Evaluating optimal therapy robustness by virtual expansion of a sample population, with a case study in cancer immunotherapy
Year: 2017
Type: article
Abstract: 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.
Language: English
Subfield: Modeling and Simulation
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2734520279