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Introduction: Chronic myelomonocytic leukemia (CMML) is a clonal hematopoietic neoplasm characterized by sustained peripheral blood (PB) monocytosis and an inherent risk for leukemic transformation. Clonal origins of the disease can be detected in hematopoietic progenitor cells (CD34+/CD38-), while the complete spectrum of mutational evolution can be seen in circulating monocytes (CD14+). Cell sorting strategies have been employed to select cells in CMML, and while there are adequate monocyte numbers in the PB, there are very few circulating progenitor cells. In addition, attrition related to the selection process significantly depletes primary cells available for biological experiments and multiomics studies such as RNA-seq, ChIP-seq, ATAC-seq, and DIP-seq. While single-cell methods may be able to overcome this challenge, bulk sequencing methods remain a robust and cost-effective approach. We hypothesized that, secondary to the stem cell origin of this disease and significant myeloproliferation, PB mononuclear cells (MNC) would provide comparable results with regards to transcriptomic analysis, in comparison to cell selection procedures. Methods: Peripheral blood obtained from 15 molecularly annotated patients with WHO-defined CMML was ACK-lysed and subjected to a Ficoll procedure for collection of MNC. MNC were left unsorted (n=5) or further selected for CD34+/CD38- (n=5) and CD14+ (n=5) using a fully automated RoboSep-S (StemCell Technologies) protocol. All samples were then subjected to bulk whole transcriptome shotgun sequencing (using Illumina TruSeq and an Illumina HiSeq 4000). After data quality control, counts of detectable transcripts were log2-normalized and Pearson's product-moment correlation coefficients were calculated to evaluate the correlation between the two cell-sorting strategies and unsorted cells in terms of detectable transcripts. To visualize sample differences log2-normalized transcripts counts were centered and scaled per gene for a select number of genes relevant to myeloid biology as well as a number of housekeeping genes. Results: Fifteen patients with WHO-defined CMML, median age 69 years (55-73 years), 66% male, were included. Next generation sequencing for somatic mutations was performed on PB MNC obtained at CMML diagnosis (Figure 1, top heatmap). Considering the small sample size, mutations were evenly distributed among groups with the exception of ASXL1 (higher frequency in CD14+ and CD34+/CD38- cells), ZRSR2 (higher frequency in unsorted cells), and TET2 (lower frequency in CD14+ cells). The three groups were also well matched with regards to other CMML-related variables such as WHO and FAB morphological subtypes, cytogenetic abnormalities, and risk stratification by the Mayo Molecular Model. Transcriptomic analysis revealed a strong positive correlation between the median number of log2-normalized detectable transcripts in unsorted cells and CD34+/CD38- cells (ρ = 0.96, p < 0.001, top scatterplot). Likewise, there was a strong positive correlation between the median number of log2-normalized detectable transcripts in unsorted cells and CD14+ cells (ρ = 0.91, p < 0.001, bottom scatterplot). The latter correlation was marginally lower, which was explained by increased global gene expression in 3 of the 5 CD14+ samples (bottom heatmap). Increased gene expression in these 3 samples involved key myeloid genes and housekeeping genes known to have stable expression across human tissues alike. In comparison to PB MNC, both cell sorting strategies resulted in significant depletion of primary cells required for other experiments, and for procedures such as ChIP-seq, DIP-seq and ATAC-seq (CD34+/CD38- had greater depletion than CD14+). Additional experiments to assess this strategy for the above mentioned epigenetic studies are currently being planned. Conclusions: Accounting for sample differences, different cell sorting strategies (unsorted, CD34+/CD38- selection, and CD14+ selection) yielded similar results when performing bulk transcriptomic assessments on PB MNC from patients with CMML. For the purpose of gene expression profiling there was no clear advantage with CD34+/CD38- or CD14+ selection. These results support the use of unsorted cells for bulk transcriptomic analysis in CMML. Figure 1 Disclosures Patnaik: Stem Line Pharmaceuticals.: Membership on an entity's Board of Directors or advisory committees.

