Mutations in NOTCH1, which occur in ~10% of Chronic Lymphocytic Leukemia (CLL) patients at diagnosis, are typically associated with unmutated (UM) B-cell receptor (BCR) subsets and define patients with earlier treatment need. Using primary CLL cells classified as NOTCH1 wild-type (CLL/NWT) or mutated (CLL/NM), both with UM-BCR, we show that BCR stimulation activates the NOTCH1 pathway, upregulating metabolic programs and mitochondrial biogenesis, selectively in CLL/NM. These cells display enhanced basal respiration and glycolysis, driven by higher mitochondrial mass, and further increase metabolic activity upon BCR triggering. To directly implicate NOTCH1 mutations, we engineered an MEC-1 model to generate wild-type (MEC-1/NWT) or mutated (MEC-1/NM) clones in a UM-BCR background. Here, NOTCH1 hyperactivation promoted mitochondrial metabolism through TFAM-dependent transcriptional control. Gene expression profiling, metabolic assays, and stable isotope tracing confirmed that MEC-1/NM cells rely on oxidative metabolism, with increased glutamine dependency and strengthened anabolic pathways, leading to augmented proliferation compared to MEC-1/NWT. Importantly, CLL/NM cells exhibit a marked vulnerability to glutamine deprivation. Combined inhibition of glutamine utilization and BCL2 triggered rapid apoptosis, providing a rationale for tailored therapeutic strategies in NOTCH1-mutated CLL. Representation of the molecular mechanism behind the metabolic reprogramming. BCR and NOTCH1 drive a dual metabolic reprogramming of glucose and glutamine pathways. In NOTCH1-mutated cells, both glucose and glutamine uptake are positively increased and even more upon BCR stimulation. Glucose is preferentially used to fuel the pentose phosphate pathway, and glutamine the TCA cycle. Concurrently, NICD accumulation, driven by BCR signaling, promotes TFAM expression and mitochondrial biogenesis. The resulting increase in mitochondrial mass underpins enhanced ATP production, oxygen consumption, and ROS generation, establishing a glutamine-dependent mitochondrial phenotype. This dependency sensitizes NOTCH1-mutated cells to glutamine blockade, which selectively induces apoptosis, further enhanced by combination with BCL-2 inhibition.

Expression of sEV-related genes correlates with disease progression and poor survival in CLL patients. Gene-expression analysis was performed by RT-qPCR for 7 genes involved in sEV biogenesis and secretion in a cohort of 144 CLL patients. The correlation between gene expression and survival was evaluated by Cox univariate regression analysis. Gene expression in clinical groups was evaluated by differential expression analysis for single genes or by logistic regression (LR) analysis for multiple genes. A, Calculated hazard ratios >1 (red dots, P < 0.05) indicate an increased risk for patients with high single-gene expression in terms of OS. B, Correlation between high or low gene expression and OS. Low and high groups are of identical size (n = 72) Median OS is indicated in months (mo). C, Correlation between high or low combined 7-gene expression and OS. D, Calculated hazard ratios >1 (red dots, P < 0.05) indicate an increased risk for patients with high multiple gene expression in terms of OS. E, Calculated hazard ratios >1 (red dots, P < 0.05) indicate an increased risk for patients with high single-gene expression in terms of TFS. F, Correlation between high or low gene expression and TFS. G and H, Standardized expression of single genes (G) or LR scores for multiple genes (H) in groups of patients according to prognostic markers (CytoG, cytogenetics, group size indicated in each panel). *, P < 0.05; **, P < 0.01. Data are mean with 95% confidence intervals.

LME-sEVs decrease CD8+ T-cell functions. A and B, Percentages (A) and numbers (B) of CD62L−KLRG1+ CD8+ T cells after 48 hours of treatment with LME- and HCME-sEVs assessed by FC. C, Expression of ICP on CD62L−KLRG1+ CD8+ T cells from B. HSNE clustering depicting treatments, cluster identity, and marker expression. D, Hierarchical clustering based on ICP expression. E, Percentages of PD1+TIM3+ICOS+ CD8+ T cells from cluster C1 (top) and of PD1+LAG3+TIM3+TIGIT+ICOS+ CD8+ T cells from cluster C8 (bottom). F–I, CD8+ T cells were isolated from C57BL/6 and CLL cells from TCL1 mice. F, Percentage of T cell–mediated killing of TCL1 cells (cytotoxic assay) in the presence of HCME-sEVs or LME-sEVs (N = 6). G, Quantification of CD8+ T-cell:TCL1 cell conjugates upon treatment with LME- or HCME-sEVs (N = 3–4) and representative images (scale bar, 10 μm). H, Quantification of immune synapse formation (F-actin area in μm2, HCME-, n = 31 and LME-sEVs, n = 39, dashed line representing median) and representative medial optical sections (scale bar, 5 μm) with arrows indicating the synapse. I, Mean Fluorescence intensity (MFI) of GzmB at the synapse between CD8+ T and CLL cells (HCME-, n = 31 and LME-sEVs, n = 51) and representative 3D volume-rendered images. J, miRNA levels quantified by RT-qPCR in CD8+ T cells treated with HCME- or LME-sEVs for 24 hours. K, Protein levels of miRNA targets determined by FC in CD8+ T cells treated for 48 hours with HCME-sEVs or LME-sEVs transfected with scramble or antagomiRs (miR-150, -155, and -378a). Preincubation of LME-sEVs with heparin was used as an inhibitor of sEV internalization. L, ICP levels determined by FC in CD8+ T cells treated for 48 hours with HCME-sEVs or LME-sEVs preincubated with blocking Abs (PD-L1, GAL9, VISTA, and MHC-II) or corresponding isotypes. *, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001 (unpaired Student t test). Data are mean.

LME-sEVs enter different lymphocyte subsets and modify CD8+ T cells in the microenvironment. A, Percentage of splenocytes internalizing MB488+ sEVs. Splenocytes from C57BL/6 mice were incubated for increasing periods of time with MB488+ LME-sEVs and analyzed by FC. sEV preincubation with heparin sulfate (Hep) was performed for 4 hours. B, Representative confocal microscopy pictures of total splenocytes after 4 hours of treatment with LME-sEVs, in the absence or presence of heparin (scale bar, 5 μm). C, Splenocytes from C57BL/6 mice were incubated for 24 hours with MB488+ LME-sEVs and then analyzed by FC with lymphocyte-lineage markers. D, FACS-sorted CD4+ and CD8+ T cells were incubated for increasing periods of time with MB488+ LME-sEVs and analyzed by FC. E, Representative confocal microscopy pictures of FACS-sorted CD4+ Tconv cells, CD8+ T cells, and Tregs after treatment with LME-sEVs (24 hours; scale bar, 5 μm). F–H, MB570+-LME-sEVs were i.v. injected in C57BL/6 mice. Total splenocytes were harvested 24 hours later and analyzed by FC directly (F) or after staining for specific immune subsets (CD19+ B cells, CD4+ and CD8+ T cells, G–H). I, Volcano plot showing differential expression of genes (DEG) with FDR <0.05 and log2FC >1 in CD8+ T cells isolated from spleens of mice treated with LME- or HCME-sEVs for 1 week. J, Hierarchical clustering of DEG from I. K, t-distributed stochastic neighbor embedding (t-SNE) of samples from I. L, Hierarchical clustering of selected genes from J, grouped by enriched gene ontologies. M and N, mRNA (M) or protein levels (N) of 3 selected DEG from I, quantified by RT-qPCR or FC, in CD8+ T cells treated in vitro for 48 hours with HCME- or LME-sEVs. *, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001 (unpaired Student t test). Data are mean.

