Distinct Tumor-Immune Ecologies in Patients with Lung Cancer Predict Progression and Define a Clinical Biomarker of Therapy Response
Integration of multiplexed imaging, spatial statistics, and machine learning identifies distinct tumor-immune ecologies that differentiate immunotherapy responders from nonresponders, improving the prediction of progression to guide precision therapy. This article is part of a special series: Driving Cancer Discoveries with Computational Research, Data Science, and Machine Learning/AI .
Authors
- Jhanelle E. Gray (ORCID: https://orcid.org/0000-0003-1304-7062)
- Theresa A. Boyle (ORCID: https://orcid.org/0000-0001-5950-3970)
- Zarifakhanim Gahramanli (ORCID: https://orcid.org/0000-0002-9353-1542)
- Amer A. Beg (ORCID: https://orcid.org/0000-0003-3834-774X)
- Robert A. Gatenby (ORCID: https://orcid.org/0000-0002-1621-1510)
- Scott J. Antonia (ORCID: https://orcid.org/0000-0002-0029-9685)
- Chandler Gatenbee (ORCID: https://orcid.org/0000-0002-9730-5964)
- Alexander R.A. Anderson (ORCID: https://orcid.org/0000-0002-2536-4383)
- Sandhya Prabhakaran (ORCID: https://orcid.org/0000-0002-9537-507X)
- Mark Robertson‐Tessi (ORCID: https://orcid.org/0000-0003-0143-9587)
Institutions
- Duke University (US)
- Moffitt Cancer Center (US)
Publication Details
- Journal
- Cancer Research
- Published
- 2025-12-19
- DOI
- https://doi.org/10.1158/0008-5472.can-25-1594
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00