Genomic classifiers in personalized prostate cancer radiotherapy approaches – a systematic review and future perspectives based on international consensus

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Genomic classifiers in personalized prostate cancer radiotherapy
approaches – a systematic review and future perspectives based on
international consensus
Simon K.B. Spohn MD , C´
edric Draulans MD ,
Amar U. Kishan MD , Daniel Spratt MD , Ashley Ross MD, PhD ,
Tobias Maurer Prof. MD , Derya Tilki Prof., MD ,
Alejandro Berlin MD , Pierre Blanchard MD, PhD ,
Sean Collins MD , Peter Bronsert MD , Ronald Chen MD, PhD ,
Alan Dal Pra MD , Gert de Meerleer MD, Prof. ,
Thomas Eade MD, Prof. , Karin Haustermans MD, Prof. ,
Tobias H¨
olscher MD , Stefan H¨
ocht MD, Prof. ,
Pirus Ghadjar MD, Prof. , Elai Davicioni PhD , Matthias Heck MD ,
Linda G.W. Kerkmeijer MD, PhD , Simon Kirste MD ,
Nikolaos Tselis MD , Phuoc T. Tran MD, PhD ,
Michael Pinkawa MD, Prof. , Pascal Pommier MD ,
Constantinos Deltas PhD , Nina-Sophie Schmidt-Hegemann MD ,
Thomas Wiegel MD, Prof. , Thomas Zilli MD , Alison C. Tree MD ,
Xuefeng Qiu MD , Vedang Murthy MD ,
Jonathan I. Epstein MD, Prof. , Christian Graztke MD, Prof. ,
Xin Gao MD , Anca L. Grosu MD, Prof. , Sophia C. Kamran MD ,
Constantinos Zamboglou MD
PII: S0360-3016(22)03691-4
DOI: https://doi.org/10.1016/j.ijrobp.2022.12.038
Reference: ROB 27992
To appear in: International Journal of Radiation Oncology, Biology, Physics
Received date: 2 June 2022
Revised date: 9 December 2022
Accepted date: 24 December 2022
Please cite this article as: Simon K.B. Spohn MD , C´
edric Draulans MD ,
Amar U. Kishan MD , Daniel Spratt MD , Ashley Ross MD, PhD , Tobias Maurer Prof. MD ,
Derya Tilki Prof., MD , Alejandro Berlin MD , Pierre Blanchard MD, PhD , Sean Collins MD ,
Peter Bronsert MD , Ronald Chen MD, PhD , Alan Dal Pra MD , Gert de Meerleer MD, Prof. ,
Thomas Eade MD, Prof. , Karin Haustermans MD, Prof. , Tobias H¨
olscher MD ,
Stefan H¨
ocht MD, Prof. , Pirus Ghadjar MD, Prof. , Elai Davicioni PhD , Matthias Heck MD ,
Linda G.W. Kerkmeijer MD, PhD , Simon Kirste MD , Nikolaos Tselis MD , Phuoc T. Tran MD, PhD ,
Michael Pinkawa MD, Prof. , Pascal Pommier MD , Constantinos Deltas PhD ,
Nina-Sophie Schmidt-Hegemann MD , Thomas Wiegel MD, Prof. , Thomas Zilli MD ,
Alison C. Tree MD , Xuefeng Qiu MD , Vedang Murthy MD , Jonathan I. Epstein MD, Prof. ,
Christian Graztke MD, Prof. , Xin Gao MD , Anca L. Grosu MD, Prof. , Sophia C. Kamran MD ,
Constantinos Zamboglou MD , Genomic classifiers in personalized prostate cancer radio-
therapy approaches – a systematic review and future perspectives based on interna-
tional consensus, International Journal of Radiation Oncology, Biology, Physics (2022), doi:
https://doi.org/10.1016/j.ijrobp.2022.12.038

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1
Title: Genomic classifiers in personalized prostate cancer radiotherapy
approaches – a systematic review and future perspectives based on
international consensus
Short Running Title: Genomic Classifier Prostate Cancer Radiation
Simon K.B. Spohn, MD a,b,c, Cédric Draulans, MD d, Amar U. Kishan, MD e, Daniel
Spratt, MD f, Ashley Ross MD, PhD g, Tobias Maurer, Prof. MD h,i, Derya Tilki Prof.,
MD h,i,j, Alejandro Berlin, MD k, Pierre Blanchard, MD, PhD l, Sean Collins, MD m,
Peter Bronsert, MD n, Ronald Chen, MD, PhD o, Alan Dal Pra, MD p, Gert de
Meerleer, MD, Prof. d, Thomas Eade, MD, Prof. q, Karin Haustermans, MD, Prof. d,
Tobias Hölscher, MD r, Stefan Höcht, MD, Prof. s, Pirus Ghadjar, MD, Prof. t, Elai
Davicioni, PhD u, Matthias Heck, MD v, Linda G.W. Kerkmeijer, MD, PhD w, Simon
Kirste, MD a,b, Nikolaos Tselis, MD x, Phuoc T. Tran, MD, PhD y, Michael Pinkawa,
MD, Prof. z, Pascal Pommier, MD aa, Constantinos Deltas, PhD bb, Nina-Sophie
Schmidt-Hegemann, MD cc, Thomas Wiegel, MD, Prof. dd, Thomas Zilli, MD ee, Alison
C. Tree, MD ff, Xuefeng Qiu, MD gg, Vedang Murthy, MDhh, Jonathan I. Epstein, MD,
Prof. ii, Christian Graztke, MD, Prof. jj, Xin Gao, MD kk, Anca L. Grosu, MD, Prof. a,b,
Sophia C. Kamran, MD ll,mm *, Constantinos Zamboglou, MD a,b,c,nn*
(a) Department of Radiation Oncology, University Medical Center Freiburg, Faculty of Medicine. University of
Freiburg, Freiburg, Germany
(b) German Cancer Consortium (DKTK). Partner Site Freiburg, Freiburg, Germany
(c) Berta-Ottenstein-Programme, Faculty of Medicine, University of Freiburg, Freiburg, Germany
(d) Department of Radiation Oncology, University Hospitals Leuven, Belgium; Department of Oncology, KU
Leuven, Belgium
(e) Department of Radiation Oncology; Department of Urology, University of California, Los Angeles, Los
Angeles, CA, USA
(f) Department of Radiation Oncology, UH Seidman Cancer Center, Case Western Reserve University, USA
(g) Department of Urology, Northwestern Feinberg School of Medicine, USA
(h) Martini-Klinik Prostate Cancer Center, University Hospital Hamburg-Eppendorf, Hamburg, Germany
(i) Department of Urology, University Hospital Hamburg-Eppendorf, Hamburg, Germany
(j) Department of Urology, Koc University Hospital, Istanbul, Turkey
(k) Department of Radiation Oncology, Temerty Faculty of Medicine, University of Toronto; Radiation
Medicine Program, Princess Margaret Cancer Centre, University Health Network. Toronto, Canada.
(l) Department of Radiation Oncology, Gustave Roussy, Oncostat U1018, Inserm, Paris-Saclay University,
Villejuif, France
(m) Department of Radiation Medicine, Medstar Georgetown University Hospital, Washington, DC USA
(n) Institute for Surgical Pathology, University Medical Center Freiburg, Faculty of Medicine. University of
Freiburg,Freiburg, Germany
(o) Department of Radiation Oncology, University of Kansas Cancer Center, Kansas City, KS 66160, USA.
(p) Department of Radiation Oncology, University of Miami, Miller School of Medicine, USA
(q) Northern Sydney Cancer Centre, Radiation Oncology Unit, Royal North Shore Hospital, Sydney, New
South Wales, Australia.
(r) Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl
Gustav Carus, Technische Universität Dresden, Dresden, Germany
(s) Xcare Practices Dept. Radiotherapy, Saarlouis, Germany
(t) Department of Radiation Oncology, Charité – Universitätsmedizin Berlin, corporate member of Freie
Universität Berlin and Humboldt-Universität zu Berlin
(u) Veracyte, Inc, San Diego, CA, USA
(v) Department of Urology, Rechts der Isar Medical Center, Technical University of Munich, Germany
(w) Department of Radiation Oncology, Radboud University Medical Center, The Netherlands
(x) Department of Radiation Oncology, University Hospital Johann Wolfgang Goethe University, Frankfurt,
Germany.
(y) Department of Radiation Oncology, University of Maryland, USA
(z) Department of Radiation Oncology, MediClin Robert Janker Klinik Bonn, Germany
(aa) Department of Radiation Oncology, Centre Léon Bérard, Lyon, France.
(bb) Molecular Medicine Research Center and Laboratory of Molecular and Medical Genetics, Department
of Biological Sciences, University of Cyprus, Nicosia, Cyprus.
(cc) Department of Radiation Oncology, University Hospital LMU Munich, Munich, Germany.
(dd) Department of Radiation Oncology, University Hospital Ulm, Ulm, Germany
(ee) Department of Radiation Oncology, Geneva University Hospital, Geneva, Switzerland
(ff) Department of Radiotherapy, The Royal Marsden Hospital and the Institute of Cancer Research, London,
UK