Background: Myelodysplastic syndrome (MDS)/myeloproliferative neoplasm (MPN) with ring sideroblasts and thrombocytosis (MDS/MPN-RS-T) was formally included as a unique WHO-defined entity in the 2016, iteration of the classification of hematological neoplasms (under MDS/MPN overlap syndromes). Patients with MDS/MPN-RS-T have features of MDS-RS-single lineage dysplasia (SLD), persistent thrombocytosis (platelet count ≥450x109/L), with proliferation of large atypical megakaryocytes. Anemia, including red blood cell transfusion dependency (RBC-TD) is a major problem. While extensive studies have documented erythroid response rates (ERR) to erythropoiesis stimulating agents (ESA) in MDS and MDS-RS-SLD, the data in MDS/MPN-RS-T is less clear. We carried out this study to asses ERR in ESA treated patients with MDS/MPN-RS-T. Methods: After approval by the IRB, patients with 2016, WHO-defined MDS/MPN-RS-T were included in the study. The bone marrow (BM) morphology, cytogenetics and BM RS% were retrospectively reviewed. All study patients underwent 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). Treatment details, including the type of ESA used, dose administered, side effects and reason for discontinuation were retrospectively abstracted. Erythroid responses were assessed using the 2006, international working group (IWG) MDS and the 2015, IWG MDS/MPN response criteria. Results: Forty seven patients with MDS/MPN-RS-T were identified, of which 37 (79%) patients received ESA treatment at any time point during their disease course; median age 73 years (range, 52-93), 46% male.The hemoglobin at diagnosis (HB) and baseline erythropoietin (EPO) levels were 9.1 gm/dL (range, 6.6-12.1) and 39 IU/L (range, 8-500), respectively. Ten (29%) patients were RBC-TD at baseline. None of the 37, ESA-treated patients were on concomitant cyto-reductive therapy for thrombocytosis. Twenty (54%) patients were treated with erythropoietin-a, 13 (35%) with darbepoetin, and 4 (11%) with both sequentially. Median doses were 40,000 IU weekly (range, 20,000-80,000) for erythropoietin-a and 200 mcg every 2 weeks (range, 100-500), for darbepoetin. Median treatment duration was 14 months (range, 1-173) and causes for discontinuation included; ESA failure/loss of response in 20 (54%), sustained HB rise in 1 (3%) and other causes, including adverse events in 4 (11%) patients. With regards to safety, the following events were considered potentially ESA related; treatment emergent hypertension in 2 (5%), venous thromboembolism, ischemic stroke and splenic infarction in 1 (3%) patient each, respectively. All patients with thrombotic/ischemic events had been on low dose aspirin prophylaxis. Erythroid responses by the IWG MDS and MDS/MPN response criteria were assessable in 35 (95%) patients: with 16 (46%) meeting criteria for an erythroid response, by both assessment systems. The median duration of ER was 20 months (range, 2-172). In comparison to ESA non-responders, those that responded were more likely to have baseline EPO levels ≤44 IU/L (ROC analysis; median, 28 vs 112 IU/L; p=0.0080), while there was a trend for inferior responses associated with RBC-TD (p=0.0857). Age, gender, baseline white blood cell and platelet counts, type of ESA used, BM RS %, JAK2V617F, SF3B1, DNMT3A, TET2 and ASXL1 mutational status did not impact ESA response (Table 1). On a univariate survival analysis (OS), survival was not significantly different between ESA responders vs non-responder (median OS, 76 vs 45 months; p=0.2929). After ESA failure, 7 (19%) patients received hypomethylating agents (HMA), while 5 (14%) received lenalidomide. There were no erythroid responses to HMA, while 1 of 3 (33%) assessable lenalidomide treated patients achieved a morphological complete response. Conclusion: Erythropoiesis stimulating agents are effective first line therapies for the management of anemia in patients with MDS/MPN-RS-T, with 46% achieving an erythroid response (median duration of response of 20 months). Low baseline endogenous EPO level (<44 IU/L) was the best predictor of response, with a trend towards an inferior response in the presence of RBC-TD. In the context of clonal thrombocytosis, potentially related thrombotic/ischemic events were documented in 8% of patients. Disclosures Al-Kali: Astex Pharmaceuticals, Inc.: Research Funding. Patnaik:Stem Line Pharmaceuticals.: Membership on an entity's Board of Directors or advisory committees.

Introduction: While the molecular target of immunomodulators such as pomalidomide (POM) and lenalidomide (LEN) has been identified, the mechanisms underlying therapeutic resistance remain incompletely understood. The uniformly emerging resistance to therapy over time in the absence of identifiable cereblon pathway mutations in the majority of patients raises questions about alternative mechanisms including aberrant gene expression. Methods: We performed gene expression profiling using an Affymetrix GeneChip Human Genome U133 Plus 2.0 microarray on CD138+ bone marrow cells from patients with relapsed / refractory (RRMM) and newly diagnosed (NDMM) multiple myeloma prior to initiating treatment. Patients were treated on two phase II clinical trial protocols (MC0789: POM ± dexamethasone in RRMM; MC0884: LEN ± dexamethasone in NDMM) between 2007 and 2012. We categorized patients based on their IMWG response as non-responders (SD) and responders (VGPR+). We selected 15 responders and 15 non-responders from MC0789 (n = 30) and compared overall survival, gene expression patterns, and involved cellular pathways between the two groups. We selected 5 responders and 5 non-responders from MC0884 (n = 10) for targeted validation of differentially expressed candidate genes. After data quality control and normalization of gene expression values, differential gene expression was estimated using limma. Statistical significance was adjusted for multiple testing in the discovery set using a false discovery rate-based approach for genome-wide experiments (q-value). We used Gene Ontology and PANTHER pathway analysis for functional annotation of differentially expressed genes. Overall survival estimates were calculated using the Kaplan-Meier method. Computation and visualization were performed in R. Results: Median age at treatment initiation on MC0789 was 65 years (40 - 82), 65% of the patients were male. Pomalidomide resistance was associated with an increase in mortality (median overall survival 1.6 versus 6.4 years, p = 0.009, Kaplan-Meier plot). There were 1076 differentially regulated genes between responders and non-responders (521 up- and 555 down-regulated, q < 0.050 for all genes, volcano plot). Expression of CRBN was 1.5-fold down-regulated in non-responders (q = 0.005). Supervised hierarchical clustering of the top 500 differentially expressed genes demonstrated distinct patterns in pomalidomide-resistant disease (heatmap). Gene ontology analysis revealed protein synthesis as one of the most enriched biological processes (bar graph). Pathway analysis showed a 6-fold enrichment (FDR = 0.007) of the ubiquitin proteasome pathway in pomalidomide-resistant disease. Differentially expressed genes involved key protein degradation pathways, epigenetic modifiers, and transcription factors. Targeted validation in MC0884 revealed 13 common genes with at least 1.5-fold differential expression (5 up- and 8 down-regulated), 12 of which have previously been implicated in the regulation of apoptosis, tumor glucose metabolism, Rho and Wnt signaling, miRNA-driven resistance to chemotherapy, and ubiquitin-dependent protein degradation (Table and Sankey diagram). The most up-regulated gene in non-responders was MYRIP, a gene coding for a vesicle trafficking protein associated with platinum resistance and suppression of pro-apoptotic BCL-2 family members in solid malignancies. The most down-regulated gene was FRZB, a gene coding for a negative regulator of Wnt signaling, previously implicated in the progression of monoclonal gammopathy of undetermined significance to multiple myeloma. Conclusions: Overall survival of patients with pomalidomide-resistant RRMM remains poor. Pomalidomide resistance was associated with differential gene expression in several potentially targetable cellular pathways beyond the known drug target cereblon. Targeted validation of candidate genes revealed common cellular pathways in immunomodulator-resistant disease. Elucidating the exact molecular mechanisms underlying immunomodulator resistance is of considerable interest for biomarker development and the identification of novel therapeutic targets and warrants further exploration. Figure Disclosures Lacy: Celgene: Research Funding. Dispenzieri:Celgene: Research Funding; Alnylam: Research Funding; Intellia: Consultancy; Janssen: Consultancy; Pfizer: Research Funding; Akcea: Consultancy; Takeda: Research Funding. Kumar:Takeda: Research Funding; Celgene: Consultancy, Research Funding; Janssen: Consultancy, Research Funding.

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.

Introduction: Chronic myelomonocytic leukemia (CMML), an aggressive myeloid malignancy, can be categorized into two subtypes, proliferative CMML (pCMML) and dysplastic (dCMML), based on a white blood cell (WBC) count of ≥ 13 x 109/L for the former (Arber et al. Blood 2016). While this WBC cut off is somewhat arbitrary, patients with pCMML have unique phenotypic features and a shorter survival. We carried out this study to assess the genomic, transcriptomic and epigenetic landscapes of these two CMML subtypes. Methods: Peripheral blood (PB) and bone marrow (BM) mononuclear cells (MNC) were obtained from WHO-defined CMML patients. Next generation sequencing (NGS) using a 36-gene panel was performed on 350 patients with Illumina HiSeq4000 platform with median read depth of 400X. RNA sequencing (RNA-seq) was performed on 25 patients by bulk whole transcriptome sequencing using Illumina TruSeq. DNA immunoprecipitation sequencing (DIP-seq) was performed on 18 patients using 5-methylcystocine (5mC), 5-hydroxymethylcytosine (5hmC) and bridging monoclonal antibodies with subsequent paired-end sequencing using HiSeq4000. Chromatin immunoprecipitation sequencing (ChIP-seq) was performed on 30 patients with Illumina HiSeq2500 to a depth of 25 million for histone 3 lysine 4 monomethylation (H3K4me1) and histone 3 lysine 4 trimethylation (H3K4me3) and 50 million reads for histone 3 lysine 27 trimethylation (H3K27me3) and Input per sample. Results: Five hundred and seventy-three patients with WHO defined CMML were included; median age 71 years (range 18-95 years), 67% males. 282 patients had pCMML (49%), while 291 (51%) had dCMML. As pre-defined, patients with pCMML were more likely to have higher absolute monocyte counts (p<0.0001), circulating immature myeloid cells (p<0.0001), PB blasts (p<0.0001), and higher lactate dehydrogenase levels (p=0.03). At last follow up 234 (41%) deaths and 70 (20%) leukemic transformations were documented. The median OS for pCMML vs dCMML in this cohort was 19 months vs 30 months (p<0.0001, Figure 1A) and validated in an independent Austrian cohort (p=0.02). Genomic profiling: NGS performed on 350 patients (BM MNC) revealed a higher frequency of NRAS (35 vs 17%, p=0.004), cumulative RAS pathway (NRAS, KRAS, CBL and PTPN11) (73 vs 47%, p=0.001), ASXL1 (p=0.003) and JAK2V617F (p=0.04) mutations in pCMML relative to dCMML (Figure 1B); while dCMML had a higher frequency of SF3B1 mutations (p=0.02). There were no differences in distribution of TET2 and SRSF2Transcriptomic analysis: RNA-seq was performed on PB MNC from RAS pathway mutant pCMML patients (n=12) and RAS pathway wildtype dCMML patients (n=13). Unsupervised clustering analysis resulted in appropriate segregation revealing distinct expression profiles between disease subtypes (Figure 1C). Compared to dCMML, 3729 genes were significantly differentially upregulated and 2658 genes were differentially downregulated in pCMML. Among genes most highly upregulated were mitotic checkpoint kinases including AURBK, PLK1, PLK2, PLK4 andEpigenetic profiling: ChIP-seq of PB and BM MNC from pCMML (n=18) and dCMML (n=12) patients and healthy, age-matched controls (n=10) revealed a global increase in H3K4me1, without significant differences in H3K4me3 or H3K27me3 occupancies (regardless of stratification by ASXL1 mutational status; 40% ASXL1mt in pCMML, 30% dCMML) in pCMML vs dCMML (Figure 1D). H3K4me1 occupancy was also increased in a sequence-specific manner at the transcription start sites of the aforementioned mitotic kinases (PLK1 and WEE1). DIP-seq was performed on PB MNC to assess global differences in 5-mC and 5-hmC levels, between pCMML (n=9) and dCMML (n=9), with no differences seen between the two subtypes (regardless of TET2 mutational status, 40% TET2mt in each subtype) (Figure 1E). Conclusions: Despite the somewhat arbitrary WBC distinction between pCMML and dCMML, clear phenotypic, genetic, transcriptomic, epigenetic and survival differences exist between the two subtypes, providing strong biological rationale for this distinction. pCMML patients have a higher frequency of oncogenic RAS pathway mutations, a unique transcriptomic profile enriched in mitotic check point kinases and a unique chromatin configuration with global and sequence specific enrichment in H3K4me1, with no significant global differences in 5mC, 5hmC, or H3K4me3 and H3K27me3 occupancy. Figure 1 Disclosures Geissler: AOP: Honoraria; Pfizer: Honoraria; AstraZeneca: Honoraria; Novartis: Honoraria; Celgene: Honoraria; Roche: Honoraria; Abbvie: Honoraria; Ratiopharm: Honoraria; Amgen: Honoraria. Al-Kali:Astex Pharmaceuticals, Inc.: Research Funding. Patnaik:Stem Line Pharmaceuticals.: Membership on an entity's Board of Directors or advisory committees.

