Age-Related Changes in Human HSC Methylomes Affect Gene Regulatory Elements Critical to HSC Function and Reveal Novel HSC Aging Candidate Genes

Abstract One of the key risk factors for developing acute myeloid leukemia (AML) is advanced age. With a median age of approximately 68 years at diagnosis, AML predominantly affects an elderly population with poor prognosis. Understanding age-related mechanisms preceding AML might foster the development of new therapeutic approaches targeting more specifically pre-malignant genetic or epigenetic changes. Age-related clonal hematopoiesis (ARCH or CHIP) increases the risk of developing leukemia and predominantly affects genes encoding epigenetic modifiers such as DNMT3A or TET2. Changes in the DNA methylome are a characteristic feature of AML and epigenetic therapies with hypomethylating agents are approved for therapy. As demonstrated in murine models, DNA methylation can shape hematopoietic stem cell (HSC) differentiation and aging phenotypes. Here, we aimed to examine genome-wide changes in the DNA methylome and transcriptome of aging human HSCs. Previous studies of aging-related changes in HSC methylomes used murine HSCs, covered only a fraction of the human methylome or were biased towards promoters and CpG islands. Here, we took advantage of tagmentation-based whole-genome bisulfite sequencing (TWGBS) to cover all CpG sites genome-wide using small amounts of input DNA (Wang et al., Nature protocols 2013). We purified HSCs from cord blood (n=3) and bone marrow of young (n=5, defined as age 23-27) and old donors (n=4, defined as age 63-72) using fluorescence-activated cell sorting (FACS) with an 8-color HSC/LSC-panel (Zeijlemaker et al., Leukemia 2016). None of the samples carried CHIP mutations. With low-input RNA-Seq and TWGBS we successfully obtained an integrative data set of the methylome and transcriptome of human HSCs from newborn, young and old individuals. We found that human HSCs show age-specific DNA methylation patterns that progressively change during aging and predominantly clusters with progressive and age-dependent degradation of methylation marks. In addition, we observed an increase in epigenetic heterogeneity in aged HSCs, extending into methylation-based proliferative clocks. We further revealed that wide-spread and progressive degradation of DNA methylation marks during HSC aging largely affected gene regulatory regions such as promoters, enhancers and transcription factor (TF) binding sites. These differentially methylated regions were highly enriched for genes playing a role in T-cell activation, cell adhesion and hematopoietic differentiation. Binding sites for transcription factors associated with AML, as for example the RUNX and GATA family of TFs were highly affected by age-dependent loss of DNA methylation. We further identified regulatory networks with target genes of transcriptional master regulators such as LYL1, regulating HSC pluripotency in combination with GATA2 and RUNX1, or ZNF639, upregulated in leukemic stem cells (LSC), or senescence and cell cycle genes to be upregulated upon aging. Besides known hallmark genes of HSC aging, such as CLU and SELP, we identified candidate genes to be differentially methylated such as HOX genes and other AML-associated genes. We observed deregulated gene expression in aged HSCs affecting HOXA cluster genes, as well as aging and longevity-associated pathways and G2M-checkpoint genes, relevant to DNA damage response. Correlation of epigenome and transcriptome identified a promising set of novel HSC aging-related candidate genes, putatively controlled by DNA methylation and functionally associated with apoptosis and cell adhesion. Furthermore, we discovered age-related epigenetic remodelling of the mTORC1 pathway, a central regulator of aging and cellular senescence. Pharmacological inhibition of mTORC1 using rapamycin was shown to increase lifespan in several model organisms. In summary, we unravel a comprehensive roadmap of the changing epigenome and transcriptome of human HSCs throughout human lifespan. This enables us to precisely pinpoint DNA methylation marks that progressively degrade during aging. Restoring these DNA methylation marks in aged HSCs could potentially ameliorate age-related decline in HSC function and might protect against leukemic transformation. Disclosures Heuser: Daiichi Sankyo: Membership on an entity's Board of Directors or advisory committees, Research Funding; Bayer Pharma AG: Research Funding; Karyopharm: Research Funding; Astellas: Research Funding; AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Honoraria; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Jazz: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Tolremo: Membership on an entity's Board of Directors or advisory committees; BMS/Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; BergenBio: Research Funding; Pfizer: Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche: Membership on an entity's Board of Directors or advisory committees, Research Funding. Buske: Pfizer: Honoraria, Speakers Bureau; Celltrion: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; MSD: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Bayer: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Amgen: Research Funding.

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Genome Wide DNA Methylation Analysis of Patients with Myelodysplastic Syndrome and Isolated Deletion (5q) Reveals Characteristic Methylation Profiles in Low and Intermediate-1 Risk Groups

Blood ◽

10.1182/blood.v120.21.3801.3801 ◽

2012 ◽

Vol 120 (21) ◽

pp. 3801-3801

Author(s):

Mark Reinwald ◽

Daniel Nowak ◽

Uwe Platzbecker ◽

Aristoteles Giagounidis ◽

Katharina S. Götze ◽

...

Keyword(s):

