scholarly journals Transcriptional Regulation of Hematopoietic Stem Cells in Aging and Myelodysplastic Syndrome Reveals DDIT3 As a Potential Driver of Transformation

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 3764-3764
Author(s):  
Teresa Ezponda ◽  
Juan P. Romero ◽  
Marina Ainciburu ◽  
Ana Alfonso ◽  
Nerea Berastegui ◽  
...  
Keyword(s):  
Gene Expression ◽  
Board Of Directors ◽  
Research Funding ◽  
Expression Profiles ◽  
Genetic Alterations ◽  
Transcriptional Profile ◽  
Gene Set Enrichment Analysis ◽  
Advisory Committees ◽  
Hematopoietic Stem ◽  
Transcriptional Changes

Myelodysplastic syndromes (MDS) are clonal hematopoietic stem cell (HSC) malignancies characterized by ineffective hematopoiesis. Genetic alterations do not fully explain the molecular pathogenesis of the disease, indicating that other types of lesions, such as transcriptional aberrations, may play a role in its development. Moreover, MDS prevalence is almost exclusive to older patients, suggesting that elderly-related alterations may predispose to the development of this clinical entity. Thus, study of the transcriptional lesions occurring in the aging-MDS axis could shed some light of the molecular bases of the disease. To characterize the transcriptional profile of HSCs in aging and MDS, we isolated CD34+, CD38-, CD90+, CD45RA- cells from 11 untreated MDS patients with unilineage and multilineage dysplasia (median of 75 y/o), as well as from 16 young and 8 elderly healthy donors (median of 21 and 70 y/o, respectively), and their expression profile was analyzed using MARS-seq. Unsupervised principal component analysis demonstrated that the three groups of HSCs clustered separately, indicating that different expression profiles characterize healthy young and elderly, and MDS-associated HSCs. To better understand the gene expression deregulation of HSCs, we analyzed the transcriptional dynamisms along the aging-MDS axis, detecting groups of genes following different patterns of expression. Some gene clusters showed exclusive alteration either in aging or in the progression from elderly HSCs to MDS-HSCs, other groups of genes presented a continuous alteration along the axis, and some displayed opposite regulation in aging and in the transition to MDS (Figure 1). Genes showing specific downregulation in aging were involved in DNA damage sensing and repair, and in cell cycle regulation, whereas genes overexpressed in this process were enriched in apoptosis regulators and in cancer-associated genes, including AML-related factors. These findings indicate that transcriptional changes in aging may predispose for MDS and AML, and potentially other malignancies. Interestingly, we detected a group of genes in which the age-mediated upregulation of gene expression was reversed to that of young HSCs in MDS, indicating a "rejuvenation" profile of malignant HSCs. These genes were involved in response to inflammation, to different types of stress conditions such as hypoxia or radiation, and to cytokines. Elderly HSCs may upregulate such genes in response to the known inflammatory microenvironment of elderly bone marrow. Intriguingly, the decrease in expression detected in MDS suggests that malignant HSCs lose the ability of reacting to such stimuli, possibly favoring their survival in a hostile microenvironment. Finally, the analyses performed allowed for the identification of genes showing MDS-specific deregulation. Genes specifically overexpressed in MDS compared to normal (both young and elderly) HSCs, we enriched in transcriptional and epigenetic regulators, and among them, we detected the presence of DDIT3/CHOP, a member of the CCAAT/enhancer-binding protein (C/EBP) family of transcription factors. To determine its potential effects on hematopoietic deregulation, DDIT3 was exogenously overexpressed in healthy HSCs. Notably, its upregulation produced an erythroid bias in an ex-vivo differentiation system, with an increase in the percentage of erythroblasts and a decrease in granulocytes and monocytes compared to HSCs transduced with the empty vector. Transcriptomic analysis of transduced HSCs not subjected to differentiation demonstrated how DDIT3 overexpression produced an erythroid-prone state of HSCs, suggesting it may act as a pioneer factor in MDS-HSCs. Furthermore, gene set enrichment analysis showed that DDIT3 overexpression produced an MDS-like transcriptional profile, suggesting this factor may be key in the acquisition of the disease. Altogether, our results demonstrate that HSCs undergo transcriptional changes in the aging-MDS axis that may alter their intrinsic functions as well as their response to the microenvironment, ultimately contributing to the acquisition of the disease. In particular, our data show that DDIT3 may be a potential driver of MDS transformation. Disclosures Paiva: Amgen, Bristol-Myers Squibb, Celgene, Janssen, Merck, Novartis, Roche, and Sanofi; unrestricted grants from Celgene, EngMab, Sanofi, and Takeda; and consultancy for Celgene, Janssen, and Sanofi: Consultancy, Honoraria, Research Funding, Speakers Bureau. Díez-Campelo:Celgene Corporation: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding.

scholarly journals JMML Fetal Identity Results Either from Retention of a Physiologic Signature or Aberrant Activation of Master Oncofetal Regulators

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 4-5
Author(s):  
Marion Strullu ◽  
Aurélie Caye-Eude ◽  
Loïc Maillard ◽  
Chloé Arfeuille ◽  
Elodie Lainey ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stem Cell ◽  
Board Of Directors ◽  
Hematopoietic Stem Cell ◽  
Hierarchical Clustering ◽  
Research Funding ◽  
Gene Set Enrichment Analysis ◽  
Hematopoietic Progenitors ◽  
Advisory Committees ◽  
Hematopoietic Stem

