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

2025-10-21

AI Summary

In cancer genomics, low-statistical-power cohorts often yield false-negative driver genes and false-positive passenger genes due to stringent multiple-testing correction. To address this, we propose a multi-evidence integration framework that transcends conventional p-value–driven approaches by jointly leveraging causal inference techniques—specifically inverse probability weighting and doubly robust estimation—and multidimensional biological evidence, including mutational signatures, expression perturbations, and literature-supported functional annotations. We formalize these components into a five-criteria decision model. Our framework markedly improves robustness and specificity in detecting weak oncogenic signals. Applied to TCGA breast cancer data, it successfully identifies *KMT2C* as a candidate driver gene while excluding well-known false positives such as *RYR2*. The method is both interpretable—enabling transparent evidence tracing—and scalable to diverse genomic data types. It establishes a novel paradigm for driver event discovery in small-sample cancer cohorts.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

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

Problem

• Rescues low-power prognostic signals in cancer genomics studies
• Distinguishes true biological drivers from statistical artifacts
• Addresses false negatives and false positives in underpowered cohorts

Innovation

• Integrates causal inference with biological validation
• Distinguishes true signals from artifacts via mutation patterns
• Prioritizes biological interpretability over statistical significance