Deep learning infers clinically relevant protein levels and drug response in breast cancer from unannotated pathology images.

ABSTRACT: The computational pathology has been demonstrated to effectively uncover tumor-related genomic alterations and transcriptomic patterns. Although proteomics has indeed shown great potential in the field of precision medicine, few studies have focused on the computational prediction of protein levels from pathology images. In this paper, we assume that deep learning-based pathological features imply the protein levels of tumor biomarkers that are indicative of prognosis and drug response. For this purpose, we propose wsi2rppa, a weakly supervised contrastive learning framework to infer the protein levels of tumor biomarkers from whole slide images (WSIs) in breast cancer. We first conducted contrastive learning-based pre-training on tessellated tiles to extract pathological features, which a

SUBMITTER: Liu H 

PROVIDER: S-EPMC10899601 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature