1230 Design of enhanced TCR against cancer antigens using an AI system

Background

Naturally occurring TCR targeting cancer antigens are associated with relatively low affinity comparatively to TCR targeting external pathogens. This might be explained by the proximity of cancer specific sequences to self. Engineering of modified affinity enhanced TCR constitutes a possible solution, however, TCR binding remains challenging to model using structural biology approaches because of the conformational flexibility of the TCR complex. The use of machine learning based methods constitutes a promising approach to design TCR of higher affinity. Herein, we report enhanced affinity TCR sequences against cancer antigens designed using TCRPPO, a proprietary pipeline for TCR sequence optimization.

Methods

TCRPPO is a new reinforcement-learning framework based on proximal policy optimization to optimize TCRs through a mutation policy. Briefly after training the system on a series of TCR sequences known to bind a given target, TCRPPO introduces mutations on existing sequence to achieve higher affinity guided by a reward function factoring in affinity of the new sequence and the likelihood for this sequence to be a valid TCRs. To validate our approach, we designed a series of candidate TCR sequences against known clinically relevant cancer antigens (KRAS G12V and MART-1) and evaluated their biological functional potency. To do so, genes encoding variable regions of the original and optimized TCRα and β chains were assembled into plasmid vectors containing a constant region of a TCRα or TCRβ chain. TAP fragments of TCRα and TCRβ together with a NFAT-Luc reporter plasmid were transfected into the ΔTCR Jurkat cell line. The cells were cultured in the presence of antigen presenting cells with or without target peptide, and then the activation of the reporter gene was measured by luciferase assay.

Results

Our AI-based TCR engineering approach generated valid enhanced TCR sequences against the selected epitopes. Engineered TCR transfected cells showed higher activity in the functional assay and demonstrated that TCR generated using a mutation policy can achieve higher biological activity than endogenous TCR. Enhanced TCR generated against KRAS G12V and MART-1 are dissimilar from already described TCR.¹

Conclusions

We successfully engineered TCRs to have better antigen recognition. The enhanced TCRs warrant further characterization to evaluate their therapeutic potential. Beyond this case, our approach constitutes a pipeline that might be applied to other targets for which alternative TCRs are required.

Reference

Chen Z, Min MR, Guo H, Cheng C, Clancy T, Ning X. T-Cell receptor optimization with reinforcement learning and mutation polices for precision immunotherapy. In: Tang, H. (eds) Research in Computational Molecular Biology. RECOMB. 2023;Lecture Notes in Computer Science(), vol 13976. Springer, Cham. https://doi.org/10.1007/978-3-031-29119-7_11.