Introduction: Additional Sex Combs-Like 1 (ASXL1) is a chromatin modifier frequently affected by truncating mutations in myeloid malignancies. These mutations are associated with poor survival outcomes and increased rates of acute leukemic transformation. In chronic myelomonocytic leukemia (CMML), ASXL1 mutations are thought to affect transcriptional activity mainly by modifying histone marks, however additional epigenomic mechanisms have not been fully explored. We interrogated the epigenome of patients with ASXL1-mutant (MT) and -wildtype (WT) CMML using a multiomics approach to define cis-regulatory elements (CREs) such as distal enhancers (DEs). Methods: Bone marrow mononuclear cells from patients with CMML 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 MT and WT CMML were performed. 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 signal peak to be considered statistically significant. Differential gene expression was estimated to define the up-regulated genes in ASXL1 MT CMML. Potential CREs were defined as sites with statistically significant signal peaks overlapping in at least two of the three epigenomic marks: H3K4me1, 5hmC, and ATAC. Potential DEs were defined as CREs in non-coding regions outside promoter regions (defined as transcription start site ±3kb) that were annotated in GeneHancer. Annotated DEs only present in ASXL1 MT but not WT CMML (specific DEs) were intersected with the list of up-regulated genes and the ReMap atlas. Results: Sixteen WHO-defined CMML patients were included, median age 69 years (48 - 77), 63% male; of which 8 patients (50%, all truncating frame shift mutations) were ASXL1 MT and 8 (50%) WT. The burden and spectrum of co-mutations was similar between ASXL1 WT and MT CMML (21 versus 23 per group; p = 0.684; heatmap). There was a predominant up-regulation of gene expression in ASXL1 MT CMML: 707 genes up- and 124 down-regulated (volcano plot, FDR < 0.050 for all genes). There were 64336 potential CREs, the vast majority (97%) being present in both ASXL1 MT and WT CMML (left Venn diagram). These CREs were most commonly located in introns, promoter regions, and distal non-coding regions (bar graph and pie chart). There were 1303 CREs unique to ASXL1 MT CMML (specific DEs), 1161 (90%) of which were annotated in GeneHancer (left Euler diagram). Of these 1161 annotated specific DEs 859 (74%) were located outside promoter regions and 34 (4%) of them were known to be associated with genes up-regulated in ASXL1 MT CMML (Euler diagrams). These specific DEs were characterized by an increase in H3K4me1 occupancy and DNA accessibility (average signal tracks, purple bars indicating annotated DEs, thin bars below peaks indicating statistical significance). We previously observed epigenomic modification of promoter regions in 519 of the 707 up-regulated genes (73%) facilitating transcriptional activity in ASXL1 MT CMML. For 13 of the up-regulated genes (right Venn diagram, blue genes in volcano plot) the specific DEs were the sole identified mechanism, while for the other 21 genes there were additional mechanisms noted in the promoter region. The top five transcription factor candidates binding the 34 specific DEs included JMJD1C, MYC, KDM5B, RCOR1, and HDAC2 (-log10(E) > 40 for all candidates). Conclusions: Using a multiomics approach based on H3K4me1, 5hmC, and ATAC data we identified potential CREs in ASXL1 MT CMML and characterized potential DEs using publicly available annotation data. Specific DEs were associated with up-regulated genes serving as a possible explanation for the observed transcriptional activity, shedding further light on the adverse prognostic impact associated with ASXL1 mutations. Figure 1 Disclosures Patnaik: Stem Line Pharmaceuticals.: Membership on an entity's Board of Directors or advisory committees.

Background: Multiple myeloma (MM) treatment has advanced considerably with proteasome inhibitors, immunomodulatory drugs (IMiDs), and, most recently, monoclonal antibodies. However, treatment response is quite heterogeneous, and many patients still progress even with novel combination therapies. Thus, understanding the factors that underlie response to treatment is a priority. Several somatic genomic aberrations and mutations predict poor response to therapy. However, germline variation has not previously been investigated as a predictor of treatment response in MM. We used genome-wide association and transcriptome-wide association approaches to identify germline genetic predictors of treatment response in MM. Methods: We included 510 MM patients from Mayo Clinic and 324 MM patients from UCSF diagnosed from 1999-2015. Basic demographics, laboratory values and pathology at diagnosis, type of initial therapy, duration of therapy, and follow-up were ascertained by chart review. Patients were grouped into categories of treatment based on their first line therapy: proteasome inhibitor-based regimen, IMiD based regimen, combination proteasome and IMiD based regimen, or other. Response was assessed using International Myeloma Working Group (IMWG) response criteria after 4-6 cycles of induction. As such, response was categorized into progressive disease (PD), minimal response (MR), partial response (PR), very good partial response (VGPR), or complete response (CR). Germline samples were genotyped using Illumina or Affymetrix arrays and were imputed based on the Haplotype Reference Consortium (HRC). For the genome-wide association study (GWAS), we included all SNPs with minor allele frequency >0.01 and imputation r2 of >0.5. We tested each SNP for association with treatment response using a linear regression model that adjusted for age, gender, and genetic ancestry (from principal components analysis (PCA)). To perform the transcriptome-wide association study (TWAS), we calculated predicted gene expression data using PREDIXCAN software on a reference cohort of 922 individuals with genotype and RNA expression data from peripheral blood. We then tested the association between predicted gene expression and response to therapy using linear regression models. Both the GWAS and TWAS were performed on subgroups of patients who received either proteasome inhibitors or IMiD therapies. The analyses of patients on proteasome inhibitors were adjusted for IMiD use as a covariate and analyses of patients on IMiDs were adjusted for proteasome inhibitor use. The threshold for genome-wide significance loci was set at P = 5 x 10-8 and the threshold for suggestive loci was set at P = 10-6. The threshold for significance for TWAS was set at P = 4 x 10-6 by using a Bonferroni correction for the number of genes for which genetic models of expression could be developed by PREDIXCAN. Results: Overall, 42.7% (59 of 138) of patients on proteasome inhibitors alone, 32.5% (66 of 203) of patients on IMiDs alone, and 58.1% (50 of 86) of patients on combination achieved at least a VGPR. There were no significant differences in response across centers in analyses that adjusted for age, sex and types of therapy. There were no genome wide significant loci to predict for response. We identified 8 suggestive SNPs associated with proteasome inhibitor response and 4 suggestive SNPs associated with IMiD response. TWAS identified ZNF622 as a candidate genetic modifier of proteasome inhibitor effect that was significant after correction for multiple hypothesis testing (P = 1.6 x 10-6). Higher genetically predicted expression was associated with improved response to proteasome inhibitor therapy. Among patients above the median of predicted expression of ZNF622 on proteasome inhibitors, 62.6% achieved at least VGPR; among patients at or below the median of expression, only 37.6% achieved at least VGPR. Conclusions: We identified an association between predicted expression of ZNF622 and clinical response to proteasome inhibitor therapy among MM patients. ZNF622 is a zinc finger binding protein which is known to be co-activator of B Myb activity and to affect apoptosis in response to oxidative stress. Our work highlights the potential importance of pharmacogenetic modifiers of treatment response in MM. Disclosures Shah: Nkarta: Consultancy, Membership on an entity's Board of Directors or advisory committees; Indapta Therapeutics: Equity Ownership; University of California, San Francisco: Employment; Celgene, Janssen, Bluebird Bio, Sutro Biopharma: Research Funding; Genentech, Seattle Genetics, Oncopeptides, Karoypharm, Surface Oncology, Precision biosciences GSK, Nektar, Amgen, Indapta Therapeutics, Sanofi: Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Poseida: Research Funding; Bristol-Myers Squibb: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Kite: Consultancy, Membership on an entity's Board of Directors or advisory committees; Teneobio: Consultancy, Membership on an entity's Board of Directors or advisory committees. Wong:Celgene: Research Funding; Janssen: Research Funding; Roche: Research Funding; Fortis: Research Funding. Martin:Roche and Juno: Consultancy; Amgen, Sanofi, Seattle Genetics: Research Funding. Kumar:Celgene: Consultancy, Research Funding; Takeda: Research Funding; Janssen: Consultancy, Research Funding.