LME-sEVs impact CD8+ T-cell transcriptome, proteome, and metabolome. A, Volcano plot showing DEG identified by RNA-seq from CD8+ T cells treated for 48 hours with LME- (n = 4) or HCME-sEVs (n = 3) with FDR <0.05 and log2FC >1. B and C, Hierarchical clustering of all DEG (B) and of selected genes from relevant cell functions (C). D, Volcano plot showing differentially expressed proteins (DEP) identified by mass spectrometry from CD8+ T cells treated for 96 hours with LME- (n = 3) and HCME-sEVs (n = 3) with FDR <0.05 and log2FC >1. E and F, GzmB mRNA expression and GzmB and perforin levels in CD8+ T cells treated for 48 hours with HCME- or LME-sEVs. G and H, Ontology analysis of enriched (G) or diminished (H) DEP in CD8+ T cells treated with LME- or HCME-sEVs (from D). I, Levels of glucose measured by mass spectrometry in culture medium in CD8+ T-cell treated with LME- or HCME-sEVs for 96 hours. Negative value represents consumption. J, Immunoblot analysis of glycolysis-related proteins from CD8+ T cells treated for 96 hours with LME- or HCME-sEVs. K, Levels of ADP and ATP generated from 13C-glucose measured by mass spectrometry in CD8+ T cells treated with LME- (n = 5) or HCME-sEVs (n = 6) for 96 hours. L, Oxygen consumption measured by SeaHorse assay from CD8+ T cells treated with LME- or HCME-sEVs for 96h. *, P < 0.05; **, P < 0.01; ****, P < 0.0001 (unpaired Student t test). Data are mean and SEM.

LME-sEVs present a specific proteome and miRNA fingerprint. A, Hierarchical clustering of sEVs differentially expressed proteins (DEP with q < 0.05) identified by mass spectrometry between HCME-sEVs (n = 3, isolated from independent pools of 5 C57BL/6 spleens) and LME-sEVs (n = 14). B, Volcano plot showing DEP between LME- and HCME-sEVs with FDR <0.05 and log2FC >1. C, PCA based on DEP. D and E, Ontology analysis of DEP between LME-sEVs and HCME-sEVs. F, Expression of ICP ligands on HCME- or LME-sEVs quantified by bead-based FC. G, Representative pictures of ICP ligand expression on single MB488+CD20+ LME-sEVs visualized by imaging FC. H and I, HSNE clustering of MB488+ LME-sEVs based on CD20, PD-L1, GAL9, and MHC-II expression analyzed by FC and related combinations of markers on CD20+ LME-sEVs. J, miRNA levels measured using RT-qPCR from HCME- (isolated from a pool of 5 C57BL/6 mice spleens) and LME-sEVs (n = 8). Data are mean.

Supplemental Table S3 - Patient clinical information

sEV are enriched in the human and murine leukemic microenvironments. A, Relative mRNA expression of selected genes involved in sEV biogenesis and secretion in B cells from PB of healthy donors (HC, n = 9) and CLL patients (CLL, n = 15; from GSE67640). B, Score based on sEV-related mRNA levels from A. C, mRNA levels of selected genes extracted from A. D, mRNA expression of selected genes according to IGHV mutational status. E, Score combining the expression of Rab10, Rab35, and Rab40C according to IGHV mutational status. F, Relative mRNA expression of selected genes involved in sEV biogenesis and secretion in B cells from C57BL/6 (WT) and Eμ-TCL1 mice (TCL1; from GSE175564). G, Score based on sEV-related RNA levels from F. H,Rab3b mRNA level extracted from F. I, Detailed protocol to isolate and purify sEVs from the murine spleen. J, Amount of proteins (in mg) recovered from LME- (n = 18) or HCME-sEVs (n = 10), normalized per gram of spleen. K, Representative TRPS analysis of ME-sEVs for size and concentration. L, Electron microscopy images of ME-sEVs. M, Western blot analysis of ME-sEVs. N, HSNE clustering analysis of MB488+ LME-sEVs based on CD63, CD81, and CD9 expression measured by bead-free FC (left) and relative percentages of combined expression (right). *, P < 0.05; **, P < 0.01; ****, P < 0.0001 (unpaired Student t test). Data are mean.

