Cell Reports MedicineGenomicsPeer-reviewed journal article
Determination of permissive and restraining cancer-associated fibroblast (DeCAF) subtypes
Xianlu Laura Peng, Ian C. McCabeiD, Elena V. KharitonovaiD, Yi XuiD, Ryan T. Zhao, Changfei Luan, Joseph F. KearneyiD, Nancy Porterfield Kren, Arthi Hariharan, Jaewon James LeeiD, Justin Su, P. ChaniD, Ashley B. MorrisoniD, Michelle E. LaBella, Silvia G. HererraiD, Ashley M. CliffiD, Hong Jin Kim, Yuliya Pylayeva‐GuptaiD, Susan TsaiiD, Brian A. Belt, Roheena Z. PanniiD, David C. LinehaniD, Shawn M. GomeziD, Jeffrey S. DamraueriD, Alina Cornelia IugaiD, , Jen Jen YehiD
Abstract
Cancer-associated fibroblast (CAF) subpopulations in pancreatic ductal adenocarcinoma (PDAC) have been identified using single-cell RNA sequencing (scRNAseq) with divergent characteristics, but their clinical relevance remains unclear. We translate scRNAseq-derived CAF cell-subpopulation-specific marker genes to bulk RNAseq data, and develop a single- sample classifier, DeCAF, for the classification of clinically restraining and permissive CAF subtypes. We validate DeCAF in 19 independent bulk transcriptomic datasets across four tumor types (PDAC, mesothelioma, bladder and renal cell carcinoma). DeCAF subtypes have distinct histology features, immune landscapes, and are prognostic and predict response to therapy across cancer types. We demonstrate that DeCAF is clinically replicable and robust for the classification of CAF subtypes in patients for multiple tumor types, providing a better framework for the future development and translation of therapies against permissive CAF subtypes and preservation of restraining CAF subtypes. SignificanceWe introduce a replicable and robust classifier, DeCAF, that delineates the significance of the role of permissive and restraining CAF subtypes in cancer patients. DeCAF is clinically tractable, prognostic and predictive of treatment response in multiple cancer types and lays the translational groundwork for the preclinical and clinical development of CAF subtype specific therapies.
- organism: human
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: protein structure prediction
- research focus: drug response / drug discovery
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 8
- FWCI
- 8.37
- Field percentile
- top 2%
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