Abstract 4030: Case classification with tumor antigen presenting and TGF-β signaling biomarkers to predict anti-PD-1 outcome in GI tract tumors using automated quantitative fluorescence multiplex IHC
Authors: Wang Xiangxue , Moh Shizen , Hubbard Antony , Muñoz-Rodríguez José L. , Khojasteh Mehrnoush , Martin Jim , Zhu Qingfeng , Anders Robert , Diaz Luis , Pestic-Dragovich Lidija , Tang Lei , Zhang Wenjun 
Published: August 2019

Introduction: Anti-PD-1/L1 immune checkpoint blockade results in tumor stabilization or shrinkage in only 15-40% of patients. Predictive biomarkers are crucial in identifying responsive patients while excluding others from the toxicities of immunotherapies. Major histocompatibility complex class I (MHC-I) downregulation is one of the most frequent mechanisms of tumor escape from the host’s immune system, but little attention has been devoted to MHC-I expression in studies of the PD-1/L1 blockade. Recently, Tauriello and Mariathasan revealed stromal transforming growth factor (TGF)-β signaling in CD8+ T lymphocytes exclusion as a key determinant of resistance to PD-1/L1 blockade in colorectal and urothelial carcinomas. We present here a multiplex panel IHC of MHC-I, β2-microglobulin (B2M), CD14, TGF-β receptor 2 (TGFBR2), and pan-cytokeratin (panCK) in tumor micro-environment, and their predictive values to anti-PD-1 treatment.

Methods: With the multiplex panel, 51 pre-pembrolizumab treatment patient specimens were stained, including pancreatic, colorectal and cholangiocarcinoma (33 non-responders: 17 PD, 13 SD, 3 NE; 18 responders: 14 PR, 4 CR). Pathologists annotated tumor areas on whole slide scans. HALO High-Plex FL module was used for image analysis. Epithelial tumor (panCK+) and stroma (panCK-) were masked with HALO’s random forest classifier. Spatial location, count, intensity, and percent abundance of each marker were identified. 43 features were designed based on the rationale of hypothesized biological significance. MATLAB was used for feature selection, ranking, and prediction of responses to anti-PD1 treatment.

Results: There was a trend of higher MHCI expression on tumor cells in the responders than non-responders to pembrolizumab treatment. Heterogeneous MHCI expression of tumor cells, and fraction of TGFBR2+ CD14+ cells in stroma were the top features ranked by Relieff k-nearest neighbor (k=30) for the prediction of the response to pembrolizumab treatment. Using Quadratic Discriminant Analysis (QDA) with five-fold cross-validation, the prediction accuracy was 76.5%. Independent validation was not performed due to small sample size.

Conclusions: Deep immune characterization of tumor microenvironments using high dimensional feature spaces derived from multiplex IHC staining may provide insightful directions on finding and validating predictive markers for various immunotherapy regiments (ex. PD-1/L1 blockade; dual TGF-β and PD-1/L1 blockade; combination of PD-1/L1 blockade with other treatments that enhance MHC-I molecules on tumor cells).

Acknowledgement: We thank Nick Cummins, Jorge Lozano, and John Hurley for their technical assistance.

Citation Format: Xiangxue Wang, Shizen Moh, Antony Hubbard, José L. Muñoz-Rodríguez, Mehrnoush Khojasteh, Jim Martin, Qingfeng Zhu, Robert Anders, Luis Diaz, Lidija Pestic-Dragovich, Lei Tang, Wenjun Zhang. Case classification with tumor antigen presenting and TGF-β signaling biomarkers to predict anti-PD-1 outcome in GI tract tumors using automated quantitative fluorescence multiplex IHC [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4030.