








































23SIUJ.ORG SIUJ  •  Volume 1, Number 1  •  October 2020

MOLECULAR BIOMARKERS IN UROLOGIC ONCOLOGY: ICUD-WUOF CONSULTATION

The Clinical Applications of Tissue  
Biomarkers in Prostate Cancer
Peter E. Lonergan,1 Samuel L. Washington III,2 Maxwell V. Meng,1 Renu Eapen3 
1 Department of Urology, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, United States, 2 Department of Epidemiology & 
Biostatistics, University of California, San Francisco, United States 3 Department of Genitourinary Oncology, Peter MacCallum Cancer Centre, Melbourne, Australia

Abstract

The clinical course of localized prostate cancer varies widely, from indolent disease unlikely to need treatment to 
aggressive disease requiring intensive, multimodal therapy. Traditionally, treatment decisions have been based on 
clinical and pathologic factors, including serum prostate specific antigen (PSA), clinical stage, and Gleason score. 
However, these factors have limited ability to describe the underlying tumor biology. Tissue-based genomic tests 
have emerged as a promising tool to more accurately characterize prostate cancer biology and predict clinical course.  
Using prostate cancer tissue obtained at pre-treatment biopsy or radical prostatectomy, these tests exploit the 
expression of specific genes involved in key biological pathways and, as a result, have the potential to aid clinical 
decision-making. The current review summarizes available data describing the clinical use of 5 commercially 
available tissue-based genomic assays in a number of clinical scenarios.

Introduction

Over the last decade, numerous tissue-based genomic classifiers have been developed, providing additional 
diagnostic, prognostic, and predictive information in the management of prostate cancer (PCa). Using prostate tissue 
obtained at pre-treatment biopsy or radical prostatectomy, these tests exploit expression of specific genes involved 
in key biological pathways. Gene expression profiling therefore has the potential to aid clinical decision-making.  
In this review, we summarize 5 commercially available tissue-based genomic classifiers and summarize the currently 
available data on their use in a number of clinical scenarios (Table 1).

ConfirmMDX
Description of assay
ConfirmMDx (MdxHealth, Irvine, United States) for Prostate Cancer is an epigenetic assay, which uses multiplex  
methylation-specific polymerase chain reaction (PCR) to measure the epigenetic status of the PCa-associated genes 
GSTP1, APC, and RASSF1 in residual cancer-negative prostate biopsy tissue samples [1].

Prediction of cancer after negative biopsy
The diagnostic performance of ConfirmMDx for predicting cancer on subsequent biopsies after an initial negative 
biopsy has been assessed in 2 retrospective studies: (1) Methylation Analysis to Locate Occult Cancer (MATLOC) [2] 
and (2) Detection Of Cancer Using Methylated Events in Negative Tissue (DOCUMENT) [3]. The MATLOC study 
generated a model with the methylation levels of the 3 genes and clinical parameters (age, PSA, DRE, initial biopsy 
pathology) in 483 men from the United Kingdom and Belgium. The studies found that this model resulted in a 
negative predictive value (NPV) of 90% [2]. The DOCUMENT validation cohort of 350 men from 5 centers across the 

Key Words Competing Interests Article Information

Prostate cancer, genomic testing, pathology, 
biopsy, prostatectomy, active surveillance

None declared. Received on July 2, 2020 
Accepted on September 8, 2020

Soc Int Urol J. 2020;1(1):23–29

http://www.siuj.org
mailto:renu.eapen%40petermac.org?subject=SIUJ


24 SIUJ  •  Volume 1, Number 1  •  October 2020 SIUJ.ORG

MOLECULAR BIOMARKERS IN UROLOGIC ONCOLOGY: ICUD-WUOF CONSULTATION

United States found similar results in which the model 
incorporating the methylation levels had an NPV of 
88%  [3]. The validity of the assay has also been assessed  
in a cohort of 211 African American men from 7 centers 
in the United States, in which methylation status  
had a NPV of 94.2% for the detection of Gleason  
score ≥ 7 cancer on repeat biopsy [4]. However, 
prospective validation studies have yet to be performed 
for this assay, and its performance compared with other 
biomarkers in men with a prior negative biopsy remains 
to be determined.

Oncotype Dx Genomic Prostate Score 
(GPS)
Description of assay
Oncotype Dx Genomic Prostate Score (Genomic Health, 
Inc., Redwood City, United States) consists of a panel of 
12 PCa-related genes representing 4 distinct biological 
pathways (androgen signaling, cellular organization, 
stromal response, and cellular organization) relative to 
5 reference genes, based on measured RNA expression 
levels by reverse transcriptase-PCR [5].

Prediction of disease progression in active 
surveillance
The GPS score was developed to determine the 
likelihood of adverse pathology in men undergoing 
immediate radical prostatectomy. Although they were 
developed in the surgical context, studies have also 
shown that the GPS correlates with disease progression 
in men on active surveillance (AS), and the current 
National Comprehensive Cancer Network (NCCN) [6] 
and American Association of Clinical Oncolog y 
(ASCO) [7] guidelines recommend considering such 
genomic tests for men qualifying for AS in situations in  
which the assay results are likely to impact clinical 
decision-making.

In the first such study in an AS cohort of 271 men 
who had GPS testing, 144 experienced biopsy upgrading, 
and GPS was associated with an increased risk of 
upgrading [8]. A further study from the same AS cohort 
assessed the ability of GPS to predict adverse pathology 
(AP) as well as the risk of biochemical recurrence (BCR) 
after radical prostatectomy (RP). In 215 men on AS who 
underwent delayed RP, GPS was associated with an 
increased risk of adverse pathology at RP and BCR [9].

GPS as a predictor of AP at RP has recently been 
reported in the multicenter Canary Prostate Active 
Surveillance Study (PASS) cohort [10]. Overall, 101 men 
on AS underwent RP after a median of 2.1 years, and 
52 men were found to have AP. GPS was significantly 
associated with AP when adjusted for diagnostic 
Gleason score, but not when also adjusted for PSA 
density [10]. Although this was a prospective multicenter 
AS cohort study with a defined AS protocol using tissue 
from original diagnostic biopsy tissue, the sample size 
for the AP endpoint was small and further validation is 
warranted.

The University of California San Francisco (UCSF) 
AS cohort has also reported the value of serial GPS 
testing in the context of AS [11]. In 111 men on AS who 
underwent serial GPS testing, the GPS at first biopsy was 
associated with an upgrade at second biopsy; however, 
the second GPS was not [11]. A further study in 1031 men 
from the UCSF cohort found that a composite score of 
3 tissue genomic markers (including GPS) predicted 
biopsy reclassification at first surveillance biopsy and 
up to 3 years after enrollment but was not associated 
with reclassification at 5 and 10 years [12]. These early 
validation studies of GPS in the context of AS are limited 
by their retrospective nature and the potential selection 
bias of those who underwent genomics testing as part of 
treatment.

A recently published study of a cohort of 296 men 
with very low-risk (37.8% ) or low-risk (62.2%) disease 
who underwent GPS testing found that GPS score 
did not differ significantly across quartiles of disease 
volume (defined as percent of positive cores, number 
of cores with > 50% involvement, largest involvement 
of any single core, and PSA density) [13]. However, the 
median likelihood of favorable pathology at RP was 
statistically different between volume quartiles. Seven 
of the 105 men (6.3%) with very low-risk disease were 
reclassified to low-risk, and 13 of 181 (7.2%) with low-
risk disease were reclassified to intermediate-risk. On 
univariate analysis, disease volume did not correlate 
with reclassification on GPS testing [13]. A further study 
to clarify the clinical utility of GPS in determining 
eligibility for AS found that no men with NCCN very 
low-risk disease were reclassified [14]. In this cohort, 
nearly one-third of men with low- or intermediate-risk 

Abbreviations 

AP adverse pathology
AS active surveillance
BCR biochemical recurrence
CAPRA  Cancer of the Prostate Risk Assessment
CCP cell cycle progression
CCR cell cycle risk
DRE digital rectal examination
GPS genomic prostate score
NCCN National Comprehensive Cancer Network
NPV negative predictive value
PCa prostate cancer
PCR polymerase chain reaction
RP radical prostatectomy

http://www.siuj.org


25SIUJ.ORG SIUJ  •  Volume 1, Number 1  •  October 2020

The Clinical Applications of Tissue Biomarkers in Prostate Cancer

disease were reclassified, but management decisions did 
not always reflect the change [14]. Thus, GPS provides 
additional risk stratification for patients, but is likely 
best reserved for patients whose clinical risk does not 
indicate a clear clinical recommendation for or against 
definitive treatment.

