Almada, Luciana L.

Abstract Pancreatic ductal adenocarcinoma (PDAC) affects many older adults in the United States. This aggressive type of pancreatic cancer is resistant to many chemotherapies, resulting in a poor prognosis and a five-year survival rate of less than ten percent. To study PDAC progression and develop novel therapies, it is important to better understand the tumor microenvironment (TME) and the contributing cell populations and molecular mechanisms. Despite the well-described role of soluble Type I collagen in the regulation of pancreatic cancer TME, the molecular events modulating its expression/activity remain elusive. Here, we describe a novel mechanism controlling the levels of soluble collagen in cancer associated fibroblasts (CAFs), a major producer of Type I collagen in pancreatic cancer TME. Specifically, we provide evidence that the transcription factor GLI2 is required for the expression of COL1A1, a key component of the Type I collagen fiber, stimulated by the transforming growth factor β (TGFβ). Our methodology included expression studies from patient samples, ChIP-qPCR, qPCR, RNA-seq analysis, gene set enrichment analysis, biological assays, and gene ontology analysis. Our results confirm that the TGFβ-GLI2 axis in CAFs is able to activate EGR1-proliferative signaling downstream of collagen signaling in pancreatic cancer cells contributing to our understanding of the molecular underpinnings of pancreatic cancer TME. Further studies that define the complete role of EGR1 in PDAC may lead to the development of novel therapies targeting EGR1 or CAFs to promote enhanced quality of life following a PDAC diagnosis.

BACKGROUND A patient-derived organoid (PDO) platform may serve as a promising tool for translational cancer research. In this study, we evaluated PDO’s ability to predict clinical response to gastrointestinal (GI) cancers. METHODS We generated PDOs from primary and metastatic lesions of patients with GI cancers, including pancreatic ductal adenocarcinoma, colorectal adenocarcinoma, and cholangiocarcinoma. We compared PDO response with the observed clinical response for donor patients to the same treatments. RESULTS We report an approximately 80% concordance rate between PDO and donor tumor response. Importantly, we found a profound influence of culture media on PDO phenotype, where we showed a significant difference in response to standard-of-care chemotherapies, distinct morphologies, and transcriptomes between media within the same PDO cultures. CONCLUSION While we demonstrate a high concordance rate between donor tumor and PDO, these studies also showed the important role of culture media when using PDOs to inform treatment selection and predict response across a spectrum of GI cancers. TRIAL REGISTRATION Not applicable. FUNDING The Joan F. & Richard A. Abdoo Family Fund in Colorectal Cancer Research, GI Cancer program of the Mayo Clinic Cancer Center, Mayo Clinic SPORE in Pancreatic Cancer, Center of Individualized Medicine (Mayo Clinic), Department of Laboratory Medicine and Pathology (Mayo Clinic), Incyte Pharmaceuticals and Mayo Clinic Hepatobiliary SPORE, University of Minnesota-Mayo Clinic Partnership, and the Early Therapeutic program (Department of Oncology, Mayo Clinic).

The Hedgehog-regulated transcription factors GLI1 and GLI2 play overlapping roles in development and disease; however, the mechanisms underlying their interplay remain elusive. We report for the first time that GLI1 and GLI2 physically and functionally interact in cancer cells. GLI1 and GLI2 were shown to co-immunoprecipitate in PANC1 pancreatic cancer cells and RMS13 rhabdomyosarcoma cells. Mapping analysis demonstrated that the zinc finger domains of both proteins are required for their heteromerization. RNAi knockdown of either GLI1 or GLI2 inhibited expression of many well-characterized GLI target genes (BCL2, MYCN, PTCH2, IL7 and CCND1) in PANC1 cells, whereas PTCH1 expression was only inhibited by GLI1 depletion. qPCR screening of a large set of putative canonical and non-canonical Hedgehog/GLI targets identified further genes (e.g. E2F1, BMP1, CDK2) strongly down-regulated by GLI1 and/or GLI2 depletion in PANC1 cells, and demonstrated that ANO1, AQP1 and SOCS1 are up-regulated by knockdown of either GLI1 or GLI2. Chromatin immunoprecipitation showed that GLI1 and GLI2 occupied the same regions at the BCL2, MYCN and CCND1 promoters. Furthermore, depletion of GLI1 inhibited GLI2 occupancy at these promoters, suggesting that GLI1/GLI2 interaction is required for the recruitment of GLI2 to these sites. Together, these findings indicate that GLI1 and GLI2 co-ordinately regulate the transcription of some genes, and provide mechanistic insight into the roles of GLI proteins in carcinogenesis.

