Using Cancer-Associated Fibroblasts as a Shear-Wave Elastography Imaging Biomarker to Predict Anti-PD-1 Efficacy of Triple-Negative Breast Cancer.

In the clinical setting, the efficacy of single-agent immune checkpoint inhibitors (ICIs) in triple-negative breast cancer (TNBC) remains suboptimal. Therefore, there is a pressing need to develop predictive biomarkers to identify non-responders. Considering that cancer-associated fibroblasts (CAFs) represent an integral component of the tumor microenvironment that affects the stiffness of solid tumors on shear-wave elastography (SWE) imaging, wound healing CAFs (WH CAFs) were identified in highly heterogeneous TNBC. This subtype highly expressed vitronectin (VTN) and constituted the majority of CAFs. Moreover, WH CAFs were negatively correlated with CD8 T cell infiltration levels and influenced tumor proliferation in the Eo771 mouse model. Furthermore, multi-omics analysis validated its role in immunosuppression. In order to non-invasively classify patients as responders or non-responders to ICI monotherapy, a deep learning model was constructed to classify the level of WH CAFs based on SWE imaging. As anticipated, this model effectively distinguished the level of WH CAFs in tumors. Based on the classification of the level of WH CAFs, while tumors with a high level of WH CAFs were found to exhibit a poor response to anti programmed cell death protein 1 (PD-1) monotherapy, they were responsive to the combination of anti-PD-1 and erdafitinib, a selective fibroblast growth factor receptor (FGFR) inhibitor. Overall, these findings establish a reference for a novel non-invasive method for predicting ICI efficacy to guide the selection of TNBC patients for precision treatment in clinical settings.