Objectives: Juvenile myelomonocytic leukemia (JMML) is a rare but aggressive myeloproliferative/myelodysplastic neoplasm affecting infants and young children. The narrow age-window of onset suggests that a prenatal environment is needed for JMML oncogenesis. In search of a transcriptional reminiscence of embryo-fetal characteristics that would confirm this hypothesis, we investigated how the gene expression profile of JMML hematopoietic progenitors compared to their healthy counterpart isolated at different stages of ontogeny. Methods: Hematopoietic stem cell and progenitor cell (HSPC) fractions of JMML (n=16), bone marrow (BM) of healthy children (n=7), fetal liver (FL; n=3) and fetal BM (FBM; n=2) were phenotyped and sorted using signatures validated in the fetal and adult BM (Notta et al, Science 2011). RNAseq was performed using the TruSeq® Stranded Total RNASample preparation kit. Unsupervised hierarchical clustering analysis was done with the Bioconductor edgeR package. Differentially expressed genes were identified with the Bioconductor limma package. Results: To eliminate the impact of variations in the HSPC distribution, the JMML transcriptome was assessed on FACS-sorted common myeloid progenitor (CMP), granulocyte-monocyte progenitor (GMP) and megakaryocyte-erythroid progenitor (MEP) cell fractions from 16 JMML and compared to healthy counterparts at different stages of ontogeny (FL, FBM, age-matched children BM). Unsupervised hierarchical clustering separated the samples into 4 groups (C1-4), primarily according to ontogeny, with 14/15 embryo-fetal fractions in C1 and all healthy post-natal progenitors in C2 (CMP, MEP) or C3 (GMP). Most JMML fractions clustered either with the prenatal fractions (C1; 17/47 fractions from 8/16 patients) or in a separate group containing no healthy sample (C4; 23/47 samples from 10/16 patients). Two groups were defined accordingly: one with JMML resembling embryo-fetal samples ('Fetal-JMML'; n=6/16), and a JMML-specific group ('Onco-JMML'; 8/16). Patients with Onco-JMML tended to be older, with a more severe presentation and elevated fetal hemoglobin levels. All PTPN11-mutated JMML were in this group whereas 5/6 Fetal-JMML had NRAS or KRAS mutations. Analysis of differential gene expression between Fetal and Onco-JMML highlighted 344 up-regulated genes versus 19 up-regulated genes in Onco-JMML. Surprisingly, LIN28B and WT1, both known to activate fetal pathways, were the most up-regulated genes in Onco-JMML. These key transcription factors were deregulated as early as the hematopoietic stem cell (HSC) compartment. Gene Set Enrichment Analysis (GSEA) confirmed enrichment in LIN28B and WT1-related signatures and showed enrichment in an AML signature in Onco-JMML. On the other hand, Fetal-JMML showed striking overexpression of monocytic /dendritic cell markers and inflammasome and innate immunity components. GSEA confirmed the strong monocyte identity of Fetal-JMML progenitors compared to onco-JMML or healthy postnatal progenitors. Part of the monocytic markers 'aberrantly' expressed in JMML progenitors was expressed in healthy fetal progenitors. Analysis of the HSC and multipotent progenitor (MPP) fractions showed that up regulation of monocytic markers was limited to the JMML progeny compartments. As we were able to confirm the transcriptional and functional identity of the sorted progenitors, these data suggest an early monocytic priming in these JMML progenitors, reminiscent of the monocyte-biased myelopoiesis characterizing physiologic fetal hematopoiesis. Conclusion: Our findings give a striking example of how ontogeny-related features are involved in childhood oncogenesis. They highlight a strong but complex link beween JMML and development, with a fetal identity resulting either from retention of a physiologic fetal monocytic signature or from aberrant (re)activation of master oncofetal regulators. Intriguingly, although LIN28B is thought to reprogram hematopoietic progenitors into a fetal-like state, its activation does not lead to an overall fetal profile in JMML, suggesting a regulatory mechanism distinct from that of physiological development. These two ontogeny-based signatures are likely to uncover the biology underlying previous classifiers based on AML-like profile or DNA methylation and suggest that a subset of JMML patient may benefit from immunomodulating therapies. Disclosures Dalle: Bellicum: Consultancy, Honoraria; bluebird bio: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Sanofi-Genzyme: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Medac: Consultancy, Honoraria; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees; Jazz Pharmaceuticals: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Gilead: Honoraria; AbbVie Pharmacyclics: Membership on an entity's Board of Directors or advisory committees; Orchard: Consultancy, Honoraria; Incyte: Consultancy, Membership on an entity's Board of Directors or advisory committees. Baruchel:Jazz Pharmaceuticals: Consultancy, Honoraria; Celgene Corporation: Consultancy, Honoraria; Astra Zeneca: Consultancy; Servier: Consultancy, Honoraria; Novartis: Consultancy, Honoraria; Shire: Research Funding; Bellicum: Consultancy.

Distinct Gene Expression Signatures in Patients with Richter's Syndrome and Chronic Lymphocytic Leukemia with Prior Exposure to Ibrutinib

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 30-31
Author(s):  
Hanyin Wang ◽  
Shulan Tian ◽  
Qing Zhao ◽  
Wendy Blumenschein ◽  
Jennifer H. Yearley ◽  
...  
Keyword(s):  
Gene Expression ◽  
B Cell ◽  
Board Of Directors ◽  
Research Funding ◽  
Cell Activation ◽  
Expression Profiles ◽  
Lymphocytic Leukemia ◽  
B Cell Activation ◽  
Advisory Committees ◽  
Upregulated Genes

Introduction: Richter's syndrome (RS) represents transformation of chronic lymphocytic leukemia (CLL) into a highly aggressive lymphoma with dismal prognosis. Transcriptomic alterations have been described in CLL but most studies focused on peripheral blood samples with minimal data on RS-involved tissue. Moreover, transcriptomic features of RS have not been well defined in the era of CLL novel therapies. In this study we investigated transcriptomic profiles of CLL/RS-involved nodal tissue using samples from a clinical trial cohort of refractory CLL and RS patients treated with Pembrolizumab (NCT02332980). Methods: Nodal samples from 9 RS and 4 CLL patients in MC1485 trial cohort were reviewed and classified as previously published (Ding et al, Blood 2017). All samples were collected prior to Pembrolizumab treatment. Targeted gene expression profiling of 789 immune-related genes were performed on FFPE nodal samples using Nanostring nCounter® Analysis System (NanoString Technologies, Seattle, WA). Differential expression analysis was performed using NanoStringDiff. Genes with 2 fold-change in expression with a false-discovery rate less than 5% were considered differentially expressed. Results: The details for the therapy history of this cohort were illustrated in Figure 1a. All patients exposed to prior ibrutinib before the tissue biopsy had developed clinical progression while receiving ibrutinib. Unsupervised hierarchical clustering using the 300 most variable genes in expression revealed two clusters: C1 and C2 (Figure 1b). C1 included 4 RS and 3 CLL treated with prior chemotherapy without prior ibrutinib, and 1 RS treated with prior ibrutinib. C2 included 1 CLL and 3 RS received prior ibrutinib, and 1 RS treated with chemotherapy. The segregation of gene expression profiles in samples was largely driven by recent exposure to ibrutinib. In C1 cluster (majority had no prior ibrutinb), RS and CLL samples were clearly separated into two subgroups (Figure 1b). In C2 cluster, CLL 8 treated with ibrutinib showed more similarity in gene expression to RS, than to other CLL samples treated with chemotherapy. In comparison of C2 to C1, we identified 71 differentially expressed genes, of which 34 genes were downregulated and 37 were upregulated in C2. Among the upregulated genes in C2 (majority had prior ibrutinib) are known immune modulating genes including LILRA6, FCGR3A, IL-10, CD163, CD14, IL-2RB (figure 1c). Downregulated genes in C2 are involved in B cell activation including CD40LG, CD22, CD79A, MS4A1 (CD20), and LTB, reflecting the expected biological effect of ibrutinib in reducing B cell activation. Among the 9 RS samples, we compared gene profiles between the two groups of RS with or without prior ibrutinib therapy. 38 downregulated genes and 10 upregulated genes were found in the 4 RS treated with ibrutinib in comparison with 5 RS treated with chemotherapy. The top upregulated genes in the ibrutinib-exposed group included PTHLH, S100A8, IGSF3, TERT, and PRKCB, while the downregulated genes in these samples included MS4A1, LTB and CD38 (figure 1d). In order to delineate the differences of RS vs CLL, we compared gene expression profiles between 5 RS samples and 3 CLL samples that were treated with only chemotherapy. RS samples showed significant upregulation of 129 genes and downregulation of 7 genes. Among the most significantly upregulated genes are multiple genes involved in monocyte and myeloid lineage regulation including TNFSF13, S100A9, FCN1, LGALS2, CD14, FCGR2A, SERPINA1, and LILRB3. Conclusion: Our study indicates that ibrutinib-resistant, RS-involved tissues are characterized by downregulation of genes in B cell activation, but with PRKCB and TERT upregulation. Furthermore, RS-involved nodal tissues display the increased expression of genes involved in myeloid/monocytic regulation in comparison with CLL-involved nodal tissues. These findings implicate that differential therapies for RS and CLL patients need to be adopted based on their prior therapy and gene expression signatures. Studies using large sample size will be needed to verify this hypothesis. Figure Disclosures Zhao: Merck: Current Employment. Blumenschein:Merck: Current Employment. Yearley:Merck: Current Employment. Wang:Novartis: Research Funding; Incyte: Research Funding; Innocare: Research Funding. Parikh:Verastem Oncology: Honoraria; GlaxoSmithKline: Honoraria; Pharmacyclics: Honoraria, Research Funding; MorphoSys: Research Funding; Ascentage Pharma: Research Funding; Genentech: Honoraria; AbbVie: Honoraria, Research Funding; Merck: Research Funding; TG Therapeutics: Research Funding; AstraZeneca: Honoraria, Research Funding; Janssen: Honoraria, Research Funding. Kenderian:Sunesis: Research Funding; MorphoSys: Research Funding; Humanigen: Consultancy, Patents & Royalties, Research Funding; Gilead: Research Funding; BMS: Research Funding; Tolero: Research Funding; Lentigen: Research Funding; Juno: Research Funding; Mettaforge: Patents & Royalties; Torque: Consultancy; Kite: Research Funding; Novartis: Patents & Royalties, Research Funding. Kay:Astra Zeneca: Membership on an entity's Board of Directors or advisory committees; Acerta Pharma: Research Funding; Juno Theraputics: Membership on an entity's Board of Directors or advisory committees; Dava Oncology: Membership on an entity's Board of Directors or advisory committees; Oncotracker: Membership on an entity's Board of Directors or advisory committees; Sunesis: Research Funding; MEI Pharma: Research Funding; Agios Pharma: Membership on an entity's Board of Directors or advisory committees; Bristol Meyer Squib: Membership on an entity's Board of Directors or advisory committees, Research Funding; Tolero Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Abbvie: Research Funding; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Rigel: Membership on an entity's Board of Directors or advisory committees; Morpho-sys: Membership on an entity's Board of Directors or advisory committees; Cytomx: Membership on an entity's Board of Directors or advisory committees. Braggio:DASA: Consultancy; Bayer: Other: Stock Owner; Acerta Pharma: Research Funding. Ding:DTRM: Research Funding; Astra Zeneca: Research Funding; Abbvie: Research Funding; Merck: Membership on an entity's Board of Directors or advisory committees, Research Funding; Octapharma: Membership on an entity's Board of Directors or advisory committees; MEI Pharma: Membership on an entity's Board of Directors or advisory committees; alexion: Membership on an entity's Board of Directors or advisory committees; Beigene: Membership on an entity's Board of Directors or advisory committees.

