Abstract 7493: Novel preclinical models for evaluating checkpoint inhibitor resistance

Abstract Cancer immunotherapy targeting the PD-1/PD-L1 axis has revolutionized oncology, producing durable responses in a range of malignancies. However, a significant number of patients either fail to respond or develop resistance following initial success. There is a critical need for robust preclinical models of anti-PD-1 resistance to elucidate underlying mechanisms and inform new therapeutic strategies. Known resistance mechanisms include impaired tumor antigen presentation, downregulation of MHC class I molecules, alterations in IFN-γ signaling, and an immunosuppressive tumor microenvironment. To address this need, GemPharmatech has developed three distinct preclinical models of anti-PD-1 resistance: Drug-Induced Resistance Model:​ CT26 tumors were engrafted in BALB/c-hPD-1 mice and subjected to repeated cycles of anti-PD-1 treatment (KEYTRUDA®) and re-implantation of non-responding tumors. This iterative in vivo selection pressure generated a stable anti-PD-1 resistant model. Genetically Engineered Model:​ Isogenic anti-PD-1 resistant models were created by knocking out genes associated with resistance (B2Mor STK11) in the CT26 cell line. Primary Resistance Model. Characterization of the drug-induced model confirmed a stable resistant phenotype in vivo, with resistant CT26 cells exhibiting accelerated tumor growth compared to parental cells. RNA sequencing identified differentially expressed genes, offering insights into potential resistance pathways. In the engineered STK11knockout model, loss of STK11 abrogated the response to anti-PD-1 therapy. This was associated with a reduced infiltration of CD8+ T cells, a significant accumulation of myeloid-derived suppressor cells (MDSCs) in the tumor microenvironment, and decreased PD-L1 expression on tumor cells, implicating immunosuppressive mechanisms in the resistance phenotype. In summary, these validated preclinical models of anti-PD-1 resistance serve as powerful tools for deconstructing resistance mechanisms, facilitating biomarker identification, and guiding the development of novel combination therapies. Citation Format: Hongyan Sun, Yan Wang, Fang Zhu, Yujing Zhang, Huixin Yang, Xiang Gao, . Novel preclinical models for evaluating checkpoint inhibitor resistance abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 7493.