U.S. patent application number 17/238794 was filed with the patent office on 2021-09-09 for molecular methods for assessing urothelial disease.
The applicant listed for this patent is City of Sapporo, Showa Denko Materials (America), Inc., Showa Denko Materials Co., Ltd.. Invention is credited to Hiroshi Harada, Masato Mitsuhashi, Taku Murakami, Cindy M. Yamamoto.
Application Number | 20210277483 17/238794 |
Document ID | / |
Family ID | 1000005583578 |
Filed Date | 2021-09-09 |
United States Patent
Application |
20210277483 |
Kind Code |
A1 |
Murakami; Taku ; et
al. |
September 9, 2021 |
MOLECULAR METHODS FOR ASSESSING UROTHELIAL DISEASE
Abstract
The present disclosure relates to methods of collecting exosomes
and microvesicles (EMV) from urine, isolating corresponding mRNA,
and analyzing expression patterns in order to diagnose and treat
various urothelial cancers. In particular, various expression
patterns are analyzed through a unique diagnostic formula.
Inventors: |
Murakami; Taku; (Irvine,
CA) ; Yamamoto; Cindy M.; (Irvine, CA) ;
Mitsuhashi; Masato; (Irvine, CA) ; Harada;
Hiroshi; (Sapporo, JP) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Showa Denko Materials Co., Ltd.
Showa Denko Materials (America), Inc.
City of Sapporo |
Tokyo
San Jose
Sapporo |
CA |
JP
US
JP |
|
|
Family ID: |
1000005583578 |
Appl. No.: |
17/238794 |
Filed: |
April 23, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15756497 |
Feb 28, 2018 |
11028443 |
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PCT/US2016/049483 |
Aug 30, 2016 |
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17238794 |
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62212501 |
Aug 31, 2015 |
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62252257 |
Nov 6, 2015 |
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62331241 |
May 3, 2016 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
C12Q 2600/158 20130101;
C12Q 1/6886 20130101 |
International
Class: |
C12Q 1/6886 20060101
C12Q001/6886 |
Claims
1. A method for treating a urothelial cancer in a human subject
comprising: identifying the subject for treating the urothelial
cancer by identifying that an expression level of at least one
marker selected from the group consisting of SLC2A1, S100A13,
KRT17, GPRC5A, P4HA1, and HSD17B2 in urinary exosomes and
microvesicles isolated from the subject is higher than an
expression level of the at least one marker in a urine sample
obtained from a non-urothelial cancer subject; and administering a
treatment selected from the group consisting of cystoscopy, tumor
resection, surgery, chemotherapy, and cystectomy to the
subject.
2. The method of claim 1, further comprising: detecting a reference
gene wherein a said reference gene is used to normalize said
expression level of said at least one marker wherein the reference
gene is selected from the group consisting of ACTB, ALDOB, DHRS2
and UPK1A.
3. The method of claim 1, further comprising: detecting a reference
gene wherein said reference gene is used to normalize the
expression level of the at least one marker, wherein said reference
gene is selected from the group consisting of ALDOB, DHRS2 and
UPK1A.
4. The method of claim 2, wherein the normalization is done by a
delta Ct method.
5. The method of claim 1, wherein the at least one marker is
selected from the group consisting of SLC2A1, S100A13, KRT17 and
GPRC5A.
6. The method of claim 1, wherein the at least one marker is
selected from the group consisting of SLC2A1, S100A13, and
KRT17.
7. The method of claim 1, wherein a value of a diagnostic formula
is obtained by machine learning technique such as logistic
regression analysis and support vector machine using the expression
level of the at least one marker.
8. A method of claim 1, wherein the urothelial cancer to be
detected is selected from the group consisting of bladder cancer,
renal pelvis cancer, and ureter cancer.
9. The method of claim 8, wherein the urothelial cancer to be
detected is recurrent bladder cancer.
10. The method of claim 1, wherein the subject does not show a
urine cytology positive result.
11. A method for treating a human subject with a urothelial cancer,
the method comprising: identifying the subject for treating the
urothelial cancer by identifying an expression of at least one RNA
associated with said urothelial cancer, comparing said at least one
RNA in a vesicle isolated from a urine sample from said subject
with an expression of said at least one RNA in a vesicle isolated
from a urine sample of a healthy human donor, wherein the at least
one RNA is selected from the group consisting of KRT17, SLC2A1,
ALDOB, LINC00967, SLC16A9, CRH, PCAT4, AQP3, THAP7, FADS2,
SERPINE1, AS1, OLFM3, S100A13, C5orf30, GINM1, GPRC5A, P4HA1,
HSD17B2, and TOP1P1, wherein an increase in said expression of said
at least one RNA of said subject compared to said expression of
said at least one RNA of said donor indicates said subject has
urothelial cancer when said increase is beyond a threshold level,
wherein said comparing said expression of said at least one RNA in
said vesicle isolated from said urine sample further comprises: (a)
capturing said vesicle from said sample from said subject by moving
said sample from said subject across a vesicle-capturing filter,
(b) loading a lysis buffer onto said vesicle-capturing filter,
thereby lysing said vesicle to release a vesicle-associated RNA,
(c) quantifying said expression of said at least one RNA associated
with urothelial cancer in said vesicle-associated RNA by PCR;
administering a treatment selected from the group consisting of
cystoscopy, tumor resection, surgery, chemotherapy, and cystectomy
to the subject having urothelial cancer.
12. The method of claim 11, wherein quantifying said expression of
said at least one RNA by PCR comprises: contacting said
vesicle-associated RNA with a reverse transcriptase to generate
complementary DNA (cDNA); contacting said cDNA with sense and
antisense primers that are specific for said at least one RNA
associated with urothelial cancer and with a DNA polymerase to
generate amplified DNA; contacting said cDNA with sense and
antisense primers that are specific for a reference RNA and with
said DNA polymerase to generate amplified DNA; and using analytical
software to determine an expression level or quantity or amount for
said at least one RNA.
13. The method of claim 12, wherein using analytical software to
determine an expression level or quantity or amount for said at
least one RNA associated with urothelial cancer comprises: using
analytical software to determine a marker cycle threshold (Ct)
value for said at least one RNA associated with urothelial cancer;
using analytical software to determine a reference Ct value for a
reference RNA; and subtracting the reference Ct value from the
marker Ct value to obtain a marker delta Ct value.
14. The method of claim 12, wherein said at least one RNA
associated with urothelial cancer is selected from the group
consisting of SLC2A1, S100A13, KRT17, GPRC5A, P4HA1, and
HSD17B2.
15. The method of claim 13, wherein said reference RNA is selected
from the group consisting of ACTB, ALDOB, DHRS2 and UPK1A.
16. The method of claim 13, wherein said increase is beyond said
threshold level when said marker delta Ct value is less than 6.
17. The method of claim 11, wherein said comparing further
comprises: determining a value of a diagnostic formula from said
expression of said at least one RNA isolated from said subject,
wherein the diagnostic formula is a linear or non-linear
mathematical formula, wherein the at least one RNA is 1 gene to 20
genes, more preferably 2 genes to 10 genes.
18. The method of claim 17, wherein the at least one RNA is
selected from the group consisting of KRT17, SLC2A1, ALDOB,
LINC00967, SLC16A9, CRH, PCAT4, AQP3, THAP7, FADS2, SERPINE1, AS1,
OLFM3, S100A13, C5orf30, GINM1, GPRC5A and TOP1P1.
19. The method of claim 18, wherein the at least one RNA is
selected from the group consisting of ALDOB, CRH, SERPINE1 and
SLC2A1.
Description
INCORPORATION BY REFERENCE TO ANY PRIORITY APPLICATIONS
[0001] Any and all applications for which a foreign or domestic
priority claim is identified in the Application Data Sheet as filed
with the present application are hereby incorporated by reference
herein and made part of the present disclosure.
REFERENCE TO SEQUENCE LISTING
[0002] A Sequence Listing submitted as an ASCII text file via
EFS-Web is hereby incorporated by reference in accordance with 37
C.F.R. .sctn. 1.52(e). The name of the ASCII text file for the
Sequence Listing is SEQUENCE_LISTING_HITACHI-127C1, the date of
creation of the ASCII text file is Apr. 22, 2021, and the size of
the ASCII text file is 17.0 KB.
BACKGROUND
Field
[0003] Several embodiments of the present disclosure relate to
devices and methods structured to isolate biomarkers from body
fluids and methods of using the expression profiles of such
biomarkers for diagnosis and treatment of diseases. Several
embodiments relate to characterizing mRNA profiles of exosomes and
microvesicles from urine samples of a patient to assess, diagnose,
or otherwise determine that patient's status with regard to
urothelial cancer, with several embodiments related to carrying out
a treatment.
Description of the Related Art
[0004] In 2015, National Cancer Institute estimated that there will
be approximately 74,000 new bladder cancer cases and 14,000 deaths
in the United States alone. The majority of bladder cancers and
other urothelial cancers (e.g., malignancy in ureters and renal
pelvises) are initiated from the transitional epithelium of urinary
tract. While urothelial cancers other than bladder cancer account
for only 5 to 10% of urothelial cancers, these cancers increase the
chance of bladder cancer in the future.
[0005] Treatment of urothelial cancer (e.g., bladder cancer)
depends on the stage and grade of the cancer. Non-muscle-invasive
cancers (Ta, Tis and T1) can be treated by transurethral tumor
removal or intravesical chemotherapy. On the other hand,
muscle-invasive cancers (T2, T3 and T4) require more aggressive
treatments such as cystectomy and intravenous chemotherapy. Because
the recurrence rate for the non-muscle-invasive cancers is 50 to
70%, and even higher for the muscle-invasive cancers, the patients
with bladder cancer history require lifelong monitoring of
recurrence, making bladder cancer the most expensive cancer in the
U.S. from diagnosis to treatment. Furthermore, about 30% of
patients with ureter or renal pelvis cancer will develop a bladder
cancer after a few years.
[0006] The current gold standard of bladder cancer detection is
cystoscopy with urine cytology. While cystoscopy with urine
cytology has a specificity of about 96%, the sensitivity is only
about 44%. For low-grade tumors, the sensitivity of cystoscopy with
urine cytology is even lower (4 to 31%). Cystoscopy is an invasive
procedure that involves inserting a thin tube with a camera and
light into the urethra and advancing the tube to the bladder.
[0007] Several FDA-approved test kits are available to screen for
urine-based urothelial cancer markers (e.g., BTA stat/BTA trak
(Polymedoco, New York), NMP22 BladderChek (Alere, Florida),
ImmunoCyt/uCyt+ (Scimedx, New Jersey), UroVysion (Abbott Molecular,
Illinois)). These diagnostic kits show very similar sensitivity and
specificity to the current gold standard, cystoscopy and cytology.
Therefore, better non-invasive biomarkers are still needed,
especially ones with higher sensitivity.
SUMMARY
[0008] As discussed, the current gold standard diagnostic method of
urothelial cancers including bladder, ureter and renal pelvis
cancers is cystoscopy and urine cytology. Considering the
invasiveness and diagnostic performance of the method as well as
the highly recurrent nature of urothelial cancers (e.g., bladder
cancer), there is a need to identify new non-invasive biomarkers
having higher sensitivity. There are provided herein, in several
embodiments, methods and systems for diagnosing and assessing
urothelial cancer with high specificity and sensitivity. In several
embodiments, the methods are minimally invasive. In several
embodiments, the methods are computer-based, and allow an
essentially real-time assessment of the presence and/or status of
urothelial cancer. In several embodiments, a specific recommended
treatment paradigm is produced (e.g., for a medical professional to
act on).
[0009] In certain aspects, various RNA can be used in the methods,
including, but not limited to detecting the presence of a
urothelial cancer in a subject. In some embodiments, the method
includes obtaining a urine sample from the subject; preparing a
urine supernatant by removing cells and large debris from the urine
sample; isolating urinary exosomes and microvesicles from the urine
supernatant; isolating an RNA from the urinary exosomes and
microvesicles; quantifying an expression level of a marker selected
from the group consisting of SLC2A1, S100A13, GAPDH, KRT17, GPRC5A,
P4HA1, and HSD17B2; and identifying the subject as having the
urothelial cancer if the expression level is higher than an
expression level of the mRNA in a urine sample obtained from a
non-urothelial cancer subject. In certain variants, the method
further includes detecting a reference gene wherein a said
reference gene is used to normalize said expression level of said
marker wherein the reference gene is selected from the group
consisting of ACTB, GAPDH, ALDOB, DHRS2 and UPK1A. In some
embodiments, the marker is selected from the list consisting of
SLC2A1, S100A13, GAPDH, KRT17 and GPRC5A. In certain embodiments, a
value of a diagnostic formula is obtained by machine learning
technique such as logistic regression analysis and support vector
machine using the expression level of more than one of the said
marker. In some embodiments, the urothelial cancer to be detected
is selected from the group consisting of bladder cancer, renal
pelvis cancer, and ureter cancer. In some aspects, the urothelial
cancer to be detected is recurrent bladder cancer. In certain
variants, the subject does not show a urine cytology positive
result.
[0010] In some aspects, a method is disclosed for screening a human
subject for an expression of an RNA associated with a urothelial
cancer, the method including the steps of comparing an expression
of the RNA in a vesicle isolated from a urine sample from the
subject with an expression of said RNA in a vesicle isolated from a
urine sample of a healthy donor, wherein an increase in said
expression of said RNA of said subject compared to said expression
of said RNA of said donor indicates said subject has urothelial
cancer when said increase is beyond a threshold level, wherein said
comparing the expression of the RNA in the vesicle isolated from
the urine sample further comprises: capturing the vesicle from the
sample from the subject by moving the sample from the subject
across a vesicle-capturing filter; loading a lysis buffer onto the
vesicle-capturing filter, thereby lysing the vesicle to release a
vesicle-associated RNA, and quantifying the expression of the RNA
associated with urothelial cancer in the vesicle-associated RNA by
PCR. In some variants, comparing further comprises: determining a
value of a diagnostic formula from the expression of the RNA
isolated from the subject, wherein the diagnostic formula is a
linear or non-linear mathematical formula, wherein the RNA is 1
gene to 60 genes, more preferably 2 genes to 10 genes. In some
aspects, the RNA is selected from the group consisting of KRT17,
SLC2A1, ALDOB, LINC00967, SLC16A9, CRH, PCAT4, AQP3, THAP7, FADS2,
SERPINE1, AS1, OLFM3, S100A13, C5orf30, GINM1, GPRC5A and TOP1P1.
In at least one embodiment, the RNA is selected from the group
consisting of ALDOB, CRH, SERPINE1 and SLC2A1.
[0011] The methods summarized above and set forth in further detail
below describe certain actions taken by a practitioner; however, it
should be understood that they can also include the instruction of
those actions by another party. Thus, actions such as "treating a
subject for a disease or condition" include "instructing the
administration of treatment of a subject for a disease or
condition."
BRIEF DESCRIPTION OF THE DRAWINGS
[0012] Various embodiments are depicted in the accompanying
drawings for illustrative purposes, and should in no way be
interpreted as limiting the scope of the embodiments. Furthermore,
various features of different disclosed embodiments can be combined
to form additional embodiments, which are part of this
disclosure.
[0013] FIG. 1A depicts a clustering analysis of EMV mRNA
profiles.
[0014] FIG. 1B depicts ingenuity pathway analysis of bladder tumors
to identify dysregulated pathways.
[0015] FIG. 1C shows comparison between gene expression in urinary
EMV mRNA and in bladder cancer biopsy mRNA.
[0016] FIG. 2A depicts organ expression of the one thousand most
abundant urinary EMV mRNA.
[0017] FIG. 2B depicts expression levels of bladder-specific genes
that are expressed abundantly in urinary EMV.
[0018] FIG. 2C depicts expression levels of kidney-specific genes
that are expressed abundantly in urinary EMV.
[0019] FIG. 2D shows box-and-whisker plots of raw Ct values in the
RT-qPCR assay of different urinary EMV mRNA in different cancer
types.
[0020] FIG. 2E shows box-and-whisker plots of raw Ct values in the
RT-qPCR assay of different urinary EMV mRNA in different bladder
cancer stages.
[0021] FIG. 2F shows box-and-whisker plots of raw Ct values in the
RT-qPCR assay of different urinary EMV mRNA in different bladder
cancer grades.
[0022] FIG. 3A shows box-and-whisker plots of RT-qPCR assay Ct
values normalized by ALDOB using delta Ct method. Normalized Ct
values are shown for different urinary EMV mRNA in different cancer
types.
[0023] FIG. 3B shows box-and-whisker plots of RT-qPCR assay Ct
values normalized by ALDOB using delta Ct method. Normalized Ct
values are shown for different urinary EMV mRNA in different
bladder cancer stages.
[0024] FIG. 3C shows box-and-whisker plots of RT-qPCR assay Ct
values normalized by ALDOB using delta Ct method. Normalized Ct
values are shown for different urinary EMV mRNA in different
bladder cancer grades.
[0025] FIG. 4A depicts gene expression of urinary EMV mRNA
candidates for different bladder cancer stages when conventional
urine cytology result was negative or suspicious.
[0026] FIG. 4B depicts gene expression of urinary EMV mRNA
candidates for different bladder cancer grades when conventional
urine cytology result was negative or suspicious.
[0027] FIG. 4C depicts gene expression of urinary EMV mRNA
candidates for different stages of recurrent bladder cancer.
[0028] FIG. 4D depicts gene expression of urinary EMV mRNA
candidates for different grades of recurrent bladder cancer.
[0029] FIG. 4E depicts gene expression of urinary EMV mRNA
candidates for different stages of non-bladder urothelial
cancer.
[0030] FIG. 4F depicts gene expression of urinary EMV mRNA
candidates for different grades of non-bladder urothelial
cancer.
[0031] FIG. 5A shows detection performance of the logistic
regression analysis formula "ALDOB+CRH+SERPINE1+SLC2A1" by bladder
cancer stage and grade.
[0032] FIG. 5B shows detection performance of the logistic
regression analysis formula "ALDOB+CRH+SERPINE1+SLC2A1" by bladder
cancer stage and grade when conventional urine cytology result was
negative or suspicious.
[0033] FIG. 5C shows detection performance of the logistic
regression analysis formula "ALDOB+CRH+SERPINE1+SLC2A1" by bladder
cancer stage and grade for detection of recurrent bladder
cancer.
[0034] FIG. 6A depicts error rate of SVM to detect bladder cancer
at various stages and grades with automatic parameter
selection.
[0035] FIG. 6B depicts parameter optimization by a grid search to
obtain the best combination of parameters, c and sigma, through 10
repeats of 5-fold cross validation.
[0036] FIG. 6C depicts error rate of SVM to detect bladder cancer
at various stages and grades with the optimized parameters
(c=2.sup.0.60 to 2.sup.0.94 sigma=2.sup.-4.5 to 2.sup.-3.5),
training and cross validation errors were 0%.+-.0% and
17.65%.+-.0.03%, respectively.
DETAILED DESCRIPTION
[0037] Certain aspects of the present disclosure are generally
directed to a minimally-invasive, or non-invasive, method that
assesses a patient's condition with regard to urothelial cancer.
Each and every feature described herein, and each and every
combination of two or more of such features, is included within the
scope of the present disclosure provided that the features included
in such a combination are not mutually inconsistent.
[0038] Exosomes and microvesicles (EMV) are released into the
urinary space from all the areas of the nephrons and encapsulate
cytoplasmic molecules of the cell of origin. Several studies showed
that tumors generate larger EMV at higher concentrations. EMV from
muscle-invasive bladder cancer cells have been shown to cause
urothelial cells to undergo epithelial-to-mesenchymal transition.
Since urothelial cancers are located on the urothelium and directly
in contact with urine, EMV from urothelial cancers may be released
into urine, suggesting that, according to several embodiments
disclosed herein, urinary EMV could be a rich source of urothelial
cancer biomarkers. Urinary cells and other markers are released
from tumors into urine only after the tumor grows significantly and
invades surrounding areas. However, urinary EMV are released not
only from tumors but also from normal and injured cells. Therefore,
molecular signatures of urothelial cancer could be obtained in
urine much earlier than the conventional biomarkers of urothelial
cancer.
[0039] The standard method to isolate urinary EMV is a differential
centrifugation method using ultracentrifugation. However, use of
ultracentrifugation may not be applicable for routine clinical
assays at regular clinical laboratories. Several embodiments of the
present disclosure employ a urinary EMV mRNA assay for biomarker
and clinical studies, which enables similar or even superior
performances to the standard method in terms of assay sensitivity,
reproducibility and ease of use. Several embodiments employ this
urinary EMV mRNA assay to screen urine samples from urothelial
cancer patients with various grades and stages of cancer were
screened to identify new biomarkers of urothelial cancer.
[0040] As described in more detail below, urinary exosomes can be
isolated from urine by passing urine samples through a vesicle
capture filter, thereby allowing the EMV to be isolated from urine
without the use of ultracentrifugation. In some embodiments, the
vesicle capture material has a porosity that is orders of magnitude
larger than the size of the captured vesicle. Although the
vesicle-capture material has a pore size that is much greater than
the size of the EMV, the EMV are captured on the vesicle-capture
material by adsorption of the EMV to the vesicle-capture material.
The pore size and structure of the vesicle-capture material is
tailored to balance EMV capture with EMV recovery so that mRNA from
the EMV can be recovered from the vesicle-capture material. In some
embodiments, the vesicle-capture material is a multi-layered filter
that includes at least two layers having different porosities. In
some embodiments, the urine sample passes first through a first
layer and then through a second layer, both made of glass fiber. In
one embodiment, the first layer has a pore-size of 1.6 .mu.m, and
the second layer has a pore size of 0.7 .mu.m. In some
configurations, the first layer has a particle retention rate of
between 0.6 and 2.7 .mu.m, preferably 1.5 and 1.8 .mu.m, and the
second layer has a particle retention rate between 0.1 and 1.6
.mu.m, preferably 0.6 and 0.8 .mu.m. In one embodiment, a particle
retention rate of the first layer is greater than that of the
second layer, thereby higher particulate loading capacity and
faster flow rates can be obtained.
[0041] Several aspects of the present disclosure employ a urinary
EMV mRNA assay in which EMV from urine of a human subject is
screened for urothelial cancer biomarkers. Urinary EMV mRNA
profiles of a healthy volunteer were analyzed by RNA-seq and found
to express not only kidney specific genes but also bladder specific
genes, suggesting that urinary EMV may be useful to detect
urothelial diseases (e.g., ureter and renal pelvis cancers) as well
as bladder cancer. Because urothelial cancers are directly in
contact with urine, it is possible molecular signatures of
urothelial cancers may be detected earlier in urinary EMV compared
to the urinary cell or protein biomarkers that are analyzed under
current clinical practice.
Patients and Samples
[0042] This study was reviewed and approved by the institutional
review board at Sapporo City General Hospital (approval no.
H25-047-197). Patients with suspected urothelial cancers were
recruited and diagnosed by cystoscopy and urine cytology. Up to 15
mL spot urine was collected prior to cystoscopy with an informed
consent (see Tables 1 and 2). Spot urine from healthy donors was
collected anonymously. The urine samples were stored at -80.degree.
C. within 3 hours after the collection.
Exosome Isolation and Characterization
[0043] In some embodiments, EMV were isolated using a differential
centrifugation method described previously by Murakami et al. (PLoS
ONE 9: e109074 (2014)). Dynamic light scattering analysis of
isolated EMV was conducted in PBS by SZ-100 (Horiba Instruments,
CA). Z-average value was obtained following the instrument control
software and used to investigate the size distribution of EMV. The
quantity of EMV was determined using EXOCET exosome quantitation
kit (System Biosciences, CA).
[0044] In some embodiments, urinary EMV were isolated using the
Exosome Isolation Tube (Hitachi Chemical Diagnostics, Inc.,
CA).
Urine EMV RNA-Seq Analysis
[0045] RNA-seq analysis of urine samples from urothelial cancer
patients was conducted in comparison with healthy, disease controls
and cancer remission in order to screen urothelial cancer
biomarkers. Urinary EMV mRNA profiles were analyzed by RNA-seq, as
described below, using EMV obtained from urine samples of healthy
donors (N=3), disease control and cancer remission patients (N=3),
patients having mild bladder cancer (N=3), and patients having
advanced urothelial cancer (N=3) (see Table 1).
TABLE-US-00001 TABLE 1 Patient samples used for urinary EMV
mRNA-seq analysis. RNAseq ID Diagnostic group Stage Grade Comments
RS01 Healthy control RS02 Healthy control RS03 Healthy control RS04
Disease control Atypical epithelium, reddened RS05 Cancer remission
No neoplastic change RS06 Cancer remission Epithelial granuloma
RS07 Mild bladder cancer pTa G2 Recurrence RS08 Mild bladder cancer
pTa G2 Recurrence RS09 Mild bladder cancer pTa G2 Flat like RS10
Advanced bladder pT4a G3 Cystectomy cancer RS11 Advanced renal pT2
G3 Nephrectomy pelvis cancer RS12 Advanced renal pT3 G3 Nephrectomy
pelvis cancer
[0046] Urinary EMV were isolated using the Exosome Isolation Tube
(Hitachi Chemical Diagnostics, Inc., CA). The captured EMV were
lysed on the filter tip, and the resultant lysates were transferred
by centrifugation to a T7 promoter oligo(dT)-immobilized microplate
for mRNA hybridization. The hybridized mRNA was amplified by
MEGAscript T7 Transcription Kit (Life Technologies, CA) directly on
the plate. RNA was purified using RNeasy MinElute Cleanup kit
before being used as starting material for TruSeq library
preparation (Illumina, CA). A 50-cycle single read run was done on
an Illumina HiSeq 2500 instrument. After the obtained raw reads
were filtered and deduplicated by FASTX-Toolkit and mapped against
hg38 by TopHat, the read counts were obtained by HTSeq and analyzed
by edgeR as described by Anders, et al. A FDR (false detection
rate)<5% criterion was used to detect differentially expressed
genes by edgeR. Additionally, ingenuity pathway analysis (Qiagen,
CA) was employed to identify dysregulated pathways in comparison
with bladder tumors using the gene expression profiles obtained
from the Cancer Genome Atlas.
[0047] Principal component analysis of the RNA-seq data detected
several possible clusters corresponding to the above sample
categories (FIG. 1A): healthy, disease controls and cancer
remission (RS01-RS06), mild bladder cancer (RS07-RS09), and
advanced urothelial cancer (RS10-RS12). Interestingly, cancer
remission samples (RS05, RS06) were located at the boundary between
healthy and disease controls and urothelial cancers. These data
suggest that urinary EMV mRNA profiles could be used to detect and
distinguish urothelial cancers by locations, grades and stages.
Pathway analysis was conducted to identify dysregulated networks in
urothelial cancer patient urine samples. In both bladder and renal
pelvis cancer urines, pathways related with organismal injury and
abnormalities and cancer, were dysregulated FIG. 1B), which is in
accordance with the patterns observed in bladder tumors. In bladder
cancer urines, additional pathways such as cellular movement,
hematological system development and function, cell-to-cell
signaling and interaction, immune cell trafficking and cell death
and survival, were also dysregulated, suggesting that immune
functions of the bladder cancer patients are compromised and can be
monitored by analyzing urinary EMV gene expression.
[0048] Unsupervised clustering analysis of urinary EMV mRNA
profiles clearly showed different clusters indicating healthy
control, bladder cancer, renal pelvis cancer, mild and advanced
urothelial cancers. By comparing among these aforementioned groups,
edgeR analysis of urinary EMV mRNA-seq data identified 94
differentially expressed genes (68 up-regulated and 26
down-regulated genes) as candidate markers to detect and
distinguish urothelial cancers by locations, grades and stages
(Table 2).
TABLE-US-00002 TABLE 2 Differentially-expressed genes in urothelial
cancer urine No. of differentially expressed genes Target group
Control group Up regulated Down regulated UC HC 2 2 UC DC and RMSN
0 2 UC HC, DC and RMSN 3 0 BC HC, DC and RMSN 36 19 BC DC and RMSN
27 1 RPC HC, DC and RMSN 1 0 RPC HC 0 0 Advanced UC HC 2 0 Advanced
UC HC, DC and RMSN 1 0 Mild UC HC 12 0 Mild UC HC, DC and RMSN 22 4
Recurrent Non recurrent 0 0 Total 68 26
[0049] Among the obtained genes, P4HA1, GPRC5A, MYC, F3, KRT17,
SHISA3, CBX7 and SPTLC3 are especially promising biomarkers of
urothelial cancers as their expression levels in urinary EMV are
highly correlated with the stages of urothelial cancers. CRH and
TMPRSS4 are also promising as these genes were differentially
expressed between mild urothelial cancer and disease control
groups. Furthermore, PRSS58, KLRC1, PRPS1L1, TRPM6, CACNA1C, FRG2,
LVRN, MFGE8, ZNF704, CDK9, MCF2L and TOMM70A are more specific to
renal pelvis cancers than bladder cancers, and useful to detect
renal pelvis cancers as well as to distinguish between bladder and
renal pelvis cancers. Interestingly, analysis of the Cancer Genome
Atlas revealed that a majority of these differentially-expressed
genes were also dysregulated in bladder cancer tumors (N=408)
compared to matched normal bladder tissues (N=19). The functional
classification analysis of these genes by PANTHER revealed many of
the molecular functions such as catalytic activity and binding and
biological processes such as metabolic and cellular processes are
dysregulated in urothelial cancers. Some of these
differentially-expressed genes and additional genes of interest
were selected for further analysis using RT-qPCR, as described
below.
Urinary EMV mRNA Analysis for RT-qPCR
[0050] Among the differentially-expressed genes in urinary EMV, 60
genes, including reference genes, were further assayed by RT-qPCR.
Urine samples were obtained from healthy donors (N=9) and from
patients having urothelial cancer or other cancers (N=245) (see
Table 3). Urinary EMV mRNA assay was conducted as previously
described by Murakami et al. (PLoS ONE 9: e109074 (2014)), except
that 10 .mu.M random hexamer was added at cDNA synthesis step. The
primer sequences are listed in Table 4. Threshold cycle (Ct) values
were obtained using the ViiA7 software (Life Technologies, CA) with
a manual threshold setting of 0.1 and a maximum Ct value of 36 (raw
Ct data). Further, data processing and analysis were performed
using R ver. 3.2. For normalization of gene expression, delta Ct
(dCt) method was employed using the following formula: dCt=Ct
[target gene]-Ct [reference gene]. A maximum dCt of 10 was used
when ALDOB was used as a reference gene. When target gene was not
detected or amplified correctly determined by melting curve
analysis, the maximum dCt was assigned. Statistical significance
was obtained by Welch's t-test or Mann-Whitney-Wilcoxon test with p
value <5%. Diagnostic performance was evaluated by the area
under the curve (AUC) in ROC curve analysis using ROCR. Sparse
logistic regression was done using glmnet. Support vector machine
(SVM) was done using kernlab.
TABLE-US-00003 TABLE 3 Sample information for urinary EMV mRNA
RT-qPCR analysis. Urine Cytology BTA*** Diagnostic group (Subject)
Stage Grade Recurrence (+) [ng/mL] Healthy control (HC) 9 (9) -- --
-- -- 3.0 .+-. 0.0 Disease control (DC)* 9 (9) -- -- -- -- 7.9 .+-.
14.8 Cancer remission (RMSN) 27 (26) -- -- -- -- 4.0 .+-. 2.8
Bladder cancer (BC) 173 (131) 0.4 .+-. 0.8 2.3 .+-. 0.5 45% 27%
10.5 .+-. 19.1 Renal pelvis cancer (RPC) 26 (25) 1.4 .+-. 1.4 2.6
.+-. 0.5 0% 17% 15.0 .+-. 27.6 Ureter cancer (URC) 7 (7) 2.2 .+-.
1.3 2.6 .+-. 0.5 0% 14% 3.9 .+-. 2.4 Other cancer (OT)** 3 (3) 3.0
3.0 -- 0% 3.0 .+-. 0.0 Total 254 (208) *DC includes non-cancer
patients such as no neoplastic change (N = 4), benign epithelium (N
= 1), chronic pyelonephritis (N = 1), inverted papilloma (N = 1),
methicillin-resistant Staphylococcus aureus (MRSA) infection (N =
1), and inflammatory polyp (N = 1). **OT includes non-urothelial
cancer patients such as adenocarcinoma (N = 1), renal cell
carcinoma (N = 1), and prostate cancer (N = 1). ***Bladder Tumor
Antigen (BTA) was assayed by a commercially-available BTA ELISA kit
(Biotang, MA).
