved AUC of 72% (with a 62%-83% confidence interval),
sensitivity of 81% (38 of 47 dysplastic biopsies predicted
correctly), and specificity of 66% (29 of 44 normal biopsies
predicted correctly).
Discussion
[0073] In the foregoing studies, the present inventors identified a
PML-associated gene expression signature in cytologically normal
bronchial brushings and characterized the biological pathways that
are dysregulated in the airway field of injury. The present
inventors established that the PML-associated airway field harbors
alterations observed in PMLs and in SCC. This evidence motivated
the development of a biomarker that reflects the presence of PMLs
and their outcome over time. The findings presented herein provide
novel insights into the earliest molecular events associated with
lung carcinogenesis and have the potential to impact lung cancer
prevention by providing novel targets (e.g., OXPHOS) and potential
biomarkers for risk stratification and monitoring the efficacy of
chemoprevention agents.
[0074] The first major finding of the foregoing studies was the
identification of a PML-associated field of injury. The most
significantly enriched pathways among up-regulated genes in
subjects with PMLs were OXPHOS, ETC, and mitochondrial protein
transport. These pathways efficiently generate energy in the form
of ATP by utilizing the ETC in the mitochondria. During cancer
development, energy metabolism alterations are described as an
increase in glycolysis and suppression of OXPHOS, known as the
Warburg effect (45); however, recent studies demonstrate that
OXPHOS is maintained in many tumors and can be important for
progression (46). The present inventors wanted to assay for OXPHOS
activation in PMLs as it may support PML progression by generating
reactive oxygen species (ROS) that can induce oxidative stress,
increase DNA damage, and HIF-1.alpha. pathway activation (pathways
observed in our analysis).
[0075] The present inventors observed increases in both the basal
OCR and the spare respiratory capacity in the PML biopsies,
suggesting that PML-derived cell cultures are more ETC and OXPHOS
dependent that the non-PML cultures. The present inventors also
demonstrated increases in the presence of mitochondria and ETC
activity marked by positive TOMM22 and COX IV staining associated
with increasing PML histological grade. Several members of the
mitochondrial protein import machinery (46) were significantly
up-regulated (FDR<0.05) in airways with PMLs including members
of the TOM complex (TOMM22, TOMM7, and TOMM20) and TIM23 complex
(TIMM23, TIMM21, and TIMM17A). We observed positive staining of
TOMM22 with increasing PML grade, suggesting that increased import
of precursor proteins from the endoplasmic reticulum may be
required to meet the energy demands of PMLs. Measurements of
mitochondrial content indicated no significant differences between
the normal and PML-derived cultures, and transcriptional levels of
PPARGC1A, associated with mitochondrial biogenesis, were not
different between subjects with and without PML indicating that
increases in OXPHOS are likely independent of mitochondrial number
(47-49). Increases in OXPHOS have been demonstrated to be
associated with PML progression in Barret's esophagus and
esophageal dysplasia (47), cervical dysplasia (48), and the
dysplastic lesions that precede oral SCC (49). Collectively, these
data suggest that the OXPHOS pathway may be a target for early
intervention. Pre-clinical studies in the NTCU mouse model of lung
SCC demonstrate the potential for targeting mitochondrial
respiration by using the natural product honokiol to inhibit tumor
development (50). Further investigations into the role of cellular
energy metabolism in the development and progression of PMLs are
needed to fully understand how to best target it for intervention
in lung cancer.
[0076] Additionally, the present inventors extended the connection
between the PML-associated airway field and PMLs beyond the OXPHOS
pathway to processes associated with squamous cell lung
carcinogenesis. By examining gene sets from multiple external
studies representative of lung cancer-related processes occurring
in the tumor, adjacent to the tumor, and in the upper airway,
significant concordant relationships were found between the
PML-associated field and processes associated with SCC tumors.
Genes are similarly altered in these varied cancer-associated
contexts and thus tissues in the field both adjacent to and far
away from the tumor may reflect basic processes and mechanisms of
lung carcinogenesis such as DNA damage as hypothesized earlier.
[0077] These observations motivated the present inventors to pursue
the most translational aspect of this study, a biomarker that can
detect PMLs and monitor their progression over time. The 200-gene
biomarker, measured in the cytologically normal bronchial airway,
achieved high performance detecting the presence of PMLs in a small
test set (AUC=0.92). This biomarker may increase the sensitivity of
bronchoscopy in detecting the presence of PMLs (which can be
difficult to observe under white light), and thus improve
identification of high-risk smokers that should be targeted for
aggressive lung cancer screening programs. Additionally, the
biomarker may offer wider clinical utility in early intervention
trials by serving as an intermediate endpoint of efficacy (beyond
Ki-67 staining for proliferation, and changes in biopsy histology).
Towards this goal, the present inventors demonstrated that the
change in biomarker scores over time reflected contemporaneous
regressive or progressive/stable disease (AUC=0.75). This result
suggests that the airway field of injury in the presence of PMLs is
dynamic and that capturing the gene expression longitudinally may
allow for further stratification of high-risk subjects. The
potential clinical utility of the biomarker is further supported by
recent work demonstrating a significant association between the
development of incident lung squamous cell carcinoma and the
frequency of sites that persist or progress to high-grade dysplasia
(24).
[0078] Further development and testing in a larger cohort is needed
to confirm the biomarker's performance, utility, and ability to
predict future PML progression or regression. Additionally,
longitudinal and spatial sampling would provide a greater
understanding of the dynamic relationship between the normal
epithelium and the PMLs as they regress or progress to SCC.
Longitudinal studies would allow for more accurate characterization
of the time intervals needed to observe gene expression dynamics
both in the PMLs and in the airway field of injury. Spatial
sampling throughout the respiratory tract, including the more
accessible nasal airway that shares the tobacco-related injury with
the bronchial airways (51), would allow for evaluation of the
impact of distance between the PMLs and the brushing site, the
range of PML histologies, and the multiplicity of PMLs that can be
present simultaneously in a patient and influence the
PML-associated airway field.
[0079] Despite these challenges and opportunities for future work,
the present inventors have comprehensively profiled gene expression
changes in airway epithelial cells in the presence of PMLs that
suggest great clinical utility. Moving therapeutics and detection
strategies towards an earlier stage in the disease process via
molecular characterization of premalignant disease holds great
promise (52, 53), and this study represents an important step
towards a precision medicine approach to lung cancer
prevention.
[0080] Materials and Methods
Software versions referenced
Data Processing
Illumina CASAVA v1.8.2
TopHat v2.0.4
RSeQC v2.3.3
HTSeq-count v0.5.4
R v3.0.0
[0081] edgeR v3.4.2
RSEM v1.2.1
Bowtie v1.0.0
Data Analysis
Limma v3.18.13
[0082] edgeR v3.4.2 sva v3.6.0
GSVA v1.10.3
Gene Expression-Based Prediction of Smoking Status
[0083] Microarray data from Beane et al. (3) Gene Expression
Omnibus [GEO] (54) Accession Number GSE7895) was re-analyzed using
using Robust Multi-array Average (RMA) (54) and the Ensembl CDF
file v16.0.0 file website
(brainarray.mbni.med.umich.edu/Brainarray/Database/CustomCDF/16.0-
.0/ensg.asp). The R package (35) was used to identify genes
differentially expressed between current (n=52) and never (n=21)
smokers, using the linear model presented in the paper additionally
correcting for quality covariates (NUSE and RLE). Ninety-four genes
(FDR<0.001) were differentially expressed between current and
never smokers. The weighted voting algorithm (55) was trained on
z-score normalized microarray data (n=73) across the 94 genes and
used to predict smoking status in z-scored log 2-transformed counts
per million (cpm) from the 82 mRNA-Seq samples.
Processing of Publically Available Datasets
[0084] Cancer Cell Line Compendium (CCLE). The Entrez ID gene
expression file labeled 10/18/2012 and the sample information file
were downloaded from CCLE website (broadinstitute.org/ccle/home).
After matching the sample annotation to the expression file, we
used ComBat (56) to adjust the data for batch effects (n=14 batches
across 1019 samples). After batch correction, the lung cell lines
(n=186) were selected and GSVA was used to calculate a pathway
enrichment score for each lung cell line for the following
pathways: KEGG oxidative phosphorylation, KEGG glycolysis
gluconeogenesis, BioCarta glycolysis, and Reactome glycolysis. The
GSVA scores for the glycolysis pathways were averaged per
sample.
[0085] The Cancer Genome Atlas (TCGA). RSEM gene-level (Entrez IDs)
counts derived from RNA-Seq data were downloaded from the TCGA data
portal on Aug. 27, 2013, for lung squamous cell carcinomas and
adjacent matched control tissue (n=100 samples from n=50 subjects).
After applying the mixture model referenced in the paper, 14,178
out of 20,531 genes were expressed as signal in at least 15% of
samples (n=15). Differential gene expression between tumor and
adjacent normal tissue was determined using limma and
voom-transformed data (38) via a linear model with cancer status as
the main effect and a random patient effect modeled using the
duplicateCorrelation function. Gene sets containing the top 200 up-
and down-regulated differentially expressed genes associated with
cancer status were used as input for GSEA.
[0086] Microarray Data. CEL files for GSE19188 and GSE18842 were
downloaded from GEO and processed using Robust Multi-array Average
(RMA) (54) and the Ensembl Gene CDF v16.0.0 file website
(brainarray.mbni.med.umich.edu/Brainarray/Database/CustomCDF/16.0.0/ensg.-
asp). Samples with a median RLE greater than 0.1 or a median NUSE
greater than 1.05 were excluded, yielding n=146 samples for
GSE19188 and n=82 samples for GSE18842. For GSE19188, differential
gene expression between squamous cell tumors (n=23) and normal lung
tissue (n=64) was conducted using limma and a linear model that
included RLE and NUSE covariates. For GSE18842, paired normal and
tumor tissue from the same subjects (n=37 subjects, n=74 samples)
were selected, and differential gene expression was conducted in an
analogous manner as described above for TCGA, additionally
correcting for RLE and NUSE metrics.
[0087] CEL files for GSE4115 were processed using RMA and the CDF
file above. The n=164 samples described in Spira et al. (9), were
used to determine genes differentially expressed in airway
brushings from subjects with and without lung cancer, using limma
and a linear model with terms for cancer status, RLE, NUSE, smoking
status, and pack-years. Gene sets containing the top 200 up- and
down-regulated differentially expressed genes associated with
cancer status were used as input for GSEA.
Immunohistochemistry
[0088] Slides were de-paraffinized, rehydrated, and heated in
citrate buffer for antigen retrieval. Slides were treated with 3%
H.sub.2O.sub.2 (in methanol) to block endogenous peroxidases,
incubated in 10% normal goat serum, and primary antibody (TOMM22:
mouse tissue 1:300 and human 1:1,200 (Abcam), and COX IV: mouse
tissue 1:500 and human 1:5,000 (Abcam)) diluted in 1% BSA. Signal
was amplified using an ABC kit (Vector Labs). Slides were next
incubated in a 3,3'-Diaminobenzidine (DAB) solution to reveal
endogenous peroxidase activity, rinsed, counterstained with
hematoxylin, dehydrated in graded alcohol followed by xylene, and
cover slipped.
Biomarker Development
[0089] Upstream gene filtering. In order to provide cross-platform
compatibility, the present inventors ran the biomarker discovery
and validation pipelines using 11,926 genes commonly present on the
RNA-Seq platform (Illumina HiSeq 2500 used with Ensembl v64 GTF)
and two microarray platforms (Affymetrix GeneChip Human Gene 1.0 ST
Array used with custom ENSG Homo sapiens CDF from Brainarray v11
and Affymetrix Human Genome U133A Array used with custom ENSG Homo
sapiens CDF from Brainarray v16).
[0090] Data generation and summarization. Samples (n=75) were run
across 4 flow cells (4 batches), and samples run in batches 1, 2,
and 3 (n=58) were assigned to a discovery set, while the remaining
samples (n=17) were used as an independent validation set and not
included in the biomarker development. Alignments and gene level
summarization were conducted as described in the paper methods.
Alignment and quality metrics were calculated using RSeQC (v2.3.3)
(57). Using the gene body measure computed by RSeQC, a ratio
between the average read coverage at 80% of the gene length and the
average coverage at 20% of the gene length was derived as an
additional quality metric (gb-ratio) to assess 3' bias per sample.
The metric was highly correlated with a surrogate variable applied
in the identification of differentially expressed genes, and was
used as a quality control metric in the biomarker pipeline.
[0091] Biomarker discovery pipeline. The biomarker discovery
pipeline has been outlined generally above. A graphical
representation of data flow as well as processing and analysis
steps is provided in FIG. 8. Each computational step outlined is
detailed in the following sections.
[0092] Balancing signature. The present inventors tested gene
signatures consisting either of an equal or unequal number of genes
up- and down-regulated in subjects with dysplastic lesions.
[0093] Input data preprocessing. The present inventors tested 3
input data types. HTSeq-count (v0.5.4) (33) was used to derive gene
count estimates (raw counts). In addition, Cufflinks (v2.0.2) (58)
was used to derive reads per kilobase per million mapped reads
(RPKM) using BAM files containing only properly paired reads. The
present inventors also calculated log 2-transformed counts per
million (CPM) by applying edgeR (v3.8.6) (36) to raw counts using
the "TMM" method (weighted trimmed mean of M-values (59)).
[0094] Gene filtering. Signal-based gene filtering was conducted as
described in detail above (Methods). In short, a gene was included
in downstream analyses if the mixture model classified it as "on"
in at least 1%, 5%, 10% or 15% of the samples. For CPM input data
type, the present inventors recalculated CPM values using raw
counts after filtering out genes.
[0095] Feature selection. To identify genes differentially
expressed (DE) between samples with and without premalignant
lesions (PMLs), the present inventors applied several algorithms to
our filtered dataset. The algorithms used were as follows:
[0096] (1) edgeR: The present inventors applied the edgeR package
(v3.8.6) (46) to raw counts only. After calculating normalization
factors (calcNormFactors) and estimating common
(estimateGLMCommonDisp) and tagwise (estimateGLMTagwiseDisp)
dispersion factors, we identified DE genes associated with the
presence of PMLs using a generalized linear model, correcting for
sex, COPD status, and smoking status covariates. For balanced
signatures, the sign of the log 2-fold change of expression between
conditions determined gene directionality. For all models
regardless of balancing, gene importance was defined by
FDR-adjusted p-value from likelihood ratio tests (glmLRT).
[0097] (2) edgeRgb: The present inventors used the edgeR package as
described in #1, additionally correcting for gb-ratio (described
above in the Data generation and summarization section).
[0098] (3) lm: The present inventors applied the limma package
(v3.22.7) (35) to CPMs, RPKMs, or voom-transformed raw counts (38).