Introduction: High-risk multiple myeloma can be defined by the presence of specific cytogenetic abnormalities (structural) or by characteristic changes in bone marrow and peripheral blood biomarkers (functional). While both entities are characterized by therapeutic resistance, frequent disease relapses, and adverse survival outcomes, the underlying molecular mechanisms remain incompletely understood. Methods: We performed gene expression profiling (GEP) using an Affymetrix GeneChip Human Genome U133 Plus 2.0 microarray on CD138+ bone marrow cells from 137 patients diagnosed with multiple myeloma between 2004 and 2012. All patients underwent Fluorescence In-situ Hybridization (FISH) evaluation, plasma cell labeling, International Staging System (ISS) risk stratification, and GEP prior to initiating treatment with novel agents. The presence of del(17p), t(4;14), t(14;16), and t(14;20) on FISH, a plasma cell labeling index (PCLI) > 2%, and ISS stage III were considered high-risk abnormalities: FISH-HR (n = 15, structural high-risk, at least one high-risk FISH lesion), PCLI-HR (n = 20; functional high-risk, PCLI > 2%), and ISS-HR (n = 12; functional high-risk, ISS stage III). For each HR group we sampled standard risk (SR) controls in a 4:1 ratio. After data quality control and normalization, differential gene expression was estimated using limma. Statistical significance was adjusted for multiple comparisons using a false discovery rate-based approach for genome-wide experiments (q-value). We employed PANTHER pathway analysis for the differentially expressed genes in each HR group. We implemented a simple gene expression score (GES) by calculating the sum of quartiles of the normalized gene expression values for genes differentially expressed in more than one HR group (GES = ΣUP(quartile - 1) + ΣDN(4 - quartile)) and externally validated its prognostic significance (UAMS TT2 / TT3, GSE24080). Survival outcomes were analyzed using the methods described by Kaplan, Meier, and Cox. Computation and visualization were performed in R. Results: Median age at diagnosis was 63 years (32 - 87), 53% of the patients were male. High-risk disease was associated with inferior overall survival, regardless of the used definition (left Kaplan-Meier plots): FISH-HR (HR 4.3, 95% CI 1.9 - 9.8, p < 0.001), PCLI-HR (HR 2.7, 95% CI 1.4 - 5.3, p = 0.004), and ISS-HR (HR 2.8, 95% CI 1.2 - 6.5, p = 0.015). There were 59 (FISH-HR), 424 (ISS-HR), and 507 (PCLI-HR) differentially expressed genes (q < 0.050 for all genes, volcano plots). PCLI-HR and FISH-HR demonstrated a predominance of transcriptional up-regulation while ISS-HR had a balanced gene expression profile with a similar number of genes being up- and down-regulated. The involved cellular pathways were different across the HR groups except for anti-apoptotic signaling (bar graphs). All HR groups had distinct gene expression profiles with no complete overlap between all HR groups. There were 71 genes with overlap between two HR groups (69 up-regulated, 2 down-regulated, Venn diagrams). The median GES was 97 (18 - 206, higher numbers indicating higher expression of up-regulated and lower numbers of down-regulated high-risk genes) in 559 patients treated on UAMS TT2 / TT3 (GSE24080). Tertiles of the GES were associated with event-free survival (HR 1.4, 95% CI 1.2 - 1.6, p < 0.001) and remained independently prognostic after adjusting for age, sex, and ISS stage (HR 1.3, 95% CI 1.1 - 1.5, p < 0.001). Conclusions: High-risk multiple myeloma remains associated with inferior overall survival, regardless of the used definition (structural or functional). The subtypes of high-risk disease have distinct gene expression profiles and involve different cellular pathways, providing important clues to the underlying biology. A 71 gene signature derived from the different high-risk subtypes was of prognostic significance in a clinical trial population after adjusting for known prognostic factors. Figure Disclosures Lacy: Celgene: Research Funding. Dispenzieri:Akcea: Consultancy; Intellia: Consultancy; Janssen: Consultancy; Pfizer: Research Funding; Takeda: Research Funding; Celgene: Research Funding; Alnylam: Research Funding. Stewart:Takeda: Consultancy; Seattle Genetics: Consultancy; Roche: Consultancy; Ono: Consultancy; Celgene: Consultancy, Research Funding; Ionis: Consultancy; Janssen: Consultancy, Research Funding; Oncopeptides: Consultancy; Amgen: Consultancy, Research Funding; Bristol Myers-Squibb: Consultancy. Bergsagel:Celgene: Consultancy; Ionis Pharmaceuticals: Consultancy; Janssen Pharmaceuticals: Consultancy. Kumar:Celgene: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Takeda: Research Funding.

