Sequential adjustment of cytotoxic T lymphocyte densities improves efficacy in controlling tumor growth

Roxana Kazhen (1) , Sabina Muller (1) , Fanny Lafouresse (1) , Salvatore Valitutti (1) , Sylvain Cussat-Blanc (2, 3)

1 CRCT - Centre de Recherches en Cancérologie de Toulouse
2 IRIT-REVA - Real Expression Artificial Life
3 UT Capitole - Université Toulouse Capitole

Understanding the human cytotoxic T lymphocyte (CTL) biology is crucial to develop novel strategies aiming at maximizing their lytic capacity against cancer cells. Here we introduce an agent-based model, calibrated on population-scale experimental data that allows quantifying human CTL per capita killing. Our model highlights higher individual CTL killing capacity at lower CTL densities and fits experimental data of human melanoma cell killing. The model allows extending the analysis over prolonged time frames, difficult to investigate experimentally, and reveals that initial high CTL densities hamper efficacy to control melanoma growth. Computational analysis forecasts that sequential addition of fresh CTL cohorts improves tumor growth control. In vivo experimental data, obtained in a mouse melanoma model, confirm this prediction. Taken together, our results unveil the impact that sequential adjustment of cellular densities has on enhancing CTL efficacy over long-term confrontation with tumor cells. In perspective, they can be instrumental to refine CTL-based therapeutic strategies aiming at controlling tumor growth.

Mots clés: Human cytotoxic T lymphocyte; CTL

Domaines: Intelligence artificielle [cs.AI]; Cancer

Roxana Kazhen, Sabina Muller, Fanny Lafouresse, Salvatore Valitutti, Sylvain Cussat-Blanc. Sequential adjustment of cytotoxic T lymphocyte densities improves efficacy in controlling tumor growth. Scientific Reports, 2019, 9 (12308), pp.0. ⟨10.1038/s41598-019-48711-2⟩. ⟨hal-02940967⟩