A Mathematical Description of the Bone Marrow Dynamics during CAR T-Cell Therapy in B-Cell Childhood Acute Lymphoblastic Leukemia

• Main Authors: Álvaro Martínez-Rubio, Salvador Chulián, Cristina Blázquez Goñi, Manuel Ramírez Orellana, Antonio Pérez Martínez, Alfonso Navarro-Zapata, Cristina Ferreras, Victor M. Pérez-García, María Rosa
• Format: Article
• Language: English
• Published: MDPI AG 2021-06-01
• Series: International Journal of Molecular Sciences
• Subjects: CAR T; mathematical model; acute lymphoblastic leukemia; B cell; bone marrow
• Online Access: https://www.mdpi.com/1422-0067/22/12/6371
• ISSN: 1661-6596; 1422-0067

Chimeric Antigen Receptor (CAR) T-cell therapy has demonstrated high rates of response in recurrent B-cell Acute Lymphoblastic Leukemia in children and young adults. Despite this success, a fraction of patients’ experience relapse after treatment. Relapse is often preceded by recovery of healthy B cells, which suggests loss or dysfunction of CAR T-cells in bone marrow. This site is harder to access, and thus is not monitored as frequently as peripheral blood. Understanding the interplay between B cells, leukemic cells, and CAR T-cells in bone marrow is paramount in ascertaining the causes of lack of response. In this paper, we put forward a mathematical model representing the interaction between constantly renewing B cells, CAR T-cells, and leukemic cells in the bone marrow. Our model accounts for the maturation dynamics of B cells and incorporates effector and memory CAR T-cells. The model provides a plausible description of the dynamics of the various cellular compartments in bone marrow after CAR T infusion. After exploration of the parameter space, we found that the dynamics of CAR T product and disease were independent of the dose injected, initial B-cell load, and leukemia burden. We also show theoretically the importance of CAR T product attributes in determining therapy outcome, and have studied a variety of possible response scenarios, including second dosage schemes. We conclude by setting out ideas for the refinement of the model.