Robertson, Keith D.

Abstract Introduction: Clonal hematopoiesis of indeterminate potential (CHIP) is defined by the presence and subsequent expansion of leukemia-associated driver mutations (MT). These MT predominantly involve epigenetic regulators such as DNMT3A, ASXL1, and TET2 (DAT, 75%). CHIP is a disease of aging and is associated with increased all-cause mortality. Clonal Hematopoiesis (CH), when present in the context of unexplained cytopenias and MT variant allele fractions (VAF) >20%, is termed as clonal cytopenias of undetermined significance (CCUS). Chronic myelomonocytic leukemia (CMML) is a hematological malignancy characterized by TET2, ASXL1, and SRSF2MT as well as sustained monocytosis. It usually arises in the setting of existing CH and clonal monocytosis. Here, we assessed the clonal compositions involving DAT genes in patients (pts) with CHIP, CCUS, and CMML. Methods: We included 1008 pts: 54 (5%) with CHIP, 334 (33%) with CCUS, and 620 (62%) with CMML. Demographics, laboratory parameters, and mutational spectrum at diagnosis were collected. We characterized and compared DAT MT in all three cohorts and assessed correlations with clinical parameters. MT locus and type along with protein sequences were analyzed using ProteinPaint. All statistical analyses were performed using R. Results: Of the 1008 pts, 427 (42%) had DAT MT; 36 had CHIP (67%); 96 had CCUS (29%), and 296 had CMML (48%). The median age was 71 years (IQR 65-76), and 68% were male. CMML pts had higher white blood cell counts, absolute neutrophil counts, absolute monocyte counts, and lower hemoglobin and platelet counts in comparison to CCUS and CHIP pts. The distribution of age and sex were similar among the three groups. After a median follow-up of 11 months (IQR 9.5-12.8), there were no myeloid progressions in the CHIP group and 17 (18%) in the CCUS group (8 of which developed CMML, Table 1). There were 738 protein-altering or splice-site DATMT in 495 unique MT hotspots. Pts with TET2MT were older (71 years, compared to DNMT3AMT: 69 years and ASXL1MT: 69 years , p<0.022). ASXL1MT were more common in males (75%, compared to DNMT3AMT: 52% and TET2MT: 66%, p=0.005). The mean VAF (mVAF) were 15.5% for DNMT3AMT, 35% for ASXL1MT and 39.2% TET2MT, respectively (p<0.001). We then compared DATMT characteristics in CHIP, CCUS, and CMML groups: DNMT3AMT were most common in CHIP, followed by CCUS, and were very rare in CMML (35% vs. 15% vs. 3%, p<0.001), with mVAF of DNMT3AMT increasing across this spectrum (CHIP: 6.5% vs. CCUS: 17.1% vs. CMML: 43.4%, p<0.0001). While DNMT3A missense MTs were most common (n=62, 67.4%), the AML-associated R882 hotspot MT was seen in 26 (28%) pts (CHIP: n=3, 5%, CCUS: n=10, 16%, and CMML n=13, 21%, Figure 1a). None of the DNMT3AMT CHIP and CCUS pts progressed to CMML.ASXL1MT were most frequently seen in CMML (n=178, 28.6%), followed by CHIP (n=4, 7.4%), and CCUS (n=17, 5%, p<0.001). The mVAF of ASXL1MT was significantly lower in CHIP, followed by CCUS, and CMML (11% vs. 34% vs. 36%, p<0.0001). Truncating ASXL1 MTs were most common (n=205, 53%). Mutational hotspots included G646W (n=81, CCUS: n=6, 3%, CMML: n=75, 37%) and E635 (n=27, CHIP: n=1, 0.5%, CCUS: n=1, 0.5%, CMML: n=27, 13%, Figure 1b). Four ASXL1MT CCUS pts transformed to CMML (R965, E635, G646W, Q910*, and Q925*); of which 2 co-mutated with TET2MT, one with SRSF2MT, and one with an additional ASXL1MT.TET2MT were frequently encountered in CMML, followed by CCUS and CHIP pts (CMML: n=205, 33% vs. CCUS: n=88, 26.3% vs. CHIP: n=15, 27.8%, p=0.02). The mVAF of TET2MT progressively increased across the 3 groups (CHIP: 14.1%, CCUS: 31.9%, CMML: 45.5%, p<0.001). TET2MT occurred across the span of the gene, with no clear hotspots identified, with nonsense MT being most frequent (n=150, 37.3%, Figure 1c). Among the nonsense and frameshift TET2MT (n=209), 131 (63%) involved the N terminal region, and 78 (37%) involved the catalytic domain. Among 8 CCUS pts that transformed to CMML, 6 had TET2MT, including 4 with spliceosome co-MT. Conclusion: While CMML, a disease of aging, arises in the background of CH, the most common CH MT, DNMT3AMT, plays a minor role in this process, and in fact, DNMT3AMT tend to occur at younger ages. While DNMT3A MT and ASXL1MT are preferentially observed in hotspots, TET2MT span the entire gene. Clonal compositions largely involving truncating TET2MT and frameshift ASXL1MT, in conjunction with splicing MT, shape the CMML genotype. Figure 1 Figure 1. Disclosures Komrokji: Geron: Honoraria; Novartis: Honoraria; Agios: Honoraria, Speakers Bureau; Abbvie: Honoraria, Speakers Bureau; JAZZ: Honoraria, Speakers Bureau; BMS: Honoraria, Speakers Bureau; Acceleron: Honoraria. Zeidan: Genentech: Consultancy; BeyondSpring: Consultancy; Kura: Consultancy, Other: Clinical Trial Committees; Amgen: Consultancy, Research Funding; Cardiff Oncology: Consultancy, Other: Travel support, Research Funding; Astex: Research Funding; Epizyme: Consultancy; Geron: Other: Clinical Trial Committees; Boehringer Ingelheim: Consultancy, Research Funding; AstraZeneca: Consultancy; Acceleron: Consultancy, Research Funding; Astellas: Consultancy; Novartis: Consultancy, Other: Clinical Trial Committees, Travel support, Research Funding; Agios: Consultancy; Aprea: Consultancy, Research Funding; AbbVie: Consultancy, Other: Clinical Trial Committees, Research Funding; Loxo Oncology: Consultancy, Other: Clinical Trial Committees; Pfizer: Other: Travel support, Research Funding; ADC Therapeutics: Research Funding; Gilead: Consultancy, Other: Clinical Trial Committees; BMS: Consultancy, Other: Clinical Trial Committees, Research Funding; Jazz: Consultancy; Incyte: Consultancy, Research Funding; Ionis: Consultancy; Jasper: Consultancy; Daiichi Sankyo: Consultancy; Janssen: Consultancy; BioCryst: Other: Clinical Trial Committees. Coombs: LOXO: Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Honoraria; AstraZeneca: Honoraria; AbbVie: Honoraria; Genentech: Honoraria; MEI Pharma: Honoraria. Madanat: Blue Print Pharmaceutical: Honoraria; Stem line pharmaceutical: Honoraria; Onc Live: Honoraria; Geron Pharmaceutical: Consultancy. Griffiths: Takeda Oncology: Consultancy, Honoraria; Novartis: Honoraria; Taiho Oncology: Consultancy, Honoraria; Apellis Pharmaceuticals: Research Funding; Alexion Pharmaceuticals: Consultancy, Research Funding; Boston Biomedical: Consultancy; Astex Pharmaceuticals: Honoraria, Research Funding; Celgene/Bristol-Myers Squibb: Consultancy, Honoraria, Research Funding; Genentech: Research Funding; Abbvie: Consultancy, Honoraria. Lai: Jazz Pharma: Consultancy, Membership on an entity's Board of Directors or advisory committees; Astellas: Speakers Bureau; Jazz Pharma: Speakers Bureau; Macrogenics: Consultancy, Membership on an entity's Board of Directors or advisory committees; Daiichi-Sankyo: Consultancy, Membership on an entity's Board of Directors or advisory committees; Agios: Consultancy, Membership on an entity's Board of Directors or advisory committees; Genentech: Consultancy, Membership on an entity's Board of Directors or advisory committees; AbbVie: Consultancy, Membership on an entity's Board of Directors or advisory committees. Savona: CTI: Consultancy, Membership on an entity's Board of Directors or advisory committees; Geron: Consultancy, Membership on an entity's Board of Directors or advisory committees; Karyopharm: Consultancy, Current equity holder in publicly-traded company, Membership on an entity's Board of Directors or advisory committees; BMS-Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; NOVARTIS: Consultancy, Membership on an entity's Board of Directors or advisory committees; Ryvu: Consultancy, Membership on an entity's Board of Directors or advisory committees; Sierra Oncology: Consultancy, Membership on an entity's Board of Directors or advisory committees; Taiho: Consultancy, Membership on an entity's Board of Directors or advisory committees; TG Therapeutics: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; ALX Oncology: Research Funding; Astex: Research Funding; Incyte: Research Funding. Litzow: AbbVie: Research Funding; Actinium: Research Funding; Pluristem: Research Funding; Amgen: Research Funding; Astellas: Research Funding; Jazz: Other: Advisory Board; Omeros: Other: Advisory Board; Biosight: Other: Data monitoring committee. Al-Kali: Novartis: Research Funding; Astex: Other: Research support to institution. Patnaik: StemLine: Research Funding; Kura Oncology: Research Funding.

Abstract Background and Aims Chronic HCV infection is a leading etiologic driver of cirrhosis and ultimately HCC. Of the approximately 71 million individuals chronically infected with HCV, 10%‐20% are expected to develop severe liver complications in their lifetime. Epigenetic mechanisms including DNA methylation and histone modifications become profoundly disrupted in disease processes including liver disease. Approach and Results To understand how HCV infection influences the epigenome and whether these events remain as “scars” following cure of chronic HCV infection, we mapped genome‐wide DNA methylation, four key regulatory histone modifications (H3K4me3, H3K4me1, H3K27ac, and H3K27me3), and open chromatin in parental and HCV‐infected immortalized hepatocytes and the Huh7.5 HCC cell line, along with DNA methylation and gene‐expression analyses following elimination of HCV in these models through treatment with interferon‐α (IFN‐α) or a direct‐acting antiviral (DAA). Our data demonstrate that HCV infection profoundly affects the epigenome (particularly enhancers); HCV shares epigenetic targets with interferon‐α targets; and an overwhelming majority of epigenetic changes induced by HCV remain as “scars” on the epigenome following viral cure. Similar findings are observed in primary human patient samples cured of chronic HCV infection. Supplementation of IFN‐α/DAA antiviral regimens with DNA methyltransferase inhibitor 5‐aza‐2’‐deoxycytidine synergizes in reverting aberrant DNA methylation induced by HCV. Finally, both HCV‐infected and cured cells displayed a blunted immune response, demonstrating a functional effect of epigenetic scarring. Conclusions Integration of epigenetic and transcriptional data elucidate key gene deregulation events driven by HCV infection and how this may underpin the long‐term elevated risk for HCC in patients cured of HCV due to epigenome scarring.

Hepatocellular carcinoma (HCC) is the second most common cause of cancer deaths worldwide. HCC is known to be a CpG Island methylator Phenotype (CIMP) cancer. Tumor development in CIMP cancers is characterized by epigenomic changes including changes in DNA methylation. We have previously shown that overlapping methylation abnormalities are conserved between primary tissue and cell culture models of HCC. Alcohol consumption is known to increase the risk of development of HCC and has also been shown to alter DNA methylation in a manner that contributes to liver disease. Here we study the effect of alcohol as a driver to tumor progression in HCC. We have developed in vitro models of HCC to identify the gene targets altered by ethanol metabolism during tumor progression over an extended period of time.