Supplemental Table S1 - Proteins detected in sEV preparations or cells treated with sEV

Supplemental Table S2 - Differential expression analysis of genes in leukemic or immune cells treated with sEV

sEVs are crucial for CLL development by impairing the antitumor immune response in vivo.A, Generation of a new TCL1-RAB27DKO mouse model. B, Detection of the human TCL1 transgene and Rab27b excision in gDNA of C57BL/6, TCL1, and TCL1-RAB27DKO mice. C, Immunoblot analysis of RAB27A and RAB27B proteins in the same mice. D, Percentage of CD5+CD19+ CLL cells in the PB of TCL1 (n = 35) or TCL1-RAB27DKO (n = 12) mice over time. E, Survival of mice from D and RAB27DKO mice (n = 10). F, Quantity of proteins recovered from LME-sEVs (n = 18) or LME-sEVsTCL1-RAB27DKO (n = 11) normalized per gram of spleen. G, PCA based on differentially expressed proteins (DEP) between LME-sEVsTCL1-RAB27DKO and LME-sEVs with FDR <0.05 and log2FC >1. H, Volcano plot showing DEP. I, Injection scheme of CLL cells competent (TCL1, red arrows) or deficient in sEV release (TCL1-RAB27DKO, green arrows) into C57BL/6 mice, with or without LME-sEVs (violet arrows). J, Percentage of CD5+CD19+ CLL cells in the blood of C57BL/6 mice injected according to I (n = 16 per condition). Four different clones for each genotype were injected into 4 mice each. K, Injection scheme of CLL cells deficient in sEV release (TCL1-RAB27DKO, green arrows) into C57BL/6 mice, treated with α-CD8 blocking or isotype-control Abs (violet arrows). L, Percentage of CD5+CD19+ CLL cells in the PB of mice injected according to K (n = 6 per group) at days 14 and 21 (left) and in the spleen of the same mice at day 21. M, Injection scheme of CLL cells deficient in sEV release (TCL1-RAB27DKO, green arrows) into C57BL/6 mice, together with α-CD8 blocking Ab (violet arrows) and followed by injection of activated CD8+ T cells treated ex vivo with HCME- (blue arrows) or LME-sEVs (red arrows). N, Percentage of CD5+CD19+ CLL cells at day 10 in the PB of mice injected according to panel M (n = 4 per group). O, Survival of mice from M (n = 4 per group). *, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001 (unpaired Student t test for F, L, and N two-way ANOVA followed by the Bonferroni multiple comparison test for D and J, log-rank test for E and O). Data are mean with SEM.

Supplemental Table S4 - Flow cytometry antibody list

Modeling tumor metabolism in vitro remains challenging. Here, we used galactose as an in vitro tool compound to mimic glycolytic limitation. In contrast to the established idea that high glycolytic flux reduces pyruvate kinase isozyme M2 (PKM2) activity to support anabolic processes, we have discovered that glycolytic limitation also affects PKM2 activity. Surprisingly, despite limited carbon availability and energetic stress, cells induce a near-complete block of PKM2 to divert carbons toward serine metabolism. Simultaneously, TCA cycle flux is sustained, and oxygen consumption is increased, supported by glutamine. Glutamine not only supports TCA cycle flux but also serine synthesis via distinct mechanisms that are directed through PKM2 inhibition. Finally, deleting mitochondrial one-carbon (1C) cycle reversed the PKM2 block, suggesting a potential formate-dependent crosstalk that coordinates mitochondrial 1C flux and cytosolic glycolysis to support cell survival and proliferation during nutrient-scarce conditions.