Voom transformation was applied using a linear model, adjusting for
sex, COPD status, and smoking status covariates, after calculating
normalization factors. The same model was used to identify DE genes
associated with the presence of PMLs. For balanced signatures, the
sign of the moderated t-statistic obtained via eBayes and topTable
determined gene directionality. For all models regardless of
balancing, gene importance was defined by the magnitude of the
t-statistic.
[0099] (4) lmgb: The present inventors used the limma package as
described in #3, additionally correcting for gb-ratio (described
above in the Data generation and summarization section).
[0100] (5) glmnet: The inventors applied the glmnet package
(v1.9-8) (60) to CPMs, RPKMs, or voom-transformed raw counts (as in
#3) to identify DE genes associated with the presence of PMLs. For
balanced signatures, gene directionality was determined by the sign
of the t-statistic obtained via limma by running a linear model
described in #3. The inventors carried out the following series of
steps using all genes for unbalanced signatures and separately
using up- and down-regulated genes for balanced signatures: First,
RPKMs and CPMs were z-score normalized, while raw counts were
voom-transformed. Then, due to the binary character of our response
variable (dysplasia status), a logistic regression model was fit
using the binomial distribution family and elastic net mixing
parameter .alpha.=0.5 (indicating a tradeoff between ridge and
lasso regressions). The standardize option was set to FALSE,
causing the coefficients to be returned on the original scale, thus
allowing their magnitude to be interpreted as gene importance.
Next, a range of regularization parameters .lamda. was generated
via leave-one-out cross-validation (nfolds=46), and the .lamda.
giving the minimum mean cross-validated error (lambda.min) was
chosen to estimate the coefficients. Finally, DE genes were defined
as having non-zero coefficients and then sorted by importance based
on the coefficients' magnitude.
[0101] (6) randomForest: The inventors applied the randomForest
package (v4.6-12) (61) to CPMs, RPKMs, and voom-transformed raw
counts (as in #3), setting the number of trees (ntree) to 100 and
importance to TRUE. For balanced signatures, the sign of the
t-statistic as described in #5 determined gene directionality. For
all models regardless of balancing, gene importance was determined
by the magnitude of the importance variable, defined as the mean
decrease in accuracy over both conditions.
[0102] (7) DESeq: The inventors applied the DESeq package (v1.18.0)
(62) to unmodified raw counts only. DE analysis to find genes
associated with the presence of PMLs included data normalization
(estimation of the effective library size), variance estimation,
and inference for two experimental conditions, as outlined in the
DESeq package vignette
(bioconductor.org/packages/3.3/bioc/vignettes/DESeq/inst/doc/DESeq.pdf).
For balanced signatures, the sign of the log 2-fold change of
expression between the two conditions determined gene
directionality. For all models regardless of balancing, gene
importance was defined by FDR.
[0103] (8) SVA: The inventors applied the sva package (v3.12.0)
(37) to CPMs, RPKMs, or voom-transformed raw counts. Raw counts
were voom-transformed using a linear model including only dysplasia
status as the predictor variable. The number of surrogate variables
(SVs) not associated with dysplasia status was estimated using the
default approach of Buja and Eyuboglu (63) ("be" method). SVs were
then identified using the empirical estimation of control probes
("irw" method), and up to 5 were added as covariates in the linear
model (limma package). The adjusted model was then used to once
again voom-transform raw counts, and subsequently fitted to
identify DE genes associated with the presence of PMLs. For
balanced signatures, the sign of the moderated t-statistic obtained
via topTable determined gene directionality. For all models
regardless of balancing, gene importance was defined by the
magnitude of the t-statistic.
[0104] (9) pAUC (partial AUC) (64): The present inventors applied
the rowpAUCs function in the genefilter package (v1.48.1) (65) to
CPMs, RPKMs, or voom-transformed raw counts (as in #3). The
inventors used 10 class label permutations and a sensitivity cutoff
of 0.1 for a specificity range of 0.9-1. For balanced signatures,
the sign of the moderated t-statistic obtained via limma's topTable
determined gene directionality. For all models regardless of
balancing, gene importance was defined by the magnitude of the
t-statistic.
[0105] Gene signature size. After the feature selection step, the
inventors selected the top scoring 10, 20, 40, 60, 80, 100, or 200
genes, making sure that for balanced signatures, half originated
from an ordered list of up-regulated genes, and half from an
ordered list of down-regulated genes.
[0106] Prediction method. For each set of genes, multiple
prediction methods were applied to predict dysplasia status
(presence of PMLs) in a training set of 46 samples and a test set
of 12 samples. These training and test set samples differed in each
iteration, which resulted from randomly splitting the 58 discovery
set samples (FIG. 8). The following prediction methods were
used:
[0107] 1. glmnet: The inventors used glmnet (v1.9-8) (60) to first
estimate a range of penalty parameters .lamda. in 10-fold cross
validation using the binomial distribution family parameter and
.alpha.=0 to ensure all feature-selected genes were included in
predictions. Dysplasia status was then predicted as a binary class,
using lambda.min penalty.
[0108] 2. wv (weighted voting) (55): Weighted voting algorithm was
used to predict dysplasia status.
[0109] 3. svm (Support Vector Machine) (66): The inventors used the
svm function in the e1071 package (v1.6-7) (66) with linear kernel
and 5-fold cross validation for class prediction.
[0110] 4. rf (random forest): The randomForest package (v4.6-12)
(61) was used with 1000 trees, requesting a matrix of class
probabilities as output.
[0111] 5. nb (Naive Bayes): The naiveBayes function was used in the
e1071 package (v1.6-7) with default parameters.
[0112] Each of the prediction algorithms generated a vector of
predicted scores and a vector of predicted labels for all samples
in the training and test sets.
[0113] Performance metrics. The present inventors considered 6,160
statistically and computationally viable combinations of the above
parameters. The predicted class labels calculated for each model
(i.e., a combination of parameters), coupled with true class labels
were then used to calculate performance metrics for the biomarker
as follows:
Accuracy T .times. P + T .times. N T .times. P + T .times. N + F
.times. P + F .times. N Sensitivity T .times. P T .times. P + F
.times. N Specificity T .times. N F .times. P + T .times. N
Positive .times. .times. Predictive .times. .times. Value T .times.
P T .times. P + F .times. P Nega .times. tive .times. .times.
Predictive .times. .times. Value T .times. N T .times. N + F
.times. N Matthew ' .times. s .times. .times. Correlation .times.
.times. Coefficient .times. .times. ( MCC ) ( TP .times. T .times.
N ) - ( F .times. P .times. F .times. N ) ( T .times. P + F .times.
P ) .times. ( T .times. P + FN ) ( T .times. N + F .times. P )
.times. ( T .times. N + F .times. N ) AUC .times. .times. for
.times. .times. ROC .times. ( Receiver .times. .times. Operating
.times. .times. Characteristic ) MAQCII .times. .times. metric 0.5
.times. AUC + 0 . 2 .times. 5 .times. ( M .times. C .times. C + 1 )
, where .times. .times. TP = true .times. .times. positives ; FP =
false .times. .times. positives ; .times. TN = true .times. .times.
negativies ; FN = false .times. .times. negatives ; MCC = Matthew '
.times. s .times. .times. Correlation .times. .times. Coefficient ;
and AUC = Area .times. .times. Under .times. .times. the .times.
.times. Curve . ##EQU00001##
[0114] For each model, we calculated these metrics for each of the
500 iterations (different training and test sets assembled from the
discovery set samples) and then averaged over all iterations. In
addition to the standard performance metrics, we calculated model
overfitting and gene selection consistency. The overfitting metric
was calculated as the difference between the train set AUC and the
test set AUC. Specifically, a model performing well on the training
set but poorly on the test set would achieve a high overfitting
score. For each model, the gene selection consistency metric was
calculated as the average ("normalized" to biomarker size in a
given model) percentage of genes passing the gene filter, that were
selected into the final gene committee in all 500 iterations:
consistency = 1 - # .times. .times. unique .times. .times. genes
.times. .times. in .times. .times. all .times. .times. iterations -
biomarker .times. .times. size ( biomarker .times. .times. size
.times. # .times. .times. iterations ) - biomarker .times. .times.
size ##EQU00002##
[0115] For example, a model requiring a 10-gene biomarker would
have the highest consistency (1) if it selected the same 10 genes
in all 500 iterations (10 unique genes selected altogether). The
same model would have the lowest consistency (0) if it selected a
different set of 10 genes in all iterations (10 genes.times.500
iterations=5000 unique genes altogether).
[0116] Selection of best model. In selecting the best model from
among the 6,160 the inventors tested and considered the degree of
model overfitting, model gene selection consistency and test set
AUC. First, top 10% (n=616) least overfitting models were
identified. Simultaneously, the inventors identified top 10%
(n=616) most consistent models. Finally, the model with the highest
test set AUC among models fulfilling both criteria (n=121) was
chosen as the final model.
[0117] Selection of final gene signature. The biomarker genes
selected may differ between iterations due to changes in the
training set. Therefore, to generate a final gene signature, the
inventors trained the biomarker using all 58 discovery set samples
and best model parameters.
[0118] Positive and negative controls. The biomarker discovery
pipeline was also used to develop control biomarkers. As positive
controls, the inventors used smoking status and sex phenotypes to
identify biomarkers that could successfully distinguish former from
current smokers (AUC=0.99), and females from males (AUC=0.96). As
negative controls, the inventors used randomly shuffled labels for
dysplasia status (AUC=0.48), smoking status (AUC=0.52), and sex
(AUC=0.51). Label shuffling was conducted preserving the
association between gene expression profiles and remaining
phenotypes; i.e., in the case of shuffled dysplasia status, only
dysplasia status was shuffled while other phenotypes and the
corresponding gene expression profile remained unchanged and linked
to the same sample ID.
[0119] Validations. The performance of the final biomarker was
tested using the biomarker discovery pipeline in validation mode.
In this mode, the pipeline takes in the entire discovery set (n=58)
as the training set, and an external validation set as the test
set. The test set is first corrected for gb-ratio (RNA-Seq quality
metric) using limma, and the residual data is used as input. Both
training and test sets are then z-score normalized. The pipeline
was run using only the final model to generate prediction labels
and prediction scores for the test set samples. Finally, pROC
package (v1.8) (67) was used to visualize and quantify biomarker
performance by plotting a ROC curve using prediction scores as the
response and the dichotomous phenotype as the predictor, and
extracting the AUC value from the resulting ROC object.
Detecting PML Presence in Validation Set Samples
[0120] In order to validate the biomarker's ability to detect the
presence of PMLs, the performance of the biomarker was tested in
smokers with and without PMLs (n=17 samples) left out of the
biomarker discovery process. To assess the robustness of the
results, we randomly permuted dysplasia status labels 100 times,
obtaining biomarker scores for all 17 samples in each of the
iterations. The present inventors then concatenated the 100 newly
generated biomarker score sets for randomized labels, creating a
predictor vector consisting of 1700 scores. Similarly, the
inventors concatenated 100 identical copies of biomarker score sets
for true labels, creating a response vector of the same length.
This allowed the inventors to visualize the performance of the
biomarker on true and randomized labels in a single ROC curve (FIG.
5).
Predicting PML Progression in Longitudinally-Collected Samples
[0121] In order to validate the biomarker's ability to predict
sample progression/regression, the present inventors first used the
biomarker to score the longitudinally collected RPCI samples
(n=51). Next, calculated the difference in scores between two
consecutive time points were calculated for each patient (later
time point biomarker score-earlier time point biomarker score). For
example, a subject with 3 samples from 3 different time points
would have 3 scores, and thus two score differences could be
calculated; a subject with 2 samples from 2 time points would have
2 scores, and thus 1 score difference.
[0122] Each pair of samples was assigned a "progressing/stable" or
"regressing" phenotype. A "progressing/stable" phenotype indicated
that the worst histological grade of PMLs sampled during the
baseline procedure increased in severity or remained unchanged at
follow-up; while a "regressing" phenotype indicated that the worst
histological grade of PMLs sampled at baseline decreased in
severity at follow-up.
[0123] The ability of the score difference to predict the
"progression/regression" phenotype was quantified by plotting a ROC
curve, using the vector of score differences as the predictor
variable, and the progression/regression phenotype as the response
variable.
[0124] Implementation of the method. The framework and structure of
this pipeline are based on principles outlined for microarray data
applications. The pipeline outlined in this paper was substantially
modified to accommodate RNA-Seq data as well as RNA-Seq-specific
methods.
Subject Inclusion/Exclusion Criteria for Samples from the British
Columbia Cancer Agency (BCCA)
[0125] The samples with normal/hyperplasia histology are part of
the Pan-Canadian Study and included subjects between 50 and 75
years old, current or former smokers who have smoked cigarettes for
20 years or more, and that had an estimated 3-year lung cancer risk
of greater than or equal to 2%. Exclusion criteria included medical
conditions, such as severe heart disease, that would jeopardize the
subject's safety during participation in the study, previously
diagnosed lung cancer, ex-smokers of greater than or equal to 15
years, anti-coagulant treatment, and pregnancy. The subjects with
airway dysplasia were participants in three different
chemoprevention studies for green tea extract (n=27 samples),
sulindac (n=4 samples), and myo-inositol (n=13 samples) or from the
Pan-Canadian Study described above (n=6). All samples were
collected at the BCCA at baseline prior to administration of
therapeutic interventions. Inclusion criteria for these
chemoprevention trials can be summarized as subjects between 40 and
79 years of age, current or former smokers with at least 30
pack-years, no lung cancer history or stage 0/I curatively treated
NSCLC either at least 1 year or 6 months prior to the trial
(depending on trial). Exclusion criteria varied by trial but
included medical conditions that would jeopardize the subject's
safety during participation of the study and pregnancy. See details
below:
Green Tea:
Inclusion Criteria
[0126] Women or men age 45 to 74 years of age [0127] Current or
former smokers who have smoked at least 30 pack-years, e.g. 1 pack
per day for 30 years or more (a former smoker is defined as one who
has stopped smoking for one or more years) [0128] ECOG performance
status 0 or 1 [0129] C-Reactive Protein >1.2 mg/L [0130] One or
more areas of dysplasia with a surface diameter larger than 1.2 mm
on autofluorescence bronchoscopy [0131] Willing to take Polyphenon
E/placebo twice a day regularly [0132] Since it is unknown if
Polyphenon E or EGCG will cause fetal harm when administered during
pregnancy, women subjects must be postmenopausal (no menstrual
periods >1 year or elevated FSH >40 mIU/ml), surgically
sterile, or using birth control pill. Women of childbearing age
must have normal .beta.-HCG within 14 days to exclude pregnancy.