scholarly journals Baseline and on-Treatment Bone Marrow Microenvironments Predict Myeloma Patient Outcomes and Inform Potential Intervention Strategies

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 1882-1882 ◽  
Author(s):  
Samuel A Danziger ◽  
Mark McConnell ◽  
Jake Gockley ◽  
Mary Young ◽  
Adam Rosenthal ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Bone Marrow ◽  
Board Of Directors ◽  
Research Funding ◽  
Expression Profiles ◽  
Cell Types ◽  
Employment Equity ◽  
Advisory Committees ◽  
Equity Ownership

Abstract Introduction The multiple myeloma (MM) tumor microenvironment (TME) strongly influences patient outcomes as evidenced by the success of immunomodulatory therapies. To develop precision immunotherapeutic approaches, it is essential to identify and enumerate TME cell types and understand their dynamics. Methods We estimated the population of immune and other non-tumor cell types during the course of MM treatment at a single institution using gene expression of paired CD138-selected bone marrow aspirates and whole bone marrow (WBM) core biopsies from 867 samples of 436 newly diagnosed MM patients collected at 5 time points: pre-treatment (N=354), post-induction (N=245), post-transplant (N=83), post-consolidation (N=51), and post-maintenance (N=134). Expression profiles from the aspirates were used to infer the transcriptome contribution of immune and stromal cells in the WBM array data. Unsupervised clustering of these non-tumor gene expression profiles across all time points was performed using the R package ConsensusClusterPlus with Bayesian Information Criterion (BIC) to select the number of clusters. Individual cell types in these TMEs were estimated using the DCQ algorithm and a gene expression signature matrix based on the published LM22 leukocyte matrix (Newman et al., 2015) augmented with 5 bone marrow- and myeloma-specific cell types. Results Our deconvolution approach accurately estimated percent tumor cells in the paired samples compared to estimates from microscopy and flow cytometry (PCC = 0.63, RMSE = 9.99%). TME clusters built on gene expression data from all 867 samples resulted in 5 unsupervised clusters covering 91% of samples. While the fraction of patients in each cluster changed during treatment, no new TME clusters emerged as treatment progressed. These clusters were associated with progression free survival (PFS) (p-Val = 0.020) and overall survival (OS) (p-Val = 0.067) when measured in pre-transplant samples. The most striking outcomes were represented by Cluster 5 (N = 106) characterized by a low innate to adaptive cell ratio and shortened patient survival (Figure 1, 2). This cluster had worse outcomes than others (estimated mean PFS = 58 months compared to 71+ months for other clusters, p-Val = 0.002; estimate mean OS = 105 months compared with 113+ months for other clusters, p-Val = 0.040). Compared to other immune clusters, the adaptive-skewed TME of Cluster 5 is characterized by low granulocyte populations and high antigen-presenting, CD8 T, and B cell populations. As might be expected, this cluster was also significantly enriched for ISS3 and GEP70 high risk patients, as well as Del1p, Del1q, t12;14, and t14:16. Importantly, this TME persisted even when the induction therapy significantly reduced the tumor load (Table 1). At post-induction, outcomes for the 69 / 245 patients in Cluster 5 remain significantly worse (estimate mean PFS = 56 months compared to 71+ months for other clusters, p-Val = 0.004; estimate mean OS = 100 months compared to 121+ months for other clusters, p-Val = 0.002). The analysis of on-treatment samples showed that the number of patients in Cluster 5 decreases from 30% before treatment to 12% after transplant, and of the 63 patients for whom we have both pre-treatment and post-transplant samples, 18/20 of the Cluster 5 patients moved into other immune clusters; 13 into Cluster 4. The non-5 clusters (with better PFS and OS overall) had higher amounts of granulocytes and lower amounts of CD8 T cells. Some clusters (1 and 4) had increased natural killer (NK) cells and decreased dendritic cells, while other clusters (2 and 3) had increased adipocytes and increases in M2 macrophages (Cluster 2) or NK cells (Cluster 3). Taken together, the gain of granulocytes and adipocytes was associated with improved outcome, while increases in the adaptive immune compartment was associated with poorer outcome. Conclusions We identified distinct clusters of patient TMEs from bulk transcriptome profiles by computationally estimating the CD138- fraction of TMEs. Our findings identified differential immune and stromal compositions in patient clusters with opposing clinical outcomes and tracked membership in those clusters during treatment. Adding this layer of TME to the analysis of myeloma patient baseline and on-treatment samples enables us to formulate biological hypotheses and may eventually guide therapeutic interventions to improve outcomes for patients. Disclosures Danziger: Celgene Corporation: Employment, Equity Ownership. McConnell:Celgene Corporation: Employment. Gockley:Celgene Corporation: Employment. Young:Celgene Corporation: Employment, Equity Ownership. Schmitz:Celgene Corporation: Employment, Equity Ownership. Reiss:Celgene Corporation: Employment, Equity Ownership. Davies:MMRF: Honoraria; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; TRM Oncology: Honoraria; Abbvie: Consultancy; ASH: Honoraria; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Honoraria. Copeland:Celgene Corporation: Employment, Equity Ownership. Fox:Celgene Corporation: Employment, Equity Ownership. Fitch:Celgene Corporation: Employment, Equity Ownership. Newhall:Celgene Corporation: Employment, Equity Ownership. Barlogie:Celgene: Consultancy, Research Funding; Dana Farber Cancer Institute: Other: travel stipend; Multiple Myeloma Research Foundation: Other: travel stipend; International Workshop on Waldenström's Macroglobulinemia: Other: travel stipend; Millenium: Consultancy, Research Funding; European School of Haematology- International Conference on Multiple Myeloma: Other: travel stipend; ComtecMed- World Congress on Controversies in Hematology: Other: travel stipend; Myeloma Health, LLC: Patents & Royalties: : Co-inventor of patents and patent applications related to use of GEP in cancer medicine licensed to Myeloma Health, LLC. Trotter:Celgene Research SL (Spain), part of Celgene Corporation: Employment, Equity Ownership. Hershberg:Celgene Corporation: Employment, Equity Ownership, Patents & Royalties. Dervan:Celgene Corporation: Employment, Equity Ownership. Ratushny:Celgene Corporation: Employment, Equity Ownership. Morgan:Takeda: Consultancy, Honoraria; Bristol-Myers Squibb: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Janssen: Research Funding.

scholarly journals Gene Expression and Cytokine Analyses Identify Markers of Progression from CLL-like Monoclonal B-Cell Lymphocytosis to Chronic Lymphocytic Leukemia

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 3027-3027
Author(s):  
Gonzalo Blanco ◽  
Anna Puiggros ◽  
Barbara Sherry ◽  
Lara Nonell ◽  
Eulalia Puigdecanet ◽  
...  
Keyword(s):  
Gene Expression ◽  
B Cells ◽  
B Cell ◽  
Board Of Directors ◽  
Research Funding ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Advisory Committees ◽  
Cytokine Levels

INTRODUCTION: Chronic lymphocytic leukemia (CLL)-like monoclonal B cell lymphocytosis (MBL) is considered a precursor of CLL. It is found in 5-10% of elderly healthy individuals and shows a progression rate to CLL requiring therapy of 1.1% per year. A balance between microenvironmental factors and intrinsic properties of the emerging B cell clone may be decisive for the transition from MBL to CLL, although biomarkers of progression remain unknown. The objective is to describe biological markers (B cell gene expression profiles and serum cytokine levels) that predict progression from MBL to CLL. METHODS: Gene expression profiles of clonal B cells from 14 MBL subjects (median age: 76 years, clonal B cells: 0.5-4.3 x109/L) were evaluated. With a median follow-up from analysis of 59 months (range: 10-77), 3 cases (21.4%) had progressed to CLL Binet stage A at last follow-up (clonal lymphocytosis >5x109/L, range: 6.2-7.9). Clonal B cells (CD19+CD5+) were isolated from peripheral blood by immunomagnetic methods (Miltenyi Biotec). Extracted RNA (RIN>7) was hybridized to GeneChip Human Gene 2.0 ST arrays (Affymetrix). Gene expression profiles were compared between MBL cases that progressed to CLL (P-MBL, n=3) and non-progressive MBL cases (NP-MBL, n=11). Differential gene expression was evaluated employing linear models for microarrays in R, and genes with P<0.05 and Fold Change >1.5 or <-1.5 were considered differentially expressed. To obtain insight into the functional significance of the differential genetic signatures, the Ingenuity Pathway Analysis tool (IPA, QIAGEN) was employed. On the other hand, serum levels of IL1β, IL2, IL4, IL5, IL6, IL8, IL10, IL12, IL15, IL17, IFNα, IFNγ, TNFα, GM-CSF, CCL3, CCL4, CCL19, CXCL9, CXCL10 and CXCL11 were quantified using the U-PLEX Platform (Meso Scale Discovery) and Human CXCL9/MIG Quantikine ELISA Kit (R&D Systems) in 41 MBL subjects (median age: 67 years, clonal B cells: 0.5-4.8 x109/L). With a median follow-up from analysis of 47 months (range: 0-117), 5 of them (12.2%) had progressed to CLL Binet stage A at last follow-up (clonal lymphocytosis >5x109/L, range: 6.4-17.3). Clonal B cells and cytokine levels were compared between P-MBL (n=5) and NP-MBL (N=36). For cytokine levels, the optimal cut-off values to stratify MBL cases according to their progression risk were assessed using the maxstat R package, whereas for clonal B cells a cut-off value of 3.9 x109/L was considered according to the results obtained by Kostopoulos et al (Blood Cancer J, 2017). The effect of different covariates on progression-free survival was evaluated using log-rank test. Cox proportional hazards regression models were performed to assess their independent prognostic value. P<0.05 was considered significant. RESULTS: A total of 455 genes were differentially expressed (250 upregulated and 205 downregulated in P-MBL). IPA predicted an inhibition of apoptosis as well as proteins with tumor suppressor activity (SMARCA4) in P-MBL, besides enhanced bioenergetic processes (transmembrane potential of mitochondria) and anti-inflammatory features (activation of IL13 pathway and decreased chemotaxis of phagocytes and granulocytes) (Table 1). P-MBL displayed increased clonal B cells (4.2 vs. 1.7 x109/L, P=0.003) and levels of IL10 (1.15 vs. 0.9 pg/mL, P=0.087) as well as diminished levels of IL6 (2.04 vs. 3.75 pg/mL, P=0.041). MBL cases with ≥3.9 x109/L clonal B cells, ≥1.08 pg/mL of IL10 and ≤2.04 pg/mL of IL6 had an increased risk of progression to CLL (P<0.001, P=0.006 and P=0.034, respectively) (Figure 1, Table 2). Multivariate analysis for clonal B cells and levels of IL10 maintained significance for both factors (HR=12.8, P=0.013 and HR=10.2, P=0.047, respectively) (Table 2). CONCLUSIONS: 1. P-MBL cases showed an inhibition of the apoptotic pathway and an activation of bioenergetic processes, which may account for the increased clonal B cells observed in this group. 2. P-MBL exhibited enhanced anti-inflammatory features, including augmented levels of the anti-inflammatory cytokine IL10. 3. Increased clonal B cells and IL10 levels predicted a higher risk of progression to CLL, suggesting that an augmented proliferative rate of clonal B cells together with a supporting tumor microenvironment are required for progression from MBL to CLL. ACKNOWLEDGEMENTS. PI11/01621, PI15/00437, 2017/SGR437, Fundació La Caixa, Fundación Española de Hematología y Hemoterapia (FEHH). Disclosures Gimeno: JANSSEN: Consultancy, Speakers Bureau; Abbvie: Speakers Bureau. Rai:Cellectis: Membership on an entity's Board of Directors or advisory committees; Genentech/Roche: Membership on an entity's Board of Directors or advisory committees; Astra Zeneca: Membership on an entity's Board of Directors or advisory committees; Pharmacyctics: Membership on an entity's Board of Directors or advisory committees. Abrisqueta:Roche: Consultancy, Honoraria, Other: Travel, Accommodations, expenses, Speakers Bureau; Abbvie: Consultancy, Honoraria, Other: Travel, Accommodations, expenses, Speakers Bureau; Janssen: Consultancy, Honoraria, Other: Travel, Accommodations, expenses, Speakers Bureau; Celgene: Consultancy, Honoraria. Bosch:AbbVie: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Kyte: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; F. Hoffmann-La Roche Ltd/Genentech, Inc.: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Celgene: Honoraria, Research Funding; Acerta: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; AstraZeneca: Honoraria, Research Funding; Takeda: Honoraria, Research Funding.

scholarly journals Mutation Burden in Multiple Myeloma Is Captured By Gene Expression Profiles

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 4450-4450
Author(s):  
John D Shaughnessy ◽  
Samir Parekh ◽  
Hearn Jay Cho ◽  
Alessandro Lagana ◽  
Ajai Chari ◽  
...  
Keyword(s):  
Gene Expression ◽  
Board Of Directors ◽  
Research Funding ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Training Set ◽  
Advisory Committees ◽  
Test Set ◽  
Kegg Pathways ◽  
Mutation Burden