TABLE-US-00004 TABLE 4 Primer sequence list. Gene Symbol Sense
primer (5' to 3') Anti sense primer (5' to 3') ACSM2A
aagacagcagccaacattcg tagcccgtcccataaactg (SEQ ID NO: 1) (SEQ ID NO:
2) ACTB atacctggcacccagcacaat tttttgccgatccacacggagtact (SEQ ID NO:
3) (SEQ ID NO: 4) ALDOB aaccaccattcaagggcttg ttggcgttttcctggatagc
(SEQ ID NO: 5) (SEQ ID NO: 6) AQP3 ttttgtttcgggccccaatg
ttgtaggggtcaacaatggc (SEQ ID NO: 7) (SEQ ID NO: 8) BANK1
tggcctggaaatgattcagc agtgggcagtaccaacc (SEQ ID NO: 9) (SEQ ID NO:
10) BKPyVgp4 acagcacagcaagaattccc tttgtgaccctgcatgaagg (SEQ ID NO:
11) (SEQ ID NO: 12) BMP2 agcagagcttcaggttttcc tttcgagttggctgttgcag
(SEQ ID NO: 13) (SEQ ID NO: 14) C5orf30 taggaggcacacgactg
atggcatggcttctgctttg (SEQ ID NO: 15) (SEQ ID NO: 16) CA1
ttgctgaagctgcctcaaag tttctgcagctttgggttgg (SEQ ID NO: 17) (SEQ ID
NO: 18) CASP7 ttccacggttccaggctattac tggcaactctgtcattcacc (SEQ ID
NO: 19) (SEQ ID NO: 20) CDCA3 tcttggtattgcacggacac
tacccagaggcaagtccaattc (SEQ ID NO: 21) (SEQ ID NO: 22) CEACAM7
tcagcgccacaaagaatgac aggtcaggtgaacttgcttg (SEQ ID NO: 23) (SEQ ID
NO: 24) CECR2 aagcatctccttgtggatcgg tggtacctgcaacgactg (SEQ ID NO:
25) (SEQ ID NO: 26) CHEK1 aggggtggtttatctgcatgg
tgttgccaagccaaagtctg (SEQ ID NO: 27) (SEQ ID NO: 28) CRH
atctccctggatctcaccttc tgtgagcttgctgtgctaac (SEQ ID NO: 29) (SEQ ID
NO: 30) DHRS2 tgagcagatctgggacaagatc aagctgcaatggaagagacc (SEQ ID
NO: 31) (SEQ ID NO: 32) F3 tcggacagccaacaattcag
agtccttgccaaaaacatccc (SEQ ID NO: 33) (SEQ ID NO: 34) FABP4
cctggtacatgtgcagaaatgg acgcctttcatgacgcattc (SEQ ID NO: 35) (SEQ ID
NO: 36) FADS2 ccaccttgtccacaaattcgtc aacacgtgcagcatgttcac (SEQ ID
NO: 37) (SEQ ID NO: 38) GAPDH cccactcctccacctttgac
cataccaggaaatgagcttgacaa (SEQ ID NO: 39) (SEQ ID NO: 40) GINM1
actggagcaacgattccc cagctgctcctgtaattccaac (SEQ ID NO: 41) (SEQ ID
NO: 42) GLI3 aatgtttccgcgactgaacc ttggactgtgtgccatttcc (SEQ ID NO:
43) (SEQ ID NO: 44) GPC5 acgtgctgctgaactttcac aaagaacaacgggggctttg
(SEQ ID NO: 45) (SEQ ID NO: 46) GPRC5A gctcatgcttcctgactttgac (SEQ
ID ttgtgagcagccaaaactcg (SEQ ID NO: 47) (SEQ ID NO: 48) HSD17B2
tttttaacaatgcatggccgtgaac (SEQ ttatatgctgctgacattcaccag (SEQ ID NO:
49) (SEQ ID NO: 50) KCNJ15 aatcgccagacccaaaaagc
aatcaccaagcacagcttcc (SEQ ID NO: 51) (SEQ ID NO: 52) KRT17
tggacaatgccaacatcctg tcaaacttggtgcggaagtc (SEQ ID NO: 53) (SEQ ID
NO: 54) LINC00967 tggagatggttggggtcaaatc (SEQ ID
tgcatccacaaagcacactg (SEQ ID NO: 55) (SEQ ID NO: 56) LRRCC1
tgagctagcagccaaggaatc ttgttgtgccagctcatgtc (SEQ ID NO: 57) (SEQ ID
NO: 58) MCM9 tgtaatgcaacggtggaagc tcatccatgatgatccctgagg (SEQ ID
NO: 59) (SEQ ID NO: 60) MYC acacatcagcacaactacgc
ggtgcattttcggttgttgc (SEQ ID NO: 61) (SEQ ID NO: 62) NRSN2-AS1
tgccaacaccaacaaggaac ttgcagttgagatgctggtc (SEQ ID NO: 63) (SEQ ID
NO: 64) OLFM3 accaaagagtgctgagcttg tcatccaagcaccaaatcgg (SEQ ID NO:
65) (SEQ ID NO: 66) P4HA1 agttggagctagtgtttggc ttgttgccaactagcactgg
(SEQ ID NO: 67) (SEQ ID NO: 68) PCAT4 ttttttcacgatgcgatgtcatgtc
ttttttgtcccaaattgtcgtccag (SEQ ID NO: 69) (SEQ ID NO: 70) PLAT
tgatcttgggcagaacataccg agcagcgcaatgtcattgtc (SEQ ID NO: 71) (SEQ ID
NO: 72) PPP2R5B acagtgcaaccacatcttc cgcaaagccattgatgatgc (SEQ ID
NO: 73) (SEQ ID NO: 74) PRDM16 ggacaaccacgcacttttagac
ttcgcgttgatgcttggttc (SEQ ID NO: 75) (SEQ ID NO: 76) RNF39
tcctgcagagactcttctgg cttgcgttgcactgattccc (SEQ ID NO: 77) (SEQ ID
NO: 78) RWDD3 tggtgttccatttgccagtc acaaaaggctctctgcttgc (SEQ ID NO:
79) (SEQ ID NO: 80) S100A13 accaccttcttcacctttgc
cagctctttgaactcgttgacg (SEQ ID NO: 81) (SEQ ID NO: 82) SERPINE1
accctcagcatgttcattgc tcatgttgcctttccagttg (SEQ ID NO: 83) (SEQ ID
NO: 84) SHISA3 tcaccccagtatttcgcttacc tggaactgaagtctggacagc (SEQ ID
NO: 85) (SEQ ID NO: 86) SLC12A1 actccagagctgctaatctcattgt
aactagtaagacaggtgggaggttct (SEQ ID NO: 87) (SEQ ID NO: 88) SLC12A3
gctctcatcgtcatcactttgc agcacgttttcctggtttcc (SEQ ID NO: 89) (SEQ ID
NO: 90) SLC16A9 acattggcgttgctttctgg attcccacagtcttcgtggtc (SEQ ID
NO: 91) (SEQ ID NO: 92) SLC2A1 tcattgtgggcatgtgcttc
accaggagcacagtgaagatg (SEQ ID NO: 93) (SEQ ID NO: 94) SLC2A3
tgttcaagagcccatctatgcc tgagcgtggaacaaaaagcc (SEQ ID NO: 95) (SEQ ID
NO: 96) SLC41A1 atcattgccatggccatcag tgcccccaacaccattaatc (SEQ ID
NO: 97) (SEQ ID NO: 98) SMCR8 tgaggccatagtcaggaaactc
aatgtttcacgggcttagcg (SEQ ID NO: 99) (SEQ ID NO: 100) THAP7-AS1
aacccgacaaaaaccagagc gcgatactgtctttctcctgtg (SEQ ID NO: 101) (SEQ
ID NO: 102) THEM45A acatctttgtgcaccagctg aaggaactctaggaaggcaacg
(SEQ ID NO: 103) (SEQ ID NO: 104) TMPRSS4 agatgatgtgtgcaggcatc
acatgccactggtcagattg (SEQ ID NO: 105) (SEQ ID NO: 106) TOP1P1
aaacagatggccttgggaac tttcaatggcccaggcaaac (SEQ ID NO: 107) (SEQ ID
NO: 108) TPX2 agtcaccagcctttgcattg taatgtggcacaggttgagc (SEQ ID NO:
109) (SEQ ID NO: 110) UMOD cctgaacttgggtcccatca gccccaagctgctaaaagc
(SEQ ID NO: 111) (SEQ ID NO: 112) UPK1A atccctgatcaccaagcagatg
aaggctgacgtgaagttcac (SEQ ID NO: 113) (SEQ ID NO: 114) UPK1B
aggcgtgcctggtttttatc aaatccaaaccaggcaaccc (SEQ ID NO: 115) (SEQ ID
NO: 116) ZBTB42 ttgtgcagcaagctgtttcc tatgtgcacgagaaggtcttcc (SEQ ID
NO: 117) (SEQ ID NO: 118) ZNF174 aactgccagactttcaaccg
atttgggggttggttcttgg (SEQ ID NO: 119) (SEQ ID NO: 120)
[0051] In order to normalize gene expression levels of marker
candidates in the RT-qPCR assay, reference gene candidates were
also screened and selected. Many reference genes such as ACTB,
GAPDH and ribosomal RNA have been used to normalize the expression
levels of mRNA in general. GAPDH has been used frequently in
urinary EMV studies as GAPDH is expressed abundantly and is
detectable in almost all the urine samples. For normalization, the
delta Ct method is frequently used in order to improve inter-assay
reproducibility. In the delta Ct method, the cycle threshold (Ct)
value of the marker candidate is subtracted from the Ct value of
the reference gene. Thus, the smaller the delta Ct value, the
higher the marker gene expression. Ideally, the gene expression
level of a reference gene should be stable independent of disease
status. However, when the number of EMV in urine is affected by
disease status, ubiquitously expressed genes may not be ideal as
reference genes because their expression levels are also affected.
For example, when bladder disease affects the number of EMV
originated in bladder, it is expected that the number of total EMV
or EMV originated in bladder in urine samples will change based on
disease status. Since ubiquitously expressed genes such as ACTB,
GAPDH and ribosomal RNA are also expressed in bladder, the
expression levels of these genes in urinary EMV will be affected by
the bladder disease status. However, it is very unlikely that
bladder disease status affects kidney function as the kidney is
located upstream in the urinary tract. Therefore, the number of
kidney-originating EMV that are released into the urine may not be
affected by bladder disease status. Therefore, kidney-specific
genes, or any other genes which are not expressed in bladder, are
ideal reference genes to normalize the expression levels of marker
genes for bladder diseases. Thus, in several embodiments, reference
genes are derived from EMV from a first organ, while EMV of
interest are from a different organ (e.g., the kidney).
[0052] In order to select organ-specific genes expressed in urinary
EMV, the 1000 most abundant mRNA expressed in urinary EMV were
selected from the RNA-seq data of urinary EMV, and the organ
specificity or the expression pattern in various organs of each
gene was investigated using GTEx database (Broad Institute). The
majority of the most abundant mRNA in urinary EMV are expressed
ubiquitously. However, some of the most abundant urinary EMV mRNA
are expressed specifically in certain organs such as kidney,
bladder and liver (FIG. 2A). The bladder-specific genes that are
expressed abundantly in urinary EMV are, for example, S100P, DHRS2,
GATA3, SNX31, UPK1A and UPK1B (FIG. 2B). The kidney-specific genes
that are expressed abundantly in urinary EMV are, for example,
ALDOB, SLC12A1, BHMT, KCNJ16, SPP1, CTXN3, DEFB1 and UMOD (FIG.
2C).
[0053] In order to select reference genes, analysis of variance
(ANOVA) was conducted using raw Ct values of tested reference gene
candidates (FIG. 2D, Table 5). Ten reference gene candidates were
selected from ubiquitously expressed genes, bladder-specific genes,
and kidney-specific genes, and their raw Ct values in the RT-qPCR
assay were compared by ANOVA among the different diagnostic groups
such as type of cancer (Dx), bladder cancer stages (Stage) or
bladder cancer grades (Grade).
TABLE-US-00005 TABLE 5 ANOVA analysis of reference gene expression.
Organ Ct ANOVA p value specificity Gene mean median Dx Stage Grade
Ubiquitous ACTB 25.6 25.8 0.1413 7E-07 0.0013 GAPDH 26.0 26.2
0.0888 2E-07 0.0002 Bladder DHRS2 27.0 26.8 0.2018 0.1423 0.1067
UPK1A 29.0 28.8 0.4592 0.3764 0.5713 UPK1B 33.0 33.1 0.105 3E-05
0.0037 Kidney ACSM2A 31.1 30.8 0.4473 0.1076 0.0805 ALDOB 24.1 23.9
0.6534 0.9641 0.5426 SLC12A1 31.9 31.6 0.4424 0.5933 0.4571 SLC12A3
31.6 31.0 0.0013 0.3554 0.0956 UMOD 31.4 31.3 0.3785 0.8876
0.6926
[0054] ANOVA indicated that ACTB, GAPDH and UPK1B are
differentially expressed among the diagnostic groups such as
bladder cancer stage and grade although they are not among the
different cancer types. On the other hand, ALDOB was highly
expressed in urinary EMV (mean Ct=24.1 and median Ct=23.9) and was
not differentially expressed among any diagnostic groups such as
cancer type, bladder cancer stage and grade, therefore ALDOB is an
ideal reference gene among the genes tested here. Alternatively,
DHRS2 and UPK1A are also ideal reference genes as their expression
levels are relatively high and not differentially expressed among
any diagnostic groups.
[0055] The gene expression profiles were normalized by ALDOB using
delta Ct method as described above and analyzed by disease status
such as cancer type (FIG. 3A), bladder cancer stage (FIG. 3B), and
bladder cancer grade (FIG. 3C). The diagnostic performances of
these urinary EMV mRNA were evaluated using ROC curve analysis by
comparing bladder cancer to disease control and cancer remission
(Table 6). The diagnostic performance of individual markers to
detect bladder cancer at various stages and grades was evaluated by
area under the curve in ROC curve analysis. The control group was
DC and RMSN (N=36). The 60 EMV mRNA markers were compared with the
conventional assays such as urine cytology and bladder tumor
antigen (BTA) ELISA assay. For urine cytology, three different
scorings were used: Cytology1; Positive (2), suspicious (1) and
negative (0), Cytology2; Positive/suspicious (1) and negative (0),
and Cytology3; Positive (1) and suspicious/negative (0).
[0056] Urinary EMV mRNA markers such as SLC2A1, S100A13, GAPDH,
KRT17, GPRC5A, P4HA1, AQP3, SLC12A3, TMPRSS4, SLC12A1, UPK1B,
FABP4, SMCR8, DHRS2, CHEK1, TOP1P1, LINC00967, CRH, MYC, ACSM2A and
GLI3 were found to be useful to detect various stages and grades of
bladder cancer (Table 6). SLC2A1, S100A13, GAPDH, KRT17 and GPRC5A
were especially able to detect bladder cancer with high specificity
and accuracy (e.g., AUC=0.64 to 0.70 for all the stages, AUC=0.56
to 0.64 for pTa, AUC=0.60 to 0.80 for pTis, AUC=0.77 to 0.86 for
pT1, AUC=0.68 to 0.90 for >pT2). SLC2A1, S100A13, GAPDH, KRT17,
and GPRC5A, were not only differentially expressed in the
urothelial cancer urine samples in comparison with the cancer
remission and disease control samples but also were able to detect
non-muscle invasive early stage urothelial cancer with high
specificity and sensitivity. These markers outperformed the
conventional urine cytology and BTA assay (Table 6).
[0057] These markers can be used complementarily with conventional
urine cytology. These EMV mRNA markers can detect bladder cancer
with high diagnostic performances (Table 7), even when conventional
urine cytology failed to detect bladder cancers (FIG. 4A), or when
the cytology result was negative or suspicious (FIG. 4B).
Diagnostic performance of individual markers to detect bladder
cancer at various stages and grades was evaluated in the patient
population whose urine cytology result was not positive by area
under the curve in ROC curve analysis. The control group was DC and
RMSN (N=26). The 60 EMV mRNA markers were compared with the
conventional assays such as urine cytology and bladder tumor
antigen (BTA) ELISA assay. For urine cytology, three different
scorings were used: Cytology1; Positive (2), suspicious (1) and
negative (0), Cytology2; Positive/suspicious (1) and negative (0),
and Cytology3; Positive (1) and suspicious/negative (0).
