•ACH-derived GCB cells with aberrant expression profiles underwent independent clonal evolution in the microenvironment of AITL.•Inhibition of the CD40–CD40LG axis, as revealed by in silico network ...analysis, is a potential novel therapeutic target.
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Angioimmunoblastic T-cell lymphoma (AITL) is proposed to be initiated by age-related clonal hematopoiesis (ACH) with TET2 mutations, whereas the G17V RHOA mutation in immature cells with TET2 mutations promotes the development of T follicular helper (TFH)-like tumor cells. Here, we investigated the mechanism by which TET2-mutant immune cells enable AITL development using mouse models and human samples. Among the 2 mouse models, mice lacking Tet2 in all the blood cells (Mx-Cre × Tet2flox/flox × G17V RHOA transgenic mice) spontaneously developed AITL for approximately up to a year, while mice lacking Tet2 only in the T cells (Cd4-Cre × Tet2flox/flox × G17V RHOA transgenic mice) did not. Therefore, Tet2-deficient immune cells function as a niche for AITL development. Single-cell RNA-sequencing (scRNA-seq) of >50 000 cells from mouse and human AITL samples revealed significant expansion of aberrant B cells, exhibiting properties of activating light zone (LZ)-like and proliferative dark zone (DZ)-like germinal center B (GCB) cells. The GCB cells in AITL clonally evolved with recurrent mutations in genes related to core histones. In silico network analysis using scRNA-seq data identified Cd40–Cd40lg as a possible mediator of GCB and tumor cell cluster interactions. Treatment of AITL model mice with anti-Cd40lg inhibitory antibody prolonged survival. The genes expressed in aberrantly expanded GCB cells in murine tumors were also broadly expressed in the B-lineage cells of TET2-mutant human AITL. Therefore, ACH-derived GCB cells could undergo independent clonal evolution and support the tumorigenesis in AITL via the CD40–CD40LG axis.
The second most common peripheral T-cell lymphoma, angioimmunoblastic T-cell lymphoma (AITL), arises from T-follicular helper cells in the context of age-related clonal hematopoiesis. Fujisawa and colleagues utilized single cell analyses of primary samples and murine models to reveal that TET methylcytosine dioxygenase 2 (TET2) mutations within clonal B cells in the tumor microenvironment are necessary for facilitating TET2-mutated AITL. Their data also suggest a novel therapeutic possibility.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Haploidentical peripheral blood stem cell transplantation (haplo-PBSCT) with post-transplant cyclophosphamide (PTCy) is an important therapeutic option for patients lacking an HLA-matched donor. ...However, the significance of CD34+ cell dose in grafts has not been fully elucidated.
We aimed to explore the impact of CD34+ cell dose on outcomes after haplo-PBSCT with PTCy.
We retrospectively investigated 111 consecutive patients who underwent haplo-PBSCT with PTCy or HLA-matched PBSCT from related donors.
There were no statistically significant differences in 3-year overall survival (p = 0.559) or progression-free survival (p = 0.974) between haplo-PBSCT and matched PBSCT. Delayed neutrophil engraftment and a lower incidence of graft-versus-host disease were observed in haplo-PBSCT. The median dose of CD34+ cells was 4.9 × 106 /kg in 57 haplo-PBSCT and 4.5 × 106 /kg in 54 matched PBSCTs. Importantly, patients who underwent haplo-PBSCT with the administration of CD34+ cell at a dose of ≥4.0 × 106 /kg significantly had improved OS (p = 0.015) and decreased incidence of disease relapse (p = 0.001) without increasing incidence of GVHD.
Our data suggest that a higher dose of CD34+ cells in haplo-PBSCT with PTCy positively impacts the outcomes without an increase of GVHD.
