Abstract 87: Path2Prot offers a new way for breast tumor subtyping and treatment response prediction from AI-inferred proteomic biomarkers.

Abstract Background: The advent of AI is revolutionizing precision medicine, including digital pathology where large foundation models (FMs) are applied to readily extract genomic/transcriptomic patterns from tumor whole-slide histopathology images (WSIs). In contrast, fewer studies have attempted to derive direct functional insights via proteomics from tumor morphology in WSIs, partly due to data scarcity. Henceforth, we propose a weakly-supervised deep learning model called Path2Prot to infer the relative abundance of 413 clinically relevant proteomic biomarkers in breast cancer (BC) from tumor H Next, using these features with matched patient-level proteomics, a multilayer perceptron is trained to infer the proteomic marker levels. To train, we used 2,074 WSIs from 841 TCGA-BRCA patients and the matched reverse-phase protein array (RPPA) data for 413 proteins (total + post-translationally modified). We leveraged both WSI types available via building three distinct models: FFPE model, trained on 893 formalin-fixed paraffin embedded WSIs (used for diagnosis); FF model, trained on 1,181 fresh-frozen WSIs (better RNA quality); and Combo model, combining the predictions of both models. Results: We assessed model performance with Pearson correlation (R) between inferred and measured proteomics, where proteins with R ≥ 0.4 are referred as the well-predicted proteins (WPPs). The Combo model performed the best with 23.7% WPPs (mean R = 0.31) in cross-validation and successfully generalized to cross-platform mass spectrometry proteomics in external validation with CPTAC-BRCA with 27.1% WPPs (mean R = 0.28; Overlap-in-WPPs = 71.8%). We further dichotomized the inferred HER2 and ER levels to identify their immunohistochemistry status and assigned patient tumors to clinically actionable subtypes (HER2+, ER+ Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 87.