Zhou, Dan

Life long high fat (HF) diet has been shown to cause more hepatic lipid accumulation in offspring as compared to a post‐weaning HF diet. However, it is still unclear what the epigenetic mechanisms are. We hypothesize that a life long HF diet will cause greater hepatic lipid accumulation as compared to a post‐weaning HF diet. The objective of this study is to understand the epigenetic mechanisms of hepatic lipid accumulation as an outcome of life long HF diet. Timed‐pregnant Sprague‐Dawley rats were given either a control (C, 16% fat) or HF (HF, 45% fat) diet during gestation and lactation. Pups from both C and HF mothers were weaned onto the same HF diet, creating two offspring groups: C/HF and HF/HF. Rats were sacrificed at 12 weeks of age and the left lobe of the liver was used for further analysis. Liver histology showed greater hepatic fat accumulation in the HF/HF group. Gene expression data was measured for multiple genes in the following pathways: glycolysis, gluconeogenesis, fatty acid synthesis, and triacylglycerol synthesis. Only glycerol‐3‐phosphate acyltransferase (GPAM) and fatty acid synthase (FASN) showed higher levels of mRNA. DNA methylation of specific genes was analyzed based on data obtained from methyl‐DNA immunoprecipitation with high‐throughput sequencing. Differential methylation was observed for both GPAM and FASN. Our data suggests that life long HF diet increases lipid storage by increased hepatic GPAM and FASN gene expression in male rats, potentially through epigenetic regulation.

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.

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.

Disruption of epigenetic mechanisms has been intimately linked to the etiology of human cancer. Understanding how these epigenetic mechanisms (including DNA methylation [5mC], hydroxymethylation [5hmC], and histone post‐translational modifications) work in concert to drive cancer initiation and progression remains unknown. Hepatocellular carcinoma (HCC) is increasing in frequency in Western countries but lacks efficacious treatments. The epigenome of HCC remains understudied. To better understand the epigenetic underpinnings of HCC, we performed a genome‐wide assessment of 5mC, 5hmC, four histone modifications linked to promoter/enhancer function (H3K4me1, H3K27ac, H3K4me3, and H3K27me3), and transcription across normal, cirrhotic, and HCC liver tissue. Implementation of bioinformatic strategies integrated these epigenetic marks with each other and with transcription to provide a comprehensive epigenetic profile of how and when the liver epigenome is perturbed during progression to HCC. Our data demonstrate significant deregulation of epigenetic regulators combined with disruptions in the epigenome hallmarked by profound loss of 5hmC, locus‐specific gains in 5mC and 5hmC, and markedly altered histone modification profiles, particularly remodeling of enhancers. Data integration demonstrates that these marks collaborate to influence transcription (e.g., hyper‐5hmC in HCC‐gained active enhancers is linked to elevated expression) of genes regulating HCC proliferation. Two such putative epigenetic driver loci identified through our integrative approach, COMT and FMO3 , increase apoptosis and decrease cell viability in liver‐derived cancer cell lines when ectopically re‐expressed. Conclusion : Altogether, integration of multiple epigenetic parameters is a powerful tool for identifying epigenetically regulated drivers of HCC and elucidating how epigenome deregulation contributes to liver disease and HCC.