Systems Biology Analysis of T-cell Activation and Anti-tumor Immunity

Introduction:: Initiation of an immune response requires activation of one or more naïve T cells. Each naïve T cell has an antigen specific T cell receptor, and these cells traffic through the blood and lymphatic vasculatures, visiting each of the ~500 lymph nodes in search of matching antigen. If a match is made, then the naïve T cell activates and proliferates. The resulting effector T cells enter the blood circulation to find and eradicate the source of antigen. Similar to pathogens like viruses, tumors can also create antigens that accumulate in lymph nodes, where they can activate T cells. Unfortunately, even when this happens and the anti-cancer effector cells find the tumor, they are prevented from killing by immune checkpoint programs that quench the immune response. Multiple drugs are now approved that block these checkpoint programs, re-enabling anti-tumor immunity, and there have been miraculous cures in a subset of patients. However, there is an urgent need to understand why most patients (~80%) do not respond to these therapies.

Materials and Methods:: Here, we propose that immune activation in these non-responding patients may be prevented or delayed due to transport limitations that prevent cognate naïve T cells and tumor antigen to colocalize in the same lymph node. We use mechanistic, multiscale computational modeling to identify potential reasons for clinical failure of immune checkpoint therapies and develop strategies for improving naïve T cell activation and tumor killing. The schematics of the multi-scale model with its different computational layers, including 1) a stochastic part to simulate activation of immunity and 2) a deterministic part to simulate tumor-immune interactions related to T cell trafficking and transport barriers, are shown in Fig.1 A, B and C, respectively.

Results, Conclusions, and Discussions::

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  Results
  
  

The model provides a mechanistic framework for optimizing cancer immunotherapy and developing testable solutions to unleash anti-tumor immune responses for more patients with cancer. The results show that tumor antigen production rate is a critical parameter, and that patients with low tumor antigen production can benefit from additional treatment to enhance neo antigen production and improve immune checkpoint inhibition therapy. The simulations show the importance of the tumor draining lymph node (TDLN) for the initiation of tumor immunity. The simulation results of anti-tumor activation are shown in Fig.1 D. We hypopthesize that removal of the TDLN results in a delay in T cell activation, because antigen accumulation in other lymph nodes is slower. Clinically, elective nodal irradiation in which the TDLNs are targeted by the radiation field, is a common strategy designed to minimize regional recurrence. We predict that this radiation disrupts the ability of the targeted lymph nodes to generate ant—cancer immune responses

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  Conclusions
  
  

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  The model provides a mechanistic framework for optimizing cancer immunotherapy and developing testable solutions to improve anti-tumor immune responses for more patients with cancer.
  
  


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  The results show that antigen production rate and trafficking of naive T cells into the lymph nodes are key parameters, and that treatments designed to enhance tumor antigen production may improve immunotherapy therapies.
  
  


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  The model can be utilized to quickly optimize radiation for each patient as personalized therapy.
  
  
  

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  Discussions
  
  Our results suggest that nodal irradiation can be counter-productive in patients with tumors that have low antigen production, because of toxic effects on proliferating T cells in the active nodes. The impact of nodal irradiation is less when there is sufficient tumor antigen because nT-cell activation is more frequent, and not rate-limiting.
  
  

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