Abstract 3371: Mapping the evolution of T cell states during DLI response and resistance using single-cell data and computational tools
Authors: Azizi Elham , Bachireddy Pavan , Nguyen Vinhkhang N. , Li Shuqiang , Neuberg Donna S. , Soiffer Robert J. , Ritz Jerome , Alyea Edwin P. , Pe'er Dana , Wu Catherine J. 
Published: August 2019

Abstract
Donor lymphocyte infusion (DLI) is a standard of care and potentially curative immunotherapy for relapsed leukemia after allogeneic hematopoietic stem cell transplant (allo-SCT). Despite low response rates for many leukemias, chronic myelogenous leukemia (CML) has historically exhibited an exquisite DLI sensitivity, and we previously reported that durable response to DLI was associated with reversal of exhaustion of bone marrow (BM) -infiltrating T cells (Bachireddy et al., Blood 2014). Critical questions remain, however, regarding the exact transcriptional states of those T cell subtypes mediating exhaustion, anti-leukemia responses, and resistance to DLI.
To map evolving phenotypic T cell states in situ at single cell resolution, we profiled viable cells isolated from cryopreserved BM mononuclear cells from a median of 3 timepoints before and after DLI from 12 patients with relapsed CML after allo-SCT, including 6 long-term responders to DLI (R’s) and 6 nonresponders (NR’s), using single cell RNA sequencing (scRNA-seq). Using our computational tools for processing and analyzing scRNA-seq data (Azizi et al., Cell 2018), we detected 381,462 cells in total derived from 43 unique patient-timepoints that met our quality metrics.
Because DLI’s anti-leukemic efficacy derives in large part from T cell activity, we sought a more refined characterization of T cells using our tool Biscuit (Azizi et al., Cell 2018) to merge, normalize and cluster T cells. We observed a marked increase in the number of T cell clusters in post-DLI samples compared to matched pre-DLI samples (p&lt;0.001). Both R and NR cases exhibited increases in phenotypic volume induced by DLI (p&lt;1x10-6), suggesting DLI induces multiple, independent gene expression components in both clinical outcomes. However, at both pre- and post-DLI timepoints, phenotypic volumes in R cases were higher than that of NR cases.
Comparing T cell exhaustion between R vs NR cases, we confirmed increased T cell exhaustion signatures in R-pre T cells. Using factor analysis techniques we found that anergy, dysfunction and tolerance are shared factors driving a subset of T cells that are enriched in NRs, suggesting multiple forms of T cell dysfunction in DLI resistance. We found that the clusters dominated by post-DLI R T cells were characterized by greater diversity of T helper subsets (Th1, Tfh, Th2, Th9, and Th22) and enrichment for exhaustion, type I and II IFN pathways, proinflammatory gene sets and CD8 T cell activation. Clusters dominated by NR T cells displayed increases in Th17 and Treg signatures, anergy and tolerance.
These data suggest that (1) pretreatment T cell phenotypic diversity may be important for DLI response; (2) that DLI increases such diversity differently in R’s than in NR’s; (3) while T cell subsets exhibit some overlap pre-therapy, responders and non-responders become increasingly dissimilar post therapy.
Citation Format: Elham Azizi, Pavan Bachireddy, Vinhkhang N. Nguyen, Shuqiang Li, Donna S. Neuberg, Robert J. Soiffer, Jerome Ritz, Edwin P. Alyea III, Dana Pe'er, Catherine J. Wu. Mapping the evolution of T cell states during DLI response and resistance using single-cell data and computational tools [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3371.