Confounder-adjusted analysis of statin selectivity across cancer cell lines identifies a colorectal-enriched SLC45A4 signal

Large-scale pharmacogenomic screens enable systematic searches for genetic determinants of drug sensitivity, but cross–cell-line analyses are confounded by lineage structure and drugfree proliferation rates. We integrate DepMap genome-scale CRISPR dependency profiles with PRISM viability responses and estimate covariate-adjusted associations between gene dependency and a statin selectivity phenotype using cross-fitted double machine learning (DML). The analysis recovers mevalonate-pathway regulators (MVK, UBIAD1) and identifies SLC45A4 as a colorectal-enriched candidate association. To assess robustness, we validate findings in independent AUC datasets, conduct leave-one-drug-out sensitivity analyses, and benchmark against empirical-null drug-bundle and gene-level negative controls. In colorectal lines, the SLC45A4 signal aligns with sterol-biosynthesis-related co-dependency and colorectal-specific shifts in polyunsaturated cholesteryl esters and putrescine in CCLE metabolomics. As positive controls, expected EGFR- and BRAF-matched drug responses were reproduced in independent datasets. These results reduce concern that the SLC45A4 association is explained solely by cross-dataset integration bias and support further experimental testing.