Evidence map›Paper›PMID 42456085›Full record

ArticleJCO precision oncology2026

Genomic Classification to Predict Survival in Metastatic Prostate Cancer: Development of Somatic Tumor Risk Assessment for Overall Survival-Prostate.

Martin W Schoen, Jiannong Li, Sihang Zeng, Heena Desai, Ryan Hausler, Candace L Haroldsen, Lukas Owens, Luca F Valle, Ruth B Etzoni, Timothy R Rebbeck and 8 more

Abstract read
In one paragraph

Article in JCO precision oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

18 authors.

Martin W SchoenSaint Louis University School of Medicine, St Louis, MO.ORCID 0000-0001-6388-5553
Jiannong LiDepartment of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center, Tampa, FL.
Sihang ZengDepartment of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA.ORCID 0009-0003-2921-829X
Heena DesaiMedical Oncology Service, Corporal Michael Crescenz VA Medical Center, Philadelphia, PA.
Ryan HauslerMedical Oncology Service, Corporal Michael Crescenz VA Medical Center, Philadelphia, PA.
Candace L HaroldsenUniversity of Utah, Salt Lake City, UT.
Lukas OwensDepartment of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA.ORCID 0000-0002-5149-5379
Luca F ValleDepartment of Radiation Oncology, David Geffen School of Medicine at the University of California Los Angeles, Los Angeles, CA.ORCID 0000-0002-5781-4174
Ruth B EtzoniDepartment of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA.ORCID 0000-0002-9164-6370
Timothy R RebbeckDepartment of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA.ORCID 0000-0002-4799-1900
Brent S RoseVeterans Affairs San Diego Healthcare System, San Diego, CA.
Michael J KelleyDepartment of Veteran Affairs, National Oncology Program, Washington, DC.ORCID 0000-0001-9523-6080
R Bruce MontgomeryFred Hutchison Cancer Institute, Seattle, WA.ORCID 0000-0003-4459-0295
Nicholas G NickolsDepartment of Radiation Oncology, David Geffen School of Medicine at the University of California Los Angeles, Los Angeles, CA.
Matthew B RettigUCLA Jonsson Comprehensive Cancer Center, Los Angeles, CA.
Kosj YamoahDepartment of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center, Tampa, FL.ORCID 0000-0001-9055-3538
Kara N MaxwellMedical Oncology Service, Corporal Michael Crescenz VA Medical Center, Philadelphia, PA.ORCID 0000-0001-8192-4202
Isla P GarrawayUCLA Jonsson Comprehensive Cancer Center, Los Angeles, CA.ORCID 0000-0002-1129-4636

Funding

TRANSLATIONAL RESEARCHP30CA076292 · NCI · UNIVERSITY OF SOUTH FLORIDA · PI John L. Cleveland · 1998 to 2026
$93.5M
CSRD VA I01 CX002946NCI NIH HHS P30 CA076292
6 · The paper itself

Abstract

purposeTumor comprehensive genomic profiling (CGP) has revolutionized cancer care and identifies patients for biomarker-specific therapy. In metastatic hormone-sensitive prostate cancer (mHSPC), although individual genes are prognostic, no comprehensive genomic classification exists using CGP that accounts for combinations of alterations to inform prognosis. We developed a DNA-based CGP classification that is prognostic for overall survival (OS) and could inform treatment.

methodsThis was a retrospective cross-sectional study using multivariable models to develop a clinicogenomic prognostic risk classification in US veterans with synchronous mHSPC. The primary outcome was OS from time of metastasis.

resultsA total of 7,201 veterans with metastatic prostate cancer and CGP were identified. There were 2,484 veterans (median [IQR] age, 72 [67-77] years) with synchronous mHSPC and tissue CGP, which were divided into training and testing data sets. Sixteen genes associated with survival were identified, and favorable, intermediate, and unfavorable genomic prognostication groups were created based on the mortality risk to generate the Somatic Tumor Risk Assessment for OS-Prostate (STRATOS-P) classification. In a multivariable model, classification into intermediate and unfavorable groups was associated with increased mortality relative to the favorable group (adjusted hazard ratio [aHR], 1.54 [95% CI, 1.33 to 1.78]; aHR, 2.37 [95% CI, 1.97 to 2.485], respectively), demonstrating an average AUC of 0.83. In an external, nonveterans validation cohort, intermediate and unfavorable classifications were associated with increased mortality (aHR, 2.45 [95% CI, 1.87 to 3.21]; aHR, 4.37 [95% CI, 3.06 to 6.22], respectively) with an AUC of 0.79. The intermediate and unfavorable genomic prognostication groups were also associated with increased mortality across multiple disease states including synchronous and metachronous diagnoses, castration resistance, and analyte type.

conclusionIn metastatic prostate cancer, tumor DNA genomic alterations are prognostic for OS. The STRATOS-P classification is a validated prognostic tool that has the potential to guide decision making in mHSPC.

Indexed as

GenomicsProstatic NeoplasmsAgedCross-Sectional StudiesHumansMaleNeoplasm MetastasisPrognosisRetrospective StudiesRisk Assessment

Identifiers

PMID42456085
PMCPMC13378758

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.