Cancer tissue functions as an ecosystem of a diverse set of cells that interact in a complex tumor microenvironment. Genomic tools applied to biopsies in bulk fail to account for this tumor heterogeneity, whereas 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 (CXCR4/CD184, CD24, THY1/CD90) to identify and interrogate distinct groups of cells in triple-negative breast cancer 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 into 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. Mol Cancer Res; 1–10. ©2016 AACR.
Note: Supplementary data for this article are available at Molecular Cancer Research Online (http://mcr.aacrjournals.org/).
Some tumor data for this paper were retrieved from the Mouse Tumor Biology Database (MTB), Mouse Genome Informatics, The Jackson Laboratory, Bar Harbor, Maine. http://www.informatics.jax.org. (October, 1998 i.e., data retrieved 8/4/16).
- Received August 26, 2016.
- Revision received November 23, 2016.
- Accepted December 21, 2016.
- ©2016 American Association for Cancer Research.