Evaluating optimal therapy robustness by virtual expansion of a sample population, with a case study in cancer immunotherapy
Open accessAbstract
Significance: A successful cancer therapy induces a strong antitumor response while causing minimal side effects. The heterogeneous nature of cancer observed across different regions of the primary tumor, across metastatic sites, across time, and across patients makes designing such a successful therapy challenging. Both standard of care and finely tailored treatment protocols run the risk of not exhibiting a robust antitumor response in the face of these uncertainties. Here we introduce a platform for exploring this robustness question using treatment response data from a sample population. Our method integrates these experimental data with statistical and mathematical techniques, allowing us to quantify therapeutic robustness. Using this approach, we identified a robust therapeutic protocol that combines oncolytic viruses with an immunotherapeutic vaccine.
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Some statistical models allow for the prediction of optimal therapy parameters by using time-course experimental data and statistical analyses for identifying robust and optimal treatment protocols.
“In addition, some statistical models allow for the prediction of optimal therapy parameters by using time-course experimental data and statistical analyses for identifying robust and optimal treatment protocols (see ref.97 for a proof of concept).”
From How to design preclinical studies in nanomedicine and cell therapy to maximize the prospects of clinical translation (2018) · cites this paper as [97]
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- Authors
- Barish, S., Ochs, M. F., Sontag, E. D., & Gevertz, J. L.
- Affiliations
- The College of New Jersey (US)
- Rutgers, The State University of New Jersey (US)
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2017-07-17
- Volume
- 114
- Issue
- 31
- Pages
- E6277-E6286
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC5547617
- OpenAlex
- W2734520279
- Cited by
- 48
- References
- 74
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 3 · 2023: 4 · 2022: 5 · 2021: 7 · 2020: 8 · 2019: 4 · 2018: 8 · 2017: 1
- FWCI
- 4.30
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- HHS | National Institutes of Health (R01 LM011000, 1R01GM100473)
- DOD | USAF | AFMC | Air Force Office of Scientific Research (FA9550-14-1-0060)
- DOD | United States Navy | Office of Naval Research (N00014-13-1-0074)
- Topics
- Physical Sciences › Mathematics › Modeling and Simulation › Mathematical Biology Tumor Growth
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Virus-based gene therapy research
- Life Sciences › Immunology and Microbiology › Immunology › Immunotherapy and Immune Responses
- Keywords
- cancer immunotherapy, oncolytic viruses, cancer vaccines, treatment response heterogeneity, antitumor immunity, tumor heterogeneity
- MeSH terms
- Algorithms, Animals, Cell Differentiation, Computer Simulation, Dendritic Cells, Disease Models, Animal, Immunotherapy, Melanoma, Models, Theoretical, T-Lymphocytes, Cytotoxic, Cancer Vaccines, Oncolytic Virotherapy, Oncolytic Viruses, Mice
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