Deep learning and transfer learning identify breast cancer survival subtypes from single-cell imaging data.

ABSTRACT:

Background

Single-cell multiplex imaging data have provided new insights into disease subtypes and prognoses recently. However, quantitative models that explicitly capture single-cell resolution cell-cell interaction features to predict patient survival at a population scale are currently missing.

Methods

We quantified hundreds of single-cell resolution cell-cell interaction features through neighborhood calculation, in addition to cellular phenotypes. We applied these features to a neural-network-based Cox-nnet survival model to identify survival-associated features. We used non-negative matrix factorization (NMF) to identify patient survival subtypes. We identified atypical subpopulations of triple-negative breast cancer (TNBC) patients with moderate prognosis and Luminal

SUBMITTER: Yadav S 

PROVIDER: S-EPMC10730890 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature