Benjamin J. Tombling, Fei Cai, Timothy J. Wendorff, Annie Ogasawara, Herman Gill, Jeff N. Tinianow, Andrew Chang, Aaron T. Balana, Lingling Peng, Stephen E. Miller, Benjamin T. Walters, Aaron Lictao, Qinying Yu, David C. DeWitt, Yuehua Wei, Sunny Z. Wu, Jawahar Sudhamsu, Akshay T. Krishnamurty, Simon P. Williams, Jan Mařı́k, Yingnan Zhang, Henry R. Maun, Daniel Kirchhofer
Leucine-rich-repeat-containing protein 15 (LRRC15) is selectively expressed on cancer-associated fibroblasts (CAFs) and constitutes a promising biomarker for imaging the tumor microenvironment. Using a combinatorial library approach, assisted by machine learning, we developed disulfide-constrained peptides (DCPs), notably ML-YSD-07 and ML-PD-03, that demonstrate subnanomolar affinities for murine LRRC15 (muLRRC15) and specifically localize onto muLRRC15-expressing fibroblasts. PET imaging with 18F-radiolabeled ML-YSD-07 exhibits specific tumor accumulation in a murine pancreatic cancer model highly enriched with LRRC15-expressing CAFs. Crystal structures of apo-muLRRC15 and of ML-YSD-07-bound muLRRC15 show that the DCPs evolved to adopt a distinct binding conformation that efficiently interacts with a flat epitope on muLRRC15. Collectively, this work identifies potent, molecularly engineered LRRC15-binding peptides and further highlights LRRC15 as a valuable CAF biomarker for cancer imaging applications. In this work, combinatorial library-derived peptides are engineered to tightly bind to LRRC15, a marker on cancer-associated fibroblasts. In mice, an isotope-labeled peptide enables PET imaging of LRRC15-rich tumors, highlighting an alternative strategy for tumor imaging.