Identifying signatures of EV secretion in metastatic breast cancer through functional single-cell profiling

Mohsen Fathi; Melisa Martinez-Paniagua; Ali Rezvan; Melisa J Montalvo; Vakul Mohanty; Ken Chen; Sendurai A Mani; Navin Varadarajan

doi:10.1016/j.isci.2023.106482

Identifying signatures of EV secretion in metastatic breast cancer through functional single-cell profiling

iScience. 2023 Mar 21;26(4):106482. doi: 10.1016/j.isci.2023.106482. eCollection 2023 Apr 21.

Authors

Mohsen Fathi¹, Melisa Martinez-Paniagua¹, Ali Rezvan¹, Melisa J Montalvo¹, Vakul Mohanty², Ken Chen², Sendurai A Mani^{3

4

5}, Navin Varadarajan¹

Affiliations

¹ Chemical and Biomolecular Engineering Department, University of Houston, 4726 Calhoun Road, Houston, TX 77204, USA.
² Department of Bioinformatics and Computational Biology, University of Texas M.D. Anderson Cancer Center, 1400 Pressler Street, Houston, TX, USA.
³ Department of Translational Molecular Pathology, University of Texas M.D. Anderson Cancer Center, 2130 W Holcombe Boulevard, Houston, TX 77030, USA.
⁴ Department of Pathology and Lab Medicine, Warren Alpert Medical School, Brown University, Providence, RI 02903, USA.
⁵ Legoretta Cancer Center, Brown University, Providence, RI 021912, USA.

Abstract

Extracellular vesicles (EVs) regulate the tumor microenvironment by facilitating transport of biomolecules. Despite extensive investigation, heterogeneity in EV secretion among cancer cells and the mechanisms that support EV secretion are not well characterized. We developed an integrated method to identify individual cells with differences in EV secretion and performed linked single-cell RNA-sequencing on cloned single cells from the metastatic breast cancer cells. Differential gene expression analyses identified a four-gene signature of breast cancer EV secretion: HSP90AA1, HSPH1, EIF5, and DIAPH3. We functionally validated this gene signature by testing it across cell lines with different metastatic potential in vitro. Analysis of the TCGA and METABRIC datasets showed that this signature is associated with poor survival, invasive breast cancer types, and poor CD8⁺ T cell infiltration in human tumors. We anticipate that our method for directly identifying the molecular determinants of EV secretion will have broad applications across cell types and diseases.

Keywords: Biotechnology; Cancer; Cancer systems biology; Transcriptomics.

Grants and funding

R01 GM143243/GM/NIGMS NIH HHS/United States