Mapping the evolution of T cell states during response and resistance to adoptive cellular therapy

Immune therapies have transformed the cancer therapeutic landscape but fail to benefit most patients. To elucidate the underlying mechanisms by which T cells mediate elimination of leukemia, we generated a high-resolution map of longitudinal T cell dynamics within the same tumor microenvironment (TME) during response or resistance to donor lymphocyte infusion (DLI), a widely used immunotherapy for relapsed leukemia. We analyzed 87,939 bone marrow-derived single T cell transcriptomes, along with chromatin accessibility and single T cell receptor clonality profiles, by developing novel machine learning tools for integrating longitudinal and multimodal data. We found that pre-treatment enrichment and post-treatment rapid, durable expansion of terminal (TEX) and precursor (TPEX) exhausted subsets, respectively, defined DLI response. A contrasting, heterogeneous pattern of T cell dysfunction marked DLI resistance. Unexpectedly, TPEX cells that expanded in responders did not arise from the infusion product but instead from both pre-existing and novel clonotypes recruited to the TME. Our unbiased dissection of the TME using a Bayesian method, Symphony, defined the T cell circuitry underlying effective human anti-leukemic immune responses that may be broadly relevant to other exhaustion antagonists across cancers. Finally, we provide a general analysis paradigm for exploiting temporal single-cell genomic profiling for deep understanding of therapeutic scenarios beyond oncology.