Book Citations: Authors, Title, HemaSphere, 2023;7(S3):pages. The individual abstract DOIs can be found at https://journals.lww.com/hemasphere/pages/default.aspx. Disclaimer: Articles published in the journal HemaSphere exclusively reflect the opinions of the authors. The authors are responsible for all content in their abstracts including accuracy of the facts, statements, citing resources, etc. 94 analysis consisting of pulsed SILAC, RNA sequencing and polysome profiling performed in CLL patient samples and cell lines treated with FL3 revealed the decreased translation of the MYC oncogene (C). Furthermore, inhibition of translation was associated with a block of proliferation (D) and a profound rewiring of MYC-driven metabolism. Interestingly, contrary to other models, in CLL, the RAS-RAF-(PHBs)-MAPK pathway is neither impaired by FL3 nor implicated in translation regulation. We rather showed that PHBs are directly associated with the translation initiation complex (E). Knock-down of PHBs resembled FL3 treatment (F), confirming the direct involvement of PHBs in translation initiation. Importantly, inhibition of translation was efficient in controlling CLL development in vivo (G). Finally, high expression of translation initiation-related genes and PHBs genes correlated with poor survival and unfavorable clinical parameters in CLL patients (H). Summary/Conclusion: We demonstrated that translation inhibition is a valuable strategy to control CLL development by blocking the translation of several oncogenic pathways including MYC. We also unraveled a new and direct role of PHBs in translation initiation, thus creating new therapeutic opportunities for CLL patients. HemaSphere | 2023;7(S3) EHA2023 Hybrid Congress Copyright Information: (Online) ISSN: 2572-9241 © 2023 the Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the European Hematology Association. This is an open access Abstract Book distributed under the Attribution-NonCommercial-NoDerivs (CC BY-NC-ND) which allows third parties to download the articles and share them with others as long as they credit the author and the Abstract Book, but they cannot change the content in any way or use them commercially. Abstract Book Citations: Authors, Title, HemaSphere, 2023;7(S3):pages. The individual abstract DOIs can be found at https://journals.lww.com/hemasphere/pages/default.aspx.Book Citations: Authors, Title, HemaSphere, 2023;7(S3):pages. The individual abstract DOIs can be found at https://journals.lww.com/hemasphere/pages/default.aspx. Disclaimer: Articles published in the journal HemaSphere exclusively reflect the opinions of the authors. The authors are responsible for all content in their abstracts including accuracy of the facts, statements, citing resources, etc. 95

Dysregulation of mRNA translation, including preferential translation of mRNA with complex 5'-UTRs such as the MYC oncogene, is recognized as an important mechanism in cancer. In this study, we show that both human and murine chronic lymphocytic leukemia (CLL) cells display a high translation rate, which can be inhibited by the synthetic flavagline FL3, a prohibitin (PHB)-binding drug. A multiomics analysis consisting of pulsed SILAC, RNA sequencing and polysome profiling performed in CLL patient samples and cell lines treated with FL3 revealed the decreased translation of the MYC oncogene and of proteins involved in cell cycle and metabolism. Furthermore, inhibition of translation was associated with a block of proliferation and a profound rewiring of MYC-driven metabolism. Interestingly, contrary to other models, the RAS-RAF-(PHBs)-MAPK pathway is neither impaired by FL3 nor implicated in translation regulation in CLL cells. Here, we rather show that PHBs are directly associated with the translation initiation complex and can be targeted by FL3. Knock-down of PHBs resembled FL3 treatment. Importantly, inhibition of translation was efficient in controlling CLL development in vivo either alone or combined with immunotherapy. Finally, high expression of translation initiation-related genes and PHBs genes correlated with poor survival and unfavorable clinical parameters in CLL patients. In conclusion, we demonstrated that translation inhibition is a valuable strategy to control CLL development by blocking the translation of several oncogenic pathways including MYC. We also unraveled a new and direct role of PHBs in translation initiation, thus creating new therapeutic opportunities for CLL patients.

Supplemental video 1. 786-0 cells described in Figure 3B were recorded by time-lapse video microscopy