AbstractThe modulation of GLI2, an oncogenic transcription factor commonly upregulated in cancer, is in many cases not due to genetic defects, suggesting dysregulation through alternative mechanisms. The identity of these molecular events remains for the most part unknown. Here, we identified TFII-I as a novel repressor of GLI2 expression. Mapping experiments suggest that the INR region of the GLI2 promoter is necessary for GLI2 repression. ChIP studies showed that TFII-I binds to this INR. TFII-I knockdown decreased the binding of NELF-A, a component of the promoter–proximal pausing complex at this site, and enriched phosphorylated RNAPII serine 2 in the GLI2 gene body. Immunoprecipitation studies demonstrate TFII-I interaction with SPT5, another pausing complex component. TFII-I overexpression antagonized GLI2 induction by TGFβ, a known activator of GLI2 in cancer cells. TGFβ reduced endogenous TFII-I binding to the INR and increased RNAPII SerP2 in the gene body. We demonstrate that this regulatory mechanism is not exclusive of GLI2. TGFβ-induced genes CCR7, TGFβ1 and EGR3 showed similar decreased TFII-I and NELF-A INR binding and increased RNAPII SerP2 in the gene body post-TGFβ treatment. Together these results identify TFII-I as a novel repressor of a subset of TGFβ-responsive genes through the regulation of RNAPII pausing.

Abstract Waldenström macroglobulinemia (WM) is a B-cell malignancy characterized by the aberrant production of a monoclonal IgM protein that may lead to hyperviscosity. Although this is a major factor causing significant morbidity in patients, little is known about the mechanisms that regulate monoclonal protein synthesis. Cytokines are protein mediators that are known to be involved in many biological processes, and can profoundly affect tumor cells and the tumor microenvironment. Many cytokines have been shown to have potent therapeutic efficacy in preclinical cancer models; however, the role of cytokine networks in WM is not fully understood and few studies have described the precise functional roles of cytokines in WM. To address this issue we performed a multiplex ELISA analysis to test cytokine levels in sera from patients with WM. We found that Rantes/CCL5 is significantly elevated in WM patients and correlates with disease activity. Elevated expression of RANTES in the serum was confirmed by ELISA and was also detected in the bone marrow of WM patients by ELISA and immunohistochemistry. RANTES expression serves as a marker for recruitment of immune cells and is associated with a wide range of immune-mediated diseases. However, the impact of RANTES in WM is not known. We analyzed the expression of receptors for Rantes by flow cytometry and found that WM B cells and stromal cells express CCR1 and CCR3, but not CCR5. Using a standard chemotaxis assay, we determined that Rantes had no effect on B cell or stromal cell recruitment. Rantes also had no effect on B cell or stromal cell survival, however it did promote stromal cell proliferation (p<0.04). The interaction between Rantes and IL-6 has been described in an autoimmune disease model. Since stromal cells secrete significantly more IL-6 than WM B cells, we treated stromal cells with Rantes for 24 hr and found that Rantes increases IL-6 secretion (p<0.03). To characterize the mechanism of Rantes-mediated IL-6 secretion, we transfected stromal cells with an IL-6 promoter construct and treated with Rantes for 12 hr and found a significant increase in IL-6 promoter activity (p<0.0162) indicating Rantes can regulate IL-6 transcription. Bioinformatics analysis of the IL-6 promoter indicates the presence of multiple candidate binding sites for transcription factors that have been previously shown to play a role in the biology of B cells, including NFkB, AP1, and GLI. Co-transfection of stromal cells with an IL-6 reporter construct and a plasmid expressing GLI1, GLI2 or GLI3 demonstrates that GLI2 and GLI3 proteins can regulate the IL-6 promoter. We then transfected stromal cells with a reporter construct containing 8X-GLI binding sites and demonstrate that Rantes can regulate GLI transcription further supporting a role for the interaction between Rantes and IL-6 through GLI transcription factors. IL-6 rich tumor microenvironment supports malignant cells. Elevated IL-6 levels have no effect on survival of BCWM.1 cells or CD19+ 138+ cells from WM patients, but leads to upregulation of IgM secretion by BCWM.1 cells and CD19+ CD138+ cells from WM patients, and increases their proliferation (p<0.0039). IL-6 activates Erk1/2 and Jak/ Stat in WM and stimulation of the IgM secreting cells BCWM.1 with IL-6 in the presence of PD98059 MAPK inhibitor had no effect on IgM secretion. However, stimulation of BCWM.1 cells with IL-6 in the presence of JakI inhibitor abolished the IL-6 mediated IgM secretion suggesting IL-6 mediated increase in IgM secretion occurs through Jak/ Stat signaling pathway. Analysis of Rantes levels in other B cell malignancies including follicular lymphoma, chronic lymphocytic leukemia, monoclonal gammopathy of undetermined significance and multiple myeloma indicates that Rantes is elevated in other B cell lymphoproliferative disorders and suggest Rantes may play a similar role in other malignancies. In summary, our data identifies a novel Rantes-GLI-IL-6 interplay in the stromal microenvironment that promotes IgM production by malignant B cells. This therefore provides multiple new potential therapeutic avenues, targeting both malignant cells and the microenvironment to control malignant cell growth, and immunoglobulin secretion in WM and Ig-mediated diseases.

