Deep learning framework for comprehensive molecular and prognostic stratifications of triple-negative breast cancer.

ABSTRACT: Triple-negative breast cancer (TNBC) is the most challenging breast cancer subtype. Molecular stratification and target therapy bring clinical benefit for TNBC patients, but it is difficult to implement comprehensive molecular testing in clinical practice. Here, using our multi-omics TNBC cohort (N = 425), a deep learning-based framework was devised and validated for comprehensive predictions of molecular features, subtypes and prognosis from pathological whole slide images. The framework first incorporated a neural network to decompose the tissue on WSIs, followed by a second one which was trained based on certain tissue types for predicting different targets. Multi-omics molecular features were analyzed including somatic mutations, copy number alterations, germline mutations, biol

SUBMITTER: Zhao S 

PROVIDER: S-EPMC11197495 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature