Multi-cellular phenotypic dynamics during the progression of breast tumors

In cancer, improving diagnostics and therapeutic interventions can benefit from understanding the cellular and phenotypic heterogeneity of the tumor microenvironment (TME). In recent years, the TME has been profiled at an unprecedented level of detail by performing single-cell RNA sequencing (scRNAseq) on patient samples. However, from patient samples, studying the temporal dynamics of the TME has been challenging. Characterizing the temporal dynamics of the TME is critical to understand how inter-tumor heterogeneity is organized into a temporally ordered sequence of causes and consequences in cellular events. Here we survey the temporal dynamics of the TME by performing longitudinal scRNAseq on mouse breast tumors at different progression time points. We find multicellular phenotypic dynamics that follow one out of three possible temporal patterns: stable colonization, wave-like, or progressive increase. In particular, IFN-responsive cancer cells, GzmB+ cytotoxic T cells, as well as macrophages of a phagocytic phenotype, progressively increase as tumors progress. These findings establish the single-cell types and phenotypes in a progressing breast tumor, and determine when these players enter and leave the TME. This single-cell dataset could serve to position clinical samples where timing is unknown on a temporal axis of progression, and suggest optimal timing for different therapies. Overall design: Clustering and comparative gene expression analysis on duplicate tumor samples from allograft model originally derived from a MMTV-PyMT GEMM of breast cancer in immunocompetent mice.