[0058] These markers are also useful to detect recurrent bladder
cancer as similar diagnostic performances were obtained even in the
recurrent bladder cancer against remission group (Table 8, FIGS.
4C-D). In Table 8, diagnostic performance of individual markers to
detect bladder cancer at various stages and grades was evaluated in
the patient population who developed bladder cancer previously, by
area under the curve in ROC curve analysis. The control group was
RMSN (N=27). The 60 EMV mRNA markers were compared with the
conventional assays such as urine cytology and bladder tumor
antigen (BTA) ELISA assay. For urine cytology, three different
scorings were used: Cytology1; Positive (2), suspicious (1) and
negative (0), Cytology2; Positive/suspicious (1) and negative (0),
and Cytology3; Positive (1) and suspicious/negative (0).
[0059] Additionally, the EMV mRNA markers such as KRT17, P4HA1,
HSD17B2, SLC2A1, S100A13, KCNJ15, SLC12A1, F3, TMEM45A, RNF39,
FABP4, TMPRSS4, UPK1B, PLAT and OLFM3 are useful to detect
non-bladder cancer urothelial cancers such as renal pelvis and
ureter cancers (Table 9, FIG. 4E-F). Especially, KRT17, P4HA1,
HSD17B2, SLC2A1 and S100A13 were able to detect non-bladder
urothelial cancer with high specificity and accuracy (e.g.,
AUC=0.69 to 0.77 for all the stages, AUC=0.58 to 0.77 for pTa,
AUC=0.64 to 0.87 for pT1, AUC=0.63 to 0.78 for >pT2). In Table
9, diagnostic performance of individual markers to detect
non-bladder urothelial cancers such as renal pelvic and ureter
cancer at various stages and grades was evaluated by area under the
curve in ROC curve analysis. The control group was DC and RMSN
(N=36). The 60 EMV mRNA markers were compared with the conventional
assays such as urine cytology and bladder tumor antigen (BTA) ELISA
assay. For urine cytology, three different scorings were used:
Cytology1; Positive (2), suspicious (1) and negative (0),
Cytology2; Positive/suspicious (1) and negative (0), and Cytology3;
Positive (1) and suspicious/negative (0).
[0060] In order to improve the diagnostic performance of bladder
cancer detection, machine learning techniques such as logistic
regression analysis, random forest, and support vector machine can
be used. Logistic regression analysis was conducted using the
urinary EMV mRNA raw Ct data. First, diagnostic scores or
probability of having a bladder cancer were assigned to the urine
samples based on the actual diagnostics such as score 0 for DC and
RMSN and score 1 for BC at pT1 and >pT2. These scores were
predicted by the combinations of mRNA data through 100-repeats of
10-fold cross validation. All the possible combinations of one to
four genes selected from the 60 gene candidates (523,685 formulas
in total) were tested and the top performing formulas were
selected. Their performances to detect bladder cancer at various
stages and grades were evaluated by area under the curve in ROC
curve analysis with DC and RMSN (N=36) as the control group. Table
10 lists the top diagnostic formulas and shows their diagnostic
performances. The genes selected frequently in the formulas with
high diagnostic performances are KRT17, SLC2A1, ALDOB, LINC00967,
SLC16A9, CRH, PCAT4, AQP3, THAP7, FADS2, SERPINE1, AS1, OLFM3,
S100A13, C5orf30, GINM1, GPRC5A and TOP1P1. The combination of
genes "ALDOB+CRH+SERPINE1+SLC2A1" showed the best performance to
detect bladder cancer, especially for detection of lower stage
and/or grade tumors (e.g., AUC 0.631 for pTa, AUC 0.822 for pTis,
AUC 0.886 for pT1, AUC 0.798 for >pT2 tumors) (Table 10, FIG.
5A). This formula was further evaluated for cytology negative
cancer detection (FIG. 5B) and for recurrent cancer detection (FIG.
5C), and was found to maintain good diagnostic performances under
both situations. Therefore, the formula is a promising diagnostic
that can be used complementarily to the conventional urine cytology
test and can be used as well in monitoring for recurrent bladder
cancer.
[0061] Support vector machine (SVM) was applied using the urinary
EMV mRNA raw Ct data to develop diagnostic formula for detecting
bladder cancer. SVM can fit the data using various linear and
non-linear functions. A non-linear radial basis function (RBF) was
selected because of its generally superior classification
performance. RBF requires tuning two parameters (c and sigma) to
obtain the best result, although the package can estimate
relatively optimum parameters automatically. SVM with automatic
parameter selection was first applied to detect bladder cancer at
various stages and grades (FIG. 6A). The training errors were 9% to
24% and the cross validation errors were 17% to 24%. In order to
improve the diagnostic formula for non-muscle invasive bladder
cancer including pTa, pTis and pT1, a grid search was conducted in
order to obtain the best combination of parameters, c and sigma,
through 10 repeats of 5-fold cross validation (FIG. 6B). With the
94, optimized parameters (c=2.sup.0.60 to 20 sigma=2.sup.-4.5 to
2.sup.-3.5), training and cross validation errors were 0%.+-.0% and
17.65%.+-.0.03%, respectively, both of which were improved further
in comparison with the automatic parameter selection (FIG. 6C).
These data indicate that bladder cancer can be detected accurately
by SVM using the urinary EMV mRNA expression profiles. The methods
and systems of the present disclosure use urinary EMV mRNA to
achieve promising non-invasive biomarkers of urothelial cancer.
[0062] The present application identifies novel methods for using
urinary EMV to diagnose, treat, and monitor urothelial cancer.
While others performed a small scale screening study of bladder
cancer markers using urinary EMV and identified several marker
candidates, the marker candidates disclosed herein were not
reported in that study. This may be because the other study was
limited due to the low recovery yields of total RNA from patient
urine samples. The present application investigated the expression
profiles of the genes detected by the other study, but none of
those reported markers appeared promising to detect and/or
distinguish urothelial cancers unlike the ones identified
herein.
[0063] As discussed above, there are provided herein several
embodiments in which nucleic acids are evaluated from blood or
urine samples in order to detect and determine an expression level
of a particular marker. In several embodiments, the determination
of the expression of the marker allows a diagnosis of a disease or
condition, for example urothelial disease. In several embodiments,
the determination is used to measure the severity of the condition
and develop and implement an appropriate treatment plan. In several
embodiments, the detected biomarker is then used to develop an
appropriate treatment regimen. In several embodiments, however, the
treatment may be taking no further action (e.g., not instituting a
treatment), for example when a subject is in remission. In several
embodiments the methods are computerized (e.g., one or more of the
RNA isolation, cDNA generation, or amplification are controlled, in
whole or in part, by a computer). In several embodiments, the
detection of the biomarker is real time.
[0064] As above, certain aspects of the methods are optionally
computerized. Also, in several embodiments, the amount of
expression may result in a determination that no treatment is to be
undertaken at that time. Thus, in several embodiments, the methods
disclosed herein also reduce unnecessary medical expenses and
reduce the likelihood of adverse effects from a treatment that is
not needed at that time.
[0065] In some embodiments, after a biological sample is collected
(e.g., a urine sample), membrane particles, cells, exosomes,
exosome-like vesicles, microvesicles and/or other biological
components of interest are isolated by filtering the sample. In
some embodiments, filtering the collected sample will trap one or
more of membrane particles, exosomes, exosome-like vesicles, and
microvesicles on a filter.
[0066] In some embodiments, after a biological sample is collected
(e.g., a urine sample), membrane particles, cells, exosomes,
exosome-like vesicles, microvesicles and/or other biological
components of interest are isolated by filtering the sample. In
some embodiments, filtering the collected sample will trap one or
more of membrane particles, exosomes, exosome-like vesicles, and
microvesicles on a filter. In some embodiments, the
vesicle-capturing material captures desired vesicles from a
biological sample. In some embodiments, therefore, the
vesicle-capturing material is selected based on the pore (or other
passages through a vesicle-capturing material) size of the
material. In some embodiments, the vesicle-capturing material
comprises a filter.
[0067] In some embodiments, the filter comprises pores. As used
herein, the terms "pore" or "pores" shall be given their ordinary
meaning and shall also refer to direct or convoluted passageways
through a vesicle-capture material. In some embodiments, the
materials that make up the filter provide indirect passageways
through the filter. For example, in some embodiments, the
vesicle-capture material comprises a plurality of fibers, which
allow passage of certain substances through the gaps in the fiber,
but do not have pores per se. For instance, a glass fiber filter
can have a mesh-like structure that is configured to retain
particles that have a size of about 1.6 microns or greater in
diameter. Such a glass fiber filter may be referred to herein
interchangeably as having a pore size of 1.6 microns or as
comprising material to capture components that are about 1.6
microns or greater in diameter. However, as discussed above, the
EMV that are captured by the filter are orders of magnitude smaller
than the pore size of the glass filter. Thus, although the filter
may be described herein as comprising material to capture
components that are about 1.6 microns or greater in diameter, such
a filter may capture components (e.g., EMV) that have a smaller
diameter because these small components may adsorb to the
filter.
[0068] In some embodiments, the filter comprises material to
capture components that are about 1.6 microns or greater in
diameter. In several embodiments, a plurality of filters are used
to capture vesicles within a particularly preferred range of sizes
(e.g., diameters). For example, in several embodiments, filters are
used to capture vesicles having a diameter of from about 0.2
microns to about 1.6 microns in diameter, including about 0.2
microns to about 0.4 microns, about 0.4 microns to about 0.6
microns, about 0.6 microns to about 0.8 microns, about 0.8 microns
to about 1.0 microns, about 1.0 microns to about 1.2 microns, about
1.2 to about 1.4 microns, about 1.4 microns to about 1.6 microns
(and any size in between those listed). In other embodiments, the
vesicle-capture material captures exosomes ranging in size from
about 0.5 microns to about 1.0 microns.
[0069] In some embodiments, the filter (or filters) comprises
glass-like material, non-glass-like material, or a combination
thereof. In some embodiments, wherein the vesicle-capture material
comprises glass-like materials, the vesicle-capture material has a
structure that is disordered or "amorphous" at the atomic scale,
like plastic or glass. Glass-like materials include, but are not
limited to glass beads or fibers, silica beads (or other
configuration), nitrocellulose, nylon, polyvinylidene fluoride
(PVDF) or other similar polymers, metal or nano-metal fibers,
polystyrene, ethylene vinyl acetate or other co-polymers, natural
fibers (e.g., silk), alginate fiber, or combinations thereof. In
certain embodiments, the vesicle-capture material optionally
comprises a plurality of layers of vesicle-capture material. In
other embodiments, the vesicle-capture material further comprises
nitrocellulose.
[0070] In some embodiments, a filter device is used to isolate
biological components of interest. In some embodiments, the device
comprises: a first body having an inlet, an outlet, and an interior
volume between the inlet and the outlet; a second body having an
inlet, an outlet, an interior volume between the inlet and the
outlet, a filter material positioned within the interior volume of
the second body and in fluid communication with the first body; and
a receiving vessel having an inlet, a closed end opposite the inlet
and interior cavity. In some embodiments, the first body and the
second body are reversibly connected by an interaction of the inlet
of the second body with the outlet of the first body. In some
embodiments, the interior cavity of the receiving vessel is
dimensioned to reversibly enclose both the first and the second
body and to receive the collected sample after it is passed from
the interior volume of the first body, through the filter material,
through the interior cavity of the second body and out of the
outlet of the second body. In some embodiments, the isolating step
comprises placing at least a portion of the collected sample in
such a device, and applying a force to the device to cause the
collected sample to pass through the device to the receiving vessel
and capture the biological component of interest. In some
embodiments, applying the force comprises centrifugation of the
device. In other embodiments, applying the force comprises
application of positive pressure to the device. In other
embodiments, applying the force comprises application of vacuum
pressure to the device. Examples of such filter devices are
disclosed in PCT Publication WO 2014/182330 and PCT Publication WO
2015/050891, hereby incorporated by reference in their
entirety.
[0071] In some embodiments, the collected sample is passed through
multiple filters to isolate the biological component of interest.
In other embodiments, isolating biological components comprises
diluting the collected sample. In other embodiments, centrifugation
may be used to isolate the biological components of interest. In
some embodiments, multiple isolation techniques may be employed
(e.g., combinations of filtration selection and/or density
centrifugation). In some embodiments, the collected sample is
separated into one or more samples after the isolating step.
[0072] In some embodiments, RNA is liberated from the biological
component of interest for measurement. In some embodiments,
liberating the RNA from the biological component of interest
comprises lysing the membrane particles, exosomes, exosome-like
vesicles, and/or microvesicles with a lysis buffer. In other
embodiments, centrifugation may be employed. In some embodiments,
the liberating is performed while the membrane particles, exosomes,
exosome-like vesicles, microvesicles and/or other components of
interest are immobilized on a filter. In some embodiments, the
membrane particles, exosomes, exosome-like vesicles, microvesicles
and/or other components of interest are isolated or otherwise
separated from other components of the collected sample (and/or
from one another--e.g., vesicles separated from exosomes).
[0073] According to various embodiments, various methods to
quantify RNA are used, including Northern blot analysis, RNase
protection assay, PCR, RT-PCR, real-time RT-PCR, other quantitative
PCR techniques, RNA sequencing, nucleic acid sequence-based
amplification, branched-DNA amplification, mass spectrometry,
CHIP-sequencing, DNA or RNA microarray analysis and/or other
hybridization microarrays. In some of these embodiments or
alternative embodiments, after amplified DNA is generated, it is
exposed to a probe complementary to a portion of a biomarker of
interest.
[0074] In some embodiments, a computerized method is used to
complete one or more of the steps. In some embodiments, the
computerized method comprises exposing a reaction mixture
comprising isolated RNA and/or prepared cDNA, a polymerase and
gene-specific primers to a thermal cycle. In some embodiments, the
thermal cycle is generated by a computer configured to control the
temperature time, and cycle number to which the reaction mixture is
exposed. In other embodiments, the computer controls only the time
or only the temperature for the reaction mixture and an individual
controls on or more additional variables. In some embodiments, a
computer is used that is configured to receive data from the
detecting step and to implement a program that detects the number
of thermal cycles required for the biomarker to reach a pre-defined
amplification threshold in order to identify whether a subject is
suffering from a gynecological disease or condition. In still
additional embodiments, the entire testing and detection process is
automated.
[0075] For example, in some embodiments, RNA is isolated by a fully
automated method, e.g., methods controlled by a computer processor
and associated automated machinery. In one embodiment a biological
sample, such as a urine sample, is collected and loaded into a
receiving vessel that is placed into a sample processing unit. A
user enters information into a data input receiver, such
information related to sample identity, the sample quantity, and/or
specific patient characteristics. In several embodiments, the user
employs a graphical user interface to enter the data. In other
embodiments, the data input is automated (e.g., input by bar code,
QR code, or other graphical identifier). The user can then
implement an RNA isolation protocol, for which the computer is
configured to access an algorithm and perform associated functions
to process the sample in order to isolate biological components,
such as vesicles, and subsequently processed the vesicles to
liberate RNA. In further embodiments, the computer implemented
program can quantify the amount of RNA isolated and/or evaluate and
purity. In such embodiments, should the quantity and/or purity
surpass a minimum threshold, the RNA can be further processed, in
an automated fashion, to generate complementary DNA (cDNA). cDNA
can then be generated using established methods, such as for
example, binding of a poly-A RNA tail to an oligo dT molecule and
subsequent extension using an RNA polymerase. In other embodiments,
if the quantity and/or purity fail to surpass a minimum threshold,
the computer implemented program can prompt a user to provide
additional biological sample(s).
[0076] Depending on the embodiment, the cDNA can be divided into
individual subsamples, some being stored for later analysis and
some being analyzed immediately. Analysis, in some embodiments
comprises mixing a known quantity of the cDNA with a salt-based
buffer, a DNA polymerase, and at least one gene specific primer to
generate a reaction mixture. The cDNA can then be amplified using a
predetermined thermal cycle program that the computer system is
configured to implement. This thermal cycle, could optionally be
controlled manually as well. After amplification (e.g., real-time
PCR), the computer system can assess the number of cycles required
for a gene of interest (e.g. a marker of urothelial disease or
condition) to surpass a particular threshold of expression. A data
analysis processor can then use this assessment to calculate the
amount of the gene of interest present in the original sample, and
by comparison either to a different patient sample, a known
control, or a combination thereof, expression level of the gene of
interest can be calculated. A data output processor can provide
this information, either electronically in another acceptable
format, to a test facility and/or directly to a medical care
provider. Based on this determination, the medical care provider
can then determine if and how to treat a particular patient based
on determining the presence of a urothelial disease or condition.
In several embodiments, the expression data is generated in real
time, and optionally conveyed to the medical care provider (or
other recipient) in real time.
[0077] In several embodiments, a fully or partially automated
method enables faster sample processing and analysis than manual
testing methods. In certain embodiments, machines or testing
devices may be portable and/or mobile such that a physician or
laboratory technician may complete testing outside of a normal
hospital or laboratory setting. In some embodiments, a portable
assay device may be compatible with a portable device comprising a
computer such as a cell phone or lap top that can be used to input
the assay parameters to the assay device and/or receive the raw
results of a completed test from the assay device for further
processing. In some embodiments, a patient or other user may be
able to use an assay device via a computer interface without the
assistance of a laboratory technician or doctor. In these cases,
the patient would have the option of performing the test "at-home."
In certain of these embodiments, a computer with specialized
software or programming may guide a patient to properly place a
sample in the assay device and input data and information relating
to the sample in the computer before ordering the tests to run.
After all the tests have been completed, the computer software may
automatically calculate the test results based on the raw data
received from the assay device. The computer may calculate
additional data by processing the results and, in some embodiments,
by comparing the results to control information from a stored
library of data or other sources via the internet or other means
that supply the computer with additional information. The computer
may then display an output to the patient (and/or the medical care
provider, and/or a test facility) based on those results.
[0078] In some embodiments, a medical professional may be in need
of genetic testing in order to diagnose, monitor and/or treat a
patient. Thus, in several embodiments, a medical professional may
order a test and use the results in making a diagnosis or treatment
plan for a patient. For example, in some embodiments a medical
professional may collect a sample from a patient or have the
patient otherwise provide a sample (or samples) for testing. The
medical professional may then send the sample to a laboratory or
other third party capable of processing and testing the sample.
Alternatively, the medical professional may perform some or all of
the processing and testing of the sample himself/herself (e.g., in
house). Testing may provide quantitative and/or qualitative
information about the sample, including data related to the
presence of a urothelial disease. Once this information is
collected, in some embodiments the information may be compared to
control information (e.g., to a baseline or normal population) to
determine whether the test results demonstrate a difference between
the patient's sample and the control. After the information is
compared and analyzed, it is returned to the medical professional
for additional analysis. Alternatively, the raw data collected from
the tests may be returned to the medical professional so that the
medical professional or other hospital staff can perform any
applicable comparisons and analyses. Based on the results of the
tests and the medical professional's analysis, the medical
professional may decide how to treat or diagnose the patient (or
optionally refrain from treating).
[0079] In several embodiments, filtration (alone or in combination
with centrifugation) is used to capture vesicles of different
sizes. In some embodiments, differential capture of vesicles is
made based on the surface expression of protein markers. For
example, a filter may be designed to be reactive to a specific
surface marker (e.g., filter coupled to an antibody) or specific
types of vesicles or vesicles of different origin. In several
embodiments, the combination of filtration and centrifugation
allows a higher yield or improved purity of vesicles.
[0080] In some embodiments, the markers are unique vesicle proteins
or peptides. In some embodiments, the severity of a particular
gynecological disease or disorder is associated with certain
vesicle modifications which can be exploited to allow isolation of
particular vesicles. Modification may include, but is not limited
to addition of lipids, carbohydrates, and other molecules such as
acylated, formylated, lipoylated, myristolylated, palmitoylated,
alkylated, methylated, isoprenylated, prenylated, amidated,
glycosylated, hydroxylated, iodinated, adenylated, phosphorylated,
sulfated, and selenoylated, ubiquitinated. In some embodiments, the
vesicle markers comprise non-proteins such as lipids,
carbohydrates, nucleic acids, RNA, DNA, etc.
[0081] In several embodiments, the specific capture of vesicles
based on their surface markers also enables a "dip stick" format
where each different type of vesicle is captured by dipping probes
coated with different capture molecules (e.g., antibodies with
different specificities) into a patient sample.
[0082] Free extracellular RNA is quickly degraded by nucleases,
making it a potentially poor diagnostic marker. As described above,
some extracellular RNA is associated with particles or vesicles
that can be found in various biological samples, such as urine.
This vesicle associated RNA, which includes mRNA, is protected from
the degradation processes. Microvesicles are shed from most cell
types and consist of fragments of plasma membrane. Microvesicles
contain RNA, mRNA, microRNA, and proteins and mirror the
composition of the cell from which they are shed. Exosomes are
small microvesicles secreted by a wide range of mammalian cells and
are secreted under normal and pathological conditions. These
vesicles contain certain proteins and RNA including mRNA and
microRNA. Several embodiments evaluate nucleic acids such as small
interfering RNA (siRNA), tRNA, and small activating RNA (saRNA),
among others.
[0083] In several embodiments the RNA isolated from vesicles from
the urine of a patient is used as a template to make complementary
DNA (cDNA), for example through the use of a reverse transcriptase.
In several embodiments, cDNA is amplified using the polymerase
chain reaction (PCR). In other embodiments, amplification of
nucleic acid and RNA may also be achieved by any suitable
amplification technique such as nucleic acid based amplification
(NASBA) or primer-dependent continuous amplification of nucleic
acid, or ligase chain reaction. Other methods may also be used to
quantify the nucleic acids, such as for example, including Northern
blot analysis, RNAse protection assay, RNA sequencing, RT-PCR,
real-time RT-PCR, nucleic acid sequence-based amplification,
branched-DNA amplification, ELISA, mass spectrometry,
CHIP-sequencing, and DNA or RNA microarray analysis.
[0084] In several embodiments, mRNA is quantified by a method
entailing cDNA synthesis from mRNA and amplification of cDNA using
PCR. In one preferred embodiment, a multi-well filterplate is
washed with lysis buffer and wash buffer. A cDNA synthesis buffer
is then added to the multi-well filterplate. The multi-well
filterplate can be centrifuged. PCR primers are added to a PCR
plate, and the cDNA is transferred from the multi-well filterplate
to the PCR plate. The PCR plate is centrifuged, and real time PCR
is commenced.
[0085] Another preferred embodiment comprises application of
specific antisense primers during mRNA hybridization or during cDNA
synthesis. It is preferable that the primers be added during mRNA
hybridization, so that excess antisense primers may be removed
before cDNA synthesis to avoid carryover effects. The oligo(dT) and
the specific primer (NNNN) simultaneously prime cDNA synthesis at
different locations on the poly-A RNA. The specific primer (NNNN)
and oligo(dT) cause the formation of cDNA during amplification.
Even when the specific primer-derived cDNA is removed from the
GenePlate by heating each well, the amounts of specific cDNA
obtained from the heat denaturing process (for example, using
TaqMan quantitative PCR) is similar to the amount obtained from an
un-heated negative control. This allows the heat denaturing process
to be completely eliminated. Moreover, by adding multiple antisense
primers for different targets, multiple genes can be amplified from
the aliquot of cDNA, and oligo(dT)-derived cDNA in the GenePlate
can be stored for future use.
[0086] Another alternative embodiment involves a device for
high-throughput quantification of mRNA from urine (or other
fluids). The device includes a multi-well filterplate containing:
multiple sample-delivery wells, an exosome-capturing filter (or
filter directed to another biological component of interest)
underneath the sample-delivery wells, and an mRNA capture zone
under the filter, which contains oligo(dT)-immobilized in the wells
of the mRNA capture zone. In order to increase the efficiency of
exosome collection, several filtration membranes can be layered
together.
[0087] In some embodiments, amplification comprises conducting
real-time quantitative PCR (TaqMan) with exosome-derived RNA and
control RNA. In some embodiments, a Taqman assay is employed. The
5' to 3' exonuclease activity of Taq polymerase is employed in a
polymerase chain reaction product detection system to generate a
specific detectable signal concomitantly with amplification. An
oligonucleotide probe, nonextendable at the 3' end, labeled at the
5' end, and designed to hybridize within the target sequence, is
introduced into the polymerase chain reaction assay. Annealing of
the probe to one of the polymerase chain reaction product strands
during the course of amplification generates a substrate suitable
for exonuclease activity. During amplification, the 5' to 3'
exonuclease activity of Taq polymerase degrades the probe into
smaller fragments that can be differentiated from undegraded probe.
In additional embodiments, the method comprises: (a) providing to a
PCR assay containing a sample, at least one labeled oligonucleotide
containing a sequence complementary to a region of the target
nucleic acid, wherein the labeled oligonucleotide anneals within
the target nucleic acid sequence bounded by the oligonucleotide
primers of step (b); (b) providing a set of oligonucleotide
primers, wherein a first primer contains a sequence complementary
to a region in one strand of the target nucleic acid sequence and
primes the synthesis of a complementary DNA strand, and a second
primer contains a sequence complementary to a region in a second
strand of the target nucleic acid sequence and primes the synthesis
of a complementary DNA strand; and wherein each oligonucleotide
primer is selected to anneal to its complementary template upstream
of any labeled oligonucleotide annealed to the same nucleic acid
strand; (c) amplifying the target nucleic acid sequence employing a
nucleic acid polymerase having 5' to 3' nuclease activity as a
template dependent polymerizing agent under conditions which are
permissive for PCR cycling steps of (i) annealing of primers and
labeled oligonucleotide to a template nucleic acid sequence
contained within the target region, and (ii) extending the primer,
wherein said nucleic acid polymerase synthesizes a primer extension
product while the 5' to 3' nuclease activity of the nucleic acid
polymerase simultaneously releases labeled fragments from the
annealed duplexes comprising labeled oligonucleotide and its
complementary template nucleic acid sequences, thereby creating
detectable labeled fragments; and (d) detecting and/or measuring
the release of labeled fragments to determine the presence or
absence of target sequence in the sample.
[0088] In additional embodiments, a Taqman assay is employed that
provides a reaction that results in the cleavage of single-stranded
oligonucleotide probes labeled with a light-emitting label wherein
the reaction is carried out in the presence of a DNA binding
compound that interacts with the label to modify the light emission
of the label. The method utilizes the change in light emission of
the labeled probe that results from degradation of the probe. The
methods are applicable in general to assays that utilize a reaction
that results in cleavage of oligonucleotide probes, and in
particular, to homogeneous amplification/detection assays where
hybridized probe is cleaved concomitant with primer extension. A
homogeneous amplification/detection assay is provided which allows
the simultaneous detection of the accumulation of amplified target
and the sequence-specific detection of the target sequence.
[0089] In additional embodiments, real-time PCR formats may also be
employed. One format employs an intercalating dye, such as SYBR
Green. This dye provides a strong fluorescent signal on binding
double-stranded DNA; this signal enables quantification of the
amplified DNA. Although this format does not permit
sequence-specific monitoring of amplification, it enables direct
quantization of amplified DNA without any labeled probes. Other
such fluorescent dyes that may also be employed are SYBR Gold,
YO-PRO dyes and Yo Yo dyes.
[0090] Another real-time PCR format that may be employed uses
reporter probes that hybridize to amplicons to generate a
fluorescent signal. The hybridization events either separate the
reporter and quencher moieties on the probes or bring them into
closer proximity. The probes themselves are not degraded and the
reporter fluorescent signal itself is not accumulated in the
reaction. The accumulation of products during PCR is monitored by
an increase in reporter fluorescent signal when probes hybridize to
amplicons. Formats in this category include molecular beacons,
dual-hybe probes, Sunrise or Amplifluor, and Scorpion real-time PCR
assays.
[0091] Another real-time PCR format that may also be employed is
the so-called "Policeman" system. In this system, the primer
comprises a fluorescent moiety, such as FAM, and a quencher moiety
which is capable of quenching fluorescence of the fluorescent
moiety, such as TAMRA, which is covalently bound to at least one
nucleotide base at the 3' end of the primer. At the 3' end, the
primer has at least one mismatched base and thus does not
complement the nucleic acid sample at that base or bases. The
template nucleic acid sequence is amplified by PCR with a
polymerase having 3'-5' exonuclease activity, such as the Pfu
enzyme, to produce a PCR product. The mismatched base(s) bound to
the quencher moiety are cleaved from the 3' end of the PCR product
by 3'-5' exonuclease activity. The fluorescence that results when
the mismatched base with the covalently bound quencher moiety is
cleaved by the polymerase, thus removing the quenching effect on
the fluorescent moiety, is detected and/or quantified at least one
time point during PCR. Fluorescence above background indicates the
presence of the synthesized nucleic acid sample.
[0092] Another alternative embodiment involves a fully automated
system for performing high throughput quantification of mRNA in
biological fluid, such as urine, including: robots to apply urine
samples, hypotonic buffer, and lysis buffer to the device; an
automated vacuum aspirator and centrifuge, and automated PCR
machinery.
[0093] The method of determining the presence of post-transplant
kidney disease or condition disclosed may also employ other methods
of measuring mRNA other than those described above. Other methods
which may be employed include, for example, Northern blot analysis,
Rnase protection, solution hybridization methods, semi-quantitative
RT-PCR, and in situ hybridization.
[0094] In some embodiments, in order to properly quantify the
amount of mRNA, quantification is calculated by comparing the
amount of mRNA encoding a marker of urothelial disease or condition
to a reference value. In some embodiments the reference value will
be the amount of mRNA found in healthy non-diseased patients. In
other embodiments, the reference value is the expression level of a
house-keeping gene. In certain such embodiments, beta-actin, or
other appropriate housekeeping gene is used as the reference value.
Numerous other house-keeping genes that are well known in the art
may also be used as a reference value. In other embodiments, a
house keeping gene is used as a correction factor, such that the
ultimate comparison is the expression level of marker from a
diseased patient as compared to the same marker from a non-diseased
(control) sample. In several embodiments, the house keeping gene is
a tissue specific gene or marker, such as those discussed above. In
still other embodiments, the reference value is zero, such that the
quantification of the markers is represented by an absolute number.
In several embodiments a ratio comparing the expression of one or
more markers from a diseased patient to one or more other markers
from a non-diseased person is made. In several embodiments, the
comparison to the reference value is performed in real-time, such
that it may be possible to make a determination about the sample at
an early stage in the expression analysis. For example, if a sample
is processed and compared to a reference value in real time, it may
be determined that the expression of the marker exceeds the
reference value after only a few amplification cycles, rather than
requiring a full-length analysis. In several embodiments, this
early comparison is particularly valuable, such as when a rapid
diagnosis and treatment plan are required (e.g., to treat heavily
damaged or malfunctioning kidneys prior to kidney failure or
transplant rejection).
[0095] In alternative embodiments, the ability to determine the
total efficiency of a given sample by using known amounts of spiked
standard RNA results from embodiments being dose-independent and
sequence-independent. The use of known amounts of control RNA
allows PCR measurements to be converted into the quantity of target
mRNAs in the original samples.
[0096] In some embodiments, a kit is provided for extracting target
components from fluid sample, such as urine. In some embodiments, a
kit comprises a capture device and additional items useful to carry
out methods disclosed herein. In some embodiments, a kit comprises
one or more reagents selected from the group consisting of lysis
buffers, chaotropic reagents, washing buffers, alcohol, detergent,
or combinations thereof. In some embodiments, kit reagents are
provided individually or in storage containers. In several
embodiments, kit reagents are provided ready-to-use. In some
embodiments, kit reagents are provided in the form of stock
solutions that are diluted before use. In some embodiments, a kit
comprises plastic parts (optionally sterilized or sterilizable)
that are useful to carry out methods herein disclosed. In some
embodiments, a kit comprises plastic parts selected from the group
consisting of racks, centrifuge tubes, vacuum manifolds, and
multi-well plates. Instructions for use are also provided, in
several embodiments.
[0097] In several embodiments, the analyses described herein are
applicable to human patients, while in some embodiments, the
methods are applicable to animals (e.g., veterinary diagnoses).
[0098] In several embodiments, presence of a urothelial condition
or disease induces the altered expression of one or more markers.
In several embodiments, the increased or decreased expression is
measured by the amount of mRNA encoding said markers (in other
embodiments, DNA or protein are used to measure expression levels).
