Immunotherapy, which activates the immune system to seek out and kill cancer cells, has significantly improved outcomes for many patients with solid tumors. However, there is still a group of patients who do not see a benefit from this type of therapy. Currently, there are no immune biomarkers to describe how patients with similar disease and patient characteristics may have different outcomes. In a new article published in Journal for Immunotherapy of CancerResearchers at Moffitt Cancer Center demonstrate how mathematical modeling can be used to analyze the effect of different cancer treatments on tumor and immune cell dynamics and help predict therapy outcomes and personalize cancer treatments. helps.
It is known that interactions between cancer cell populations with the surrounding immune environment influence cancer development and progression and patient responses to immunotherapy. Some patients respond well to immunotherapy, while other patients do not. However, it is not clear what differentiates these patients.
“Just as early-stage cancers are treated differently than late-stage cancers, tumors with different types of immune involvement may require very different therapeutic approaches,” Rebecca Baker, the first of the article Author and Cancer Biology Ph.D. he said. Student at Moffitt.
The Moffitt researchers wanted to improve their understanding of tumor and immune cell interactions to help predict patients’ outcomes and identify the best therapeutic options. Knowing these dynamics is extremely complex and difficult to study in the laboratory, the team used an alternative approach to conceptualize these interactions with mathematical modeling. They developed a model that simulated interactions between all possible combinations of tumor cell and immune cell population numbers over time. They included parameters for the rate of tumor cell growth and elimination, and for immune cell recruitment and exhaustion. The results of their model were either immune escape, in which tumor cells grew to their maximum potential, or tumor control through the antitumor activity of immune cells.
The researchers then used their model to simulate and predict the outcomes of a variety of treatments, including cytotoxic chemotherapy and cell-based immunotherapy, which affect the size of a tumor cell or immune cell population, and immune checkpoint inhibitors, that affect nature. Interaction between tumor and immune cell populations. He also addressed the potential consequences of the combination therapies.
These models help to understand how therapies can be combined to achieve optimal outcomes for patients through immune-cell control of tumor cell populations. In the future, the researchers hope that mathematical modeling can be used in the clinic to help predict patient responses to therapy and guidance treatments.
“Mathematical oncology abstraction provides a novel and promising way to conceptualize the effect of different cancer treatments on a patient’s tumor and local immune environment and gives us the opportunity to rethink the immunotherapy numbers game,” said Heiko Anderling, PhD, study Said author and associate member of the Department of Integrated Mathematical Ontology at Moffitt.
This study was supported by the National Cancer Institute (U01CA244100 and R21CA263911) and the Ocala Royal Dames for Cancer Research.
material provided by H. Lee Moffitt Cancer Center and Research Institute, Note: Content can be edited for style and length.