Abstract Background. While expression-based signatures inform adjuvant therapy in breast cancer (BC), no approved molecular biomarkers exist for the neoadjuvant setting, where early prediction of response could guide treatment. Identifying such biomarkers is challenging given the molecular heterogeneity of breast cancer, where multiple malignant subtypes may coexist within a tumor and influence therapy response. Methods. We developed BRIDGE, a computational framework that deconvolves the pretreatment bulk tumor transcriptome to estimate molecular subtype composition and predict pathological complete response (pCR) to neoadjuvant therapy. BRIDGE was trained on 9 transcriptomics datasets and tested on 23 independent ones spanning different subtypes, composing one of the most variable multi-cohort validations to date. Six additional datasets with pre-treatement H in HER2+ disease, an AUC of 0.78 (OR = 7.2); and in TNBC, an AUC of 0.71 (OR = 4.4). We further developed BRIDGE-Slide, which applies BRIDGE to pre-treatment histopathology slides via deep learning-inferred transcriptomics. BRIDGE-Slide outperforms direct slide-to-response models, underscoring its potential as a first-of-its-kind, fast, low-cost biomarker. Finally, spatial transcriptomics shows that BRIDGE-derived subtype assignments form spatially cohesive regions aligned with canonical molecular features, reinforcing its biological interpretability. Conclusions. BRIDGE is a biologically grounded framework for neoadjuvant BC response prediction, validated on a rich set of diKerent patients cohorts. Its histopathology based version opens the door for fast and low cost prediction in the neoadjuvant setting, upon further prospective testing and validation. Citation Format: Thomas Cantore, Danh-Tai Hoang, Lipika Ray, Amos Stemmer, Tiangen Chang, Saugato Rahman Dhruba, Eldad David Shulman, Emma M. Campagnolo, Joo Sang Lee, Salomon M. Stemmer, Stephen-John Sammut, Yuan Yuan, Stan Lipkowitz, Sheila Rajagopal, Carlos M. Caldas, Nishanth Ulhas Nair, Eytan Ruppin. A biologically grounded predictor of neoadjuvant breast cancer therapy response from tumor transcriptomics and histopathology abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 3729.
Cantore et al. (2026) studied this question.