In some embodiments urine is collected from a patient and directly
evaluated. In some embodiments, vesicles are concentrated, for
example by use of filtration or centrifugation. Isolated vesicles
are then incubated with lysis buffer to release the RNA from the
vesicles, the RNA then serving as a template for cDNA which is
quantified with methods such as quantitative PCR (or other
appropriate amplification or quantification technique). In several
embodiments, the level of specific marker RNA from patient vesicles
is compared with a desired control such as, for example, RNA levels
from a healthy patient population, or the RNA level from an earlier
time point from the same patient or a control gene from the same
patient.
Implementation Mechanisms
[0099] According to some embodiments, the methods described herein
can be implemented by one or more special-purpose computing
devices. The special-purpose computing devices may be hard-wired to
perform the techniques, or may include digital electronic devices
such as one or more application-specific integrated circuits
(ASICs) or field programmable gate arrays (FPGAs) that are
persistently programmed to perform the techniques, or may include
one or more general purpose hardware processors programmed to
perform the techniques pursuant to program instructions in
firmware, memory, other storage, or a combination. Such
special-purpose computing devices may also combine custom
hard-wired logic, ASICs, or FPGAs with custom programming to
accomplish the techniques. The special-purpose computing devices
may be desktop computer systems, server computer systems, portable
computer systems, handheld devices, networking devices or any other
device or combination of devices that incorporate hard-wired and/or
program logic to implement the techniques.
[0100] Computing device(s) are generally controlled and coordinated
by operating system software, such as iOS, Android, Chrome OS,
Windows XP, Windows Vista, Windows 7, Windows 8, Windows Server,
Windows CE, Unix, Linux, SunOS, Solaris, iOS, Blackberry OS,
VxWorks, or other compatible operating systems. In other
embodiments, the computing device may be controlled by a
proprietary operating system. Conventional operating systems
control and schedule computer processes for execution, perform
memory management, provide file system, networking, I/O services,
and provide a user interface functionality, such as a graphical
user interface ("GUI"), among other things.
[0101] In some embodiments, the computer system includes a bus or
other communication mechanism for communicating information, and a
hardware processor, or multiple processors, coupled with the bus
for processing information. Hardware processor(s) may be, for
example, one or more general purpose microprocessors.
[0102] In some embodiments, the computer system may also includes a
main memory, such as a random access memory (RAM), cache and/or
other dynamic storage devices, coupled to a bus for storing
information and instructions to be executed by a processor. Main
memory also may be used for storing temporary variables or other
intermediate information during execution of instructions to be
executed by the processor. Such instructions, when stored in
storage media accessible to the processor, render the computer
system into a special-purpose machine that is customized to perform
the operations specified in the instructions.
[0103] In some embodiments, the computer system further includes a
read only memory (ROM) or other static storage device coupled to
bus for storing static information and instructions for the
processor. A storage device, such as a magnetic disk, optical disk,
or USB thumb drive (Flash drive), etc., may be provided and coupled
to the bus for storing information and instructions.
[0104] In some embodiments, the computer system may be coupled via
a bus to a display, such as a cathode ray tube (CRT) or LCD display
(or touch screen), for displaying information to a computer user.
An input device, including alphanumeric and other keys, is coupled
to the bus for communicating information and command selections to
the processor. Another type of user input device is cursor control,
such as a mouse, a trackball, or cursor direction keys for
communicating direction information and command selections to the
processor and for controlling cursor movement on display. This
input device typically has two degrees of freedom in two axes, a
first axis (e.g., x) and a second axis (e.g., y), that allows the
device to specify positions in a plane. In some embodiments, the
same direction information and command selections as cursor control
may be implemented via receiving touches on a touch screen without
a cursor.
[0105] In some embodiments, the computing system may include a user
interface module to implement a GUI that may be stored in a mass
storage device as executable software codes that are executed by
the computing device(s). This and other modules may include, by way
of example, components, such as software components,
object-oriented software components, class components and task
components, processes, functions, attributes, procedures,
subroutines, segments of program code, drivers, firmware,
microcode, circuitry, data, databases, data structures, tables,
arrays, and variables.
[0106] In general, the word "module," as used herein, refers to
logic embodied in hardware or firmware, or to a collection of
software instructions, possibly having entry and exit points,
written in a programming language, such as, for example, Java, Lua,
C or C++. A software module may be compiled and linked into an
executable program, installed in a dynamic link library, or may be
written in an interpreted programming language such as, for
example, BASIC, Perl, or Python. It will be appreciated that
software modules may be callable from other modules or from
themselves, and/or may be invoked in response to detected events or
interrupts. Software modules configured for execution on computing
devices may be provided on a computer readable medium, such as a
compact disc, digital video disc, flash drive, magnetic disc, or
any other tangible medium, or as a digital download (and may be
originally stored in a compressed or installable format that
requires installation, decompression or decryption prior to
execution). Such software code may be stored, partially or fully,
on a memory device of the executing computing device, for execution
by the computing device. Software instructions may be embedded in
firmware, such as an EPROM. It will be further appreciated that
hardware modules may be comprised of connected logic units, such as
gates and flip-flops, and/or may be comprised of programmable
units, such as programmable gate arrays or processors. The modules
or computing device functionality described herein are preferably
implemented as software modules, but may be represented in hardware
or firmware. Generally, the modules described herein refer to
logical modules that may be combined with other modules or divided
into sub-modules despite their physical organization or storage
[0107] In some embodiments, a computer system may implement the
methods described herein using customized hard-wired logic, one or
more ASICs or FPGAs, firmware and/or program logic which in
combination with the computer system causes or programs the
computer system to be a special-purpose machine. According to one
embodiment, the methods herein are performed by the computer system
in response to hardware processor(s) executing one or more
sequences of one or more instructions contained in main memory.
Such instructions may be read into main memory from another storage
medium, such as a storage device. Execution of the sequences of
instructions contained in main memory causes processor(s) to
perform the process steps described herein. In alternative
embodiments, hard-wired circuitry may be used in place of or in
combination with software instructions.
[0108] The term "non-transitory media," and similar terms, as used
herein refers to any media that store data and/or instructions that
cause a machine to operate in a specific fashion. Such
non-transitory media may comprise non-volatile media and/or
volatile media. Non-volatile media includes, for example, optical
or magnetic disks, or other types of storage devices. Volatile
media includes dynamic memory, such as a main memory. Common forms
of non-transitory media include, for example, a floppy disk, a
flexible disk, hard disk, solid state drive, magnetic tape, or any
other magnetic data storage medium, a CD-ROM, any other optical
data storage medium, any physical medium with patterns of holes, a
RAM, a PROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip
or cartridge, and networked versions of the same.
[0109] Non-transitory media is distinct from but may be used in
conjunction with transmission media. Transmission media
participates in transferring information between nontransitory
media. For example, transmission media includes coaxial cables,
copper wire and fiber optics, including the wires that comprise a
bus. Transmission media can also take the form of acoustic or light
waves, such as those generated during radio-wave and infra-red data
communications.
[0110] Various forms of media may be involved in carrying one or
more sequences of one or more instructions to a processor for
execution. For example, the instructions may initially be carried
on a magnetic disk or solid state drive of a remote computer. The
remote computer can load the instructions into its dynamic memory
and send the instructions over a telephone line using a modem or
other network interface, such as a WAN or LAN interface. A modem
local to a computer system can receive the data on the telephone
line and use an infra-red transmitter to convert the data to an
infra-red signal. An infra-red detector can receive the data
carried in the infra-red signal and appropriate circuitry can place
the data on a bus. The bus carries the data to the main memory,
from which the processor retrieves and executes the instructions.
The instructions received by the main memory may retrieve and
execute the instructions. The instructions received by the main
memory may optionally be stored on a storage device either before
or after execution by the processor.
[0111] In some embodiments, the computer system may also include a
communication interface coupled to a bus. The communication
interface may provide a two-way data communication coupling to a
network link that is connected to a local network. For example, a
communication interface may be an integrated services digital
network (ISDN) card, cable modem, satellite modem, or a modem to
provide a data communication connection to a corresponding type of
telephone line. As another example, a communication interface may
be a local area network (LAN) card to provide a data communication
connection to a compatible LAN (or WAN component to communicate
with a WAN). Wireless links may also be implemented. In any such
implementation, a communication interface sends and receives
electrical, electromagnetic or optical signals that carry digital
data streams representing various types of information.
[0112] A network link may typically provide data communication
through one or more networks to other data devices. For example, a
network link may provide a connection through a local network to a
host computer or to data equipment operated by an Internet Service
Provider (ISP). The ISP in turn provides data communication
services through the world wide packet data communication network
now commonly referred to as the "Internet." The local network and
Internet both use electrical, electromagnetic or optical signals
that carry digital data streams. The signals through the various
networks and the signals on the network link and through a
communication interface, which carry the digital data to and from
the computer system, are example forms of transmission media.
[0113] In some embodiments, the computer system can send messages
and receive data, including program code, through the network(s),
the network link, and the communication interface. In the Internet
example, a server might transmit a requested code for an
application program through the Internet, ISP, local network, and
communication interface.
[0114] The received code may be executed by a processor as it is
received, and/or stored in a storage device, or other non-volatile
storage for later execution.
[0115] It is contemplated that various combinations or
subcombinations of the specific features and aspects of the
embodiments disclosed above may be made and still fall within one
or more of the inventions. Further, the disclosure herein of any
particular feature, aspect, method, property, characteristic,
quality, attribute, element, or the like in connection with an
embodiment can be used in all other embodiments set forth herein.
Accordingly, it should be understood that various features and
aspects of the disclosed embodiments can be combined with or
substituted for one another in order to form varying modes of the
disclosed inventions. Thus, it is intended that the scope of the
present inventions herein disclosed should not be limited by the
particular disclosed embodiments described above. Moreover, while
the invention is susceptible to various modifications, and
alternative forms, specific examples thereof have been shown in the
drawings and are herein described in detail. It should be
understood, however, that the invention is not to be limited to the
particular forms or methods disclosed, but to the contrary, the
invention is to cover all modifications, equivalents, and
alternatives falling within the spirit and scope of the various
embodiments described and the appended claims. Any methods
disclosed herein need not be performed in the order recited. The
methods disclosed herein include certain actions taken by a
practitioner; however, they can also include any third-party
instruction of those actions, either expressly or by implication.
For example, actions such as "treating a subject for a disease or
condition" include "instructing the administration of treatment of
a subject for a disease or condition."
[0116] Conditional language, such as, among others, "can," "could,"
"might," or "may," unless specifically stated otherwise, or
otherwise understood within the context as used, is generally
intended to convey that certain embodiments include, while other
embodiments do not include, certain features, elements and/or
steps. Thus, such conditional language is not generally intended to
imply that features, elements and/or steps are in any way required
for one or more embodiments.
[0117] Terms, such as, "first", "second", "third", "fourth",
"fifth", "sixth", "seventh", "eighth", "ninth", "tenth", or
"eleventh" and more, unless specifically stated otherwise, or
otherwise understood within the context as used, are generally
intended to refer to any order, and not necessarily to an order
based on the plain meaning of the corresponding ordinal number.
Therefore, terms using ordinal numbers may merely indicate separate
individuals and may not necessarily mean the order therebetween.
Accordingly, for example, first and second biomarkers used in this
application may mean that there are merely two sets of biomarkers.
In other words, there may not necessarily be any intention of order
between the "first" and "second" sets of data in any aspects.
[0118] The ranges disclosed herein also encompass any and all
overlap, sub-ranges, and combinations thereof. Language such as "up
to," "at least," "greater than," "less than," "between," and the
like includes the number recited. Numbers preceded by a term such
as "about" or "approximately" include the recited numbers. For
example, "about 10 nanometers" includes "10 nanometers."
