Digital versus manual pathology scoring of PD-L1 in patients with esophageal squamous cell carcinoma (ESCC).

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e16023 Background: Immune checkpoint inhibitors (ICIs) are standard-of-care for patients with ESCC, but the association between PD-L1 and clinical outcomes of patients with ESCC receiving ICIs treatment is unclear. Currently, different PD-L1 antibody clones, scoring, and cutoffs were used for different ICIs development in ESCC. Thus, we develop a digital pathology (DP) algorithm to generate multiple scores at the same time with high reproducibility. We evaluate DP algorithm by: (1) comparing the digital and manual scores of SP263 and 22C3 assays; (2) evaluating the association with OS. Methods: Tumor tissue samples were collected from ESCC patients treated at National Taiwan University Hospital between 2017 and 2020. Slides were tested for PD-L1 using SP263 and 22C3 assays at a College of American Pathologists accredited central laboratory. H&E and PD-L1 slides were scanned for whole slide images (WSIs) using DP200 scanner at 20x. HALO (v3.5) by indica labs was used for DP development by deep learning neural network algorithms. WSIs of SP263 were annotated for region of interest (ROI), tumor/stroma area, and tumor, immune and fibroblast cells by board certified pathologists. After the algorithm is developed and run on SP263 WSIs, a board-certified pathologist reviewed the images and provided an agreement score of DP markups. The images were excluded if an agreement was less than 80%. This algorithm was then applied to 22C3 WSIs. Data of areas and cells were exported for subsequent statistical analysis using R. We calculated the DP scores based on the definition of manual readouts of TAP, CPS and s/iTILs in ROI and tumor and stroma regions in both clones’ WSIs. These DP scores were compared with manual scores using Spearman’s rank-order correlation. Association with OS were evaluated using cox proportional hazard regression. Results: Of 235 evaluable images, twelve (5.1%) were excluded due to the low agreement with the pathologist's judgment. DP generated scores showed a strong correlation with pathologists scoring (rho = 0.80 for SP263 and rho = 0.74 for 22C3). DP generated CPS-like and TAP-like scores in ROI, and tumor and stroma regions from SP263 and 22C3 also demonstrated a strong correlation between each other (rho: 0.77 - 0.86). However, s/iTILs correlation between DP and pathologist score is negligible (rho: 0.002 - 0.34). While examining the association of PD-L1 scores and s/iTILs with OS, no clear associations were found with either DP or pathologists’ scores, after adjusting for stage, ECOG, age and gender. Conclusions: DP algorithms developed in this study demonstrated the efficiency of generating multiple DP scores of CPS, TAP, and s/iTILs from one image at the same time, which performed comparable to manual scores. DP algorithm developed based on SP263 is generalizable to 22C3 and showed a good performance. Further algorithm adjustment will be made to overcome the misclassification in the 12 failed cases.