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Cited by 280Cancer Immunotherapy and BiomarkersAI in cancer detectionRadiomics and Machine Learning in Medical Imaging

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Artificial intelligence-powered spatial analysis of tumor-infiltrating lymphocytes as complementary biomarker for immune checkpoint inhibition in non-small-cell lung cancer

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Park, Sehhoon, Ock, Chan-Young, Kim, Hyojin, Pereira, Sergio, Park, Seonwook, Ma, Minuk, Choi, Sangjoon, Kim, Seokhwi, Shin, Seunghwan, Aum, Brian Jaehong, Paeng, Kyunghyun, Yoo, Donggeun, Cha, Hongui, Park, Sunyoung, Suh, Koung Jin, Jung, Hyun Ae, Kim, Se Hyun, Kim, Yu Jung, Sun, Jong-Mu, Chung, Jin-Haeng, Ahn, Jin Seok, Ahn, Myung-Ju, Lee, Jong Seok, Park, Keunchil, Song, Sang Yong, Bang, Yung-Jue, Choi, Yoon-La, Mok, Tony S., Lee, Se-Hoon·2022·Journal of Clinical Oncology
DOI 10.1200/jco.21.02010PMC PMC9177249
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Abstract

PURPOSE 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). METHODS 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). RESULTS 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. CONCLUSION 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.

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FIG 1. Development of AI-powered tissue analyzer, Lunit SCOPE IO, in non–small-cell lung cancer. (A) The scheme of Lunit SCOPE IO development and the workflow of the current study. (B) Composition of database under Lunit SCOPE IO development. (C) Representative image of H&E original image (left) and Lunit SCOPE IO–inferenced segmentation of CE, purple, CS, green, TIL, cyan, respectively (right). (D) ROC curves to segmentize CE, CS, and to detect TIL in an internal validation cohort. (E) ROC curves to segmentize and detect CE, CS, and to detect TIL, in an external validation cohort (n = 110). The Cancer Genome Atlas lung adenocarcinoma and lung squamous cell carcinoma). AI, artificial intelligence; AUROC, area under the receiver operating characteristic; CE, cancer epithelium; CS, cancer stroma; H&E, hematoxylin and eosin; IP, immune phenotype; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; NLST, National Lung Screening Trial; PLCO, Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial; ROC, receiver operating characteristic; SMC, Samsung Medical Center; SNUBH, Seoul National University Bundang Hospital; TCGA, The Cancer Genome Atlas; TIL, tumor-infiltrating lymphocytes.
FIG 1. Development of AI-powered tissue analyzer, Lunit SCOPE IO, in non–small-cell lung cancer. (A) The scheme of Lunit SCOPE IO development and the workflow of the current study. (B) Composition of database under Lunit SCOPE IO development. (C) Representative image of H&E original image (left) and Lunit SCOPE IO–inferenced segmentation of CE, purple, CS, green, TIL, cyan, respectively (right). (D) ROC curves to segmentize CE, CS, and to detect TIL in an internal validation cohort. (E) ROC curves to segmentize and detect CE, CS, and to detect TIL, in an external validation cohort (n = 110). The Cancer Genome Atlas lung adenocarcinoma and lung squamous cell carcinoma). AI, artificial intelligence; AUROC, area under the receiver operating characteristic; CE, cancer epithelium; CS, cancer stroma; H&E, hematoxylin and eosin; IP, immune phenotype; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; NLST, National Lung Screening Trial; PLCO, Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial; ROC, receiver operating characteristic; SMC, Samsung Medical Center; SNUBH, Seoul National University Bundang Hospital; TCGA, The Cancer Genome Atlas; TIL, tumor-infiltrating lymphocytes.CC BY-NC-ND · PubMed Central
FIG 2. Classification of immune phenotype on the basis of spatial analysis of TIL powered by Lunit SCOPE IO. (A) Distribution of TIL density in CE in TCGA-LUAD and LUSC (WSI n = 923, 1 mm2-sized grid n = 165,646, blue), SMC (WSI n = 1,205, 1 mm2-sized grid n = 121,122, red), and SNUBH (WSI n = 261, 1 mm2-sized grid n = 34,297, teal, top left). Distribution of TIL density in CS in same cohorts (bottom left). Sample H&E images and Lunit SCOPE IO–inferenced masked images of a 1 mm2-sized grid (CE: yellow, CS: purple, and TIL: red) of three immune phenotypes (middle left). Sample WSI images masked with three immune phenotypes of 1 mm2-sized grid (inflamed: red, immune-excluded: teal, immune-desert: blue, middle right). Distribution of inflamed score (red) and immune-excluded score (teal) and proportion of representative immune phenotype in TCGA-LUAD and LUSC (right). (B) and (C) Correlation of representative IP (x-axis) and local immune cytolytic activity measured by gene expressions of GZMA and PRF1 (y-axis, B) as well as tumor mutational burden (y-axis, C) in TCGA-LUAD and LUSC. **Represents P < .001 and *represents P < .05. (D) Summary of ssGSEA using HALLMARK gene sets in TCGA-LUAD and LUSC, filtered by FDR (or q value) < 1%. The mean ratios of ssGSEA scores times –log2 FDR value of inflamed IP or others (red, first column) and those of immune-excluded IP or others (teal, second column). (E) Summary of Cibersort analysis in TCGA-LUAD and LUSC, filtered by P value < .05. Subsets of immune cells enriched in inflamed IP (red), those in immune-excluded IP (teal), and those in immune-desert IP (blue) were annotated. Diameter from zero point to each dot reflected the mean ratio of each IP compared with other IPs times –log2 P value. (F) Summary of multiplex IHC in SMC cohort (n = 99). Quantification of expressing cells was normalized by number of DAPI-stained cells. The normalized count of each expressing cells enriched in inflamed IP (red), that in immune-excluded IP (teal), and that in immune-desert IP (blue) was annotated. Diameter from zero point to each dot reflected the mean ratio of each IP compared with other IPs times –log2 P value. *Represents P < .05. (G) Representative multiplex IHC images of inflamed IP (left), immune-excluded IP (middle), and immune-desert IP (right). AI, artificial intelligence; AUROC, area under the receiver operating characteristic; CE, cancer epithelium; CS, cancer stroma; FDR, false discovery rate; H&E, hematoxylin and eosin; IHC, immunohistochemistry; IP, immune phenotype; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; SMC, Samsung Medical Center; SNUBH, Seoul National University Bundang Hospital; ssGSEA, single-sample gene set enrichment analysis; TCGA, The Cancer Genome Atlas; TIL, tumor-infiltrating lymphocytes; TMB, tumor mutational burden; WSI, whole-slide image.
FIG 2. Classification of immune phenotype on the basis of spatial analysis of TIL powered by Lunit SCOPE IO. (A) Distribution of TIL density in CE in TCGA-LUAD and LUSC (WSI n = 923, 1 mm2-sized grid n = 165,646, blue), SMC (WSI n = 1,205, 1 mm2-sized grid n = 121,122, red), and SNUBH (WSI n = 261, 1 mm2-sized grid n = 34,297, teal, top left). Distribution of TIL density in CS in same cohorts (bottom left). Sample H&E images and Lunit SCOPE IO–inferenced masked images of a 1 mm2-sized grid (CE: yellow, CS: purple, and TIL: red) of three immune phenotypes (middle left). Sample WSI images masked with three immune phenotypes of 1 mm2-sized grid (inflamed: red, immune-excluded: teal, immune-desert: blue, middle right). Distribution of inflamed score (red) and immune-excluded score (teal) and proportion of representative immune phenotype in TCGA-LUAD and LUSC (right). (B) and (C) Correlation of representative IP (x-axis) and local immune cytolytic activity measured by gene expressions of GZMA and PRF1 (y-axis, B) as well as tumor mutational burden (y-axis, C) in TCGA-LUAD and LUSC. **Represents P < .001 and *represents P < .05. (D) Summary of ssGSEA using HALLMARK gene sets in TCGA-LUAD and LUSC, filtered by FDR (or q value) < 1%. The mean ratios of ssGSEA scores times –log2 FDR value of inflamed IP or others (red, first column) and those of immune-excluded IP or others (teal, second column). (E) Summary of Cibersort analysis in TCGA-LUAD and LUSC, filtered by P value < .05. Subsets of immune cells enriched in inflamed IP (red), those in immune-excluded IP (teal), and those in immune-desert IP (blue) were annotated. Diameter from zero point to each dot reflected the mean ratio of each IP compared with other IPs times –log2 P value. (F) Summary of multiplex IHC in SMC cohort (n = 99). Quantification of expressing cells was normalized by number of DAPI-stained cells. The normalized count of each expressing cells enriched in inflamed IP (red), that in immune-excluded IP (teal), and that in immune-desert IP (blue) was annotated. Diameter from zero point to each dot reflected the mean ratio of each IP compared with other IPs times –log2 P value. *Represents P < .05. (G) Representative multiplex IHC images of inflamed IP (left), immune-excluded IP (middle), and immune-desert IP (right). AI, artificial intelligence; AUROC, area under the receiver operating characteristic; CE, cancer epithelium; CS, cancer stroma; FDR, false discovery rate; H&E, hematoxylin and eosin; IHC, immunohistochemistry; IP, immune phenotype; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; SMC, Samsung Medical Center; SNUBH, Seoul National University Bundang Hospital; ssGSEA, single-sample gene set enrichment analysis; TCGA, The Cancer Genome Atlas; TIL, tumor-infiltrating lymphocytes; TMB, tumor mutational burden; WSI, whole-slide image.CC BY-NC-ND · PubMed Central
FIG 3. Landscape and clinical significance of Lunit SCOPE IO–powered immune phenotype. (A) Proportion of inflamed score (red), immune-excluded score (green), and immune-desert score (blue, first row), representative IP (second row), EGFR mutation status, ALK translocation status (black: positive, gray: negative, and blank: unknown), PD-L1 immunohistochemistry TPS (pink: ≥ 50%, green: 1%-49%, and teal: 0%), and best overall response by RECIST v1.1 (purple: complete response or partial response, and blank: stable disease or progressive disease) of each patient were shown (left: SMC, right: SNUBH). (B) Kaplan-Meier analysis of progression-free survival (left) and overall survival after ICI treatment (right) on the basis of representative immune phenotype in the merged cohort from SMC and SNUBH. (C) Kaplan-Meier analysis of PFS (left) and OS after first-line platinum-based chemotherapy (right) on the basis of representative immune phenotype in a merged cohort. ALK, anaplastic lymphoma kinase; EGFR, epidermal growth factor receptor; HR, hazard ratio; ICI, immune checkpoint inhibitor; IP, immune phenotype; mOS, median overall survival; mPFS, median progression-free survival; NA, not applicable; NR, not reported; ORR, overall response rate; OS, overall survival; PD-L1, programmed death ligand-1; PFS, progression-free survival; SMC, Samsung Medical Center; SNUBH, Seoul National University Bundang Hospital; TPS, tumor proportion score.
FIG 3. Landscape and clinical significance of Lunit SCOPE IO–powered immune phenotype. (A) Proportion of inflamed score (red), immune-excluded score (green), and immune-desert score (blue, first row), representative IP (second row), EGFR mutation status, ALK translocation status (black: positive, gray: negative, and blank: unknown), PD-L1 immunohistochemistry TPS (pink: ≥ 50%, green: 1%-49%, and teal: 0%), and best overall response by RECIST v1.1 (purple: complete response or partial response, and blank: stable disease or progressive disease) of each patient were shown (left: SMC, right: SNUBH). (B) Kaplan-Meier analysis of progression-free survival (left) and overall survival after ICI treatment (right) on the basis of representative immune phenotype in the merged cohort from SMC and SNUBH. (C) Kaplan-Meier analysis of PFS (left) and OS after first-line platinum-based chemotherapy (right) on the basis of representative immune phenotype in a merged cohort. ALK, anaplastic lymphoma kinase; EGFR, epidermal growth factor receptor; HR, hazard ratio; ICI, immune checkpoint inhibitor; IP, immune phenotype; mOS, median overall survival; mPFS, median progression-free survival; NA, not applicable; NR, not reported; ORR, overall response rate; OS, overall survival; PD-L1, programmed death ligand-1; PFS, progression-free survival; SMC, Samsung Medical Center; SNUBH, Seoul National University Bundang Hospital; TPS, tumor proportion score.CC BY-NC-ND · PubMed Central
FIG 4. Lunit SCOPE IO–powered immune phenotype dictates the responder of immune checkpoint inhibitors especially in PD-L1 tumor proportion score 1%-49%. (A) Forest plot of HR (dot) and 95% CI (arrow) for PFS according to each clinicopathologic subgroup. (B) Proportion of inflamed IP (red), immune-excluded IP (teal), and immune-desert IP (blue) according to PD-L1 TPS. (C) Kaplan-Meier analysis of PFS grouped by PD-L1 TPS status and representative IP in the merged cohort from SMC and SNUBH. (D) ROC curves of Lunit SCOPE IO inflamed score (red) and PD-L1 TPS (blue) to predict immune checkpoint inhibitor responder in PD-L1 TPS 1%-49% subgroup. ALK, anaplastic lymphoma kinase; AUROC, area under the receiver operating characteristic; ECOG, Eastern Cooperative Oncology Group; HR, hazard ratio; ICI, immune checkpoint inhibitors; IP, immune phenotype; mPFS, median progression-free survival; NA, not applicable; ORR, overall response rate; PD-L1, programmed death ligand-1; PFS, progression-free survival; ROC, receiver operating characteristic; SMC, Samsung Medical Center; SNUBH, Seoul National University Bundang Hospital; TPS, tumor proportion score.
FIG 4. Lunit SCOPE IO–powered immune phenotype dictates the responder of immune checkpoint inhibitors especially in PD-L1 tumor proportion score 1%-49%. (A) Forest plot of HR (dot) and 95% CI (arrow) for PFS according to each clinicopathologic subgroup. (B) Proportion of inflamed IP (red), immune-excluded IP (teal), and immune-desert IP (blue) according to PD-L1 TPS. (C) Kaplan-Meier analysis of PFS grouped by PD-L1 TPS status and representative IP in the merged cohort from SMC and SNUBH. (D) ROC curves of Lunit SCOPE IO inflamed score (red) and PD-L1 TPS (blue) to predict immune checkpoint inhibitor responder in PD-L1 TPS 1%-49% subgroup. ALK, anaplastic lymphoma kinase; AUROC, area under the receiver operating characteristic; ECOG, Eastern Cooperative Oncology Group; HR, hazard ratio; ICI, immune checkpoint inhibitors; IP, immune phenotype; mPFS, median progression-free survival; NA, not applicable; ORR, overall response rate; PD-L1, programmed death ligand-1; PFS, progression-free survival; ROC, receiver operating characteristic; SMC, Samsung Medical Center; SNUBH, Seoul National University Bundang Hospital; TPS, tumor proportion score.CC BY-NC-ND · PubMed Central

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  1. US Food and Drug Administration, 2020, PD-L1 IHC 22C3 pharmDx—P150013/S014

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Metadata

Authors
Park, S., Ock, C.-Y., Kim, H., Pereira, S., Park, S., Ma, M., Choi, S., Kim, S., Shin, S., Aum, B. J., Paeng, K., Yoo, D., Cha, H., Park, S., Suh, K. J., Jung, H. A., Kim, S. H., Kim, Y. J., Sun, J.-M., Chung, J.-H., Ahn, J. S., Ahn, M.-J., Lee, J. S., Park, K., Song, S. Y., Bang, Y.-J., Choi, Y.-L., Mok, T. S., & Lee, S.-H.
Affiliations
  • Samsung Medical Center (KR)
  • Lunit (South Korea) (KR)
  • Seoul National University Bundang Hospital (KR)
  • Lunit, Seoul, Republic of Korea
  • Ajou University (KR)
  • Seoul National University (KR)
  • Chinese University of Hong Kong (HK)
Corresponding author
Lee, Se-Hoon
ORCID
Venue
Journal of Clinical Oncology
Publisher
American Society of Clinical Oncology (ASCO)
Published
2022-06-10
Volume
40
Issue
17
Pages
1916-1928
ISSN
  • 0732-183X (ISSN-L)
  • 1527-7755
DOI
10.1200/jco.21.02010
PMC
PMC9177249
OpenAlex
W4220803499
Cited by
280
References
29
Citations by year
2026: 46 · 2025: 76 · 2024: 75 · 2023: 56 · 2022: 27
FWCI
24.47
Type
journal article
Language
en
Open access
Licence
cc-by-nc-nd
Locations
  • Journal of Clinical Oncology (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • AJOU Open Repository (Ajou University) (submittedVersion, cc-by-nc-nd)
  • Seoul National University Open Repository (Seoul National University) (submittedVersion)
Funders
Lunit
Topics
  • Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Keywords
tumor-infiltrating lymphocytes, immune checkpoint inhibitors, non-small cell lung cancer, tumor microenvironment, immunophenotype, immune-excluded tumors, tumor proportion score, PD-L1 expression, whole slide image analysis, artificial intelligence, predictive biomarkers, progression-free survival, overall survival
MeSH terms
Immune Checkpoint Inhibitors, Artificial Intelligence, Carcinoma, Non-Small-Cell Lung, Humans, Lung Neoplasms, Biomarkers, Lymphocytes, Tumor-Infiltrating, Tumor Microenvironment, B7-H1 Antigen, Spatial Analysis

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