Introduction: Truncating mutations in Additional Sex Combs-Like 1 (ASXL1) are associated with a high-risk disease phenotype in myeloid malignancies. In chronic myelomonocytic leukemia (CMML), truncating ASXL1 mutations are known to increase transcriptional activity of leukemic driver genes and have been associated with gene body hypermethylation. We interrogated the transcriptome and methylome of patients with ASXL1-mutant (MT) and -wildtype (WT) CMML using a multi-omics approach to test the hypothesis that gene expression is mediated through gene body methylation. Methods: Bone marrow mononuclear cells from patients with ASXL1 WT (n=8) and MT (n=8) CMML were subjected to targeted NGS of DNA, whole transcriptome shotgun sequencing (RNA-seq), and immunoprecipitation of DNA methyl residues (DIP-seq). After quality control all samples were sequenced on an Illumina HiSeq 4000 before further processing and data analysis. Differential gene expression analysis was performed to identify genes up-regulated in MT CMML. The samples in the two groups were treated as biological replicates and subjected to a consensus peak calling strategy requiring an overlap of at least 30% between samples and an adjusted p-value < 5x10-5 for a methylation peak to be considered statistically significant. For validation purposes methylation analysis was performed on 3 ASXL1 MT and 3 WT CMML patients using Illumina Infinium MethylationEPIC microarrays and differentially methylated regions were identified using a bump hunting strategy. Gene body methylation was defined as methylation in gene bodies (outside the promoter region, i.e. transcription start site ±2kb). Gene body methylation was compared between WT and MT CMML for the up-regulated genes and correlated with expression of all genes in MT CMML. Results: Sixteen WHO-defined CMML patients were included, median age 69 years (48-77), 63% male, 50% had truncating ASXL1 frame shift mutations. Abnormal karyotypes were observed in the same number of patients and the burden of co-mutations was similar between the two groups (median number per group 3 vs. 3, p=0.508). This included several modulators of DNA methylation including TET2, DNMT3A, and IDH2 (median number per group 1 vs. 1, p=0.699). There was a predominant up-regulation of gene expression in MT CMML: 707 genes up- and 124 down-regulated (FDR<0.050) without evidence of differential methylation in promoter regions of the differentially expressed genes. There was no difference in the number of genes with consensus methylation peaks in the gene body or the extent of gene body methylation (area under the consensus peaks) between MT and WT CMML for the differentially expressed genes (Figure 1a). We further validated these results using methylation microarrays (Figure 1b). Among the MT CMML patients, there was a curvilinear relationship between gene body methylation and gene expression across all genes with intermediate gene body methylation being associated with the highest gene expression (Figure 1c). As an alternative unbiased approach, we identified 1595 differentially methylated regions (DMR, regions with FDR<0.05) in the validation data set. We mapped all hypermethylated regions to gene bodies requiring that there was no concurrent hypermethylation of the promoter region of the same gene. With this approach we identified one of the 707 up-regulated genes (0.14%) with evidence of isolated gene body hypermethylation (HBZ, log2-fold change in gene expression 4.25, FDR=0.0008, DMR area=1.61, DMR FDR=0.018). Representative examples of up-regulated mitotic kinases without evidence of differential gene body methylation are shown in Figure 1d (pie charts represent the mean β-values of microarray probes, p-values represent the comparisons of mean β-values between MT and WT CMML per gene). Conclusions: Gene body methylation was positively associated with gene expression in MT CMML. However, the lack of differential gene body methylation between WT and MT CMML for the up-regulated genes make it an unlikely explanation for the observed increase in transcriptional activity among patients with MT CMML. Figure 1 Disclosures Ordog: Millipore Sigma: Patents & Royalties.