[0133] Normal renal and liver function defined as serum creatinine
bilirubin, AST, ALT or alkaline phosphatase levels below the upper
limit of normal [0134] Agreeing to sign, on initial interview,
informed consent forms for screening procedures (sputum cytometry
analysis, fluorescence bronchoscopy, and low dose spiral thoracic
CT scan). Once eligibility has been determined for the
chemoprevention trial participation, agreeing to sign a
study-specific treatment informed consent form.
Exclusion Criteria
[0134] [0135] Consumption of more than 7 cups of tea a week [0136]
Use of other natural health products containing green tea compounds
[0137] Chronic active hepatitis/liver cirrhosis [0138] Severe heart
disease, e.g. unstable angina, chronic congestive heart failure,
use of antiarrhythmic agents [0139] Ongoing gastric ulcer [0140]
Have on-going rectal bleeding [0141] Have a history of chronic
diverticulitis and/or colitis [0142] Experiencing symptoms of
gastritis or hemorrhoids in which medical treatment is required
[0143] Experiencing any symptomatic gastrointestinal condition that
may predispose the individual to gastrointestinal bleeding [0144]
Acute bronchitis or pneumonia within one month [0145] Carcinoma
in-situ or invasive cancer on bronchoscopy or abnormal spiral chest
CT suspicious of lung cancer [0146] Known reaction to Xylocaine
salbutamol, midazolam, and alfentanil [0147] Known allergy to green
tea and/or corn starch, gelatin, or other nonmedicinal ingredients
[0148] Any medical condition, such as acute or chronic respiratory
failure, or bleeding disorder, that in the opinion of the
investigator could jeopardize the subject's safety during
participation in the study [0149] On anti-coagulant treatment such
as warfarin or heparin [0150] Breastfeeding [0151] Pregnancy [0152]
Unwilling to have a bronchoscopy [0153] Unwilling to have a spiral
chest CT [0154] Unwilling to sign a consent
Sulindac:
Inclusion Criteria
[0154] [0155] Men and women 40 through 79 years of age [0156]
Current or former smokers with a >30 pack-year smoking history
and (a) no prior lung cancer, (b) stage I NSCLC resected at least
one year prior to Registration/Randomization, or (c) stage I
Non-Small Cell Lung Cancer (NSCLC) with a >1 year interval since
adjuvant chemotherapy conclusion [0157] Women of childbearing
potential and men must agree to use adequate contraception
(hormonal or barrier method of birth control; abstinence) prior to
study entry and for the duration of study participation. Should a
woman become pregnant or suspect she is pregnant while
participating in this study, she should inform her treating
physician immediately. [0158] A negative (serum or urine) pregnancy
test done <7 days prior to [0159] Registration/Randomization,
for women of childbearing potential only [0160] Willingness to
provide tissue blocks and sputum samples for research purposes
[0161] Participants must have normal organ and marrow function as
defined below and obtained .ltoreq.45 days prior to
Registration/Randomization: [0162] Hemoglobin .gtoreq.lower limit
of institutional normal (LLN) [0163] Leukocytes .gtoreq.3,000/.mu.L
[0164] Absolute neutrophil count .gtoreq.1,500/.mu.L [0165]
Platelets .gtoreq.100,000/.mu.L [0166] Direct bilirubin
.ltoreq.1.5.times.institutional upper limit of normal (ULN) [0167]
ALT (SGPT).ltoreq.1.5.times.institutional ULN [0168] Creatinine
.ltoreq.1.5.times.institutional ULN or calculated creatinine
clearance .gtoreq.30 ml/min [0169] .gtoreq.1 site of
histologically-confirmed bronchial dysplasia [0170] ECOG
performance status .ltoreq.1 [0171] Negative chest x-ray [0172]
Negative electrocardiogram
[0173] Exclusion Criteria [0174] Prior history of cancer (within
the previous 3-years). Exception: Stage I NSCLC as outlined above,
nonmelanomatous skin cancer, localized prostate cancer, carcinoma
in situ (CIS) of cervix, or superficial bladder cancer with
conclusion of treatment >6 months prior to
Registration/Randomization. [0175] Prior pneumonectomy [0176] Solid
organ transplant recipients [0177] History of GI ulceration,
bleeding or perforation [0178] Uncontrolled intercurrent illness
including, but not limited to: ongoing or active infection,
symptomatic congestive heart failure, unstable angina pectoris,
cardiac arrhythmia, recent (<6 months) history of MI, chronic
renal disease, chronic liver disease, difficult to control
hypertension or psychiatric illness/social situations that would
limit compliance with study requirements. [0179] Recent (<6
months) participation in another chemoprevention trial [0180]
Participant currently receiving any other investigational agents
[0181] Any supplemental oxygen use (continuous or intermittent use)
or documented [0182] Room Air (RA) SaO2<90% [0183] Pregnant
women. Note: because there are no adequate, well-controlled studies
in pregnant women and sulindac is absolutely contraindicated in the
3rd trimester. [0184] Breastfeeding women. Note: because there is
an unknown but potential risk for adverse events in nursing infants
secondary to treatment of the mother with sulindac, women who are
breast-feeding will be excluded. [0185] Individuals who are known
to be HIV positive. Note: HIV positive individuals are excluded for
the following two reasons. First, HIV positive individuals are
known to have altered immune function. Since one of the potential
mechanisms of action of sulindac is proposed to be enhancement of
immune function in preventing lung cancer progression, it is not
known how the presence of HIV infection would alter this
enhancement of immune function as compared to non-HIV infected
individuals. Second, individuals with HIV are also known to be at
higher risk for lung cancer then non-HIV infected individuals which
would alter the risk/incidence of lung cancer in our study
population. [0186] Regular NSAID or corticosteroid use during the
6-month period prior to intervention (may be eligible after washout
period of 12 weeks for NSAIDs and 6 weeks for corticosteroids)
[0187] Regular aspirin use. Exception: Aspirin can be used if
prescribed by a physician for prevention. Maximum of one aspirin
(81 mg) per day allowed. [0188] History of allergic reactions or
hypersensitivity to sulindac or other NSAIDS, including
aspirin-sensitive asthma [0189] Women of childbearing potential who
are unwilling to employ adequate contraception (hormonal or barrier
method of birth control; abstinence) prior to study entry and for
the duration of study participation. Note: Effects of sulindac on
the developing human fetus at the recommended therapeutic dose are
fetal harm early in pregnancy. However, there are known harmful
adverse events in the third trimester of pregnancy. Should a woman
become pregnant or suspect she is pregnant while participating in
this study, she should inform her treating physician immediately.
[0190] Current use of methotrexate, corticosteroids, (anti-platelet
agents) warfarin, ticlopidine, clopidogrel, aspirin, abciximab,
dipyridamole, eptifibatide, tirofiban, lithium, cyclosporine,
hydralazine, ACE inhibitors
Myo-Inositol:
Inclusion Criteria
[0190] [0191] Ability to understand and willingness to sign a
written informed consent document [0192] Age .gtoreq.45 to
.ltoreq.79 [0193] ECOG performance status (PS) 0 or 1 [0194] One or
both of the following: Stage 0/I curatively treated non-small cell
lung cancer (NSCLC) with a .gtoreq.30 pack-year smoking history
(surgery, adjuvant chemotherapy or radiotherapy must be completed
.gtoreq.6 months prior to screening); OR Current or former smokers
with a .gtoreq.30 pack-year smoking history without a history of
lung cancer. Pack-years is determined by multiplying the number of
packs smoked per day by the number of years smoked. [0195] Women of
childbearing capacity who agree to use an acceptable form of birth
control for the duration of the study (e.g. condom, oral
contraceptives, etc.)
Exclusion Criteria
[0195] [0196] Prior history of cancer, with the following
exceptions: [0197] .gtoreq.3-year disease free interval (with the
exception of stage I NSCLC as described above) [0198]
Non-melanomatous skin cancer [0199] Localized prostate cancer with
conclusion of treatment .gtoreq.6 months prior to screening [0200]
Carcinoma in situ (CIS) of cervix with conclusion of treatment
.gtoreq.6 months prior to screening [0201] Superficial bladder
cancer with conclusion of treatment .gtoreq.6 months prior to
screening [0202] Prior pneumonectomy [0203] Solid organ transplant
recipients [0204] Uncontrolled intercurrent illness including, but
not limited to: ongoing or active infection, symptomatic congestive
heart failure, unstable angina pectoris, cardiac arrhythmia, severe
chronic obstructive pulmonary disease requiring supplemental
oxygen, difficult to control hypertension, or psychiatric
illness/social situations that would limit compliance with study
requirements. [0205] Schizophrenia [0206] Bipolar disorder [0207]
Lithium treatment [0208] Carbamazepine treatment [0209] Valproate
treatment [0210] Diabetes [0211] Currently using other natural
health products containing inositol [0212] Anticoagulant use such
as Coumadin or heparin. Exception: participant is off those drugs
for .gtoreq.7 days prior to pre-registration. [0213] Recent
(.ltoreq.6 months) participation in another chemoprevention trial
[0214] Participant currently receiving any other investigational
agents [0215] Any supplemental oxygen use (continuous or
intermittent use) or documented Room Air (RA) SaO.sub.2<90%
[0216] Pregnant women. (Excluded because the effects of high doses
of myo-inositol on the fetus or newborn are not known.) [0217]
Breastfeeding women. (Excluded because the risk for adverse events
in nursing infants secondary to treatment of the mother with high
doses of myo-inositol are not known.) [0218] History of allergic
reactions attributed to myo-inositol [0219] History of allergies to
any ingredient in the study product or placebo
Early Detection of Lung Cancer--A Pan-Canadian Study:
Inclusion Criteria
[0219] [0220] Women or men age 50 to 75 years [0221] Current or
former smokers who have smoked cigarettes for 20 years or more (a
former smoker is defined as one who has stopped smoking for one or
more years) [0222] An estimated 3-year lung cancer risk of >2%
based on the risk prediction model. [0223] ECOG performance status
0 or 1 [0224] Capable of providing, informed consent for screening
procedures (low dose spiral CT, AFB, spirometry, blood
biomarkers)
Exclusion Criteria
[0224] [0225] Any medical condition, such as severe heart disease
(e.g. unstable angina, chronic congestive heart failure), acute or
chronic respiratory failure, bleeding disorder, that in the opinion
of the investigator could jeopardize the subject's safety during
participation in the study or unlikely to benefit from screening
due to shortened life-expectancy from the co-morbidities [0226]
Have been previously diagnosed with lung cancer [0227] Have had
other cancer with the exception of the following cancers which can
be included in the study: non-melanomatous skin cancer, localized
prostate cancer, carcinoma in situ (CIS) of the cervix, or
superficial bladder cancer. Treatment of the exceptions must have
ended >6 months before registration into this study. [0228]
Ex-smoker for .gtoreq.15 years [0229] On anti-coagulant treatment
such as warfarin or heparin [0230] Known reaction to Xylocaine,
salbutamol, midazolam, and alfentanil [0231] Pregnancy [0232]
Unwilling to have a spiral chest CT [0233] Chest CT within 2 years
[0234] Unwilling to sign a consent Subject Inclusion/Exclusion
Criteria for Samples from RPCI
[0235] Subjects met the following high-risk lung screening
criteria: 1) Personal cancer history of the lung, bronchus,
head/neck, and/or esophagus and no evidence of disease at the time
of enrollment, or 2.) No personal history of upper aerodigestive
cancer, age 50+, and a current smoker or a former smoker with 20+
pack years. In addition, subjects in the second group had to have
one or more risk factors including chronic lung disease such as
emphysema, chronic bronchitis, or chronic obstructive pulmonary
disease, occupationally related asbestos disease, or a family
history of lung cancer in a first degree relative.
TABLE-US-00001 TABLE 1 Demographic and clinical characteristics
stratified by premalignant lesion status. Data are means (SD) for
continuous variables and proportions with percentages for Overall
No Lesions Lesions Factor (n = 82) (n = 25) (n = 50) P* Age 62.9
(7.2) 64.5 (5.8) 62.2 (8.0) 0.16 Male 54/82 (65.9) 16/25 (64) 35/50
(70) 0.61 Current smoker 40/82 (48.8) 11/25 (44) 25/50 (50) 0.81
Pack-years 47.3 (15.7) 47.6 (17.9) 47.2 (15.2) 0.93 FEV1% Predicted
82.5 (18.6) 84.5 (17.9) 81.7 (19.2) 0.54 FEV1/FVC Ratio 71.2 (7.9)
73.4 (7.4) 69.6 (8.1) 0.05 COPD (FEV1% < 80 & 24/82 (29.3)
5/25 (20) 17/50 (34) 0.28 FEV1/FVC < 70) Histology <10.001
Normal 12/82 (14.6) 12/25 (48) Hyperplasia 13/82 (15.9) 13/25 (52)
Metaplasia 7/82 (8.5) Mild Dysplasia 35/82 (42.7) 35/50 (70)
Moderate Dysplasia 12/82 (14.6) 12/50 (24) Severe Dysplasia 3/82
(3.7) 3/50 (6) dichotomous variables. P* values are for the
comparison of subjects with and without premalignant lesions. Two
sample t-tests were used for continuous variables; Fisher's exact
test was used for categorical variables.
TABLE-US-00002 TABLE 2 Alignment statistics stratified by
premalignant lesion status Data are means (SD) for continuous
variables and proportions with percentages for dichotomous
variables. Reads are expressed in millions denoted by M. P* values
are Overall No Lesions Lesions Factor (n = 82) (n = 25) (n = 50) P*
Total Alignments 90M (17M) 90M (15M) 91M (19M) 0.78 Unique
Alignments 83M (16M) 83M (14M) 84M (17M) 0.76 Properly Paired 66M
(12M) 66M (11M) 67M (14M) 0.75 Alignments Genebody 80/20 Ratio 1.3
(0.2) 1.3 (0.1) 1.3 (0.2) 0.84 Mean GC Content 47.8 (3.4) 47.4
(2.9) 48.2 (3.7) 0.34
for the comparison of subjects with and without premalignant
lesions. Two sample t-tests were used for continuous variables;
Fisher's exact test was used for factors.
