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Integrative Analysis and Machine Learning based Characterization of Single Circulating Tumor Cells

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...

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Bibliographic Details
Published in:Journal of clinical medicine 2020-04, Vol.9 (4), p.1206
Main Authors: 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, Sengupta, Debarka
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Language:English
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Summary: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.
ISSN:2077-0383
2077-0383
DOI:10.3390/jcm9041206