Introduction: Additional Sex Combs-Like 1 (ASXL1) is a chromatin modifier frequently affected by truncating mutations in myeloid malignancies. In chronic myelomonocytic leukemia (CMML), truncating ASXL1 mutations are associated with the overexpression of leukemogenic driver genes, however the molecular mechanisms underlying this transcriptional activation remain controversial. We performed single-cell chromatin accessibility studies of ASXL1-mutant (MT) and -wildtype (WT) CMML to identify cis-regulatory elements (CREs) such as distal enhancer elements that may explain the observed transcriptional activity in MT CMML. Methods: Bone marrow mononuclear cells from a patient with WT and a patient with MT CMML were subjected to single-cell DNA transposase accessibility studies (scATAC-seq). The cryopreserved cells were thawed and resuspended and approximately 100000 viable mononuclear cells per sample were subjected to transposase assays before proceeding to single-cell partitioning into gel beads in emulsion, barcoding, library construction, and sequencing following an established 10X Genomics workflow. The target cell recovery was approximately 2000 cells per sample. Genomic libraries were sequenced on an Illumina HiSeq 4000 before demultiplexing, alignment to the reference genome, and post-alignment quality control. The 10X Genomics Cell Ranger ATAC software was used for demultiplexing, alignment of the reads to the GRCh38 reference genome, filtering and quality control, counting of barcodes and unique molecular identifiers, identification of transposase cut sites, detection of accessible chromatin peaks, count matrix generation for peaks and transcription factors, clustering, and differential accessibility analysis. The two-sample Kolmogorov-Smirnov test for equality of distribution functions was used to compare the distributions of single-cell entropies. Single-cell cis-co-accessibility scores were estimated using Cicero. Results: To mitigate potential effects on the analysis introduced by unequal cell recovery due to experimental conditions, we randomly sampled 1500 single cells from each genotype. Visualizing the first two principal components of the chromatin accessibility data revealed the differences in chromatin conformation between genotypes and also the increased heterogeneity within the MT sample (Figure 1a). To quantify this increase in intratumoral heterogeneity of chromatin accessibility in MT CMML, we calculated the single-cell entropies for both genotypes. There was an increase in the dispersion of single-cell entropy values (mean 0.793±0.026 versus 0.811±0.012, p=6.19x10-151) in MT compared to WT disease (Figure 1b). To leverage the power of the single-cell chromatin accessibility data, we estimated cis-co-accessibility scores (two-way chromatin interactions). There were 631 genes involved in two-way chromatin interactions that were either specific to MT CMML or were shared between MT and WT CMML and showed an increase in co-accessibility in MT CMML. Co-accessibility was higher in MT compared to WT CMML, regardless whether the chromatin interaction was MT-specific or shared between the genotypes (Figure 1c). Gene ontology analysis revealed increased chromatin accessibility in genes involved in the regulation of cell cycle, nuclear division, chromosome organization, cell cycle phase transition, DNA conformation change, and DNA replication (FDR<1.00x10-5). The construction of cis-co-accessibility networks demonstrated a remodeling of the chromatin landscape with MT-specific chromatin interactions involving key mitotic kinases such as MAP2K3 (Figure 1d). Conclusions: MT CMML is characterized by an increase in intratumoral heterogeneity and increased chromatin accessibility. The increased chromatin co-accessibility involved key mitotic kinases and may serve as a plausible explanation for the increased transcriptional activity observed in MT CMML. Figure 1 Disclosures Ordog: Millipore Sigma: Patents & Royalties.

