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Integrative analysis and machine learning based characterization of single circulating tumor cells

Iyer, A and Gupta, K and Sharma, S and Hari, K and Lee, YF and Ramalingam, N and Yap, YS and West, J and Bhagat, AA and Subramani, BV and Sabuwala, B and Tan, TZ and Thiery, JP and Jolly, MK and Ramalingam, N and Sengupta, D (2021) Integrative analysis and machine learning based characterization of single circulating tumor cells. In: Journal of Clinical Medicine, 9 (4). pp. 1-2.

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Official URL: https://doi.org/10.3390/jcm9041206


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.

Item Type: Journal Article
Publication: Journal of Clinical Medicine
Publisher: MDPI
Additional Information: The copyright for this article belongs to the Authors.
Keywords: Blood; CTC; High-throughput sequencing; Machine learning; Rare cell type; RNA-seq; Single-cell
Department/Centre: Division of Interdisciplinary Sciences > Centre for Biosystems Science and Engineering
Date Deposited: 22 May 2023 04:29
Last Modified: 22 May 2023 04:29
URI: https://eprints.iisc.ac.in/id/eprint/81711

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