Biomarkers on the basis of tumor-infiltrating lymphocytes (TIL) are potentially valuable in predicting the effectiveness of immune checkpoint inhibitors (ICI). However, clinical application remains challenging because of methodologic limitations and laborious process involved in spatial analysis of TIL distribution in whole-slide images (WSI).
We have developed an artificial intelligence (AI)–powered WSI analyzer of TIL in the tumor microenvironment that can define three immune phenotypes (IPs): inflamed, immune-excluded, and immune-desert. These IPs were correlated with tumor response to ICI and survival in two independent cohorts of patients with advanced non–small-cell lung cancer (NSCLC).
Inflamed IP correlated with enrichment in local immune cytolytic activity, higher response rate, and prolonged progression-free survival compared with patients with immune-excluded or immune-desert phenotypes. At the WSI level, there was significant positive correlation between tumor proportion score (TPS) as determined by the AI model and control TPS analyzed by pathologists (P < .001). Overall, 44.0% of tumors were inflamed, 37.1% were immune-excluded, and 18.9% were immune-desert. Incidence of inflamed IP in patients with programmed death ligand-1 TPS at < 1%, 1%-49%, and ≥ 50% was 31.7%, 42.5%, and 56.8%, respectively. Median progression-free survival and overall survival were, respectively, 4.1 months and 24.8 months with inflamed IP, 2.2 months and 14.0 months with immune-excluded IP, and 2.4 months and 10.6 months with immune-desert IP.
The AI-powered spatial analysis of TIL correlated with tumor response and progression-free survival of ICI in advanced NSCLC. This is potentially a supplementary biomarker to TPS as determined by a pathologist.
Sehhoon Park , MD, PhD1; Chan-Young Ock , MD, PhD2; Hyojin Kim , MD, PhD3; Sergio Pereira, PhD2; Seonwook Park , PhD2; Minuk Ma , MS2; Sangjoon Choi , MD4; Seokhwi Kim , MD, PhD5; Seunghwan Shin , MD2; Brian Jaehong Aum , PhD2; Kyunghyun Paeng, MS2; Donggeun Yoo, PhD2; Hongui Cha , PhD1; Sunyoung Park, PhD1; Koung Jin Suh, MD6; Hyun Ae Jung , MD, PhD1; Se Hyun Kim , MD, PhD6; Yu Jung Kim , MD, PhD6; Jong-Mu Sun , MD, PhD1; Jin-Haeng Chung , MD, PhD3; Jin Seok Ahn, MD, PhD1; Myung-Ju Ahn , MD, PhD1; Jong Seok Lee, MD, PhD6; Keunchil Park , MD, PhD1; Sang Yong Song, MD, PhD4; Yung-Jue Bang , MD, PhD7; Yoon-La Choi , MD, PhD4; Tony S. Mok , MD8; and Se-Hoon Lee , MD, PhD1,9
1Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea
2Lunit, Seoul, Republic of Korea
3Department of Pathology, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
4Department of Pathology and Translational Genomics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
5Department of Pathology, Ajou University School of Medicine, Suwon, Republic of Korea
6Division of Hematology-Oncology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
7Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
8State Key Laboratory of Translational Oncology, Department of Clinical Oncology, Chinese University of Hong Kong, Hong Kong, China
9Department of Health Sciences and Technology, Samsung Advanced Institute of Health Sciences and Technology, Sungkyunkwan University, Seoul, Republic of Korea
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
Reducing Domain Gap by Reducing Style Bias
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: Distinct subset of immune cells assessed by multiplex immunohistochemistry correlates with immune phenotype classified by ...
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
Deep Learning Analysis of CT Images Reveals High-Grade Pathological Features to Predict Survival in Lung Adenocarcinoma
Artificial Intelligence–Powered Hematoxylin and Eosin Analyzer Reveals Distinct Immunologic and Mutational Profiles among Immune Phenotypes in Non–Small-Cell Lung Cancer
Abstract : Artificial Intelligence-Powered Analyzer Reduces Inter-observer Variation in PD-L1 Tumor Proportion Score of Non-Small Cell Lung Cancer
Abstract : Artificial Intelligence-Powered Tumor Purity Assessment From H&E Whole Slide Images Correlates...
Abstract : Deep learning-based H&E analyzer reveals distinct immune profiles and clinical outcomes among immune phenotypes in uterine corpus endometrial carcinoma
Abstract : Artificial intelligence-powered spatial analysis of tumor-infiltrating lymphocytes reveals immune-excluded phenotype is correlated with TGF-beta pathway related genomic aberrations
Observer Performance Study to Examine the Feasibility of the AI-powered PD-L1 Analyzer to Assist Pathologists’ Assessment of PD-L1 Expression Using Tumor Proportion Score in Non-Small Cell Lung Cancer
Artificial intelligence-powered human epidermal growth factor receptor 2 (HER2) analyzer in breast cancer as an assistance tool for pathologists to reduce interobserver variation
Artificial intelligence-powered whole-slide image analyzer reveals a distinctive distribution of tumor-infiltrating lymphocytes in neuroendocrine tumors and carcinomas
Artificial Intelligence (AI) - powered spatial analysis of tumor-infiltrating lymphocytes (TIL) for prediction of response to neoadjuvant chemotherapy (NAC) in triple negative breast cancer (TNBC)
Artificial intelligence (AI)-powered pathology image analysis merged with spatial transcriptomics reveals distinct TIGIT expression in the immune-excluded tumor-infiltrating lymphocytes
Trastuzumab plus FOLFOX for Gemcitabine/Cisplatin refractory HER2-positive biliary tract cancer: a multi-institutional phase II trial of the Korean Cancer Study Group (KCSG-HB19-14)
The Inflamed Immune Phenotype (IIP): a clinically actionable artificial intelligence (AI)-based biomarker predictive of immune checkpoint inhibitor (ICI) outcomes across >16 primary tumor types
Safety and efficacy of YBL-006, an anti-PD-1 monoclonal antibody in advanced solid tumors: a phase I study
Robust artificial intelligence-powered imaging biomarker based on mammography for risk prediction of breast cancer.