Hepatocellular Carcinoma (HCC) is a highly fatal liver cancer with no effective treatments. HCC tumors, formed after long‐term viral or alcohol related inflammation and cirrhosis, have a surprisingly low number of genetic mutations. Prior research has found that epigenetic patterns, such as DNA methylation at gene promoter sites, are common in HCC patients. Recent improvements in genetic sequencing and online genetic databases, have allowed us to identify a set of gene targets, including FMO3, for HCC detection or treatment. The hypothesis is that FMO3 is reduced by epigenetic dysregulation, leading to tumor formation. To find the connection between FMO3, epigenetic events and HCC tumor pathways, our lab created novel HCV‐stable and non‐infected HCC cell lines. We also used Huh7 and Huh7.5 cells to show the epigenetic changes in stressed cells and methylation inhibited cells. To mimic inflammation and stress leading to epigenetic changes, Huh7 cells were exposed to heat shock at 40°C for 40 min. Huh7.5 cells were treated with epigenetic inhibitor 5‐Aza‐2 deoxycytidine. RT‐qPCR and Western Blot show that FMO3 is significantly reduced in stressed cells and upregulated when treated with the methylation inhibitor. These results indicate that FMO3 may be used for early cancer risk detection and/or to improve treatment options by identifying the relationship between epigenetic regulation and tumorigenesis. Support or Funding Information NIH NIDDK 1R01DK110024‐01A1

Abstract Motivation DNA methylation can be measured at the single CpG level using sodium bisulfite conversion of genomic DNA followed by sequencing or array hybridization. Many analytic tools have been developed, yet there is still a high demand for a comprehensive and multifaceted tool suite to analyze, annotate, QC and visualize the DNA methylation data. Results We developed the CpGtools package to analyze DNA methylation data generated from bisulfite sequencing or Illumina methylation arrays. The CpGtools package consists of three types of modules: (i) ‘CpG position modules’ focus on analyzing the genomic positions of CpGs, including associating other genomic and epigenomic features to a given list of CpGs and generating the DNA motif logo enriched in the genomic contexts of a given list of CpGs; (ii) ‘CpG signal modules’ are designed to analyze DNA methylation values, such as performing the PCA or t-SNE analyses, using Bayesian Gaussian mixture modeling to classify CpG sites into fully methylated, partially methylated and unmethylated groups, profiling the average DNA methylation level over user-specified genomics regions and generating the bean/violin plots and (iii) ‘differential CpG analysis modules’ focus on identifying differentially methylated CpGs between groups using different statistical methods including Fisher’s Exact Test, Student’s t-test, ANOVA, non-parametric tests, linear regression, logistic regression, beta-binomial regression and Bayesian estimation. Availability and implementation CpGtools is written in Python under the open-source GPL license. The source code and documentation are freely available at https://github.com/liguowang/cpgtools. Supplementary information Supplementary data are available at Bioinformatics online.

Introduction: Myelodysplastic syndrome/myeloproliferative neoplasm (MDS/MPN) overlap syndromes consist of 5 distinct WHO-defined entities; namely chronic myelomonocytic leukemia (CMML), atypical chronic myeloid leukemia, BCR/ABL1- (aCML), juvenile myelomonocytic leukemia (JMML), MDS/MPN with ring sideroblasts and thrombocytosis (MDS/MPN-RS-T), and MDS/MPN, unclassifiable (MDS/MPN-U) (Arber et al., Blood 2016). With the notable exception of JMML, a bona fide RASopathy, the other entities are characterized by clinical heterogeneity and molecular diversity. Loss of function TET2 mutations (TET2MT) are common in myeloid neoplasms, especially CMML (60%), and are known leukemogenic drivers. We carried out this study to assess the TET2 mutational landscape and phenotypic correlates in patients with MDS/MPN overlap syndromes. Methods: After approval by the institutional review board, adult patients with WHO defined MDS/MPN overlap syndromes were included; with the exception of JMML. The BM morphology, cytogenetics and 2016, WHO-diagnoses were retrospectively reviewed and all patients underwent targeted next generation sequencing for 29 myeloid-relevant genes, obtained on BM mononuclear cells, at diagnosis, or at first referral, by previously described methods (Patnaik et al., BCJ 2016). Results: Five hundred and four patients were included in the study; including 387 (77%) with CMML, 48 (10%) with MDS/MPN-RS-T, 17 (3%) with aCML and 52 (10%) with MDS/MPN-U. The median age at diagnosis was 71 (range, 18-99) years, and 333 (66%) were male. TET2MT were seen in 212 (42%) patients, with the frequency of other mutations being: ASXL1 45%, SRSF2 40%, NRAS 15%, SF3B1 13%, CBL, RUNX1 and SETBP1 12% each, and JAK2 V617F 11% (Figure B). Among the MDS/MPN overlap syndromes, TET2 was more frequently mutated in CMML (49%) and aCML (47%) compared to MDS/MPN-RS-T (10%) and MDS/MPN-U (15%). The prevalence of patients with TET2MT increased with age, a finding consistent across all MDS/MPN subtypes (Figure C). Overall, 341 TET2MT were identified in 212 patients (mean 1.6 variants/patient, range 0-5): 120 (24%) had >1 TET2MT, while 113 (22%), 5 (1%) and 2 (0.4%) had 2, 3 and 5 mutations, respectively. CMML and aCML patients were more likely to have an age-independent increase in multiple TET2MT (28% and 24%), in comparison to MDS/MPN-RS-T (4%) and MDS/MPN-U (8%). TET2 MT spanned the entire coding sequence and were mostly truncating (78%, Figure A): 59 (17%) were missense, 14 (4%) involved the splice-donor/acceptor sites, 2 (0.5%) were in-frame deletions, 129 (38%) were nonsense, and 137 (40%) were frameshift mutations. Overall, the distribution of TET2MT was superimposable across CMML, aCML, and MDS/MPN-U; the only exception being the absence of splice site mutations in the latter two. One hundred and eighty-seven (55%) TET2MT were secondary to pathogenic single nucleotide variants (SNV), while the remainders were secondary to deletions (25%) and insertions (15%). Transitions comprised the most frequent type of SNV (65%), with the C:G>T:A being the most common (56%). Patients with MDS/MPN overlap syndrome and TET2MT were more likely to have additional gene mutations compared to wild type patients (mean mutation number 3.1 vs 2.1, p<0.0001), with common co-mutations being SRSF2 (51%), ASXL1 (42%), and CBL (17%). The median overall survival (OS) of the entire cohort was 29 (range, 0-170) months; 29 months for CMML, 63 months for MDS/MPN-RS-T, 14 months for aCML, and 25 months for MDS/MPN-U. On univariate analysis, OS was superior in CMML patients with TET2MT (35 months) compared to wild type cases (21 months, p<0.0001, Figure D), and in CMML patients with >1 TET2MT (41 months) in comparison to wild type (21 months, p<0.0001) and single TET2MT (29 months, p=0.0476) cases (Figure E). These observations were not seen in patients with aCML, MDS/MPN-RS-T, and MDS/MPN-U. Conclusion: Our study demonstrates that TET2MT are among the most frequent mutations in patients with MDS/MPN overlap syndromes (42%), especially CMML (49%), with an age-dependent increase in the frequency and number of TET2MT. Mutations in TET2 were found to span the entire coding sequence, with truncating mutations being more common (78%). Importantly, in CMML, TET2MT, including number of TET2MT, were associated with favorable survival outcomes. Figure Disclosures Al-Kali: Astex Pharmaceuticals, Inc.: Research Funding. Patnaik:Stem Line Pharmaceuticals.: Membership on an entity's Board of Directors or advisory committees.

