Background Thousands of patients have benefited from the growing use of cancer immunotherapies. However, the success of these therapies can be highly variable. Strikingly, some murine tumor models show large variability in the outcome of cancer immunotherapies, even when the mice, tumor cells and anti-tumor immune cells injected into mice are all genetically identical. Here, we sought to analyze this variability in adoptive cell therapies, in order to identify the immune population driving this variability.

Methods To search for the occurrence of large variability ex vivo, we extracted mouse TCR-transgenic CD8+ T cells, and co-cultured them with antigen-expressing tumor cells. We used multiplexed in vitro assays and single-cell analysis of thousands of samples to analyze the inter-replicate variability of tumor cell killing and immune activation. We then developed a quantitative framework that uses statistical modeling and machine learning to extract useful information from this immunotherapeutic variability.

Results We identified conditions (e.g. cell numbers, antigen quality, tumor cell types) where large variations in the immune activation against cancer cells is observed even between technical replicates. Our framework allowed the prediction and identification of a rare population of naïve CD8+ T cells (‘Spark T cells’) that is necessary and sufficient to spark massive anti-tumor immune reactions. Applying this same framework to human TCR-engineered T cell blasts, we identified a different subpopulation of CD8+ T cells that has similar properties as the Spark T cells identified in naïve mouse CD8+ T cells, with potential relevance to predict the efficacy of immunotherapies for cancer patients.

Conclusions We envision this framework being applied to identify other relevant immune cell types that act as catalysts for successful cancer immunotherapies.