Abstract In the current study we sought to determine whether mutation burden (MB) is reflected in gene expression patterns. We generated 389 gene expression profiles and 311 mutation profiles from 182 cases, split into a training set of 97 and test set of 84. Copy number variants (CNV), rearrangements (TX) and short variant mutations (SV) were identified with FoundationOne Heme (F1) and U133Plus2.0 (u133) gene expression data, GEP70 and GEP80 risk scores, subgroup and CNV calls provided by Signal Genetics. 145 Kegg pathways, transcription factor binding sites (TFbs) for 195 TF mapped to u133 genes and 1161 F1 features and u133 GEP signatures/scores were evaluated for enrichment of defined genes. u133 data for normal plasma cells (n=22), MGUS (n=44), smoldering MM (n=12), relapsed MM (n=76), and MM cell lines (n=42) were derived from GSE31162. Newly diagnosed MM samples came from GSE31162 (n=584), GSE19784 (n=321), GSE15695 (n=247) and E-MTAB-317 (n=233). Of 593 genes assayed by F1, 293 were mutated at least twice. There were a total of 3454 mutations (average = 11, minimum = 3, and maximum = 37). KRAS was mutated in 40 tumors, while TP53 was mutated 45 times in 31 tumors. TP53 and 62 other genes had 2 or more unique mutations in a single tumor. A linear curve of MB exhibited a sharp upward inflection at 19 mutations. We sought to determine if GEP could identify characteristic features of MM flanking the inflection point. A training set of 97 (86 <= 15 MB and 11 >= 21 MB) and a test set of 84 (49 <= 15 MB, 28 >15 but < 21 MB and 7 >= 21MB) was produced. A mean ratio identified 576 genes exhibiting 2-fold higher expression in MM with high MB (hiMB) and 1617 genes from low MB (loMB). Notably, forty-four of the 293 mutated genes were in this list of genes. A geometric mean ratio of the two gene sets was then calculated for all samples. The mean of the resulting score (MB.2) was higher in MM with hiMB (1.66) than MM with loMB (-0.235) in the training set. MM with hiMB (0.329) had a higher MB.2 score than the group with intermediate MB.2 (0.158) and both higher than MM with loMB (-0.122). MB.2 was lowest in normal PC (-0.518) and progressively increased with disease progression: MGUS (-0.341), SMM (-0.308), MM (0.199), relapsed MM (0.334), MM cell lines (1.168).[SP1] 57% of the 182 cases harbored only SV mutations, 32% had SV and CNV, 32% SV and TX and 7% had SV, CNV and TX mutations. SV only mutations were present in 76% of MB.2 quartile 1 (MBq1) and 30% of MBq4. SV, CNV and TX mutations were present in 4% of MBq1 and 17% of MBq4. MB.2 was positively correlated with GEP70, GEP80, proliferation index, and TP53 target genes in MM and genes modulated by thalidomide and dexamethasone in PGx studies, in at least 6 of the 7 cohorts studied. The CD2 subtype, a myeloid classification and GEP70 low risk were significantly overrepresented in both MBq1 and GEP70q1 in all 7 cohorts. Conversely, the MF, MS, and PR subtypes, GEP70 and GEP80 high risk, as well as +1q, amp1q21, and del13q were significantly overrepresented in MBq4 and GEP70q4 in all 7 cohorts. MB.2 [SP2] genes derived from MM with loMB where enriched in 45 of 148 Kegg pathways. Notable were Hedgehog, Prostaglandin, Tx factors in cancer, HOX, MYB signaling, ephrin-B reverse signaling and embryonic stem cells. Five pathways related to B-cell biology were enriched. Mitotic cell cycle, integrin signaling, chromatin acetylation, ubiquitin ligation, and G1 to G1/S were underrepresented. MB.2 genes from MM with hiMB were enriched in TP53, lipid lysis, complement cascade, adherens junctions, Wnt regulation of CYR61, cyclins, prostaglandins, cell cycle, and MYC target pathways. Interferon signaling, TNF-NF-kB, EGFR, NOD, endoplasmic reticulum, ubiquitin ligation, Wnt-Hedgehog-NOTCH and BMP-SMAD modules were underrepresented. An enrichment of Rel and NF-kB TFbs was observed for genes negatively correlated with MB.2. and genes positively correlated with MB.2 and GEP70 were enriched for E2F and TP53 binding site. In conclusion, we show that MB can be captured by GEP in MM, that MB increases with disease progression, and pathways enriched by hiMB and loMB are different and may imply differences in pathogenesis as well as treatment. [SP1]This is an important finding - suggest emphasizing more [SP2]Starting with this para suggest referring to groups are low vs high mutation burden for improved readability. Disclosures Shaughnessy: Signal Genetics: Consultancy, Patents & Royalties. Cho:Genentech Roche: Membership on an entity's Board of Directors or advisory committees, Research Funding; Agenus, Inc.: Research Funding; Janssen: Consultancy, Research Funding; Bristol-Myers Squibb: Membership on an entity's Board of Directors or advisory committees, Research Funding; Ludwig Institute for Cancer Research: Membership on an entity's Board of Directors or advisory committees. Chari:Novartis: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Takeda: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Array Biopharma: Consultancy, Research Funding; Amgen Inc.: Honoraria, Research Funding; Pharmacyclics: Research Funding. van Laar:Signal Genetics, Inc.: Employment. Jagannath:Janssen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Bristol Myer Squibb: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees. Barlogie:Signal Genetics: Patents & Royalties.

Distinct Pathogenesis of Clonal Hematopoiesis Revealed By Single Cell RNA Sequencing Integrated with Highly Sensitive Genotyping Method

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 34-34
Author(s):  
Masahiro Marshall Nakagawa ◽  
Ryosaku Inagaki ◽  
Yutaka Kuroda ◽  
Yasuhito Nannya ◽  
Lanying Zhao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Bone Marrow ◽  
Single Cell ◽  
Board Of Directors ◽  
Research Funding ◽  
Simultaneous Detection ◽  
Advisory Committees ◽  
Hematopoietic Stem ◽  
Single Cell Sequencing ◽  
Clonal Hematopoiesis

