bioRxivCancer BiologyPreprint
Redefining colorectal cancer classification and clinical stratification through a single-cell atlas
A. M. Khaliq, z. kurt, M. W. Grunvald, C. Erdogan, S. S. Turgut, T. Rand, S. Khare, J. A. Borgia, D. M. Hayden, S. G. Pappas, H. R. Govekar, A. R. Bhama, A. Singh, R. A. Jacobson, A. E. Kam, A. Zloza, J. Reiser, D. V. Catenacci, K. Turaga, M. Radovich, S. Thyparambil, M. A. Levy, J. Subramanian, T. M. Kuzel, A. Sadanandam, , A. Masood
Abstract
Colorectal cancer (CRC), a disease of high incidence and mortality, has had few treatment advances owing to a large degree of inter- and intratumoral heterogeneity. Attempts to classify subtypes of colorectal cancer to develop treatment strategies has been attempted by Consensus Molecular Subtypes (CMS) classification. However, the cellular etiology of CMS classification is incompletely understood and controversial. 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 (CAF-S1 and CAF-S4) in more than 1,500 CRC patients using bulk transcriptomic data that significantly stratifies overall survival in multiple independent cohorts. We also uncovered two CAF-S1 subpopulations, ecm-myCAF and TGF{beta}-myCAF, known to be associated with primary resistance to immunotherapies. 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.
- organism: human
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: clustering
- approach: deep learning
- approach: statistical model
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