Citations

Abstract Mutations in chromatin regulator ASXL1 are frequently identified in myeloid malignancies, in particular ∼40% of patients with chronic myelomonocytic leukemia (CMML). ASXL1 mutations are associated with poor prognosis in CMML and significantly co-occur with NRAS mutations. Here, we show that concurrent ASXL1 and NRAS mutations defined a population of CMML patients who had shorter leukemia-free survival than those with ASXL1 mutation only. Corroborating this human data, Asxl1−/− accelerated CMML progression and promoted CMML transformation to acute myeloid leukemia (AML) in NrasG12D/+ mice. NrasG12D/+;Asxl1−/− (NA) leukemia cells displayed hyperactivation of MEK/ERK signaling, increased global levels of H3K27ac, upregulation of Flt3. Moreover, we find that NA-AML cells overexpressed all the major inhibitory immune checkpoint ligands: programmed death-ligand 1 (PD-L1)/PD-L2, CD155, and CD80/CD86. Among them, overexpression of PD-L1 and CD86 correlated with upregulation of AP-1 transcription factors (TFs) in NA-AML cells. An AP-1 inhibitor or short hairpin RNAs against AP-1 TF Jun decreased PD-L1 and CD86 expression in NA-AML cells. Once NA-AML cells were transplanted into syngeneic recipients, NA-derived T cells were not detectable. Host-derived wild-type T cells overexpressed programmed cell death protein 1 (PD-1) and T-cell immunoreceptor with immunoglobulin and ITIM domains (TIGIT) receptors, leading to a predominant exhausted T-cell phenotype. Combined inhibition of MEK and BET resulted in downregulation of Flt3 and AP-1 expression, partial restoration of the immune microenvironment, enhancement of CD8 T-cell cytotoxicity, and prolonged survival in NA-AML mice. Our study suggests that combined targeted therapy and immunotherapy may be beneficial for treating secondary AML with concurrent ASXL1 and NRAS mutations.

Abstract Purpose: Structural variants (SV) of the MYC gene region are common in multiple myeloma and influence disease progression. However, the prognostic significance of different MYC SVs in multiple myeloma has not been clearly established. Experimental Design: We conducted a retrospective study of multiple myeloma comparing MYC SV subtypes identified by next-generation sequencing (NGS) and FISH to MYC expression and disease survival using 140 cases from Mayo Clinic and 658 cases from the MMRF CoMMpass study. Results: MYC SVs were found in 41% of cases and were classified into nine subtypes. A correlation between the presence of a MYC SV and increased MYC expression was identified. Among the nine MYC subtypes, the non-immunoglobulin (non-Ig) insertion subtype was independently associated with improved outcomes, while the Ig insertion subtype, specifically involving the IgL gene partner, was independently associated with poorer outcomes compared with other MYC SV subtypes. Although the FISH methodology failed to detect approximately 70% of all MYC SVs, those detected by FISH were associated with elevated MYC gene expression and poor outcomes suggesting a different pathogenic role for FISH-detected MYC subtypes compared with other MYC subtypes. Conclusions: Understanding the impact of different MYC SVs on disease outcome is necessary for the reliable interpretation of MYC SVs in multiple myeloma. NGS approaches should be considered as a replacement technique for a more comprehensive evaluation of the multiple myeloma clone.