2
(gg) Department of Urology, Medical School of Nanjing University, Affiliated Drum Tower Hospital, Nanjing,
China
(hh) Department of Radiation Oncology, ACTREC, Tata Memorial Centre, Homi Bhabha National University,
India
(ii) Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
(jj) Department of Urology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg,
Freiburg, Germany
(kk) Department of Internal Medicine, Division of Hematology and Oncology, Massachusetts General
Hospital, Boston, MA, USA.
(ll) Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston,
MA
(mm) Broad Institute of MIT and Harvard, Cambridge, MA
(nn) German Oncology Center, European University of Cyprus, Limassol, Cyprus
* Contributed equally
Corresponding author
Simon K.B. Spohn
Robert-Koch-Straße 3, 79106 Freiburg, Germany
Tel: +49 761 270 94610; Fax: +49 761 270 94720
Simon.Spohn@uniklinik-freiburg.de
Word count: 3907
Author contribution
Conceptualization: SKBS, SCK and CZ; Methodology: SKBS and CZ; Resources:
SKBS, CD and CZ, Investigation: CD. (Leuven), AUK, DS, AR, RM, DT, AB, PB
(Paris), SC, PB (Freiburg), RC, ADP, GM, TE, KH, TH, SH, PG, ED, MH, LGWK, SK,
NT, PTT, MP, PP, NS, TW, TZ, ACT, XQ, VM, JIE; Data curation: SKBS, SCK and
CZ, Formal analysis: SKBS SCK and CZ, Supervision: CD (Cyprus), CG, XG, ALG;
Writing-Original draft: SKBS, SCK and CZ; Writing Review & Editing: All authors
Author responsible for statistical analysis
Simon Spohn
Email: Simon.Spohn@uniklinik-freiburg.de
Disclosures
A.U.K. reports funding support from grant P50CA09213 from the Prostate Cancer
National Institutes of Health Specialized Programs of Research Excellence and grant
W81XWH-22-1-0044 from the Department of Defense, as well as grant RSD1836
from the Radiological Society of North America, the STOP Cancer organization, the
Jonsson Comprehensive Cancer Center, and the Prostate Cancer Foundation.
C.Z. received founding from the Klaus Tschira foundation, Naslund Medical and from
the German research foundation. CZ received speaker fees from Johnson and
Johnson and from Novocure, all outside the submitted work.
E.D. is an employee of Veracyte, manufacturer of Decipher.
A.R. is a consultant and / or speaker for astellas, bayer, pfizer, blue earth, lantheus,
janssen, tempus, veracyte
X.G. is in the consulting/advisory board for Bayer, Myovant, Guardant Health.
Funding
This study is supported by the German Federal Ministry of Education and Research
(BMBF) as part of the ERA PER Joint Funding Call 2019.
Grant No.: Med-Call / JTC2019-299 01KU2015
Role of the funder
The funders had no role in study design, data collection and analysis, decision to
publish, or preparation of the manuscript

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Data Availability Statement
The results of the systematic review are included in the manuscript. All data
generated during the DELPHI consensus are included in the manuscript and
supplementary material. The corresponding author is available for any further
questions on the data.
Acknowledgements
AT acknowledges support from Cancer Research UK (C33589/A28284 and
C7224/A28724 CRUK RadNet). This project represents independent research
supported by the National Institute for Health research (NIHR) Biomedical Research
Centre at The Royal Marsden NHS Foundation Trust and the Institute of Cancer
Research, London. The views expressed are those of the authors and not
necessarily those of the NIHR or the Department of Health and Social Care.
We thank Fotini Miltiadous for the administrative support.
Abstract
Background:
Current risk-stratification systems for prostate cancer (PCa) do not sufficiently reflect
the disease heterogeneity. Genomic classifiers (GC) enable improved risk-
stratification after surgery, but less data exists for patients treated with definitive
radiotherapy (RT) or RT in oligo-/metastatic disease stages. In order to guide future
perspectives of GCs for RT, we conducted (i) a systematic review on the evidence of
GCs for patients treated with RT and (ii) a survey of experts using the DELPHI
method, addressing the role of GCs in personalized treatments to identify relevant
fields of future clinical and translational research.
Methods:
We performed a systematic review and screened ongoing clinical trials on
“clinicaltrials.gov”. Based on these results a multidisciplinary international team of
experts received an adapted DELPHI method survey. 31 and 30 experts answered
round 1 and round 2, respectively. Questions with ≥ 75% agreement were considered
as relevant and included into the qualitative synthesis.

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Results:
Evidence for GCs as predictive biomarkers is mainly available to the postoperative
RT setting. Validation of GCs as prognostic markers in the definitive RT settings is
emerging. Experts used GCs in PCa patients with extensive metastases (30%), in
postoperative settings (27%) and newly diagnosed PCa (23%). 47% of experts do not
currently use GCs in clinical practice. Expert consensus demonstrates that GCs are
promising tools to improve risk-stratification in primary and oligo-/metastatic patients
in addition to existing classifications. Experts were convinced that GCs might guide
treatment decisions in terms of RT-field definition and intensification/de-intensification
in various disease stages.
Conclusions:
This work confirms the value of GCs and the promising evidence of GC utility in the
setting of RT. Additional studies of GCs as prognostic biomarkers are anticipated and
form the basis for future studies addressing predictive capabilities of GCs to optimize
RT and systemic therapy. The expert consensus points out future directions for GC
research in the management of PCa.

