Optimization of CAR T cells treatments of hematological malignancies: An integrated human data-based approach using mathematical models


Cancer immunotherapies use components of the patient immune system to selectively target and attack tumor cells. Immunotherapies are an effective treatment option for hematological malignancies. The most successful type of immunotherapy today is Chimeric antigen receptor (CAR) T cell. This treatment consists of helping T cells to fight cancer by changing them in the lab so they can find and destroy cancer cells. CAR T cell therapies have shown relevant results in patients with B cell malignancies such as leukemia, lymphoma, and multiple myeloma. However, relapsed, or refractory B-cell malignancies after two or more lines of therapy have a poor prognosis.

Why Mathematics?

The problem is the correct identification of non-responders to CAR T cell therapy at the time of decision to treat. To consider this problem, this project aims to use mathematical models to describe disease dynamics in patients. These models will be used to optimize, personalize, and improve CAR T cells treatment of B cell malignancies.

The Team

The team for this project is integrated by experts in mathematical modeling and data analysis from MOLAB, medical doctors specialized in hematological malignancies and nuclear medicine experts. The tasks involve both the theoretical modeling of the disease and the effect of the therapy, as well as the use of medical data from the patients, which comes mainly in the form of PET scans.


The results are of interest for the medical doctors applying the CAR T cell therapy as it will make it possible to stratify the patients that will and will not respond. In this project there Is also potential interest for pharma companies developing the pharmaceuticals, as there is room for improvement and possible optimization.