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Cited by 89Cancer Immunotherapy and BiomarkersAI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Spatial interplay patterns of cancer nuclei and tumor-infiltrating lymphocytes (TILs) predict clinical benefit for immune checkpoint inhibitors

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Wang, Xiangxue, Barrera, Cristian, Bera, Kaustav, Viswanathan, Vidya Sankar, Azarianpour-Esfahani, Sepideh, Koyuncu, Can, Velu, Priya, Feldman, Michael D. +7 more·2022·Science Advances
DOI 10.1126/sciadv.abn3966PMC PMC9159577
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Abstract

Immune checkpoint inhibitors (ICIs) show prominent clinical activity across multiple advanced tumors. However, less than half of patients respond even after molecule-based selection. Thus, improved biomarkers are required. In this study, we use an image analysis to capture morphologic attributes relating to the spatial interaction and architecture of tumor cells and tumor-infiltrating lymphocytes (TILs) from digitized H&E images. We evaluate the association of image features with progression-free (PFS) and overall survival in non–small cell lung cancer (NSCLC) ( N = 187) and gynecological cancer ( N = 39) patients treated with ICIs. We demonstrated that the classifier trained with NSCLC alone was associated with PFS in independent NSCLC cohorts and also in gynecological cancer. The classifier was also associated with clinical outcome independent of clinical factors. Moreover, the classifier was associated with PFS even with low PD-L1 expression. These findings suggest that image analysis can be used to predict clinical end points in patients receiving ICI.

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Authors
Wang, X., Barrera, C., Bera, K., Viswanathan, V. S., Azarianpour-Esfahani, S., Koyuncu, C., Velu, P., Feldman, M. D., Yang, M., Fu, P., Schalper, K. A., Mahdi, H., Lu, C., Velcheti, V., & Madabhushi, A.
Affiliations
  • Case Western Reserve University (US)
  • Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, USA.
  • Weill Cornell Medicine (US)
  • University of Pennsylvania (US)
  • University of Colorado Anschutz (US)
  • Yale University (US)
  • Magee-Womens Research Institute (US)
  • NYU Langone Health (US)
  • Louis Stokes Cleveland VA Medical Center (US)
Corresponding author
  • Wang, Xiangxue
  • Madabhushi, Anant
ORCID
Venue
Science Advances
Publisher
American Association for the Advancement of Science (AAAS)
Published
2022-06-03
Volume
8
Issue
22
Pages
eabn3966-eabn3966
ISSN
2375-2548 (ISSN-L)
DOI
10.1126/sciadv.abn3966
PMC
PMC9159577
OpenAlex
W4281767664
Cited by
89
References
59
Citations by year
2026: 11 · 2025: 28 · 2024: 22 · 2023: 22 · 2022: 6
FWCI
8.16
Type
journal article
Language
en
Open access
Licence
cc-by-nc
Locations
  • Science Advances (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
Funders
  • U.S. Department of Veterans Affairs (I01 BX004121)
  • National Institutes of Health (5u54ca254566-04, UL1TR002548, UL1TR001863)
  • National Cancer Institute (U01CA248226, U54CA254566, U24CA199374, R01CA216579, U01CA239055, R01CA208236, R01CA202752, R01CA220581, R01CA257612, R01CA249992)
  • National Institute of Biomedical Imaging and Bioengineering (R43EB028736)
  • National Center for Research Resources (C06RR012463)
  • National Center for Advancing Translational Sciences
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
immune checkpoint inhibitors, tumor-infiltrating lymphocytes, spatial interaction, tumor architecture, predictive biomarkers, non-small cell lung cancer, gynecologic cancer, progression-free survival, overall survival, PD-L1 expression

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