Published in Cell Reports Medicine, researchers have defined cancer-associated fibroblast (CAF) subtypes that are clinically robust, prognostic, and predictive of immunotherapy response.
In a collaboration between several labs at UNC Lineberger, researchers have defined cancer-associated fibroblast (CAF) subtypes that are clinically robust, prognostic, and predictive of immunotherapy response and developed a clinical classifier designed to distinguish between these subtypes in patients. These results were reported in a paper recently published in Cell Reports Medicine.
The research was led by authors Laura Peng, PhD, and 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. The team leveraged diverse expertise from the Yeh Lab and Rashid Lab, as well as the labs of William Kim, MD, and Alina Iuga, MD, to define the CAF subtypes and develop the single sample classifier known as DeCAF. CAFs play an important role in the tumor microenvironment (TME) of pancreatic ductal adenocarcinoma (PDAC), acting as key regulators in the dense tissue around pancreatic tumors 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 multidisciplinary team of researchers identified specific gene pairs that can accurately predict patient prognosis and therapeutic response in PDAC tumors as well as various other cancers.
Development of the DeCAF classifier builds on the Yeh and Rashid Labs’ previous investigations into methods of classification and treatment optimization for PDAC patients. This latest study reveals 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. Ultimately, the framework provides a more precise biological basis for selecting targeted therapies based on a patient’s unique 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.”
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Media Contact: Brittany Phillips, Communications Specialist, UNC Health | UNC School of Medicine