ABSTRACT
Quantitative models that explicitly capture single-cell resolution cell-cell interaction features to predict patient survival at population scale are currently missing. Here, we describe the first quantitative model that extracted hundreds of features describing single-cell based cell-cell interactions and cellular phenotypes from a large, published cohort of cyto-images of breast cancer patients. We applied these features to a neural-network based Cox-nnet survival model and obtained high accuracy in predicting patient survival in test data (Concordance Index > 0.8). We identified seven survival subtypes using the top survival features, which present distinct profiles of epithelial, immune, fibroblast cells, and their interactions. We identified atypical subpopulations of TNBC patients with moderate prognosis (GATA3 over-expression) and Liminal A patients with poor prognosis (KRT6 and ACTA2 over-expression and CDH1 under-expression). These atypical subpopulations are validated in TCGA-BRCA and METABRIC datasets. This work outlines the roadmap to integrate single-cell data for survival prediction at population scale.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
Email addresses: SY: shyadav{at}med.umich.edu
SZ: shuzh{at}med.umich.edu
BH: hbing{at}med.umich.edu