e19003 Background: Acute panmyelosis with myelofibrosis (APMF) is a rare subtype of acute myeloid leukemia characterized by acute panmyeloid proliferation with increased blasts, cytopenias with bone marrow fibrosis, and absence of splenomegaly. There is a paucity of population-based studies of APMF. Methods: We queried the United States Surveillance, Epidemiology, and End Results (SEER) database and National Cancer Database (NCDB) using the ICD-O-3 code 9931/3. The SEER 17 registries (2004-2015) were used to find data on incidence. For NCDB data, comorbid disease burden was calculated using the Charlson-Deyo Score (CDS). Hazard ratios (HR) with confidence intervals (CI) were calculated using a Cox proportional hazards model. Overall survival (OS) was estimated with the Kaplan-Meier method. Variables significant in univariable analysis were included in a multivariable analysis. Results: We identified 260 APMF patients using the SEER database. Incidence was 0.3 cases/million individuals and did not change significantly from 2004-2015. With a median follow up of 6.9 years (95% CI 6.1-7.8), the median OS was 2.3 years (95% CI 1.7-2.8). We identified 530 APMF patients using the NCDB. The median age at diagnosis was 67 years (range 22-90) and 311 (59%) were male. With a median follow up of 5.0 years (95% CI 3.0-7.6), the median OS was 2.3 years (95% CI: 0.8-6.5). OS was 69%, 31%, and 18% at 1-, 5-, and 10- years, respectively. Patients diagnosed in 2012-2015 had a significantly improved OS compared to those diagnosed in 2004-2007 (HR 0.65, 95% CI 0.49-0.85; p=0.002). 271 patients (53%) patients received chemotherapy. The OS for those that received chemotherapy was 70% at 1 year and 30% at 5 years versus 70% and 32% at 1- and 5- years for those who did not ( p=0.99). The median time to chemotherapy from time of diagnosis was 25 days (range 0-532 days). 52 patients (10%) underwent transplantation and the OS of those patients was 90% at 1 year and 45% at 5 years versus 67% and 29% at 1- and 5- years for those who did not (HR: 1.7 (95% CI: 1.2-2.6), p=0.006). In univariable analysis, factors predicting inferior OS were age ≥ 65 years old at diagnosis (HR 1.8, 95% CI 1.5-2.3; p<0.001), male sex (HR 1.5, 95% CI 1.2-1.8; p<0.001), CDS ≥ 1 (HR: 1.5 (95% CI: 1.2-2.0), p<0.001), government insurance (HR 1.8, 95% CI 1.4-2.3; p<0.001), diagnosis at a non-academic facility (HR 1.6, 95% CI 1.2-2.0, p<0.001), and not receiving a hematologic transplant (HR 1.7, 95% CI 1.2-2.6; p=0.006). Multivariable-adjusted analysis is shown in the Table. Conclusions: The overall incidence of APMF has not changed between 2004 and 2015, but OS for 2012-2015 was improved compared to 2004-2007. Age ≥ 65 years old, male sex, CDS ≥ 1, and diagnosis at a nonacademic facility predicted inferior OS. Multivariable-adjusted analysis.[Table: see text]

Key Points Systemic immune microenvironment signatures in CMML indicate an altered T- and natural killer cell balance. CMML bone marrow dendritic cell aggregates associate with disease progression and systemic regulatory T-cell phenotypic switch.

