Based on shifted Poisson statistics, Salazar-Cavazos et al. developed the StoICS pipeline and found that stochastic activation of rare “spark T cells” – CD5lowC11ahigh in mice and CCR7lowCD45ROhigh in humans – accounts for variable cancer immunotherapy outcomes, even when immunological settings appear identical. Spark T cells demonstrate a distinct chromatin accessibility map and can rapidly produce IFNγ (and other cytokines) upon TCR triggering, which entrains neighboring T cells and decides the overall immunotherapeutic outcome. An ENTPD1highCD2high gene signature in human spark T cells predicted response to anti-CTLA-4 treatment.
Contributed by Ute Burkhardt
ABSTRACT: Cancer immunotherapies trigger highly variable responses in patients and in genetically identical mouse models. To assess the intrinsic stochasticity of these therapies, we performed thousands of well-controlled ex vivo immunoassays. We show that leukocyte responses and tumor cytotoxicity are highly variable at the macroscopic level and statistically distributed as a shifted Poisson process. Stochastic activation of a rare subpopulation of T cells (so-called Spark T cells), coupled with a paracrine interferon (IFN)-_-driven positive feedback, accounts for this measured "noise" in immunotherapeutic reactions. We integrated these quantitative insights into a custom-designed machine-learning pipeline to analyze immune reactions with single-cell resolution. This led us to phenotypically and functionally identify Spark T cells in murine naive T cells and in human T cell blasts as prepared for adoptive T cell therapy. We then demonstrate their relevance in explaining variable outcomes in cancer immunotherapies.
