Studies Reveal Distinct CAF Fibrotic Responses and New DeCAF Classifier
Two studies characterize cancer-associated fibroblast (CAF) heterogeneity. One finds distinct collagen biomarker responses to profibrotic stimuli, and another introduces DeCAF, a single-sample classifier that predicts CAF subtypes and immunotherapy response in pancreatic cancer.
Two new studies add to the understanding of cancer-associated fibroblast (CAF) heterogeneity, one identifying distinct fibrotic responses and collagen biomarkers among CAFs and another developing a single-sample classifier to predict CAF subtypes and immunotherapy response.
In the first study, published in Scientific Reports, researchers evaluated the production of three extracellular matrix proteins — type I (PRO-C1), type III (PRO-C3), and type VI (PRO-C6) collagen — by four distinct CAFs cultured in vitro for up to 12 days. Cells were stimulated with profibrotic or inflammatory factors (TGF-β1, PDGF-AB, IL-1α, IL-6) and/or treated with antifibrotic compounds (ALK5i, Fresolimumab), with collagen production quantified by competitive ELISA. The results revealed distinct fibrotic responses: two CAFs displayed high intrinsic fibrotic activity and minimal additional responsiveness to profibrotic stimuli, while two CAFs exhibited low intrinsic fibrotic activity and significant increases in PRO-C1, PRO-C3, and PRO-C6 upon stimulation. TGF-β1 was the primary driver of PRO-C3, PDGF-AB was the primary driver of PRO-C6, and IL-1α and IL-6 had no effect on the three collagen biomarkers. Antifibrotic treatments with ALK5i and Fresolimumab effectively reduced collagen biomarkers elevated by TGF-β1 to baseline levels or below. The authors noted that these results underscore the heterogeneity of CAFs in extracellular matrix remodeling and highlight the need for tailored therapeutic strategies.
In the second study, published in Cell Reports Medicine, researchers from UNC Lineberger defined CAF subtypes that are clinically robust, prognostic, and predictive of immunotherapy response and developed a single-sample classifier called DeCAF. CAFs play an important role in the tumor microenvironment of pancreatic ductal adenocarcinoma (PDAC), acting as key regulators with both tumor-restraining (restCAF) and tumor-promoting (proCAF) properties. By integrating single-cell RNA sequencing, bulk RNA sequencing, spatial transcriptomics, pathology, and clinical data, the team identified specific gene pairs that can accurately predict patient prognosis and therapeutic response in PDAC tumors as well as various other cancers. The study revealed that proCAF environments are linked to aggressive basal-like subtype tumor cells and immunosuppressive landscapes, whereas restCAF dominance correlates with better survival and improved sensitivity to immune checkpoint inhibition. Unlike previous clustering methods, DeCAF offers a robust, single-sample tool that remains stable across different sequencing platforms, providing a more precise biological basis for selecting targeted therapies based on a patient's unique stromal profile.