A machine learning framework for supervised treatment response prediction from tumor transcriptomics: A large-scale pan-cancer study

ABSTRACT Precision oncology aims to guide treatment decisions using biomarkers. While DNA-based panels are increasingly applied, RNA transcriptomics remain underused due to limited datasets and the absence of robust models. We assembled the largest transcriptomic resource for drug response prediction to date, spanning 69 cohorts, 3,729 patients, nine cancer types, and six frontline therapies: anti-PD-1/PD-L1 immune-checkpoint inhibitors, trastuzumab, bevacizumab, BRAF inhibitors, paclitaxel, and FAC/FEC (Fluorouracil-Adriamycin-Cyclophosphamide/Fluorouracil-Epirubicin-Cyclophosphamide) chemotherapy. We developed EXPRESSO (EXpression-Profile-RESponSe-Optimizer), a supervised machine-learning framework that predicts treatment response from pre-treatment transcriptomes by integrating drug targets and context-specific biomarkers. EXPRESSO achieves ROC-AUCs of 0.64–0.73 and odds ratios of 2.4–4.6 across therapies, outperforming 20 published transcriptomic signatures. Robustness analysis reveals that predictive performance plateaued for some therapies with increasing training cohorts but continued to improve for others. These findings suggest inherent limits of supervised brute-force learning for certain treatments, but additional data and deeper mechanistic modeling may further enhance transcriptomics-based predictors. Competing Interest Statement E.R. is a cofounder of MedAware Ltd. and a cofounder (divested) and nonpaid scientific consultant of Pangea Therapeutics. E.R. is a member of the scientific advisory board of GSK Oncology, the WIN consortium and the ProCan program. All other authors have declared no conflicts of interest. Footnotes We mentioned EXPRESSO as a machine-learning-based framework instead of AI-based framework and changed the whole manuscript with that.