Dna Methylation ◽

Board Of Directors ◽

Research Funding ◽

Bone Marrow Cells ◽

Risk Groups ◽

Differential Methylation ◽

Aberrant Methylation ◽

Methylation Analysis ◽

Advisory Committees ◽

Genome Wide

Abstract Abstract 3801 Background Aberrant DNA methylation at promoter CpG islands is recognized as one of the hallmarks driving the pathogenesis of myeloid malignancies, especially myelodysplastic syndromes (MDS). Shen et al. recently characterized an algorithm of 10 aberrantly methylated gene loci which was predictive for overall survival and progression-free survival in a large cohort of MDS patients. A particular cytogenetic subgroup of MDS patients with a deletion on the long arm of chromosome 5 (5q-syndrome) has been shown to benefit from treatment with lenalidomide. However, the exact underlying molecular mechanism of MDS with isolated deletion (5q) is still not understood. To further elucidate on the role of deregulated DNA methylation we analyzed DNA-methylation profiles of bone marrow cells from patients with MDS with an isolated deletion (5q). Methods All patients were diagnosed and treated within a German multicenter trial investigating the safety of Lenalidomide in patients with low risk myelodysplastic syndromes and an isolated deletion (5q) after informed consent and according to the declaration of Helsinki. Bone marrow cells of 47 MDS patients with deletion (5q) at initial diagnosis were analyzed (median age 70 years, range 41 – 88 years, IPSS score: low n= 22; intermediate-1 n = 25). DNA was extracted using the QIAGEN Allprep Kit® (Qiagen, Hilden, Germany). Genome wide DNA methylation analysis was performed using the HumanMethylation450 BeadChip (Illumina, San Diego, USA). Differential methylation of CpGs was defined by a minimum mean methylation difference of 15% as expressed by the beta-value of the array data and statistical significance set at q ≤ 0.01 according to the Benjamini-Hochberg-method for multiple significance testing. Analysis of array data was performed using Genome-Studio Software® (Illumina, San Diego, USA), Qlucore Omics explorer 2.3 (Qlucore software. Lund, Sweden) and Microsoft Excel 10.1® (Microsoft Software, Redmond, USA). Gene ontology analysis was performed using GATHER (http://http://gather.genome.duke.edu/). Results Using a q-value of ≤ 0.05 for the beta-value of the array and excluding gender-specific chromosomal CpGs, 473,929 CpGs were evaluable for analysis. Gene Ontology analysis using the GATHER Tool showed a significant enrichment of genes mapped to 5q31 (p<0.0001). Highly significant differential methylation profiles between MDS patients with isolated (5q) were found between patients with low and intermediate-1 IPSS score. CpGs differentially hypermethylated in intermediate-1 risk versus lows risk patients affected the coding regions of interesting candidate genes such as platelet-derived growth factor receptor, beta polypeptide (PDGFRB), clathrin interactor 1 (CLINT1), both located at 5q33 and suspected to be involved in the pathogenesis of 5q deleted MDS. Furthermore, transcriptional regulators such as proline, glutamate and leucine rich protein 1 (PELP1), v-myb myeloblastosis viral oncogene homolog (avian) (MYB), genes known to be involved in cancer like trichorhinophalangeal 1(TRPS1), and tumor suppressors like forkhead box P1 (FOXP1) and genes thought to be involved in the pathogenesis of MDS like Minichromosome maintenance protein2(MCM2) did show differentially DNA-methylation according to our selection criteria. Conclusions We present a comprehensive genome wide methylation analysis of MDS patients with an isolated deletion (5q) with low and intermediate-1 risk according to IPSS. Thereby we detected sets of significantly differentially methylated CpGs between both risk groups. Correlation of these data to clinical parameters might help to further elucidate the contribution of aberrant methylation to the phenotype of MDS with isolated deletion (5q) and could possibly help establishing novel prognostic markers based on differential methylation. Moreover, unraveling the role of aberrant methylation patterns might result in new therapeutic treatment approaches at least in a subset of patients. Disclosures: Nowak: Celgene: Research Funding. Platzbecker:Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Giagounidis:Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees. Götze:Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees. Ottmann:Celgene: Clinical trial participation Other. Haase:Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees. Schlenk:Celgene: Research Funding. Ganser:Celgene: Research Funding. Germing:Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Hofmann:Celgene: Honoraria, Research Funding. Nolte:Celgene: Research Funding.

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The Genomic and Epigenomic Landscapes of Blast Crisis Transformation in Chronic Myeloid Leukemia

Blood ◽

10.1182/blood.v126.23.3737.3737 ◽

2015 ◽

Vol 126 (23) ◽

pp. 3737-3737 ◽

Cited By ~ 3

Author(s):

S. Tiong Ong ◽

Thushangi Pathiraja ◽

Asif Javed ◽

Xin Xuan Sheila Soh ◽

Simeen Malik ◽

...

Keyword(s):

Gene Expression ◽

Dna Methylation ◽

Chronic Myeloid Leukemia ◽

Board Of Directors ◽

Candidate Genes ◽

Myeloid Leukemia ◽

Research Funding ◽

Blast Crisis ◽

Advisory Committees ◽

Cpg Sites

Abstract The transition from chronic phase (CP) to blast crisis (BC) chronic myeloid leukemia (CML) is characterized by reprogramming of the CML transcriptome (Radich et al. PNAS 2006), and shortened survival. Current models propose genomic instability as causal in BC transformation with enhanced DNA damage and impaired DNA repair inducing genetic mutations (ranging from large chromosomal aberrations to point mutations), altered gene function, and eventually BC transformation (Perrotti et al. JCI 2010). Consistent with this model are the phenomena of BC clonal evolution, and the increased frequency of ABL kinase domain mutations found in BC. Because different mutational processes are associated with distinct cancer-specific mutation signatures (Alexandrov et al. Nature 2013), this model also predicts the existence of a CML-specific mutation signature. In addition, recent work has highlighted the importance of epigenetic alterations in hematologic malignancies (Shih et al., Nat. Rev. Cancer, 2012). However, we lack a complete understanding of the type or frequency of genetic alterations in BC, and the relative contribution of genetic vs. epigenetic events in reprogramming the BC transcriptome. To address these knowledge gaps, we analyzed the CML progression genome, epigenome, and transcriptome in 12 CP/BC sample pairs. Whole-genome sequencing revealed the CML genome to be relatively stable with respect to structural variations, indels, and somatic single nucleotide variants. The average number of nonsynonymous coding mutations per BC genome was 5, placing the BC coding genome in the same mutation frequency range as AML and ALL genomes (Alexandrov et al. Nature 2013). In addition, we identified a novel mutation signature in all CML samples suggesting a CML-specific mutational process. 1175 genes were 'hit' by genomic, mostly copy number, alterations in >1 sample, and included TCR genes and Ikaros (IKZF1) among lymphoid BC pairs. Only 21 recurrently altered genes were affected by somatic SNVs or indels, with resistance-associated ABL1 mutations being commonest. We next used DNA methylation arrays to assess the BC epigenome, and found 20,651 CpG sites (out of 455,187) to be hyper-methylated, and 3225 to be hypo-methylated in BC compared to CP. Combined methylome and transcriptome analysis demonstrated an inverse relationship between methylation and expression changes at a subset of CpG sites enriched at promoters. Genes with increased methylation/decreased expression or decreased methylation/increased expression included those involved in cell cycle control/heme biosynthesis, and molecular mechanisms of cancer/G-protein coupled receptor signaling/MAPK signaling respectively. Unsupervised methylation-based clustering segregated samples into CP, lymphoid BC and myeloid BC groups, recapitulating expression-based clustering, and further supporting a functional role for DNA methylation in BC transcriptional reprogramming. We next performed an integrative analysis by combining the genome, methylome, and transcriptome datasets, and included data from 34 additional CML samples. Top ranking candidate genes included epigenetic modifiers, and hematopoetic differentiation- and stem cell-related genes. Functional analysis of candidate genes and epigenetic processes using genetic and epigenetic drug-based approaches are ongoing. In summary, we conclude that: 1. The genomic and epigenomic landscapes in BC are characterized by a modest number of recurring events in the former, but consistent and striking differences in the latter, 2. The BC methylome is functionally associated with the robust gene expression changes found in BC, and 3. Epigenetic modifier drugs may be of use in reversing the gene expression changes characteristic of BC. Disclosures Chuah: Children International: Honoraria; Novartis: Honoraria; Bristol Meyers Squibb: Honoraria. Takahashi:Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Sysmex: Research Funding, Speakers Bureau; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Celgene: Speakers Bureau; Masis: Consultancy; Otsuka: Membership on an entity's Board of Directors or advisory committees; Astellas: Speakers Bureau; BMS: Honoraria, Research Funding, Speakers Bureau.

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Molecular Subgroups of T Cell Acute Lymphoblastic Leukemia in Adults Treated According to GMALL Protocols

Blood ◽

10.1182/blood-2020-141921 ◽

2020 ◽

Vol 136 (Supplement 1) ◽

pp. 37-38

Author(s):

Martin Neumann ◽

Lorenz Bastian ◽

Sonja Hänzelmann ◽

Alina Hartmann ◽

Heiko Trautmann ◽

...

