The causal integration ladder: a multilevel evidence framework for therapeutic target evaluation in cervical cancer

Cervical-cancer genomic studies nominate many altered genes, but the evidence needed to distinguish disease association from a causal, therapeutically tractable mechanism is rarely stated explicitly. We present the Causal Integration Ladder (CIL), an evidence-accounting framework that distinguishes Level I association, inherited Level II-G evidence, acquired Level II-S evidence, Level III computational and direct functional evidence, and Level IV translational validation, while treating viral etiology as an explicit context modifier. An exploratory TCGA-CESC screen (306 tumours, 3 normal samples) supplied 25 Level I candidates. HPV annotations were available for 291 primary tumours (280 positive, 9 negative, 2 indeterminate). Restriction to HPV-positive tumours preserved the direction of all 25 Level I effects; HPV-positive versus HPV-negative comparisons were exploratory because the negative group was small and histologically heterogeneous. Somatic analysis used 194 mutation-evaluable and 295 copy-number-evaluable tumours. Ten candidates showed false-discovery-rate-significant copy-number-expression associations, including CDKN2A, whereas recurrent protein-altering mutation was uncommon. Eight genes were additionally audited using public cis-eQTL, GWAS, dependency, pharmacogenomic, and cell-compartment resources. The revised CIL reports inherited, somatic, etiological, and functional evidence independently; absence of germline support is not interpreted as evidence against somatic, viral, or functional relevance. No observational result is presented as experimental validation, and Level III direct perturbation and Level IV translational claims remain prospective.