A tissue-aware computational framework for confounding-controlled co-expression network analysis: Context-dependent...

Pan-cancer pharmacogenomic modelling using co-expression networks faces a fundamental challenge: topological metrics predominantly encode tissue identity rather than genuine pharmacological signal, inflating apparent predictive performance and undermining biological interpretability. We introduce a tissue-aware computational framework integrating tissue-specific WGCNA construction, within-tissue edge disruption profiling, and per-gene z-score standardisation, with tissue prediction accuracy as a quantitative acceptance criterion for confounder control. The framework was applied to 660 cancer cell lines across three targeted therapies (osimertinib, crizotinib, KRAS G12C Inhibitor-12) and evaluated under repeated stratified cross-validation with FDR correction. Within-tissue standardisation reduced tissue identity encoding in topological features from 88.9% to 12.9%. Within-tissue WGCNA modules outperformed a confounder-free PCA baseline across all three drugs, confirming genuine co-regulatory structure beyond dimensionality reduction. Osimertinib met the pre-specified primary improvement criterion (Δρ=+0.063, p_adj<0.001 versus tissue-residualised expression), while crizotinib and KRAS G12C Inhibitor-12 showed no meaningful improvement, establishing that the predictive benefit of co-expression topology is highly drug-context-dependent. A proof-of-concept feasibility check in an independent cohort (GSE255958) showed that baseline expression features discriminated drug-tolerant persister cells from responders in the EGFR context (AUC =0.758), though the small sample size precludes strong inferential claims and the network component could not be evaluated externally. This work establishes a reproducible computational standard for confounder-controlled co-expression network analysis in pan-cancer pharmacogenomics, with direct applicability to any CCLE- or GDSC-scale study.