Abstract: Loss-of-function mutations in ten-eleven translocation-2 (TET2) are recurrent events in acute myeloid leukemia (AML) as well as in preleukemic hematopoietic stem cells (HSCs) of age-related clonal hematopoiesis. TET3 mutations are infrequent in AML, but the level of TET3 expression in HSCs has been found to decline with age. We examined the impact of gradual decrease of TET function in AML development by generating mice with Tet deficiency at various degrees. Tet2f/f and Tet3f/f mice were crossed with mice expressing Mx1-Cre to generate Tet2f/wtTet3f/fMx-Cre+ (T2ΔT3), Tet2f/fTet3f/wtMx-Cre+ (ΔT2T3), and Tet2f/fTet3f/fMx-Cre+ (ΔT2ΔT3) mice. All ΔT2ΔT3 mice died of aggressive AML at a median survival of 10.7 weeks. By comparison, T2ΔT3 and ΔT2T3 mice developed AML at longer latencies, with a median survival of ∼27 weeks. Remarkably, all 9 T2ΔT3 and 8 ΔT2T3 mice with AML showed inactivation of the remaining nontargeted Tet2 or Tet3 allele, respectively, owing to exonic loss in either gene or stop-gain mutations in Tet3. Recurrent mutations other than Tet3 were not noted in any mice by whole-exome sequencing. Spontaneous inactivation of residual Tet2 or Tet3 alleles is a recurrent genetic event during the development of AML with Tet insufficiency.

Abstract: 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 & gt;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. Disclosures 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.

Abstract: Background: Activities of nonhematopoietic cells (NHCs) reportedly underlie lymphomagenesis. In follicular lymphoma (FL), mesenchymal stromal cells (SCs) including follicular dendritic cells (FDCs) have been shown to facilitate FL expansion. However, comprehensive understanding of lymphoma NHC activities have been hampered by indefinite NHC heterogeneity even in normal human lymph node (LN). Indeed, human LN blood endothelial cells (BECs) and non-endothelial stromal cells (NESCs) have not been analyzed at single-cell resolution. Here, we aimed to construct a single-cell atlas of NHCs in human LN applicable to lymphoma researches. We also sought to reveal the landscape of stromal remodeling in lymphomas, particularly in FL, to advance understanding of stromal contributions in lymphomagenesis. Methods: We prospectively performed single-cell RNA sequencing of NHCs ( & gt;100,000 cells) extracted from 27 human samples including metastasis-free LN (MFLN; n=9), nodal FL (n=10), peripheral T-cell lymphoma (PTCL; n=5), and diffuse large B-cell lymphoma transformed from FL (tDLBCL; n=3). Data from MFLN samples were used for the construction of NHC atlas. Immunofluorescence (IF) staining was performed to investigate the existence and topological localizations of each NHC subcluster in the LN. Using the NHC atlas, we performed comprehensive comparative analysis with FL NHCs by differentially-expressed gene (DEG) and intercellular ligand-receptor analyses. We also investigated the prognostic impact of putative stroma-derived biomarkers using deposited microarray data of FL patients. Finally, we examined the applicability of the atlas to NHCs from other lymphoma subtypes by analyzing PTCL and tDLBCL NHCs. Data analysis was performed through multiple pipelines including Seurat, Monocle3, and CellphoneDB. Results: Graph-based clustering analysis revealed that the transcriptional features of NHC subpopulations in MFLN are detectable in FL NHCs. Unsupervised sub-clustering analysis of BECs, lymphatic endothelial cells (LECs), and NESCs revealed 10, 8, and 12 subclusters, respectively, including some lacking mouse counterpart. IF staining successfully identified each NHC subcluster and its localization in the LN. In FL NHCs, the proportion of arterial BEC subclusters markedly increased relative to MFLN, while the proportion of LECs decreased. In FL NESCs, the proportion of marginal reticular cells (MRCs) as well as FDCs greatly increased. DEG analysis revealed that the greatest changes in gene expression occurs in NESC subclusters, particularly in MRCs, T-zone reticular cells (TRCs), pericytes, and FDCs. Notably, in some NESC subclusters, we observed marked upregulation of genes relevant to solid cancers but previously not described in lymphomas (e.g. POSTN, EGFL6, and FAP). Combined interactome and DEG analysis revealed 60 FL-specific interactions between NHC subclusters and malignant B cells. For example, interactions mediated through stroma-derived CD70 were enhanced at medullary SC subclusters and SCs at LN capsule adventitia. Additionally, the CCR7-CCL19 interaction and interactions via B-cell activating factor (BAFF) were unexpectedly upregulated at non-TRC SC and medullary SC subclusters, respectively. Also, the CXCL13-CXCR5 axis was highly activated in MRCs, collectively indicating that non-FDC SCs vigorously participate in FL cell expansion and/or infiltration into extra-follicular lesions. Some intercellular interactions were functionally validated by in vitro binding assays. Based on this dataset, we identified putative stroma-derived biomarkers linked to unfavorable prognosis in FL patients including TDO2, encoding immune-modulators, and LY6H and LOX, tip cell markers. We finally confirmed that NHC subclusters identified in our atlas were also detectable in NHCs of more aggressive lymphoma subtypes including PTCL and tDLBCL. Notably, we found that extra-follicular SCs had further differentiated into follicular SCs in tDLBCL, likely representing a terminal form of stromal remodeling in FL. Conclusion: We constructed a comprehensive single-cell atlas of NHCs in human LN highly applicable to lymphoma NHC researches and revealed a total of 30 NHC subclusters. Our study largely updates NHC taxonomy in LNs and provides a rich resource and deeper insights into lymphoma biology, a contribution that should advance lymphoma management and therapy. Figure 1 Figure 1. Disclosures Usuki: 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; Daiichi Sankyo Co., Ltd.: Research Funding, Speakers Bureau; Sumitomo-Dainippon Pharma Co., Ltd.: Research Funding; SymBio Pharmaceuticals Ltd.: Research Funding, Speakers Bureau; Gilead Sciences, Inc.: Research Funding; Bristol-Myers-Squibb K.K.: Research Funding, Speakers Bureau; Apellis Pharmaceuticals, Inc.: Research Funding; AbbVie GK: Research Funding, Speakers Bureau; Astellas Pharma Inc.: Research Funding, Speakers Bureau; 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.

