Predicting Pathway Dependency in Head and Neck Squamous Cell Carcinoma from Hallmark Transcriptomic Features

Despite frequent pathway activation, targeted molecular therapy remains limited in head and neck squamous cell carcinoma (HNSCC), highlighting the need to distinguish activation from dependence. Dependency is functional, context-specific, and coupled; mutations or expression alone cannot establish it. No validated framework identifies tumor pathway dependencies from baseline molecular profiles. We developed a transcriptome-based framework to infer pathway dependency in HNSCC. Nine Elastic Net models used 50 MSigDB Hallmark activity profiles from 953 GDSC cell lines and drug responses pooled across compounds annotated to each pathway. Cross-validated R2 ranged from 0.17 (IGF1R) to 0.48 (P53); leave-one-pathway-out validation retained rank agreement (Spearman ρ = 0.19–0.56) for pathways with no drug response observed during training. We validated the models using pharmacological, genomic, and proteomic data, then applied them to TCGA-HNSC (n = 565) and GSE65858 (n = 256), examining HPV status, age, stage, tobacco history, and CD8+ infiltration. Seven of nine pathways were higher in HPV+ tumors and replicated directionally in GSE65858. Among TP53-wild-type tumors, HPV remained linked to greater P53 dependency (β = +0.37, q = 0.010); HPV+ tumors also had higher p53 transcriptional activity, suggesting residual p53 function despite E6 expression. The codependency signature had a dominant global axis (PC1 = 62.3% variance; Cronbach's α = 0.91), replicated across cohorts (Tucker congruence = 0.996), but largely reflected CD8+ T-cell infiltration (ρ = 0.80). The framework uses RNA-sequenced bulk tumor cohorts to assess baseline transcriptome-based dependencies and match pathway-directed agents to likely responders before treatment. It is not prognostic.