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1276 From TCR-pMHC binding kinetic characteristics to TCR phosphorylation and initiation of signal transduction
  1. Jonathan Desponds,
  2. Mayra Cruz Tleugabulova,
  3. Jeanne Cheung,
  4. Shirley Ng Palace,
  5. Martine Darwish,
  6. Geraldine Strasser,
  7. Andrey Shaw,
  8. Ira Mellman,
  9. Andrei Chernyshev and
  10. Darya Orlova
  1. Genentech, South San Francisco, CA, USA


Background The development of adoptive cell therapy has made the understanding of T-cell target recognition and activation crucial to cancer treatment. Indeed, the success of many of these therapies directly depends on our ability to predict or measure the specificity of T-cell receptors (TCR) to neoantigens and understand how these specificities translate into activation. While the dynamics of TCR-pMHC binding have been under careful scrutiny for a long time, many fundamental unknowns remain regarding the aspects of the dynamics that relate to immune function. Among the key factors that have impeded progress is the fact that most experimental approaches have relied on using soluble TCRs, as opposed to receptors properly assembled in the plasma membrane.

Methods To overcome these limitations, we developed a cell-based assay (figure 1A) to monitor the binding and unbinding of whole CD8 T-cells to a variety of target peptide-MHC monomers in solution using flow cytometry (figure 1B). Using anti-CD8 antibodies, we also measured the contribution of co-receptors to the formation and stability of the TCR-pMHC complex. In parallel, we measured the phosphorylation of CD3 ζ (figure 1C) to quantify the first step of T-cell activation (figure 1D) and its relation to the TCR occupancy and TCR-pMHC binding kinetics. Using a mix of deterministic and stochastic mathematical tools, we developed a minimal model that captures the kinetics of TCR-pMHC interaction.

Results We find that binding kinetics exhibit complex, peptide- and concentration-dependent non-linear behavior that is time-sensitive. Our minimal effective model succeeded at separating peptides based on the strength of the interaction (including functional avidity) and recapitulating the key features of binding dynamics. Importantly, that model is able to extrapolate experimental measurements to other ranges of concentrations for a given TCR, making it a precious tool for the selection of optimal cognate TCRs. We explored hypotheses regarding mechanistic interpretation of the observed dynamics. In particular, using blocking anti-CD8 antibodies, we separated effects of TCR binding from those of co-receptor binding. We also examined the connection between TCR occupancy and TCR-pMHC binding kinetic rates and the phosphorylation of the CD3 ζ chain.

Conclusions Our unique approach, combining novel experimental methods and mathematical modeling, can extract biomolecular TCR-pMHC interaction rates from TCRs assembled in the plasma membrane. It captures the complexity of TCR-pMHC interactions and connects it to the intracellular signal transduction.

Abstract 1276 Figure 1

Outline of the TCR-pMHC quantitative assessment workflowCell-based monomer assay (A) measurement of TCR-pMHC binding (B) and TCR phosphorylation (C) combined with mathematical modeling to understand and predict T-cell activation (D)

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