•Clinical outcomes of haplo-PBSCT were comparable to matched-PBSCT without increasing severe GVHD.•Delayed neutrophil engraftment and lower incidence of GVHD were observed in haplo-PBSCT.•Haplo-PBSCT receiving CD34+ cell dose of ≥ 4.0x106 /kg improved OS and decreased incidence of disease relapse without increasing GVHD.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Background: Comprehensive gene mutation profiling of primary central nervous system lymphoma (PCNSL) revealed genomic abnormalities associated with the NFκB signaling pathway and immune escape ...(Chapuy B, et al. Blood 2016). Although this has led to advances in targeted therapy, there are only a few candidate biomarkers for diagnosis and prediction of survival in PCNSL. Recurrence still occurs at high rate of over 60% and therapeutic resistance is a significant challenge in the management of recurrent PCNSL. Until now, the systemic profiling of tumor micro-environment (TME) was performed through gene expression analysis by whole transcriptome analysis (WTA) and single-cell RNA sequencing analysis (Heming M, et al. Genome medicine 2022), and stratification in PCNSL was attempted by using spatial transcriptome analysis (Xia Y, et al. Leukemia 2023). However, TME of PCNSL and their impact on prognosis remain uncertain. Objective: We performed this study to investigate the prognostic impact on survival and establish novel prognostic biomarkers in PCNSL. Methods: We analyzed the expression levels of 770 neuroinflammation-related (NFR) genes by the NanoString nCounter technology in tumor samples from 30 PNCSL patients. The clinical significance of genes and their association with prognosis were assessed. Genes related to “worse prognosis (WP)” or “better prognosis (BP)” were identified. We performed the univariable analysis using a cox proportional hazards model to evaluate the predictive value of expression of genes related to WP and clinical risk factors, such as age, sex, serum lactate dehydrogenase level, Karnofsky Performance Status (KPS), consciousness levels at diagnosis and lymphoma involvement of the deep brain structure. Gene expression data related to WP were subjected to multivariate analysis with clinical variables with p values less than 0.15 in the univariate analysis. The genes associated with WP were further validated using WTA of an independent PCNSL cohort (n=30, previously published by Fukumura K, et al. Acta neuropathologica 2016). Results: The median age at diagnosis was 69 years old (range, 32-83). The median follow-up period was 25 months (range, 1-110 months), with overall survival (OS) at 3 years of 39.7% and progression free survival at 3 years of 25.4%. Notably, 48 of 770 NFR genes were highly expressed in the WP group (3-year OS, 22.2%), compared with the BP group (3-year OS, 66.7%) (Figure 1). We found that oligodendrocyte and astrocyte-related signatures were enriched in the WP-associated gene set. Among clinical prognostic factors, KPS > 70 (p=0.0293), and consciousness levels at diagnosis (other than JCS 0-1) (p=0.104) were relatively associated with poor OS (p < 0.15) in univariate analysis. Multivariate analysis revealed that high expressions of TUBB4A (p=0.028, HR:3.88), S100B (p=0.046, HR:3.093) and SLC6A1 (p=0.034, HR:3.765) were significantly related to death independent from KPS and consciousness levels at diagnosis. Expression levels of these 3 genes were also significantly associated with poor OS in a validation cohort. Conclusion: Our observations suggest high expression of TUBB4A, S100B and SLC6A1 are the clinical indicators to predict poor prognosis in PCNSL patients. Furthermore, these data suggest that genes related to TME may play a crucial role in the pathogenesis of PCNSL, complementing the well-known involvement of the NF-kB signaling pathway. Therefore it is necessary to continue research focused on TME-targeted therapeutics to encounter drug resistance and refractoriness in PCNSL.
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Introduction: Clonal hematopoiesis (CH) is an age-related change in which blood cells with somatic mutations are clonally expanded. CH is known to be a predisposing factor for various age-related ...diseases. However, the characteristics of blood cells with somatic mutations derived from CH at the single-cell level is not fully understood. Objective: We performed this study to explore the comprehensive properties of mutant cells derived from CH. Methods: We enrolled 51 healthy elderly individuals (male, 13; female, 38) from the Kashiwanoha cohort for this study. To investigate somatic mutations frequently mutated in CH, we designed a custom panel targeting 49 genes. Targeted deep sequencing (TDS) was performed on mononuclear cells, and T, B, and monocyte fractions of peripheral blood (PB). Error-correction process was performed on TDS data. The error-correction process to the TDS data was as follows: Firstly, read families which have more than 5 identical unique molecular identifier (UMI) amplicon were included. Second, at each position, nucleotides were compared and a consensus nucleotide was called if at least 90% the nucleotides were identical. If the agreement was below 90%, the nucleotides were changed to “N ”at that position. Third, if the “N” constituted less than 10% of a read family, it was recognized as a consensus read. Single-cell multiome analysis (sc Multiome) was performed on PB samples of 43 individuals by 10xGenomics Chromium Next GEM Single Cell Multiome ATAC + Gene Expression. Sc Multiome analysis was conducted using Seurat and Signac for quality control, integration, and clustering. Additionally, we employed long-read sequencing with the PromethION platform to identify CH mutations in the sc Multiome libraries. Results: The median age of this cohort was 72 years old (range, 50 - 85). After error correction was applied to the TDS data, a total of 56 mutations were detected in 34 individuals ( DNMT3A R882, 1; DNMT3A nonR882, 16; TET2, 10; GNAS, 4; STAT3, 3; KRAS, 1; MYD88, 1; others, 20). The median VAF was 0.0091 (range, 0.003 to 0.164). The number of mutated genes in each individual increased with age, and individuals of under the age of 69 had significantly fewer mutations compared to those over 70 years old. Regarding the TDS data of each fraction, DNMT3A mutations were detected in both monocyte and B-cell fractions in 5 cases, while they were restricted to T-cell fraction in one case. In 3 cases, mutations were detected in all fractions. TET2 mutations were present in both monocyte and B-cell fractions in all 5 cases. On the other hand, KRAS, STAT3 and MYD88 mutations were restricted to T-, T-, and B-cell fractions, respectively. In sc Multiome data, the median number of cells included was 7931 (range, 2998 to 13413). By the sc Multiome long read sequencing, 39 mutations were detected in 22 individuals ( DNMT3A R882, 1; DNMT3A nonR882, 5; TET2, 6; GNAS, 1; STAT3, 3; KRAS, 1; others, 22). The median rate of DNMT3A mutated cells was 0.007(range, 0.003 to 0.014). Similarly, the median TET2 mutated cell population rate was 0.007(range, 0.004 to 0.044). The average cell number of B cells, T cells, monocytes, and NK cells in sc Muliome data were 955 (range, 305 to 2275), 3875 (range, 955 to 7726), 1812 (range, 352 to 4750), and 868 (range, 289 to 6022) respectively. Among B cells, 16 mutations were detected in 13 individuals ( DNMT3A nonR882, 2; TET2, 2; STAT3, 3; others, 9). For T cells, 27 mutations were detected in 20 individuals ( DNMT3A R882, 1; DNMT3A nonR882, 4; TET2, 4; GNAS, 1; STAT3, 3;others, 12). In Monocytes cells 24, mutations were detected in 16 individuals ( DNMT3A R882, 1; DNMT3A nonR882, 4; TET2, 5; GNAS, 1; STAT3, 3;others, 10). Lastly, among NK cells, 23 mutations were detected in 17 individuals ( DNMT3A nonR882, 5; TET2, 4; GNAS, 1; STAT3, 3;others, 10). The mutated cell populations in each cell fraction were 0.037(range, 0.014 to 0.208), 0.013 (range, 0.002 to 0.180), 0.013(range, 0.003 to 0.178), and 0.043(range, 0.009 to 0.284). Conclusion: This study clarified the detailed distribution of CH-derived mutant cells in PB of elderly individuals. We identified 17 mutations that expanded in myeloid fraction and 12 mutations expanded in lymphoid fraction. Sc Multiome analysis combined with long-read sequencing allowed us to gain a deeper understanding of CH mutated cell populations. These findings provide the valuable insights into properties of CH mutant cells.
