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From pathways to prediction: a comparative machine learning framework for prostate cancer survival.

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A new genomic risk score for prostate cancer improves prognostic accuracy by analyzing pathway-level alterations. This tool helps stratify patients, offering better predictions than traditional methods.

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Area of Science:

  • Genomic analysis of cancer signaling pathways.
  • Prognostic biomarker development in oncology.

Background:

  • Prostate cancer exhibits significant heterogeneity, challenging conventional prognostic models.
  • Single-gene mutations and tumor mutational burden offer limited predictive power.

Purpose of the Study:

  • To develop and validate a pathway-based genomic risk score for prostate cancer.
  • To improve prognostic accuracy by capturing cumulative oncogenic disruption.

Main Methods:

  • Analysis of somatic mutations mapped to 11 cancer-related pathways in 2231 prostate adenocarcinoma patients.
  • Development of a composite risk score integrating pathway burden, p53 status, and co-alterations.
  • Evaluation using survival analysis, Cox regression, ROC curves, and machine learning, validated in an external cohort.

Main Results:

  • The pathway-based risk score significantly stratified patients by overall survival (P < .0001).
  • Each one-point increase in the score correlated with a 31% higher mortality risk (HR 1.31).
  • The model demonstrated moderate discrimination (C-index 0.5897) and outperformed tumor mutational burden alone.

Conclusions:

  • A mutation-based genomic risk score provides interpretable prognostic stratification for prostate cancer.
  • Pathway-level genomic analysis offers a more robust approach than single-gene or tumor mutational burden metrics.
  • The developed framework shows performance comparable to machine-learning models for risk stratification.