TABLE-US-00003 TABLE 3 280 genes differentially expressed between
subjects with PMLs and without PMLs Ensembl entrezgene hgnc_symbol
gene_biotype wikigene_description Direction ENSG00000223959 172
AFG3L1P pseudogene AFG3 ATPase Down-regulated in family gene 3-like
the presence of 1 (S. cerevisiae), dyplasia pseudogene
ENSG00000115282 64427 TTC31 protein_coding tetratricopeptide
Down-regulated in repeat domain 31 the presence of dyplasia
ENSG00000139631 51380 CSAD protein_coding cysteine sulfinic
Down-regulated in acid decarboxylase the presence of dyplasia
ENSG00000198198 23334 SZT2 protein_coding seizure threshold 2
Down-regulated in homolog (mouse) the presence of dyplasia
ENSG00000167524 124923 protein_coding uncharacterized
Down-regulated in serine/threonine- the presence of protein kinase
dyplasia SgK494 ENSG00000242028 25764 Cl5orf63 protein_coding
chromosome 15 Down-regulated in open reading frame the presence of
63 dyplasia ENSG00000235194 NA PPP1R3E protein_coding
Down-regulated in the presence of dyplasia ENSG00000179979 285464
CRIPAK protein_coding cysteine-rich PAK1 Down-regulated in
inhibitor the presence of dyplasia ENSG00000164970 203259 FAM219A
protein_coding family with Down-regulated in sequence similarity
the presence of 219, member A dyplasia ENSG00000162231 10482 NXF1
protein_coding nuclear RNA Down-regulated in export factor 1 the
presence of dyplasia ENSG00000010322 11188 NISCH protein_coding
nischarin Down-regulated in the presence of dyplasia
ENSG00000121310 55268 ECHDC2 protein_coding enoyl CoA
Down-regulated in hydratase domain the presence of containing 2
dyplasia ENSG00000167978 23524 SRRM2 protein_coding serine/arginine
Down-regulated in repetitive matrix 2 the presence of dyplasia
ENSG00000229180 NA lincRNA Down-regulated in the presence of
dyplasia ENSG00000108799 2145 EZH1 protein_coding enhancer of zeste
Down-regulated in homolog 1 the presence of (Drosophila) dyplasia
ENSG00000070476 79364 ZXDC protein_coding ZXD family zinc
Down-regulated in finger C the presence of dyplasia ENSG00000186088
54103 PION protein_coding pigeon homolog Down-regulated in
(Drosophila) the presence of dyplasia ENSG00000132680 22889
KIAA0907 protein_coding KIAA0907 Down-regulated in the presence of
dyplasia ENSG00000122965 9904 RBM19 protein_coding RNA binding
motif Down-regulated in protein 19 the presence of dyplasia
ENSG00000130766 83667 SESN2 protein_coding sestrin 2 Down-regulated
in the presence of dyplasia ENSG00000064607 10147 SUGP2
protein_coding SURP and G patch Down-regulated in domain containing
the presence of 2 dyplasia ENSG00000184863 155435 RBM33
protein_coding RNA binding motif Down-regulated in protein 33 the
presence of dyplasia ENSG00000214021 26140 TTLL3 protein_coding
tubulin tyrosine Down-regulated in ligase-like family, the presence
of member 3 dyplasia ENSG00000080603 10847 SRCAP protein_coding
Snf2-related Down-regulated in CREBBP activator the presence of
protein dyplasia ENSG00000072121 23503 ZFYVE26 protein_coding zinc
finger, FYVE Down-regulated in domain containing the presence of 26
dyplasia ENSG00000182873 NA antisense Down-regulated in the
presence of dyplasia ENSG00000104365 3551 IKBKB protein_coding
inhibitor of kappa Down-regulated in light polypeptide the presence
of gene enhancer in dyplasia B-cells, kinase beta ENSG00000167522
29123 ANKRD11 protein_coding ankyrin repeat Down-regulated in
domain 11 the presence of dyplasia ENSG00000213190 10962 MLLT11
protein_coding myeloid/lymphoid Down-regulated in or mixed-lineage
the presence of leukemia (trithorax dyplasia homolog, Drosophila);
translocated to, 11 ENSG00000135407 10677 AVIL protein_coding
advillin Down-regulated in the presence of dyplasia ENSG00000185219
353274 ZNF445 protein_coding zinc finger protein Down-regulated in
445 the presence of dyplasia ENSG00000163486 23380 SRGAP2
protein_coding SLIT-ROBO Rho Down-regulated in GTPase activating
the presence of protein 2 dyplasia ENSG00000087266 6452 SH3BP2
protein_coding SH3-domain Down-regulated in binding protein 2 the
presence of dyplasia ENSG00000198563 692199 DDX39B protein_coding
DEAD (Asp-Glu- Down-regulated in Ala-Asp) box the presence of
polypeptide 39B dyplasia ENSG00000142528 25888 ZNF473
protein_coding zinc finger protein Down-regulated in 473 the
presence of dyplasia ENSG00000123064 79039 DDX54 protein_coding
DEAD (Asp-Glu- Down-regulated in Ala-Asp) box the presence of
polypeptide 54 dyplasia ENSG00000042062 140876 FAM65C
protein_coding family with Down-regulated in sequence similarity
the presence of 65, member C dyplasia ENSG00000247484 NA NA NA NA
Down-regulated in the presence of dyplasia ENSG00000100201 10521
DDX17 protein_coding DEAD (Asp-Glu- Down-regulated in Ala-Asp) box
the presence of helicase 17 dyplasia ENSG00000125633 54520 CCDC93
protein_coding coiled-coil domain Down-regulated in containing 93
the presence of dyplasia ENSG00000257479 NA lincRNA Down-regulated
in the presence of dyplasia ENSG00000076108 11176 BAZ2A
protein_coding bromodomain Down-regulated in adjacent to zinc the
presence of finger domain, 2A dyplasia ENSG00000137221 93643 TJAP1
protein_coding tight junction Down-regulated in associated protein
1 the presence of (peripheral) dyplasia ENSG00000215424 114044 MCM3
lincRNA MCM3AP Down-regulated in AP-AS1 antisense RNA 1 the
presence of (non-protein dyplasia coding) ENSG00000100941 5411 PNN
protein_coding pinin, desmosome Down-regulated in associated
protein the presence of dyplasia ENSG00000170949 90338 ZNF160
protein_coding zinc finger protein Down-regulated in 160 the
presence of dyplasia ENSG00000240053 58496 LY6G5B protein_coding
lymphocyte antigen Down-regulated in 6 complex, locus the presence
of G5B dyplasia ENSG00000181523 6448 SGSH protein_coding N-
Down-regulated in sulfoglucosamine the presence of sulfohydrolase
dyplasia ENSG00000131398 3748 KCNC3 protein_coding potassium
voltage- Down-regulated in gated channel, the presence of
Shaw-related dyplasia subfamily, member 3 ENSG00000129933 23383
MAU2 protein_coding MAU2 chromatid Down-regulated in cohesion
factor the presence of homolog (C. dyplasia elegans)
ENSG00000161010 51149 C5orf45 protein_coding chromosome 5
Down-regulated in open reading frame the presence of 45 dyplasia
ENSG00000110888 65981 CAPRIN2 protein_coding caprin family
Down-regulated in member 2 the presence of dyplasia ENSG00000130254
9667 SAFB2 protein_coding scaffold attachment Down-regulated in
factor B2 the presence of dyplasia ENSG00000184634 9968 MED12
protein_coding mediator complex Down-regulated in subunit 12 the
presence of dyplasia ENSG00000077157 4660 PPP1R12B protein_coding
protein phosphatase Down-regulated in 1, regulatory the presence of
subunit 12B dyplasia ENSG00000133624 79970 ZNF767 pseudogene zinc
finger family Down-regulated in member 767 the presence of dyplasia
ENSG00000227372 57212 TP73-AS1 lincRNA TP73 antisense
Down-regulated in RNA 1 (non- the presence of protein coding)
dyplasia ENSG00000100813 22985 ACIN1 protein_coding apoptotic
Down-regulated in chromatin the presence of condensation dyplasia
inducer 1 ENSG00000127511 23309 SIN3B protein_coding SIN3
transcription Down-regulated in regulator homolog the presence of B
(yeast) dyplasia ENSG00000155363 4343 MOV10 protein_coding Mov10,
Moloney Down-regulated in leukemia virus 10, the presence of
homolog (mouse) dyplasia ENSG00000124222 8675 STX16 protein_coding
syntaxin 16 Down-regulated in the presence of dyplasia
ENSG00000099331 4650 MYO9B protein_coding myosin IXB Down-regulated
in the presence of dyplasia ENSG00000169246 NA NPIPL3
protein_coding Down-regulated in the presence of dyplasia
ENSG00000137343 79969 ATAT1 protein_coding alpha tubulin
Down-regulated in acetyltransferase 1 the presence of
dyplasia ENSG00000169045 3187 HNRNPH1 protein_coding heterogeneous
Down-regulated in nuclear the presence of ribonucleoprotein
dyplasia H1 (H) ENSG00000205047 NA protein_coding Down-regulated in
the presence of dyplasia ENSG00000198853 9853 RUSC2 protein_coding
RUN and SH3 Down-regulated in domain containing the presence of 2
dyplasia ENSG00000197375 6584 SLC22A5 protein_coding solute carrier
Down-regulated in family 22 (organic the presence of
cation/carnitine dyplasia transporter), member 5 ENSG00000182796
440104 TMEM198B pseudogene transmembrane Down-regulated in protein
198B, the presence of pseudogene dyplasia ENSG00000182944 2130
EWSR1 protein_coding Ewing sarcoma Down-regulated in breakpoint
region 1 the presence of dyplasia ENSG00000065526 23013 SPEN
protein_coding spen homolog, Down-regulated in transcriptional the
presence of regulator dyplasia (Drosophila) ENSG00000137337 9656
MDC1 protein_coding mediator of DNA- Down-regulated in damage
checkpoint the presence of 1 dyplasia ENSG00000186174 283149 BCL9L
protein_coding B-cell Down-regulated in CLL/lymphoma 9- the
presence of like dyplasia ENSG00000075568 23505 TMEM131
protein_coding transmembrane Down-regulated in protein 131 the
presence of dyplasia ENSG00000170322 4798 NFRKB protein_coding
nuclear factor Down-regulated in related to kappaB the presence of
binding protein dyplasia ENSG00000171456 171023 ASXL1
protein_coding additional sex Down-regulated in combs like 1 the
presence of (Drosophila) dyplasia ENSG00000044446 5256 PHKA2
protein_coding phosphorylase Down-regulated in kinase, alpha 2 the
presence of (liver) dyplasia ENSG00000166436 9866 TRIM66
protein_coding tripartite motif Down-regulated in containing 66 the
presence of dyplasia ENSG00000255847 NA antisense Down-regulated in
the presence of dyplasia ENSG00000245149 100507018 lincRNA
uncharacterized Down-regulated in LOC100507018 the presence of
dyplasia ENSG00000253200 NA protein_coding Down-regulated in the
presence of dyplasia ENSG00000100226 9567 GTPBP1 protein_coding GTP
binding Down-regulated in protein 1 the presence of dyplasia
ENSG00000146828 56996 SLC12A9 protein_coding solute carrier
Down-regulated in family 12 the presence of (potassium/chloride
dyplasia transporters), member 9 ENSG00000215769 NA protein_coding
Down-regulated in the presence of dyplasia ENSG00000168297 54899
PXK protein_coding PX domain Down-regulated in containing the
presence of serine/threonine dyplasia kinase ENSG00000225828
100128071 protein_coding uncharacterized Down-regulated in
LOC100128071 the presence of dyplasia ENSG00000115459 84173 ELMOD3
protein_coding ELMO/CED-12 Down-regulated in domain containing the
presence of 3 dyplasia ENSG00000224660 100505696 lincRNA
uncharacterized Down-regulated in LOC100505696 the presence of
dyplasia ENSG00000090905 27327 TNRC6A protein_coding trinucleotide
repeat Down-regulated in containing 6A the presence of dyplasia
ENSG00000205885 283314 antisense uncharacterized Down-regulated in
LOC283314 the presence of dyplasia ENSG00000117616 57035 Clorf63
protein_coding chromosome 1 Down-regulated in open reading frame
the presence of 63 dyplasia ENSG00000114841 25981 DNAH1
protein_coding dynein, axonemal, Down-regulated in heavy chain 1
the presence of dyplasia ENSG00000132382 10514 MYBBP1A
protein_coding MYB binding Down-regulated in protein (P160) 1a the
presence of dyplasia ENSG00000061936 6433 SFSWAP protein_coding
splicing factor, Down-regulated in suppressor of the presence of
white-apricot dyplasia homolog (Drosophila) ENSG00000168763 26505
CNNM3 protein_coding cyclin M3 Down-regulated in the presence of
dyplasia ENSG00000214765 641977 SEPT7P2 pseudogene septin 7
Down-regulated in pseudogene 2 the presence of dyplasia
ENSG00000119321 23307 FKBP15 protein_coding FK506 binding
Down-regulated in protein 15, 133 kDa the presence of dyplasia
ENSG00000047056 22884 WDR37 protein_coding WD repeat domain
Down-regulated in 37 the presence of dyplasia ENSG00000165699 7248
TSC1 protein_coding tuberous sclerosis 1 Down-regulated in the
presence of dyplasia ENSG00000168970 100137047 JMJD7-
protein_coding JMJD7-PLA2G4B Down-regulated in PLA2G4B readthrough
the presence of dyplasia ENSG00000079277 8569 MKNK1 protein_coding
MAP kinase Down-regulated in interacting the presence of
serine/threonine dyplasia kinase 1 ENSG00000115568 7701 ZNF142
protein_coding zinc finger protein Down-regulated in 142 the
presence of dyplasia ENSG00000167615 114823 LENG8 protein_coding
leukocyte receptor Down-regulated in cluster (LRC) the presence of
member 8 dyplasia ENSG00000100083 26088 GGA1 protein_coding
golgi-associated, Down-regulated in gamma adaptin ear the presence
of containing, ARF dyplasia binding protein 1 ENSG00000139436 9815
GIT2 protein_coding G protein-coupled Down-regulated in receptor
kinase the presence of interacting ArfGAP dyplasia 2
ENSG00000168066 7536 SF1 protein_coding splicing factor 1
Down-regulated in the presence of dyplasia ENSG00000099917 51586
MED15 protein_coding mediator complex Down-regulated in subunit 15
the presence of dyplasia ENSG00000091831 2099 ESR1 protein_coding
estrogen receptor 1 Down-regulated in the presence of dyplasia
ENSG00000234420 100129482 ZNF37BP pseudogene zinc finger protein
Down-regulated in 37B, pseudogene the presence of dyplasia
ENSG00000178971 80169 CTC1 protein_coding CTS telomere
Down-regulated in maintenance the presence of complex dyplasia
component 1 ENSG00000114982 55683 KANSL3 protein_coding KAT8
regulatory Down-regulated in NSL complex the presence of subunit 3
dyplasia ENSG00000148840 23082 PPRC1 protein_coding peroxisome
Down-regulated in proliferator- the presence of activated receptor
dyplasia gamma, coactivator-related 1 ENSG00000112941 11044 PAPD7
protein_coding PAP associated Down-regulated in domain containing
the presence of 7 dyplasia ENSG00000143624 65123 INTS3
protein_coding integrator complex Down-regulated in subunit 3 the
presence of dyplasia ENSG00000139990 8816 DCAF5 protein_coding DDB1
and CUL4 Down-regulated in associated factor 5 the presence of
dyplasia ENSG00000100650 6430 SRSF5 protein_coding
serine/arginine-rich Down-regulated in splicing factor 5 the
presence of dyplasia ENSG00000133460 66035 SLC2A11 protein_coding
solute carrier Down-regulated in family 2 (facilitated the presence
of glucose dyplasia transporter), member 11 ENSG00000102125 6901
TAZ protein_coding tafazzin Down-regulated in the presence of
dyplasia ENSG00000136828 9649 RALGPS1 protein_coding Ral GEF with
PH Down-regulated in domain and SH3 the presence of binding motif 1
dyplasia ENSG00000235027 NA antisense Down-regulated in the
presence of dyplasia ENSG00000235706 400242 DICER1- lincRNA DICER1
antisense Down-regulated in AS1 RNA 1 (non- the presence of protein
coding) dyplasia ENSG00000205890 100128770 antisense
uncharacterized Down-regulated in LOC100128770 the presence of
dyplasia ENSG00000133943 80017 C14orf159 protein_coding chromosome
14 Down-regulated in open reading frame the presence of 159
dyplasia ENSG00000100068 91355 LRP5L protein_coding low density