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Background: The presence and role of follicular T-cell populations other than T follicular helper (Tfh) cells, such as T follicular regulatory (Tfr) and cytotoxic (Tfc) cells, are gaining increasing ...attention in certain pathological states. However, the ecosystem of follicular T cells in the tumor microenvironment (TME) has not been fully elucidated. In particular, the significance of minor follicular T-cell subsets in the neoplastic follicular environment remains elusive. Here, we aimed to reveal the landscape of follicular T-cell alterations in various cancers, with a particular emphasis on the follicular lymphoma (FL) TME. Methods: We analyzed single-cell RNA/TCR sequencing data of >500,000 human T cells from FL (obtained from four cohorts) and 25 other cancer types, as well as homeostatic and reactive lymph nodes (LNs), to construct a comprehensive single-T-cell atlas. We investigated differentially expressed genes, RNA velocity, and TCR clonality using this atlas. To determine the functions of neoplastic follicular regulatory (Tnfr) and cytotoxic (Tnfc) T cells, we performed in vitro cytokineproduction and co-culture assays, in combination with cell activation/suppression, cell division, and apoptosis assays, using human FL samples. With the PhenoCycler-Fusion system, we conducted multiplex digital spatial profiling (DSP) of 169 FL samples from two independent cohorts (now being extended to 242 FL samples from three cohorts) for >25 antibodies. We also performed single-cell spatial and protein expression profiling and prognostic analysis. Results: In FL, distinct minor neoplastic follicular T-cell subsets-Tnfr and CD4 (Tnfc4) and CD8 (Tnfc8) Tnfc cells-increased relative to homeostatic LNs. The TCR repertoire analysis revealed that Tnfr cells shared clonotypes with conventional effector regulatory T (Trg) and Tfh cells, whereas Tnfc4 and Tnfc8 cells shared clonotypes with Tfh cells and effector and exhausted (Tcex) cytotoxic CD8 T cells, respectively. In line with these findings, the RNA velocity survey suggested that Tnfr, Tnfc4, and Tnfc8 cells originated from Trg, Tfh, and naïve-like CD8 T cells, respectively. Tnfr and Tnfc cells expressed higher levels of effector genes, including those involved in cytokine release, chemokine response, migration, and PD-1 signaling, than their reactive LN counterparts. The pan-cancer survey revealed that Tfr and CD4 Tfc cells were exclusive to FL, whereas the prevalence and gene expression profiles of CD8 Tfc cells varied across cancers. Tnfr cells were marked by abundant expression of IL10 and IL21, whereas Tnfc cells displayed a unique phenotype, as they concomitantly expressed markers of effector Tfh (e.g., CXCL13, CXCR5, and PDCD1), naïve/stem (e.g., CCR7 and TCF7), central memory (e.g., CD27, CD28, and SELL), and tissue-resident memory (e.g., ITGAE) cells. Hierarchical clustering demonstrated that Tnfc8 cells had transcriptional profiles similar to those of melanoma TCF1 +PD-1 +CD8 + stem-like T cells. DSP of FL detected Tnfr and Tnfc cells frequently localized within and around neoplastic follicles, forming a cellular neighborhood that allowed them to interact closely. Tnfr cells were distributed predominantly near Tfh cells. The functional co-culture assays demonstrated that Tnfr cells suppressed Tfh-cell activation and division, thereby inhibiting Tfh-mediated malignant B-cell activation and survival. Tnfc8 cells showed a higher cell division capability than that of Tcex cells, suggesting that Tnfc8 cells function as a pool of CD8 T cells in neoplastic follicles. The prognostic analysis revealed that Tnfr and Tnfc cell proportions correlated with early disease relapse (i.e., POD24) and predicted a significantly longer time-to-relapse ( P <0.05 for Tnfr and <0.001 for Tnfc cells) in FL. In the multivariate analysis, the prognostic impact of these two cell subsets was independent of the FLIPI. The prognostic analysis findings were confirmed using a validation cohort. Conclusions: Our multi-omics approach identified the expansion of minor neoplastic follicular T-cell subsets that carry unique transcriptional and functional profiles and robust prognostic impacts. These findings deepen our understanding of the biological and immunological roles of non-Tfh follicular T cells in the lymphoma TME and highlights their clinical potential for patient risk stratification and future therapeutic interventions.