Keyword(s):

Dna Methylation ◽

Risk Stratification ◽

Board Of Directors ◽

Financial Support ◽

Research Funding ◽

Good Prognosis ◽

Molecular Subgroups ◽

Advisory Committees ◽

Molecular Subgroup ◽

Age Dependent

Introduction: In T-ALL, in contrast to BCP-ALL, molecular subgroups are less well defined. Molecular subgroups are based on aberrant expression of oncogenes often resting upon structural aberrations (chromosomal translocation, copy number variations, point mutations). So far, comprehensive studies on molecular subgroups have been predominantly performed in pediatric patients. The clinical relevance of molecular characteristics is not taken into account for risk stratification and targeted therapeutic options for T-ALL patients are limited. Risk stratification is mainly based on immunophenotype and MRD response. Thus, in the German Multicenter Study Group for Adult ALL (GMALL) protocols early and mature T-ALL and molecular failure after first consolidation are high risk features. Whole RNA transcriptome sequencing (RNAseq) enables molecular subgroup allocation profiles with potential prognostic relevance. Herein, we investigated a large cohort of 161 adult T-ALL patients with RNAseq for molecular subgroups and their clinical implication. Patients and methods : RNAseq data (Illumina HiSeq 4000, 100 or 125 bp paired-end, average read count ~30 million reads/sample) were generated from 161 adult T-ALL patients from diagnostic bone marrow samples. For 84 of these T-ALL patients, we additionally investigated DNA methylation using Infinium 450k methylation bead arrays and mutation status using deep targeted DNA sequencing with a gene panel consisting of 206 genes (HiSeq 1500, 100 bp paired-end, average ~800 reads/bp). All patients were treated likewise within pediatric inspired protocols of the GMALL. Clinical follow up and outcome data were available for 129 patients aged between 18 and 55 years. Results: Based on oncogene expression, we could assign molecular subgroups to 160 out of the 161 T-ALL patients: HOXA: n=37 (23%), TLX1: n=37 (23%), TAL/LMO: n=33 (20%), LYL1/LMO2: n=31 (19%), TLX3: n=17 (11%) , NKX2-1: n=4 (2%), TAL2: n=1 (1%). Among others, gene fusion events incorporating TAL1 (n=16), HOXA (n=6), TLX1 (n=14), TLX3 (n=2) and NKX2 (n=2) were detected as underlying mechanisms. Age distribution revealed more TAL/LMO in the younger population (16-25 years: 35% versus >35 years: 3%; p=0.001) and more LYL1/LMO2 and HOXA in the older patients (16-25 years: 23% vs. >35 years: 40%; n.s.). DNA methylation analyses for 84 of the 161 patients support the molecular classification with four distinct subgroups demonstrating a homogenous methylation profile for the TLX1 driven subgroup (methylation cluster M3, n=30). In contrast, methylation cluster M2 (n=21) showed a hypomethylation in CpG islands and comprises the majority of TAL/LMO positive cases (19/21) including all samples with STIL-TAL1 fusions. M1 (n=25) and M4 (n=7) clusters comprised TLX3 (mainly cluster M1, 8/10), HOXA and LYL1/LMO2 cases. Mutation analysis showed a high rate of NOTCH1 (n=71%) mutations in the TLX1 subgroup and an increased rate of mutations in the JAK/STAT pathway (n=29%) and epigenetic regulators (n=50%) in HOXA and LYL1/LMO2 subgroups. Regarding clinical outcome, 126 out of 129 (98%) patients achieved a morphologically complete remission (CR), two patients failed CR, one patient died during induction therapy. Regarding MRD response, 12% of the investigated T-ALL patients showed a molecular failure after consolidation. Noteworthy, we found a molecular CR for 28/30 (93%) patients in the TLX1 subgroup, but only 11/19 (58%) respectively 2/6 (33%) in the HOXA and the LYL1/LMO2 subgroup. This finding results in exceptional five year-overall survival (5y-OS) of 93% in TLX1 patients. Together with the NKX2-1 subgroup (n=4, with 100% 5y-OS), these patients build a prognostically favourable risk group in the overall cohort (94% 5y-OS versus 76% in TAL-LMO patients versus 62% in all other subgroups, p=0.007). This result could not only be found in the overall cohort, but also within the already good risk subgroup of thymic T-ALL patients (93% vs. 75%, p=0.02). Conclusion: This is to our knowledge the largest cohort of adult T-ALL patients characterized by RNAseq on molecular level. Our findings point towards an age dependent distribution of molecular subgroups contributing to age-dependent outcome differences. Patients with TLX1 reveal a molecular subgroup with extraordinary good prognosis. Within thymic T-ALL, the subgroup of patients without TLX1 (~53%) has an inferior but still good prognosis. Disclosures Fiedler: Gilead: Other: support for meeting attendance; Jazz Pharmaceuticals: Honoraria, Other: support for meeting attendance; Abbvie: Membership on an entity's Board of Directors or advisory committees; Morphosys: Consultancy, Honoraria; Celgene: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Consultancy, Honoraria; ARIAD/Incyte: Consultancy, Honoraria; Amgen: Consultancy, Honoraria, Other: support for meeting attendance, Patents & Royalties, Research Funding; Daiichi Sankyo: Other: support for meeting attendance. Alakel:Pfizer: Consultancy. Schwartz:AMGEN: Other: personal fees and non-financial support; Novartis: Other: personal fees and non-financial support; Jazz Pharmaceuticals: Other: personal fees and non-financial support; Pfizer: Other: personal fees ; BTG Intl Inc: Other: personal fees ; Gilead Sciences: Other: personal fees and non-financial support ; MSD Sharp & Dohme: Other: personal fees ; Basilea: Other: non-financial suppor. Müller-Tidow:Pfizer: Research Funding, Speakers Bureau; Daiichi Sankyo: Research Funding; Janssen-Cilag GmbH: Speakers Bureau; BiolineRx: Research Funding. Brüggemann:Regeneron: Research Funding; Affimed: Research Funding; Amgen: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria; Incyte: Consultancy; Roche: Consultancy; Celgene: Consultancy. Goekbuget:Servier: Consultancy, Research Funding; Amgen: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Novartis: Consultancy, Research Funding; Gilead: Consultancy; Erytech: Consultancy; Kite: Consultancy; Jazz: Consultancy, Research Funding.

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Dynamic Epigenetic Landscapes Define Multiple Myeloma Progression and Drug Resistance

Blood ◽

10.1182/blood-2020-142872 ◽

2020 ◽

Vol 136 (Supplement 1) ◽

pp. 32-33

Author(s):

Rafael Renatino-Canevarolo ◽

Mark B. Meads ◽

Maria Silva ◽

Praneeth Reddy Sudalagunta ◽

Christopher Cubitt ◽

...

