| Literature DB >> 34758319 |
Pavan Bachireddy1, Elham Azizi2, Cassandra Burdziak3, Vinhkhang N Nguyen4, Christina S Ennis4, Katie Maurer5, Cameron Y Park6, Zi-Ning Choo7, Shuqiang Li8, Satyen H Gohil9, Neil G Ruthen10, Zhongqi Ge11, Derin B Keskin12, Nicoletta Cieri9, Kenneth J Livak10, Haesook T Kim13, Donna S Neuberg13, Robert J Soiffer14, Jerome Ritz14, Edwin P Alyea14, Dana Pe'er15, Catherine J Wu16.
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.Entities:
Keywords: ATAC-seq; allogeneic hematopoietic stem cell transplant; donor lymphocyte infusion; exhaustion; gene regulatory networks; immunotherapy; leukemia; probabilistic models; scRNA-seq; statistical machine learning
Mesh:
Year: 2021 PMID: 34758319 PMCID: PMC9035342 DOI: 10.1016/j.celrep.2021.109992
Source DB: PubMed Journal: Cell Rep Impact factor: 9.995