Díaz‐Montero, C. Marcela

686 Background: Immune checkpoint inhibitors (ICI) have been used to treat advanced muscle invasive and/or metastatic bladder cancer. However, ICIs are only effective in 30-40% of cases. In light of potentially significant IRAE’s, tools to predict individual patient level benefits are desirable. AI technology offers the opportunity to apply computational methods to predict outcomes from readily available digitized pathology slides. We investigated the utility of an AI platform integrating computational biomarkers based on morphological characterization of digitized H&E slide whole images (WSI) to predict response to ICI. Methods: We analyzed H&E-stained whole slide images (WSI) of bladder tumor tissue collected from transurethral resection of bladder tumor (TURBT) from 116 patients with advanced or metastatic bladder cancer at a single institution between 2015-2020. Adjacent multiplex IHC stained specimens were analyzed myeloid and lymphoid marker panels. 20 patients from the overall cohort treated with ICI were designated ‘responders’ (complete response, partial response, stable disease) and 20 patients were identified as non-responders (progressive disease) based on ICI best clinical/radiographic response. WSIs were divided into small non-overlapping image patches. These image tiles were processed into multiple AI encoder models to extract morphological features. The tile morphological features, represented by high dimensional vectors, were combined by an aggregation model to represent the whole slide morphology and then used to classify the patients into responders vs non-responders to immunotherapy. Area-under-the-receiver operating characteristic (AUC) was used to measure the performance of response prediction. Results: Our method shows AUC of 0.708 at classification of the patients (n=40) into responder and non-responder groups. With a cutoff that identifies the top 50% patients (n=20) of high probability of responding to immunotherapy predicted by our models, 75% of them are responders. In the bottom 50% patients (n=20) of the low scores, 75% of them are non-responders. . Multiplex IHC (myeloid and lymphoid panel) was performed using adjacent sections and were characterized yielding deeper mechanistic insight into the inflammatory populations driving predictive morphologic patterns. Conclusions: By applying innovative AI morphological analysis on patient WSIs, we generated a computational biomarker that shows promising ability to predict patient response to immunotherapy in bladder cancer. This approach merits future prospective validation and additional patient level analysis in broader cohorts. [Table: see text]

Abstract Purpose: Little is known about the association between myeloid-derived suppressor cell (MDSC) subsets and various chemokines in patients with renal cell carcinoma (RCC) or the factors that draw MDSC into tumor parenchyma. Experimental Design: We analyzed polymorphonuclear MDSC (PMN-MDSC), monocytic MDSC (M-MDSC), and immature MDSC (I-MDSC) from the parenchyma and peripheral blood of 48 patients with RCC, isolated at nephrectomy. We analyzed levels of IL1β, IL8, CXCL5, Mip-1α, MCP-1, and Rantes. Furthermore, we performed experiments in a Renca murine model to assess therapeutic synergy between CXCR2 and anti-PD1 and to elucidate the impact of IL1β blockade on MDSC. Results: Parenchymal PMN-MDSC have a positive correlation with IL1β, IL8, CXCL5, and Mip-1α, and I-MDSC correlate with IL8 and CXCL5. Furthermore, peripheral PMN-MDSC correlate with tumor grade. Given that PMN-MDSC express CXCR2 and parenchymal PMN-MDSC correlated with IL8 and CXCL5, we assessed the response of CXCR2 blockade with or without anti-PD1. Combination therapy reduced tumor weight and enhanced CD4+ and CD8+ T-cell infiltration. In addition, anti-IL1β decreased PMN-MDSC and M-MDSC in the periphery, PMN-MDSC in the tumor, and peripheral CXCL5 and KC. Anti-IL1β also delayed tumor growth. Conclusions: Parenchymal PMN-MDSC have a positive correlation with IL1β, IL8, CXCL5, and Mip-1α, suggesting they may attract PMN-MDSC into the tumor. Peripheral PMN-MDSC correlate with tumor grade, suggesting prognostic significance. Anti-CXCR2 and anti-PD1 synergized to reduce tumor weight and enhanced CD4+ and CD8+ T-cell infiltration in a Renca murine model, suggesting that CXCR2+ PMN-MDSC are important in reducing activity of anti-PD1 antibody. Finally, anti-IL1β decreases MDSC and delayed tumor growth, suggesting a potential target for MDSC inhibition. Clin Cancer Res; 23(9); 2346–55. ©2016 AACR.