Local modeling

The size of tumor and immune cell populations and their interactions over time impact treatment outcomes

Immunotherapies that activate the immune system to seek out and kill cancer cells have dramatically improved outcomes for many patients with solid tumors. However, there is still a subset of patients who do not see the benefits of this type of therapy. Currently, there are no immune biomarkers that explain how patients with similar disease and characteristics may have different outcomes. In a new article published in the Journal for Cancer ImmunotherapyMoffitt Cancer Center researchers demonstrate how mathematical modeling can be used to analyze the impact of different cancer treatments on tumor and immune cell dynamics and help predict therapy outcomes and personalize cancer treatment .

Interactions between cancer cell populations with the surrounding immune environment are known to impact the development and progression of cancer and patient responses to immunotherapy. Some patients respond well to immunotherapies, while other patients do not. However, it is unclear what differentiates these patients.

“Just as early-stage cancers are treated differently than late-stage diseases, tumors with different degrees of immune involvement may require very different therapeutic approaches,” said Rebecca Bekker, first author of the paper and incumbent. a doctorate in cancer biology. student at Moffitt.

The Moffitt researchers wanted to improve their understanding of tumor-immune cell interactions to help predict patient outcomes and identify the best treatment options. Knowing that these dynamics are extremely complex and difficult to study in the laboratory, the team used an alternative approach to conceptualize these interactions with mathematical modelling. They developed a model that simulates the interactions between all possible combinations of tumor cell and immune cell numbers over time. They included metrics for tumor cell growth and clearance rate, as well as immune cell recruitment and exhaustion. The results of their model were either immune evasion, in which tumor cells reached 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 different types of therapies, including cytotoxic chemotherapy and cellular immunotherapies, which impact the size of tumor cell or immune cell populations, and inhibitors of immune checkpoints, which impact the nature of the interactions between tumor and immune cell populations. They also discussed the potential outcomes of combination therapies.

These models help conceptualize how therapies can be combined to achieve optimal patient outcomes 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 guide treatment.

“Mathematical abstraction in oncology provides a new and promising way to conceptualize the effect of various cancer treatments on a patient’s tumor and local immune environment and gives us the opportunity to rethink the numbers game of ‘immunotherapy,'” said Heiko Enderling, Ph.D., study author and associate member of Moffitt’s Department of Integrated Mathematical Oncology.

This study was supported by the National Cancer Institute (U01CA244100 and R21CA263911) and the Ocala Royal Dames for Cancer Research.

Source of the story:

Material provided by H. Lee Moffitt Cancer Center and Research Institute. Note: Content may be edited for style and length.