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1. Introduction
Prostate cancer (PCa) is the second most common malignancy in men worldwide [1].
Improvements in screening and diagnostics have led to an increased number of
patients diagnosed with all stages of PCa [2, 3]. Radiotherapy (RT) plays a central
role in the management of PCa patients and can be applied in a curative setting or as
part of a palliative treatment concept. However, current risk stratification systems are
imperfect — the use of novel prognostic or predictive biomarkers are urgently needed
to enable better patient selection for appropriate treatment in the future. Several
biomarkers evaluating blood, urine, or tissue have been developed to aid with risk
stratification. Genomic classifiers (GC), or mRNA-based gene expression profiles
from tissue, have shown promise to reliably enable identification of aggressive PCa
and guide treatment decisions with different commercially available profiling panels,
including Prolaris, Oncotype DX and Decipher for overview see [4, 5]). Additionally,
the PAM50 classifier has been demonstrated to differentiate between luminal and
basal PCa, with luminal B tumours being associated with favorable response to
postoperative ADT [6].
Most evidence is available for the Decipher GC after radical prostatectomy (RP),
improving risk stratification and consequently guiding postoperative disease
management [7]. Less data exists for patients treated with definitive RT or RT in the
(oligo)metastatic setting, but GC might facilitate personalized oncologic treatments in
various perspectives in all disease stages. The aim of this work is to (i) summarize
the role of GCs for PCa patients in all disease stages treated with RT, since this is
aspect has not previously been highlighted and (ii) point out relevant clinical and
translational issues for future fields of research. We therefore conducted (i) a
systematic review and (ii) a survey of experts and key opinion leaders using the
DELPHI method. See Figure 1 for a summarizing overview.

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2. Methodology
2.1 Systematic Review
Studies eligible for inclusion were original articles on GC in PCa in the setting of RT,
comprising primary definitive, post-operative, as well as metastasis-directed (MDT)
RT. In general, 3 types of manuscript were included: (A) manuscripts on oncologic
outcomes after RT, (B) the role of GCs in RT decision process and (C) correlation
studies between other biomarkers and GCs. Inclusion criteria were: A1) patients
treated with RT (definitive, postoperative, MDT); A2) clinical results with the following
endpoints: clinical recurrence (CR), biochemical recurrence (BR), distant metastases
(DM), prostate-cancer specific mortality (PCSM), overall survival (OS). A3) Articles
with retrospective and prospective data were allowed (n patients > 50); B1)
Translational work addressing correlation of GCs with imaging, biomarkers or
biological features (radioresistance, androgen signaling etc); C1) Impact of GCs in
RT treatment decision processes. Exclusion criteria were: 1) articles not written in
English 2) non-original articles. SKBS and CZ performed a PubMed/Medline,
EMBASE and Cochrane Library database search for the terms:
PubMed:
(prostatic neoplasms [MeSH Terms6) or (prostatic neoplas*[tiab OR prostate
neoplas*[tiab] OR prostatic cancer*[tiab] OR prostate cancer*[tiab] OR prostatic
carcinoma*[tiab] OR prostate carcinoma*[tiab] OR prostatic adenocarcinoma*[tiab]
OR prostate adenocarcinoma*[tiab] OR prostatic tumor*[tiab] OR prostate
tumor*[tiab] OR prostatic tumour*[tiab] OR prostate tumour*[tiab]) AND
(Radiotherapy"[8]) or (radiotherapy [Subheading]) or (radiotherap*[tiab] OR
radiati*[tiab] OR irradiati*[tiab] OR "x ray therapy" [tiab] OR "x ray therapies" [tiab] OR
radioimmunotherap*[tiab] OR immunoradiotherap*[tiab]) AND (genomic* classif* [tiab]
OR decipher* [tiab])

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EMBASE and Cochrane:
('prostate cancer'/exp OR 'prostate cancer') AND ('radiotherapy'/exp OR
'radiotherapy') AND ('genomic classifier'/exp OR 'genomic classifier')
Mapped terms ''genomic classifier'' mapped to 'genomic classifier'.
In case of discrepant findings (n=3), a third reviewer (CeDr) provided a final decision.
The time period considered in this review was from June 6th 2013 until December 1st
2021. One hundred twenty-six articles were identified and 32 duplicates removed.
After applying inclusion and exclusion criteria, 26 studies were included for qualitative
review according to PRISMA [9] (Figure 2). This version was sent to the experts for
the first round of the survey. Between round 1 and 2 of the survey, a second round of
literature research was performed considering articles until the December 31st 2021.
No additional studies matching in- and exclusion criteria were found. During the peer-
review process an update of the literature search was performed:5 more studies and
one more clinical trial were included by considering a time period from June 6th 2013
until December 1st 2022.
2.2 Ongoing clinical trials
In order to provide an overview of clinical trials implementing GCs in treatment
decision, which serves to classify results of the expert survey, ongoing clinical trials
were screened on “clinicaltrials.gov”. Studies needed to be ongoing trials on GCs in
PCa in the setting of RT. SKBS performed the search for the terms (“Condition or
disease: prostate cancer” AND “genomic classifier” OR “radiotherapy”). Five clinical
trials on GCs in RT were located.
2.3 Expert opinion
The multidisciplinary team of expert professionals included radiation oncologists,
urological oncologists and pathologists. Experts were characterized by long-time

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experience in care and/or clinical trials of PCa patients, scientific research and their
role as key opinion leaders. An adapted DELPHI method was used to identify the
most relevant questions for future perspectives of GC. Since predictive biomarkers
are ultimately warranted to guide personalized treatments, we focused on the
putative capability of GCs to identity patients who might benefit from a certain
treatment. In round 1 (R1) preliminary results of literature search and key questions
were prepared by SKBS and CZ and emailed to 46 PCa experts, receiving 27 replies.
The survey was designed using the online tool Surveymonkey. Based on the
recommendations of the participants, we sent additional 9 invitations, of which 4
replied. In total 31 experts answered R1 (response rate: 56%). After completion of
R1, SKBS and CZ consolidated questionnaires and prepared round 2 (R2), in which
participant’s feedback and questions that did not reach consensus (defined as 50-
75% of votes) were included. These results were prepared by SKBS and CZ and
distributed to all participants (n=31). 30 experts provided answers in R2 (response
rate: 97%). Finally, only questions with ≥75% agreement were considered as relevant
and included into the qualitative synthesis. The detailed results of the adapted Delphi
rounds can be found in the supplementary information.
3. Results
3.1 GC in the Literature – methodological aspects
Literature search revealed 31 original papers addressing GCs in the setting of RT
(see Table 1 for details). Most of the studies (n=26) included retrospectively collected
patient collectives whilst 5 studies analyzed GC in prospectively collected patient
cohorts. Only two studies performed an external validation [11][36]. With 20 (65%)
studies, the Decipher test was used in the vast majority [10-29].
In 24 (77%) studies RP specimens were used to obtain tissue for genomic analyses
[10-24, 26, 28, 30-36]. In one study (3%) the Decipher test was applied to both RP-

