癌症免疫治疗中的随机性源于罕见但功能关键的 Spark T 细胞

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)-γ-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.