1. Integrative Analysis and Machine Learning based Characterization of Single Circulating Tumor Cells.
- Author
-
Iyer, Arvind, Gupta, Krishan, Sharma, Shreya, Hari, Kishore, Lee, Yi Fang, Ramalingam, Neevan, Yap, Yoon Sim, West, Jay, Bhagat, Ali Asgar, Subramani, Balaram Vishnu, Sabuwala, Burhanuddin, Tan, Tuan Zea, Thiery, Jean Paul, Jolly, Mohit Kumar, Ramalingam, Naveen, and Sengupta, Debarka
- Subjects
MACHINE learning ,BLOOD cells ,BREAST cancer ,TRANSCRIPTOMES ,GENE expression - Abstract
We collated publicly available single-cell expression profiles of circulating tumor cells (CTCs) and showed that CTCs across cancers lie on a near-perfect continuum of epithelial to mesenchymal (EMT) transition. Integrative analysis of CTC transcriptomes also highlighted the inverse gene expression pattern between PD-L1 and MHC, which is implicated in cancer immunotherapy. We used the CTCs expression profiles in tandem with publicly available peripheral blood mononuclear cell (PBMC) transcriptomes to train a classifier that accurately recognizes CTCs of diverse phenotype. Further, we used this classifier to validate circulating breast tumor cells captured using a newly developed microfluidic system for label-free enrichment of CTCs. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF