Redefining tumor classification and clinical stratification through a colorectal cancer single-cell atlas

Khaliq, Ateeq M, Kurt, Zeyneb, Grunvald, Miles W, Erdogan, Cihat, Turgut, Sultan Sevgi, Rand, Tim, Khare, Sonal, Borgia, Jeffrey A, Hayden, Dana M, Pappas, Sam G, Govekar, Henry R, Bhama, Anuradha R, Singh, Ajaypal, Jacobson, Richard A, Kam, Audery E, Zloza, Andrew, Reiser, Jochen, Catenacci, Daniel V, Turaga, Kiran, Radovich, Milan, Thyparambil, Sheeno, Levy, Mia A, Subramanian, Janakiraman, Kuzel, Timothy M, Sadanandam, Anguraj, Hussain, Arif, El-Rayes, Bassel, Salahudeen, Ameen and Masood, Ashiq (2021) Redefining tumor classification and clinical stratification through a colorectal cancer single-cell atlas. bioRxiv. p. 429256. (Submitted)

2021.02.02.429256v3.full.pdf - Submitted Version
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Colorectal cancer (CRC), a disease of high incidence and mortality, exhibits a large degree of inter- and intra-tumoral heterogeneity. The cellular etiology of this heterogeneity is poorly understood. Here, we generated and analyzed a single-cell transcriptome atlas of 49,859 CRC cells from 16 patients, validated with an additional 31,383 cells from an independent CRC patient cohort. We describe subclonal transcriptomic heterogeneity of CRC tumor epithelial cells, as well as discrete stromal populations of cancer-associated fibroblasts (CAFs). Within CRC CAFs, we identify the transcriptional signature of specific subtypes that significantly stratifies overall survival in more than 1,500 CRC patients with bulk transcriptomic data. We demonstrate that scRNA analysis of malignant, stromal, and immune cells exhibit a more complex picture than portrayed by bulk transcriptomic-based Consensus Molecular Subtypes (CMS) classification. By demonstrating an abundant degree of heterogeneity amongst these cell types, our work shows that CRC is best represented in a transcriptomic continuum crossing traditional classification systems boundaries. Overall, this CRC cell map provides a framework to re-evaluate CRC tumor biology with implications for clinical trial design and therapeutic development.
Competing Interest Statement: The authors have declared no competing interest.

Item Type: Article
Subjects: A300 Clinical Medicine
B200 Pharmacology, Toxicology and Pharmacy
G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: John Coen
Date Deposited: 09 Feb 2021 14:20
Last Modified: 31 Jul 2021 14:50

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