Researchers at the University of North Carolina Lineberger Comprehensive Cancer Center have developed a new clinical tool designed to predict cancer-associated fibroblast (CAF) subtypes in patient tumor samples. The findings, recently published in Cell Reports Medicine, describe a single sample classifier named DeCAF that can distinguish between CAF subtypes and help inform treatment decisions for patients with pancreatic ductal adenocarcinoma (PDAC) and other cancers.
The study was led by Laura Peng, PhD, Ian McCabe, a doctoral candidate from the lab of Jen Jen Yeh, MD, and Elena Kharitonova, a doctoral candidate from the lab of Naim Rashid, PhD. Researchers collaborated across several labs at UNC Lineberger—including those led by William Kim, MD, and Alina Iuga, MD—to define clinically robust CAF subtypes that are prognostic and predictive of response to immunotherapy.
CAFs play an important role in the tumor microenvironment of PDAC. They act as key regulators in the dense tissue surrounding pancreatic tumors and exhibit both tumor-restraining (restCAF) and tumor-promoting (proCAF) properties. By integrating data from single-cell RNA sequencing, bulk RNA sequencing, spatial transcriptomics, pathology, and clinical sources, the research team identified specific gene pairs capable of predicting patient prognosis and therapeutic response not only in PDAC but also in various other cancers.
According to their findings, proCAF environments are associated with more aggressive basal-like subtype tumor cells and immunosuppressive landscapes. In contrast, dominance of restCAFs is linked to better survival rates and increased sensitivity to immune checkpoint inhibitors. The DeCAF classifier differs from previous clustering methods by offering a robust tool that remains stable across different sequencing platforms on an individual sample basis. This approach aims to provide a more precise biological framework for selecting targeted therapies tailored to each patient’s stromal profile.
“This innovative classifier creates new possibilities to guide treatment for PDAC and other cancers that see limited success with immunotherapy,” said Peng. “As far as translational impact, we hope to leverage better definition of tumor heterogeneity to improve treatment for our patients.”