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and biopsy-specimens [26], whilst six studies (19%) solely used biopsy specimens for
further analyses [27-29, 37-39]. Most studies (n=18, 58%) investigated the
associations between GCs and oncological outcomes such as DM (n=12, 39%) [12-
14, 26-32, 38, 39], prostate cancer specific mortality (PCSM) (n=3, 10%) [14, 28, 38],
overall survival (OS) (n=1, 3%)[14], clinical recurrence (CR) (n=3, 10%) [10, 11, 15]
and biochemical recurrence (BR) (n=6, 19%) [13, 15, 27, 33, 37, 38]. The other
studies reported on the role of GCs on treatment decision making (n=9, 29%) [16-24]
or correlated GCs with other biomarkers (n=4, 13%) [25, 34-36].
All studies included in this review reported on the role of GC as prognostic
biomarkers for PCa patients. To correctly assess the predictive value of a biomarker
in a study, at least two comparison groups must be available (in the best case, two
treatment arms in a RCT) [40]. This pre-requirement was not fulfilled by any study in
this review. However, five studies suggested a predictive role for GC in the setting of
adjuvant RT after surgery and one study supposed a predictive role of GC in the
response to androgen deprivation therapy (ADT) in the definitive RT setting [11-13,
30, 31, 39].
3.2 GC in the Literature – GCs for outcome prediction in RT for primary
localized PCa
In total seven studies included 1551 patients treated with definitive RT [26-29, 37-39].
Tosoian et al. performed a retrospective analysis of 405 men with high-risk PCa, of
which 80 were treated with definitive RT +/- ADT. A subset analysis showed that GC
was an independent prognosticator for patients treated with RT (HR 1.61, 95%CI
1.08–2.40,) [26]. Berlin et al. analyzed 121 patients with National Comprehensive
Cancer Network (NCCN) intermediate-risk PCa treated with definitive RT without
ADT. The GC outperformed all other indices in prediction of DM (HR 2.05, 95%CI

10
1.24 – 4.24) [27]. Nguyen et al. investigated retrospectively the Decipher biopsy test
as a prognosticator for DM and PCSM in intermediate- and high-risk patients treated
with RP or RT +/- ADT, respectively. In the mixed cohort the GC test was a
significant predictor for DM (HR 1.37 per 0.1 score increase, 95% CI: 1.06–1.78) and
PCSM (HR 1.57 per 0.1 score increase, 95%CI: 1.03–2.48) [28]. Another study by
Nguyen et al. included patients with intermediate- and high-risk PCa. Each GC score
increase was a significant predictor for DM in multivariate analysis (HR 1.36, 95%CI:
1.04–1.83). Furthermore, patients with a GC>0.6 (high-risk) had a 20% cumulative
incidence of metastasis at 5 years after RT, whereas patients with a low-risk GC
score of ≤0.2 had 0% cumulative incidence [29]. Tward et al. showed that a clinical
cell-cycle risk score (CAPRA score + Prolaris GC) prognosticated DM with a HR per
unit score of 2.22 (95%CI: 1.71-2.89) after dose-escalated RT +/- ADT in
intermediate- and high-risk patients [39]. Additionally, the authors suggested a
multimodality threshold defining men in which adding ADT may not significantly
reduce their risk of DM. Freedland et al. included patients with low-, intermediate-
and high-risk PCa and evaluated the prognostic utility of the Polaris score for BR
after primary RT +/- ADT. In the multivariate analysis the GC was a significant
predictor for BR (HR 2.11, 95%CI: 1.05-4.25) [37]. Comparable results in a similar
collective were observed by the study from Janes et al. by also considering the
endpoints DM (HR 4.28, 95%CI:2.43 – 7.75) and PCSM (HR 6.11, 95%CI:2.93 –
14.33) [38].
3.3 GC in the Literature – GCs for outcome prediction in RT for postoperative
PCa
In total 10 studies (ART or SRT: n=7, SRT: n=3) with 9792 patients evaluated the
role of GC in the postoperative RT setting [10-15, 30-33]. Dalela et al. [11] proposed

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a nomogram for the prediction of clinical progression in the postoperative RT setting.
By including the Decipher score in the model a C-index of 0.85 was obtained. Lee et
al. observed a C-Index of 0.84 in an external validation of this model [10]. The group
by Den et al. evaluated the prognostic role of the Decipher score for DM prediction in
the postoperative setting and observed a HR of 1.61 (95%CI: 1.2 – 2.15) and of 0.78
(95%CI: 0.64 – 0.91) in multicentric and monocentric retrospective cohorts,
respectively [12, 13]. Both studies suggested that patients with low GC scores are
best treated with SRT, whereas those with high GC scores benefit from ART. Similar
results for DM prediction after postoperative RT were observed by other studies
incorporating PORTOS [31], the genomic expression of stromal infiltration markers
[30] and the clinical genomic risk [32]. Feng et al. showed a significant impact of the
Decipher score on DM (HR, 1.17 95%CI: 1.05 – 1.32) and PCSM (HR 1.39,
95%CI1.20 – 1.63) in the prospective RTOG 9601 trial cohort treated with SRT +/-
ADT [14]. Dal Pra et al. examined the prognostic impact of the Decipher score in the
SAKK09/10 study collective which was treated with SRT for recurrent PCa after
surgery and observed a HR of 2.21 (95%CI: 1.41 – 3.47) for BR [15].
3.4 GC in the Literature – GCs for outcome prediction in RT for oligometastatic
PCa
Deek et al. analyzed the impact of genetic features on outcomes in a pooled cohort
of the STOMP and ORIOLE trial [41]. Patients without a high-risk mutational status
experienced favorable progression-free survival rates (HR 0.57, 95%CI: 0.32 – 1.03).
The authors observed a potential larger benefit for MDT in patients with high-risk
mutations.
3.5 GC in the Literature – GCs for treatment decision making

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Eight studies evaluated the effect of the 22-gene Decipher score on postoperative
treatment decision making and showed that high GC risk scores were associated
with intensification of treatment in terms of admission of ADT, RT dose and
expansion of RT fields, independent from clinicopathological factors [16-23]. Five
studies assessed treatment recommendations before and after addition of GC score
information in patients treated with RP and adverse pathological features such as
pT3 stage and positive margins [16-20]. Post-Decipher recommendations changed in
up to 40% [20], the number needed to test for a change in recommendation varied
between 3 and 4 [16, 17]. Badani et al. showed similar results in a retrospective
cohort of low-, intermediate- and high-risk patients according to D’Amico risk
classification [21]. Furthermore, implementation of GC testing and its results in
clinical practice decreased cancer specific anxiety [16, 18] and decisional conflict
scores [18, 19]. Nguyen et al. assessed the treatment recommendations from 20 US
board certified urologists and 26 radiation oncologists with high rates of
recommendation change, identifying the GC risk score as the strongest influencing
factor [22]. Lobo et al. developed a Markov Model for decision of postoperative
treatment decision [23] and for cost effectiveness, which demonstrated improved cost
effectiveness and quality adjusted life years (QALYs) [24].
3.6 GC in the Literature – GCs in correlation with other biomarkers
In addition to the 22-gene Decipher score, the Genomics Resource for Intelligent
Discovery (GRID) database provides comprehensive transcriptomic profiles and thus
enables additional genomic studies. Ben-Salem et al. analyzed androgen receptor
(AR) target genes in the GRID database and validated their results in smaller cohorts
and could identify a baseline heterogeneity in AR action and found that specific up-
or downregulation of AR genes was associated with treatment response prediction