Abstract Background : The 2016 revision to the World Health Organization classification of myeloid neoplasms has recommended distinction between "proliferative" (WBC ≥ 13 x 10(9)/L) and "dysplastic" (WBC < 13 X 10(9)/L) subtypes of chronic myelomonocytic leukemia (CMML). In addition, CMML is characterized by a spectrum of cytogenetic and molecular abnormalities (Patnaik Leukemia 2014). In the current study, we looked for correlations between gene expression profiles (GEP) and morphologic or molecular categories of previously untreated patients with CMML. Methods : 35 patients with CMML were included in the study. Targeted capture assays were carried out on BM DNA specimens obtained at diagnosis for 28 myeloid-relevant genes (Patnaik Blood C. J.). RNA sequencing (Stranded Total RNA- Illumina, San Diego, CA), was performed on 9 normal BM controls, and 35 CMML samples (25 peripheral blood (PB) and 10 BM). The RNA-Seq data was analyzed using MAPRSeq v.2.0. Normalization and differential expression analysis was performed using edgeR 2.6.2. We selected genes that were differentially expressed with a False Discovery Rate (FDR) <0.1 and a fold change bigger than 2. Pathway enrichment analysis was performed using Ingenuity Pathway Analysis (IPA). Results: Among the 35 study patients, 75% were males and median age was 71 years (range, 55-87). 21 (60%), 9 (25%) and 5 (15%) patients were classified as CMML-0, 1 and 2, respectively. 16 (45%) had a dysplastic while 19 (55%) had a proliferative phenotype. At a median follow-up of 16 months, 5 (14%) deaths and 4 (14%) leukemic transformations were documented. Mutational frequencies included; TET2 45%, ASXL1 45%, SRSF2 40%, NRAS 14%, SETBP1 13%, CBL 10%, JAK2 7%, RUNX1 6%, DNMT3A 6%, U2AF1 6%, SF3B1 5%, ZRSR2 4%, Tp53 4%, and IDH2 4%. Abnormal cytogenetics (n=9, 35%) included; -Y 8%, trisomy 8 6%, monosomy 7 3%, del(13q) 3% and monosomal karyotype 3%. i) Differential GEP in PB samples: 25 PB CMML samples were analyzed. Unsupervised GEP demonstrated two unique clusters (figure one); cluster one was enriched with dysplastic CMML (90%) and cluster two with proliferative CMML (64%). Consistent with the difference in the distribution of morphologic categories, in comparison to cluster one, patients in cluster two had a higher white blood count (p=0.003), higher monocyte count (p=0.02) and higher risk prognostication by the Mayo Model (p=0.016); there was no significant difference between the two clusters in terms of age and gender distribution, hemoglobin level, neutrophil and platelet counts, PB or BM blast content, cytogenetic risk stratification, ASXL1, TET2, RAS, CBL and SETBP1 mutational status. Genes significantly upregulated (FDR<1e-5 and fold change ≥ 16x); in cluster two (proliferative CMML) in comparison to cluster one included; PRG2, CTSG, BEX1, CD34, CRISP2, while those significantly down-regulated included (FDR<1e-15 and fold change ≥ 5x); IL2RB, PRF1, GNLY, PDGFRB. IPA analysis identified the following pathways preferentially expressed in cluster two (proliferative CMML) 1) mitotic roles of polo-like kinase (KIF23, WEE-1, PLK1, PLK4), 2) Cell cycle: G2/M checkpoint regulation (CDC25C, WEE-1, AURKA, CDK1), 3) Cell cycle: chromosomal replication (CDC45, RPA3, MCM2, CDC7), and 4) BRCA1 in DNA damage response (BARD1, RBBP8, PLK1, RAD51). Whereas in the cluster one (dysplastic CMML), preferentially expressed pathways included; 1) T-cell receptor signalling (CD247 PRKCQ, CD4, PLCG1, CD28) and 2) STAT3 signalling (SOCS3, MAP3K9). ii) Differential GEP in BM samples: 10 BM CMML samples were analyzed along with 10 normal controls. Samples and controls clustered separately and within the CMML samples two clusters were identified (figure two). These clusters had equal representations of dysplastic (n=5) and proliferative (n=5) CMML sub-types. Additional samples are being processed. Conclusions: Differential gene expression profiling in patients with CMML identifies two unique clusters, clinically demarcated, at least in PB samples, by "proliferative" and "dysplastic" CMML subtypes. The proliferative cluster is enriched in genes involved in cell cycle regulation and DNA damage response; whereas the dysplastic cluster is enriched in T-cell receptor and STAT3 signalling. The prognostic and therapeutic impact of these gene clusters remains to be assessed in a larger group of informative patients. Figure 1. Figure 1. Figure 2. Figure 2. Disclosures Al-Kali: Novartis: Research Funding; Celgene: Research Funding.