Background: Chronic Lymphocytic Leukemia (CLL), the most common type of leukemia in adults, is characterized by the clonal expansion of CD5+ CD19+ B cells. Despite great advance in the standard of care in the last decades, there are still unmet medical needs (long-life treatment, resistance…). Altered cellular metabolism has emerged as a hallmark of cancer by sustaining the uncontrolled growth of cancer cells. The one-carbon (1C) pathway is a major driver for tumor proliferation, providing building blocks for biosynthesis of nucleotides through pyrimidines and purines synthesis (A). Understanding the metabolic reprogramming occurring in cancer, notably in CLL, may provide insights to support the development of novel therapies. Aims: We aim to pre-clinically test a novel nanomolar MTHFD1/2 inhibitor (MTHFD1/2i) for CLL treatment. Recently, our collaborators and us described that this compound mainly inhibits the dehydrogenase/cyclohydrolase (DC) activity of MTHFD1, leading to a complete block in thymidylate synthesis in SW620 colon cancer cells (Green et al, Nature Metabolism, in press). Methods: Here we evaluate the cytotoxic activity of MTHFD1/2i on a panel of murine and human CLL cells, and also on others B cell malignancies. The molecular mechanisms sustaining the cytotoxic activity were evaluated by performing metabolic tracing, rescue experiments and CRISPR/Cas9 KO. Finally, the impact of MTHFD1/2i on CLL development in vivo was assessed in a xenograft mouse model. Results: Higher expression of MTHFD2 was observed in patients and murine CLL cells compared to normal B cells, suggesting 1C metabolism over-activation. Moreover, we showed a strong expression of both MTHFD1 and 2 enzymes in all the CLL, MCL, DLBCL and MM cancer cell lines tested. In vitro treatment with MTHFD1/2i efficiently reduced cell viability of CLL cell lines, as for MCL and DLBCL cell lines, at low nanomolar dose while no effect was observed on MM cell lines (B). We identified specific mechanism of resistance in non-responding MM cells. In CLL cells, the cytotoxic effect of MTHFD1/2i is associated with a blockade of cell proliferation. Using 13C-serine isotope tracing, we showed that MTHFD1/2i did not prevent formate release but significantly reduced ATP production from serine. By performing rescue experiments, we confirmed that the cytotoxic effect of MTHFD1/2i on CLL cells is mediated through the inhibition of the DC domain of MTHFDH1, resulting in a defect in thymidylate synthesis (C). Some 1C metabolites, notably thymidine and folate, are much higher in mouse plasma compared to human. We demonstrated in vitro that thymidine and chronical exposure with folic acid compromised MTHFD1/2i cytotoxic activity. To validate the in vivo efficacy of MTHFD1/2i, we developed a murine model in which we aim (i) to reduce the use of plasmatic thymidine, and (ii) to prevent folate to fuel the 1C cycle. To do so, we performed our in vivo experiment by using custom diet and a genetically engineered CLL cell line model (CRISPR-Cas9 KO). We demonstrated that in vivo treatment of NSG mice with MTHFD1/2i after subcutaneous engraftment of CRISPR-Cas9 KO OSU-CLL cells significantly increased survival and completely eradicated established primary tumor (D and E) Summary/Conclusion: We identified a novel nanomolar MTHFD1/2i displaying a high cytotoxic activity in CLL and other B-cell malignancies by impairing cell proliferation through a defect in pyrimidine synthesis. This inhibitor also exhibits a potent anticancer activity in a pre-clinical murine model of CLL, reinforcing its therapeutic potential for CLL treatment in clinic.Keywords: B cell chronic lymphocytic leukemia, Inhibitor

Abstract Small extracellular vesicle (sEV, or exosome) communication among cells in the tumor microenvironment has been modeled mainly in cell culture, whereas their relevance in cancer pathogenesis and progression in vivo is less characterized. Here we investigated cancer–microenvironment interactions in vivo using mouse models of chronic lymphocytic leukemia (CLL). sEVs isolated directly from CLL tissue were enriched in specific miRNA and immune-checkpoint ligands. Distinct molecular components of tumor-derived sEVs altered CD8+ T-cell transcriptome, proteome, and metabolome, leading to decreased functions and cell exhaustion ex vivo and in vivo. Using antagomiRs and blocking antibodies, we defined specific cargo-mediated alterations on CD8+ T cells. Abrogating sEV biogenesis by Rab27a/b knockout dramatically delayed CLL pathogenesis. This phenotype was rescued by exogenous leukemic sEV or CD8+ T-cell depletion. Finally, high expression of sEV-related genes correlated with poor outcomes in CLL patients, suggesting sEV profiling as a prognostic tool. In conclusion, sEVs shape the immune microenvironment during CLL progression. Significance: sEVs produced in the leukemia microenvironment impair CD8+ T-cell mediated antitumor immune response and are indispensable for leukemia progression in vivo in murine preclinical models. In addition, high expression of sEV-related genes correlated with poor survival and unfavorable clinical parameters in CLL patients. See related commentary by Zhong and Guo, p. 5. This article is highlighted in the In This Issue feature, p. 1