13
[34]. Two studies additionally assessed the immune content score (ICS) derived from
immune cell-specific genes [25, 35]. Yamoah et al. showed that patients with high
Decipher GC and ICS scores have a higher risk of DM and PCSM and are
associated with genes correlated to radiosensitivity [25]. Awashti et al. assessed
difference in immune specific genes between African-American (AAM) and
European-American (EAM) patients and identified that PCa of AAM patients exhibit
higher ICS scores, lower DNA damage repair and higher radiosensitivity [35].
3.7 Ongoing prospective clinical trials
In total nine studies were identified via “clinicaltrials.gov” incorporating GC and RT
treatment. Additionally, five studies were included based on the recommendations of
the authors of this work. Eight and one studies incorporate GCs in the primary and
salvage PCa setting, respectively. See Table 2 and supplementary material for
synthesis of ongoing clinical trials.
3.8 Survey
Thirty experts answered R2 of the modified DELPHI survey. Half of the participating
experts reported on using GCs in clinical practice, mostly in extensive metastatic
disease (30%) and postoperative settings (27%). See Figure 3 for details.
Please see Figure 4 and Table 3 for the detailed results of the expert survey
concerning the clinical and research setting, respectively. Considering primary PCa
patients, the majority (97%) of experts were convinced that GCs could be
implemented as a dedicated feature into PCa risk group stratification systems in the
future. Consensus was reached, that additional tools for risk stratifications are
needed across NCCN risk groups (low/favorable intermediate-risk: 83%; unfavorable
intermediate-risk: 100%; high-risk: 100%) and that GCs are likely to be useful tool in
this setting (low/favorable intermediate-risk: 83%; unfavorable intermediate-risk: 90%;

14
high-risk: 93%). Experts were convinced that GCs might be applied as a predictive
biomarker and to determine optimal treatments across various risk groups, including
administration and duration of ADT, intensification of systemic therapies or addition of
radiation to elective pelvic nodes.
Considering metastatic disease, 100% of experts agreed that additional tools for
improved risk stratification are needed and 76% believed that GCs might be a useful
tool in this scenario. Relevant scenarios identified by experts were administration of
MDT and the combination of MDT and systemic therapies.
Considering the postoperative setting, 97% of experts agreed that additional tools for
improved risk stratification are needed and 89% believed that GCs might be a useful
tool in this scenario. Relevant questions identified by experts were administration of
adjuvant vs early-salvage RT, administration and duration of ADT and additional
pelvic irradiation in salvage RT.
MFS was considered as the most appropriate endpoint to evaluate the role of GCs in
clinical studies for non-metastasized PCa. Consensus that GCs might be relevant in
translational research fields was reached, in strategies to cope with intertumoral
heterogeneity (between the primary and metastatic lesions), alteration in androgen
receptor signaling and decision making for physicians.
Discussion
This work incorporated a systematic review and a modified Delphi survey to assess
the role of GC in PCa RT and to define future directions. Despite the fact that 47% of
the participants of the survey do not use GC in clinical routine, the vast majority
agreed that GC should be incorporated in RT strategies in all PCa disease stages in
the future. Differences in clinical utilization of GC is explainable by regional
differences in the distribution of facilities capable to perform GC tests and

15
reimbursement issues. However, our work shows, that the potential clinical utility is of
GCs is expected to be relevant. In line with a previous systemic review and meta-
analysis [7], our current synthesis shows that the Decipher test is the most commonly
utilized GC in PCa patients and the highest level of evidence for the Decipher GC
exists in the setting of risk stratification, outcome prediction and treatment guidance
after RP [7].
Prognostic biomarkers are helpful tools to identify patients who are at high or low risk
of recurrence and thus are candidates for treatment intensification or de-
intensification. Predictive biomarkers are warranted in order to truly guide
personalized treatments. All studies demonstrate the prognostic value of the GCs,
but due to the methodological design of the studies, no clear conclusions regarding
the predictive value of GCs can be drawn. None of the studies assessed the
predictive capacity of a GC within dedicated treatment arm of a RCT. Additionally,
only two of the studies included external validation cohorts, which underlines the
need of more high quality studies. Therefore, the results of the expert consensus may
help to guide clinical decision making, the design of future clinical trials and
translational research. Fortunately, some of the presented clinical questions are
addressed by currently ongoing clinical trials. However, we want to mention, that
these studies are designed to evaluate the prognostic capability of GCs in these new
clinical scenarios, which might form the basis for future studies addressing the
predictive capabilities. Despite the absence of prospective and externally validated
studies addressing predictive values of GC in patients treated with definitive RT, we
could identify seven studies including in total 1551 patients in which GCs were
analyzed as a prognosticator of DM and BR after definitive RT [26, 27, 29, 37-39].
See Figure 5 for details. We did not include the study by Ngyuen et al. in Figure 4,
since no individual data on the prognostic value of GCs in the cohort of patients

16
treated only with RT are presented [28]. This moderate sample size contrasts the
larger number of GC analyses in patients treated with RP (n=9792), but
demonstrates similar results with the Decipher GC score being an independent
prognosticator after both treatments. Furthermore, additional information will be
provided by forthcoming studies, validating the Decipher GC in phase III studies,
such as the NRG/RTOG 9202, 9314, 9902, 0126 and STAMPEDE trials, which have
been presented at the ASTRO 2021, ASCO GU 2022 and ESMO 2022 annual
congresses [42-44]. Thus, performing GC tests on biopsy cores yields promising
results to predict PCa aggressiveness suggesting implementation in risk and
treatment stratification after definitive RT. Confirming these results, expert consensus
was reached that GCs are a promising tool to improve PCa risk stratification in the
primary PCa setting. However, we demonstrate a lack of data for RT, pointing out the
need for additional studies, including validation of GCs in patient cohorts staged with
state-of-the-art imaging, such as PSMA-PET and multiparametric magnetic
resonance imaging (MRI), and treated with modern radiation approaches, including
stereotactic body radiotherapy (SBRT) or brachytherapy (BT). Future studies should
clarify whether the applied biopsy method (MRI-fusion vs. MRI-guided vs. no
implementation of MRI information) influences the GC results.
Extrapolating the results of Tward et al. [39] Feng et al. [14] and Ben-Salem et al.
[34], GC scores might help to stratify patients that benefit the most from concomitant
ADT, define the optimal duration of ADT, as well as identify those that may benefit
from systemic treatment intensification in definitive RT settings. Consequently,
consensus was reached by experts, that GCs are promising tools to improve
recommendations for administration of systemic therapies in the primary setting
across all NCCN risk groups including duration of ADT and addition of new hormonal

17
agents, particularly in high-risk patients. Furthermore, alterations in androgen
receptor signaling were considered to be a relevant translational research field.
Additional predicative markers are urgently needed addressing this vague clinical
issue in order to guide personalized treatment decisions. Four ongoing prospective
trials will contribute to this field of research by stratifying patients according to
genomic risk groups into intensification or de-intensification of systemic treatments
and thus optimize therapies based on GCs.
Treatment intensification in the setting of definitive RT can also be achieved by dose
escalation in all primary PCa risk stages (delivered dose to the tumor >80-90 Gy,
EQD2 α/β=1.6 Gy). Dose escalation improves BR-free survival and can be performed
via BT [45] or focal boosting [46]. Kishan et al. analyzed the genomic heterogeneity
of patients with Grade group 4-5 who underwent prostatectomy within the GRID
database and could identify four distinct clusters, of which one was enriched with
genes related to cell cycle and proliferation and was associated with worse DM-free
survival [47]. Furthermore, GRID analysis revealed PCa subtypes with increased
genomic radiosensitivitiy [25, 35], therefore GC scores might be utilized to identify
patients with radioresistent or radiosensitive PCa and thus guide treatment decision
in terms of (focal) dose escalation and the optimal dose in the primary setting.
However, these aspects were not considered relevant by experts, with approximately
50% agreement that GCs might be useful to identify patients who benefit from focal
dose escalation due to increased radio-resistance. On the basis of a high
fractionation sensitivity of PCa [48], moderately hypofractionated RT (MHRT) [49, 50]
and ultra-hypofractionated RT or SBRT [51, 52] have been analyzed in randomized
controlled trials and were demonstrated to have comparable relapse rates to
conventionally fractionated RT. The consideration that genomic alterations might
influence the individual α/β-value [48] and that GCs might thus be used to predict

