Hlady, Ryan A.

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.

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.

With the epidemic of obesity, nonalcoholic fatty liver disease (NAFLD) has become the most common pediatric liver disease. The influence of a perinatal obesity‐inducing diet (OID) on the development and progression of NAFLD in offspring is important but incompletely studied. Hence, we fed breeding pairs of C57BL/6J mice during gestation and lactation (perinatally) either chow or an OID rich in fat, fructose, and cholesterol (FFC). The offspring were weaned to either chow or an FFC diet, generating four groups: perinatal (p)Chow‐Chow, pChow‐FFC, pFFC‐Chow, and pFFC‐FFC. Mice were sacrificed at 10 weeks of age. We examined the whole‐liver transcriptome by RNA sequencing (RNA‐seq) and whole‐liver genome methylation by reduced representation bisulfite sequencing (RRBS). Our results indicated that the pFFC‐FFC mice had a significant increase in hepatic steatosis, injury, inflammation, and fibrosis, as assessed histologically and biochemically. We identified 189 genes that were differentially expressed and methylated in the pFFC‐FFC mice versus the pChow‐FFC mice. Gene set enrichment analysis identified hepatic fibrosis/hepatic stellate cell activation as the top canonical pathway, suggesting that the differential DNA methylation events in the mice exposed to the FFC diet perinatally were associated with a profibrogenic transcriptome. To verify that this finding was consistent with perinatal nutritional reprogramming of the methylome, we exposed pFFC‐Chow mice to an FFC diet in adulthood. These mice developed significant hepatic steatosis, injury, inflammation, and more importantly fibrosis when compared to the appropriate controls.Conclusion: Perinatal exposure to an OID primes the immature liver for an accentuated fibrosing nonalcoholic steatohepatitis (NASH) phenotype, likely through nutritional reprogramming of the offspring methylome. These data have potential clinical implications for monitoring children of obese mothers and risk stratification of children with NAFLD.

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.

Abstract Hepatocellular carcinoma (HCC) is the second most common cause of cancer deaths worldwide. Hepatitis C virus (HCV) infection is a major risk factor for HCC in the US. HCV infection may cause cancer by suppressing host defense that controls the propagation of repetitive element (RE) sequences, including LINE-1 and Alu, the most representative REs in the human genome. DNA methylation of RE helps stabilize the genome, while reduced RE methylation, also referred as global hypomethylation, leads to genomic instability and is known as a hallmark of cancer. However, current available high-throughput technology for DNA methylation profiling only enables genomewide RE methylation study on a global scale. The role of individual RE methylation in cancer is largely understudied. In this study, using our recently developed and published machine learning algorithm to enhance the RE coverage of the Illumina methylation array platform, we examined the role of individual RE methylation in HCV-induced HCC across the entire genome. We conducted an epigenome-wide association study on 22 HCC, 20 cirrhotic, and 44 normal liver tissues. We included equal numbers of HCV-induced and alcohol-induced HCC/cirrhosis cases. The Illumina 450k array profiled 1939 REs and we computationally predicted 33184 REs; correlations between the overlapping 1541 profiled and predicted RE methylation were &gt; 0.91. Sex, tumor differentiation, and tumor stage were similarly distributed across the HCV and alcohol groups. We adjusted for batch effects and used Benjamini-Hochberg adjusted false discovery rate (FDR) to account for multiple testing. Relative to normal liver tissue, we observed over twice as many differentially methylated (FDR &lt; 0.05) REs in HCV-HCC compared to alcohol-HCC tissue (2911 vs 1304, 749 in common). We validated our results in The Cancer Genome Atlas (TCGA) (325 vs 129, 74 in common) with high correlation (r = 0.90). We also observed that 169 REs were significantly altered in HCV-cirrhosis tissue (compared to none in alcohol-cirrhosis tissue), of which, 17 REs in 13 genes, including PIK3CD, LY9, GCK, NLRC3, and PRKCZ, were consistently hypomethylated in both HCC and cirrhotic liver tissue induced by HCV (r = 0.87). Using TCGA RNA sequencing data, we found that these REs were located in gene body and their hypomethylation was associated with lower expression of their host genes (r &gt; 0.3, p &lt; 0.05). Gene Ontology Enrichment analysis showed that these genes were enriched in T-cell activation & differentiation and cytokine production (FDR &lt; 0.05). Our findings indicate that HCV infection has a more extensive impact on the DNA methylation landscape in RE than alcohol consumption, and that HCV infection may also induce similar hypomethylation in cirrhotic liver. Hypomethylated RE may alter local chromatin environment and disrupt expression of genes involved in immune and inflammatory responses, both of which may promote HCC development. RE hypomethylation in specific genomic locus may serve as early detection biomarker of HCC in patients with HCV-induced cirrhosis. Citation Format: Yinan Zheng, Jimmy Ren, Ryan A. Hlady, Keith D. Robertson, Robert L. Murphy, Lewis R. Roberts, Lifang Hou. DNA methylation of individual repetitive elements in HCV infection-induced HCC [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr LB-235.

AbstractHepatocellular carcinoma (HCC) is the most prevalent primary tumor of the liver, and is steadily becoming one of the most lethal cancers worldwide. Liver resection, which is the recommended procedure for early localized HCC, results in frequent recurrence (50–70%), while the standard of care for late-stage HCC, multikinase inhibitors, only improves survival by a few months. The lack of success for these treatment modalities is attributable, at least in part, to marked phenotypic heterogeneity within the tumor. Intratumoral heterogeneity (ITH) has emerged as a defining characteristic of human tumors, with individual cancer cells displaying distinct differences in properties including growth rate, metastatic capacity, and response to treatment. This heterogeneity, which is unlikely to be captured from a biopsy, impacts outcome because a single treatment targeting one cancer-specific pathway would spare tumor cells having distinct characteristics. Development of effective biomarkers remains a major challenge for similar reasons. Understanding, interpreting, and circumventing the impact of ITH is therefore paramount for developing reliable biomarkers and designing effective individualized treatment strategies for HCC.