Robertson, Keith D.

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.

Abstract Background: We previously demonstrated that putative clinical epidemiologic exposures associated with leukemia risk are prevalent among AML patients, and some are associated with unique cytogenetic risk group and with clinical phenotype (Finn, Cancer Epidemiol, 2015). Herein, we studied genome-wide DNA methylation in a cohort of AML patients to evaluate the association of individual hyper- and hypo-methylated CpG sites with epidemiologic exposures and overall survival. Methods: The Mayo Clinic AML Epidemiology Cohort is a highly annotated retrospective case series of 295 consecutive patients (pts) with AML diagnosed and treated at Mayo Clinic Florida and Arizona, with central cytogenetics performed in all cases. The prevalence of clinical epidemiologic exposures, past medical and family history as well as medication use and lifestyle was systematically obtained. After IRB approval, we interrogated the cytogenetic database and successfully obtained leukemia DNA from available remnant diagnostic cytogenetic cell pellets in a cohort of 148 AML patients in the Mayo epidemiology case series and performed an assessment of genome-wide DNA methylation using the Infinium HumanMethylation450K BeadChip. Samples were processed using the R Bioconductor package 'minfi' using Subset Within Array Quantile Normalization (PMID: 22703947). Individual CpGs that did not reach a detection p-value of <0.05 were filtered out. An internal control was included on each array to control for significant batch effects. To determine differential methylation status of 473,864 individual CpG sites (after exclusion of 11,648 that did not pass detection p-value QC), CpG sites with low interpatient variability, a standard deviation <0.05 of methylation values, and an interquartile range <0.05 of methylation values and X/Y chromosome probes were excluded from analysis. Epidemiologic and important clinical variables occurring in at least 10 pts or previously-identified as of unique interest but in fewer than 10 pts were evaluated, including sex, BMI, performance status and comorbidities, tobacco and alcohol use, family history of hematologic malignancy, medications of interest, history of toxin exposure, and history of immunosuppression and/or solid organ transplant or of secondary and therapy-related AML (t-AML). Associations of differential hypo or hypermethylation at 281,259 CpG sites with epidemiologic exposures/clinical variables were evaluated using Spearman's test of correlation (continuous or ordinal exposures), a Wilcoxon rank sum test (dichotomous exposures), or a Kruskal-Wallis rank sum test (multi-category exposures). A Bonferroni correction was applied for multiple testing, after which p <1.8 x 10-7 was considered as significant. To reduce the likelihood of false-negative findings, we additionally considered p <5 x 10-6 as indicating suggestive evidence of an association. CpG sites and associated gene and function was determined using Illumina manifest file & www.ncbi.nlm.nih.gov/gene. Results: Statistically significant associations (p <1.8 x 10-7) with individual epidemiologic and clinical exposures were identified for 109 unique CpG sites, corresponding to differential methylation in the genes listed in Table. Specifically, obesity (predominantly hypomethylation), specific cytogenetic lesions and risk group, and gender were highly significantly associated with unique CpG methylation status, but not smoking, toxin exposure, family history, comorbidity or performance status, secondary or t-AML, immunosuppression, medication use, of family history in the analysis. A further 353 unique CpG sites had defined suggestive associations (ongoing analysis). We also identified 8 additional CpG sites where differential methylation was associated with overall survival (p <5 x 10-6, Table). Conclusion: Obesity, cytogenetic lesions, and sex are associated with differential methylation of unique CpG sites at AML diagnosis, using stringent univariate statistical criteria. Significant CpG sites were identified in genes previously linked to AML biology and prognosis (DOCK6, HOXB3, MIR10A, FOXN3/CHES1, GPX1, MYST2/KAT7, PTPRD), but some represent novel findings in AML. These preliminary results suggest an association of some AML risk factors and clinical variables with unique gene methylation and will undergo validation in a prospective AML epidemiology dataset. Disclosures Cerhan: Jannsen: Other: Scientific Advisory Board; Nanostring: Research Funding; Celgene: Research Funding. Foran:Agios: Research Funding; Xencor, Inc.: Research Funding.