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T follicular helper (T
) cell lymphomas (TFHLs) are characterized by T
-like properties and accompanied by substantial immune-cell infiltration into tumor tissues. Nevertheless, the comprehensive ...understanding of tumor-cell heterogeneity and immune profiles of TFHL remains elusive. To address this, we conducted single-cell transcriptomic analysis on 9 lymph node (LN) and 16 peripheral blood (PB) samples from TFHL patients. Tumor cells were divided into 5 distinct subclusters, with significant heterogeneity observed in the expression levels of T
markers. Copy number variation (CNV) and trajectory analyses indicated that the accumulation of CNVs, together with gene mutations, may drive the clonal evolution of tumor cells towards T
-like and cell proliferation phenotypes. Additionally, we identified a novel tumor-cell-specific marker, PLS3. Notably, we found a significant increase in exhausted CD8
T cells with oligoclonal expansion in TFHL LNs and PB, along with distinctive immune evasion characteristics exhibited by infiltrating regulatory T, myeloid, B, and natural killer cells. Finally, in-silico and spatial cell-cell interaction analyses revealed complex networking between tumor and immune cells, driving the formation of an immunosuppressive microenvironment. These findings highlight the remarkable tumor-cell heterogeneity and immunoevasion in TFHL beyond previous expectations, suggesting potential roles in treatment resistance.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Activating mutations in the Vav guanine nucleotide exchange factor 1 (VAV1) gene are reported in various subtypes of mature T-cell neoplasms (TCNs). However, oncogenic activities associated with VAV1 ...mutations in TCNs remain unclear. To define them, we established transgenic mice expressing VAV1 mutants cloned from human TCNs. Although we observed no tumors in these mice for up to a year, tumors did develop in comparably aged mice on a p53-null background (p53−/−VAV1-Tg), and p53−/−VAV1-Tg mice died with shorter latencies than did p53-null (p53−/−) mice. Notably, various TCNs with tendency of maturation developed in p53−/−VAV1-Tg mice, whereas p53−/− mice exhibited only immature TCNs. Mature TCNs in p53−/−VAV1-Tg mice mimicked a subtype of human peripheral T-cell lymphoma (PTCL-GATA3) and exhibited features of type 2 T helper (Th2) cells. Phenotypes seen following transplantation of either p53−/−VAV1 or p53−/− tumor cells into nude mice were comparable, indicating cell-autonomous tumor-initiating capacity. Whole-transcriptome analysis showed enrichment of multiple Myc-related pathways in TCNs from p53−/−VAV1-Tg mice relative to p53−/− or wild-type T cells. Remarkably, amplification of the Myc locus was found recurrently in TCNs of p53−/−VAV1-Tg mice. Finally, treatment of nude mice transplanted with p53−/−VAV1-Tg tumor cells with JQ1, a bromodomain inhibitor that targets the Myc pathway, prolonged survival of mice. We conclude that VAV1 mutations function in malignant transformation of T cells in vivo and that VAV1-mutant–expressing mice could provide an efficient tool for screening new therapeutic targets in TCNs harboring these mutations.
•Expression of VAV1 mutants on a p53-null background accelerated development of various types of TCNs.•Myc pathway activation is marked in TCNs with VAV1 mutants, accompanying focal somatic copy-number alterations of the Myc locus.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Introductions:
Loss-of-function TET2 mutations are frequent in clonal hematopoiesis in patients with solid cancers as well as that in healthy individuals. It remains to be elucidated whether and how ...TET2-mutated immune cells affect cancer progression in patients with TET2-mutated clonal hematopoiesis. Here, we assessed activity of Tet2-deficient immune cells using a mouse lung cancer model.
Methods:
Lewis Lung Carcinoma (LLC) cells were subcutaneously transplanted into blood-specific Mx-Cre or myeloid-specific LysM-Cre x Tet2 f/f mice (Tet2 -/- or Tet2 mye-) or control mice (CT). Single-cell RNA sequencing (scRNA-seq) was performed to determine the immune-cell profiles and mediators in tumors of Tet2 -/- mice (Tet2 -/- tumors). Whole transcriptome analysis (WTA) was also performed for granulocytic myeloid-derived cells (GMD), monocytic myeloid-derived cells (MMD), and tumor associated macrophages (TAM), as well as LLC cells sorted from Tet2 -/- tumors and CT tumors.
