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

Pavan Bachireddy, Elham Azizi, Cassandra Burdziak, Vinhkhang N. Nguyen, Christina S. Ennis, Katie Maurer, Cameron Y. Park, Zi Ning Choo, Shuqiang Li, Satyen H. Gohil, Neil G. Ruthen, Zhongqi Ge, Derin B. Keskin, Nicoletta Cieri, Kenneth J. Livak, Haesook T. Kim, Donna S. Neuberg, Robert J. Soiffer, Jerome Ritz, Edwin P. AlyeaDana Pe'er, Catherine J. Wu

Research output: Contribution to journalArticlepeer-review

32 Scopus citations

Abstract

To elucidate mechanisms by which T cells eliminate leukemia, we study donor lymphocyte infusion (DLI), an established immunotherapy for relapsed leukemia. We model T cell dynamics by integrating longitudinal, multimodal data from 94,517 bone marrow-derived single T cell transcriptomes in addition to chromatin accessibility and single T cell receptor sequencing from patients undergoing DLI. We find that responsive tumors are defined by enrichment of late-differentiated T cells before DLI and rapid, durable expansion of early differentiated T cells after treatment, highly similar to “terminal” and “precursor” exhausted subsets, respectively. Resistance, in contrast, is defined by heterogeneous T cell dysfunction. Surprisingly, early differentiated T cells in responders mainly originate from pre-existing and novel clonotypes recruited to the leukemic microenvironment, rather than the infusion. Our work provides a paradigm for analyzing longitudinal single-cell profiling of scenarios beyond adoptive cell therapy and introduces Symphony, a Bayesian approach to infer regulatory circuitry underlying T cell subsets, with broad relevance to exhaustion antagonists across cancers.

Original languageEnglish (US)
Article number109992
JournalCell Reports
Volume37
Issue number6
DOIs
StatePublished - Nov 9 2021

Keywords

  • ATAC-seq
  • allogeneic hematopoietic stem cell transplant
  • donor lymphocyte infusion
  • exhaustion
  • gene regulatory networks
  • immunotherapy
  • leukemia
  • probabilistic models
  • scRNA-seq
  • statistical machine learning

ASJC Scopus subject areas

  • General Biochemistry, Genetics and Molecular Biology

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