RT Journal Article
T1 A tissue-aware computational framework for confounding-controlledco-expression network analysis: Context-dependent utility in drug sensitivitymodelling
A1 Rios Cadenas, Marc
A1 Segura Carmona, Iván
A1 López Fernández, Aurelio
A1 Gómez-Vela, Francisco Antonio
K1 Co-expression networks
K1 Pharmacogenomics
K1 Drug sensitivity
K1 Pan-cancer
K1 Confounder control
K1 WGCNA
AB 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 -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 (,  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 ), 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.
PB Elsevier
YR 2026
FD 2026-07-11
LK https://hdl.handle.net/10433/27276
UL https://hdl.handle.net/10433/27276
LA en
NO Marc Ríos-Cadenas, Iván Segura-Carmona, Aurelio López-Fernández, Francisco A. Gómez-Vela, A tissue-aware computational framework for confounding-controlled co-expression network analysis: Context-dependent utility in drug sensitivity modelling, Computational Biology and Chemistry, Volume 124, Part 2, 2026, 109235, ISSN 1476-9271, https://doi.org/10.1016/j.compbiolchem.2026.109235.
NO Universidad Pablo de Olavide de Sevilla, Departamento de deporte e informática
DS RIO
RD Aug 5, 2026