Cancer tissue functions as an ecosystem of a diverse set of cells that interact in a complex tumor microenvironment (TME). Genomic tools applied to biopsies in bulk fail to account for this tumor heterogeneity while single cell imaging methods limit the number of cells which can be assessed or are very resource intensive. The current study presents methods based on flow cytometric analysis and cell sorting using known cell surface markers (eg, CD184, CD24, CD90) to identify and interrogate distinct groups of cells in triple-negative breast cancer (TNBC) clinical biopsy specimens from patient-derived xenograft (PDX) models. The results demonstrate that flow cytometric analysis allows a relevant subgrouping of cancer tissue and that sorting of these subgroups provides insights on cancer cell populations with unique, reproducible and functionally divergent gene expression profiles. The discovery of a drug resistance signature implies that uncovering the functional interaction between these populations will lead to deeper understanding of cancer progression and drug response. Implications: PDX-derived human breast cancer tissue was investigated at the single cell level and cell subpopulations defined by surface markers were identified which suggest specific roles for distinct cellular compartments within a solid tumor