Keyword(s):

Gene Expression ◽

Drug Resistance ◽

Disease Progression ◽

Board Of Directors ◽

Research Funding ◽

Chromatin Accessibility ◽

Refractory Disease ◽

Advisory Committees ◽

Cytogenetic Abnormalities ◽

Genome Wide

Multiple myeloma (MM) is an incurable cancer of bone marrow-resident plasma cells, which evolves from a premalignant state, MGUS, to a form of active disease characterized by an initial response to therapy, followed by cycles of therapeutic successes and failures, culminating in a fatal multi-drug resistant cancer. The molecular mechanisms leading to disease progression and refractory disease in MM remain poorly understood. To address this question, we have generated a new database, consisting of 1,123 MM biopsies from patients treated at the H. Lee Moffitt Cancer Center. These samples ranged from MGUS to late relapsed/refractory (LR) disease, and were comprehensively characterized genetically (844 RNAseq, 870 WES, 7 scRNAseq), epigenetically (10 single-cell chromatin accessibility, scATAC-seq) and phenotypically (537 samples assessed for ex vivo drug resistance). Mutational analysis identified putative driver genes (e.g. NRAS, KRAS) among the highest frequent mutations, as well as a steady increase in mutational load across progression from MGUS to LR samples. However, with the exception of KRAS, these genes did not reach statistical significance according to FISHER's exact test between different disease stages, suggesting that no single mutation is necessary or sufficient to drive MM progression or refractory disease, but rather a common "driver" biology is critical. Pathway analysis of differentially expressed genes identified cell adhesion, inflammatory cytokines and hematopoietic cell identify as under-expressed in active MM vs. MGUS, while cell cycle, metabolism, DNA repair, protein/RNA synthesis and degradation were over-expressed in LR. Using an unsupervised systems biology approach, we reconstructed a gene expression map to identify transcriptomic reprogramming events associated with disease progression and evolution of drug resistance. At an epigenetic regulatory level, these genes were enriched for histone modifications (e.g. H3k27me3 and H3k27ac). Furthermore, scATAC-seq confirmed genome-wide alterations in chromatin accessibility across MM progression, involving shifts in chromatin accessibility of the binding motifs of epigenetic regulator complexes, known to mediate formation of 3D structures (CTCF/YY1) of super enhancers (SE) and cell identity reprograming (POU5F1/SOX2). Additionally, we have identified SE-regulated genes under- (EBF1, RB1, SPI1, KLF6) and over-expressed (PRDM1, IRF4) in MM progression, as well as over-expressed in LR (RFX5, YY1, NBN, CTCF, BCOR). We have found a correlation between cytogenetic abnormalities and mutations with differential gene expression observed in MM progression, suggesting groups of genetic events with equivalent transcriptomic effect: e.g. NRAS, KRAS, DIS3 and del13q are associated with transcriptomic changes observed during MGUS/SMOL=>active MM transition (Figure 1). Taken together, our preliminary data suggests that multiple independent combinations of genetic and epigenetic events (e.g. mutations, cytogenetics, SE dysregulation) alter the balance of master epigenetic regulatory circuitry, leading to genome-wide transcriptional reprogramming, facilitating disease progression and emergence of drug resistance. Figure 1: Topology of transcriptional regulation in MM depicts 16,738 genes whose expression is increased (red) or decreased (green) in presence of genetic abnormality. Differential expression associated with (A) hotspot mutations and (B) cytogenetic abnormalities confirms equivalence of expected pairs (e.g. NRAS and KRAS, BRAF and RAF1), but also proposes novel transcriptomic dysregulation effect of clinically relevant cytogenetic abnormalities, with yet uncharacterized molecular role in MM. Figure 1 Disclosures Kulkarni: M2GEN: Current Employment. Zhang:M2GEN: Current Employment. Hampton:M2GEN: Current Employment. Shain:GlaxoSmithKline: Speakers Bureau; Amgen: Speakers Bureau; Karyopharm: Research Funding, Speakers Bureau; AbbVie: Research Funding; Takeda: Honoraria, Speakers Bureau; Sanofi/Genzyme: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen: Honoraria, Speakers Bureau; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Adaptive: Consultancy, Honoraria; BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Siqueira Silva:AbbVie: Research Funding; Karyopharm: Research Funding; NIH/NCI: Research Funding.

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Global DNA Methylation Analysis Identifies Key Pathway Differences Between Poor (Low OCT-1 Activity) and Standard Risk CP-CML Patients At Diagnosis

Blood ◽

10.1182/blood.v120.21.3730.3730 ◽

2012 ◽

Vol 120 (21) ◽

pp. 3730-3730

Author(s):

Dale B. Watkins ◽

Chung Hoow Kok ◽

Richard J. D'Andrea ◽

Timothy P. Hughes ◽

Deborah L. White

Keyword(s):

Dna Methylation ◽

Board Of Directors ◽

Research Funding ◽

Global Dna Methylation ◽

Advisory Committees ◽

Poor Risk ◽

Normal Individuals ◽

Risk Patients ◽

Dna Methylation Analysis ◽

Standard Risk

Abstract Abstract 3730 Background: DNA methylation, specifically CpG methylation, is an essential mediator of epigenetic gene expression which is of vital importance to many biological processes and human malignancies. DNA hypermethylation has been previously described in a small number of genes in chronic myeloid leukemia (CML); however, current published studies have only examined the methylation status of selected genes, often based on the results of studies in other malignancies. Therefore, the global DNA methylation profile of chronic phase-CML (CP-CML) remains poorly understood, as does the impact of the epigenome on patient response to tyrosine kinase inhibitors (TKIs) including imatinib. The organic cation transport-1 (OCT-1) protein is the major active protein involved in imatinib transport, and we have previously demonstrated that measuring its function in leukemic mononuclear cells, expressed as OCT-1 activity (OA), in patient cells prior to imatinib therapy, provides a strong prognostic indicator. Notably, very low OA (poor risk cohort) is associated with patients at significant risk for poor molecular response, mutation development and leukemic transformation on imatinib therapy. Therefore, it is of particular interest to ascertain whether epigenetic changes are distinct and potentially biologically relevant in these poor risk patients. Aim: To investigate the global DNA methylation profile in CP-CML patients with a particular focus on poor risk patients (very low OCT-1 activity), and to ascertain whether aberrant epigenetic programming may underlie their poor response. Method: Cells were isolated from the blood of 55 CP-CML patients at diagnosis and 5 normal individuals. CP-CML patients were classified according to their OA values, with 29 classified as poor (very low OA) and 26 standard risk (high OA). Whole genome DNA methylation analysis was performed using the Illumina Infinium® HumanMethylation450 BeadChip. Analysis of array data was performed with R v2.15.0, using the minfiBioconductor package. Results: The methylation profile of CP-CML was significantly different to that of normal individuals, as shown in Table 1. GeneGo enrichment analysis revealed a significant enrichment in CML for genes known to be involved in other leukemias (p=4.92e−26) particularly AML and CLL, suggesting similar pathways may be under epigenetic control in CML. A significant number of polycomb group (BMI1 and EZH2) target genes were also identified, suggesting the likely involvement of this pathway in CML. Table 1: Summary of significant CpGs and corresponding genes when comparisons of CP-CML to normal individuals, and poor to standard risk patients, are made using methylation profiles. A significant difference was also observed when the methylation profiles of poor and standard risk CP-CML patients were analysed (Table 1). GeneGo analysis again identified polycomb group (SUZ12 and EZH2) target enrichment and significant enrichment of NOTCH, Hedgehog and WNT signalling (p=7.93e−9, p=2.42e−5 and p=3.66e−5 respectively) in poor risk patients, indicating these pathways may play a significant role in the unfavourable responses observed in many of these patients. Of particular interest were the ten CpGs where a fold change >4 was observed between the methylation profiles of poor and standard risk patients. Using the Prediction Analysis of Microarrays supervised learning algorithm, a classifier for patient OA prediction based on this 10 CpG signature was evaluated. This classifier had an overall accuracy of 94% (sensitivity: 95%, specificity: 93%). Conclusion: We present a comprehensive global DNA methylation analysis of CP-CML that indicates significant and widespread changes to the CML epigenome, compared with that of normal individuals. Importantly, we have generated a classifier, which identifies the poor risk patient subgroup (very low OA) with 94% accuracy. Validation of this classifier is currently in progress. The epigenetic changes identified here may contribute to CML pathogenesis, and also to the response heterogeneity observed between CP-CML patients treated with TKI therapy. Disclosures: Hughes: Novartis Oncology: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Ariad: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. White:Novartis Oncology: Honoraria, Research Funding; BMS: Research Funding; CSL: Research Funding.