Prediction of adverse pathology at surgery
The panel of genes used in GPS was initially identified 
from 732 candidate genes selected through a meta-

analysis of publicly available microarray datasets, 
based on the ability to predict cancer-specific mortality 
across multiple foci of cancer within RP specimens.  
It has been validated for predicting AP on RP specimens 
using biopsy specimens [15]. GPS has a lso been 
validated prospectively as an independent predictor 
of AP at RP [16]. On multivariable analysis in models 
that included clinical variables (PSA, clinical stage, 
and biopsy grade), GPS was a significant predictor 
of AP at RP in both intermediate-risk and favorable 

TABLE 1.

Summary of tissue genomic biomarkers used in prostate cancer

Genomic Test Tissue Source
Clinical 

Requirements
Marker Measurement Reported Outcomes

ConfirmMDx
Negative biopsy 

tissue
Prior negative biopsy, 

no ASAP
DNA methylation (GSTP1, APC, 

RASSF1, ACTB )
 • Any cancer [2,3]
 • Gleason score ≥ 7 [4]

Oncotype Dx GPS Positive biopsy tissue
Gleason 3+3 or 3+4, 
NCCN very low to 
intermediate risk

RNA (17 gene expression: AZGP1, 
FAM13C, KLK2, SRD5A2, FLNC, 

GSN, GSTM2, TPM2, BGN, COL1A1, 
SFRP4, TPX2, ARF1, ATP5E, CLTC, 

GPS1, PGK1)

 • Adverse pathology at RP (Gleason 
≥ 4+3 or ≥ pT3a) [8,15,16]

 • Metastasis after RP [17]
 • Prostate cancer-specific death 
after RP [17]

 • Biopsy upgrade on active 
surveillance [8,11]

 • Adverse pathology at RP while  
on AS [10]

ProMark Positive biopsy tissue Gleason 3+3 or 3+4
Protein (DERL1, CUL2, SMAD4, 

PDSS2, HSPA9, FUS, pS6, YBOX1)

 • Unfavorable pathology at RP 
(Gleason ≥ 3+4 or ≥ pT3a), pN1, 
pM1 [18,20]

Prolaris
Positive biopsy tissue 

or RP
None

RNA (46 gene expression:  
31 cell cycle progression genes,  

15 housekeeping genes)

 • Biochemical recurrence after 
RP [21,23–25]

 • Prostate cancer-specific death 
after RP [22]

 • Selection for active  
surveillance [22]

 • Metastasis after RP [23]
 • Risk stratification [25]
 • Metastasis after definitive 
treatment [27]

Decipher
Positive biopsy tissue 

or RP
None

RNA (22 gene expression:  
NFIB, NUSAP1, ZWILCH, ANO7,  

PCAT-32, UBE2C, CAMK2N1, 
MYBPC1, PBX1, THBS2, EPPK1, 

IQGAP3, LASP1, PCDH7, RABGAP1, 
GLYATL1P4, S1PR4, TNFRSF19, TSBP, 

3 RNA markers not associated  
with genes)

 • Metastasis after RP [28-32]
 • Prostate cancer-specific death 
after RP [28]

 • Biochemical recurrence after  
RP [29]

 •  Prostate cancer-specific death 
after RP [33,34]

 • Adverse pathology at RP [35,36]

http://www.siuj.org


26 SIUJ  •  Volume 1, Number 1  •  October 2020 SIUJ.ORG

MOLECULAR BIOMARKERS IN UROLOGIC ONCOLOGY: ICUD-WUOF CONSULTATION

intermediate-risk groups. The AUC of the adverse 
pathology predictive model was 0.726 when combining 
CAPRA and GPS versus 0.633 with CAPRA alone [16]. 
This study confirms prior validations that employed a 
prospective–retrospective design in men who underwent 
immediate RP.

Prediction of post-operative outcomes
In a retrospective cohort study of 279 men treated with 
RP from 1995 to 2010 for localized prostate cancer 
spanning low to high NCCN risk groups, GPS from 
biopsy tissue was found to be independently associated 
with BCR, metastasis, and death after adjusting for 
CAPRA score and NCCN risk category [17]. The c-index 
for PCa metastasis at 10 years increased from 0.65 for 
CAPRA alone to 0.73 with the addition of GPS. For 
prostate cancer death, a similar increase of 0.78 to 0.84 
was found with the addition of GPS to CAPRA [17]. 
However, it is unclear what impact GPS has on clinical 
decisions, particularly as models in the study were not 
adjusted for receipt of any adjuvant post-RP treatment.