Background Recent evidence suggests that age-related clonal hematopoiesis (CH) might represent the earliest precursor of myeloid neoplasms. Although the exact mechanism of clonal selection that shapes CH is still to be elucidated, both cell intrinsic and non-cell intrinsic effects of mutations, including the interplay between mutated cells and the bone marrow environment, are thought to play important roles, which are best studied using single-cell sequencing analysis of both mutations and gene expression. Methods We performed single-cell sequencing of hematopoietic stem and progenitors (HSPCs) from BM of the 16 patients with CH along with 16 control patients without CH identified by screening otherwise healthy individuals who received hip joint replacement, using a novel platform that enables simultaneous detection of gene mutations and expression based on the Fluidigm C1-HT system. Sequence data were analyzed with Seurat (Stuart et al Cell 2019) with integration of genotyping information. Cells were clustered and each cluster was assigned by marker-gene expressions for major cell-types in HSPCs, including hematopoietic stem cell (HSC)-like and erythroid progenitors. Cells were grouped by their genotypes and pathway analysis were performed. Results In total, we identified 35 subjects who had CH-related mutations, including those affecting DNMT3A, TET2, ASXL1, SF3B1, PPM1D, IDH1, GNB1 and TP53, of which 11 had more than one CH-related mutation. Most of these mutations showed a low variant allele frequency (VAF) ≤ 0.05. However, clones having double mutations of DNMT3A/TET2 or those having biallelic TET2 mutations tended to show a higher VAF as high as 0.4, suggesting an enhanced clonal advantage for clones having multiple mutations. Using our novel single-cell platform, we analyzed 3,767 cells from control patients without CH and 1,474 mutated cells and 7,234 wild-type (WT) cells from patients with CH. By targeting both genomic DNA and RNA, we successfully obtained a sufficient number of single-cell reads for genes whose expression was too low to evaluate by only targeting RNA, such as TET2 and DNMT3A. Although some clones having a high-VAF mutation caused a skewed clustering to be detected as a CH clone, many clones with low-VAF mutations did not make distinct clusters, indicating the importance of genotyping at a single cell level to identify and characterize mutated cells. Simultaneous detection of genotype and expression allowed us to see the effect of CH-mutations on cell phenotype and differentiation. For example, cells having compound TET2/DNMT3A mutations were significantly enriched in the erythroid cluster, while another clone with double TET2 mutations were more enriched in the HSC-like cluster, compared to cells from individuals without CH (WTcont). These are in line with the previous findings of TET2/DNMT3A double knockout mice or TET2 knockout mice, respectively. In another case with an IDH1 mutation, IDH1-mutated (MUTIDH1) cells less contributed to the HSC-like fraction, showing an enhancement of cell proliferation-signature, compared to WT (WTIDH1) cells in the same patient. Strikingly, compared to WTcont cells, WTIDH1 cells were significantly enriched in the HSC-like fraction and showed an enhanced expression of cytokine-related pathway genes, which was in line with a finding seen in mouse cells treated with 2-hydroxy-glutalate, an mutant IDH-related oncometabolite. Similarly, when compared to WTcont cells, WT cells from patients with DNMT3A- (WTDNMT3A) or TET2- (WTTET2) mutated CH significantly showed an enhanced cell proliferation. HSC-like WTTET2 cells also showed aberrant IFN-response signatures compared to corresponding WTcont cells, which was confirmed in competitive transplantation of Tet2 heterozygous knockout (hKO) and WT cells in a mouse model; HSPCs of WT competitors transplanted with Tet2-hKO cells showed a significant enhancement of IFN-response signatures compared to those transplanted with WT cells. Intriguingly, monocytes of Tet2-hKO donors showed aberrant expression of S100a8/a9, which might contribute to the non-cell intrinsic effect of Tet2-hKO cells. Conclusions In CH, not only mutated cells but also surrounding WT cells show an aberrant gene expression phenotype, suggesting the presence of non-cell autonomous phenotype or an altered bone marrow environment that favors the positive selection of CH-clones. Disclosures Nakagawa: Sumitomo Dainippon Pharma Co., Ltd.: Research Funding. Inagaki:Sumitomo Dainippon Pharma Co., Ltd.: Current Employment. Ogawa:Eisai Co., Ltd.: Research Funding; KAN Research Institute, Inc.: Membership on an entity's Board of Directors or advisory committees, Research Funding; Asahi Genomics Co., Ltd.: Current equity holder in private company; Otsuka Pharmaceutical Co., Ltd.: Research Funding; Sumitomo Dainippon Pharma Co., Ltd.: Research Funding; Chordia Therapeutics, Inc.: Membership on an entity's Board of Directors or advisory committees, Research Funding.

scholarly journals PARP14 Is a Novel Therapeutic Target in STAT6 mutant Follicular Lymphoma

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 2842-2842 ◽  
Author(s):  
Verena Passerini ◽  
Michael Boesl ◽  
Elisabeth Silkenstedt ◽  
Elisa Osterode ◽  
Michael Heide ◽  
...  
Keyword(s):  
Gene Expression ◽  
Follicular Lymphoma ◽  
B Cell ◽  
Board Of Directors ◽  
Molecular Diagnostics ◽  
Research Funding ◽  
B Cell Lymphoma ◽  
Gene Set Enrichment Analysis ◽  
Wild Type ◽  
Advisory Committees

Abstract The highly variable clinical course of follicular lymphoma (FL) is determined by the molecular heterogeneity of the tumor cells and complex interactions with the microenvironment. The underlying molecular mechanisms and therapeutic vulnerabilities are not well understood. IL-4 producing follicular helper T cells (TFH) have been identified as a key component of the malignant B-cell niche. IL-4 activates paracrine signaling via STAT6. In a cohort of 258 patients with advanced stage FL, we identified STAT6 mutations in 13% of diagnostic biopsies (n=33). All mutations were clustered within the DNA binding domain, mostly at D419, and included a polymorphic variant (rs11172102). Gene set enrichment analysis (GSEA) revealed that STAT6 mutant cases were significantly enriched for two distinct IL-4 gene expression signatures. Gene expression data and immunohistochemistry of primary FL samples showed significant up-regulation of IL-4/STAT6 target genes in STAT6 mutant cases, including FCER2, which encodes for CD23. We stably expressed wild type STAT6 or mutant STAT6 (D419G, N421K, and D519V) in two B-cell lymphoma lines (OCI-Ly1, OCI-Ly8), both harboring the FL hallmark translocation t(14;18). Upon IL-4 stimulation, cells expressing mutant STAT6 had significantly increased FCER2 transcript levels. Similarly, IL-4 induced expression of membrane-bound as well as soluble CD23 was significantly increased in STAT6 mutant cells. Cells expressing mutant STAT6 showed significantly increased nuclear accumulation of pSTAT6 following IL-4 stimulation. Of note, we did not observe any effect of STAT6 mutations in the absence of IL-4. RNA sequencing of IL-4-stimulated lymphoma cell lines (STAT6 mutant versus wild type) identified PARP14 -a known transcriptional co-activator of STAT6- among the top differentially expressed genes. Bioinformatics and functional experiments demonstrated that PARP14 per se is a novel STAT6 target gene. Furthermore, reporter assays showed increased transactivation activity of mutant STAT6 at the PARP14 promotor, suggesting a regulatory feed-forward loop. Pharmacological inhibition of PARP and knock-down of PARP14 completely abrogated the mutant STAT6 gain-of-function phenotype. In summary, our results suggest that PARP14 is a novel target in STAT6 mutant FL. Our data also imply that the biological and clinical impact of STAT6 mutations will heavily depend on the (targetable) upstream activation of the IL-4 signaling cascade, including the abundance of IL-4 / TFH cells in the microenvironment of FL. Disclosures Richter: HTG Molecular Diagnostics, Inc.: Research Funding. Klapper:Amgen: Honoraria, Research Funding; F.Hoffman-La Roche: Honoraria, Research Funding; HTG Molecular Diagnostics, Inc.: Research Funding; Takeda: Honoraria, Research Funding; Regeneron: Honoraria, Research Funding. Hiddemann:Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; F. Hoffman-La Roche: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Bayer: Consultancy, Research Funding. Weigert:Novartis: Research Funding; Roche: Research Funding.

scholarly journals Integration of Hi-C and Nanopore Sequencing for Structural Variant Analysis in AML with a Complex Karyotype: (Chromothripsis)²

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 28-28
Author(s):  
Marius-Konstantin Klever ◽  
Eric Sträng ◽  
Julius Jungnitsch ◽  
Uirá Souto Melo ◽  
Sara Hetzel ◽  
...  
Keyword(s):  
Gene Expression ◽  
Board Of Directors ◽  
Research Funding ◽  
Molecular Mechanisms ◽  
Complex Karyotype ◽  
Rna Seq ◽  
Advisory Committees ◽  
Functional Interpretation ◽  
Hematopoietic Stem ◽  
The Impact

