Abstract 6191: Simultaneous prediction of tumor microenvironment biomarkers from pathology slides using multi-task deep regression

Abstract

Abstract Understanding the Tumor Microenvironment (TME) is crucial for tailoring personalized cancer treatments and improving patient outcomes. Using deep learning, we can predict properties of the TME from standard Hematoxylin and Eosin (H&E) stained slides, including specific gene expression signatures. While past models were limited to single signature predictions, we explore the potential of multi-task deep learning to identify multiple biomarkers at once, allowing more complex relationships and interactions to be modeled simultaneously. Our study focuses on the prediction of expression patterns of tumor, stromal, and immune cells in breast cancer (BC). We developed a multi-task transformer-based deep regression model, trained in a weakly-supervised setting, to predict biomarkers in the TME from routine H&E-stained pathology slides of a BC cohort (n=876). The performance was evaluated using Pearson’s correlation coefficient (r) and spatial heatmaps generated via gradient-weighted class activation mapping. Our model achieved a significant correlation coefficient above 0.40 (p<0.0001) for all predicted TME biomarkers in the holdout test set (n=176). This includes the biomarkers lymphocyte infiltrating signature score (r=0.46, p<0.0001), tumor infiltrating lymphocyte regional fraction (r=0.48, p<0.0001), leukocyte fraction (r=0.43, p<0.0001), stromal fraction (r=0.42, p<0.0001), and tumor cell proliferation (r=0.47, p<0.0001). Notably, the model's heatmaps highlighted the pertinent cells and histological characteristics for its predictions. These data show that our deep learning model accurately predicts multiple TME biomarkers concurrently from routine pathology slides. This approach offers a promising avenue for cost-effective and efficient biomarker quantification in the TME, with potential applicability across various cancer types. Citation Format: Omar S. El Nahhas, Marta Ligero, Jakob N. Kather. Simultaneous prediction of tumor microenvironment biomarkers from pathology slides using multi-task deep regression [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6191.