ProMark
Description of assay
ProMa rk (Meta ma rk Genet ics, Inc., Wa lt ha m, 
United States) is a test that uses quantitative tissue 
proteomics [18] to generate a risk score from the levels 
of 8 target proteins, derived from a candidate biomarker 
study [19], for men with Gleason score 3+3 and 3+4 
cancer on biopsy tissue. The score gives the probability 
of having AP at RP.

Prediction of adverse pathology following 
surgery
A clinical prognostic risk model was developed in 
381 patients with biopsy and RP specimens and then 
validated in a separate cohort of 276 patients, assessing 
their proteomic panel risk against NCCN risk category 
and D’Amico classification [20]. In the validation cohort, 
the authors reported an AUC for predicting AP at RP 
for the proteomic panel alone was 0.68 compared with 
the NCCN model of 0.69 and D’Amico model of 0.65. 
However, when the proteomic panel was added to both 
the NCCN and D’Amico models, the AUCs increased 
to 0.75. Additionally, when the authors used clinical 
cutoffs for the protein panel combined with NCCN risk 
categories, they reported the positive predictive value for 
favorable pathology was 82% for NCCN intermediate-
risk, 82% for low-risk, and 95% for very low-risk. Finally, 
a decision curve analysis was performed that found 
a net benefit with the addition of the protein panel for 
patients in NCCN intermediate- and high-risk groups, 
which may change treatment decision-making [20]. 
However, for patients with lower clinical risk disease, 
the ProMark proteomic panel may be more suitable to 
reassure and confirm that men are indeed low risk. To 

date, the ProMark genomic panel has been validated 
only for predicting AP at RP. Although AP is associated 
with an overall less favorable prognosis, no study to  
date has validated ProMark for predicting survival or 
post-treatment disease recurrence.

Prolaris
Description of assay
Prolaris (Myriad Genetics, Inc., Salt Lake City, United 
States) is a panel that measures the RNA expression 
levels of 31 genes involved in cell cycle progression (CCP) 
relative to the expression of 15 housekeeping genes in 
cancerous tissue [21].

Identification of men suitable for active 
surveillance
A model combining CCP scores and clinical parameters, 
termed cell cycle risk (CCR) score, has been developed 
and applied to the identification of men with low-risk 
disease who are appropriate for AS. In a validation 
cohort of 585 men conservatively managed with CCR 
scores below the threshold, the predicted mean 10-year 
PCa mortality was 2.7%, and the CCR significantly 
dichotomized low- and high-risk disease. The potential 
clinical benefit of selecting men for AS using the CCR 
score was evaluated in a sequential, unselected cohort 
of 19 215 men. The proportion of men identified as 
candidates was substantially higher when the CCR 
score was used than when clinicopathologic features 
alone were used, while the mean 10-year predicted 
PCa mortality risks remained identical (1.9% versus 
2.0%) [22]. However, the validation cohort in this study 
was composed of men who deferred curative therapy 
and were therefore not a “true” AS cohort as there 
was minimal scheduled surveillance in the absence of 
symptoms of clinical progression.

Prediction of post-operative outcomes
The CCP score has also been assessed on biopsy 
specimens of men who later underwent definitive 
treatment. In a cohort of 582 men who were treated with 
RP, biopsy CCP scores were significantly associated with 
BCR and metastases on multivariable analyses; it should 
be noted that only 2% of men developed metastases [23]. 
The CCP score was not significantly associated with 
clinical variables in any models, suggesting that it 
provides additional predictive information beyond 
traditional clinical parameters. However, the prognostic 
value of CCP was not directly compared with any 
established clinical risk calculators. In a further 
study using the same cohort, the CCP score provided 
additional prognostic value for BCR after RP in men 
with NCCN low-risk disease beyond the CAPRA score, 
with 35% of men with high CCP score experiencing BCR 
at 5 years. The c-index was 0.56 for the CAPRA score 
alone and 0.66 for CAPRA combined with CCP score. 

http://www.siuj.org


27SIUJ.ORG SIUJ  •  Volume 1, Number 1  •  October 2020

The Clinical Applications of Tissue Biomarkers in Prostate Cancer

A decision curve analysis which found that the CCP 
score provided net clinical benefit beyond CAPRA [24]. 
Therefore, using only pre-treatment data, the CCP 
score allows for prognostic stratification within the  
low-risk category, and this information could inform  
decision-making regarding AS and local treatments in 
certain patients.

