Mathematical modelling of tumour-immune interactions in presence of checkpoint inhibitor-based therapies for the treatment of cancer

Arora, Vaibhav; Ali, Sk. Meheraj; Karmakar, Arka Das; Sen, Pramita

doi:10.1016/j.matpr.2021.11.481

Cited by 1 publication

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“…Studies have shown that formation of CD28-B7 complexes is responsible for generating positive stimulatory signals for activation of T cells and the checkpoint protein CTLA4, upregulated on binding of tumour antigens with T cell receptors, acts as a competitive inhibitor to the CD28-B7 complex formation resulting in diminution of T cell activation (Wei et al 2017;Arora et al 2022;Shiravand et al 2022a). The checkpoint protein PD-1 attenuates T cell activation in two ways, firstly it binds with PD-L1 and PD-L2 ligands, generates a negative signal and inhibits the T cell activity and secondly, the PD1-PDL1 complex inhibits CD28 binding with B7 ligands leading to T cell deactivation (Wei et al 2017;Liu et al 2019;Shiravand et al 2022b).…”

Section: Introductionmentioning

confidence: 99%

A mathematical modeling framework for deciphering the dynamics of tumour-immune interactions in presence of checkpoint inhibitors

Sen¹,

Ghosh²

2022

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Targeting checkpoint proteins expressed on surfaces of T cells as a consequence of sustained tumour antigen stimulation leads to reinvigoration of immune responses and durable clinical benefits in cancer patients. However, the therapeutic interventionsusing checkpoint inhibitors like anti-CTLA4 and anti-PD1 produce different outcomes in different individuals as a consequence of complex interactions between tumour antigens, immune cells and immune responses. Here a discrete time mathematical model is proposed for establishing the quantitative underpinningsof the tumour-immune interplay occurring during tumour growth. The mathematical motif succintlydescribes the tumour growth history with time, the dynamics of tumour antigens and effector T cells and emphasizes on the effect of primary immune responses alone and combination of primary and secondary immune responses on tumour regression. The model compares the tumour growth dynamics observed in persons lacking secondary immune responses with that observed in persons with both immune responses. The anti-tumour immune responses comprising the primary and secondary responses are downregulated by the checkpoint proteins like PD1 and CTLA4 upon continuous stimulation by tumour antigens. The anti-PD1 and anti-CTLA4 inhibitors inhibit the checkpoint proteins and re-strengthen the immune responses resulting in sustained tumour growth or elimination of the tumour. The model developed further forecasts the effect of variation in the strength of immune responses on tumour growth dynamics, and the various outcomes observed when the monotherapy or combination therapy is discontinued after a certain period of time. Further, the model predictions are used to explore the synergistic interactions between the inhibitors and report the maximum synergy achievable for the drug combination.

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