Down-regulated in lipoprotein the presence of receptor-related
dyplasia protein 5-like ENSG00000234616 NA JRK processed_
Down-regulated in transcript the presence of dyplasia
ENSG00000115687 23178 PASK protein_coding PAS domain Down-regulated
in containing the presence of serine/threonine dyplasia kinase
ENSG00000243335 154881 KCTD7 protein_coding RAB guanine
Down-regulated in nucleotide the presence of exchange factor
dyplasia (GEF) 1 ENSG00000131149 23199 KIAA0182 protein_coding
KIAA0182 Down-regulated in the presence of dyplasia ENSG00000184677
9923 ZBTB40 protein_coding zinc finger and Down-regulated in BTB
domain the presence of
containing 40 dyplasia ENSG00000116580 54856 GON4L protein_coding
gon-4-like (C. Down-regulated in elegans) the presence of dyplasia
ENSG00000130684 26152 ZNF337 protein_coding zinc finger protein
Down-regulated in 337 the presence of dyplasia ENSG00000143442
23126 POGZ protein_coding pogo transposable Down-regulated in
element with ZNF the presence of domain dyplasia ENSG00000249093 NA
NA NA NA Down-regulated in the presence of dyplasia ENSG00000173064
283450 C12orf51 protein_coding chromosome 12 Down-regulated in open
reading frame the presence of 51 dyplasia ENSG00000215039 678655
lincRNA uncharacterized Down-regulated in LOC678655 the presence of
dyplasia ENSG00000178038 259173 ALS2CL protein_coding ALS2
C-terminal Down-regulated in like the presence of dyplasia
ENSG00000258461 NA processed_ Down-regulated in transcript the
presence of dyplasia ENSG00000146830 64599 GIGYF1 protein_coding
GRB10 interacting Down-regulated in GYF protein 1 the presence of
dyplasia ENSG00000234290 NA antisense Down-regulated in the
presence of dyplasia ENSG00000120318 64411 ARAP3 protein_coding
ArfGAP with Down-regulated in RhoGAP domain, the presence of
ankyrin repeat and dyplasia PH domain 3 ENSG00000162241 283130
SLC25A45 protein_coding solute carrier Down-regulated in family 25,
member the presence of 45 dyplasia ENSG00000205268 5150 PDE7A
protein_coding phosphodiesterase Down-regulated in 7A the presence
of dyplasia ENSG00000160712 3570 IL6R protein_coding interleukin 6
Down-regulated in receptor the presence of dyplasia ENSG00000119906
55719 FAM178A protein_coding family with Down-regulated in sequence
similarity the presence of 178, member A dyplasia ENSG00000166762
117155 CATSPER2 protein_coding cation channel, Down-regulated in
sperm associated 2 the presence of dyplasia ENSG00000203709 NA
Clorf132 protein_coding Down-regulated in the presence of dyplasia
ENSG00000167202 23102 TBC1D2B protein_coding TBC1 domain
Down-regulated in family, member 2B the presence of dyplasia
ENSG00000140326 146059 CDAN1 protein_coding congenital
Down-regulated in dyserythropoietic the presence of anemia, type I
dyplasia ENSG00000238105 55592 pseudogene golgin A2 Down-regulated
in pseudogene 5 the presence of dyplasia ENSG00000167395 9726
ZNF646 protein_coding zinc finger protein Down-regulated in 646 the
presence of dyplasia ENSG00000109063 4621 MYH3 protein_coding
myosin, heavy Down-regulated in chain 3, skeletal the presence of
muscle, embryonic dyplasia ENSG00000196689 7442 TRPV1
protein_coding transient receptor Down-regulated in potential
cation the presence of channel, subfamily dyplasia V, member 1
ENSG00000168488 11273 ATXN2L protein_coding ataxin 2-like
Down-regulated in the presence of dyplasia ENSG00000230124
100527964 antisense uncharacterized Down-regulated in LOC100527964
the presence of dyplasia ENSG00000184551 NA pseudogene
Down-regulated in the presence of dyplasia ENSG00000198026 63925
ZNF335 protein_coding zinc finger protein Down-regulated in 335 the
presence of dyplasia ENSG00000166887 23339 VPS39 protein_coding
vacuolar protein Down-regulated in sorting 39 homolog the presence
of (S. cerevisiae) dyplasia ENSG00000006530 55750 AGK
protein_coding acylglycerol kinase Down-regulated in the presence
of dyplasia ENSG00000128191 100302197 DGCR8 protein_coding DiGeorge
Down-regulated in syndrome critical the presence of region gene 8
dyplasia ENSG00000109118 57649 PHF12 protein_coding PHD finger
protein Down-regulated in 12 the presence of dyplasia
ENSG00000068400 56850 GRIPAP1 protein_coding GRIP1 associated
Down-regulated in protein 1 the presence of dyplasia
ENSG00000228544 100131193 antisense uncharacterized Down-regulated
in LOC100131193 the presence of dyplasia ENSG00000204842 6311 ATXN2
protein_coding ataxin 2 Down-regulated in the presence of dyplasia
ENSG00000084774 790 CAD protein_coding carbamoyl- Down-regulated in
phosphate the presence of synthetase 2, dyplasia aspartate
transcarbamylase, and dihydroorotase ENSG00000184787 7327 UBE2G2
protein_coding ubiquitin- Down-regulated in conjugating the
presence of enzyme E2G 2 dyplasia ENSG00000173120 22992 KDM2A
protein_coding lysine (K)-specific Down-regulated in demethylase 2A
the presence of dyplasia ENSG00000215012 79680 C22orf29
protein_coding chromosome 22 Down-regulated in open reading frame
the presence of 29 dyplasia ENSG00000135365 51317 PHF21A
protein_coding PHD finger protein Down-regulated in 21A the
presence of dyplasia ENSG00000157827 114793 FMNL2 protein_coding
formin-like 2 Down-regulated in the presence of dyplasia
ENSG00000112659 23113 CUL9 protein_coding cullin 9 Down-regulated
in the presence of dyplasia ENSG00000108509 23125 CAMTA2
protein_coding calmodulin binding Down-regulated in transcription
the presence of activator 2 dyplasia ENSG00000170919 100190939
TPT1-AS1 lincRNA TPT1 antisense Down-regulated in RNA 1 (non- the
presence of protein coding) dyplasia ENSG00000197622 56882 CDC42SE1
protein_coding CDC42 small Down-regulated in effector 1 the
presence of dyplasia ENSG00000100888 57680 CHD8 protein_coding
chromodomain Down-regulated in helicase DNA the presence of binding
protein 8 dyplasia ENSG00000213983 8906 AP1G2 protein_coding
adaptor-related Down-regulated in protein complex 1, the presence
of gamma 2 subunit dyplasia ENSG00000130827 55558 PLXNA3
protein_coding plexin A3 Down-regulated in the presence of dyplasia
ENSG00000198169 90987 ZNF251 protein_coding zinc finger protein
Down-regulated in 251 the presence of dyplasia ENSG00000132424
25957 PNISR protein_coding PNN-interacting Down-regulated in
serine/arginine-rich the presence of protein dyplasia
ENSG00000120709 51307 FAM53C protein_coding family with
Down-regulated in sequence similarity the presence of 53, member C
dyplasia ENSG00000131067 2686 GGT7 protein_coding gamma-
Down-regulated in glutamyltransferase the presence of 7 dyplasia
ENSG00000166888 6778 STAT6 protein_coding signal transducer
Down-regulated in and activator of the presence of transcription 6,
dyplasia interleukin-4 induced ENSG00000258727 NA antisense
Down-regulated in the presence of dyplasia ENSG00000141867 23476
BRD4 protein_coding bromodomain Down-regulated in containing 4 the
presence of dyplasia ENSG00000005339 1387 CREBBP protein_coding
CREB binding Down-regulated in protein the presence of dyplasia
ENSG00000165275 158234 RG9MTD3 protein_coding RNA (guanine-9-)
Down-regulated in methyltransferase the presence of domain
containing dyplasia 3 ENSG00000196535 399687 MYO18A protein_coding
myosin XVIIIA Down-regulated in the presence of dyplasia
ENSG00000125814 63908 NAPB protein_coding N-ethylmaleimide-
Down-regulated in sensitive factor the presence of attachment
protein, dyplasia beta ENSG00000092421 57556 SEMA6A protein_coding
sema domain, Down-regulated in transmembrane the presence of domain
(TM), and dyplasia cytoplasmic domain, (semaphorin) 6A
ENSG00000137497 4926 NUMA1 protein_coding nuclear mitotic
Down-regulated in apparatus protein 1 the presence of dyplasia
ENSG00000100416 55687 TRMU protein_coding tRNA 5- Down-regulated in
methylaminomethy the presence of 1-2-thiouridylate dyplasia
methyltransferase ENSG00000110274 22897 CEP164 protein_coding
centrosomal protein Down-regulated in 164 kDa the presence of
dyplasia ENSG00000104885 84444 DOT1L protein_coding DOT1-like,
histone Down-regulated in H3 the presence of methyltransferase
dyplasia (S. cerevisiae) ENSG00000244161 100506906 FLNB- antisense
FLNB antisense Down-regulated in AS1 RNA 1 (non- the presence of
protein coding) dyplasia ENSG00000218418 NA pseudogene
Down-regulated in the presence of dyplasia ENSG00000171163 55657
ZNF692 protein_coding zinc finger protein Down-regulated in 692 the
presence of
dyplasia ENSG00000184313 374977 HEATR8 protein_coding HEAT repeat
Down-regulated in containing 8 the presence of dyplasia
ENSG00000156858 78994 PRR14 protein_coding proline rich 14
Down-regulated in the presence of dyplasia ENSG00000247743 NA NA NA
NA Down-regulated in the presence of dyplasia ENSG00000213015 51157
ZNF580 protein_coding zinc finger protein Down-regulated in 580 the
presence of dyplasia ENSG00000142937 94163 RPS8 protein_coding
ribosomal protein Up-regulated in the S8 presence of dyplasia
ENSG00000129518 55837 EAPP protein_coding E2F-associated
Up-regulated in the phosphoprotein presence of dyplasia
ENSG00000213326 NA RPS7P11 pseudogene Up-regulated in the presence
of dyplasia ENSG00000177889 7334 UBE2N protein_coding ubiquitin-
Up-regulated in the conjugating presence of enzyme E2N dyplasia
ENSG00000185834 NA RPL12P4 pseudogene Up-regulated in the presence
of dyplasia ENSG00000166171 25911 DPCD protein_coding deleted in
primary Up-regulated in the ciliary dyskinesia presence of homolog
(mouse) dyplasia ENSG00000235297 NA pseudogene Up-regulated in the
presence of dyplasia ENSG00000181163 4869 NPM1 protein_coding
nucleophosmin Up-regulated in the (nucleolar presence of
phosphoprotein dyplasia B23, numatrin) ENSG00000177600 619565 RPLP2
protein_coding ribosomal protein, Up-regulated in the large, P2
presence of dyplasia ENSG00000082515 29093 MRPL22 protein_coding
mitochondrial Up-regulated in the ribosomal protein presence of L22
dyplasia ENSG00000185068 404672 GTF2H5 protein_coding general
Up-regulated in the transcription factor presence of IIH,
polypeptide 5 dyplasia ENSG00000134248 10542 HBXIP protein_coding
hepatitis B virus x Up-regulated in the interacting protein
presence of dyplasia ENSG00000186198 123264 protein_coding organic
solute Up-regulated in the transporter beta presence of dyplasia
ENSG00000186132 130355 C2orf76 protein_coding chromosome 2
Up-regulated in the open reading frame presence of 76 dyplasia
ENSG00000185641 NA pseudogene Up-regulated in the presence of
dyplasia ENSG00000168653 4725 NDUFS5 protein_coding NADH
Up-regulated in the dehydrogenase presence of (ubiquinone) Fe-S
dyplasia protein 5, 151 kDa (NADH-coenzyme Q reductase)
ENSG00000100554 51382 ATP6V1D protein_coding ATPase, H+
Up-regulated in the transporting, presence of lysosomal 34 kDa,
dyplasia V1 subunit D ENSG00000161016 6132 RPL8 protein_coding
ribosomal protein Up-regulated in the L8 presence of dyplasia
ENSG00000111775 1337 COX6A1 protein_coding cytochrome c
Up-regulated in the oxidase subunit VIa presence of polypeptide 1
dyplasia ENSG00000183978 28958 CCDC56 protein_coding coiled-coil
domain Up-regulated in the containing 56 presence of dyplasia
ENSG00000236552 728658 RPL13AP5 pseudogene ribosomal protein
Up-regulated in the L13a pseudogene 5 presence of dyplasia
ENSG00000236801 NA pseudogene Up-regulated in the presence of
dyplasia ENSG00000131100 529 ATP6V1E1 protein_coding ATPase, H+
Up-regulated in the transporting, presence of lysosomal 31 kDa,
dyplasia V1 subunit E1 ENSG00000235174 NA RPL39P3 pseudogene
Up-regulated in the presence of dyplasia ENSG00000169740 7580 ZNF32
protein_coding zinc finger protein Up-regulated in the 32 presence
of dyplasia ENSG00000129562 1603 DAD1 protein_coding defender
against Up-regulated in the cell death 1 presence of dyplasia
ENSG00000144713 6161 RPL32 protein_coding ribosomal protein
Up-regulated in the L32 presence of dyplasia ENSG00000197756 6168
RPL37A protein_coding ribosomal protein Up-regulated in the L37a
presence of dyplasia ENSG00000164751 5828 PEX2 protein_coding
peroxisomal Up-regulated in the biogenesis factor 2 presence of
dyplasia ENSG00000010278 100652804 CD9 protein_coding CD9 molecule
Up-regulated in the presence of dyplasia ENSG00000140988 26784 RPS2
protein_coding ribosomal protein Up-regulated in the S2 presence of
dyplasia ENSG00000198618 NA PPIAP22 pseudogene Up-regulated in the
presence of dyplasia ENSG00000151465 8872 CDC123 protein_coding
cell division cycle Up-regulated in the 123 homolog (S. presence of
cerevisiae) dyplasia ENSG00000143543 10899 JTB protein_coding
jumping Up-regulated in the translocation presence of breakpoint
dyplasia ENSG00000244398 NA pseudogene Up-regulated in the presence
of dyplasia ENSG00000232856 NA protein_coding Up-regulated in the
presence of dyplasia ENSG00000108100 219771 CCNY protein_coding
cyclin Y Up-regulated in the presence of dyplasia ENSG00000118939
7347 UCHL3 protein_coding ubiquitin carboxyl- Up-regulated in the
terminal esterase presence of L3 (ubiquitin dyplasia thiolesterase)
ENSG00000169021 7386 UQCRFS1 protein_coding ubiquinol- Up-regulated
in the cytochrome c presence of reductase, Rieske dyplasia
iron-sulfur polypeptide 1 ENSG00000172809 6169 RPL38 protein_coding
ribosomal protein Up-regulated in the L38 presence of dyplasia
ENSG00000137154 6194 RPS6 protein_coding ribosomal protein
Up-regulated in the S6 presence of dyplasia ENSG00000164405 27089
UQCRQ protein_coding ubiquinol- Up-regulated in the cytochrome c
presence of reductase, complex dyplasia III subunit VII, 9.5 kDa
ENSG00000143457 55204 GOLPH3L protein_coding golgi Up-regulated in
the phosphoprotein 3- presence of like dyplasia ENSG00000138297
100287932 TIMM23 protein_coding translocase of inner Up-regulated
in the mitochondrial presence of membrane 23 dyplasia homolog
(yeast) ENSG00000228474 100128731 OST4 protein_coding
oligosaccharyltrans Up-regulated in the ferase 4 homolog presence
of (S. cerevisiae) dyplasia ENSG00000112981 8382 NME5
protein_coding non-metastatic cells Up-regulated in the 5, protein
presence of expressed in dyplasia (nucleoside- diphosphate kinase)
ENSG00000112667 10591 C6orf108 protein_coding chromosome 6
Up-regulated in the open reading frame presence of 108 dyplasia
ENSG00000183617 116541 MRPL54 protein_coding mitochondrial
Up-regulated in the ribosomal protein presence of L54 dyplasia
ENSG00000188873 NA RPL10AP2 pseudogene Up-regulated in the presence
of dyplasia ENSG00000131143 1327 COX411 protein_coding cytochrome c
Up-regulated in the oxidase subunit IV presence of isoform 1
dyplasia ENSG00000178741 9377 COX5A protein_coding cytochrome c
Up-regulated in the oxidase subunit Va presence of dyplasia
ENSG00000232112 51372 CCDC72 protein_coding coiled-coil domain
Up-regulated in the containing 72 presence of dyplasia
ENSG00000178449 84987 COX14 protein_coding COX14 Up-regulated in
the cytochrome c presence of oxidase assembly dyplasia homolog (S.