Use in risk prediction
In addition to biopsy tissue, the CCP score has also been 
derived from RP tissue to prognosticate and stratify 
according to risk of BCR. In a study of 413 men who 
underwent RP, CCP score was able to effectively stratify 
patients within CAPRA-S risk groups [25]. Regardless of 
CAPRA-S risk group, men with a very low CCP score did 
not experience cancer recurrence within 5 years of RP, 
whereas recurrence was almost 50% in men with a high 
CCP score. In the overall recurrence predictive model, 
the c-index of CAPRA-S alone was 0.73 and increased 
to 0.77 with the addition of the CCP score. Therefore, 
the CCP score provides some additional prognostic 
information, beyond pathologic factors, on the risk of 
BCR following RP. This additional information may aid 
decision-making regarding adjuvant treatments.

Risk stratification is often more challenging in 
certain cohorts such as African American men, for 
whom non-clinical social determinants of health may 
influence receipt of treatment and ultimate oncologic 
outcomes [26]. In a retrospective cohort of 767 men of 
whom 281 (37%) were African American, the CCP 
score was a significant predictor of metastatic disease 
on multivariable analysis including clinical parameters; 
however, there was no interaction with ancestry or 
treatment [27]. Of note, ancestry was self-reported, and 
this may have introduced error thereby limiting the 
generalizability of the conclusions, as population-based 
genetic heterogeneity was not addressed.

Decipher
Description of assay
The Decipher genomic classifier (Decipher Biosciences, 
San Diego, United States) is a genomic assay based 
on a full transcriptome microarray, including both 
protein coding and non-coding RNA, and measures 
RNA expression levels from 22 genes that were initially 
selected from 1.4 million candidate RNA probes [28].

Prediction of post-operative outcomes
Decipher was initia l ly va lidated for predicting 
metastases in a cohort of 545 patients who underwent RP 
at the Mayo Clinic, of whom 213 subsequently developed 
metastases. Decipher has been further validated in 
multiple studies for predicting outcomes after RP [28]. 
In a study of 85 men who developed BCR following RP, 
Decipher predicted metastases with an AUC of 0.82, and 

in modeling with clinicopathologic variables, Decipher 
was the only significant predictor of metastasis [29]. 
In a larger cohort of 260 men with intermediate- and 
high-risk disease at the time of RP who did not receive 
adjuvant treatment, Decipher added prognostic accuracy 
to the CAPRA-S for estimating metastatic disease 
at 10 years. The c-index of Decipher and CAPRA-S 
alone was 0.76 and 0.77, respectively; when Decipher 
and CAPRA-S were combined, the c-index improved 
to 0.87. The greater net benefit of the combination 
was also confirmed with decision curve analysis [30].  
A similar finding was observed in a cohort of 169 men 
that included 15 who had metastatic progression within 
5 years of RP. In multivariable analysis, after adjusting 
for clinical risk factors, Decipher predicted metastases 
and also had the highest c-index (0.77), compared with 
CAPRA-S and Stephenson nomogram, as well as a 
panel of previously reported, unrelated prostate cancer 
biomarkers [31].

Use in risk stratification
Decipher has also been assessed in determining risk 
in men with a persistently detectable PSA after RP, 
as a subset of these patients likely harbor metastatic 
disease. In a cohort of 477 men who underwent RP at 
3 academic centers, 150 had an immediately detectable  
post-operative PSA. The 5-year metastasis rate was 
0.90% for Decipher low/intermediate and 18% for 
Decipher high-risk [32]. On multivariable analysis, only 
Decipher high-risk, detectable PSA, and lymph node 
invasion remained prognostic factors for metastasis [32]. 
The sample size of men with a detectable PSA  
post-RP in this study may have limited the power to 
detect differences in various prognostic variables.

Decipher has also been shown to prognosticate 
prostate cancer death. Combining CAPR A-S and 
Decipher predicted prostate cancer death at 5 years after 
RP in 185 men with high-risk disease. The combination 
of CAPRA-S and Decipher had greater clinical benefit 
on decision curve analysis (DCA) than either alone [33]. 
Supporting this finding, a multicenter study of 561 men 
with adverse pathologic features at RP, the combination 
of Decipher and CAPRA-S improved the prediction of 
to 10-year prostate cancer death following RP (c-index 
0.73) compared with either test alone (c-index 0.73 
for each) [34]. DCA also confirmed the net benefit of 
Decipher combined with CAPRA-S for 10-year prostate 
cancer mortality [34].