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The Genomic Landscape of Childhood and Adult Acute Erythroid Leukemia

Blood ◽

10.1182/blood.v128.22.39.39 ◽

2016 ◽

Vol 128 (22) ◽

pp. 39-39 ◽

Cited By ~ 2

Author(s):

Ilaria Iacobucci ◽

Ji Wen ◽

Manja Meggendorfer ◽

Catherine Carmichael ◽

John K Choi ◽

...

Keyword(s):

Dna Methylation ◽

Tyrosine Kinase ◽

Board Of Directors ◽

Transcription Regulation ◽

Research Funding ◽

Complex Karyotype ◽

Advisory Committees ◽

Nih3t3 Cells ◽

Genomic Landscape

Abstract Introduction: The genetic basis of several acute myeloid leukemia (AML) subtypes remains poorly characterized, such as that of acute erythroid leukemia (AEL, AML M6) which is currently subclassified by morphology alone, and is associated with poor outcome. Here we sought to perform a definitive genomic analysis of AEL and translate these findings into faithful experimental models and novel therapeutic approaches. Methods: We studied 151 AEL cases (19.2% pediatric, 4.6% young adult, 21.9% adult and 54.3% older adult). Diagnosis of AEL was centrally confirmed and subclassified according to WHO 2008 and revised 2016 criteria. Whole exome and/or genome sequencing, RNA-sequencing and SNP array analysis were performed on all cases and in 2 AEL cell lines (TF-1 and Hel). Genomic data were compared to those from non-M6 childhood and adult AML from TARGET (n=192) and TCGA (n=197) studies. The functional effects of fusion transcripts and mutated genes were examined in IL-3 dependent Ba/F3 cells, NIH3T3 cells for focus formation assays and/or mouse lineage negative hematopoietic stem cells (lin- HSC) for colony forming and transplantation assays. Avatars of human AEL were established in immunocompromised NSGS and MISTRG mice for preclinical studies. Results: a) Genomic landscape of AEL. We identified 2,250 non-synonymous clonal and subclonal somatic mutations in 1,723 genes with a mean of 16.4 per case (range 2-88) and with missense and frameshift mutations accounting for 47.1% and 22.5% of all mutations, respectively. 78 genes were recurrently mutated in at least 3 cases. In frame fusions were detected in 31% of childhood and 27.5% of adult cases, and were more frequent in cases with complex karyotype. 124 potential driver genes were identified by statistical analysis or known pathogenic role in cancer, 9 of which were recurrent novel targets of mutation, most commonly involving chromatin modification (60.3%), cell cycle/tumor suppression (TP53, 33.8%), DNA methylation (28.5%), transcription regulation (26.5%), splicing (15.9%), NPM1 (11.9%), Ras (11.3%), JAK-STAT signaling (9.9%), the cohesin complex (8.6%), ALK/NTRK1 (4.6%) and PI3K signaling (3.3%). Overall, 33% of cases harbored a mutation in signaling genes amenable to inhibition by tyrosine kinase/Ras inhibitors. Mutations in TP53 and DNA methylation genes were significantly more frequent in adults while mutations in transcription regulators and Ras pathway were more frequent in children. Splicing mutations correlated with MDS phenotype and PI3K alterations with therapy-related AEL. Based on the co-occurrence and exclusivity of mutations 7 main distinct AEL genetic subtypes were defined: 1) pediatric AEL with NUP98-rearrangements (3.3% of all cases); 2) adult complex karyotype AEL with TP53 mutations (33.8%); 3) AEL with MLL-rearrangements (12.6%); 4) NPM1-mutated AEL (11.9%); 5) DNA-methylation/splicing mutated AEL (17.8%); 6) splicing/Ras/transcription regulation mutated AEL (21.2%) and 7) Other (8.6%) (Fig.1A). Mutations of chromatin modifiers occurred independently of karyotype, age and subtype (Fig.1B). NUP98-fusions and mutations in PTPN11, UBTF and GATA1 were more frequent in pediatric AEL compared to non-M6 AML. Among adults, mutations in TP53 and MLL were more frequent in AEL while FLT3, NPM1, DNMT3A and IDH1 were more in frequent in non-M6 subtypes. A complex karyotype, therapy-related AEL, TP53 mutations and NUP98-rearrangements were associated with poor outcome. b) Functional AEL modeling and therapeutic translations. Expression of NUP98-JARID1A in lin- HSC resulted in sustained self-renewal and development of an aggressive transplantable leukemia. At least three classes of signaling pathway mutations are targetable in AEL. ALK mutations in the extracellular MAM domain transformed Ba/F3 cells which were sensitive to crizotinib in vitro. Mutations in the tyrosine kinase domain of NTRK1 transformed NIH3T3 cells and were sensitive to entrectinib in vitro. Targeting of JAK-STAT, mTOR and PI3K pathways were examined in xenografts and sensitivity to JAK2 inhibitor ruxolitinib was confirmed in vivo. Conclusions: We provided the first comprehensive landscape of genomic alterations in AEL and defined distinct genomic groups with unique patterns of mutation occurrence compared to non-M6 AML. Finally, we showed that several pathogenic pathways are amenable to inhibition by approved targeted compounds. Disclosures Meggendorfer: MLL Munich Leukemia Laboratory: Employment. Wei:Novartis: Honoraria, Research Funding. Loh:Abbvie: Research Funding; Bristol Myers Squibb: Membership on an entity's Board of Directors or advisory committees. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Mullighan:Amgen: Speakers Bureau; Incyte: Membership on an entity's Board of Directors or advisory committees; Loxo Oncology: Research Funding.

