The size of tumour and immune cell populations and their interactions over time impact outcomes to treatment.
Immunotherapies that activate the immune system to seek and kill cancer cells have greatly improved outcomes for many patients with solid tumours. There is still, however, a subset of patients who do not see benefit from this type of therapy. Currently, there are no immune biomarkers that explain how patients with similar disease and patient characteristics can have different outcomes. In an article published in the Journal for ImmunoTherapy of Cancer, Moffitt Cancer Center researchers in Florida, US, have demonstrated how mathematical modelling can be used to analyse the impact of different cancer treatments on tumour and immune cell dynamics and help predict outcomes to therapy and personalise cancer treatment.
It is known that interactions between cancer cell populations with the surrounding immune environment 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 like early stage cancers are treated differently than late-stage disease, tumours with different degrees of immune involvement may need very different therapeutic approaches,” said Rebecca Bekker, article first author and Cancer Biology Ph.D. student at Moffitt.
Moffitt researchers wanted to improve their understanding of tumour and immune cell interactions to help predict outcomes for patients and identify the best therapeutic options. Knowing these dynamics are extremely complex and difficult to study in a laboratory setting, the team used an alternative approach to conceptualise these interactions with mathematical modelling. They developed a model that simulates interactions between all possible combinations of tumour cell and immune cell population numbers over time. They included parameters for the rate of tumour cell growth and elimination, and immune cell recruitment and exhaustion. The outcomes of their model were either immune escape, wherein the tumour cells grew to their maximum potential, or tumour control through the anti-tumour activity of immune cells.
The researchers then used their model to simulate and predict outcomes to different types of therapies, including cytotoxic chemotherapy and cell-based immunotherapies, which impact the size of the tumour cell or immune cell populations, and immune checkpoint inhibitors, which impact the nature of the interactions between tumour and immune cell populations. They also addressed potential outcomes to combination therapies.
These models help conceptualise how therapies can be combined to achieve optimal outcomes for patients through immune-cell control of tumour cell populations. In the future, the researchers hope mathematical modelling can be used in the clinic to help predict patient responses to therapy and guide treatment.
“Mathematical oncology abstraction provides a novel and promising way to conceptualise the effect of various cancer treatments on a patient’s tumour and the local immune environment and gives us an opportunity to rethink the immunotherapy numbers game,” said Heiko Enderling, Ph.D., study author and associate member of the Integrated Mathematical Oncology Department at Moffitt.
This study was supported by the National Cancer Institute (U01CA244100 and R21CA263911) and the Ocala Royal Dames for Cancer Research.