Decipher has also been applied to prostate biopsies 
from patients to predict AP at RP [35]. In a retrospective 
multicenter cohort of 266 men with NCCN very low-, 
low- and favorable intermediate-risk PCa, Decipher 
from diagnostic biopsies was an independent predictor 
of AP at RP. This study did not have long-term  
follow-up to evaluate survival outcomes, and the sample 

http://www.siuj.org


28 SIUJ  •  Volume 1, Number 1  •  October 2020 SIUJ.ORG

MOLECULAR BIOMARKERS IN UROLOGIC ONCOLOGY: ICUD-WUOF CONSULTATION

size and low number of events did not allow Decipher to 
be assessed in the individual NCCN risk (eg, favorable 
intermediate only) groups. A further study using this 
cohort also found that NCCN favorable intermediate-
risk disease with Decipher low or intermediate score 
was not associated with significantly higher odds of AP 
compared to very low-risk/low-risk disease [36].

Taken together, these studies demonstrate that the 
Decipher has a prognostic value for both metastatic 
disease and PCa mortality following RP. This can 
potentially assist patients and clinicians in decision-
ma k ing around adjuvant therapy—if a patient’s 
Decipher risk for metastasis and prostate cancer death is 
low, then he could potentially forgo adjuvant treatments, 
even if his pathological characteristics are less favorable.

Emerging tissue biomarkers
A benefit of the technology from GenomeDx, is the 

ability to study other RNA expression signatures, which 
may be associated with outcomes beyond the scope of 
the original gene expression panel. Retrospectively, 
GenomeDx has used their technolog y to identif y 
signatures related to other treatment modalities 
such as radiation, androgen deprivation therapy,  
and chemotherapy sensitivity. Zhao et al. identified 
a 24-gene molecular signature that predicts patient 
response to adjuvant radiotherapy following radical 
prostatectomy. The gene signature, termed t he 
Post-Operative Radiation Therapy Outcomes Score 
(PORTOS) was identified from patients included in 
1 of 5 published studies of men with prostate cancer 
who had radical prostatectomy (with or without  

post-operative radiotherapy) and had gene expression 
analysis of the tumor, with long-term follow-up and for 
whom complete clinicopathological data were available. 
The authors demonstrated that in those patients who 
had a high PORTOS score, those who had radiotherapy 
had a lower 10 year metastases rate than those who did 
not (5% versus 63%, HR 0.12, P < 0.001) [37]. PORTOS 
remains to be validated prospectively, and its association 
with RT sensitivity has been shown only in post-RP 
patients. Therefore, further investigation is required to 
validate the gene panel prospectively and in the setting 
of RT for primary treatment of prostate cancer.

Future Challenges and Directions
Many of the growing number of tissue-based genomic 
tests are clinically validated to improve on clinical 
parameters alone by more accurately determining 
prognosis and specific oncologic outcomes. However, 
whether a genomic test may be predictive of a clinically 
significant response to a particular management strategy 
is a more complex question. Many of the genomic 
tests outlined have been shown to predict multiple 
endpoints, including AP, BCR, metastasis-free survival, 
and/or cancer-specific mortality, but one endpoint is 
not a surrogate for another. The temporal associations 
of many tissue genomic tests and outcomes remain 
obscure, but there are current prospective trials designed 
to address this issue.

Tissue-based genomic tests have the potential to 
optimize the care for men with prostate cancer, but 
determining the optimal genomic test for each patient to 
ensure the best outcome requires continued study. 

References

1. Van Neste L., Bigley J, Toll A, et al. A tissue biopsy-based epigenetic 
multiplex PCR assay for prostate cancer detection. BMC Urol. 
2012;12:16.

2. Stewar t GD, Van Neste L, Delvenne P, et al., Clinical utilit y 
of an epigenetic assay to detect occult prostate cancer in 
histopathologically negative biopsies: results of the MATLOC study. 
J Urol. 2013; 189(3):1110-6.

3. Partin AW, Van Neste L, Klein EA, et al. Clinical validation of an 
epigenetic assay to predict negative histopathological results in 
repeat prostate biopsies. J Urol. 2014;192(4):1081-7.