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Racial and Age-Related Differences in Impacts of High-Risk Cytogenetic Abnormalities on Survival in Multiple Myeloma in a Nationwide Electronic Health Record-Derived Database

Blood ◽

10.1182/blood-2021-152774 ◽

2021 ◽

Vol 138 (Supplement 1) ◽

pp. 4121-4121

Author(s):

Gregory S Calip ◽

Mustafa S Ascha ◽

Xiaoliang Wang ◽

Amy E Pierre ◽

Kathleen Maignan ◽

...

Keyword(s):

High Risk ◽

Board Of Directors ◽

Research Funding ◽

Publicly Traded ◽

Advisory Committees ◽

Cytogenetic Abnormalities ◽

Age Related ◽

Black Patients ◽

Double Hit ◽

White Patients

Abstract Background: The incidence of multiple myeloma (MM) and enrichment of cytogenetic abnormalities differ significantly between racial/ethnic groups in the US, and their significance in determining myeloma progression and survival is not well understood. Whole genome sequencing has identified unique mutational signatures in MM, including an age-related process common in hyperdiploid myeloma. Our purpose was to describe racial and age-related differences in the impact of high-risk cytogenetic abnormalities (HRCAs) on survival in MM. Methods: We conducted a retrospective cohort study of adult MM patients starting first-line therapy between January 2011 and May 2021 using the nationwide Flatiron Health electronic health record-derived de-identified database. Patient-level demographic and clinical characteristics were ascertained using structured and unstructured data, curated via technology-enabled abstraction. Patients who had documented fluorescence in situ hybridization testing within 30 days prior to or 90 days following the start of first-line treatment were included. HRCAs, including gain or amplification 1q21, deletion 17p, t(4;14), t(14;16) and t(14;20), were identified and categorized as 0, 1, or 2+ HRCAs. Our outcomes of interest were real world progression free survival (rwPFS) and overall survival (rwOS). Cox proportional hazards models were used to calculate adjusted hazard ratios (HR) and 95% confidence intervals (CI), adjusted for demographic and clinical characteristics and treatment including time-dependent receipt of autologous stem cell transplantation. Results: From a cohort of 4889 MM patients, there were 790 (16%) Black and 2995 (61%) White patients with median ages at diagnosis of 68 and 70 years, respectively. Compared to White patients, a higher proportion of Black patients had IgG M-protein (61% vs 55%) and a lower proportion had 1+ HRCAs identified (31% vs 34%). Among all racial groups, compared to patients aged <65 years (N=1771), a higher proportion of patients aged 65+ years (N=3118) had IgA M-protein (21% vs 17%) and 1+ HRCAs identified (35% vs 33%). Multivariable models showed evidence of significant statistical interaction between age and prevalence of HRCA for rwPFS (P-int: 0.02). Among White patients, having 2+ HRCAs ("double-hit MM") compared to no HRCAs was associated with worse rwPFS in both younger and older patients (<65 years: HR 2.88, 95% CI 1.93-4.32, P<0.01; 65+ years: HR 1.51, 95% CI 1.18-1.94, P<0.01). Among Black patients, associations between double-hit MM and rwPFS were attenuated and not statistically significant regardless of age (<65 years: HR 1.81, 95% CI 0.69-4.74, P=0.23; 65+ years: HR 1.61, 95% CI 0.92-2.81, P=0.09). Similarly, we also found evidence of statistical interaction between age and prevalence of HRCA for rwOS (P-int: 0.02). Among White patients, double-hit MM was significantly associated with worse rwOS but the magnitude of increased risk differed for younger (HR 3.39, 95% CI 2.24-5.14, P<0.01) and older (HR 1.61, 95% CI 1.27-2.05, P<0.01) patients. Double-hit MM was significantly associated with worse rwOS among older Black patients (HR 1.78, 95% CI 1.03-3.06, P=0.04), but not younger Black patients (HR 1.60, 95% CI 0.58-4.40, P=0.36). Conclusions: In this cohort of newly diagnosed MM patients treated in routine practice, having double-hit MM was differentially predictive of poor survival across age groups. Double-hit MM was associated with worse rwPFS and rwOS among White patients, but these trends were less consistent among Black patients. Our current understanding of cytogenetic risk stratification of MM requires further study and additional data for identifying low- and high-risk subsets of patients across different ages and racial groups. Figure. Kaplan-Meier survivor functions for rwPFS in White (Panel A) and Black (Panel B) patients by age group and number of HRCAs Figure 1 Figure 1. Disclosures Calip: Flatiron Health: Current Employment; Roche: Current equity holder in publicly-traded company; Pfizer: Research Funding. Ascha: Flatiron Health: Current Employment; Roche: Current equity holder in publicly-traded company. Wang: Roche: Current equity holder in publicly-traded company; Flatiron Health: Current Employment. Pierre: Flatiron Health, Inc: Current Employment; Roche: Current holder of stock options in a privately-held company. Maignan: Flatiron Health: Current Employment; Roche: Current equity holder in publicly-traded company. Wadé: Roche: Current equity holder in publicly-traded company; Flatiron Health: Current Employment. Leng: Roche: Current equity holder in publicly-traded company; Flatiron Health: Current Employment. Seymour: Karyopharm: Honoraria, Membership on an entity's Board of Directors or advisory committees; Roche: Current equity holder in publicly-traded company; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; Flatiron Health Inc: Current Employment. Patel: Janssen: Consultancy; Amgen: Consultancy; Celgene: Consultancy. Neparidze: Eidos Therapeutics: Membership on an entity's Board of Directors or advisory committees; GlaxoSmithKline: Research Funding; Janssen: Research Funding.

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Modeling of the Epigenome of the Cell-of-Origin Identifies Cancer-Specific DNA Methylation Patterns in CLL

Blood ◽

10.1182/blood-2018-99-115941 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 3885-3885

Author(s):

Justyna Anna Wierzbinska ◽

Reka Toth ◽

Naveed Ishaque ◽

Jan-Phillip Mallm ◽

Karsten Rippe ◽

...

