A Multi-Evidence Framework Rescues Low-Power Prognostic Signals and Rejects Statistical Artifacts in Cancer Genomics

TL;DR

This paper introduces a multi-evidence computational framework that improves detection of true cancer prognostic signals in underpowered genomic studies by integrating causal inference and biological validation, reducing false positives and negatives.

Contribution

The authors present a novel five-criteria framework combining causal inference methods with biological validation to better identify true signals in underpowered cancer genomics data.

Findings

Standard GWAS fails in underpowered cohorts, detecting no significant genes.

The framework correctly identified false positives like RYR2.

It successfully prioritized candidate genes like KMT2C despite low statistical power.

Abstract

Motivation: Standard genome-wide association studies in cancer genomics rely on statistical significance with multiple testing correction, but systematically fail in underpowered cohorts. In TCGA breast cancer (n=967, 133 deaths), low event rates (13.8%) create severe power limitations, producing false negatives for known drivers and false positives for large passenger genes. Results: We developed a five-criteria computational framework integrating causal inference (inverse probability weighting, doubly robust estimation) with orthogonal biological validation (expression, mutation patterns, literature evidence). Applied to TCGA-BRCA mortality analysis, standard Cox+FDR detected zero genes at FDR<0.05, confirming complete failure in underpowered settings. Our framework correctly identified RYR2 -- a cardiac gene with no cancer function -- as a false positive despite nominal significance (p=0.024), while identifying KMT2C as a complex candidate requiring validation despite marginal significance (p=0.047, q=0.954). Power analysis revealed median power of 15.1% across genes, with KMT2C achieving only 29.8% power (HR=1.55), explaining borderline statistical significance despite strong biological evidence. The framework distinguished true signals from artifacts through mutation pattern analysis: RYR2 showed 29.8% silent mutations (passenger signature) with no hotspots, while KMT2C showed 6.7% silent mutations with 31.4% truncating variants (driver signature). This multi-evidence approach provides a template for analyzing underpowered cohorts, prioritizing biological interpretability over purely statistical significance. Availability: All code and analysis pipelines available at github.com/akarlaraytu/causal-inference-for-cancer-genomics