18
whether a specific fractionation scheme is beneficial, was not considered to be
relevant by experts (≤37% agreement across NCCN risk groups). Nevertheless,
future research might provide new insights into the heterogeneity of PCa and the
linkage between the genomic signatures and radiation- and fractionation sensitivity.
For example, Dal Pra et al. reported in a congress abstract that Patients with high
PORTOS score that received 70Gy SRT dose to the fossa had better 5-year clinical
progress-free survival (94% vs. 49%, p=0.006) compared to patients that received
the 64Gy dose [53].
GCs might help to improve risk stratification for treatment escalation or de-escalation
in terms of RT to pelvic lymphatics. Prophylactic whole pelvis radiation has recently
been shown to improve BR free survival at the cost of late genitourinary toxicities
[54], thus improved patient selection to prevent overtreatment is needed. Currently, a
Phase II study adapts RT fields based on GC and includes pelvic lymphatics only in
GC high-risk patients (NCT05169970). Consensus was reached that GCs might be a
useful predictive marker to identify high-risk PCa patients who benefit the most from
elective pelvic irradiation and alleviate this controversial discussion.
Incorporating recent improvements in diagnostics, in particular PSMA-PET, might
further facilitate treatment personalization [55, 56]. Since PSMA-PET was used in
none of the identified studies, there are many open questions to what extent GC and
advanced imaging methods give complementary or redundant information addressing
outcome prediction or decision management. Expert’s answers were inconsistent
with 45% agreeing that image features might be utilized to predict GC scores in R1
and 61% agreeing that GC might be useful to predict imaging results in R2. Only one
trial analyzed the ability of GC scores to predict PET-positive extraprostatic lesions
and found a significant association with pelvic nodal disease [57]. Interestingly,
Hectors et al. extracted imaging features from multiparametric magnetic resonance

19
tomography and developed a machine-learning model, which excellently predicted a
Decipher score of ≥0.60 (AUC=0.84). These promising results should be validated in
larger patient cohorts. Considering the possible capability to depict intratumoral
molecular characteristics on PSMA-PET [58, 59], this imaging method should be
included for future genomic-imaging correlations.
Combination of genomic signatures and PSMA-PET-based staging of tumor
localization could be of particular interest in oligometastatic and oligorecurrent
disease stages, enabling identification of patients who benefit from metastasis- [60,
61] or primary- directed therapies [62]. Stopsack et al. reported on specific genomic
features associated with poor survival in metastatic PCa, which might be used to
intensify systemic therapies or develop targeted therapies [63]. The pooled analysis
of the STOMP and ORIOLOE trial links outcome after MDT to mutational burden in
oligometastatic PCa patients, encouraging that further research will possibly add
value to define patients with “genomic” low- and high metastatic burden and guide
treatment in these stages. Consequently, consensus was reached that GCs might be
helpful as a predictive factor for progression-free survival/ prostate cancer specific
survival after MDT or systemic therapies or the combination of both in
oligometastatic, oligorecurrent or oligoprogressive patients. In contrary,
implementation of GC as a predictor for primary-directed therapies was not
considered relevant in this setting. Additionally, experts agreed that strategies to
cope with intertumoral heterogeneity between primary tumours and metastases
should be assessed in future.
A factor that should not be underestimated is decisional conflict and patient’s anxiety.
Luckily, patients with PCa have multiple treatment options in the primary,
postoperative and metastatic setting [64, 65]. Likely, the implementation of GCs as a
tool to guide treatment decision will not only be beneficial in patients aiming for RP

20
[16-22] but also for RT concepts, an opinion that is confirmed by the expert
consensus.
Further clinical trials are needed to tackle the scarcity of studies addressing the
predictive value of GC in RT and therefore accurate definition of study endpoints is
warranted. Experts reached consensus that the validated surrogate parameter MFS
is an appropriate endpoint across all NCCN risk groups. However, future research
will assess the role of MFS a surrogate end point in the era of molecular imaging [66].
We want to highlight, that most of the available prospective data on GCs still only
exists for the Decipher GC. The PORTOS signature complies with high
methodological standards since a statistical analysis of treatment interaction and an
external validation was performed in the study by Zhao et al. [31].
However, due to the lack of prospective data, external validation and its benefit on
long-term outcomes GCs are still not recommended for routine use [67].
Nevertheless, most of the presented studies included multicenter cohorts, partly from
RCTs, and potentially some of the ongoing studies will provide data to support
broader use of these assays to enable improved treatments for PCa patients.
We want to acknowledge the limitations of this work. Due to a lack in evidence for the
role of GC in the primary PCa RT setting, the discussion is mainly based on
extrapolation from data obtained from RP cohorts. However, we brought together an
internationally-recognized expert panel to optimize conclusions and to highlight future
directions in GC research for RT patients. Finally, it is important to mention that
implementation of GC in PCa is a fast-moving field and conclusions will need to be
iterated in light of rapidly evolving evidence. For example, several studies reported
their results in current congresses analyzing the role of GC in the context of primary-
definitive RT [43] or salvage RT [53] .

21
In summary, this work confirms the value of GCs and, in particular, of the Decipher
GC as a prognostic biomarker in patients undergoing RP and its predictive value for
postoperative RT. Additionally, we summarize the scarce, but promising evidence
that GCs might be equivalently useful in the setting of definitive RT. Nevertheless we
highlight that GCs currently do not comply with its great potential to function as
predictive markers and thus guide personalized treatment decisions. In this regard,
we await the highly anticipated prospective clinical trials, which will further inform the
role of GCs in the setting of RT and present an expert consensus, which can help to
design studies capable to validate GCs as predictive biomarkers and thus ultimately
guide personalized treatments. The authors want to emphasize that the development
and establishment of tumor biomarkers for PCa patients is complex. Thus, a
dedicated system for biomarker study design, conduct, analysis, and evaluation that
incorporates a hierarchal level of evidence should be applied. The presented expert
consensus might help to guide future research perspectives.

22
Data availability
The results of the systematic review are included in the manuscript. All data
generated during the DELPHI consensus are included in the manuscript and
supplementary material. The corresponding author is available for any further
questions on the data.

23
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Secondary Analysis of a Randomized Clinical Trial. JAMA Oncology 2021;7(4):555-563.
63. Stopsack KH, Nandakumar S, Wibmer AG, et al. Oncogenic Genomic Alterations, Clinical
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Prostate Cancer-2020 Update. Part 1: Screening, Diagnosis, and Local Treatment with Curative Intent.
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66. Dess RT, Jackson WC, Spratt DE. End Point Definitions and Surrogacy in Prostate Cancer: Will
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Cancer: ASCO Guideline. J Clin Oncol 2020;38(13):1474-1494.

