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    Bioinformatics, Computational and Systems Biology

    Keytruda: The Key to reversing CD8+ T cell exhaustion?

    (B-52) Keytruda: The Key to reversing CD8+ T cell exhaustion?

    Thursday, October 12, 2023
    9:30 AM – 10:30 AM PDT
    Location: Exhibit Hall - Row B - Poster # 52
    Introduction::

    Upon stimulation, naïve CD8+ T cells proliferate and differentiate into a variety of memory and effector cell types; however, failure to clear antigens in chronic infections causes prolonged stimulation of CD8+ T cells, ultimately leading to T cell exhaustion (TCE). Many experimental studies have shown that dysfunctional CD8+ T cells in cancer are characterized by high expression levels of inhibitory receptors, including PD1 (programmed death receptor 1), a major regulator of TCE. T cells with high PD1 expression lose the ability to eliminate cancer. Recently, the U.S. Food and Drug Administration approved a new immunotherapy, Keytruda (pembrolizumab) to target the PD1 pathway in hopes of reversing TCE. Keytruda is a drug that binds to PD1 to help immune cells kill cancer cells better and is used to treat many different types of cancer. However, clinical trials have shown that 30-40% of patients relapse, motivating a need to mechanistically understand why some T cells respond well to this treatment while others do not. Various models have been proposed to account for the progression of T cell states towards terminal differentiation, from the acute phase of immune responses to exhaustion. Here, we utilize a previously published data-driven Boolean model of gene regulatory interactions to quantitively investigate the effects of Keytruda on the evolution of T cell states of gene regulation along the path to the terminal state.



    Materials and Methods:: We adapted a data-driven Boolean model of gene regulation (Ildefonso et. al 2023) to investigate the effects of introducing Keytruda, an FDA approved drug that targets TCE. The previous model comprised a 25-node network and included regulators that promote “proliferative pro-memory” (PP) or “effector exhausted” (EE) states. We incorporated additional rules to introduce Keytruda into our model (Figure 1A) to compare wild-type (WT, no treatment) to Keytruda treatment. Ten thousand model simulations were implemented as a series of logic statements that are executed in an asynchronous update paradigm.

    Results, Conclusions, and Discussions::

    We applied the model to quantitively investigate the evolution of T cell states of gene regulation when perturbed with the PD1 inhibitor, Keytruda, along the path to the terminal state (Figure 1A). Our systems analysis identifies distinct gene expression patterns corresponding to terminal pro-memory states in response to Keytruda, even when starting from identical underlying networks. The sequence of changes in gene expression that gives rise to different cell states (Figure 1B, example from attractor end state 1 [ES1]) include five attractor states for WT and seven attractor states for Keytruda. The represented key gene drivers for WT and Keytruda reveal a shift from terminal exhaustion to pro-memory (Figure 1C, example from ES1) with increased metabolic activity and IL signaling.



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