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Related Experiment Video

Updated: Jun 9, 2026

Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer
13:19

Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer

Published on: November 2, 2013

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

Ewan K Cobran1, N Jewel Samadder2, Daniel J Schaid3

  • 1Department of Quantitative Health Science, Mayo Clinic College of Medicine and Science, Scottsdale, Arizona, USA.

Cancer Genetics
|June 7, 2026
PubMed
Summary

Genomic testing in prostate cancer care has prognostic and predictive roles that differ by disease stage. Understanding these distinctions and implementation barriers is crucial for effective, equitable genomics-informed treatment.

Keywords:
Artificial intelligenceBiomarkers, tumorGenetic testingPatient navigationProstatic neoplasms

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Area of Science:

  • Oncology
  • Genetics
  • Medical Informatics

Background:

  • Genomic testing is integral to prostate cancer management, but its application varies significantly with disease state and clinical context.
  • Tissue-based genomic classifiers are mainly prognostic in localized disease, while germline and somatic testing identify predictive biomarkers for therapy selection in advanced disease.

Purpose of the Study:

  • To present a unified clinical framework for understanding diverse genomic platforms in prostate cancer, from localized disease to metastatic progression.
  • To critically compare available genomic assays and evaluate emerging technologies like liquid biopsy and AI-enabled biomarkers.
  • To address implementation challenges hindering the real-world impact and equitable access of genomic testing.

Main Methods:

  • Review and synthesis of current literature on genomic testing in prostate cancer.
  • Comparative analysis of tissue-based assays, germline/somatic sequencing, circulating tumor DNA (ctDNA), and AI biomarkers.
  • Evaluation of prognostic classifiers versus predictive biomarkers (e.g., HRD, MMR deficiency).

Main Results:

  • Genomic classifiers refine risk in localized prostate cancer; predictive biomarkers guide therapy in advanced stages.
  • Emerging approaches include liquid biopsies, multimodal imaging integration, and digital pathology algorithms.
  • Key implementation barriers include reimbursement, access disparities, provider education, and patient navigation.

Conclusions:

  • A clinically actionable framework for prostate cancer genomics must clarify when results change management and where evidence is still developing.
  • Addressing implementation barriers is essential to improve equity and the real-world utility of genomic information in patient care.
  • Future strategies should focus on improving actionability and equitable access to genomics-informed prostate cancer care.