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• W4362545054 abstract "Abstract Background: Immune checkpoint inhibitors (ICIs) have revolutionized the treatment of patients with deficient DNA mismatch repair (d-MMR) metastatic colorectal cancer (CRC), yet less than half of patients receive clinical benefit. We hypothesized that immune contexture including spatial distribution of cells expressing immune molecules in the tumor microenvironment may predict immunotherapy outcome. Methods: Primary CRC tissues from consecutive patients with d-MMR metastatic CRC (N=33) were treated with anti-PD-1 antibodies at Mayo Clinic Comprehensive Cancer Center (2015-2018). Tumors were stained for PD-L1, PD-1, CD8, CD3, CD68, LAG3, TGFβR2, MHC-I, CD14, B2M, DAPI and pan-cytokeratin in 5 compartments (overall tumor, tumor epithelia, tumor stroma, peritumor inside vs outside) by multiplex immunofluorescence with digital image analysis. Features computed within each image analysis region included positive cell density, fraction of positive cell types, intensity of positive cells, and spatial distribution between distinct cell types (distances measured using image analysis software). Prior to model fitting, feature selection was performed using regularized Cox regression with LASSO. Regularization parameter was chosen based on 5-fold cross validation. A Cox proportional hazards model was fitted to predict patient progression-free survival (PFS). Results: Among patients with d-MMR CRCs, 16/33 (48.4%) were female, 10 received first-line ICI therapy, and 23 had received &gt; 1 prior chemotherapy regimen. Median age was 61.6 years (IQR of 49.4, 73.7). Eight patients (24.2%) harbored BRAFV600E, 9 (27.2%) had mutant KRAS, and median PFS was 22.2 months (95% CI: 11.8, NR) with 20 events. PD-L1 was expressed in tumor cells, CD68+ macrophages, and CD3+ T lymphocytes. PD-1 expression on CD8+ T lymphocytes was also observed. By univariate analysis, only cell-cell distance readouts achieved statistical significance. Multivariable feature selection identified the mean number of PD-1+ cells within 10 microns of a PD-L1+ cell in the overall tumor as the strongest predictor of anti-PD-1 efficacy, and was significantly associated with PFS (HR=0.87, 95% CI: 0.79-0.95, p&lt; 0.001). In contrast, the ratio of PD-1+/PD-L1+ cells was not predictive of treatment efficacy (p= 0.47), thereby underscoring the importance of spatial distribution for PFS prediction. Conclusion: The mean number of PD-1+ cells in proximity to PD-L1+ cells in tumors was a predictive biomarker of anti-PD-1 efficacy. Confirmatory studies are warranted. Citation Format: Bahar Saberzadeh-Ardestani, Rondell P. Graham, Qian Shi, Eze Ahanonu, Sara McMahon, Crystal Williams, Antony Hubbard, Wenjun Zhang, Andrea Muranyi, Dongyao Yan, Kandavel Shanmugam, Frank A. Sinicrope. Prediction of anti-PD-1 efficacy based on immune marker densities and their spatial distribution in colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6648." @default.
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• W4362545054 date "2023-04-04" @default.
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• W4362545054 title "Abstract 6648: Prediction of anti-PD-1 efficacy based on immune marker densities and their spatial distribution in colorectal cancer" @default.
• W4362545054 doi "https://doi.org/10.1158/1538-7445.am2023-6648" @default.
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