Journal article
Mathematical prediction of clinical outcomes in advanced cancer patients treated with checkpoint inhibitor immunotherapy
SCIENCE ADVANCES, v 6(18), eaay6298
Apr 2020
PMID: 32426472
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
We present a mechanistic mathematical model of immune checkpoint inhibitor therapy to address the oncological need for early, broadly applicable readouts (biomarkers) of patient response to immunotherapy. The model is built upon the complex biological and physical interactions between the immune system and cancer, and is informed using only standard-of-care CT. We have retrospectively applied the model to 245 patients from multiple clinical trials treated with anti-CTLA-4 or anti-PD-1/PD-L1 antibodies. We found that model parameters distinctly identified patients with common (n = 18) and rare (n = 10) malignancy types who benefited and did not benefit from these monotherapies with accuracy as high as 88% at first restaging (median 53 days). Further, the parameters successfully differentiated pseudo-progression from true progression, providing previously unidentified insights into the unique biophysical characteristics of pseudo-progression. Our mathematical model offers a clinically relevant tool for personalized oncology and for engineering immunotherapy regimens.
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Details
- Title
- Mathematical prediction of clinical outcomes in advanced cancer patients treated with checkpoint inhibitor immunotherapy
- Publication Details
- SCIENCE ADVANCES, v 6(18), eaay6298
- Publisher
- AMER ASSOC ADVANCEMENT SCIENCE; WASHINGTON
- Grant note
- We gratefully acknowledge support from the Andrew Sabin Family Fellowship, Center for Radiation Oncology Research, the Sheikh Ahmed Center for Pancreatic Cancer Research, institutional funds from The University of Texas MD Anderson Cancer Center, GE Healthcare, Philips Healthcare, and Cancer Center Support (Core) Grant CA016672 from the National Cancer Institute (NIH) to MD Anderson. E.J.K. was also supported by Project Purple, NIH (U54CA210181-01, U01CA200468, and U01CA196403), and the Pancreatic Cancer Action Network (16-65-SING). Z.W. and V.C. were also supported by NSF grant DMS-1716737 and NIH grants 1U01CA196403, 1U01CA213759, 1R01CA226537, 1R01CA222007, and U54CA210181. V.C. was also supported by the University of Texas System STARS Award. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Drexel University
- Web of Science ID
- WOS:000531089700008
- Scopus ID
- 2-s2.0-85084682191
- Other Identifier
- 991021860766204721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Oncology