Abstract: 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.

Abstract: Background: Aplastic anemia (AA), paroxysmal nocturnal hemoglobinuria (PNH) and myelodysplastic syndrome (MDS) are the heterogeneous group of bone marrow failure syndrome (BMFs). AS they often show profound hypocellular marrow, the diagnosis is often difficult by bone marrow and laboratory examination alone. Red to yellow marrow conversion occurs with age in the appendicular skeleton (AS), where red marrow is converted to yellow marrow until the age of early 20s. Although abnormal distribution of red marrow in appendicular skeleton were previously reported in small series of patients with MDS, leukemia and lymphoma by MRI, no further study has published so far. Here, we examined distribution of red marrow in AS by low-dose multi-detector CT (MDCT) in BMFs patients, and analyzed the relationship between the abnormal medullary pattern in AS and laboratory variables. The relationship between the MDCT pattern and subsequent development of leukemic transformation on survivals was analyzed in patients with BMFs. Patients: We retrospectively reviewed the medical records of 138 untreated patients (AA n=36, PNH n=5, and MDS n=97) with BMFs diagnosed in the Department of Hematology/Oncology at Kameda Medical Center, Kamogawa, Japan, from July 2008 to June 2014. Follow-up MDCTs were evaluated in 28 MDS patients when they were diagnosed as overt AML (MDS/tAML). Retrospective review of clinical and laboratory features including complete blood count, % of bone marrow blast, chromosomal analysis, and International Prognostic Scoring System (IPSS) at diagnosis was performed. WHO classification of patients with MDS was as follows: RCUD (n=21), RARS (n=2), RCMD (n=26), RAEB (n=43), and, MDS unclassified (MDS-U) (n=5). Leukemia-free survival (LFS) and overall survival (OS) were analyzed in 73 patients with MDS who were ≥65 years of age and ineligible for allogeneic stem cell transplantation (allo SCT) by the Kaplan-Meier. CT image acquisition and Image analysis: Non-enhanced CT examinations were performed from the skull to the knees by MDCT scanner (Aquilion 64, Tohshiba, Tokyo, Japan). Bony canal of humeral and femoral bone were visualized by coronal and sagittal axis image reconstruction. Medullary CT density of humerus and femurs were measured and the results were expressed as Hounsfield unit (HU). As the normal adult bone marrow was composed of rich adipocytes and called yellow marrow, it is represented by low density CT value between -30 to -100 HU. The value above -30 HU observed in long bony canals was considered as high density. Medullary pattern of AS were categorized as follows: (1) fatty pattern; showing a low signal density marrow (2) focal pattern; showing abnormally focal high density lesions (3) diffuse pattern; showing uniformly high density marrow. Results: All 36 patients with AA showed a fatty (n=13, 36.1%) or focal (n=23, 63.9%) pattern in medullary AS on MDCT, and none of them showed diffuse pattern. Five patients with PNH showed as follows: fatty/focal/diffuse, 1/3/1. Ninety-seven patients with MDS showed as follows: fatty/focal/diffuse, 24/46/27. Patients with MDS who showed diffuse pattern had a significantly low hemoglobin concentration compared to those with fatty or focal pattern (p=0.03). Among the patients with MDS, most of the