PKIDB-informed molecular profiling improves reproducible prediction of cancer kinase-inhibitor response.

BACKGROUND

Protein kinase inhibitors are central drugs in precision oncology, but cell-line response to these agents is affected by lineage and molecular context rather than target identity alone. 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.

METHODS

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.

RESULTS

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. Continuous-response validation in the same GDSC1 test set produced Spearman correlations of 0.453 for AUC and 0.457 for LN_IC50, with continuous-derived sensitivity ROC AUCs of 0.703 and 0.710. Molecular summaries improved external ROC AUC over drug-lineage context by 0.052 for AUC sensitivity and 0.046 for LN_IC50 sensitivity. ERK/MAPK signalling showed the strongest pathway-level signal, with ROC AUC 0.825 for AUC sensitivity and 0.780 for LN_IC50 sensitivity. PRISM cross-platform validation was more modest (best ROC AUC 0.597), consistent with assay and drug-overlap differences.

CONCLUSION

PKIDB-informed molecular profiling improves externally reproducible kinase-inhibitor response modelling across public cancer cell-line screens, especially for ERK/MAPK inhibitors. These findings support a pharmacogenomic modelling and validation study rather than direct patient-level or experimental validation.