Frontiers in pharmacology · 2026
PKIDB-informed molecular profiling improves reproducible prediction of cancer kinase-inhibitor response.
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Liu Miao, Song Jiayang, Liu Xianbin, Zhao Linlin, Xiong Yu
Аннотация
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.
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.