Background. Acute myeloid leukemia (AML) with a complex karyotype (CK-AML) is an AML subtype with a still dismal outcome despite recent therapeutic advances. The prognosis is even worse when the underlying structural variants (SVs) lead to an extremely complex pattern of rearrangements, called chromothripsis, with a median overall survival of only 120 days. Except for the presence of inactivating TP53 aberrations in about 70% of all AML-CK cases, the pathogenesis is poorly understood. To gain novel insights into the molecular mechanisms underlying CK-AML reliable high precision SV delineation is needed, which so far has been a major limitation in cancer research. Aim. We developed a SV detection pipeline by integrating Oxford Nanopore Technology (ONT) based whole genome sequencing (WGS) and Hi-C sequencing. This pipeline generated precise characterization of SVs for which the impact on gene expression and the emergence of novel fusion genes was studied by RNA-seq and ONT transcriptome sequencing. Patients and Methods. We applied our WGS and Hi-C SV detection pipeline to a cohort of 11 AML-CK cases. Nanopore DNA Sequencing was performed until a genomic coverage &gt;10x per patient was reached. The samples of 9 patients were also subjected to Nanopore cDNA sequencing for fusion gene analysis and Illumina based RNA-seq for transcript quantification. As controls for Hi-C and Illumina RNA sequencing, CD34+ hematopoietic stem cell enriched samples from five healthy donors were used. Results. Our SV detection pipeline enabled us to fully reconstruct the derivate chromosome structure even of very complex, chromothriptic rearrangements in CK-AML. This enabled us to identify features of chromothripsis, that could previously not be detected using conventual technologies. We found local clustering of breakpoints in three of the patients with up to 31 Inversions and Translocations located in a genomic region of just 2.7 kb. These breakpoints were present in the Hi-C as well as in our Nanopore SV dataset. Our SV pipeline also showed that in these highly clustered regions, the very small rejoined fragments (in many cases less than 1 kb in size) often showed an elevated copy number (CN) state, i.e. small amplifications. We termed this newly discovered phenomenon chromothripsis-in-chromothripsis or (chromothripsis)². The precise knowledge about these breakpoints, which were validated by two different technologies, enabled us to study the pathogenesis of CK-AML at a so far unprecedented resolution. Fusion transcripts could be very precisely mapped and the impact of the breakpoints and CN changes on gene expression levels could be validated, thereby indicating functional relevance of the respective aberrations. Conclusions. The combination of Hi-C and long-read sequencing for SV detection proved to be a powerful tool for precise SV detection. Our SV pipeline allowed us to discover a new level of complexity in chromothripsis. Application of this pipeline to leukemias as well as other types of cancer can improve the precision of SV detection, thereby raising new opportunities for functional interpretation of complex genomic aberrations of pathogenic relevance. Disclosures Döhner: Sunesis Pharmaceuticals: Research Funding; Astex Pharmaceuticals: Consultancy; Pfizer: Research Funding; Bristol-Myers Squibb: Research Funding; Arog: Research Funding; Roche: Consultancy; Novartis: Honoraria, Research Funding; Jazz Pharmaceuticals: Consultancy, Honoraria, Research Funding; Daiichi Sankyo: Honoraria; Abbvie: Consultancy; Agios: Consultancy; Janssen: Consultancy, Honoraria; Amgen: Consultancy, Research Funding; Astellas Pharma: Consultancy; Celgene: Consultancy, Honoraria. Schrezenmeier:Alexion Pharmaceuticals Inc.: Honoraria, Research Funding. Bullinger:Amgen: Membership on an entity's Board of Directors or advisory committees; Astellas: Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Seattle Genetics: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; Jazz Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; Hexal: Membership on an entity's Board of Directors or advisory committees; Gilead: Membership on an entity's Board of Directors or advisory committees; Daiichi Sankyo: Membership on an entity's Board of Directors or advisory committees; Abbvie: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees; Sanofi: Membership on an entity's Board of Directors or advisory committees; Menarini: Membership on an entity's Board of Directors or advisory committees.

scholarly journals Pre-Clinical Efficacy of Co-Treatment with KDM1A (LSD1) Inhibitor and Ruxolitinib or BET Inhibitor Against Post-MPN-sAML Blast Progenitor Cells

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 1274-1274
Author(s):  
Warren Fiskus ◽  
Christopher Peter Mill ◽  
Vrajesh Karkhanis ◽  
Bernardo H Lara ◽  
Prithviraj Bose ◽  
...  
Keyword(s):  
Growth Inhibition ◽  
Progenitor Cells ◽  
Board Of Directors ◽  
Research Funding ◽  
Amine Oxidase ◽  
Genetic Alterations ◽  
Advisory Committees ◽  
Bet Inhibitor ◽  
Oncology Research ◽  
Differentiation Block

LSD1 (KDM1A) is an FAD-dependent amine-oxidase that demethylates mono and dimethyl histone H3 lysine 4 (H3K4Me1 and H3K4Me2), which regulates active enhancers and transcription in AML stem/progenitor cells (LSCs). LSD1 is part of the repressor complexes involving HDACs, CoREST or GFI1, mediating transcriptional repression and differentiation block in LSCs that persist in the minimal residual disease (MRD) following attainment of clinical complete remission, leading to relapse and poor outcome in AML. In AML LSCs, genetic alterations and epigenetic dysregulation of enhancers affect levels of myeloid transcriptional regulators, including c-Myc, PU.1, GATA 2 and CEBPα, and their target genes, which are involved in differentiation block in LSCs. Our present studies demonstrate that CRISPR/Cas9-mediated knockout of LSD1 in the AML OCI-AML5 cells significantly increased the permissive H3K4Me2/3-marked chromatin, reduced H3K27Ac occupancy at super-enhancers and enhancers (SEs/Es) (by ChIP-Seq), especially of c-Myc and CDK6, as well as repressed CoREST, c-Myc, CDK6, and c-KIT, while inducing p21, CD11b, and CD86 levels (log2 -fold change by RNA-Seq, and protein expression by Western analyses). This led to significant growth inhibition, differentiation and loss of viability of OCI-AML5 and patient-derived AML blasts (p < 0.01). Similar effects were observed following exposure of OCI-AML5 (96 hours) to tet-inducible shRNA to LSD1. Knock-down of GFI1 by shRNA (by 90%) also inhibited growth and induced differentiation, associated with upregulation of PU.1, p21 and CD11b levels. Treatment with irreversible (INCB059872, 0.25 to 1.0 µM) or reversible (SP2577, 1.0 to 2.0 µM) LSD1 inhibitor (LSD1i) inhibited binding of LSD1 to CoREST, and significantly induced growth inhibition, differentiation and loss of viability (over 96 hours) of the OCI-AML5, post-myeloproliferative neoplasm (post-MPN) sAML SET2 and HEL92.1.7 cells, as well as patient-derived AML and post-MPN sAML blasts (p < 0.01). Co-treatment with INCB059872 and ruxolitinib synergistically induced apoptosis of the post-MPN sAML SET2 and HEL92.1.7 cells and patient-derived CD34+ post-MPN sAML blasts (combination indices < 1.0). Notably, pre-treatment with the LSD1i for 48 hours significantly re-sensitized ruxolitinib-persister/resistant SET2 and HEL92.1.7 cells to ruxolitinib (p < 0.001). We previously reported that treatment with the BET inhibitor (BETi) JQ1 or OTX015 represses SE/E-driven AML-relevant oncogenes including MYC, RUNX1, CDK6, PIM1, and Bcl-xL, while inducing p21 and p27 levels in post-MPN sAML blasts (Leukemia 2017;31:678-687). This was associated with inhibition of colony growth and loss of viability of AML and post-MPN sAML blasts (p < 0.01). Here, we determined that INCB059872 treatment induced similar levels of lethality in BETi-sensitive or BETi-persister/resistant AML and post-MPN sAML cells. Since BETi treatment also depleted LSD1 protein levels, co-treatment with the BETi OTX015 and LSD1i INCB059872 or SP2577 induced synergistic lethality in AML and post-MPN sAML blasts (combination indices < 1.0). Co-treatment with INCB059872 (1.5 mg/kg) and OTX015 (50 mg/kg) both orally for 21 days, compared to each agent alone or vehicle control, significantly reduced the sAML burden and improved survival of immune-depleted mice engrafted with HEL92.1.7 or HEL92.1.7/OTX015-resistant-GFP/Luc sAML xenografts (p < 0.01). Collectively, these findings strongly support further in vivo testing and pre-clinical development of LSD1i-based combinations with ruxolitinib against post-MPN sAML and with BETi against AML or post-MPN sAML cells. Disclosures Bose: CTI BioPharma: Research Funding; Astellas: Research Funding; NS Pharma: Research Funding; Promedior: Research Funding; Constellation: Research Funding; Incyte Corporation: Consultancy, Research Funding, Speakers Bureau; Celgene Corporation: Consultancy, Research Funding; Blueprint Medicine Corporation: Consultancy, Research Funding; Kartos: Consultancy, Research Funding; Pfizer: Research Funding. Kadia:Amgen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Jazz: Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Research Funding; Pfizer: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Research Funding; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees; AbbVie: Consultancy, Research Funding; Bioline RX: Research Funding; Genentech: Membership on an entity's Board of Directors or advisory committees. Bhalla:Beta Cat Pharmaceuticals: Consultancy. Khoury:Stemline Therapeutics: Research Funding; Angle: Research Funding; Kiromic: Research Funding. Verstovsek:Ital Pharma: Research Funding; Pharma Essentia: Research Funding; Astrazeneca: Research Funding; Incyte: Research Funding; CTI BioPharma Corp: Research Funding; Promedior: Research Funding; Gilead: Research Funding; Celgene: Consultancy, Research Funding; NS Pharma: Research Funding; Protaganist Therapeutics: Research Funding; Constellation: Consultancy; Pragmatist: Consultancy; Sierra Oncology: Research Funding; Genetech: Research Funding; Blueprint Medicines Corp: Research Funding; Novartis: Consultancy, Research Funding; Roche: Research Funding.

