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2393 Single-Cell and Spatial Analyses Characterize Distinct Subsets of Malignant T Cells in Angioimmunoblastic T Cell Lymphoma

Program: Oral and Poster Abstracts
Session: 621. Lymphomas: Translational—Molecular and Genetic: Poster II
Hematology Disease Topics & Pathways:
Genomics, Adults, Translational Research, Lymphomas, Bioinformatics, T Cell Lymphoma, Immunology, Diseases, Lymphoid Malignancies, Biological Processes, Genomic Profiling, Technology and Procedures, Study Population
Sunday, December 12, 2021, 6:00 PM-8:00 PM

Francois Lemonnier1,2*, Chuang Dong3*, Bruno Tesson, PhD4*, Laurine Gil3*, Noudjoud Attaf, PhD3*, Diana-Laure Mboumba2*, Philippe Gaulard, MD, PhD2,5* and Pierre Milpied, PhD3*

1Assistance Publique-Hôpitaux de Paris (AP-HP), Hôpitaux Universitaires Henri Mondor, Unité Hémopathies Lymphoïdes, Créteil, France
2Université Paris Est Créteil, INSERM, Institut Mondor de Recherche Biomédicale, Créteil, France
3Aix Marseille Université, CNRS, INSERM, Centre d'Immunologie de Marseille-Luminy, Marseille, France
4Institut Carnot CALYM, Pierre-Bénite, France
5Département de Pathologie, Hôpitaux Universitaires Henri Mondor, Créteil, France


Angioimmunoblastic T-cell lymphoma (AITL) is the most frequent of nodal peripheral T-cell lymphomas. AITL results from the transformation of T follicular helper (TFH) cells and is characterized by chemo-resistance and poor survival (5-year OS around 30%). Recent data from prospective clinical trials suggest that disease outcome may be impacted by factors other than genomic features, such as the tumor microenvironment (TME) and overall intra-tumoral heterogeneity. Our understanding of AITL intra-tumoral genetic, transcriptional and functional heterogeneity is limited because most molecular data generated so far have come from bulk analyses. Single-cell RNA sequencing (scRNA-seq) enables fine characterization of cell types and functional cell states. When focused on T or B cells, 5’-end scRNA-seq also yields the TCR or BCR sequences that allow tracking clonally related cells. Here we studied the intra-tumor heterogeneity of AITL tumors using integrative scRNA-seq.


We analyzed lymph node live cell suspensions from AITL patients (n=10) using droplet-based 10x Genomics 5’-end scRNA-seq. Malignant T cells from 4 AITL samples were also analyzed by FACS index sorting and plate-based 5’-end scRNA-seq to link cell surface phenotype and gene expression profile. We identified malignant T cell clones by intersecting the gene expression and TCR sequencing data, and performed separate focused analyses of TME subsets and malignant T cells. We compared subsets of malignant T cells from all patients using marker gene-based metaclustering to identify AITL T cell states conserved across patients. We explored the genetic heterogeneity of malignant T cells by mapping RHOAG17V mutations and inferring copy number variation (CNV) subclones from scRNA-seq data. In select cases, we performed in situ analysis by immunohistochemistry (IHC) or spatial transcriptomics to characterize the spatial distribution of malignant T cell subsets identified by scRNA-seq.


Based on gene expression, malignant T cells grouped in patient-specific clusters, while non-malignant T, B and myeloid TME cells from all patients clustered by cell type or cell state. Among TME cells, we identified 7 subsets of B cells (including activated B cells, plasma cells, and one patient-specific monoclonal B cell proliferation), 6 subsets of myeloid cells (including macrophages, conventional and plasmacytoid dendritic cells), and 8 subsets of non-malignant T cells (including activated cytotoxic T lymphocytes (CTL) with clonal expansions). Patient-specific malignant T cells were heterogeneous and divided into several gene-expression based clusters. Metaclustering of malignant T cell subsets identified T central memory (TCM)-like and TFH-like states in 10/10 samples. We also identified in 3/10 samples clusters of CTL-like malignant T cells expressing characteristic marker genes (including NKG7, GNLY, GZMK, PRF1). We observed an intra-sample continuum of gene expression states from quiescent TCM-like to proliferating TFH-like states. TFH-like cells were larger in size and expressed higher levels of surface PD1 and ICOS than TCM-like and CTL-like subsets. We detected the RHOAG17V mutation in malignant T cells of 4/4 mutated cases, with no evidence of subclonal heterogeneity for that mutation. We detected clonal and subclonal CNV in most AITL malignant T cells. CTL-like states were enriched in specific CNV subclones, but the TCM-like to TFH-like continuum was observed in all CNV subclones, suggesting that functional plasticity and subclonal genetic evolution may occur independently. In situ staining of markers for TFH-like (PD1, ICOS, CD200) and CTL-like (GZMK, GZMA) cells showed that TFH-like and CTL-like cells occupied distinct tissue niches within the tumor. In spatial transcriptomics analysis, TFH-like cells mapped to follicular dendritic cell (FDC)-rich areas, while TCM-like cells were associated with T-zone reticular cells.


Our analyses recapitulate known characteristics of AITL TME, and uncover previously unrecognized heterogeneity among malignant T cells across multiple patients. The distinct gene expression programs, phenotypes, genetics, and locations of TFH-like, TCM-like and CTL-like states suggest that AITL malignant T cells undergo significant functional plasticity and genetic divergence, which could influence response to therapy and overall clinical course.

Disclosures: Lemonnier: Institut Roche: Research Funding; Gilead: Other: travel grant. Gaulard: Gilead: Consultancy; Innate Pharma: Research Funding; Sanofi: Research Funding; Alderaan: Research Funding; Takeda: Consultancy, Honoraria. Milpied: Institut Roche: Research Funding; Innate Pharma: Research Funding; Bristol Myers Squibb: Research Funding.

*signifies non-member of ASH