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.

Waldenström macroglobulinemia (WM) is a low-grade B-cell lymphoma associated with the accumulation of lymphoplasmacytic cells in the bone marrow (BM) and a high level of monoclonal IgM protein in the serum. Genome-wide studies revealed genomic alternations in WM, with the most prevalent being a point mutation in the myeloid differentiation primary response gene 88 (MyD88) resulting in an amino acid substitution (L265P) that is present in over 90% of WM patients. MyD88 is an effector of Toll-like receptor (TLR) signaling pathway that mediates inflammatory cytokine expression and secretion upon activation. Here, we found that activation of the TLR-MyD88 pathway in WM cells carrying the MyD88 L265P somatic mutation increases the expression of IL-6 and CCL2, two cytokines known to participate in the pathobiology of several neoplasms including B cell malignancies. Furthermore, IL-6 has been shown to modulate IgM secretion and malignant B cell growth in WM. We demonstrate that an active ERK1/2 pathway, which is elevated in WM patient samples, is required to maintain IL-6 and CCL2 expression in BCWM.1 (p=0.0339) and RPCI-WM1 (p=0.0005) cells. Further characterization identified a change in the chromatin landscape in response to TLR-MyD88 activation in WM cells. We found increased levels of trimethylation of histone 3 lysine 4 (H3K4me3) at the promoters of the inflammatory cytokines IL-6 and CCL2 by chromatin immunoprecipitation (ChIP) assay followed by qPCR (ChIP-qPCR). The H3K4me3 modification is catalyzed by six related homologs of the yeast histone methyltransferase (HMT) family. Further analysis identified the Mixed-lineage leukemia 1 (MLL1) as the enzyme bound to these promoters in response to TLR-MyD88 stimulation of WM cells. Analysis of WM cell lines and primary WM patient cells showed that MLL1 and its binding partner, menin, are expressed at significantly higher levels in CD19+CD138+cells from WM patients compared to CD19+B cells from peripheral blood of healthy donors (p<0.001). We also found an increase in H3K4me3 deposition on IL-6 and CCL2 promoters during early (1-3 hr) and late (12-24 hr) kinetics following TLR-MyD88 stimulation. This also coincided with increased deposition of MLL1 on these cytokine promoters by ChIP-qPCR. Disruption of menin-MLL1 using the MI-2 or MI-503 menin-MLL1 inhibitors significantly reduced IL-6 and CCL2 expression in WM cell lines (BCWM.1 p=0.039; RPCI-WM1 p=0.0041). This also results in significantly reduced IgM expression (p<0.0001) and secretion at inhibitor dose (5 μM) that has no effect on cell proliferation. Finally, MLL1 knockdown using RNAi significantly reduces IgM expression in BCWM.1 (p<0.0001), MWCL (p=0.0016) and RPCI-WM1 (p<0.0001) and IgM secretion in BCWM.1 (p<0.0001) and MWCL-1 (p<0.0001). Taken together, these results identify a novel role for menin-MLL1 in regulating inflammatory cytokines and IgM expression and secretion in WM and provide the rationale for targeting these molecules in WM patients. Disclosures Ansell: Mayo Clinic Rochester: Employment; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; Trillium: Research Funding; Affimed: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; LAM Therapeutics: Research Funding; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding.

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.