Background: The past two decades have seen an explosion in the development of new drugs for the treatment of myeloma, and previous studies have shown that these advances have translated into improved survival. It is unclear if we have continued to maintain the pace of improvement in the recent years. We designed this study to examine the patterns of survival in patients with newly diagnosed MM, specifically examining the impact on patient subgroups by age and disease risk. Patients and Methods: We included patients with newly diagnosed MM, who were seen at Mayo Clinic, Rochester between 2004 and 2018. We grouped them into three 5-year groups: group 1 (2004-2008), group 2 (2009-2013) and group 3 (2014-2018) to assess the trends over time. Only those who were seen within 6 months of diagnosis were included in the current cohort. Fluorescence in situ hybridization (FISH) was performed using clg based approach as previously described and high risk (HR) was defined as those with t(4;14), t(14;16), t(14;20) and del17p. Survival was estimated using the Kaplan-Meier method and curves were compared using the log rank test. The Cox proportional hazards model was used to examine the prognostic impact of various clinical and laboratory factors. Results: The study cohort had 3783 patients: 61% were male, median age was 64 years (range 22-96), 47% were ≥65 and 14% ≥75 years. There was no change over time in the median age or the gender distribution. No clear trends were evident across the groups in terms of the disease characteristics including high risk status. The patients in the cohort were divided into three 5 year groups: Group 1(2004-2008; n=1045), Group 2(2009-2013; n=1237) and Group 3(2014-2018; n=1501). The initial treatment regimen changed over time to include proteasome inhibitors (PI) and immunomodulatory drug (IMiD) based regimens with a PI-IMiD combination being 2%, 18% and 55% respectively in groups 1, 2 and 3 (Figure 1). The median follow-up for the entire cohort was 6.8 years (95% CI; 6.5,6.9), and for groups 1, 2 and 3 were 13 years, 8 years and 3.2 years respectively. The median OS for the entire group was 6.8 years (95% CI; 6.4, 7.2). The median OS from diagnosis has improved over time with the groups 1, 2, and 3 having a median OS (95% CI) of 5.5 years (5.1, 6.1), 7.3 years (6.7, 8.0) and NR respectively(P=0.0001) (Figure 2). We specifically looked at the 1, 2 and 3-year survival over the years and found a steady trend for improvement (Figure 3). When examining trends by age, those under 65 years had improvement in survival from group 1 to 2; median OS was 7.3 years, 10 years and NR for groups 1, 2 and 3 respectively (P=0.0001) . Those above 65 had major improvement from group 2 to 3; median OS was 4.4 years, 5.2 years and NR for groups 1,2 and 3 respectively (P=0.0001). When examined by FISH risk status, improvement in OS was seen among with Standard Risk (SR) status, especially during the last 5-year period. The median OS for patients with HR status was 3.5 years, 4.5 years and NR (P=0.07); and 7.6 years, 8.6 years and NR in patients with SR status (P=0.0012) for groups 1, 2 and 3 respectively. Conclusion: In a large cohort of patients with newly diagnosed MM, we have been able to demonstrate sustained improvement in OS over the years, likely driven by introduction of new drugs and continued use of transplant. The impact of newer generation of drugs, especially monoclonal antibodies is not reflected in this analysis and will likely become obvious over the next five years. The data will help identify the areas that require focus for continued improvements in order to derive the maximum impact on outcomes. Disclosures Kapoor: Celgene: Honoraria; Amgen: Research Funding; Sanofi: Consultancy, Research Funding; Janssen: Research Funding; Cellectar: Consultancy; GlaxoSmithKline: Research Funding; Takeda: Honoraria, Research Funding. Gertz:Alnylam: Other: personal fee; Prothena: Other: personal fee; Teva: Speakers Bureau; Johnson and Johnson: Speakers Bureau; DAVA oncology: Speakers Bureau; Proclara: Other; Aurora Bio: Other; Springer Publishing: Patents & Royalties; Sanofi: Other; Research to Practice: Other; Celgene: Other; Physicians Education Resource: Other: personal fee; Medscape: Other: personal fee, Speakers Bureau; Amgen: Other: personal fee; Appellis: Other: personal fee; Annexon: Other: personal fee; Spectrum: Other: personal fee, Research Funding; Janssen: Other: personal fee; Ionis/Akcea: Other: personal fee; Abbvie: Other. Dispenzieri:Intellia: Research Funding; Alnylam: Research Funding; Takeda: Research Funding; Pfizer: Research Funding; Janssen: Research Funding; Celgene: Research Funding. Dingli:Rigel: Consultancy; Bristol Myers Squibb: Research Funding; Apellis: Consultancy; Alexion: Consultancy; Sanofi-Genzyme: Consultancy; Karyopharm Therapeutics: Research Funding; Millenium: Consultancy; Janssen: Consultancy. Russell:Imanis: Other: Equity Ownership. Lin:Juno: Consultancy; Bluebird Bio: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Novartis: Consultancy; Takeda: Research Funding; Legend BioTech: Consultancy; Merck: Research Funding; Vineti: Consultancy; Janssen: Consultancy, Research Funding; Kite, a Gilead Company: Consultancy, Research Funding; Sorrento: Consultancy, Membership on an entity's Board of Directors or advisory committees; Gamida Cells: Consultancy. Kumar:Carsgen: Other, Research Funding; Adaptive Biotechnologies: Consultancy; Tenebio: Other, Research Funding; Celgene/BMS: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Cellectar: Other; Genecentrix: Consultancy; Dr. Reddy's Laboratories: Honoraria; Janssen Oncology: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Novartis: Research Funding; Kite Pharma: Consultancy, Research Funding; Takeda: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; MedImmune: Research Funding; Sanofi: Research Funding; Karyopharm: Consultancy; Oncopeptides: Consultancy, Other: Independent Review Committee; IRC member; BMS: Consultancy, Research Funding; Genentech/Roche: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Merck: Consultancy, Research Funding; Amgen: Consultancy, Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments, Research Funding; AbbVie: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments.