TABLE-US-00006 TABLE 6 Diagnostic performance of urinary EMV mRNA
for bladder cancer detection (control group = RMSN (N = 36)). All
pTa pTis pT1 >pT2 G1 G2 G3 N = N = N = N = N = N = N = N =
Marker 173 115 10 37 11 4 110 59 Cytology1 0.62 0.53 0.77 0.82 0.72
0.67 0.52 0.81 Cytology2 0.63 0.54 0.73 0.81 0.72 0.72 0.53 0.79
Cytology3 0.57 0.51 0.78 0.72 0.66 0.57 0.53 0.74 BTA 0.58 0.56
0.54 0.60 0.72 0.54 0.58 0.59 SLC2A1 0.70 0.64 0.68 0.86 0.76 0.71
0.63 0.82 S100A13 0.66 0.61 0.74 0.77 0.68 0.52 0.63 0.73 GAPDH
0.65 0.58 0.60 0.85 0.75 0.69 0.59 0.75 KRT17 0.64 0.57 0.62 0.82
0.90 0.64 0.59 0.75 GPRC5A 0.64 0.56 0.80 0.85 0.72 0.59 0.58 0.77
P4HA1 0.63 0.58 0.53 0.82 0.71 0.51 0.60 0.70 AQP3 0.63 0.55 0.66
0.79 0.81 0.60 0.57 0.74 SLC12A3 0.62 0.63 0.61 0.61 0.63 0.76 0.62
0.62 TMPRSS4 0.62 0.56 0.67 0.78 0.66 0.60 0.59 0.68 SLC12A1 0.62
0.61 0.59 0.66 0.63 0.54 0.62 0.62 UPK1B 0.61 0.57 0.66 0.69 0.80
0.55 0.58 0.68 FABP4 0.61 0.58 0.68 0.67 0.67 0.69 0.58 0.67 SMCR8
0.61 0.60 0.55 0.65 0.61 0.61 0.62 0.58 DHRS2 0.61 0.60 0.64 0.61
0.64 0.64 0.61 0.62 ACTB 0.61 0.55 0.56 0.76 0.70 0.63 0.56 0.69
FADS2 0.60 0.55 0.62 0.71 0.63 0.60 0.55 0.69 CHEK1 0.59 0.55 0.69
0.70 0.64 0.54 0.57 0.66 TPX2 0.59 0.53 0.62 0.76 0.70 0.57 0.53
0.71 TOP1P1 0.59 0.55 0.63 0.64 0.80 0.67 0.57 0.63 CASP7 0.59 0.56
0.63 0.67 0.60 0.68 0.54 0.67 LINC00967 0.59 0.51 0.85 0.75 0.61
0.53 0.54 0.69 KCNJ15 0.59 0.56 0.52 0.70 0.59 0.65 0.57 0.62
HSD17B2 0.59 0.53 0.63 0.75 0.63 0.63 0.55 0.64 GINM1 0.58 0.54
0.59 0.72 0.57 0.67 0.54 0.67 SLC2A3 0.58 0.52 0.60 0.76 0.64 0.65
0.54 0.65 CRH 0.58 0.50 0.77 0.78 0.62 0.53 0.51 0.71 PPP2R5B 0.58
0.55 0.53 0.68 0.64 0.81 0.55 0.62 RNF39 0.58 0.52 0.62 0.71 0.66
0.52 0.53 0.68 MYC 0.58 0.51 0.74 0.71 0.65 0.73 0.52 0.67 SHISA3
0.57 0.56 0.62 0.61 0.55 0.81 0.56 0.59 SLC41A1 0.57 0.55 0.53 0.64
0.61 0.58 0.54 0.64 NRSN2-AS1 0.57 0.55 0.63 0.61 0.66 0.73 0.54
0.62 UPK1A 0.57 0.55 0.61 0.59 0.63 0.67 0.56 0.60 MCM9 0.56 0.51
0.54 0.70 0.60 0.68 0.52 0.62 OLFM3 0.56 0.56 0.54 0.58 0.52 0.55
0.58 0.52 ACSM2A 0.56 0.59 0.57 0.53 0.61 0.61 0.59 0.51 GLI3 0.55
0.54 0.50 0.55 0.73 0.63 0.54 0.58 RWDD3 0.55 0.51 0.62 0.63 0.67
0.57 0.53 0.61 C5orf30 0.55 0.51 0.63 0.64 0.61 0.61 0.53 0.59 F3
0.55 0.51 0.52 0.65 0.68 0.68 0.51 0.61 PLAT 0.55 0.51 0.52 0.68
0.57 0.54 0.51 0.67 LRRCC1 0.55 0.53 0.51 0.60 0.59 0.59 0.53 0.58
THAP7-AS1 0.55 0.56 0.59 0.51 0.50 0.56 0.56 0.52 UMOD 0.54 0.54
0.63 0.54 0.54 0.65 0.54 0.53 CA1 0.54 0.54 0.60 0.50 0.58 0.53
0.56 0.50 BMP2 0.54 0.53 0.53 0.71 0.65 0.51 0.51 0.60 PRDM16 0.54
0.53 0.52 0.58 0.54 0.67 0.51 0.58 BANK1 0.54 0.55 0.67 0.53 0.60
0.52 0.56 0.50 CDCA3 0.53 0.51 0.63 0.54 0.66 0.53 0.52 0.56
SLC16A9 0.53 0.50 0.54 0.60 0.69 0.76 0.52 0.54 ZBTB42 0.53 0.51
0.55 0.53 0.65 0.53 0.51 0.58 TMEM45A 0.52 0.52 0.56 0.63 0.55 0.51
0.51 0.59 SERPINE1 0.52 0.53 0.56 0.67 0.55 0.69 0.52 0.60 PCAT4
0.52 0.50 0.51 0.59 0.56 0.71 0.51 0.53 CECR2 0.52 0.53 0.58 0.59
0.69 0.65 0.52 0.58 CEACAM7 0.50 0.52 0.51 0.54 0.57 0.55 0.50 0.50
ZNF174 0.50 0.53 0.51 0.58 0.54 0.57 0.50 0.50 ALDOB 0.50 0.50 0.50
0.50 0.50 0.50 0.50 0.50 BKPyVgp4 0.50 0.50 0.50 0.50 0.50 0.50
0.50 0.50 GPC5 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50
TABLE-US-00007 TABLE 7 Diagnostic performance of urinary EMV mRNA
for bladder cancer detection in urine-cytology-negative or
suspicious population (control group DC/RMSN (N = 26)). All pTa
pTis pT1 >pT2 G1 G2 G3 N = N = N = N = N = N = N = N = Marker
115 92 3 15 5 3 90 22 Cytology1 0.59 0.56 0.56 0.78 0.64 0.78 0.55
0.72 Cytology2 0.59 0.56 0.56 0.78 0.64 0.78 0.55 0.72 Cytology3
0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 BTA 0.56 0.56 0.62 0.53
0.63 0.58 0.57 0.52 SLC2A1 0.68 0.64 0.87 0.84 0.67 0.88 0.63 0.83
S100A13 0.68 0.66 0.87 0.78 0.60 0.62 0.66 0.73 SMCR8 0.64 0.63
0.67 0.74 0.78 0.62 0.66 0.58 GAPDH 0.64 0.60 0.64 0.86 0.79 0.77
0.62 0.70 FABP4 0.63 0.60 0.79 0.75 0.68 0.64 0.60 0.73 DHRS2 0.62
0.62 0.51 0.62 0.79 0.62 0.64 0.60 ACTB 0.62 0.59 0.60 0.79 0.81
0.64 0.60 0.71 BANK1 0.62 0.62 0.65 0.62 0.76 0.53 0.64 0.56 GPRC5A
0.62 0.57 0.81 0.86 0.65 0.51 0.60 0.72 SHISA3 0.62 0.62 0.56 0.67
0.52 0.85 0.61 0.61 TMPRSS4 0.61 0.58 0.51 0.82 0.57 0.53 0.61 0.61
SLC12A3 0.60 0.62 0.51 0.58 0.55 0.76 0.61 0.55 SLC12A1 0.60 0.60
0.62 0.66 0.57 0.51 0.61 0.56 P4HA1 0.60 0.57 0.69 0.78 0.67 0.51
0.61 0.58 UPK1A 0.58 0.59 0.53 0.53 0.74 0.64 0.60 0.56 KCNJ15 0.58
0.57 0.54 0.73 0.55 0.63 0.58 0.60 UPK1B 0.58 0.57 0.63 0.60 0.90
0.65 0.58 0.56 SLC41A1 0.58 0.58 0.69 0.66 0.54 0.50 0.57 0.63
ACSM2A 0.57 0.57 0.79 0.52 0.68 0.60 0.58 0.58 TOP1P1 0.57 0.55
0.59 0.63 0.74 0.60 0.58 0.54 KRT17 0.57 0.53 0.60 0.74 0.89 0.53
0.56 0.61 FADS2 0.57 0.55 0.60 0.68 0.63 0.60 0.56 0.60 RNF39 0.56
0.54 0.54 0.69 0.62 0.58 0.54 0.65 CHEK1 0.56 0.56 0.62 0.55 0.65
0.54 0.58 0.52 AQP3 0.56 0.53 0.58 0.73 0.75 0.52 0.55 0.64 C5orf30
0.56 0.56 0.60 0.57 0.64 0.63 0.57 0.50 RWDD3 0.56 0.55 0.50 0.65
0.58 0.61 0.56 0.56 CASP7 0.56 0.55 0.54 0.66 0.53 0.64 0.54 0.61
SLC2A3 0.56 0.52 0.58 0.76 0.71 0.66 0.55 0.59 CA1 0.56 0.55 0.76
0.56 0.58 0.53 0.57 0.53 NRSN2-AS1 0.55 0.55 0.51 0.59 0.53 0.70
0.55 0.57 PRDM16 0.55 0.55 0.58 0.64 0.56 0.69 0.55 0.56 PPP2R5B
0.55 0.55 0.60 0.59 0.53 0.86 0.54 0.53 GLI3 0.55 0.54 0.50 0.57
0.70 0.50 0.54 0.59 TPX2 0.55 0.51 0.52 0.72 0.69 0.52 0.53 0.62
ZNF174 0.54 0.53 0.51 0.65 0.55 0.55 0.56 0.51 OLFM3 0.54 0.54 0.51
0.56 0.54 0.71 0.56 0.53 BMP2 0.54 0.51 0.51 0.73 0.68 0.53 0.53
0.58 MCM9 0.54 0.52 0.55 0.67 0.51 0.66 0.53 0.58 LINC00967 0.54
0.51 0.75 0.65 0.64 0.59 0.53 0.58 F3 0.54 0.52 0.63 0.63 0.70 0.69
0.53 0.58 SERPINE1 0.54 0.57 0.54 0.62 0.59 0.67 0.55 0.50
THAP7-AS1 0.54 0.55 0.55 0.55 0.58 0.51 0.54 0.53 GINM1 0.54 0.52
0.59 0.69 0.54 0.66 0.52 0.57 UMOD 0.53 0.53 0.56 0.56 0.51 0.62
0.55 0.52 PCAT4 0.53 0.54 0.53 0.51 0.56 0.67 0.55 0.53 MYC 0.53
0.50 0.55 0.69 0.52 0.71 0.51 0.58 LRRCC1 0.52 0.53 0.63 0.54 0.52
0.59 0.52 0.55 TMEM45A 0.52 0.50 0.54 0.64 0.50 0.59 0.51 0.58 CRH
0.52 0.50 0.56 0.62 0.65 0.58 0.50 0.61 HSD17B2 0.52 0.51 0.51 0.68
0.63 0.72 0.51 0.52 SLC16A9 0.52 0.50 0.54 0.58 0.62 0.79 0.51 0.52
PLAT 0.52 0.50 0.58 0.60 0.54 0.58 0.50 0.59 CDCA3 0.51 0.51 0.65
0.55 0.68 0.52 0.51 0.50 CEACAM7 0.51 0.52 0.58 0.54 0.64 0.58 0.51
0.50 ZBTB42 0.51 0.50 0.71 0.54 0.63 0.65 0.51 0.55 CECR2 0.50 0.52
0.56 0.62 0.67 0.63 0.51 0.54 ALDOB 0.50 0.50 0.50 0.50 0.50 0.50
0.50 0.50 BKPyVgp4 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 GPC5
0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50
TABLE-US-00008 TABLE 8 Diagnostic performance of urinary EMV mRNA
for bladder cancer detection in urine-cytology-negative or
suspicious population (control group is RMSN (N = 27)). All pTa
pTis pT1 >pT2 G1 G2 G3 N = N = N = N = N = N = N = N = Marker 77
67 5 10 0 4 59 14 Cytology1 0.51 0.52 0.62 0.64 -- 0.70 0.53 0.58
Cytology2 0.51 0.52 0.61 0.66 -- 0.74 0.52 0.59 Cytology3 0.53 0.55
0.63 0.51 -- 0.57 0.55 0.54 BTA 0.55 0.53 0.66 0.60 -- 0.53 0.55
0.55 SLC12A3 0.70 0.69 0.64 0.75 -- 0.82 0.69 0.68 S100A13 0.64
0.61 0.72 0.76 -- 0.53 0.62 0.72 GPRC5A 0.62 0.56 0.84 0.91 -- 0.64
0.59 0.76 KRT17 0.62 0.60 0.60 0.74 -- 0.70 0.61 0.62 SLC2A1 0.62
0.56 0.81 0.86 -- 0.74 0.56 0.82 SLC12A1 0.61 0.60 0.52 0.76 --
0.52 0.60 0.70 SHISA3 0.61 0.61 0.50 0.68 -- 0.86 0.59 0.65 DHRS2
0.61 0.61 0.56 0.64 -- 0.63 0.62 0.63 UMOD 0.60 0.56 0.77 0.72 --
0.70 0.56 0.74 LRRCC1 0.59 0.60 0.51 0.55 -- 0.63 0.59 0.58 FABP4
0.58 0.56 0.64 0.66 -- 0.71 0.56 0.63 ACSM2A 0.58 0.60 0.56 0.53 --
0.65 0.59 0.60 SMCR8 0.58 0.57 0.61 0.76 -- 0.65 0.59 0.53 AQP3
0.58 0.57 0.61 0.64 -- 0.64 0.55 0.67 GAPDH 0.58 0.55 0.51 0.80 --
0.66 0.54 0.70 PPP2R5B 0.57 0.59 0.60 0.56 -- 0.89 0.56 0.52
HSD17B2 0.57 0.56 0.50 0.67 -- 0.68 0.58 0.52 TOP1P1 0.57 0.56 0.55
0.66 -- 0.70 0.56 0.56 THAP7-AS1 0.57 0.58 0.63 0.51 -- 0.56 0.57
0.57 TPX2 0.56 0.53 0.57 0.78 -- 0.58 0.54 0.65 NRSN2-AS1 0.56 0.55
0.54 0.62 -- 0.76 0.54 0.56 SLC41A1 0.55 0.54 0.52 0.66 -- 0.56
0.52 0.68 UPK1A 0.55 0.55 0.50 0.58 -- 0.64 0.55 0.61 KCNJ15 0.55
0.54 0.53 0.67 -- 0.65 0.56 0.53 ACTB 0.55 0.53 0.51 0.70 -- 0.63
0.54 0.59 MYC 0.55 0.53 0.70 0.60 -- 0.76 0.52 0.61 OLFM3 0.55 0.53
0.58 0.67 -- 0.56 0.56 0.55 C5orf30 0.55 0.54 0.56 0.57 -- 0.61
0.55 0.54 F3 0.54 0.55 0.61 0.56 -- 0.74 0.54 0.50 P4HA1 0.54 0.53
0.62 0.69 -- 0.54 0.55 0.53 CASP7 0.54 0.51 0.61 0.68 -- 0.65 0.50
0.70 LINC00967 0.54 0.52 0.83 0.52 -- 0.57 0.53 0.57 CHEK1 0.54
0.53 0.66 0.51 -- 0.54 0.54 0.56 SLC2A3 0.53 0.51 0.56 0.66 -- 0.66
0.53 0.50 CRH 0.53 0.51 0.73 0.59 -- 0.56 0.51 0.61 TMPRSS4 0.53
0.51 0.64 0.73 -- 0.60 0.50 0.64 PRDM16 0.53 0.51 0.56 0.70 -- 0.69
0.50 0.59 GINM1 0.53 0.51 0.57 0.68 -- 0.67 0.51 0.57 ZNF174 0.52
0.54 0.53 0.56 -- 0.59 0.53 0.53 RWDD3 0.52 0.51 0.55 0.63 -- 0.57
0.52 0.50 CECR2 0.52 0.54 0.54 0.57 -- 0.69 0.54 0.51 FADS2 0.52
0.51 0.50 0.60 -- 0.61 0.51 0.62 GLI3 0.52 0.52 0.50 0.50 -- 0.63
0.52 0.50 SLC16A9 0.52 0.52 0.50 0.51 -- 0.76 0.53 0.59 BMP2 0.52
0.51 0.54 0.65 -- 0.54 0.51 0.53 CEACAM7 0.52 0.51 0.51 0.59 --
0.57 0.52 0.50 SERPINE1 0.52 0.53 0.55 0.54 -- 0.74 0.54 0.51 MCM9
0.51 0.50 0.59 0.63 -- 0.69 0.51 0.50 TMEM45A 0.51 0.51 0.51 0.56
-- 0.54 0.50 0.55 CDCA3 0.51 0.52 0.67 0.54 -- 0.54 0.51 0.50 UPK1B
0.51 0.51 0.57 0.56 -- 0.54 0.50 0.55 PCAT4 0.51 0.53 0.60 0.60 --
0.70 0.54 0.56 ZBTB42 0.50 0.50 0.54 0.53 -- 0.52 0.53 0.65 PLAT
0.50 0.51 0.59 0.57 -- 0.55 0.51 0.53 BANK1 0.50 0.53 0.72 0.55 --
0.52 0.53 0.61 CA1 0.50 0.51 0.54 0.57 -- 0.51 0.52 0.59 RNF39 0.50
0.52 0.56 0.59 -- 0.51 0.51 0.52 ALDOB 0.50 0.50 0.50 0.50 -- 0.50
0.50 0.50 BKPyVgp4 0.50 0.50 0.50 0.50 -- 0.50 0.50 0.50 GPC5 0.50
0.50 0.50 0.50 -- 0.50 0.50 0.50
TABLE-US-00009 TABLE 9 Diagnostic performance of urinary EMV mRNA
for renal pelvis and ureter cancer detection (control group is
DC/RMSN (N = 36)). All pTa pTis pT1 >pT2 G1 G2 G3 N = N = N = N
= N = N = N = N = Marker 32 10 1 7 14 0 13 19 Cytology1 0.64 0.70
0.93 0.79 0.52 -- 0.68 0.61 Cytology2 0.66 0.72 0.88 0.81 0.54 --
0.69 0.63 Cytology3 0.51 0.54 0.93 0.58 0.57 -- 0.55 0.51 BTA 0.60
0.58 1.00 0.56 0.58 -- 0.64 0.57 KRT17 0.77 0.66 0.97 0.87 0.78 --
0.75 0.78 P4HA1 0.72 0.77 1.00 0.77 0.63 -- 0.75 0.70 HSD17B2 0.71
0.67 0.97 0.74 0.69 -- 0.73 0.69 SLC2A1 0.70 0.58 1.00 0.77 0.70 --