4. Waterhouse RL Jr, Van Neste L, Moses K A, et al. Evaluation of an 
epigenetic assay for predicting repeat prostate biopsy outcome in 
African American men. Urology. 2019;128:62-65.

5. Knezevic D, Goddard AD, Natraj N, et al. Analytical validation of 
the Oncotype DX prostate cancer assay - a clinical RT-PCR assay 
optimized for prostate needle biopsies. BMC Genomics. 2013;14: 
690.

6. Mohler JL, Antonarakis ES, Armstrong AJ, et al. Prostate Cancer, 
Version 2.2019, NCCN Clinical Practice Guidelines in Oncology. J 
Natl Compr Canc Netw. 2019;17(5):479-505.

7. Eggener SE, Rumble RB, Armstrong AJ, et al. Molecular biomarkers 
in localized prostate cancer: ASCO guideline. J Clin Oncol. 
2020;38(13):1474-1494.

8. Kornberg Z, Cowan JE, Westphalen AC, et al. Genomic Prostate 
Score, PI-RADS version 2 and progression in men with prostate 
cancer on active surveillance. J Urol. 2019;201(2): 300-307.

9. Kor nberg Z , Cooper berg MR, Cowan JE, et al. A 17- gene 
genomic prostate score as a predictor of adverse pathology for 
men on active sur veillance. J Urol. 2019; 202(4):702-709. doi: 
101097ju0000000000000290

10. Lin DW, Zheng Y, McKenney JK, et al. 17-Gene Genomic prostate 
score test results in the Canary Prostate Active Surveillance Study 
(PASS) Cohort. J Clin Oncol. 2020: 10;38(14):1549-1557. doi: 10.1200/
JCO.19.02267

http://www.siuj.org


29SIUJ.ORG SIUJ  •  Volume 1, Number 1  •  October 2020

The Clinical Applications of Tissue Biomarkers in Prostate Cancer

11. Cedars BE, Washington SL 3rd, Cowan JE, et al. Stabilit y of a 
17-gene genomic prostate score in serial testing of men on active 
surveillance of early stage prostate cancer. J Urol. 2020(4):696-701. 
doi: 10.1097/JU.0000000000000271

12. L oner gan P E , Washing ton SL 3r d, C ow an JE , et al. Risk 
factors for biopsy reclassification over time in men on active 
sur veillance for early stage prostate cancer. J Urol. 20 20: 
101097JU0000000000001186.

13. Nyame YA, Grimberg DC, Greene DJ, et al. Genomic scores are 
independent of disease volume in men with favorable risk prostate 
cancer: implications for choosing men for active surveillance. J Urol. 
2018;199(2):438-444.

14. Gaffney C, Golan R, Cantu MD, et al. The clinical utility of the 
genomic prostate score in men with very low to intermediate risk 
prostate cancer. J Urol. 2019; 202(1): 96-101.

15. Klein EA, Cooperberg MR, Magi-Galluzzi C, et al. A 17-gene assay 
to predict prostate cancer aggressiveness in the context of Gleason 
grade heterogeneity, tumor multifocality, and biopsy undersampling. 
Eur Urol. 2014; 66(3):550-60.

16. Eggener S, Karsh LI, Richardson T, et al. A 17-gene panel for 
prediction of adverse prostate cancer pathologic features: 
prospective clinical validation and utility. Urology. 2019;126:76-82.

17. Van Den Eeden SK, Lu R, Zhang N, et al. A biopsy-based 17-gene 
genomic prostate score as a predictor of metastases and prostate 
cancer death in surgically treated men with clinically localized 
disease. Eur Urol. 2018;73(1):129-138.

18. Shipitsin M, Small C, Giladi E, et al. Automated quantitative 
multiplex immunofluorescence in situ imaging identifies phospho-S6 
and phospho-PRAS40 as predictive protein biomarkers for prostate 
cancer lethality. Proteome Sci. 2014; 12:40.

19. Shipitsin M, Small C, Choudhury S, et al. Identification of proteomic 
biomarkers predicting prostate cancer aggressiveness and lethality 
despite biopsy-sampling error. Br J Cancer. 2014;111(6): 1201-12.

20. Blume-Jensen P, Berman DM, Rimm DL, et al. Development and 
clinical validation of an in situ biopsy-based multimarker assay 
for risk stratification in prostate cancer. Clin Cancer Res. 2015; 
21(11):2591-600.