Introduction: Germline RUNX1 mutations/deletions result in a Familial Platelet Disorder with propensity to Myeloid Malignancy (FPDMM); an autosomal dominant condition characterized by thrombocytopenia, qualitative platelet defects, and myeloid clonal evolution (MDS and AML). The advent of next generation sequencing (NGS) has allowed detection of clonal cytopenias of unclear significance (CCUS) prior to bone marrow (BM) morphological changes. Somatic RUNX1 mutations (RUNX1MT) can be seen in myeloid malignances, including MDS/MPN overlap syndromes, with an indeterminate prognostic impact. We carried out this study to define the landscape of clonal evolution in FPDMM patients and to compare germline RUNX1MT with somatic mutations. Methods After IRB approval, families with RUNX1MT FPDMM (detected by germline sequencing and deletion/duplication assays) and patients with 2016, WHO-defined MDS/MPN overlap syndromes (except JMML) were included in the study. Index patients with FPDMM underwent BM biopsies and in select cases NGS at diagnosis and then as indicated for follow up, or at disease progression. All MDS/MPN overlap syndrome patients underwent a 29-gene panel NGS assay on BM samples obtained at diagnosis by previously described methods (Patnaik et al., BCJ 2016). Results Eight index patients with FPDMM were included; median age at diagnosis 58 (range, 0-71) years, 3 (38%) males (Figure A). Four (50%) had intragenic deletions involving multiple exons in 3 cases and 2 nucleotides in 1 case, 1 (13%) each had missense, frameshift and splice site mutations, while 1 (13%) patient had a whole gene deletion (Figure C). Notably, in 4 (100%) of 4 patients with large deletions, NGS testing was negative for RUNX1 abnormalities. Four (50%) index patients presented with a myeloid neoplasm; JAK2, SRSF2, ASXL1 mutant CMML, MDS/MPN-U with trisomy 8, AML with del7q and normal karyotype AML (negative NGS). Three (38%) index patients had CCUS at diagnosis with TET2 (67%) being the most common acquired mutation, and others including SRSF2, EZH2, and SF3B1. Cumulatively, myeloid clonal evolution was seen in 7 (88%) patients. Three (38%) index patients (AML-2, CMML-1) underwent allogeneic HCT, and at last follow up were in remission. Median age at myeloid clonal evolution was 61 (range, 4-71) years; median overall survival (OS) from date of myeloid clonal evolution was 12 (range, 7-53) months. For the comparative analysis, 499 patients with MDS/MPN overlap syndromes were included; 382 (77%) with CMML, 48 (10%) with MDS/MPN-RS-T (MDS/MPN with ring sideroblasts and thrombocytosis), 17 (3%) with aCML (atypical CML), and 52 (10%) with MDS-MPN-U (unclassifiable). Median age at diagnosis was 71 (range, 18-99) years, and 329 (66%) were male. RUNX1MT were detected in 57 (11%) patients; 43 (11%) with CMML, 5 (30%) with aCML, and 9 (17%) with MDS-MPN-U. No RUNX1MT were found in MDS-MPN-RS-T. Overall, 62 RUNX1MT were identified in 57 patients, with 52 (39 CMML, 4 aCML, 9 MDS-MPN-U) and 5 (4 CMML, 1 aCML) patients harboring 1 and 2 RUNX1MT, respectively. RUNX1MT spanned the entire coding sequence without specific hotspots (Figure B): 27 (44%) were missense, 2 (3%) involved the splicing sites, 5 (8%) were nonsense and 28 (45%) were frameshift. There was only one RUNX1MT (R273K), seen in both germline and somatic patients (Figures B and C). Compared to wild type patients, RUNX1MT patients with MDS/MPN overlap syndromes had significantly lower platelet counts (median 72 vs 117 x109/L, p<0.0001), without any other phenotypic differences. In the somatic RUNX1MT cohort, co-occurring mutations included ASXL1 (54%), SRSF2 (54%), TET2 (49%), and NRAS (28%), with NRAS (28% vs 13%, p=0.0028), and SRSF2 (54% vs 39%, p=0.0218) mutations clustering with RUNX1MT. Median OS of the entire cohort was 29 (range, 0-170) months. On univariate analysis, while there was a trend for an inferior OS in RUNX1MTvs wild type patients (20 vs 29 months, p=0.0523), the leukemia-free survival was clearly inferior (p=0.0175, Figure D). Conclusion RUNX1 MT FPDMM is a germline predisposition syndrome with a high rate of myeloid clonal evolution (88%), including clonal cytopenias (38%). Germline RUNX1MT have minimal overlap with somatic mutations seen in myeloid malignancies, with NGS testing often missing RUNX1 deletions. Somatic RUNX1MT in myeloid malignancies are significantly associated with thrombocytopenia and an inferior leukemia-free survival. Figure Disclosures Patnaik: Stem Line Pharmaceuticals.: Membership on an entity's Board of Directors or advisory committees.