Abstract Introduction: Chronic myelomonocytic leukemia (CMML) is a chronic myeloid malignancy associated with monocytosis, autoimmunity (~30%) & an inherent risk for leukemic transformation. Bone marrow (BM) dendritic cell (DC) populations occur in ~30% of patients, with a poorly defined biological & prognostic role. The malignant immune microenvironment is regulated by indoleamine 2,3-dioxygenase-1 (IDO-1) expressing DCs, which modulate regulatory T (Treg) cells & block their conversion into proinflammatory T helper (Th17)-like cells. IDO-1 is a known immune checkpoint & functions by catabolizing tryptophan, an amino acid essential for T cell function. We hypothesized that distinct IDO-1 expressing DC populations in CMML modulate Tregs & contribute towards immune tolerance & aggressive disease biology. Methods: Primary diagnostic CMML peripheral blood mononuclear cells (PBMC) & BM biopsy specimens were obtained after Mayo Clinic IRB approval. A DC population was defined on H&E stained biopsy sections as focal collections (>10) of cells with characteristic elongated nuclei & cytoplasmic extensions. Transcriptomic & protein expression studies assessing IDO-1 expression were done by previously described methods. In addition, IHC expression of PD-1, PD-L1 & CTLA-4 was also done. IDO-1 promoter methylation studies with DIP-seq were performed. The impact on immune tolerance was assessed using mass cytometry (CyTOF). Results: Cohort: Twenty eight patients with CMML were included in the study, median age 70 (range: 51-80) years; 71% males. Eleven (39%) patients had coexisting autoimmune conditions. Of these, 8 (73%) had detectable DC populations either at diagnosis, or during the course of their disease. At a median follow-up of 46 (95% CI 27, 84) months, there were 14 (50%) deaths & 9 (32%) leukemic transformations.IHC results: Nine (32%) patients were identified to have a DC population at CMML diagnosis. CD123 & TCL1 staining was performed in 5 (56%) patients, with 3 being positive for both, & 2 positive for CD123 only (additional IHC studies ongoing). IDO1 expression by IHC was documented in all 9 (100%) cases (Fig 1A & 1B), while rare populations of PD-1, PD-L1 & CTLA-4 lymphocytes were also seen in all cases. Due to the low DC burdens (median cellularity ≤ 5%) & uniform staining intensity, IHC-based grading was not done. Samples at serial time-points, post-HMA therapy & at the time of blast transformation, were available in 5 & 3 patients respectively. Among the patients who did not have DC populations at diagnosis, 5 (42%) developed them post-HMA therapy, while 3 (50%) developed them at the time of LT. The development of DC populations was associated with loss of response to HMA (50%) & disease progression (50%).Transcriptomic analysis: RNA expression data was available on 7 (25%) patients, of whom only 1 (14%) had DC populations at diagnosis. The IDO-1 RPKM value in the former was higher than the mean pooled value in the latter group (330 versus 74, p=0.05).Methylation studies: DIP-seq was performed on 12 (43%) cases from the primary IHC cohort. Qualitative analysis of IDO-1 promoter hypomethylation was conducted & confirmed in all 9 (100%) cases with 5-mC & 5-hmC marks compared to input as displayed in figure 1C.Immune profiling: CyTOF was performed on 4 CMML samples (3 with IDO-1 expressing DC populations at diagnosis) from the primary IHC cohort & compared to a normal PBMC control. Results confirmed an increase in DC populations (fig 1D& 1E), & reduced % of Th17-like T cells in CMML samples compared to control (1.1 versus 5.07, p=0.05, fig 1F).Clinical correlates & survival analysis: With the exception that CMML patients with DC populations had a higher frequency of NRAS (P=0.007) mutations, the two groups were comparable for cytogenetic & molecular abnormalities. The median OS for the cohort was 45 (95% CI 29, 84) months. CMML patients with IDO-1 expressing DC populations at diagnosis had a shorter median OS, in comparison to those without (median OS 30 vs 45, p=0.03, Kaplan-Meier analysis in fig 1G). Conclusions: In conclusion, we demonstrate that DC populations are seen in ~30% of patients with CMML with a uniform expression of IDO-1 & limited expression of PD-1, PD-L1 & CTLA-4. CMML patients with BM DC populations have a higher frequency of NRAS mutations & DC IDO-1 expression is associated with tumor induced immune tolerance. Additional IHC, genomic & preclinical studies with IDO-1 inhibitors are ongoing. Figure 1. Figure 1. Disclosures Al-Kali: Novartis: Research Funding.