21. Cuzick J, Swanson GP, Fisher G, et al. Prognostic value of an RNA 
expression signature derived from cell cycle proliferation genes in 
patients with prostate cancer: a retrospective study. Lancet Oncol. 
2011;12(3):245-55.

22. Lin DW, Crawford ED, Keane T, et al. Identification of men with 
low-risk biopsy-confirmed prostate cancer as candidates for active 
surveillance. Urol Oncol. 2018;36(6):310 e7-310 e13.

23. Bishoff JT, et al. Prognostic utility of the cell cycle progression 
score generated from biopsy in men treated with prostatectomy.  
J Urol. 2014;192(2):409-14.

24. Tosoian JJ, Chappidi MR, Bishoff JT, et al. Prognostic utility of 
biopsy-derived cell cycle progression score in patients with 
National Comprehensive Cancer Network low-risk prostate cancer 
undergoing radical prostatectomy: implications for treatment 
guidance. BJU Int. 2017; 120(6):808-814.

25. Cooperberg MR, Simko JP, Cowan JE, et al. Validation of a 
cell-cycle progression gene panel to improve risk stratification 
in a c o n t e mp o r ar y p r o s t a t e c t o m y c o h o r t . J Clin O n col. 
2013;31(11):1428-34.

26. Kagawa-Singer M, Valdez Dadia A, Yu MC, et al. Cancer, culture, 
and health disparities: time to char t a new course? CA Cancer  
J Clin. 2010; 60(1):12-39.

27. Canter DJ, Reid J, Latsis M, et al. Comparison of the prognostic 
utility of the cell cycle progression score for predicting clinical 
outcomes in African American and Non-African American men with 
localized prostate cancer. Eur Urol. 2019;75(3):515-522.

28. Erho N, Crisan A, Vergara IA, et al. Discovery and validation of a 
prostate cancer genomic classifier that predicts early metastasis 
following radical prostatectomy. PLoS One. 2013;8(6):e66855.

29. Ross AE, Feng FY, Ghadessi M, et al. A genomic classifier predicting 
metastatic disease progression in men with biochemical recurrence 
after prostatectomy. Prostate Cancer Prostatic Dis. 2014; 17(1):64-9.

30. Ross AE, Johnson MH, Yousefi K, et al. Tissue-based genomics 
augments post-prostatectomy risk stratification in a natural 
histor y cohor t of intermediate- and high-risk men. Eur Urol. 
2016;69(1):157-65.

31. Klein EA, Yousefi K, Haddad Z, et al. A genomic classifier improves 
prediction of metastatic disease within 5 years after surgery in 
node -negative high-risk prost ate cancer patients managed 
by radical prostatectomy without adjuvant therapy. Eur Urol. 
2015;67(4):778-86.

32. Sprat t DE, Dai DLY, Den RB, et al. Per formance of a prostate 
cancer genomic classifier in predicting metastasis in men with 
prostate-specific antigen persistence postprostatectomy. Eur Urol. 
2018;74(1):107-114.

33. Cooperberg MR, Davicioni E, Anamaria Crisan A, et al. Combined 
value of validated clinical and genomic risk stratification tools for 
predicting prostate cancer mortality in a high-risk prostatectomy 
cohort. Eur Urol. 2015;67(2):326-33.

34. Karnes RJ, Choeurng V, Ross AE, et al. Validation of a genomic risk 
classifier to predict prostate cancer-specific mortality in men with 
adverse pathologic features. Eur Urol. 2018;73(2):168-175.

35. Kim HL, Li P, Huang H-C, et al. Validation of the Decipher test 
for predicting adverse pathology in candidates for prostate 
cancer active sur veillance. Prostate Cancer Prostatic Dis. 2019; 
22(3):399-405.

36. Herlemann A, Huang H-C, Alam R, et al. Decipher identifies men 
with otherwise clinically favorable-intermediate risk disease who 
may not be good candidates for active surveillance. Prostate Cancer 
Prostatic Dis. 2020;23(1):136-143. doi: 10.1038/s41391-019-0167-9

37. Zhao SG, Chang SL, Spratt DE, et al. Development and validation 
of a 24-gene predictor of response to postoperative radiotherapy 
in prostate cancer: a matched, retrospective analysis. Lancet Oncol 
2016;17(11):1612-1620.

http://www.siuj.org