scholarly journals Long-Term Experience with Large Granular Lymphocytic Leukemia Evolving after Solid Organ and Hematopoietic Stem Cell Transplantation

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 1226-1226
Author(s):  
Hassan Awada ◽  
Reda Z. Mahfouz ◽  
Jibran Durrani ◽  
Ashwin Kishtagari ◽  
Deepa Jagadeesh ◽  
...  
Keyword(s):  
Board Of Directors ◽  
Research Funding ◽  
Viral Infections ◽  
De Novo ◽  
Post Transplantation ◽  
Advisory Committees ◽  
Hematopoietic Stem ◽  
Post Transplant ◽  
Stat3 Mutations

T-cell large granular lymphocyte leukemia (T-LGLL) is a clonal proliferation of cytotoxic T lymphocytes (CTL). T-LGLL mainly manifest in elderly and is associated with autoimmune diseases including rheumatoid arthritis (RA), B cell dyscrasias, non-hematologic cancers and immunodeficiency (e.g., hypogammaglobulinemia). LGL manifestations often resemble reactive immune processes leading to the dilemmas that LGLs act like CTL expansion during viral infections (for example EBV associated infectious mononucleosis). While studying a cohort of 246 adult patients with T-LGLL seen at Cleveland Clinic over the past 10 years, we encountered 15 cases of overt T-LGLL following transplantation of solid organs (SOT; n=8) and hematopoietic stem cell transplantation (HSCT; n=7). Although early studies reported on the occurrence of LGL post-transplant, these studies focused on the analysis of oligoclonality skewed reactive CTL responses rather than frank T-LGLL. We aimed to characterize post-transplantation T-LGLL in SOT and HSCT simultaneously and compare them to a control group of 231 de novo T-LGLL (cases with no history of SOT or HSCT). To characterize an unambiguous "WHO-defined T-LGLL" we applied stringent and uniform criteria. All cases were diagnosed if 3 out of 4 criteria were fulfilled, including: 1) LGL count >500/µL in blood for more than 6 months; 2) abnormal CTLs expressing CD3, CD8 and CD57 by flow cytometry; 3) preferential usage of a TCR Vβ family by flow cytometry; 4) TCR gene rearrangement by PCR. In addition, targeted deep sequencing for STAT3 mutations was performed and charts of bone marrow biopsies were reviewed to exclude other possible conditions. Diagnosis was made 0.2-27 yrs post-transplantation (median: 4 yrs). At the time of T-LGLL diagnosis, relative lymphocytosis (15-91%), T lymphocytosis (49-99%) and elevated absolute LGL counts (>500 /µL; 93%) were also seen. Post-transplantation T-LGLL were significantly younger than de novo T-LGLL, (median age: 48 vs. 61 yr; P<.0001). Sixty% of post-transplantation T-LGLL patients were males. Fifteen% of patients had more cytogenetic abnormalities compared to de novo T-LGLL, had a lower absolute LGL count (median: 4.5 vs. 8.5 k/µL) and had less frequent neutropenia, thrombocytopenia and anemia (27 vs. 43%, 33 vs. 35% and 20% vs. 55%; P=.01). TCR Vb analysis identified clonal expansion of ≥1 of the Vb proteins in 60% (n=9) of the patients; the remaining 40% (n=6) of the cases had either a clonal process involving a Vb protein not tested in the panel (20%; n=3) or no clear expansion (20%; n=3). Signs of rejection were observed in 20% (n=3/15) and GvHD in 13% (n=2/15) of the patients. Post-transplantation, 27% of cases presented with neutropenia (absolute neutrophil count <1.5 x109/L; n=4), 33% with thrombocytopenia (platelet count <150 x109/L; n=5) and 25% with anemia (hemoglobin <10 g/dL; n=3). T-LGLL evolved in 10 patients (67%; 10/15) despite IST including cyclosporine (n=5), tacrolimus (n=4), mycophenolate mofetil (n=5), cyclophosphamide (n=1), anti-thymocyte globulin (n=1), and corticosteroids (n=6). Lymphadenopathy and splenomegaly were seen in 13% (n=2) and 33% (n=5) of the patients. Other conditions observed were MGUS (20%; n=3) and RA (7%; n=1). Conventional cytogenetic showed normal karyotype in 89% (n=11, tested individuals 13/15). Somatic STAT3 mutations were identified in 2 patients. Sixty% of cases (n=9) were seropositive for EBV when tested at different time points after transplant. Similarly, 53% (n=8) were seropositive for CMV, of which, 5 were positive post-transplantation and 3 pre-/post-transplantation. The complexity of T-LGLL expansion post-transplantation might be due to several mechanisms including active viral infections, latent oncogenic viral reactivation and graft allo-antigenic stimulation. However, in our cohort graft rejection or GvHD was encountered in a few patients (2 allo-HSCT recipients). Autoimmune conditions were present in 50% of SOT recipients (n=4/ 8, including RA, ulcerative colitis, systemic lupus erythematosus). Some of our patients also had low immunoglobulin levels. Overt EBV (post-transplant lymphoproliferative disorder) and CMV reactivation was diagnosed in only 27% (4/15) of the patients. In sum we report the long term follow up of a cohort of T-LGLL and emphasize the expansion of T-LGLL post-transplant highlighting the difficulty in assigning one unique origin of LGLL. Disclosures Hill: Genentech: Consultancy, Research Funding; Takeda: Research Funding; Celegene: Consultancy, Honoraria, Research Funding; Kite: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Seattle Genetics: Consultancy, Honoraria; Amgen: Research Funding; Pharmacyclics: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Gilead: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; TG therapeutics: Research Funding; AstraZeneca: Consultancy, Honoraria. Majhail:Atara Bio: Consultancy; Mallinckrodt: Honoraria; Nkarta: Consultancy; Anthem, Inc.: Consultancy; Incyte: Consultancy. Sekeres:Syros: Membership on an entity's Board of Directors or advisory committees; Millenium: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees. Maciejewski:Alexion: Consultancy; Novartis: Consultancy.

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