27
Figure legend
Figure 1: Summarizing overview
Figure 2: PRISMA Flow diagram of the systematic database search and excluded
records. Abbreviations: GC=genomic classifier, RT=radiotherapy

28
Figure 3: Clinical applications of genomic classifiers in various prostate cancer (PCa)
stages.

29
Figure 4 Consensus answers: Bars show agreement on genomic classifiers (GC)
being a useful tool to improve risk stratification across national cancer
comprehensive network (NCCN) risk groups, recurrent and metastatic disease and in
postoperative settings or as a predictive factor for various parameters across risk
groups and disease stages. In this context “predictive factor” intends to represent the
ability of genomic classifiers to identify patients who might benefit from a certain
treatment. Abbreviations: PCa=prostate cancer, ADT= androgen deprivation therapy,
RT=radiotherapy

30
Figure 5: For each study assessing definitive radiotherapy, the hazard ratio (HR) and
95% confidence intervals on multivariate analysis of genomic classifiers predicting
distant metastases and biochemical recurrence is shown. For the Decipher score
(Studies by Ngyuen et al., Berlin et al. and Toisan et al.) HR per 0.1 unit increase is
shown. For the Oncotype DX Prostate Score (Study by Janes et al.) HR per 20 unit
increase is shown. For the Cell Cycle Progression score (Studies by Freedland and
Tward et al.) HR per 1 unit increase is shown.

31
Table legend
Table 1: List of included articles on GC in prostate cancer in the setting of
radiotherapy structured after prognostic and predictive oncological endpoints,
decision making and other biomarker studies. The second column includes endpoints
and the third column identifies whether patients were primarily treated with RP or RT.
Abbreviations:
CR=clinical recurrence, GC=genomic classifier, DM=distant metastases,
ART=adjuvant radiotherapy, BR=biochemical recurrence, RP= radical prostatectomy,
PCSM=prostate cancer specific mortality, OS=overall survival, SRT= salvage
radiotherapy, ADT=androgen deprivation therapy, RT = radiotherapy, AAM = Afro-
American Men, EAM=European American Men, MDT= metastases directed therapy,
PFS= progression free survival, PCa=prostate cancer, HR=Hazard Ratio, C-
Index=concordance index, TCGA=The Cancer Genome Atlas, PORTOS=Post

32
Operative Radiation Therapy Outcome Score, CAPRA=Cancer of the Prostate Risk
Assessment
Table 2: Summary of ongoing prospective clinical trials for prostate cancer RT
applying GCs.
Table 3: Expert consensus on endpoints to be addressed in clinical studies on GC
and translational research fields. Abbreviations: PCa=prostate cancer,
NCCN=national comprehensive cancer network, GC=genomic classifier,
MFS=metastases-free survival, PCSS=prostate cancer specific survival,
PFS=progression free survival

33
Table 1:
Studies analyzing oncological endpoints - Primary localized PCa
Author
Genomic
classifier /
function
Endpoint(s)
Treatment
Cohort Details:
Number of
patients (n)/study
design/validation
C-Index/Hazard
Ratio (HR) with
95% confidence
interval
(CI)/Pinteraction (if
analyzed)
Tosoian
et al.
[26]
Decipher /
prognostic
DM
Definitive RP
or definitive RT
+/- ADT
n=405
Multicentric
Retrospective
No dedicated
external validation
HR for DM = 1.33
(1.19 – 1.48)
Berlin et
al. [27]
Decipher /
prognostic
BR, DM
Definitive RT
n=121
Prospective
registry
Monocentric
No dedicated
external validation
HR for BR = 1.36
(1.09 – 1.71)
HR for DM = 2.05
(1.24 – 4.24)
Nguyen
et al.
[28]
Decipher /
prognostic
DM, PCSM
Definitive RP
or definitive RT
+/- ADT
n=235
Multicentric
Retrospective
No dedicated
external validation
HR for DM = 1.37
(1.06 – 1.78),
HR for PCSM =
1.57 (1.03 – 2.48)
Nguyen
et al.
[29]
Decipher /
prognostic
DM
Definitive RT +
ADT
n=100
Retrospective
Monocentric
No dedicated
external validation
HR for DM = 1.36
(1.04 – 1.83)
Freedla
nd et al.
[37]
Prolaris /
prognostic
BR
Definitive RT
+/- ADT
n=141
Retrospective
Monocentric
No dedicated
external validation
HR for BR:
Per one unit
change in score:
2.11 (1.05-4.25)
Janes et
al. [38]
Ocotype DX
/ prognostic
BR, DM,
PCSM
Definitive RT
+/- ADT
n=238
Retrospective
Multicentric
No dedicated
external validation
HR for BR:
Per 20-unit
increase: 3.62
(2.59-5.02)
HR for DM:
Per 20-unit
increase: 4.48
(2.75-7.38)
HR for PCSM:
Per 20-unit
increase: 5.36
(3.06-.9.76)
Tward
et al.
[39]
Clinical cell-
cycle risk
score
(CAPRA
score +
DM
Definitive
dose-
escalated RT
n=741
Retrospective
Multicentric
No dedicated
external validation
HR for DM = 2.22
(1.71 – 2.89)

34
Prolaris) /
prognostic
and
predictive
Studies analyzing oncological endpoints – Adjuvant RT or SRT after primary RP
First
author
Genomic
classifier /
function
Endpoint(s)
Treatment
Cohort Details:
Number of
patients (n)/study
design/validation
C-Index/Hazard
Ratio (HR) with
95% confidence
interval
(CI)/Pinteraction (if
analyzed)
Lee et
al. [10]
Decipher /
prognostic
CR,
External
validation of
a GC based
risk-
stratification
nomogram
ART or SRT
after RP
n=350
Monocentric
Retrospective
External validation
of the [11]
nomogram
C-index = 0.84
Mahal
et al.
[30]
Genomic
expression
of stromal
infiltration
markers /
prognostic
and
predictive
DM
ART or SRT
after RP
Three cohorts:
Prospective
Registry cohort
(n=5239)
retrospective
multicenter cohort
(n=1135)
TCGA cohort
(n=498)
No dedicated
external validation
HR for DM = 2.15
(1.25 – 3.7)
10 year MFS for
patient with high
stromal scores
24% (no ART) vs
68%, p=0.0015
(ART)
pinteraction = 0.02
Dalela
et al.
[11]
Decipher /
prognostic
and
predictive
CR
ART or SRT
after RP
n= 512
Multicentric
Retrospective
No dedicated
external validation
C-index:
Decipher = 0.71,
Decipher +
clinical model
0.85
HR for CR (GC
high vs low) =
2.93 (1.58 – 5.55)
Den et
al. [12]
Decipher /
prognostic
and
predictive
DM
ART or SRT
after RP
n=188
Bicentric
Retrospective
No dedicated
external validation
HR for clinical
metastasis. =
1.61 (1.2 – 2.15).
Patients with high
risk GC: ART vs
SRT HR = 0.2
(0.04 – 0.90)
Den et
al. [13]
Decipher /
prognostic
and
predictive
BR, DM
ART or SRT
after RP
n=143
Monocentric
Retrospective
No dedicated
external validation
HR for BR = 0.75
(0.67 – 0.94)
HR for DM = 0.78
(0.64 – 0.91)

