ArticleBriefings in bioinformatics2026
Cross-cohort projection of clinically anchored latent risk enables multi-omics interpretation without refitting.
Article in Briefings in bioinformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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6 authors.
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Abstract
Linking clinically derived risk signals to reproducible molecular states across independent cohorts remains a major challenge in translational bioinformatics. Existing approaches often rely on cohort-specific model fitting, limiting cross-dataset comparability and downstream biological interpretation. We developed a cross-cohort projection framework that maps baseline clinical variables to a clinically anchored latent risk coordinate, $\mu$, enabling application across external datasets without refitting. The fixed projector was trained in a local imaging cohort and applied unchanged to independent cohorts. Projected $\mu$ was evaluated across multiple molecular layers, including bulk transcriptomics, single-cell-guided deconvolution, spatial transcriptomics, and circulating cell-free DNA (cfDNA). In an independent external cohort, projected $\mu$ preserved separation of time to castration resistance across predefined strata (P = .002), with 30-month risk increasing from 0.13 to 0.86 across ordered $\mu$ bins. In bulk transcriptomics, higher projected $\mu$ was associated with increased proliferation-related signaling and reduced androgen receptor/lineage programs ($\rho$ = 0.40 and -0.26; both P < .001). Deconvolution analyses linked higher projected $\mu$ to reduced AR-high epithelial cell fractions ($\rho$ = -0.17, P = .001). Spatial transcriptomics demonstrated organized tissue-level structure of prespecified molecular programs. In cfDNA, higher projected $\mu$ was associated with a more negative RB1 copy-number signal in the detectable subset ($\rho$ = -0.49, P = .0278). This study presents a projection-based framework for cross-cohort translation of clinically anchored latent risk into interpretable multi-omics context. By enabling reuse of a fixed coordinate without refitting, the approach provides a practical strategy for linking clinical risk to molecular programs and blood-based readouts across datasets.
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