Deep-learning analysis of H&E images to define three immune phenotypes to reveal loss-of-target in excluded immune cells as a novel resistance mechanism of immune checkpoint inhibitor in non-small cell lung cancer.

Abstract

3120 Background: Discovery of predictive biomarker to enrich the responder of immune checkpoint inhibitor (ICI) in PD-L1-low ( < 50%) non-small cell lung cancer (NSCLC) is still challenging. Recent study showed that loss of heterozygosity (LOH) of HLA led to immune evasion. In the current study, we hypothesized that 3 immune phenotype (3IP): inflamed, excluded and desert would be reliably classified by deep-learning algorithm of H&E image, called Lunit-SCOPE, which would dictate the responder in PD-L1-low NSCLC patients and discover a unique resistance pathway in excluded phenotype. Methods: Lunit-SCOPE was trained with 1,824 H&E Whole-Slide Image (WSI) of NSCLC from Samsung Medical Center (SMC). WSI was divided into patches (~10 high-power fields) which was classified for 3IP, based on both quantity and localization of immune cells. The 3IP was trained and optimized by considering clinical outcome of 119 NSCLC patients with PD-(L)1 inhibitor (training cohort, patches = 25,897), and validated in 62 patients enrolled in LC-biomarker study (NCT03578185, validation cohort, patches = 8,929). Tumor Proportion Score (TPS) of PD-L1 22C3 immunohistochemistry was assessed by pathologists. Tumor Mutational Burden (TMB) was calculated as number of nonsynonymous alterations throughout whole-exome and HLA LOH was called by LOHHLA algorithm. Results: Interactive analysis to classify 3IP in training cohort showed that 8,726 (33.7%), 10,965 (42.3%), and 6,206 (24.0%) patches were classified as inflamed, excluded, and desert, respectively. In validation cohort, median progression-free survival (mPFS) of inflamed phenotype was 10.1 m, significantly prolonged compared to either excluded phenotype (3.0 m, P= 0.0053) or desert phenotype (1.4 m, P= 0.0011). Inflamed phenotype independently dictated favorable ICI outcome in PD-L1-low (TPS < 50%, mPFS of inflamed: 14.3 m vs excluded/desert: 1.4 m, P= 0.0233) as well as in PD-L1-high (TPS≥50%, 10.1 m vs 4.2 m, P= 0.0361), respectively. Excluded phenotype had higher TMB compared to inflamed phenotype had (median 177 vs 107), and HLA LOH was also enriched in excluded phenotype (31.0%) compared to inflamed (17.6%) and desert (16.7%) phenotypes. Conclusions: Lunit-SCOPE based 3IP classification can predict ICI outcome especially in PD-L1-low ( < 50%) patients. Excluded phenotype showed poor ICI outcome even with high TMB, partially explained by HLA LOH resulting in loss-of-target, as a novel resistance mechanism of ICI.