Results:
We found that tumor growth was enhanced in both Tet2 -/- and Tet2 mye- comparing to CT. Unsupervised clustering of scRNA-seq data identified 14 cell clusters: GMD into 3 (GMD1, GMD2, and GMD3), MMD into 5 (MMD1, MMD2, MMD3, MMD4, and MMD5), TAMs into 4 (TAM1, TAM2, TAM3, and TAM4), and DCs into 2 (DC1 and DC2). Notably, among all subclusters, the proportions of GMD1, GMD3, TAM3 and TAM4 were markedly expanded in Tet2 -/- tumors comparing to CT. Differentially expressed gene (DEG) analysis of scRNA-seq data found that S100a8 and S100a9 were highly expressed in Tet2-deficient GMD1 compared to CT. Furthermore, S100a8/S100a9 proteins were elevated in plasmas of Tet2 -/- comparing to those of CT. Pathway analysis using DEGs (p < 0.05) from WTA of GMD determined interleukin 1b (Il1b) signaling as upstream of S100a8/S100a9 activity. Gene set enrichment analysis (GSEA) also showed that 6 pathways related to Il1b were enriched in Tet2-deficient group compared to CT group. Gene ontology analysis (GO) for DEGs of GMD, MMD, and TAMs by WTA as well as 13 subclusters by scRNA-seq revealed that the “cellular response to IL-1” pathway was enriched in Tet2-deficient group compared to CT group. To define the downstream effectors in LLC cells, we performed WTA for LLC cells sorted from Tet2 -/- and CT tumors. We found that Vegfa, encoding a mediator for angiogenesis was highly upregulated in LLC cells sorted from Tet2 -/- tumors comparing to CT tumors. GSEA for WTA further identified that multiple Vegfa-related pathways as well as MAPK cascade were enriched in LLC cells from Tet2 -/- tumors comparing to those from CT tumors . Furthermore, S100a8/S100a9 induced Vegfa secretion from LLC cells in vitro. Remarkably, the area of blood vessels was increased in Tet2 -/- tumors comparing to CT tumors. Immunostaining exhibited that the number of Ly6g +GMD foci (>1000 px 2) expressing S100a8/S100a9 was increased in Tet2 -/- tumors comparing to CT tumors. Furthermore, LLC cells surrounding GMD foci highly expressed Vegfa in Tet2 -/- tumors. Finally, administration of an antibody against Emmprin, a receptor for S100a8/S100a9 inhibited the tumor growth in Tet2 -/-. Notably, the area of blood vessels in Tet2 -/- tumors with anti-Emmprin group was decreased at 2-fold compared to that seen in isotype group (p < 0.05). Consistently, S100A8/S100A9 induced VEGFA production in human lung cancer cells in vitro.
Conclusions:
Tet2-deificient immune cells promote lung cancer progression through S100a8/S100a9-Emmprin-Vegfa axis. Our study suggests a novel role of TET2-mutated clonal hematopoiesis in cancer progression and even provides a novel therapeutic target.
No relevant conflicts of interest to declare.
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Background
Angioimmunoblastic T-cell lymphoma (AITL) is proposed to be initiated by age-related clonal hematopoiesis (ACH) with TET2mutations, whereas the G17V RHOA mutation in TET2-mutated immature ...cells facilitates development of T follicular helper (T FH)-like tumor cells. Notably, we and others have reported that immune cells derived from ACH with TET2 mutations infiltrate AITL tissues. However, how ACH-derived immune cells function as a microenvironmental niche in AITL remains largely unknown.
Objective
To elucidate the role of TET2-mutated immune cells in AITL tumorigenesis.
Methods
The G17V RHOA transgenic mice were crossed with mice lacking Tet2 in all blood cells (Mx-Crex Tet2f/f, A) and in T cells (Cd4-Crex Tet2f/f, B), respectively. Single-cell RNA sequencing (Sc-seq) was performed on >60,000 cells from AITL in mice (AITLm, n=2) and human (AITLh, n=5), and their controls to reveal the immune profiles. We used Seurat and Monocle3 pipelines for analysis of Sc-seq. Whole genome bisulfite sequencing (WGBS) was used to analyze the methylome of germinal center B (GCB) cells in AITLm and control.
