Predicting Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Using Multi-Omics and Machine Learning

Abstract

Pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer remains a clinically important endpoint, but accurate prediction before treatment is challenging. We developed an attention-based multi-omics framework that integrates pretreatment genomics, transcriptomics, proteomics, epigenomics, and clinical variables to predict pCR in early-stage breast cancer. The model was trained on the I-SPY2 neoadjuvant cohort and externally evaluated using The Cancer Genome Atlas Breast Cancer and independent NAC datasets. Performance was assessed using discrimination, calibration, and subtype-specific analyses, while explainability was examined using SHAP-based feature importance and pathway enrichment testing. In the I-SPY2 test set, the multi-omics model achieved an area under the receiver operating characteristic curve of 0.81 and outperformed clinical-only and single-omics baselines across subtypes. Improvements were most apparent in triple-negative and HER2-positive disease. The model showed acceptable calibration and maintained performance in external and transfer analyses, in which higher predicted risk scores were associated with poorer recurrence-related outcomes. Explainability analyses identified proliferation, immune activity, and PI3K/AKT signaling as major contributors to prediction. These findings indicate that integrating pretreatment multi-omics data with clinical variables improves prediction of NAC response while producing interpretable outputs. Further prospective validation is required before clinical application.