Abstract Background : TET2, located on chromosome 4q24, is frequently mutated in the majority of patients with chronic myelomonocytic leukemia (CMML). TET2 has 11 exons, and variations, especially in exon 3 have been described as a part of age related clonal hematopoiesis (Jaiswal NEJM 2015). In CMML, somatic TET2 mutations in the absence of ASXL1 mutations (ASXL1wt/TET2mt) were previously shown to impart a favorable outcome on survival (Patnaik BCJ 2015). In the current larger CMML patient cohort, we describe the number and type of TET2 mutations and examine their phenotypic and prognostic effects. Methods : 261 patients with CMML were included in the study. All patients had bone marrow (BM) biopsies and cytogenetics performed at diagnosis. Targeted capture assays were carried out on BM DNA specimens obtained at diagnosis for the following genes; TET2, DNMT3A, IDH1, IDH2, ASXL1, EZH2, SUZ12, SRSF2, SF3B1, ZRSR2, U2AF1, PTPN11, Tp53, SH2B3, RUNX1, CBL, NRAS, KRAS, JAK2, CSF3R, FLT3, KIT, CALR, MPL, NPM1, CEBPA, IKZF, and SETBP1. TET2 (NM_001127208.2) coverage extended from exons 3-11, with frame shift, non-sense, and missense variations considered pathogenic. Previously annotated single nucleotide polymorphisms (http//www.hapmap.org) were considered non-pathogenic. Results: Among the 261 study patients, 65% were males and median age was 70 years (range, 28-91). 154 (59%), 64 (25%) and 43 (16%) patients were classified as CMML-0, 1 and 2, respectively. At a median follow-up of 23 months, 174 (67%) deaths and 37 (14%) leukemic transformations were documented. Mutational frequencies ≥4% were encountered in; ASXL1 45%, TET2 42%, 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%. i) TET2 mutations type, number and phenotypic correlates: TET2 mutations were seen in 109 (42%) patients; these included frameshift 33 (30%), nonsense 29 (27%) and missense 11 (10%) variants whereas 36 (33%) had more than one type of mutation. Overall, 57 (52%) patients had more than 1 TET2 mutations: 52 (48%) patients had 1, 46 (42%) 2 and 11 (10%) ≥3 TET2 mutations. Among all 109 TET2 mutated patients, 65% were male, and median age was 71 years with no significant difference in age and gender distribution between mutated and un-mutated cases, or type of TET2 mutations; however, older patients were more likely to carry multiple TET2 mutations (p=0.01). Compared to their un-mutated counterparts, TET2 mutated cases were less likely to have a low hemoglobin (p<0.001), circulating immature myeloid cells (IMC) (p=0.001), peripheral blood (PB) (p=0.009) and BM blasts (p=0.009), and have high-risk stratification per Mayo Molecular Model (p<0.001); these differences were not affected by the type or number of TET2 mutations. TET2 mutated cases were more likely to have a higher frequency of SRSF2 (p=0.004) and lower frequency of ASXL1 (p=0.03), Tp53 (p=0.04) and IDH1/2 mutations (p<0.001); these associations were also not affected by the type or number of TET2 mutations. ii) Impact on survival: Median survival for the entire cohort (n=261) was 24 months. In univariate analysis, survival was superior in TET2 mutated (median 33 months) versus wild-type (median 21 months) patients [p=0.03; HR 1.3 95% CI 1.12-1.86] (figure 1A). This survival difference remained significant after adjustment for age (p=0.04), leukocyte count (p=0.017), absolute monocyte count (p=0.02), absolute lymphocyte count (p=0.02), platelet count (p=0.015), circulating IMC (p=0.03), DNMT3A (p=0.02) and ASXL1 (p=0.045) mutations; however, significance was lost after adjustment for abnormal karyotype (p=0.32) and the Mayo Molecular Model (p=0.0003). These observations were not affected by the type or number of TET2 mutations. Finally, our previous observation regarding the survival advantage of ASXL1wt/TET2mt versus other genotypes was most apparent for patients with multiple TET2 mutations (p=0.02) (figure 1B). Conclusions: TET2 mutations are common in CMML and constitute approximately equal proportions of frameshift and non-sense mutations, while missense mutations are less frequent. Majority of TET2 mutated CMML cases harbor more than one mutant variant. Regardless, the relevance of type and number of TET2 mutations in CMML was limited to an association between older age and number of mutations and the latter with possibly improved survival in the absence of clonal ASXL1 mutations. Disclosures No relevant conflicts of interest to declare.

