Data Sheet 1_PKIDB-informed molecular profiling improves reproducible prediction of cancer kinase-inhibitor response.docx

Many drug-response models report optimistic internal performance; stricter evaluation requires held-out drugs, independent screens and cross-platform validation. This study evaluates a focused, auditable pharmacogenomic modelling framework for kinase-inhibitor response prediction.</p>Methods<p>Protein kinase inhibitors were curated from PKIDB and matched to GDSC release 8.5 response data and DepMap 26Q1 molecular profiles.

The full matched screens contained GDSC2 (40,403 drug-cell line pairs; 42 drugs; 945 models), GDSC1 (40,900 pairs; 48 drugs; 945 models) and PRISM secondary screen data (7,090 pairs; 23 drugs; 336 models). GDSC2-to-GDSC1 external testing was restricted to compounds shared by both GDSC releases (23,340 GDSC2 training pairs and 22,570 GDSC1 test pairs; 25 drugs). Models were evaluated using cell-line cross-validation, drug cross-validation, independent-screen validation, continuous-response prediction and pathway/drug subgroup analysis.</p>Results<p>In the GDSC1 shared-drug external set, molecular-summary models achieved ROC AUC 0.706 for GDSC AUC-defined sensitivity and 0.692 for LN_IC50-defined sensitivity.