Mathematical deconvolution of CAR T-cell proliferation and exhaustion from real-time killing assay data

Sahoo, Prativa; Yang, Xin; Abler, Daniel; Maestrini, Davide; Adhikarla, Vikram; Frankhouser, David; Cho, Heyrim; Machuca, Vanessa; Wang, Dongrui; Barish, Michael; Gutova, Margarita; Branciamore, Sergio; Brown, Christine E.; Rockne, Russell C. (2020). Mathematical deconvolution of CAR T-cell proliferation and exhaustion from real-time killing assay data. Journal of The Royal Society Interface, 17(162), p. 20190734. The Royal Society 10.1098/rsif.2019.0734

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Chimeric antigen receptor (CAR) T-cell therapy has shown promise in the treatment of haematological cancers and is currently being investigated for solid tumours, including high-grade glioma brain tumours. There is a desperate need to quantitatively study the factors that contribute to the efficacy of CAR T-cell therapy in solid tumours. In this work, we use a mathematical model of predator-prey dynamics to explore the kinetics of CAR T-cell killing in glioma: the Chimeric Antigen Receptor T-cell treatment Response in GliOma (CARRGO) model. The model includes rates of cancer cell proliferation, CAR T-cell killing, proliferation, exhaustion, and persistence. We use patient-derived and engineered cancer cell lines with an in vitro real-time cell analyser to parametrize the CARRGO model. We observe that CAR T-cell dose correlates inversely with the killing rate and correlates directly with the net rate of proliferation and exhaustion. This suggests that at a lower dose of CAR T-cells, individual T-cells kill more cancer cells but become more exhausted when compared with higher doses. Furthermore, the exhaustion rate was observed to increase significantly with tumour growth rate and was dependent on level of antigen expression. The CARRGO model highlights nonlinear dynamics involved in CAR T-cell therapy and provides novel insights into the kinetics of CAR T-cell killing. The model suggests that CAR T-cell treatment may be tailored to individual tumour characteristics including tumour growth rate and antigen level to maximize therapeutic benefit.

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