Abstract Background: Large case-control studies have identified common epidemiologic exposures associated with risk of AML development, including obesity [BMI ≥30 kg/m2] associated with 2-fold increase risk. However it is not known whether individual risk factors are associated with unique AML genetic lesions. We have recently identified a significant association of some common exposures including obesity with unique disease cytogenetic subgroups in a cohort of n=295 consecutive AML patients diagnosed and treated at Mayo Clinic [Cancer Epidemiol 39:1084, 2015]. Aberrant DNA methylation is a hallmark of cancer and has been reported in AML, and we therefore performed an analysis of global DNA methylation in patients in this cohort to determine whether specific epidemiologic exposures are associated with a unique pattern of DNA methylation. Methods: Patients in the initial cohort were previously reported. An available baseline AML bone marrow cytogenetic pellet from diagnosis was identified for n=148 patients (50.2%). Following IRB approval and using previously reported techniques [Leukemia 24:1283, 2010], leukemia DNA was isolated from the remnant diagnostic cytogenetic samples and processed on the HumanMethylation450 BeadChip array (Illumina). Individual CpG dinucleotides were assigned a Beta value (i.e. methylation value) ranging from 0 (unmethylated) to 1 (methylated). We excluded those with minor methylation changes (<0.2 between any two samples), resulting in 383,524 CpG's. For the purposes of this first analysis we have focused only on CpG's with an inter-quartile range (IQR, i.e. difference between 75th and 25th percentiles) of greater than 0.25, comprising a total of 39,657 CpG's. An unsupervised cluster analysis of differential methylation was generated ("Heatmap", Figure). Univariate and multivariable analyses were performed adjusting for age as a continuous variable to determine the association of individual exposures with unique methylation clusters (primary endpoint), and we also evaluated the association of clinical characteristics and clinical outcomes after therapy (secondary endpoint). The median age of patients was 69 yrs (range 19-89), male 65%, and Caucasian 89%. The median BMI was 27.2 (range 16.9-59.9), and n=47 patients (32%) had BMI ≥30. Standard cytogenetic risk groups were applied [favorable (n=8), normal (n=61), intermediate abnormal (n=32) and poor (n=47)]. Results: We identified 4 distinct methylation clusters (Figure: Cluster #1, Black horizontal bar; Cluster #2, Green; Cluster #3, Blue; and Cluster #4, Red). Multivariate analysis of select variables is shown in Table. Patients in cluster #1 were significantly more likely to have a BMI ≥30 [Odds Ratio (OR) 2.49, p=0.017] and more likely to have favorable risk cytogenetics although this was not statistically significant [OR 6.71, p=0.088], while those in cluster #2 were significantly more likely to be have a history of regular NSAID use [OR 3.26, p=0.03] and less likely to have normal [OR 0.40, p=0.044] and possibly intermediate abnormal cytogenetics [OR 0.34, p=0.051]. Those in cluster #3 were significantly younger [median age 48 yrs, p<0.001] and were also more likely to have prior history MDS although this was not statistically significant [OR 3.7, p=0.082]. Cluster #4 was not correlated with any distinct exposure apart from possible decreased alcohol use [OR 0.3, p=0.079]. History of past or current tobacco use, regular acetaminophen use, documented myelotoxin exposure, family history, and secondary AML did not correlate with a unique methylation cluster. There was no independent association of methylation cluster with overall survival (p=0.46), although among patients receiving intensive therapy cluster #2 was associated with lower complete remission rate [OR 0.28 (95% CI 0.09-0.86), p=0.026]. Conclusion: This first analysis of AML molecular epidemiology and clinical outcome suggests that specific disease risk factors (esp. obesity and age) are associated with unique leukemia DNA methylation clusters. In addition, aberrant methylation is associated with some clinical features and with outcome after therapy, suggesting that epidemiologic risk factors may have a prognostic impact. This provides novel insight into leukemogenesis, and will guide prospective studies of AML epidemiology and outcome. Figure. Figure. Disclosures Foran: medscape: Honoraria; pfizer: Honoraria; Millennium Pharmaceuticals, Inc.: Research Funding; novartis: Honoraria; karyopharm: Honoraria; boehringer ingelheim: Research Funding; agios: Research Funding; Cellerant: Research Funding.

