Evidence map›Paper›PMID 42251787›Full record

ReviewCancer genetics2026

A decision-oriented framework for genomic testing across the prostate cancer continuum.

Ewan K Cobran, N Jewel Samadder, Daniel J Schaid, David V Conti, Christopher Haiman, Luciana Vargas, Michael R Gionfriddo, Jon C Tilburt

Abstract readReview
In one paragraph

Review in Cancer genetics, 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

8 authors.

Ewan K CobranDepartment of Quantitative Health Science, Mayo Clinic College of Medicine and Science, Scottsdale, Arizona, USA. Electronic address: cobran.ewan@mayo.edu.
N Jewel SamadderDivision of Gastroenterology and Hepatology, Mayo Clinic College of Medicine and Science, Phoenix, Arizona, USA.
Daniel J SchaidDepartment of Quantitative Health Science, Mayo Clinic College of Medicine and Science, Rochester, Minnesota, USA.
David V ContiCenter for Genetic Epidemiology, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, California, USA.
Christopher HaimanCenter for Genetic Epidemiology, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, California, USA.
Luciana VargasDepartment of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA.
Michael R GionfriddoKnowledge and Evaluation Research Unit, Mayo Clinic College of Medicine and Science, Rochester, Minnesota, USA.
Jon C TilburtDepartment of Internal Medicine, Mayo Clinic College of Medicine and Science, Phoenix, Arizona, USA.

Funding

Genetic Literacy and Patient-Caregiver Communication of Prognostic Genetic Technology for Localized Prostate CancerK01CA230193 · NCI · UNIVERSITY OF GEORGIA · PI COBRAN, EWAN KEMAR · 2020 to 2024
$849k
NCI NIH HHS K01 CA230193
6 · The paper itself

Abstract

Genomic testing is now embedded in contemporary prostate cancer care, yet the clinical meaning of different genomic platforms varies substantially by disease state and clinical context. In localized disease, tissue-based genomic classifiers primarily serve prognostic functions by refining risk estimates beyond clinicopathologic variables, whereas in advanced disease, germline and somatic testing identify predictive biomarkers linked to therapy selection. This distinction is clinically consequential because the supporting evidence, endpoints, and implementation challenges differ across assays and across points on the disease continuum. In this review, we position tissue-based assays, germline testing, somatic sequencing, circulating tumor DNA (ctDNA), and artificial intelligence-enabled biomarkers within a unified clinical framework spanning localized disease, biochemical recurrence, and metastatic progression. We critically compare commercially available genomic assays with respect to methodology, specimen type, intended use, validation cohorts, and clinically relevant outcomes. We distinguish prognostic classifiers from predictive biomarkers such as homologous recombination repair deficiency and mismatch repair deficiency, and we evaluate emerging approaches, including liquid biopsy, multimodal integration with imaging, and digital pathology-based algorithms. We further address implementation barriers that may limit real-world impact, including reimbursement uncertainty, disparities in access to next-generation sequencing, limited provider familiarity with genomic interpretation, and the need for patient-centered communication and navigation in genomics-informed care. A clinically useful framework for prostate cancer genomics must therefore move beyond cataloging tests and instead clarify when genomic results change management, where evidence remains immature, and how implementation strategies can improve equity and actionability.

Indexed as

Biomarkers, TumorGenetic TestingGenomicsProstatic NeoplasmsArtificial IntelligenceHumansMalePrognosisBiomarkers, TumorArtificial intelligenceBiomarkers, tumorGenetic testingPatient navigationProstatic neoplasms

Identifiers

PMID42251787
PMCPMC13425411

What OpenQuestion holds

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Registered trials

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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.