Transcriptome-Informed Digital Twins Reveal a Functional Response Landscape in Prostate Cancer

Prostate cancer (PCa) shows substantial inter-sample molecular heterogeneity, yet static molecular profiles provide limited information about how individual tumours respond to changes in signalling activity. We developed a transcriptome-informed digital-twin framework that translates tumour-specific molecular states into simulated functional responses. RNA-sequencing data from 492 primary TCGA-PRAD tumour samples were scored across 1628 Reactome pathways. Of these, 220 unique pathways (232 pathway-to-node assignments) were selected through biologically informed mapping and aggregated into eight functional nodes used to personalise individual digital twins (DTs). Each DT was simulated under six conditions: baseline and five perturbations involving AR (androgen receptor) suppression, PI3K/AKT suppression, TGF-β induction, combined AR and PI3K/AKT suppression, and combined AR suppression with TGF-β induction. These simulations produced a 42-feature behavioural fingerprint for each tumour. PCA identified a continuous functional response space, with the first three components accounting for 80.75% of behavioural variance. The dominant response axis was associated with Gleason score (Spearman ρ=0.30, FDR-adjusted p<0.001), while TP53 mutation status was associated with PC1 and PC3 after multiple-testing correction. Responses to individual perturbations varied between tumour samples and were coordinated across pathway interventions. Combined perturbations were largely additive, with relative deviations below 3% across the principal functional outputs. The dominant simulation-derived axis closely corresponded to the major static transcriptomic axis, indicating that the model primarily re-expressed, rather than replaced, the underlying molecular structure. The major variance structure was preserved across personalisation strengths, whereas discrete HDBSCAN solutions depended on the parameter setting. Ablation analysis showed that transcriptome-informed parameter personalisation was necessary to preserve inter-sample differences in baseline steady states. Overall, the results describe PCa heterogeneity as a continuous functional response space and position transcriptome-informed DTs as a mechanistic re-expression of molecular profiles that enables explicit, hypothesis-generating counterfactual perturbation analyses.