Introduction: Truncating mutations in the Additional Sex Combs-Like 1 (ASXL1) gene are associated with a proliferative disease phenotype and poor survival outcomes across the spectrum of myeloid malignancies including chronic myelomonocytic leukemia (CMML). ASXL1 is thought to act as a chromatin modifier regulating transcriptional activity, however the exact mechanisms and resulting chromatin states remain controversial. We interrogated the epigenome of 16 patients with ASXL1-mutant and -wildtype CMML using a multiomics approach. Methods: Bone marrow mononuclear cells from patients with CMML (8 ASXL1-mutant, 8 -wildtype) were subjected to targeted NGS of DNA, whole transcriptome shotgun sequencing (RNA-seq), immunoprecipitation (IP) of DNA (hydroxy-)methyl residues (DIP-seq), IP of the histone modifications H3K4me1, H3K4me3, and H3K27me3 (ChIP-seq), and DNA transposase accessibility studies (ATAC-seq). After quality control all samples were sequenced on an Illumina HiSeq 4000 before further processing and data analysis. Global assessments of DNA (hydroxy-)methylation, DNA accessibility, and histone modifications between ASXL1-mutant and -wildtype CMML were performed. Differential gene expression was performed to define the up-regulated genes in ASXL1-mutant disease. The promoter regions of these up-regulated genes (defined as transcription start site ±3kb) were compared using the aforementioned multiomics approach. Epigenomic modification of the promoter region facilitating up-regulation of transcription was defined as the presence of a signal peak in ASXL1-mutant disease (in the absence of a signal peak in -wildtype disease) or 25% increase in a common signal peak (H3K4me1/3, 5hmC), the presence of a unique signal peak in ASXL1-mutant disease (ATAC), or the absence of a signal peak in ASXL1-mutant disease (in the presence of a signal peak in -wildtype disease), or 25% decrease in a common signal peak (H3K27me3, 5mC). Results: Sixteen patients with CMML, median age 69 years (48 - 77), 63% male, were included. Half of the patients had proliferative disease (pCMML) and half of them had truncating frameshift mutations in ASXL1 (heatmap). All ASXL1 variant allele frequencies were compatible with heterozygosity (31 - 48%). The burden of co-mutations was similar between ASXL1-wildtype and ASXL1-mutant disease (21 versus 23 per group; no difference in the median number of co-mutations, p = 0.684). The spectrum of co-mutations was typical for CMML, involving spliceosome components, epigenetic regulators, chromatin regulators, and cell signaling molecules (heatmap). There was a predominant up-regulation of gene expression in ASXL1-mutant patients: 707 genes up- and 124 down-regulated (volcano plot, FDR < 0.05 for all genes). Functional annotation of the up-regulated genes showed cell division, mitotic nuclear division, sister chromatid cohesion, DNA replication, and G1/S transition to be the 5 most enriched processes (accounting for 29% of all up-regulated genes, FDR < 1x10-10 for all terms). The up-regulated genes included several potential therapeutic targets and HOXA family members (including HOXA9). There were global increases in H3K4me1/3, 5mC, and 5hmC, decreases in H3K27me3, as well as a more relaxed chromatin conformation (bar graphs). Many of these epigenomic changes affected non-coding regions. When focusing on the promoter regions of the 707 up-regulated genes there was evidence of one or more of the interrogated epigenomic mechanisms facilitating transcription for 519 of the genes (73%). The most abundant mechanism was histone modification, followed by changes in DNA (hydroxy-)methylation, and increased chromatin accessibility, with considerable overlap (Venn diagram). For HOXA9, a known driver of leukemogenesis, the data supported a loss of H3K27me3 as the most prominent among the interrogated epigenomic regulatory mechanisms (average signal tracks). Conclusions: The transcriptome and chromatin conformation of ASXL1-mutant CMML are skewed towards proliferation and mirror the aggressive disease phenotype observed in practice. There is evidence of histone modification as well as changes in DNA methylation, and chromatin conformation facilitating transcriptional activity including known leukemogenic drivers. Additional regulatory mechanisms such as gene body methylation and enhancer elements require further exploration. Figure Disclosures Patnaik: Stem Line Pharmaceuticals.: Membership on an entity's Board of Directors or advisory committees.