Iyer, Arvind ; Gupta, Krishan ; Sharma, Shreya ; Hari, Kishore ; Lee, Yi ; Ramalingam, Neevan ; Yap, Yoon ; West, Jay ; Bhagat, Ali ; Subramani, Balaram ; Sabuwala, Burhanuddin ; Tan, Tuan ; Thiery, Jean ; Jolly, Mohit ; Ramalingam, Naveen ; Sengupta, Debarka (2020) Integrative Analysis and Machine Learning Based Characterization of Single Circulating Tumor Cells Journal of Clinical Medicine, 9 (4). p. 1206. ISSN 2077-0383
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Official URL: https://doi.org/10.3390/jcm9041206
Related URL: http://dx.doi.org/10.3390/jcm9041206
Abstract
e 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: | Article |
|---|---|
| Source: | Copyright of this article belongs to MDPI |
| Keywords: | High-throughput sequencing; Rare cell type; Single-cell; RNA-seq; Machine learning; CTC; Blood. |
| ID Code: | 142508 |
| Deposited On: | 24 Jan 2026 07:22 |
| Last Modified: | 24 Jan 2026 07:22 |
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