Integration of Gene Expression and Digital Histology to Predict Treatment-Specific Responses in Breast Cancer.

ABSTRACT: Deep learning models applied to digital histology can predict gene expression signatures (GES) and offer a low-cost, rapidly available alternative to molecular testing at the time of diagnosis. We optimized transformer-based models to infer GES results and applied this approach to pre-treatment H&E-stained biopsies from 1,940 breast cancer patients treated with neoadjuvant chemotherapy in clinical trial and real-world cohorts. The most predictive histology-derived GES for pathologic complete response (pCR) in the I-SPY2 trial was validated in four external cohorts: CALGB 40601, CALGB 40603, a trial of durvalumab plus CT, and standard-of-care CT-treated patients from the University of Chicago. Among HER2-negative patients, a transformer-based model trained using a signature composed of estr

SUBMITTER: Howard FM 

PROVIDER: S-EPMC12407626 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature