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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
Open accessAbstract
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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“Numerous studies have demonstrated the potential for using AI to predict immunotherapy biomarkers from H&E images of patients with NSCLC, including tumor mutation burden (TMB; ref. 76), PD-L1 expression (105), and TILs (106, 107).”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [106]
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“This AI-powered spatial TIL analyzer may potentially supplement PD-L1 TPS as a supporting biomarker for ICI.”
ResultInfluentialHerbst, R. S., Baas, P., Kim, D.-W., Felip, E., Pérez-Gracia, J. L., Han, J.-Y., Molina, J., Kim, J.-H., Arvis, C. D., Ahn, M.-J., et al. (2016) · The Lancet · doi:10.1016/s0140-6736(15)01281-7 · Cited by 6,449 · cited 1×
“5%).(24,25) Distribution of IP by baseline clinical attributes and smoking history was investigated, and there was no correlation between IP and smoking status, liver metastasis, and lung immune prognostic index score(26); however, some correlation existed between IP and pleural effusion andbrainmetastasis at the poin…”
BackgroundPembrolizumab for the Treatment of Non–Small-Cell Lung Cancer
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“8% of the PD-L1 1%-49% subgroup from the KEYNOTE 001 study.(24) In the small subset of patients (n 5 10) who received firstline ICI, the TRR of patients with inflamed versus noninflamed IP was 66.”
BackgroundSelf-citationTGFβ attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells
Mariathasan, S., Turley, S. J., Nickles, D., Castiglioni, A., Yuen, K., Wang, Y., Kadel III, E. E., Koeppen, H., Astarita, J. L., Cubas, R., et al. (2018) · Nature · doi:10.1038/nature25501 · Cited by 5,400
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“IP was de fi ned as follows 13,14 : in fl amed as TIL density in CE area above the threshold (106/mm 2 ); immune-excluded as TIL density in CE area below the threshold and TIL density in CS area above the threshold (357/mm 2 ); and immune-desert as both TIL density in CE area and that in CS area…”
BackgroundIFN-γ–related mRNA profile predicts clinical response to PD-1 blockade
Ayers, M., Lunceford, J., Nebozhyn, M., Murphy, E., Loboda, A., Kaufman, D. R., Albright, A., Cheng, J. D., Kang, S. P., Shankaran, V., et al. (2017) · Journal of Clinical Investigation · doi:10.1172/jci91190 · Cited by 3,788
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“Pembrolizumab is superior to standard chemotherapy in terms of tumor response rate (TRR) and survival in patients with tumor proportion score (TPS)$ 50%(3); however, treatment outcomes were similar between pembrolizumab and chemotherapy in patients with PD-L1 TPS 1%-49%.(4) Therefore, development of a novel biomarker…”
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- US Food and Drug Administration, 2020, PD-L1 IHC 22C3 pharmDx—P150013/S014
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Pathological Digital Biomarkers: Validation and Application
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InfluentialRoznovan, C. N., Marutescu, L. G., & Pîrcălăbioru, G. G. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26136177 · Cited by 9
“In addition, an artificial intelligence-powered immune phenotype based on spatial TILs analysis was able to predict the ICIs’ efficacy in patients with biliary tract cancer [130], high-grade serous ovarian carcinoma [121], and lung adenocarcinoma [115].”
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“15 We hypothesized that TIL density or immune phenotype (IP) determined by TIL may predict response to neoadjuvant therapy in advanced TET and possibly related to survival outcomes.”
BackgroundThe role of artificial intelligence in immune checkpoint inhibitor research: A bibliometric analysis
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Tumor infiltrating lymphocytes (TILs) – Pathologia, quo vadis? – A global survey
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“For instance, analyzing the distribution of tumor-infiltrating lymphocytes and their interactions with tumors and immune phenotypes may offer valuable information regarding the potential efficacy of ICI treatment [50, 51].”
Deep Learning Predicts EGFR Mutation Status from Histology Images in Non–Small Cell Lung Cancer
Park, J., Shin, S., Hwang, W., Keum, S., Brattoli, B., Rawson, J. H., Lee, T., Pereira, S., Ahn, C. H., Senior, M. J. T., et al. (2025) · Cancer Research Communications · doi:10.1158/2767-9764.crc-25-0155 · Cited by 6
“Sub-sequently, the Lunit SCOPE IO model (37, 38) is used to perform two tasks.”
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Artificial Intelligence and Lung Pathology
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“35 Guo and colleagues used a deep learning algorithm for segmenting tumor cells and TILs, extracting features related to survival.”
Liu, J., Sun, D., Xu, S., Shen, J., Ma, W., Zhou, H., Ma, Y., Zhang, Y., Fang, W., Zhao, Y., et al. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1485703 · Cited by 6
Papillary thyroid cancer immune phenotypes via tumor-infiltrating lymphocyte spatial analysis
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The Tumor Immune Microenvironment Is Associated With Recurrence in Early-Stage Lung Adenocarcinoma
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“A previous study conducted on non-small cell lung cancer demonstrated an increase in positive cases using artificial intelligence-powered analysis to aid in the interpretation of equivocal IHC cases.(27) Significant higher agreement of high-TIL group implies that the high TIL level also increases the possibility of PD…”
MethodMachine learning to detect melanoma exploiting nuclei morphology and Spatial organization
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“Previous studies have elucidated the correlation between histology-depicted TME and clinical outcomes [16–18].”
Characterization of the immune cell infiltration landscape in myxofibrosarcoma to aid immunotherapy
Zhao, Z., Chen, Z., Yu, B., Xiao, B., Liu, L., Xia, Y., Li, A.-Y., Wang, P., Xiang, C., Liu, C., et al. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.916915 · Cited by 5
“Our results were consistent with the literature (36, 38, 39), which proved that typing ICI and gene clusters could guide clinical immunotherapy of MFS.”
ResultPan, X., Feng, S., Wang, Y., Chen, J., Lin, H., Wang, Z., Hou, F., Lu, C. Y., Chen, X., Liu, Z., et al. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e30779 · Cited by 5
WhARIO: whole-slide-image-based survival analysis for patients treated with immunotherapy
Tourniaire, P., Ilié, M., Mazières, J., Vigier, A., Ghiringhelli, F., Piton, N., Sabourin, J., Bibeau, F., Hofman, P. M., Ayache, N., et al. (2024) · Journal of medical imaging · doi:10.1117/1.jmi.11.3.037502 · Cited by 6
GJA4 expressed on cancer associated fibroblasts (CAFs)—A ‘promoter’ of the mesenchymal phenotype
Ye, Q., Liu, Y.-J., Li, J.-Q., Han, M., Bian, Z.-R., Chen, T.-Y., Li, J.-P., Liu, S.-L., & Zou, X. (2024) · Translational Oncology · doi:10.1016/j.tranon.2024.102009 · Cited by 5
“Currently, there are three widely recognized immune phenotypes in cancer: immune-inflamed (tumor infiltrating lymphocyte (TIL) distributed intratumorally), immune-desert (scant TIL in TME), and immune-excluded (TIL excluded, out of tumor stroma) [43,44].”
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“From the perspective of TIL, a report documents no significant difference between primary and metastatic lesions in NSCLC [15], whereas in various other cancer types, there are reports show decreased TILs in recurrence [28–30].”
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“Their density and spatial organization within the TME may indicate a pre-existing antitumor immune response and an inflamed TME, both of which have been associated with improved outcomes following ICB in multiple tumor types, including NSCLC [62–64].”
Hata, T., Yamada, T., Gotô, Y., Amano, A., Negi, Y., Watanabe, S., Furuya, N., Oba, T., Ikoma, T., Nakao, A., et al. (2025) · Cancer Research Communications · doi:10.1158/2767-9764.crc-25-0157 · Cited by 4
“However, measuring these factors in routine clinical practice is challenging (15–17).”
Detection of Muscularis propria Invasion in Urothelial Carcinoma Using Artificial Intelligence
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“Recent evidence highlights a significant correlation between TIL density and improved prognosis in LUAD in the context of immunotherapy [13, 15, 41].”
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“In fact, the presence of lymphocytes infiltrating tumor (TILs) is often associated with longer patient survival [18,19].”
The role of tumor microenvironment in regulating tumor cell dormancy
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Radiotherapy and immunotherapy in cancer treatment: mechanisms of clinical synergy
Lu, L., & Deng, L. (2026) · Journal of Clinical Investigation · doi:10.1172/jci207550 · Cited by 3
“AI is also increasingly used to identify predictive biomarkers, including signatures associated with tumor-infiltrating lymphocytes, PD-L1 expression, and immune phenotypes, thereby supporting patient stratification for radioimmunotherapy (212, 214).”
Global trends in tertiary lymphoid structures: a bibliometric analysis from 2014 to 2023
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YTHDF1 shapes “cold” tumor and inhibits CD8+ T cells infiltration and function in breast cancer
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Application value of early lung cancer screening based on artificial intelligence
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“13,14 Prior studies applying automated TIL scoring in breast cancer have demonstrated strong associations with clinical outcomes, using diverse computational methodologies.”
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“In digital pathology, accurate segmentation and classification of cells are crucial for many diagnostic, prognostic, and predictive analyses [1, 2, 3, 4].”
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“To assess spatial TIL densities of whole-slide images (WSIs) from the TCGA-CRC dataset, an artificial intelligence-powered model, Lunit SCOPE IO (Lunit), was used for classifying immune phenotypes (IPs) of the TME [ 42 ].”
Artificial Intelligence for Predicting Immunotherapy Efficacy in Non-Small Cell Lung Cancer
Cao, P., Jia, X., Yang, Y., Wang, X., Zhu, J., Li, X., Yang, Z., & Wen, Q. (2026) · Journal of Inflammation Research · doi:10.2147/jir.s581764 · Cited by 2
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“65 68 71 In contrast to TILs, quantitative analysis of tumor-associated macrophages from H&E images remains more challenging.”
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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
- Park, Sehhoon
- Ock, Chan-Young
- Kim, Hyojin
- Park, Seonwook
- Ma, Minuk
- Choi, Sangjoon
- Kim, Seokhwi
- Shin, Seunghwan
- Aum, Brian Jaehong
- 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
- Bang, Yung-Jue
- Choi, Yoon-La
- Mok, Tony S.
- Lee, Se-Hoon
- Venue
- Journal of Clinical Oncology
- Published
- 2022-06-10
- Volume
- 40
- Issue
- 17
- Pages
- 1916-1928
- ISSN
- 0732-183X (ISSN-L)
- 1527-7755
- 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
- hybrid
- doi.org
- 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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