期刊
SCIENCE ADVANCES
卷 8, 期 22, 页码 -出版社
AMER ASSOC ADVANCEMENT SCIENCE
DOI: 10.1126/sciadv.abn3966
关键词
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资金
- National Cancer Institute [1U24CA199374-01, R01CA249992-01A1, R01CA202752-01A1, R01CA208236-01A1, R01CA216579-01A1, R01CA220581-01A1, R01CA257612-01A1, 1U01CA239055-01, 1U01CA248226-01, 1U54CA254566-01]
- National Heart, Lung and Blood Institute [1R01HL15127701A1]
- National Institute of Biomedical Imaging and Bioengineering [1R43EB028736-01]
- National Center for Research Resources [1 C06 RR12463-01]
- VA Merit Review Award from the United States Department of Veterans Affairs Biomedical Laboratory Research and Development Service [IBX004121A]
- Office of the Assistant Secretary of Defense for Health Affairs, through the Breast Cancer Research Program [W81XWH-19-1-0668]
- Prostate Cancer Research Program [W81XWH-15-1-0558, W81XWH-20-1-0851]
- Lung Cancer Research Program [W81XWH-18-1-0440, W81XWH-20-1-0595]
- Peer Reviewed Cancer Research Program [W81XWH-18-1-0404]
- Kidney Precision Medicine Project (KPMP) Glue Grant
- Ohio Third Frontier Technology Validation Fund
- Clinical and Translational Science Collaborative of Cleveland from the National Center for Advancing Translational Sciences (NCATS) component of the NIH [UL1TR0002548]
- NIH Roadmap for Medical Research
- Wallace H. Coulter Foundation Program in the Department of Biomedical Engineering at Case Western Reserve University
- Bristol Myers Squibb
- AstraZeneca
- Boehringer Ingelheim
This study utilizes image analysis to capture morphologic attributes and evaluates their association with survival in lung cancer and gynecological cancer patients treated with immune checkpoint inhibitors (ICIs).
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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