Introduction Clonal cytopenias of undetermined significance (CCUS) is defined by the presence of somatic driver mutations/copy number variations in hematopoietic cells in patients with low blood counts, in the absence of morphological evidence for myeloid neoplasms. Patients with CCUS are often symptomatic and can be transfusion dependent (TD), with high rates of progression to myeloid neoplasms, especially myelodysplastic syndromes (MDS). In addition, similar to MDS, there are reports of CCUS patients responding to hypomethylating agents. Due to the lack of a formal diagnosis/morphological dysplasia, CCUS patients are often denied therapies or clinical trial enrollment. We carried out this study to validate our hypothesis that CCUS shares similar clinical and survival characteristics with lower risk MDS (LR-MDS). Methods CCUS patients were prospectively identified from the clonal hematopoiesis clinic, whereas LR-MDS patients were retrospectively identified from our institutional database. LR-MDS was defined as very low, low and intermediate risk MDS based on the R-IPSS categorization. Baseline demographics, blood counts, bone marrow (BM) morphology, cytogenetics, and NGS results were abstracted. Transfusion dependency was defined as requiring at least one unit of red cell or platelets every 4 weeks. The Mann-Whitney-U and Fischer's exact test were used to compare quantitative and qualitative data in subgroups. Kaplan-Meier overall survival (OS) estimates were used for survival analysis and compared using the log-rank test. Results 186 patients were included in the study; 74 (40%) with CCUS and 112 (60%) with LR-MDS, median age 66 years, with 63% being male (Table 1). In the CCUS group, 93% had one or more mutations (67% &gt;1 mutation) detected by NGS, while 7% had clonal cytogenetic abnormalities. Common mutations in CCUS included TET2 (30%), SRSF2 (20%), DNMT3A (13%) and ASXL1 (11%); with 28% of patients being red cell TD and 15% being platelet TD. On application of the R-IPSS stratification, 40%, 45%, 12% and 3% were in the very low, low, intermediate and high risk categories, respectively. LR-MDS subtypes included MDS-RS (50%), MDS-MLD (21%), MDS-EB (10%), MDS del5q (7%), and MDS-U (12%). SF3B1 mutations were seen in 67% (95% of MDS-RS), while TET2 and DNMT3A mutations were seen in 29% and 21%, respectively. 60% of LR-MDS patients had &gt;1 mutation. 44% were red cell TD, while 10% were platelet TD. In comparison to patients with CCUS, LR-MDS patients were more likely to have higher white blood cell counts (p=0.002), higher neutrophil counts (p=0.009), higher platelet counts (p&lt;0.001), be red cell TD (p=0.031), have BM ring sideroblasts (p&lt;0.001), have higher BM blast% (p=0.004) and carry SF3B1 mutations (p&lt;0.001); whereas CCUS patients were more likely to have higher monocyte counts (p=0.009) and more likely to carry IDH1 (p=0.005), ZRSR2 (p=0.005) and ATM (p=0.012) mutations. Importantly there were no differences with regards to R-IPSS cytogenetic risk groups, R-IPSS prognostic categories, platelet TD, and mutations involving TET2, DNMT3A, SRSF2 and ASXL1 and TP53. Notably signal pathway mutations and mutations involving tumor suppressor genes were infrequent (&lt;5%) in both groups. The LR-MDS patients had a longer median follow up (53 vs 15 months) and at last follow up 72 (64%) and 11 (15%) deaths had been documented in the LR-MDS and CCUS groups respectively. 13 (18%) CCUS patients progressed to MDS (10) and AML (3) over a median of 15 months, while 9 (8%) LR-MDS patients progressed to higher grade MDS (1) and AML (8), respectively (median follow up 53 months). There was no difference in the median survival between CCUS (median OS not reached) versus LR-MDS (median OS 8.3 years) (p=0.372, Figure 1). Conclusion In spite of subtle phenotypic/molecular differences between LR-MDS and CCUS, both entities had similar prognostication, distribution of high risk mutations and survival outcomes. Within a short follow-up, 18% of CCUS patients progressed to MDS/AML, indicating that regardless of the absence of dysplasia these entities are a continuum of myeloid neoplasms. In order to improve access to treatments and clinical trial opportunities, we recommend that close consideration be given to consider CCUS as a MDS subtype. Disclosures Shah: Dren Bio: Consultancy.

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 &lt; 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&lt;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&lt;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&lt;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.