0.70 0.69 S100A13 0.69 0.65 1.00 0.64 0.74 -- 0.64 0.73 GAPDH 0.67
0.63 0.97 0.77 0.63 -- 0.70 0.66 MYC 0.67 0.63 0.64 0.75 0.68 --
0.69 0.65 KCNJ15 0.67 0.63 1.00 0.73 0.64 -- 0.61 0.71 SLC12A1 0.66
0.77 1.00 0.65 0.57 -- 0.66 0.65 GPRC5A 0.66 0.60 0.75 0.74 0.67 --
0.66 0.65 ACTB 0.65 0.60 0.89 0.71 0.64 -- 0.64 0.66 F3 0.65 0.67
0.65 0.64 0.69 -- 0.67 0.63 SLC2A3 0.65 0.61 0.92 0.69 0.65 -- 0.64
0.65 TMEM45A 0.64 0.58 1.00 0.58 0.71 -- 0.58 0.69 C5orf30 0.64
0.67 0.74 0.70 0.61 -- 0.63 0.65 BMP2 0.63 0.60 1.00 0.61 0.65 --
0.58 0.68 FADS2 0.63 0.69 1.00 0.63 0.58 -- 0.65 0.62 RNF39 0.63
0.57 0.65 0.66 0.69 -- 0.58 0.66 FABP4 0.63 0.64 0.57 0.82 0.51 --
0.73 0.54 DHRS2 0.61 0.60 1.00 0.60 0.59 -- 0.60 0.63 TMPRSS4 0.61
0.75 0.68 0.68 0.53 -- 0.75 0.51 CA1 0.61 0.68 0.67 0.80 0.53 --
0.69 0.55 GLI3 0.61 0.65 0.50 0.57 0.61 -- 0.64 0.58 TOP1P1 0.61
0.67 0.58 0.55 0.58 -- 0.60 0.61 SHISA3 0.60 0.62 0.69 0.69 0.60 --
0.54 0.65 UPK1B 0.60 0.68 1.00 0.65 0.51 -- 0.71 0.53 UMOD 0.60
0.64 1.00 0.60 0.53 -- 0.61 0.59 GINM1 0.60 0.65 0.75 0.65 0.58 --
0.60 0.60 ZNF174 0.60 0.53 1.00 0.56 0.65 -- 0.54 0.64 SERPINE1
0.60 0.50 0.68 0.69 0.65 -- 0.59 0.60 AQP3 0.59 0.59 0.78 0.70 0.54
-- 0.70 0.51 PLAT 0.59 0.58 0.60 0.80 0.52 -- 0.61 0.58 CRH 0.59
0.58 0.58 0.58 0.63 -- 0.53 0.64 LINC00967 0.59 0.61 1.00 0.56 0.57
-- 0.54 0.63 SLC41A1 0.58 0.50 1.00 0.64 0.61 -- 0.51 0.65 MCM9
0.58 0.56 0.68 0.57 0.63 -- 0.53 0.61 NRSN2-AS1 0.57 0.70 0.69 0.55
0.59 -- 0.61 0.55 TPX2 0.57 0.62 0.54 0.54 0.57 -- 0.57 0.57 UPK1A
0.57 0.51 1.00 0.58 0.57 -- 0.54 0.59 SLC16A9 0.57 0.52 1.00 0.59
0.59 -- 0.51 0.61 ACSM2A 0.56 0.52 0.99 0.65 0.62 -- 0.53 0.58
CECR2 0.55 0.59 0.72 0.63 0.53 -- 0.53 0.57 PPP2R5B 0.55 0.59 0.72
0.63 0.52 -- 0.54 0.56 THAP7-AS1 0.54 0.54 0.69 0.56 0.63 -- 0.55
0.61 ZBTB42 0.54 0.54 1.00 0.66 0.53 -- 0.51 0.58 CDCA3 0.54 0.53
0.53 0.55 0.59 -- 0.53 0.58 SLC12A3 0.53 0.58 1.00 0.77 0.55 --
0.53 0.57 PRDM16 0.53 0.53 0.74 0.52 0.55 -- 0.54 0.58 RWDD3 0.53
0.54 1.00 0.57 0.57 -- 0.51 0.54 BANK1 0.52 0.53 0.97 0.54 0.51 --
0.55 0.51 SMCR8 0.52 0.55 0.86 0.54 0.51 -- 0.54 0.51 CASP7 0.52
0.50 1.00 0.55 0.53 -- 0.52 0.55 CHEK1 0.52 0.51 0.54 0.54 0.53 --
0.53 0.51 OLFM3 0.52 0.53 0.69 0.80 0.59 -- 0.55 0.51 PCAT4 0.51
0.55 0.97 0.62 0.50 -- 0.52 0.54 CEACAM7 0.51 0.69 0.58 0.52 0.58
-- 0.60 0.55 LRRCC1 0.51 0.53 0.79 0.50 0.54 -- 0.52 0.53 ALDOB
0.50 0.50 0.50 0.50 0.50 -- 0.50 0.50 BKPyVgp4 0.50 0.50 0.50 0.50
0.50 -- 0.50 0.50 GPC5 0.50 0.50 0.50 0.50 0.50 -- 0.50 0.50
TABLE-US-00010 TABLE 10 Diagnostic performance of urinary EMV mRNA
formula. All pTa pTis pT1 >pT2 G1 G2 G3 Formula N = 173 N = 115
N = 10 N = 37 N = 11 N = 4 N = 110 N = 59 ALDOB + CRH + SERPINE1 +
SLC2A1 0.71 0.63 0.82 0.89 0.80 0.53 0.64 0.85 ALDOB + CRH + SLC2A1
+ THAP7.AS1 0.69 0.61 0.81 0.88 0.82 0.69 0.61 0.85 CRH + SLC2A1 +
THAP7.AS1 + TOP1P1 0.69 0.61 0.85 0.88 0.83 0.87 0.61 0.83 CRH +
GINM1 + SLC2A1 + TOP1P1 0.68 0.59 0.80 0.87 0.85 0.64 0.62 0.81
ALDOB + KRT17 + SERPINE1 + SLC2A1 0.69 0.62 0.64 0.89 0.90 0.62
0.63 0.82 KRT17 + LINC00967 + SERPINE1 + SLC2A1 0.69 0.61 0.74 0.87
0.88 0.53 0.63 0.81 ALDOB + CRH + OLFM3 + SLC2A1 0.69 0.61 0.74
0.89 0.80 0.61 0.62 0.81 CRH + SLC16A9 + SLC2A1 + TOP1P1 0.68 0.59
0.84 0.89 0.81 0.60 0.60 0.84 KRT17 + LINC00967 + SLC16A9 + SLC2A1
0.68 0.58 0.78 0.88 0.87 0.57 0.60 0.84 KRT17 + S100A13 + SLC16A9 +
SLC2A1 0.68 0.59 0.74 0.89 0.86 0.55 0.60 0.83 ALDOB + KRT17 +
SLC2A1 + THAP7.AS1 0.68 0.60 0.65 0.87 0.88 0.65 0.61 0.81 CRH +
SLC2A1 + THAP7.AS1 + TPX2 0.67 0.58 0.81 0.89 0.82 0.83 0.58 0.84
ALDOB + CHEK1 + KRT17 + SLC2A1 0.67 0.59 0.67 0.87 0.84 0.58 0.60
0.80 ALDOB + KRT17 + OLFM3 + SLC2A1 0.67 0.59 0.62 0.87 0.86 0.62
0.61 0.79 KRT17 + LINC00967 + S100A13 + SLC16A9 0.67 0.57 0.78 0.87
0.88 0.52 0.59 0.82 CRH + GPRC5A + SLC16A9 + SLC2A1 0.66 0.56 0.88
0.89 0.76 0.51 0.57 0.85 ALDOB + KRT17 + PCAT4 + SLC2A1 0.66 0.57
0.64 0.90 0.84 0.72 0.58 0.81 CRH + KRT17 + SLC16A9 + SLC2A1 0.66
0.56 0.75 0.90 0.87 0.51 0.57 0.83 ALDOB + AQP3 + KRT17 + SERPINE1
0.65 0.57 0.64 0.85 0.92 0.56 0.59 0.78 KRT17 + LINC00967 + PCAT4 +
SLC16A9 0.65 0.54 0.77 0.90 0.87 0.63 0.56 0.81 ALDOB + FADS2 +
KRT17 + SLC16A9 0.65 0.55 0.65 0.87 0.93 0.51 0.58 0.80 KRT17 +
LINC00967 + PCAT4 + S100A13 0.64 0.53 0.75 0.90 0.81 0.75 0.56 0.78
ALDOB + CRH + THAP7.AS1 + TPX2 0.64 0.53 0.80 0.87 0.84 0.58 0.54
0.84 ALDOB + AQP3 + KRT17 + LINC00967 0.64 0.54 0.71 0.85 0.92 0.65
0.57 0.78 ALDOB + GAPDH + KRT17 + PCAT4 0.64 0.54 0.59 0.91 0.84
0.76 0.56 0.78 ALDOB + AQP3 + KRT17 0.64 0.54 0.66 0.85 0.92 0.65
0.57 0.77 KRT17 + LINC00967 + OLFM3 + PCAT4 0.64 0.53 0.74 0.90
0.82 0.70 0.56 0.78 AQP3 + KRT17 + SLC16A9 0.64 0.54 0.64 0.85 0.92
0.51 0.56 0.79 FADS2 + GINM1 + KRT17 + SLC16A9 0.64 0.54 0.65 0.86
0.92 0.53 0.56 0.79 KRT17 + LINC00967 + PCAT4 + PRDM16 0.64 0.53
0.72 0.90 0.82 0.78 0.55 0.78 C5orf30 + FADS2 + KRT17 + SLC16A9
0.63 0.53 0.63 0.86 0.92 0.54 0.55 0.80 ALDOB + AQP3 + CASP7 +
KRT17 0.63 0.54 0.64 0.85 0.92 0.61 0.56 0.77 KRT17 + LINC00967 +
PCAT4 + SLC41A1 0.63 0.52 0.73 0.90 0.83 0.74 0.55 0.78 KRT17 +
LINC00967 + PCAT4 0.63 0.51 0.73 0.90 0.83 0.71 0.54 0.78 KRT17 +
LINC00967 + PCAT4 + PLAT 0.62 0.51 0.73 0.90 0.81 0.70 0.54 0.78
GPRC5A + KRT17 + LINC00967 + PCAT4 0.62 0.51 0.78 0.89 0.78 0.69
0.54 0.78 ALDOB + FADS2 + KRT17 + LRRCC1 0.62 0.52 0.59 0.85 0.94
0.56 0.55 0.76 C5orf30 + KRT17 + SLC16A9 + TPX2 0.61 0.51 0.62 0.85
0.92 0.61 0.53 0.79
Sequence CWU 1
1
120120DNAhuman 1aagacagcag ccaacattcg 20220DNAhuman 2tttgcccgtc
ccataaactg 20323DNAhuman 3tttttcctgg cacccagcac aat 23425DNAhuman
4tttttgccga tccacacgga gtact 25520DNAhuman 5aaccaccatt caagggcttg
20620DNAhuman 6ttggcgtttt cctggatagc 20720DNAhuman 7ttttgtttcg
ggccccaatg 20820DNAhuman 8ttgtaggggt caacaatggc 20920DNAhuman
9tggcctggaa atgattcagc 201020DNAhuman 10ttgtgggcag tttccttacc
201120DNAhuman 11acagcacagc aagaattccc 201220DNAhuman 12tttgtgaccc
tgcatgaagg 201320DNAhuman 13agcagagctt caggttttcc 201420DNAhuman
14tttcgagttg gctgttgcag 201520DNAhuman 15tttggttggc ttcacgactg
201620DNAhuman 16atggcatggc ttctgctttg 201720DNAhuman 17ttgctgaagc
tgcctcaaag 201820DNAhuman 18tttctgcagc tttgggttgg 201922DNAhuman
19ttccacggtt ccaggctatt ac 222020DNAhuman 20tggcaactct gtcattcacc
202120DNAhuman 21tcttggtatt gcacggacac 202222DNAhuman 22tacccagagg
caagtccaat tc 222320DNAhuman 23tcagcgccac aaagaatgac 202420DNAhuman
24aggtcaggtg aacttgcttg 202521DNAhuman 25aagcatctcc ttgtggatcg g
212620DNAhuman 26tggtttcctg caacgttctg 202721DNAhuman 27aggggtggtt
tatctgcatg g 212820DNAhuman 28tgttgccaag ccaaagtctg 202921DNAhuman
29atctccctgg atctcacctt c 213020DNAhuman 30tgtgagcttg ctgtgctaac
203122DNAhuman 31tgagcagatc tgggacaaga tc 223220DNAhuman
32aagctgcaat ggaagagacc 203320DNAhuman 33tcggacagcc aacaattcag
203421DNAhuman 34agtccttgcc aaaaacatcc c 213522DNAhuman
35cctggtacat gtgcagaaat gg 223620DNAhuman 36acgcctttca tgacgcattc
203722DNAhuman 37ccaccttgtc cacaaattcg tc 223820DNAhuman
38aacacgtgca gcatgttcac 203920DNAhuman 39cccactcctc cacctttgac
204024DNAhuman 40cataccagga aatgagcttg acaa 244120DNAhuman
41ttctggagca acgttttccc 204222DNAhuman 42cagctgctcc tgtaattcca ac
224320DNAhuman 43aatgtttccg cgactgaacc 204420DNAhuman 44ttggactgtg
tgccatttcc 204520DNAhuman 45acgtgctgct gaactttcac 204620DNAhuman
46aaagaacaac gggggctttg 204722DNAhuman 47gctcatgctt cctgactttg ac
224820DNAhuman 48ttgtgagcag ccaaaactcg 204925DNAhuman 49tttttaacaa
tgcatggccg tgaac 255025DNAhuman 50tttttatgct gctgacattc accag
255120DNAhuman 51aatcgccaga cccaaaaagc 205220DNAhuman 52aatcaccaag
cacagcttcc 205320DNAhuman 53tggacaatgc caacatcctg 205420DNAhuman
54tcaaacttgg tgcggaagtc 205522DNAhuman 55tggagatggt tggggtcaaa tc
225620DNAhuman 56tgcatccaca aagcacactg 205721DNAhuman 57tgagctagca
gccaaggaat c 215820DNAhuman 58ttgttgtgcc agctcatgtc 205920DNAhuman
59tgtaatgcaa cggtggaagc 206022DNAhuman 60tcatccatga tgatccctga gg
226120DNAhuman 61acacatcagc acaactacgc 206220DNAhuman 62ggtgcatttt
cggttgttgc 206320DNAhuman 63tgccaacacc aacaaggaac 206420DNAhuman
64ttgcagttga gatgctggtc 206520DNAhuman 65accaaagagt gctgagcttg
206620DNAhuman 66tcatccaagc accaaatcgg 206720DNAhuman 67agttggagct
agtgtttggc 206820DNAhuman 68ttgttgccaa ctagcactgg 206925DNAhuman
69ttttttcacg atgcgatgtc atgtc 257025DNAhuman 70ttttttgtcc
caaattgtcg tccag 257122DNAhuman 71tgatcttggg cagaacatac cg
227220DNAhuman 72agcagcgcaa tgtcattgtc 207320DNAhuman 73acagtgcaac
cacatcttcc 207420DNAhuman 74cgcaaagcca ttgatgatgc 207522DNAhuman
75ggacaaccac gcacttttag ac 227620DNAhuman 76ttcgcgttga tgcttggttc
207720DNAhuman 77tcctgcagag actcttctgg 207820DNAhuman 78cttgcgttgc
actgattccc 207920DNAhuman 79tggtgttcca tttgccagtc 208020DNAhuman
80acaaaaggct ctctgcttgc 208120DNAhuman 81accaccttct tcacctttgc
208222DNAhuman 82cagctctttg aactcgttga cg 228320DNAhuman
83accctcagca tgttcattgc 208420DNAhuman 84tcatgttgcc tttccagtgg
208522DNAhuman 85tcaccccagt atttcgctta cc 228621DNAhuman
86tggaactgaa gtctggacag c 218725DNAhuman 87actccagagc tgctaatctc
attgt 258826DNAhuman 88aactagtaag acaggtggga ggttct 268922DNAhuman
89gctctcatcg tcatcacttt gc 229020DNAhuman 90agcacgtttt cctggtttcc
209120DNAhuman 91acattggcgt tgctttctgg 209221DNAhuman 92attcccacag
tcttcgtggt c 219320DNAhuman 93tcattgtggg catgtgcttc 209421DNAhuman
94accaggagca cagtgaagat g 219522DNAhuman 95tgttcaagag cccatctatg cc
229620DNAhuman 96tgagcgtgga acaaaaagcc 209720DNAhuman 97atcattgcca
tggccatcag 209820DNAhuman 98tgcccccaac accattaatc 209922DNAhuman
99tgaggccata gtcaggaaac tc 2210020DNAhuman 100aatgtttcac gggcttagcg
2010120DNAhuman 101aacccgacaa aaaccagagc 2010222DNAhuman
102gcgatactgt ctttctcctg tg 2210320DNAhuman 103acatctttgt
gcaccagctg 2010422DNAhuman 104aaggaactct aggaaggcaa cg
2210520DNAhuman 105agatgatgtg tgcaggcatc 2010620DNAhuman
106acatgccact ggtcagattg 2010720DNAhuman 107aaacagatgg ccttgggaac
2010820DNAhuman 108tttcaatggc ccaggcaaac 2010920DNAhuman
109agtcaccagc ctttgcattg 2011020DNAhuman 110taatgtggca caggttgagc
2011120DNAhuman 111cctgaacttg ggtcccatca 2011219DNAhuman
112gccccaagct gctaaaagc 1911322DNAhuman 113atccctgatc accaagcaga tg
2211420DNAhuman 114aaggctgacg tgaagttcac 2011520DNAhuman
115aggcgtgcct ggtttttatc 2011620DNAhuman 116aaatccaaac caggcaaccc
2011720DNAhuman 117ttgtgcagca agctgtttcc 2011822DNAhuman
118tatgtgcacg agaaggtctt cc 2211920DNAhuman 119aactgccaga
ctttcaaccg 2012020DNAhuman 120atttgggggt tggttcttgg 20
* * * * *