Abstract Background: Epigenetic dysregulation is involved in the etiology and progression of various human diseases. Formalin-fixed paraffin-embedded (FFPE) samples represent the gold standard for archiving pathology samples, and thus FFPE samples are a major resource of samples in clinical research. However, chromatin-based epigenetic assays in the clinical settings are limited to fresh or frozen samples and are hampered by low chromatin yield in FFPE samples due to the lack of reliable and efficient chromatin preparation methods. Here, we introduce a new chromatin extraction method from FFPE tissues (Chrom-EX PE) for chromatin-based epigenetic assays. Results: During rehydration of FFPE tissues, applying tissue-level cross-link reversal to the deparaffinized tissue at 65°C dramatically increased chromatin yield in the soluble fraction. We found the resulting chromatin to be compatible with ChIP-qPCR and ChIP-seq analysis of histone marks (H3K4me3, H3K27me3, and H3K27Ac) and RNA polymerase II. The chromatin prepared by Chrom-EX PE showed a gradual fragmentation pattern with varying incubation temperature. At temperatures below 37°C, the majority of soluble chromatin was over 1 kb in size. The soluble chromatin prepared at 45-60°C showed a typical nucleosomal pattern shown in cell lines and frozen tissues. The majority of the chromatin prepared at 65°C is close to mononucleosomal size. These observations indicate that chromatin preparation from FFPE samples can be controlled for downstream chromatin-based epigenetic assays. Conclusions: Our method achieves efficient extraction of high-quality chromatin from FFPE samples suitable for targeted and genome-wide epigenetic assays while minimizing degradation and damages. This approach provides a new platform for developing chromatin-based assays applicable to archived FFPE samples to circumvent the over-fixed nature of FFPE tissues. This work was supported, in part, by National Institutes of Health grant P01DK068055 and the Mayo Clinic Center for Individualized Medicine Epigenomics Program. Citation Format: Jian Zhong, Zhenqing Ye, Chad Clark, Samuel Lenz, Justin Nguyen, Huihuang Yan, Keith Robertson, Gianrico Farrugia, Zhiguo Zhang, Tamas Ordog, Jeong-Heon Lee. Enhanced and controlled chromatin extraction for chromatin-based epigenetic assays in FFPE tissues [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 5180.

AbstractThere are currently no effective treatments for advanced‐stage papillary renal cell carcinoma (PRCC). The goal of this study is to define potential DNA methylation‐based markers and treatment targets for advanced‐stage type 2 PRCC. Progressive DNA methylation changes and copy number variation (CNV) from localized to advanced‐stage type 2 PRCC are analyzed by using methylation data generated by TCGA's kidney renal papillary cell carcinoma (TCGA‐KIRP, 450k array) project. Survival analyses are performed for the identified biomarkers and genes with CNV. In addition, expression of the corresponding genes is investigated by RNA‐seq analysis. Progressive methylation changes in several CpGs from localized to advanced‐stage type 2 PRCC are observed. Four CpGs (cg00489401, cg27649239, cg20555674, and cg07196505) in particular are identified as markers for differentiating between localized and advanced‐stage type 2 PRCC. Copy number analysis reveals that copy gain of PTK7 mostly occurs in advanced‐stage type 2 PRCC. Both the four CpG methylation changes and PTK7 copy number gain are associated with patient survival. RNA‐seq analysis demonstrates that PTK7 copy gain leads to higher PTK7 expression relative to tumors without copy number gain. Moreover, PTK7 is significantly upregulated from localized to advanced‐stage type 2 PRCC and is linked to cancer cell invasion. In conclusion, DNA methylation markers that differentiate between localized and advanced‐stage type 2 PRCC may serve as useful markers for disease staging or outcome, while PTK7 copy gain represents a potential treatment target for advanced‐stage type 2 PRCC. Stepwise methylation changes and copy number gain also associate with disease stage in PRCC patients.

Alpha‐1 antitrypsin deficiency (AATD) liver disease is characterized by marked heterogeneity in presentation and progression, despite a common underlying gene mutation, strongly suggesting the involvement of other genetic and/or epigenetic modifiers. Variation in clinical phenotype has added to the challenge of detection, diagnosis, and testing of new therapies in patients with AATD. We examined the contribution of DNA methylation (5‐methylcytosine [5mC]) to AATD liver disease heterogeneity because 5mC responds to environmental and genetic cues and its deregulation is a major driver of liver disease. Using liver biopsies from adults with early‐stage AATD and the ZZ genotype, genome‐wide 5mC patterns were interrogated. We compared DNA methylation among patients with early AATD, and among patients with normal liver, cirrhosis, and hepatocellular carcinoma derived from multiple etiologic exposures, and linked patient clinical/demographic features. Global analysis revealed significant genomic hypomethylation in AATD liver–impacting genes related to liver cancer, cell cycle, and fibrosis, as well as key regulatory molecules influencing growth, migration, and immune function. Further analysis indicated that 5mC changes are localized, with hypermethylation occurring within a background of genome‐wide 5mC loss and with patients with AATD manifesting distinct epigenetic landscapes despite their mutational homogeneity. By integrating clinical data with 5mC landscapes, we observed that CpGs differentially methylated among patients with AATD disease are linked to hallmark clinical features of AATD (e.g., hepatocyte degeneration and polymer accumulation) and further reveal links to well‐known sex‐specific effects of liver disease progression. Conclusion: Our data reveal molecular epigenetic signatures within this mutationally homogeneous group that point to ways to stratify patients for liver disease risk.