RT Journal Article
SR Electronic
T1 Integrative analysis and machine learning based characterization of single circulating tumor cells
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 867200
DO 10.1101/867200
A1 Arvind Iyer
A1 Krishan Gupta
A1 Shreya Sharma
A1 Kishore Hari
A1 Yi Fang Lee
A1 Neevan Ramalingam
A1 Yoon Sim Yap
A1 Jay West
A1 Ali Asgar Bhagat
A1 Balaram Vishnu Subramani
A1 Burhanuddin Sabuwala
A1 Tuan Zea Tan
A1 Jean Paul Thiery
A1 Mohit Kumar Jolly
A1 Naveen Ramalingam
A1 Debarka Sengupta
YR 2019
UL http://biorxiv.org/content/early/2019/12/06/867200.abstract
AB 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 systems for label-free enrichment of CTCs.