Proceedings: ASCO Annual Meeting 2021; June 4-8; Chicago, IL
Background : Pathologic classification of immune phenotype is challenging since there is no consensus on how to assess spatial relations of tumor-infiltrating lymphocyte (TIL) on cancer epithelium (CE) and cancer stroma (CS) in whole-slide images (WSI). We previously suggested that the artificial intelligence (AI)-powered tissue analyzer, Lunit SCOPE IO, can classify immune phenotype, and that its predictions are correlated with the clinical outcome of immune checkpoint inhibitor (ICI) in non-small cell lung cancer (NSCLC). In this study, we designed a pathologic validation of immune phenotype using multiplex immunohistochemistry.
Methods : Lunit SCOPE IO was developed based on a 2.8 x 109 micrometer2 area of CE or CS, and 5.9 x 106 TILs from 3,166 H&E Whole-Slide Image (WSI) of multiple cancer types, annotated by board-certified pathologists. H&E WSIs were divided into 1 mm2-sized tiles, where we classified immune phenotype (IP) based on TIL density on CE and TIL density on CS. Representative IP was determined based on the overall proportion of tile-level IPs in each WSI. Multiplex immunohistochemistry (mIHC) staining with CD3, CD8, CD20, CD68, FOXP3, CK, and DAPI was performed in NSCLC tumor tissues (n = 99) treated with immune checkpoint inhibitors (ICI) at the Samsung Medical Center. A normalized number of cells expressing each marker was calculated by dividing the total number of marker-positive cells by the number of DAPI-positive cells in each WSI.
Results : The proportions of inflamed IP, immune-excluded IP, and immune desert IP in the analysis set were 46.5%, 29.3%, and 24.2%. respectively. Median progression-free survival of ICI was 6.4 m in inflamed IP, 1.9 m in immune-excluded IP, and 1.6 m in immune-desert IP (hazard ratio of inflamed versus others: 0.43, confidence interval 0.27-0.68, P = 0.000188). Multiplex IHC results showed that the normalized CD3-positive cells and CD8-positive cells, which play a role of anti-tumor activity, were highly enriched in inflamed IP compared to those in other IPs (CD3: fold change [FC] 1.57, P = 0.0182; CD8: FC 1.24, P = 0.0697), whereas FOXP3-positive cells, linked to the immunosuppressive activity, were enriched in immune-excluded IP (FC 1.26, P = 0.0656). We also noted that CD68-positive cells were significantly enriched in immune-desert IP (FC 1.76, P = 0.00467).
Conclusions:The immune cell subset in WSI is distinct according to the immune phenotype, as CD3- or CD8-positive cells are enriched in inflamed IP rather than immune-excluded IP as classified by AI-powered TIL analysis of H&E image.
Yoon-La Choi, Sehhoon Park, Sergio Pereira, Seonwook Park, Minuk Ma, Jiwon Shin, Jisoo Shin, Kyunghyun Paeng, Donggeun Yoo, Chan-Young Ock, Se-Hoon Lee
Abstract: Deep learning-based predictive biomarker for adjuvant chemotherapy in early-stage hormone receptor-positive breast cancer
Abstract: Pan-cancer analysis of tumor microenvironment using deep learning-based cancer stroma and immune profiling in H&E images
Abstract: Deep learning-based predictive biomarker for immune checkpoint inhibitor response in metastatic non-small cell lung cancer
Abstract: Comprehensive deep learning analysis of H&E tissue phenomics reveals distinct immune landscape and transcriptomic enrichment profile among immune inflamed, excluded and desert subtypes...
Abstract: 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...
Abstract: Deep learning-based immune phenotype analysis reveals distinct resistance pattern of immune checkpoint inhibitor in non-small cell lung cancer
Predicting breast tumor proliferation from whole-slide images: The TUPAC16 challenge
Accuracy and efficiency of an artificial intelligence tool when counting breast mitoses
Abstract: Artificial intelligence-powered spatial analysis of tumor infiltrating lymphocytes (TIL) to reflect target gene expressions of novel immuno-oncology agents.
Abstract : Pathologic validation of artificial intelligence-powered prediction of combined positive score of PD-L1 immunohistochemistry in urothelial carcinoma.
Abstract : Clinical performance of artificial intelligence-powered annotation of tumor cell PD-L1 expression for treatment of immune-checkpoint inhibitor (ICI) ...
Abstract : Artificial intelligence-powered spatial analysis of tumor-infiltrating lymphocytes predicts survival after immune checkpoint inhibitor therapy across multiple cancer types.
Abstract : Deep learning based radiomic biomarker for predicting the presence of high-grade histologic patterns in lung adenocarcinoma
Abstract : Artificial intelligence-powered tissue analysis reveals distinct tumor-infiltrating lymphocyte profile as a potential biomarker of molecular subtypes in endometrial cancer
Abstract : Artificial intelligence-powered spatial analysis of tumor-infiltrating lymphocytes reveals distinct genomic profile of immune excluded phenotype in pan-carcinoma
Deep learning from HE slides predicts the clinical benefit from adjuvant chemotherapy in hormone receptor-positive breast cancer patients
Abstract : Interim results of phase I dose escalation study of YBL-006: A novel anti-PD-1 monoclonal antibody in advanced solid tumors
Abstract : Assistance with an artificial intelligence-powered PD-L1 analyzer reduces interobserver variation in pathologic reading of tumor proportion score in non-small cell lung cancer
Abstract : AI-powered whole-slide image analysis of tumor-infiltrating lymphocytes for prediction of prognosis in colorectal cancer