cerevisiae) ENSG00000138663 51138 COPS4 protein_coding COP9
constitutive Up-regulated in the photomorphogenic presence of
homolog subunit 4 dyplasia (Arabidopsis) ENSG00000149547 9538 E124
protein_coding etoposide induced Up-regulated in the 2.4 mRNA
presence of dyplasia ENSG00000173660 440567 UQCRH protein_coding
ubiquinol- Up-regulated in the cytochrome c presence of reductase
hinge dyplasia protein ENSG00000125356 4694 NDUFA1 protein_coding
NADH Up-regulated in the dehydrogenase presence of (ubiquinone) 1
dyplasia alpha subcomplex, 1, 7.5 kDa ENSG00000162244 6159 RPL29
protein_coding ribosomal protein Up-regulated in the L29 presence
of dyplasia ENSG00000174444 595097 RPL4 protein_coding ribosomal
protein Up-regulated in the L4 presence of dyplasia
ENSG00000145247 132299 OCIAD2 protein_coding OCIA domain
Up-regulated in the containing 2 presence of dyplasia
ENSG00000178980 6415 SEPW1 protein_coding selenoprotein W, 1
Up-regulated in the presence of dyplasia ENSG00000169020 521 ATP5I
protein_coding ATP synthase, H+ Up-regulated in the transporting,
presence of mitochondrial Fo dyplasia complex, subunit E
ENSG00000125743 6633 SNRPD2 protein_coding small nuclear
Up-regulated in the ribonucleoprotein presence of D2 polypeptide
dyplasia 16.5 kDa ENSG00000101928 56180 MOSPD1 protein_coding
motile sperm Up-regulated in the domain containing presence of 1
dyplasia ENSG00000151366 100532726 NDUFC2 protein_coding NADH
Up-regulated in the dehydrogenase presence of (ubiquinone) 1,
dyplasia subcomplex unknown, 2, 14.5 kDa ENSG00000171421 64979
MRPL36 protein_coding mitochondrial Up-regulated in the ribosomal
protein presence of L36 dyplasia ENSG00000198755 4736 RPL10A
protein_coding ribosomal protein Up-regulated in the L10a presence
of dyplasia ENSG00000232119 28985 MCTS1 protein_coding malignant T
cell Up-regulated in the amplified sequence presence of 1 dyplasia
ENSG00000198643 131177 FAM3D protein_coding family with
Up-regulated in the sequence similarity presence of 3, member D
dyplasia ENSG00000123144 79002 C19orf43 protein_coding chromosome
19 Up-regulated in the open reading frame presence of 43 dyplasia
ENSG00000111669 7167 TPI1 protein_coding triosephosphate
Up-regulated in the isomerase 1 presence of dyplasia
ENSG00000089063 29058 TMEM230 protein_coding chromosome 20
Up-regulated in the open reading frame presence of 30 dyplasia
ENSG00000214026 6150 MRPL23 protein_coding mitochondrial
Up-regulated in the ribosomal protein presence of L23 dyplasia
ENSG00000119421 4702 NDUFA8 protein_coding NADH Up-regulated in the
dehydrogenase presence of (ubiquinone) 1 dyplasia alpha subcomplex,
8, 19 kDa ENSG00000135940 1329 COX5B protein_coding cytochrome c
Up-regulated in the oxidase subunit Vb presence of dyplasia
ENSG00000146066 192286 HIGD2A protein_coding HIG1 hypoxia
Up-regulated in the inducible domain presence of family, member 2A
dyplasia ENSG00000170892 79042 TSEN34 protein_coding tRNA splicing
Up-regulated in the endonuclease 34 presence of homolog (S.
dyplasia cerevisiae) ENSG00000166920 84419 C15orf48 protein_coding
chromosome 15 Up-regulated in the open reading frame presence of 48
dyplasia ENSG00000140307 2958 GTF2A2 protein_coding general
Up-regulated in the transcription factor presence of IIA, 2, 12 kDa
dyplasia ENSG00000184831 79135 APOO protein_coding apolipoprotein O
Up-regulated in the presence of dyplasia ENSG00000205544 254863
C17orf61 protein_coding chromosome 17 Up-regulated in the open
reading frame presence of 61 dyplasia
TABLE-US-00004 Supplemental Table 1. ANOVA derived p-values for the
association between the surrogate variables and
demographic/phenotypic variables Variable SV1 SV2 SV3 SV4 SV5 SV6
SV7 SV8 SV9 Presence of 0.549 0.376 0.964 0.500 0.118 0.481 0.046
0.166 0.652 premalignant lesion (2-level) Smoking status 0.000
0.655 0.191 0.084 0.689 0.804 0.308 0.719 0.761 Smoking status by
0.000 0.363 0.801 0.045 0.819 0.780 0.130 0.827 0.663 Gene
Expression Sex 0.961 0.058 0.000 0.032 0.492 0.801 0.433 0.884
0.991 COPD status 0.612 0.866 0.047 0.161 0.973 0.129 0.083 0.007
0.592 Pack-years 0.398 0.293 0.523 0.576 0.845 0.399 0.875 0.428
0.178 Age 0.300 0.153 0.562 0.845 0.166 0.618 0.037 0.050 0.528
FEV1 0.050 0.391 0.046 0.009 0.123 0.150 0.171 0.028 0.691 FEV1/FVC
ratio 0.023 0.670 0.172 0.056 0.491 0.107 0.028 0.011 0.708 Barcode
0.870 0.605 0.006 0.500 0.745 0.444 0.695 0.119 0.187 Lane 0.335
0.748 0.682 0.351 0.037 0.792 0.402 0.996 0.549 Batch 0.676 0.730
0.474 0.426 0.861 0.037 0.145 0.688 0.261 GC content 0.599 0.886
0.057 0.902 0.257 0.157 0.001 0.416 0.210 Genebody 80/20 ratio
0.000 0.245 0.633 0.271 0.000 0.736 0.015 0.319 0.048 (gb-ratio)
Number of Uniquely 0.302 0.154 0.726 0.948 0.055 0.120 0.036 0.163
0.586 Aligning Reads Number of Reads 0.545 0.605 0.498 0.442 0.000
0.383 0.170 0.745 0.942 Aligning to Splice Junctions Z-score
(sample mean 0.514 0.371 0.238 0.595 0.024 0.031 0.005 0.353 0.021
of z-score normalized data by gene) Relative Expression 0.814 0.615
0.996 0.740 0.918 0.887 0.214 0.274 0.111 (sample median of ratios
computed for each gene by dividing the expression by the median
expression)
TABLE-US-00005 Supplemental Table 2. Phenotypic information about
the human biopsy cell cultures used in the bioenergetics
experiments. Smoking Bio- Mito- Histology Gender Status energetics
TrackerFM Normal F Current X Normal M Current X Normal F Former X
Normal M Former X Normal F Current X X Normal F Current X X
Moderate M Current X Dysplasia Severe M Former X Dysplasia Severe M
Current X Dysplasia Low grade M Former X dysplasia Severe M Current
X X Dysplasia Low grade M Former X X dysplasia
TABLE-US-00006 Supplemental Table 3. Phenotypic information about
the human biopsies used in the IHC experiments. Smoking Stain PtID
Status WorstHistology_Description Tomm-22 Pt 3 FS 0 Normal,
Negative, Benign Mucosa Cox-IV Pt 3 FS 0 Normal, Negative, Benign
Mucosa Tomm-22 Pt 4 FS 23 Squamous Metaplasia (non-specific),
Mature Metaplasia, Squamous Hyperplasia Cox-IV Pt 4 FS 23 Squamous
Metaplasia (non-specific), Mature Metaplasia, Squamous Hyperplasia
Tomm-22 Pt 3 FS 25 Moderate Dysplasia, Squamous Pre-invasive Cox-IV
Pt 3 FS 25 Moderate Dysplasia, Squamous Pre-invasive Tomm-22 Pt 1
CS 27 CIS Squamous Carcinoma In-Situ Cox-IV Pt 1 CS 27 CIS Squamous
Carcinoma In-Situ (*CS refers to current smoker and FS to former
smoker)
TABLE-US-00007 Supplemental Table 4. Demographic and clinical
characteristics of the British Columbia Lung Health Study
stratified by premalignant lesions status Discovery Set Validation
Set Overall No Lesions Lesions Overall No Lesions Lesions Factor (n
= 58) (n = 20) (n = 38) P* (n = 17) (n = 5) (n = 12) P* Age 62.7
(7.1) 64.1 (5.8) 61.9 (7.6) 0.24 63.9 (8.6) 66 (5.8) 63 (9.7) 0.45
Male 37/58 (63.8) 12/20 (60) 25/38 (65.8) 0.78 14/17 (82.4) 4/5
(80) 10/12 (83.3) 1 Current smoker 28/58 (48.3) 9/20 (45) 19/38
(50) 0.79 8/17 (47.1) 2/5 (40) 6/12 (50) 1 Pack-years 48.2 (16.9)
49.4 (18.9) 47.5 (15.9) 0.71 44.6 (12.9) 40.5 (11.6) 46.3 (13.5)
0.39 FEV1% Predicted 86.5 (17.7) 87.8 (16.7) 85.7 (18.5) 0.66 69.5
(16.2) 71 (17.7) 68.9 (16.3) 0.83 FEV1/FVC Ratio 72.1 (7.7) 75.1
(6.3) 70.4 (8) 0.02 67 (8.1) 66.8 (8.5) 67.1 (8.3) 0.95 COPD (FEV1
% < 80 & 11/58 (19) 2/20 (10) 9/38 (23.7) 0.3 11/17 (64.7)
3/5 (60) 8/12 (66.7) 1 FEV1/FVC < 70) Histology <0.001
<0.001 Normal 11/58 (19) 11/20 (55) 1/17 (5.9) 1/5 (20)
Hyperplasia 9/58 (15.5) 9/20 (45) 4/17 (23.5) 4/5 (80) Metaplasia
0/58 (0) 0/17 (0) Mild Dysplasia 29/58 (50) 29/38 (76.3) 6/17
(35.3) 6/12 (50) Moderate Dysplasia 6/58 (10.3) 6/38 (15.8) 6/17
(35.3) 6/12 (50) Severe Dysplasia 3/58 (5.2) 3/38 (7.9) 0/12 (0)
Data are means (SD) for continuous variables and proportions (%)
dichotomous variables. Reads are expressed in millions denoted by
M. P* values are for the comparison of subjects with and without
premalignant lesions. Two sample t-tests were used for continuous
variables; Fisher's exact test was used for factors.
TABLE-US-00008 Supplemental Table 5. Alignment statistics of the
British Columbia Lung Health Study Discovery and the Roswell Park
Cancer Institute cohort BC-LHS Discovery Set BC-LHS Validation Set
RPCI Overall No Lesions Lesions Overall No Lesions Lesions Overall
Factor (n = 58) (n = 20) (n = 38) P* (n = 17) (n = 5) (n = 12) P*
(n = 51) Total Alignments 90M (16M) 98M (15M) 91M (17M) 0.67 93M
(22M) 94M (18M) 92M (24M) 0.86 95M (15M) Unique Alignments 83M
(15M) 82M (13M) 83M (16M) 0.65 85M (20M) 86M (16M) 84M (22M) 0.85
Properly Paired Alignments 66M (1.2M) 65M (11M) 67M (12M) 0.63 68M
(16M) 69M (13M) 67M (17M) 0.86 65M (9.6M) Genebody 80/20 Ratio 1.3
(0.2) 1.3 (0.1) 1.3 (0.2) 0.39 1.3 (0.3) 1.2 (0.1) 1.4 (0.3) 0.15
1.8 (0.2) Mean GC Content 48.1 (3.4) 47.5 (2.7) 48.4 (3.6) 0.33
47.4 (3.8) 46.9 (3.8) 47.6 (3.9) 0.74 49.2 (1.4) Data are means
(SD). Reads are expressed in millions denoted by M. P* values are
for two sample t-tests for comparison of subjects with and without
premalignant lesions.