Keyword(s):

Dna Methylation ◽

Cell Differentiation ◽

B Cells ◽

B Cell ◽

Board Of Directors ◽

Research Funding ◽

B Cell Differentiation ◽

Advisory Committees ◽

Normal Differentiation ◽

Methylation Patterns

Abstract Normal B cells undergo extensive epigenetic programming during normal differentiation and distinct B cell differentiation stages represent unique DNA methylation patterns. Chronic Lymphocytic Leukemia (CLL) originates from rapidly differentiating B cells and their DNA methylation signature is stably propagated in CLL. Consequently, CLL methylome data can be used to infer the putative cell-of-origin (COO) for each individual CLL case. We define the COO of CLL as the cell that has acquired a first oncogenic hit and which will initiate tumorigenic growth if one or more additional hits have been acquired. This means that two factors contribute to the epigenetic profile of CLL cells: first, the epigenetic profile of the founder B cell at the time of malignant transformation and second, CLL-specific epigenetic alterations that are acquired during leukemogenesis and progression of the disease. Previous studies using peripheral blood CD19+ B cells as a reference for aberrant methylation calls completely neglected the massive epigenetic programming that occurs during normal B cell differentiation. Thus, novel strategies aiming at identifying truly CLL-specific methylation changes considering the highly dynamic methylome during normal B cell differentiation were urgently needed. Here we outline a new analytical framework to delineate CLL-specific DNA methylation. We demonstrate how this approach can be applied to detect epigenetically deregulated transcripts in CLL. Firstly, we modeled the epigenome dynamics occurring during normal B cell differentiation using linear regression. The DNA methylomes of CLL cells were then precisely positioned onto the normal B cell differentiation trajectory to define the closest normal B cell methylome for every CLL patient, the COO. The epigenome of the COO then served as a reference for aberrant DNA methylation calls. We dissected two categories of CLL-specific methylation events: those occurring at sites undergoing epigenetic programming during B cell differentiation and those that normally do not change during B cell differentiation. The first group was further subdivided into class A and B, displaying exaggerated methylation loss or gain, respectively, and class C showing both hyper- and hypomethylation relative to the normal differentiation. The second group was classified into class D displaying hypo- and class E showing hypermethylation. Overall, only 1.6% of the CpG-sites (7,248 CpGs) represented on the Illumina 450k array were affected by disease-specific methylation programming, mostly hypomethylation (6,680 CpGs). Next, the molecular programs underlying the CLL-specific methylation patterns were investigated. We tested enrichment of chromatin states and of transcription factor binding sites (TFBS) as identified in an immortalized B cell line (GM12878). This indicated that disease-specific methylation events target transcriptionally relevant cis-regulatory elements in CLL (enhancers, weak and poised promoters and insulator regions). In line with this, CLL-specific differentially methylated regions affected TFBS associated with signaling pathways known to be important in normal B-cell differentiation (i.e. BATF, EBF1). We also observed altered methylation at CTCF binding sites suggesting their involvement in CLL pathogenesis. In the present work, we dissected CLL methylomes to distinguish between normal B cell differentiation-associated methylation patterns and CLL-specific methylation events. We showed that this approach is indispensable to identify key pathogenic events driving CLL pathogenesis. The relevance of our approach was demonstrated by contrasting the number of epigenetically deregulated miRNAs and protein-coding genes to those determined with a classic analysis using CD19+ B cells as controls. This highlights the extent of overcalling of CLL-specific methylation patterns in previous studies (~30-fold for protein-coding genes and ~10-fold for miRNAs) and stresses the importance to consider normal differentiation trajectories for the identification of aberrant DNA methylation events. Here we propose 11 protein-coding genes (e.g. DOK2, CLLU1) and 4 miRNAs (e.g. miR-486, miR-195) as being epigenetically deregulated in CLL. Our analytical approach provides a general framework for the identification of disease-specific epigenomic changes that should be applicable to other cancers in the future. Disclosures Küppers: the Takeda Advisory Board: Membership on an entity's Board of Directors or advisory committees. Stilgenbauer:AbbVie: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Genzyme: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Boehringer-Ingelheim: 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, Research Funding; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pharmcyclics: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Mundipharma: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; GSK: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Hoffmann La-Roche: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding.

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Germline-Somatic Interactions in Myelofibrosis Susceptibility

Blood ◽

10.1182/blood-2021-150634 ◽

2021 ◽

Vol 138 (Supplement 1) ◽

pp. 313-313

Author(s):

Derek W Brown ◽

Youjin Wang ◽

Andrew St. Martin ◽

Stephen R. Spellman ◽

Shu-Hong Lin ◽

...

Keyword(s):

Telomere Length ◽

Board Of Directors ◽

Research Funding ◽

Jak2 V617f ◽

Jak2 V617f Mutation ◽

Risk Haplotype ◽

Advisory Committees ◽

Genome Wide ◽

Increased Risk ◽

V617f Mutation

Abstract Introduction: Myelofibrosis (MF) is a rare myeloproliferative neoplasm (MPN) characterized by bone marrow fibrosis, progressive bone marrow failure, and increased risk of acute myeloid leukemia. While MF arises from somatic driver mutations in JAK2, MPL, and CALR, some MPN patients may have a heritable component. To comprehensively examine the genetic etiology of MF, we performed the first integrative analysis of SNP array genotyping (using Infinium Global Screening Array), targeted long-read sequencing (using PacBio SMRT sequencing) and telomere length (TL, using qPCR assay). Methods: Our study included 937 MF patients who received an allogeneic hematopoietic cell transplant (HCT) between 2000 and 2016 and had an available pre-HCT blood sample at the Center for International Blood and Marrow Transplant Research Repository. Somatic mosaic chromosomal alterations (mCAs, including deletions, duplications, or copy-neutral losses-of-heterozygosity (CNLOH)) were called with the Mosaic Chromosomal Alteration (MoChA) algorithm using raw genotyping intensity data. A genome-wide association study (GWAS) was restricted to include 827 MF patients of European ancestry and utilized 4,135 genetically-matched healthy controls. Results: GWAS identified six independent MF susceptibility loci at genome-wide significance (P< 5×10 -8); four of which replicate prior MPN susceptibility loci [9p24.1(JAK2), 5p15.33(TERT), 3q25.33(IFT80), and 4q24(TET2)] and two novel MF loci [6p21.35(HLA-DQB1-AS1) and 17p13.1(TP53)] (Figure 1). A transcriptome-wide association analysis using whole blood GTEx data highlighted the 9p24.1 locus with increased JAK2 expression associated with elevated risk of MF (P= 2.18×10 -19). A strong colocalization statistic further indicated shared genetic component between eQTL and this JAK2 locus (HyPrColoc Posterior Probability= 0.6) (Figure 2). Based on the strong signal identified at TERT (Figure 1), we investigated the relationship between MF risk and genetically-inferred telomere length using a panel of 19 germline variants previously found to be associated with telomere length. Of the 19 telomere-length associated variants investigated, 7 were found to be associated with MF risk (binomial P= 2.31×10 -5, linear trend P= 5.48×10 -4) (Figure 3). Both Mendelian randomization and genome-wide genetic correlation analyses further indicated that increased risk of MF was associated with longer inherited telomere length. Utilizing available clinical mutation data on a subset of 185 patients, MF cases carrying the germline risk haplotype of the 9p24.1(JAK2) susceptibility locus were observed to more frequently have the JAK2 V617F mutation (71% vs 59%; P= 0.02). Targeted PacBio long-read sequencing around JAK2 provided further evidence of linkage between the germline risk allele and the JAK2 V617F mutation. Detectable autosomal mCAs were also abundant in MF cases with 67.4% having at least one mCA (compared to ~3% in the general population) and 27.6% having an mCA spanning JAK2 (mostly CNLOH) (Figure 4). In addition, using a binomial test for biased allelic imbalance, a cis relationship was identified at 9p24.1 in which the MF risk haplotype was predominantly duplicated by CNLOH (binomial P=1.36×10 -9). Regional sequencing of JAK2 further confirmed duplication of JAK2 V617F by CNLOH. Finally, we observed an inverse association between autosomal mCAs and qPCR measured telomere length (OR= 0.22, 95% CI= 0.07-0.65, P= 6.40×10 -3). These results were consistent by mCA chromosomal region and copy number state. Conclusion: Our results suggest a molecular framework for the genetic etiology of MF in which both genetically-inferred telomere length and germline variation at JAK2 are associated with increased MF risk. The 9p24.1 risk haplotype predisposes to the acquisition of a somatic JAK2 V617F mutation in cis and subsequent duplication of JAK2 V617F by mCAs (usually CNLOH). This process leads to aberrant JAK2 activity and increased clonal proliferation, accelerating telomere length shortening and increasing genomic instability in patients with MF. Figure 1 Figure 1. Disclosures Gupta: AbbVie: Consultancy, Honoraria; Constellation Pharma: Consultancy, Honoraria; Roche: Consultancy; Pfizer: Consultancy; BMS-Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Sierra Oncology: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Incyte: Honoraria, Research Funding. Lee: Janssen: Other; Incyte: Research Funding; AstraZeneca: Research Funding; Kadmon: Research Funding; National Marrow Donor Program: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding; Pfizer: Research Funding; Syndax: Research Funding; Takeda: Research Funding; Amgen: Research Funding. Saber: Govt. COI: Other.