Results
AITLm occurred only in A, but not in B. Then, we intraperitoneally transplanted Cd4 + tumor-containing cells together with various lineages of immune cells sorted from AITLm into nude mice. AITLm developed only when B-lineage cells were cotransplanted with Cd4 + tumor-containing cells. Unsupervised clustering of the Sc-seq data identified 6 T-, 6 B- and 3 myeloid clusters in AITLm. B-cell clusters were annotated into naïve B-, memory B-, GCB-, and plasma clusters along the B-cell differentiation through Geneset variable analysis (GSVA) and trajectory analysis. We found that the aberrant GCB clusters, simultaneously exhibiting DZ-like proliferation markers (Aicda and Mki67) and LZ-like activation markers (Cd40, Cd83) were markedly expanded in AITLm. Geneset Enrichment Analysis (GSEA) revealed that MYC targets and other signaling pathways involved in cell proliferation were highly enriched in the GCB clusters in AITLm. WGBS showed that the number of hypermethylated regions (HyperDMRs) was markedly higher than that of hypomethylated regions (HypoDMRs) at all the regions; promoters, exons, introns, untranslated and intergenic regions. Among HyperDMRs, Atp13a2, Pdzd2, Rapgef4, Irf4 and Egr3 expressions were downregulated in the GCB clusters of Sc-seq in AITLm. Remarkably, the number of BCR clones in GCB of AITLm were significantly less than those in controls. In addition, in AITLm mice, the number of somatic mutations in GCB cells was significantly higher than that in T FH-like tumor cells. Remarkably, we detected unique core histone mutations in the GCB cells of AITLm, including the recurrent p.Ser87Asn Histone3 mutations. Next, In silico network analysis using Sc-seq data between GCB and T FH-like clusters identified that 11 interactions, including Cd40-Cd40lg were significantly enhanced in AITLm compared to controls. Flowcytomeric analysis revealed that cell-surface expression of Cd40 were significantly higher in the GCB cells of AITLm than those of control. Pathologically, the follicular structure was disrupted in AITLm. Consequently, Cd40lg +Cd4 +tumor cells and Cd40 +Cd19 + cells were both diffusely distributed and sometimes localized adjacent to each other. Finally, administration of an anti-Cd40lg antibody prolonged the survival of nude mice transplanted with AITLm.
In AITLh with TET2 mutations, unsupervised clustering of Sc-seq identified T-, B-, and myeloid-cell clusters and a cluster characterized by proliferative markers. In B-lineage cells, 9 clusters were re-clustered and annotated to naïve or memory B-, GCB- and plasmablast clusters under the same manner of mouse data. Gene ontology analysis from differential expression genes in each cluster showed that the GCB- and CD40-related genesets were enriched not only in the GCB cluster but also in the naive to memory B clusters. Furthermore, the AITL-B-specific geneset, which referred from genes (CD40, CD83, AICDA, MKI67) highly expressed in the GCB cluster in AITLm was enriched not only in the GCB cluster, but also in the naive to memory B clusters in AITLh.
Conclusion
This study suggests a new concept that ACH-derived GCB cells with TET2 mutations can undergo independent clonal evolution and function as microenvironmental cells to support tumorigenesis in AITL via the CD40-CD40LG axis.
Usuki: Astellas Pharma Inc.: Research Funding, Speakers Bureau; AbbVie GK: Research Funding, Speakers Bureau; Gilead Sciences, Inc.: Research Funding; SymBio Pharmaceuticals Ltd.: Research Funding, Speakers Bureau; Daiichi Sankyo Co., Ltd.: Research Funding, Speakers Bureau; Sumitomo-Dainippon Pharma Co., Ltd.: Research Funding; Otsuka Pharmaceutical Co., Ltd.: Research Funding, Speakers Bureau; Novartis Pharma K.K.: Research Funding, Speakers Bureau; Ono Pharmaceutical Co., Ltd.: Research Funding, Speakers Bureau; Janssen Pharmaceutical K.K.: Research Funding; Celgene K.K.: Research Funding, Speakers Bureau; Takeda Pharmaceutical Co., Ltd.: Research Funding, Speakers Bureau; Nippon-Boehringer-Ingelheim Co., Ltd.: Research Funding; Mundipharma K.K.: Research Funding; Amgen-Astellas Biopharma K.K.: Research Funding; Nippon-Shinyaku Co., Ltd.: Research Funding, Speakers Bureau; Kyowa-Kirin Co., Ltd.: Research Funding, Speakers Bureau; Pfizer Japan Inc.: Research Funding, Speakers Bureau; Alexion Pharmaceuticals, Inc.: Research Funding, Speakers Bureau; Eisai Co., Ltd.: Speakers Bureau; MSD K.K.: Research Funding, Speakers Bureau; PharmaEssentia Japan KK: Research Funding, Speakers Bureau; Yakult Honsha Co., Ltd.: Research Funding, Speakers Bureau; Bristol-Myers-Squibb K.K.: Research Funding, Speakers Bureau; Apellis Pharmaceuticals, Inc.: Research Funding; Incyte Biosciences Japan G.K.: Research Funding; Chugai Pharmaceutical Co., Ltd.: Research Funding, Speakers Bureau; Sanofi K.K.: Speakers Bureau; Amgen K.K.: Research Funding.
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