TABLE-US-00009 Supplemental Table 6. Demographic and clinical
characteristics of the Roswell Park Cancer Institute Cohort (n = 51
samples from n = 23 subjects) Progressing Factor Overall Regressing
Stable P* No. Samples 51 34 22 No. Sample 28 17 11 Pairs No.
Patients** 23 16 10 Time between 343.8 (171.9) 350.9 (199.6) 332.8
(125.9) 0.77 Procedures (Days) Histological -0.9 (1.7) -1.9 (1.0)
0.7 (1.3) <0.001 Grade Change Worst Histological Lesion Observed
Normal 5/51 (9.8) 4/34 (11.8) 2/22 (9.1) 0.038 Hyperplasia 6/51
(11.8) 5/34 (14.7) 1/22 (4.5) Metaplasia 9/51 (17.6) 8/34 (23.5)
1/22 (4.5) Mild Dysplasia 3/51 (5.9) 3/34 (8.8) 0 (0) Moderate
20/51 (39.2) 9/34 (26.5) 15/22 (68.2) Dysplasia Severe 8/51 (15.7)
5/34 (14.7) 3/22 (13.6) Dysplasia Age at 58.1 (6.5) 58.4 (6.9) 57.6
(6.1) 1 Baseline Male 13/28 (46.4) 7/17 (41.2) 6/11 (54.5) 0.7 Ever
smoker at 27/28 (96.4) 17/17 (100) 10/11 (90.9) 0.39 Baseline
Pack-years at 48.1 (22) 49.8 (24.8) 45.4 (17.6) 1 Baseline Data are
means (SD) for continuous variables and proportions (%) for
dichotomous variables. P*values are for the comparison of samples,
sample pairs, or patients classified as having regressing or
progressing/stable PMLs. Two sample t-tests were used for
continuous variables; Fisher's exact test was used for factors.
**Among the 23 patients, 3 patients had 2 sample pairs where one
pair was classified as regressing and the other as
progressing/stable. These patients are counted in both the
regressing and progressing/stable columns.
TABLE-US-00010 Dataset 1. Ensembl IDs for genes used to predict
smoking status. ENSG00000151632 ENSG00000125398 ENSG00000159228
ENSG00000109586 ENSG00000049089 ENSG00000198431 ENSG00000140961
ENSG00000117450 ENSG00000111058 ENSG00000198074 ENSG00000001084
ENSG00000168309 ENSG00000108602 ENSG00000065833 ENSG00000215182
ENSG00000079819 ENSG00000117983 ENSG00000163931 ENSG00000173376
ENSG00000197838 ENSG00000176153 ENSG00000136810 ENSG00000137642
ENSG00000134873 ENSG00000172765 ENSG00000154040 ENSG00000048707
ENSG00000123124 ENSG00000102359 ENSG00000197747 ENSG00000103222
ENSG00000103647 ENSG00000099968 ENSG00000196344 ENSG00000140939
ENSG00000167996 ENSG00000006125 ENSG00000149256 ENSG00000010404
ENSG00000023909 ENSG00000077147 ENSG00000134775 ENSG00000177156
ENSG00000123700 ENSG00000124664 ENSG00000197712 ENSG00000154822
ENSG00000086548 ENSG00000137573 ENSG00000100012 ENSG00000136205
ENSG00000138061 ENSG00000104341 ENSG00000151012 ENSG00000039537
ENSG00000181458 ENSG00000006210 ENSG00000078596 ENSG00000117394
ENSG00000106541 ENSG00000125798 ENSG00000109854 ENSG00000196139
ENSG00000162496 ENSG00000181019 ENSG00000140526 ENSG00000166670
ENSG00000198417 ENSG00000162804 ENSG00000105388 ENSG00000069764
ENSG00000108924 ENSG00000171903 ENSG00000085662 ENSG00000137648
ENSG00000125144 ENSG00000113924 ENSG00000134827 ENSG00000142655
ENSG00000139629 ENSG00000160180 ENSG00000124107 ENSG00000119514
ENSG00000227051 ENSG00000144711 ENSG00000101445 ENSG00000137337
ENSG00000114638 ENSG00000142657 ENSG00000130595 ENSG00000145147
ENSG00000087842 ENSG00000133985 ENSG00000125813
TABLE-US-00011 Dataset 2. Results of pathway enrichment using ROAST
(FDR < 0.05). The column "Direction" refers to pathway
enrichment among genes up-regulated (Up) or down-regulated (Down)
in the presence of PMLs. Pathway NGenes PropDown PropUp Direction
PValue FDR REACTOME_METABOLISM_OF_PROTEINS 382 0.091623 0.544503 Up
0.002 0.0128 REACTOME_METABOLISM_OF_RNA 251 0.139442 0.494024 Up
0.002 0.0128 REACTOME_METABOLISM_OF_MRNA 206 0.131068 0.533981 Up
0.002 0.0128 KEGG_HUNTINGTONS_DISEASE 158 0.126582 0.607595 Up
0.002 0.0128 KEGG_ALZTIEIMERS_DISEASE 141 0.120567 0.631206 Up
0.002 0.0128 REACTOME_TRANSLATION 141 0.042553 0.780142 Up 0.002
0.0128 REACTOME_INFLUENZA_LIFE_CYCLE 133 0.075188 0.691729 Up 0.002
0.0128 REACTOME_TCA_CYCLE_AND_RESPIRATORY_ELECTRON_ 125 0.088 0.64
Up 0.002 0.0128 TRANSPORT KEGG_OXIDATIVE_PHOSPHORYLATION 117
0.042735 0.692308 Up 0.002 0.0128 KEGG_PARKINSONS_DISEASE 113
0.079646 0.699115 Up 0.002 0.0128
REACTOME_SRP_DEPENDENT_COTRANSLATIONAL_ 105 0.019048 0.885714 Up
0.002 0.0128 PROTEIN_TARGETING_TO_MEMBRANE
REACTOME_NONSENSE_MEDIATED_DECAY_ 103 0.07767 0.776699 Up 0.002
0.0128 ENHANCED_BY_THE_EXON_JUNCTION_COMPLEX
REACTOME_3_UTR_MEDIATED_TRANSLATIONAL_ 102 0.029412 0.843137 Up
0.002 0.0128 REGULATION REACTOME_SIGNALING_BY_RHO_GTPASES 93
0.387097 0.150538 Down 0.002 0.0128
REACTOME_RESPIRATORY_ELECTRON_TRANSPORT_ 91 0.021978 0.758242 Up
0.002 0.0128 ATP_SYNTHESIS_BY_CHEMIOSMOTIC_CO
AT_PRODUCTION_BY_UNCOUPLING_PROTEINS_KEGG_
JAK_STAT_SIGNALING_PATHWAY 87 0.321839 0.126437 Down 0.002 0.0128
KEGG_PYRIMIDINE_METABOLISM 84 0.154762 0.380952 Up 0.002 0.0128
KEGG_RIBOSOME 83 0.012048 0.939759 Up 0.002 0.0128
REACTOME_PEPTIDE_CHAIN_ELONGATION 82 0.012195 0.939024 Up 0.002
0.0128 REACTOME_RESPIRATORY_ELECTRON_TRANSPORT 74 0.013514 0.756757
Up 0.002 0.0128 PID_HDAC_CLASSI_PATHWAY 60 0.366667 0.15 Down 0.002
0.0128 PID_MYC_REPRESSPATHWAY 55 0.381818 0.127273 Down 0.002
0.0128 REACTOME_ACTIVATION_OF_THE_MRNA_UPON_ 55 0.054545 0.745455
Up 0.002 0.0128 BINDING_OF_THE_CAP_BINDING_COMPLEX_A _
SUBSEQUENT_BINDING_TO_43S PID_AVB3_INTEGRIN_PATHWAY 53 0.320755
0.132075 Down 0.002 0.0128 KEGG_ADIPOCYTOKINE_SIGNALING_PATHWAY 51
0.411765 0.176471 Down 0.002 0.0128
REACTOME_MITOCHONDRIAL_PROTEIN_IMPORT 49 0.102041 0.530612 Up 0.002
0.0128 REACTOME_FORMATION_OF_THE_TERNARY_ 47 0.042553 0.829787 Up
0.002 0.0128 COMPLEX_AND_SUBSEQUENTLY_THE_43S_COMP
KEGG_CARDIAC_MUSCLE_CONTRACTION 43 0.116279 0.55814 Up 0.002 0.0128
KEGG_LYSINE_DEGRADATION 42 0.428571 0.166667 Down 0.002 0.0128
PID_IL4_2PATHWAY 42 0.380952 0.119048 Down 0.002 0.0128
REACTOME_FORMATION_OF_RNA_POL_II_ 41 0.170732 0.439024 Up 0.002
0.0128 ELONGATION_COMPLEX_ KEGG_NOTCH_SIGNALING_PATHWAY 40 0.425
0.125 Down 0.002 0.0128 PID_RHOA_REG_PATHWAY 40 0.475 0.125 Down
0.002 0.0128 REACTOME_NRAGE_SIGNALS_DEATH_THROUGH_INK 39 0.358974
0.153846 Down 0.002 0.0128 REACTOME_PRE_NOTCH_EXPRESSION_AND_ 38
0.342105 0.131579 Down 0.002 0.0128 PROCESSING
REACTOME_NCAM_SIGNALING_FOR_NEURITE_OUT_ 37 0.459459 0.108108 Down
0.002 0.0128 GROWTH ST_GA13_PATHWAY 33 0.424242 0.121212 Down 0.002
0.0128 PID_RAC1_REG_PATHWAY 33 0.454545 0.121212 Down 0.002 0.0128
REACTOME_BMAL1_CLOCK_NPAS2_ACTIVATES_ 33 0.484848 0.090909 Down
0.002 0.0128 CIRCADIAN_EXPRESSION BIOCARTA_CARM_ER_PATHWAY 32
0.34375 0.125 Down 0.002 0.0128 REACTOME_G1_PHASE 32 0.09375 0.5 Up
0.002 0.0128 REACTOME_FORMATION_OF_THE_HIV1_EARLY_ 31 0.129032
0.483871 Up 0.002 0.0128 ELONGATION_COMPLEX
KEGG_PROPANOATE_METABOLISM 30 0.1 0.433333 Up 0.002 0.0128
PID_FRA_PATHWAY 28 0.428571 0.071429 Down 0.002 0.0128
REACTOME_PURINE_METABOLISM 28 0.178571 0.392857 Up 0.002 0.0128
KEGG_BUTANOATE_METABOLISM 27 0.037037 0.481481 Up 0.002 0.0128
BIOCARTA_MYOSIN_PATHWAY 27 0.296296 0.111111 Down 0.002 0.0128
REACTOME_MRNA_CAPPING 27 0.111111 0.481481 Up 0.002 0.0128
REACTOME_FORMATION_OF_TRANSCRIPTION_ 27 0.074074 0.518519 Up 0.002
0.0128 COUPLED_NER_TC_NER_REPAIR_COMPLEX
REACTOME_PRE_NOTCH_TRANSCRIPTION_AND_ 25 0.48 0.12 Down 0.002
0.0128 TRANSLATION ST_GAQ_PATHWAY 24 0.5 0.166667 Down 0.002 0.0128
REACTOME_RORA_ACTIVATES_CIRCADIAN_EXPRESSION 24 0.5 0.041667 Down
0.002 0.0128 REACTOME_ENDOSOMAL_SORTING_COMPLEX_ 24 0.083333
0.541667 Up 0.002 0.0128 REQUIRED_FOR_TRANSPORT_ESCRT
BIOCARTA_HDAC_PATHWAY 23 0.478261 0.130435 Down 0.002 0.0128
PID_HDAC_CLASSIII_PATHWAY 22 0.454545 0.136364 Down 0.002 0.0128
PID_RXR_VDR_PATHWAY 22 0.409091 0.045455 Down 0.002 0.0128
REACTOME_PREFOLDIN_MEDIATED_TRANSFER_OF_ 21 0.047619 0.571429 Up
0.002 0.0128 SUBSTRATE_TO_CCT_TRIC
REACTOME_SIGNALING_BY_FGFR1_MUTANTS 19 0.421053 0.157895 Down 0.002
0.0128 REACTOME_SIGNALING_BY_FGFR1_FUSION_MUTANTS 18 0.444444
0.111111 Down 0.002 0.0128 BIOCARTA_TNER2_PATHWAY 17 0.529412
0.117647 Down 0.002 0.0128 BIOCARTA_RELA_PATHWAY 15 0.533333 0.2
Down 0.002 0.0128 REACTOME_FORMATION_OF_ATP_BY_CHEMIOSMOTIC_ 15 0
0.866667 Up 0.002 0.0128 COUPLING
REACTOME_EARLY_PHASE_OF_HIV_LIFE_CYCLE 13 0 0.538462 Up 0.002
0.0128 BIOCARTA_VDR_PATHWAY 12 0.583333 0 Down 0.002 0.0128
BIOCARTA_CARM1_PATHWAY 12 0.416667 0.166667 Down 0.002 0.0128
REACTOME_SEMA3A_PLEXIN_REPULSION_ 12 0.5 0.166667 Down 0.002 0.0128
SIGNALING_BY_INHIBITING_INTEGRIN_ADHESION BIOCARTA_ETC_PATHWAY 11 0
0.727273 Up 0.002 0.0128 BIOCARTA_EGER_SMRTE_PATHWAY 11 0.454545 0
Down 0.002 0.0128 BIOCARTA_P27_PATHWAY 11 0.090909 0.454545 Up
0.002 0.0128 PID_LPA4_PATHWAY 11 0.545455 0 Down 0.002 0.0128
REACTOME_PURINE_SALVAGE 11 0.181818 0.727273 Up 0.002 0.0128
BIOCARTA_RAB_PATHWAY 10 0 0.9 Up 0.002 0.0128
REACTOME_ASSOCIATION_OF_LICENSING_FACTORS_ 9 0.111111 0.555556 Up
0.002 0.0128 WITH_THE_PRE_REPLICATIVE_COMPLEX