patients with RCUD (n=21), RARS (n=2), RCMD (n=26), MDS-U (n=5) showed the fatty or focal pattern (fatty or focal/diffuse pattern; RCUD (18/3), RARS (2/0), RCMD (21/5), MDS-U (5/0)), but approximately half (46%) of patients with RAEB showed diffuse pattern (fatty/focal/diffuse pattern; 7/17/19). In addition, patients with transformed to MDS/tAML showed either focal (n=10, 35.7%) or diffuse (n=18, 64.3%) pattern and none of them showed fatty pattern. In 73 patients with MDS who were ≥65 years of age and ineligible for allo SCT, the group with focal or diffuse pattern had significantly shorter LFS and OS compared to the group with fatty pattern (p=0.01, p=0.05, respectively). Patients with focal pattern in AS showed longer LFS than those with diffuse pattern (p=0.05), but difference was not statistically significant in OS (p=0.22). Conclusions: This study showed that MDCT imaging of the appendicular skeletons provided important information for the diagnosis and prognosis of patients with BM. In patients with MDS, focal or diffuse pattern on MDCT showed negative prognostic impact on LFS and OS, and these patterns appeared to reflect the status of disease. Figure 2. Figure 2. Figure 3. Figure 3. Disclosures No relevant conflicts of interest to declare.

Abstract: 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 & gt; 70 (p=0.0293), and consciousness levels at diagnosis (other than JCS 0-1) (p=0.104) were relatively associated with poor OS (p & lt; 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.

Abstract: Primary central nervous system lymphoma ( PCNSL ) is a rare subtype of lymphoma that arises within the brain or the eyes. PCNSL recurs within the central nervous system ( CNS ) in most relapsed cases, whereas extra‐ CNS relapse is experienced in rare cases. The present study aimed at identifying the presence of common precursor cells ( CPC ) for primary intra‐ and relapsed extra‐ CNS tumors, and further assessing the initiating events in bone marrow ( BM ). Targeted deep sequencing was carried out for five paired primary intra‐ and relapsed extra‐ CNS tumors of PCNSL . Two to five mutations were shared by each pair of intra‐ and extra‐ CNS tumors. In particular, MYD 88 mutations, L265P in three and P258L in one, were shared by four pairs. Unique somatic mutations were observed in all five intra‐ CNS tumors and in four out of five extra‐ CNS tumors. Remarkably, IgH clones in the intra‐ and the extra‐ CNS tumors in two pairs were distinct from each other, whereas one pair of tumors shared identical monoclonal IgH rearrangement. In a cohort of 23 PCNSL patients, L265P MYD 88 mutations were examined in tumor‐free BM mononuclear cells ( MNC ) in which the PCNSL tumors had L265P MYD 88 mutations. L265P MYD 88 mutations were detected by a droplet digital PCR method in nine out of 23 bone marrow mononuclear cells. These results suggest that intra‐ and extra‐tumors are derived from CPC with MYD 88 mutations in most PCNSL , arising either before or after IgH rearrangement. The initiating MYD 88 mutations may occur during B‐cell differentiation in BM .

Abstract: 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 & gt;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 & gt;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 & lt;0.05 for Tnfr and & lt;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.