Title:	Computationally-optimised immunotherapy
DNr:	NAISS 2024/22-1267
Project Type:	NAISS Small Compute
Principal Investigator:	Luca Panconi <luca.panconi@scilifelab.se>
Affiliation:	Stockholms universitet
Duration:	2024-10-08 – 2025-11-01
Classification:	10610
Keywords:

Abstract

Cancer is the second leading cause of mortality worldwide, accounting for over 10 million deaths each year. During a healthy immune response, antigens bind to T cell receptors (TCR) on adjacent lymphocytes and trigger TCR oligomerisation. It is well-documented that clustering of TCR amplifies the signal from antigen-presenting cells and serves as a fundamental precursor of the immune response. The success of immunotherapy depends largely on expression of neoantigens, such as programmed death-ligand 1 (PD-L1), and nanoscale organisation of T cell receptors (TCR), which aggregate to promote signal digitisation and activation. Despite exhibiting the hallmarks of an immunogenic cancer, immunotherapy often fails to induce TCR oligomerisation in TNBC. However, therapeutic mediators can directly elicit aggregation of T cell receptors, inducing immunological activation in the presence of cancer cells, to target and destroy tumours. Asymmetric DNA origami constructs can be coated with affibodies which bind specifically to neoantigens on cancer cells while inducing TCR oligomerisation on local T cells. DNA origami is highly versatile, and allows for tailoring of therapeutic affinity and avidity. Such therapeutic mediators could target many cancer antigens, but this process would differ on a cell-to-cell and antigen-to-antigen basis. Immunotherapeutic treatment strategies depend heavily on nanoscale receptor organisation in pre- and post-activated T cells and neoantigen prevalence in cancerous targets. In vitro testing is expensive, time-consuming, and limited to mediator availability – the only viable solution is in silico therapeutic design. Computational optimisation for immunotherapy has been hindered by a lack of receptor visualisation, but with the advent of super-resolution, we now have unique insights into protein maps at nanoscale resolution. Protein aggregation dynamics (PAD) simulators, founded on agent-based modelling (ABM) approaches, show promise in simulating transmembrane receptor motility. By constructing simulations, learned from protein localisation data, it is possible to track receptor dynamics, determine requirements for clustering, and predict signal digitisation. As such, the goal of this project is to develop in silico methods of quantifying immunotherapeutic efficiency and optimising treatment parameters for eliciting T cell activation. I will take TNBC as a specific case study, with intent to generalise towards other hormone-negative cancers. This will yield an open-source framework for modelling arbitrary transmembrane protein dynamics, with an emphasis on targeting T cell receptors and neoantigens with therapeutic mediators. Fundamentally, this framework can be used to optimise DNA origami structures, as nanotherapeutics for immunotherapy, in treating TNBC and other immunogenic cancers.