REACTOME_GLUTAMATE_NEUROTRANSMITTER_ 9 0.555556 0 Down 0.002 0.0128
RELEASE_CYCLE REACTOME_INTEGRATION_OF_PROVIRUS 8 0 0.625 Up 0.002
0.0128 BIOCARTA_NUCLEARRS_PATHWAY 6 0.5 0 Down 0.002 0.0128
REACTOME_ACYL_CHAIN_REMODELLING_OF_PI 6 0 0.666667 Up 0.002 0.0128
REACTOME_ENDOGENOUS_STEROLS 6 0.5 0.166667 Down 0.002 0.0128
REACTOME_SYNTHESIS_SECRETION_AND_ 6 0 0.833333 Up 0.002 0.0128
DEACYLATION_OF_GHRELIN REACTOME_INTERACTION_BETWEEN_L1_AND_ANKYRINS
6 1 0 Down 0.002 0.0128 KEGG_TAURINE_AND_HYPOTAURINE_METABOLISM 5
0.4 0.2 Down 0.002 0.0128 REACTOME_DOPAMINE_NEUROTRANSMITTER_ 5 0.6
0.2 Down 0.002 0.0128 RELEASE_CYCLE
REACTOME_ACETYLCHOLINE_NEUROTRANSMITTER_ 4 0.75 0 Down 0.002 0.0128
RELEASE_CYCLE REACTOME_NUCLEAR_RECEPTOR_TRANSCRIPTION_ 34 0.294118
0.058824 Down 0.002 0.0128 PATHWAY KEGG_PROTEIN_EXPORT 23 0.043478
0.652174 Up 0.002 0.0128 ST_INTERLEUKIN_4_PATHWAY 23 0.391304
0.086957 Down 0.002 0.0128 REACTOME_TRAF6_MEDIATED_IRF7_ACTIVATION
17 0.529412 0 Down 0.002 0.0128 PID_CIRCADIANPATHWAY 15 0.533333
0.066667 Down 0.002 0.0128 REACTOME_VIRAL_MESSENGER_RNA_SYNTHESIS
14 0.071429 0.642857 Up 0.002 0.0128
REACTOME_METABOLISM_OF_POLYAMINES 13 0.076923 0.538462 Up 0.002
0.0128 REACTOME_NOTCH_HLH_TRANSCRIPTION_PATHWAY 11 0.454545
0.090909 Down 0.002 0.0128 REACTOME_ADENYLATE_CYCLASE_ACTIVATING_ 7
0.571429 0 Down 0.002 0.0128 PATHWAY ST_STAT3_PATHWAY 9 0.555556 0
Down 0.002 0.0128 REACTOME_BINDING_AND_ENTRY_OF_HIV_VIRION 4 0 0.5
Up 0.002 0.0128 PID_CD40_PATHWAY 27 0.333333 0.037037 Down 0.002
0.0128 REACTOME_CD28_DEPENDENT_PI3K_AKT_SIGNALING 19 0.473684
0.052632 Down 0.002 0.0128 BIOCARTA_RARRXR_PATHWAY 15 0.4 0.066667
Down 0.002 0.0128 BIOCARTA_PITX2_PATHWAY 13 0.384615 0 Down 0.002
0.0128 REACTOME_INCRETIN_SYNTHESIS_SECRETION_AND_ 9 0 0.444444 Up
0.002 0.0128 INACTIVATION
REACTOME_CLASS_C_3_METABOTROPIC_GLUTAMATE_ 2 0.5 0 Down 0.002
0.0128 PHEROMONE_RECEPTORS BIOCARTA_EGF_PATHWAY 31 0.258065
0.032258 Down 0.002 0.0128 REACTOME_HDL_MEDIATED_LIPID_TRANSPORT 11
0.454545 0 Down 0.002 0.0128 REACTOME_GENERIC_TRANSCRIPTION_PATHWAY
292 0.349315 0.10274 Down 0.004 0.0283
REACTOME_DEVELOPMENTAL_BIOLOGY 270 0.333333 0.188889 Down 0.004
0.0283 REACTOME_SIGNALING_BY_PDGF 94 0.361702 0.148936 Down 0.004
0.0283 PID_SMAD2_3NUCLEARPATHWAY 68 0.411765 0.102941 Down 0.004
0.0283 PID_REG_GR_PATHWAY 60 0.366667 0.15 Down 0.004 0.0283
KEGG_ECM_RECEPTOR_INTERACTION 51 0.352941 0.117647 Down 0.004
0.0283 REACTOME_CIRCADIAN_CLOCK 48 0.416667 0.125 Down 0.004 0.0283
KEGG_PPAR_SIGNALING_PATHWAY 43 0.348837 0.162791 Down 0.004 0.0283
SIG_BCR_SIGNALING_PATHWAY 41 0.317073 0.04878 Down 0.004 0.0283
REACTOME_TRANSCRIPTION_COUPLED_NER_TC_NER 41 0.097561 0.439024 Up
0.004 0.0283 REACTOME_RNA_POL_II_TRANSCRIPTION_PRE_ 38 0.131579
0.447368 Up 0.004 0.0283 INITIATION_AND_PROMOTER_OPENING
KEGG_AMYOTROPHIC_LATERAL_SCLEROSIS_ALS 37 0.189189 0.324324 Up
0.004 0.0283 KEGG_ABC_TRANSPORTERS 31 0.516129 0.129032 Down 0.004
0.0283 BIOCARTA_PAR1_PATHWAY 31 0.290323 0.16129 Down 0.004 0.0283
REACTOME_COLLAGEN_FORMATION 31 0.451613 0.096774 Down 0.004 0.0283
PID RETINOIC_ACID_PATHWAY 28 0.392857 0.178571 Down 0.004 0.0283
REACTOME_CIRCADIAN_REPRESSION_OF_EXPRESSION_ 22 0.5 0.045455 Down
0.004 0.0283 BY_REV_ERBA KEGG_O_GLYCAN_BIOSYNTHESIS 21 0.047619
0.619048 Up 0.004 0.0283 REACTOME_YAP1_AND_WWTR1_TAZ_STIMULATED_ 20
0.4 0.1 Down 0.004 0.0283 GENE_EXPRESSION BIOCARTA_AKT_PATHWAY 18
0.444444 0.166667 Down 0.004 0.0283 BIOCARTA_IL7_PATHWAY 16 0.4375
0.125 Down 0.004 0.0283 REACTOME_OXYGEN_DEPENDENT_PROLINE_ 15
0.066667 0.533333 Up 0.004 0.0283
HYDROXYLATION_OF_HYPOXIA_INDUCIBLE_FA BIOCARTA_IL22BP_PATHWAY 14
0.5 0 Down 0.004 0.0283 REACTOME_NCAM1_INTERACTIONS 14 0.571429 0
Down 0.004 0.0283 REACTOME_EFFECTS_OF_PIP2_HYDROLYSIS 14 0.428571
0.071429 Down 0.004 0.0283 KEGG_RIBOFLAVIN_METABOLISM 13 0.076923
0.461538 Up 0.004 0.0283 REACTOME_TRAF3_DEPENDENT_IRF_ACTIVATION_
13 0.461538 0 Down 0.004 0.0283 PATHWAY BIOCARTA_EPONFKB_PATHWAY 9
0.666667 0 Down 0.004 0.0283 REACTOME_IL_6_SIGNALING 9 0.444444 0
Down 0.004 0.0283 REACTOME_SYNTHESIS_SECRETION_AND_ 7 0 0.571429 Up
0.004 0.0283 INACTIVATION_OF_GIP BIOCARTA_GABA_PATHWAY 3 0 0.666667
Up 0.004 0.0283 REACTOME_INFLUENZA_VIRAL_RNA_TRANSCRIPTION_ 98
0.020408 0.867347 Up 0.004 0.0283 AND_REPLICATION
REACTOME_LIPOPROTEIN_METABOLISM 19 0.315789 0.052632 Down 0.004
0.0283 REACTOME_ACYL_CHAIN_REMODELLING_OF_PG 7 0 0.571429 Up 0.004
0.0283 BIOCARTA_PDGF_PATHWAY 30 0.266667 0.033333 Down 0.004 0.0283
REACTOME_SYNTHESIS_SECRETION_AND_ 8 0 0.5 Up 0.004 0.0283
INACTIVATION_OF_GLP1 BIOCARTA_SALMONELLA_PATHWAY 11 0 0.636364 Up
0.004 0.0283 REACTOME_AXON_GUIDANCE 173 0.34104 0.179191 Down 0.006
0.0386 REACTOME_SIGNALING_BY_NOTCH 90 0.311111 0.2 Down 0.006
0.0386 KEGG_PEROXISOME 71 0.098592 0.352113 Up 0.006 0.0386
ST_INTEGRIN_SIGNALING_PATHWAY 71 0.323944 0.126761 Down 0.006
0.0386 REACTOME_SEMAPHORIN_INTERACTIONS 58 0.310345 0.224138 Down
0.006 0.0386 REACTOME_RNA_POL_II_PRE_TRANSCRIPTION_EVENTS 57
0.175439 0.385965 Up 0.006 0.0386 KEGG_ACUTE_MYELOID_LEUKEMIA 53
0.320755 0.132075 Down 0.006 0.0386
REACTOME_NUCLEOTIDE_EXCISION_REPAIR 46 0.108696 0.391304 Up 0.006
0.0386 REACTOME_EXTRACELLULAR_MATRIX_ORGANIZATION 43 0.372093
0.093023 Down 0.006 0.0386 KEGG_VALINE_LEUCINE_AND_ISOLEUCINE_ 40
0.075 0.45 Up 0.006 0.0386 DEGRADATION PID_HDAC_CLASII_PATHWAY 31
0.419355 0.16129 Down 0.006 0.0386
REACTOME_ELONGATION_ARREST_AND_RECOVERY 31 0.193548 0.451613 Up
0.006 0.0386 KEGG_RNA_POLYMERASE 27 0.074074 0.481481 Up 0.006
0.0386 SIG_IL4RECEPTOR_IN_B_LYPHOCYTES 25 0.32 0.04 Down 0.006
0.0386 PID_REELINPATHWAY 24 0.416667 0.166667 Down 0.006 0.0386
REACTOME_ABC_FAMILY_PROTEINS_MEDIATED_ 23 0.521739 0.217391 Down
0.006 0.0386 TRANSPORT REACTOME_ABORTIVE_ELONGATION_OF_HIV1_ 23
0.130435 0.478261 Up 0.006 0.0386 TRANSCRIPT_IN_THE_ABSENCE_OF_TAT
BIOCARTA_GH_PATHWAY 22 0.363636 0.045455 Down 0.006 0.0386
REACTOME_RNA_POL_III_CHAIN_ELONGATION 16 0.0625 0.4375 Up 0.006
0.0386 BIOCARTA_CD40_PATHWAY 14 0.5 0.071429 Down 0.006 0.0386
REACTOME_ACYL_CHAIN_REMODELLING_OF_PC 12 0.166667 0.5 Up 0.006
0.0386 REACTOME_CASPASE_MEDIATED_CLEAVAGE_OF_ 11 0.545455 0.272727
Down 0.006 0.0386 CYTOSKELETAL_PROTEINS
REACTOME_ORGANIC_CATION_ANION_ZWITTERION_ 5 0.6 0 Down 0.006 0.0386
TRANSPORT KEGG_FOCAL_ADHESION 145 0.296552 0.151724 Down 0.006
0.0386 PID_TNFPATHWAY 43 0.395349 0.093023 Down 0.006 0.0386
REACTOME_APC_CDC20_MEDIATED_DEGRADATION_OF_ 18 0.111111 0.388889 Up
0.006 0.0386 NEK2A BIOCARTA_ETS_PATHWAY 17 0.352941 0.117647 Down
0.006 0.0386
PID_HIF1APATHWAY 18 0.166667 0.333333 Up 0.006 0.0386
KEGG_TRYPTOPHAN_METABOLISM 25 0.08 0.28 Up 0.006 0.0386
REACTOME_N_GLYCAN_ANTENNAE_ELONGATION 10 0.1 0.5 Up 0.006 0.0386
REACTOME_AMINO_ACID_TRANSPORT_ACROSS_THE_ 18 0.388889 0 Down 0.006
0.0386 PLASMA_MEMBRANE indicates data missing or illegible when
filed
TABLE-US-00012 Dataset 3. GSEA results detailing lung cancer
associated dataset enrichment among genes differentially expressed
in the airway field associated with PMLs NOM FDR FWER RANK Gene Set
SIZE ES NES p-val q-val p-val AT MAX LEADING EDGE OOI ET AL. EARLY,
DN-REG, 26 -0.56 -1.87 0.002 0.005 0.017 2634 tags = 46%, list =
19%, signal = 57% PVN P < 0.05, TVN P < 0.05 OOI ET AL.
EARLY, UP-REG, 487 0.36 2.11 0 0 0.001 3850 tags = 43%, list = 28%,
signal = 58% PVN P < 0.05, TVN P < 0.05 OOI ET AL. STEPWISE,
DN-REG, 111 -0.31 -1.4 0.028 0.064 0.794 3041 tags = 27%, list =
22%, signal = 54% PVN P < 0.05, TVP P < 0.05, TVN P < 0.05
OOI ET AL. STEPWISE, UP-REG, 518 0.29 1.73 0. 0.005 0.076 2858 tags
= 29%, list = 21%, signal = 35% PVN P < 0.05, TVP P < 0.05,
TVN P < 0.05 OOI ET AL. LATE, DN-REG, 12 -0.64 -1.74 0.012 0.009
0.082 1784 tags = 58%, list = 13%, signal = 67% TVP P < 0.05,
TVN P < 0.05 OOI ET AL. LATE, UP-REG, 54 0.53 2.24 0 0 0 3052
tags = 46%, list = 22%, signal = 59 TVP P < 0.05, TVN P <
0.05 TCGA, SCCVN, DN-REG, 200 119 -0.37 -1.67 0.001 0.014 0.152
3526 tags = 36%, list = 25%, signal = 48% TCGA, SCCVN, UP-REG, 200
146 0.28 1.41 0.013 0.048 0.6 3950 tags = 40%, list = 28%, signal =
55% GSE18842, TVN, DN-REG, 200 111 -0.42 -1.87 0 0.007 0.016 3526
tags = 41%, list = 25%, signal = 54% GSE18842, TVN, UP-REG, 200 149
0.43 2.14 0 0 0.001 4601 tags = 52%, list = 33%, signal = 77%
GSE19188, SCCVN, DN-REG, 200 115 -0.35 -1.55 0.006 0.027 0.371 4837
tags = 50%, list = 35%, signal = 75% GSE19188, SCCVN, UP-REG, 200
147 0.42 2.14 0 0 0.001 3596 tags = 41%, list = 26%, signal = 55%
GSE4115, CAVN, DN-REG, 200 108 -0.35 -1.56 0.005 0.031 0.365 3066
tags = 31%, list = 22%, signal = 39% GSE4115, CAVN, UP-REG, 200 197
0.45 2.36 0 0 0 3781 tags = 55%, list = 27%, signal = 74%
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