Systematic identification of pan-cancer single-gene expression biomarkers in drug high-throughput screens

Abstract

Precision oncology relies on molecular biomarkers to stratify patients into responders and non-responders to a given treatment. Although gene expression profiles have historically been explored for biomarker discovery, fewer studies investigated single-gene expression biomarkers. Additionally, many approaches are limited to cancer type-specific associations, which constrain statistical power. To address these limitations, we developed a regression-based framework that corrects for tissue-specific biases and enhances detection of pan-cancer single-gene expression biomarkers of drug sensitivity in cancer cell line high-throughput drug screens. Our method maintains predictive performance post-correction, and successfully recovers established biomarkers, such as SLFN11 expression for DNA damaging agents. Notably, we identified SPRY4 and NES expression as biomarkers of sensitivity for compounds targeting ERK/MAPK signaling (adjusted p-value = 4.016 × 10 - 5 and 7.221 × 10 - 6, respectively). This approach offers a scalable strategy for biomarker discovery and holds potential for translation to more complex biological models and patient-derived datasets. Ultimately, pan-cancer single-gene expression biomarkers may inform patient stratification and warrant clinical validation in precision oncology.