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Single-Cell Transcriptomics Study of Human Hematopoietic Progenitors Reveals Alterations Associated with Aging and Myeloid Malignancies

Blood ◽

10.1182/blood-2021-153550 ◽

2021 ◽

Vol 138 (Supplement 1) ◽

pp. 1082-1082

Author(s):

Marina Ainciburu ◽

Teresa Ezponda ◽

Nerea Berastegui ◽

Ana Alfonso Pierola ◽

Amaia Vilas-Zornoza ◽

...

Keyword(s):

Single Cell ◽

Board Of Directors ◽

Research Funding ◽

Healthy Aging ◽

The Elderly ◽

Advisory Committees ◽

Myeloid Malignancies ◽

Age Related ◽

Healthy Donors ◽

Transcriptional Alterations

Abstract Hematopoietic stem and progenitor cells (HSPCs) comprise a continuum of cells with varying differentiation potential and priming toward specific lineages. During both healthy aging and myeloid malignancies, changes occur in the composition and regulation of HSPCs. In this study, we evaluated human HSPCs obtained from young and elderly healthy donors using single-cell RNA sequencing to identify the transcriptional and regulatory alterations associated with aging at single cell resolution. We then applied this knowledge to the study of specific perturbations associated with the development of myeloid pathologies. We isolated >90,000 bone marrow CD34+ cells from 5 young (18-20 y/o), 3 elderly (>65 y/o) healthy donors, 1 patient with myelodysplastic syndrome (MDS) and 1 patient with acute myeloid leukemia (AML), using fluorescence-activated cell sorting. scRNA libraries were prepared with the 10X chromium platform and sequenced. Finally, bioinformatic analysis was performed using available R and Python algorithms such as Seurat, Palantir and Scenic. First, we characterized HSPC subpopulations in young donors by unsupervised clustering and manual annotation. Taking the previous findings as reference, we then classified the elderly and pathological HSPC using elastic-net regularization prediction models (Figure 1A). Comparison of subpopulations in young and elderly donors confirmed the age-related increase in HSC, as well as reduction of lymphoid progenitors and myelomonocytic compartments. Next, we performed differential expression and pathways analysis to uncover age-associated alterations in the transcriptional profile of cells with the same identity. We found a generalized enrichment in elderly HSPC of pathways activated upon stress and inflammation, such as p53, hypoxia and TNF alpha response. This suggests an age-related increased response to the more inflammatory microenvironment of elderly individuals. On the other hand, young HSPC were enriched for cell cycle activation and proliferation pathways, as well as metabolic processes (Figure 1B). Using trajectory analysis, we recovered 6 differentiation paths present in our young donor's data. When compared to the elderly, the greatest changes occurred along the monocytic trajectory. For some genes, expression differed through the whole trajectory, indicating the existence of original transcriptional alterations already at the HSC compartment. On the other hand, expression of myelomonocytic differentiation markers, such as MPO and CD74, reached lower levels in our elderly HSPC data, pointing towards a loss of capacity for monocytic differentiation in progenitors from elderly individuals. Finally, to identify key transcription factors regulating the progression of differentiation routes, we built gene regulatory networks. Overall, we found lower activation levels for transcriptional programs in the early progenitors from elderly donors. In addition, gene ontology enrichment analysis showed that the active networks in the young were enriched for differentiation-related terms, while networks from the elderly were not. These results also indicate an age-associated loss of differentiation capability. We then applied the same computational tools to analyze aberrant hematopoiesis in samples from 2 patients suffering from myeloid malignancies (MDS and AML). On one hand, we subjected the MDS sample to trajectory analysis, focusing on the erythroid lineage. We observed perturbations in the expression dynamics of genes playing a role in erythropoiesis. In the AML sample, we encountered a significant expansion of the most immature cell compartments (HSC, LMPP and MEP). In addition, GRN reconstruction showed up the specific activity of transcription programs activated by factors deregulated during leukemia, such as ZSCAN18 and GFI1. In conclusion, our work described the transcriptional alterations that occur in early hematopoiesis, both during healthy aging and myeloid pathology. We used multiple approaches, such as the study cellular proportions, differentiation trajectories and GRNs. The inclusion of samples from patients with myeloid pathology provided insights into the potential role of single-cell technologies for understanding and treating hematological malignancies. Figure 1 Figure 1. Disclosures Sanchez-Guijo: Gilead: Consultancy, Honoraria; Celgene/Bristol-Myers-Squibb,: Consultancy, Honoraria; Incyte: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria; Takeda: Honoraria, Research Funding; Roche: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Novartis: Consultancy, Honoraria, Research Funding. Diez-Campelo: Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; BMS: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Takeda Oncology: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau. Valcarcel: BMS: Consultancy, Honoraria, Speakers Bureau; CELGENE: Consultancy, Honoraria, Speakers Bureau; ASTELLAS: Consultancy, Honoraria, Speakers Bureau; AMGEN: Consultancy, Honoraria, Speakers Bureau; NOVARTIS: Consultancy, Honoraria, Speakers Bureau; TAKEDA: Consultancy, Honoraria, Speakers Bureau; JAZZ: Consultancy, Honoraria, Speakers Bureau; SOBI: Consultancy, Honoraria, Speakers Bureau; SANOFI: Consultancy, Honoraria, Speakers Bureau. Romero: 10X Genomics: Current Employment. Prosper: Janssen: Honoraria; Oryzon: Honoraria; BMS-Celgene: Honoraria, Research Funding.

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