Deep learning-based immune phenotype analysis reveals distinct resistance pattern of immune checkpoint inhibitor in non-small cell lung cancer

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Background: Resistance pattern and biological mechanism of immune checkpoint inhibitor (ICI) has been poorly understood. Sine suggested resistance mechanisms would be either innate resistance caused by lack of immune recruitment or acquired immune evasion after durable response of ICI treatment, we hypothesized that resistance pattern of tumor microenvironment would be distinct according to duration of ICI response in non-small cell lung carcinoma (NSCLC). In the current study, we applied deep-learning-based classification of three immune phenotypes (3IP): inflamed, excluded, and desert, to objectively assess the immunologic status of tumor microenvironment. Methods: Deep-learning algorithm of H&E Whole-Slide Images (WSI), called Lunit-SCOPE, was trained with 1,824 H&E WSI of NSCLC from Samsung Medical Center (SMC). WSI was divided into patches and each patch (~10 high-power fields) was classified as inflamed, excluded and desert, based on both quantity and localization of immune cells. Among NSCLC patients treated with ICI in SMC, 87 paired treatment-naïve (Pre, patch N = 15,415) and post-progression (Post, patch N = 18,197) tumor tissues were analyzed for Lunit-SCOPE. Results: In 87-paired samples, proportions of excluded and desert phenotypes were increased in post-progression tumor tissues (excluded; Pre 26.8% versus Post 32.5%, desert; Pre 19.5% versus Post 25.3%). Focused on 29 patients classified as inflamed in treatment-naïve, proportion of immune phenotypes of post-progression were clearly different according to duration of response, divided by median progression-free survival (PFS) of 3.7 m. Patients with rapid progression without ICI response (PFS < 3.7 m) turned into desert type (46.2%), whereas durable responder (PFS ≥ 3.7 m) either still remained on inflamed phenotype (42.9%) or turned into excluded phenotype (21.4%). Patients who remained on inflamed phenotype had favorable overall survival after progression on ICI, compared to turned into desert type (median survival not reached versus 6.6 m, P= 0.0296). Conclusions: Resistance patterns of ICI are distinct according to duration of response in patients with inflamed phenotype. Rapid progressor turns off immune into desert phenotype whereas most durable responder keeps immune recruitment into tumor microenvironment, which needs tailored strategy to overcome ICI resistance.

Deep learning-based predictive biomarker for immune checkpoint inhibitor response in metastatic non-small cell lung cancer

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Background: In the era of immunotherapy, immune checkpoint inhibitor (ICI) has changed the treatment paradigm in metastatic non-small cell lung cancer (NSCLC). Along with clinical trials, there is an ongoing investigation to discover the predictive biomarker of ICI which so far has unsatisfactory reliability. As an effort to enhance the predictive value of ICI treatment, we applied deep learning and developed artificial intelligent (AI) score (range from 0 to 1) to analyze the specific context of immune-tumor microenvironment (TME) extracted by scanned images from H&E slides. Methods: As a ground work, deep learning-based H&E image analyzer, Lunit SCOPE, has been trained with H&E images (n = 1824) from ICI naive NSCLC samples. For the calculation of AI score, training was conducted using responder/non-responder labeled ICI treated samples from the exploratory cohort. The ICI responder was defined as the patient with a best overall response of partial or complete response and stable disease for more than 6 months. The positivity of PD-L1 immunohistochemistry (IHC) was assessed manually by pathologists. Results: The exploratory cohort is composed of NSCLC patients treated with ICI (n = 189) in Samsung Medical Center, and response to ICI was observed in 72 (38.1%) patients. Median follow-up duration was 6.8 months (6.6~8.2). Samples with PD-L1 IHC positive, defined by ≥ 1%, was observed in 138 (73.0%) patients. AI score was significant higher in the responder group (median: 0.391 vs 0.205, P = 6.14e-5), and the patients with AI score above the cut-off (0.337) showed a better response to ICI (odds ratio [OR] 3.47 P = 7.34e-5) which is higher than patients with PD-L1 ≥ 1% (OR 1.92, P = 0.069). High AI score group (n = 83) showed significantly favorable PFS compared to low AI score group (n = 106, median PFS: 5.1m vs 1.9m, hazard ratio [HR] 0.51, P = 9.6e-5) and this outcome was independent with PD-L1 status (P = 6.0e-5). In subgroup analysis, PFS of PD-L1 high / AI score high group (n = 63) had longer median PFS (6.7m) compared to both PD-L1 high / AI score low group (n = 70, 4.0m, P = 0.001) and PD-L1 low/AI score low group (n = 35, 1.9m, P = 4.0e-6). Tumor infiltrating lymphocyte (TIL) density of cancer epithelium was significantly correlated with AI score (Pearson’s r = 0.310, P = 1.43e-5), which suggests that AI score may partly reflect TME represented by TIL. Conclusions: The AI score by machine-learned information, extracted from H&E images without additional IHC stain, could predict responsiveness and PFS of ICI treatment independent of PD-L1 IHC positivity.

Calcium extrusion regulatory molecules: differential expression during pregnancy in the porcine uterus

Calcium ions in the uterine endometrium are essential for the establishment and maintenance of pregnancy, but the cellular and molecular mechanisms of calcium ion regulation in the endometrium are not fully understood. Our previous study in pigs found that calcium regulatory molecules, transient receptor potential, vanilloid type 6 and calbindin-D9K, are expressed in the uterine endometrium during the estrous cycle and pregnancy. However, we did not determine the expression of calcium extrusion regulatory molecules, plasma membrane calcium ATPases (ATP2Bs), sodium/calcium exchangers (SLC8As), or potassium-dependent sodium/calcium exchangers (SLC24As), in the uterine endometrium and conceptuses. Thus, in this study we determine whether ATP2Bs, SCL8As, and SLC24As are expressed in the uterine endometrium during the estrous cycle and pregnancy and in conceptuses during early pregnancy. Real-time RT-PCR analysis showed that ATP2Bs, SLC8As, and SLC24As were expressed in the uterine endometrium in a pregnancy status- and stage-specific manner. Conceptuses during early pregnancy also expressed these molecules. In situ hybridization analysis showed that ATP2B1, SLC8A1, and SLC24A4 were localized mainly to luminal and glandular epithelium and stromal cells in the endometrium during pregnancy. These results indicate that calcium extrusion regulatory molecules are expressed in the uterine endometrium during the estrous cycle and pregnancy and in conceptuses during early pregnancy, indicating that calcium extrusion regulatory molecules may play important roles in the establishment and maintenance of pregnancy by regulating calcium ion concentration in the uterine endometrium in pigs.