A novel multiomics machine learning signature identifies rapid progression in clinically low risk prostate cancer.
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Rafeletou A, Fathi F, Kiseļova T, Taheri G, Lundberg A
NPJ Digit Med 9 (1) -
[2026-09-14; online 2026-09-14]
Risk stratification in primary prostate cancer remains heavily reliant on clinicopathological criteria that frequently miss the heterogeneity underlying early aggressive disease. We present a novel machine learning non-linear prognostic framework encoding somatic copy-number alterations and biological information associated with gene products, along with an integrative multi-omics approach including epigenomics and transcriptomics into a patient-specific biological network. Applied to the TCGA-PRAD (n = 498), our weighted graph-based feature selection and LASSO-Cox model identified ZNF268 as a master regulator gene, in which the hypermethylation of its promoter region is linked to a distinct oncogenic transition exclusive to Low/Intermediate-risk disease. Post-hoc analysis of Low-ZNF268 tumors showed a distinct somatic landscape enriched for driver mutations and predicted sensitivity to MAPK, ATR, and PI3K/mTOR inhibitors, providing potential therapeutic vulnerabilities alongside the prognostic signal. Topological network analysis further revealed that ZNF268 loss impacts a co-expression rewiring gene network, quantified as a Rewiring Score: associated with Progression-Free Survival in the TCGA-PRAD (HR: 2.79, 95% CI: 1.36-5.71, p = 0.0049) and Biochemical Recurrence in two external cohorts. By capturing tumors at an active molecular transition state preceding systemic progression, this framework offers a prognostic tool to identify biologically aggressive prostate cancer disease within patients currently undertreated by standard risk criteria.
PubMed 42736338
DOI 10.1038/s41746-026-03254-5
Crossref 10.1038/s41746-026-03254-5
pii: 10.1038/s41746-026-03254-5
pmc: PMC13575102