A blueprint for mutation-defined hallmark vulnerabilities across human cancers

Hallmark gene mutations shape cancer cell vulnerabilities and inform drug discovery1–3. A systematic map of hallmark gene mutation-defined cancer dependencies and therapeutic responses is essential to uncover novel targets and refine therapeutic strategies. Here, we present the first pan-cancer blueprint of hallmark vulnerabilities, systematically linking hallmark gene mutation markers to cancer cell dependencies and drug sensitivities across 22 cancer cohorts. We integrated multi-omics data from patient tumours with large-scale CRISPR-Cas9 screens and pharmacologic profiling of over a thousand cancer cell lines. Our analysis revealed the cancer type-specific nature of hallmark gene expression programs, uncovered previously unrecognised mutation-target gene dependencies, and highlighted metabolic programs as a dominant class of functional vulnerabilities. Notably, we identified oxidative phosphorylation (OXPHOS) addiction in CDKN2A-loss lung squamous cell carcinoma (LUSC) and experimentally validated this dependency. Our validation highlights the greater selectivity of CDKN2A-loss LUSC cells to metformin, an FDA-approved antidiabetic drug known for its OXPHOS inhibitory activity. Proteogenomic integration further prioritised targets overexpressed in mutant tumours, constituting therapeutic windows. Pharmacologic profiling identified both oncology and non-oncology agents with selective activity in mutation-defined subgroups, revealing opportunities for drug repurposing. Our machine learning framework, Comet-X, for the first time fully leveraged gene mutation combinations to predict these target dependencies and drug responses. The resulting pan-cancer mutation-dependency map provides a comprehensive resource of hallmark gene targets and candidate therapeutics, stratified by mutation markers, to pave the way for drug development, clinical trial design and discovery research.