35
Zhao et
al. [31]
PORTOS /
prognostic
and
predictive
DM
ART or SRT
after RP
n=196 matched
training cohort
n=330 pooled
matched validation
cohort
Multicentric
Retrospective
HR for DM after
RT in the high
PORTOS Group:
0.15 (0.04 - 0.6)
pinteraction = 0.016
Ross et
al. [32]
Clinical-
genomic
risk
(CAPRA
score +
Decipher) /
prognostic
DM
ART or SRT
after RP
n=422
Multicentric
Retrospective
No dedicated
external validation
HR for DM= 1.28
(1.08 – 1.52)
Studies analyzing oncological endpoints – Salvage RT after primary RP
First
author
Genomic
classifier /
function
Endpoint(s)
Treatment
Cohort Details:
Number of
patients (n)/study
design/validation
C-Index/Hazard
Ratio (HR) with
95% confidence
interval
(CI)/Pinteraction (if
analyzed)
Feng et
al. [14]
Decipher /
prognostic
DM, PCSM,
OS
SRT +- ADT
after RP
n=486
Multicentric
Prospective
No dedicated
external validation
HR for DM = 1.17
(1.05 – 1.32),
HR for PCSM =
1.39 (1.20 –
1.63),
HR for OS = 1.17
(1.06 – 1.29)
Koch et
al. [33]
Prolaris /
prognostic
BR
SRT after RP
n=47
Retrospective
Monocentric
No dedicated
external validation
Odds ratio for DM
or non-response
to SRT:
Per one unit
change in score:
10.4 (2.05-90.1)
Dal Pra
et al.
[15]
Decipher /
prognostic
BR, CR
SRT after RP
n=226
Cohort from RCT
No dedicated
external validation
HR for BR:
GC continuous =
1.14 (1.04 –
1.25);
GC categorical
high vs low-
intermediate =
2.21 (1.41 –
3.47). HR for CR:
GC categorical
(high vs low-
intermediate) =
2.29 (1.32 – 3.98)
Studies analyzing oncological endpoints – Metastasis-directed therapy in oligometastatic
PCa
Deek et
High-risk
PFS
MDT (RT or
n=70
HR for PFS:

36
al. [41]
mutational
signature (A
TM, BRCA1/
2, Rb1,
or TP53) /
prognostic
surgery) in
oligometastatic
castration
sensitive PCa
Cohort from two
prospective trials
No dedicated
external validation
low vs high
mutational
burden): 0.57 (
0.32 – 1.03)
MDT vs
observation in
patients with high
mutational
burden: 0.05
(0.01 – 0.28)
MDT vs
observation in
patients without
high mutational
burden: 0.42
(0.23 – 0.77)
Studies analyzing treatment decision making
First
author
Genomic
classifier
Endpoint
Primary
Treatment
Gore et
al. [16]
Decipher
Postoperative treatment decision
RP
Marasci
o et al.
[17]
Decipher
Postoperative treatment decision
RP
Gore et
al. [18]
Decipher
Postoperative treatment decision (ART or SRT)
RP
Michalo
poulos
et al.
[19]
Decipher
Postoperative treatment decision in high risk
patients
RP
Badani
et al.
[20]
Decipher
Postoperative treatment decision in high-risk
patients
RP
Badani
et al.
[21]
Decipher
Postoperative treatment decision
RP
Nguyen
et al.
[22]
Decipher
Postoperative treatment recommendations from 20
US board certificated urologist and 26 radiation
oncologist
RP
Lobo et
al. [23]
Decipher
Markov Model for decision of ART vs SRT after RP
RP
Lobo et
al. [24]
Decipher
Markov Model for cost effectiveness
RP
Biomarker studies
First
author
Genomic
classifier
Endpoint(s)
Ben-
Salem
et al.
[34]
Androgen
Receptor
gene
signatures
Androgen Receptor Activity in localized treatment naive PCa and
association with clinical risk factors, molecular markers and PCa
subtypes.

37
Yamoah
et al.
[25]
Decipher
Transcriptomic interactions between tumor immune content score (ICS)
and Decipher GC
Awasthi
et al.
[35]
Whole
transcripto
me data
from the
Decipher
GRID
registry
Differences of immune-specific genes between AAM and EAM PCa
tumor environment
Mahal
et al.
[36]
Decipher
Genomic
Resource
Information
Database
PCSM, all-cause mortality and genomic characterization of PCa
patients with low PSA and high grade PCa
Table 2:
Ongoing Trials
Trial number
Study type
Patient
characteristics
Applied GC
Treatment
decision based on
GC
NCT04513717
(NRG-GU009)
Parallel
Phase III,
randomized
NCCN high-risk
Decipher
Escalation or de-
escalation of
systemic therapy
NCT05100472
(SHORTER)
Phase II, non-
randomized
NCCN high-risk
Decipher
ADT de-escalation
NCT05050084
(NRG-GU10)
Parallel
Phase III,
randomized
NCCN
unfavorable
intermediate-
risk
Decipher
Escalation or de-
escalation of
systemic therapy
NCT04025372
(INTREPID)
Phase II,
randomized
NCCN
intermediate-
risk
Decipher
N/A (GC is required
and serves as
stratification
variable)
NCT05169970
Phase II, non-
randomized
NCCN
unfavorable
intermediate-
risk
Decipher
Inclusion of elective
pelvic lymphatics in
RT field
NCT02783950
(G-Minor)
Randomized,
parallel
assignment
RPE with pT3
or positive
margins
Decipher
Adjuvant treatment
decision (RT or
ADT)
NCT04984343
(FORT)
Phase II,
randomized
NCCN low- and
intermediate-
risk
Decipher
N/A (GC>0.6
serves as inclusion
criterion)
NCT04396808
Crossover
assignment,
NCCN low- and
intermediate-
Decipher,
Prolaris and
N/A (Impact of GC
on treatment

38
randomized
risk
Oncotype DX
decision)
NCT02723734
(VANDAAM)
Cohort
NCCN low- and
intermediate-
risk
Decipher
N/A (Impact of GC
on outcome
prediction)
NCT03495427
Cohort
NCCN low- and
intermediate-
risk
Decipher
N/A (concordance
between GC and
PSMA-PET)
(subgroup of
VANDAAM)
NCT03371719
(NRG-GU006)
Phase II,
randomized
SRT
PAM50 gene
expression
N/A (gene
expression
clustering)
NCT03770351
Cohort
NCCN low- and
intermediate-
risk
Decipher
ProstateNext
N/A (Impact of GC
on outcome
prediction
NCT03141671
Phase II,
randomized
SRT
Decipher
N/A (high risk
Decipher score as
inclusion criterion)
NCT04134260
Phase III,
randomized
SRT
Decipher
PAM50 gene
expression
N/A (Impact of GC
on outcome
prediction

39
Table 3:
Primary PCa -
NCCN
low/favorable
intermediate-risk
Which of the following endpoints do you consider
relevant to be addressed with GCs as predictors
for treatment
% of
answers
MFS
82.8%
Primary PCa - NCCN
unfavorable
intermediate-risk
Which of the following endpoints do you consider
relevant to be addressed with GCs as predictors
for treatment
MFS
96.6%
Time to distant metastases
82.1%
Primary PCa - NCCN high-
risk
Which of the following endpoints do you consider
relevant to be addressed with GCs as predictors
for treatment
MFS
96.6%
PCSS
79.3%
Time to distant metastases
93.1%
Oligometastastaticoligoprogres
sive or oligorecurrent disease
Which of the following endpoints do you consider
most relevant to be addressed with genomic
classifiers as predictors for treatment
management
PCSS
82.8%
Time to castration-resistance
75.9%
PFS
75.9%
Translationa
l research
fields
Which of the following translational research fields
do you consider as relevant to be addressed in
future research incorporating GCs?

40
Strategies to cope with intertumoral heterogeneity in
case GCs are obtained from biopsy cores in metastatic
disease (evaluation of intertumoral heterogeneity
between primary and metastases)
75.0%
Alteration in androgen signaling
75.0%
Decision making for physicians
85.7%