Citations

ABSTRACTEpithelial‐mesenchymal transition (EMT) is a process whereby epithelial cells assume mesenchymal characteristics to facilitate cancer metastasis. However, EMT also contributes to the initiation and development of primary tumors. Prior studies that explored the hypothesis that EMT gene variants contribute to epithelial ovarian carcinoma (EOC) risk have been based on small sample sizes and none have sought replication in an independent population. We screened 15,816 single‐nucleotide polymorphisms (SNPs) in 296 genes in a discovery phase using data from a genome‐wide association study of EOC among women of European ancestry (1,947 cases and 2,009 controls) and identified 793 variants in 278 EMT‐related genes that were nominally (P < 0.05) associated with invasive EOC. These SNPs were then genotyped in a larger study of 14,525 invasive‐cancer patients and 23,447 controls. A P‐value <0.05 and a false discovery rate (FDR) <0.2 were considered statistically significant. In the larger dataset, GPC6/GPC5 rs17702471 was associated with the endometrioid subtype among Caucasians (odds ratio (OR) = 1.16, 95% CI = 1.07–1.25, P = 0.0003, FDR = 0.19), whereas F8 rs7053448 (OR = 1.69, 95% CI = 1.27–2.24, P = 0.0003, FDR = 0.12), F8 rs7058826 (OR = 1.69, 95% CI = 1.27–2.24, P = 0.0003, FDR = 0.12), and CAPN13 rs1983383 (OR = 0.79, 95% CI = 0.69–0.90, P = 0.0005, FDR = 0.12) were associated with combined invasive EOC among Asians. In silico functional analyses revealed that GPC6/GPC5 rs17702471 coincided with DNA regulatory elements. These results suggest that EMT gene variants do not appear to play a significant role in the susceptibility to EOC.

AbstractBackground Electronic health records (EHRs) are increasingly used for clinical and translational research through the creation of phenotype algorithms. Currently, phenotype algorithms are most commonly represented as noncomputable descriptive documents and knowledge artifacts that detail the protocols for querying diagnoses, symptoms, procedures, medications, and/or text-driven medical concepts, and are primarily meant for human comprehension. We present desiderata for developing a computable phenotype representation model (PheRM).Methods A team of clinicians and informaticians reviewed common features for multisite phenotype algorithms published in PheKB.org and existing phenotype representation platforms. We also evaluated well-known diagnostic criteria and clinical decision-making guidelines to encompass a broader category of algorithms.Results We propose 10 desired characteristics for a flexible, computable PheRM: (1) structure clinical data into queryable forms; (2) recommend use of a common data model, but also support customization for the variability and availability of EHR data among sites; (3) support both human-readable and computable representations of phenotype algorithms; (4) implement set operations and relational algebra for modeling phenotype algorithms; (5) represent phenotype criteria with structured rules; (6) support defining temporal relations between events; (7) use standardized terminologies and ontologies, and facilitate reuse of value sets; (8) define representations for text searching and natural language processing; (9) provide interfaces for external software algorithms; and (10) maintain backward compatibility.Conclusion A computable PheRM is needed for true phenotype portability and reliability across different EHR products and healthcare systems. These desiderata are a guide to inform the establishment and evolution of EHR phenotype algorithm authoring platforms and languages.

We have previously shown that amino acid changes in the human Kruppel-Like Factor (KLF) 11 protein is associated with the development of maturity onset diabetes of the young VII, whereas complete inactivation of this pathway by the −331 human insulin mutation causes neonatal diabetes mellitus. Here, we report that Klf11−/− mice have decreased circulating insulin levels, alterations in the control of blood glucose and body weight, as well as serum dyslipidemia, but do not develop diabetes. Functional assays using ex vivo liver tissue sections demonstrate that Klf11−/− mice display increased insulin sensitivity. Genome-wide experiments validated by pathway-specific quantitative PCR arrays reveal that the Klf11−/− phenotype associates to alterations in the regulation of gene networks involved in lipid metabolism, in particular those regulated by peroxisome proliferator-activated receptor-γ. Combined, these results demonstrate that the major phenotype given by the whole-body deletion of Klf11 in mouse is not diabetes but increased insulin sensitivity, likely due to altered transcriptional regulation in target tissues. The absence of diabetes in the Klf11−/− mouse either indicates an interspecies difference for the role of this transcription factor in metabolic homeostasis between mouse and humans, or potentially highlights the fact that other molecular factors can compensate for its absence. Nevertheless, the data of this study, gathered at the whole-organism level, further support a role for KLF11 in metabolic processes like insulin sensitivity, which regulation is critical in several forms of diabetes.

Abstract Introduction Epigenetic changes are reversible features of the genome that regulate gene transcription and protein expression on several levels including DNA methylation, histone modification or miRNA expression. We investigated the association between inherited variation in genes of key epigenetic processes and risk of multiple cancers within the GAME-ON consortium. Methods We performed a pathway based meta-analysis using genotypes from more than 50,000 cases of breast, lung, prostate, ovarian and colorectal cancer cases and more than 60,000 controls from various genome wide association studies participating in the GAME-ON consortium to estimate associations with cancer risk. Using the 1000GenomeProject database, we selected 505,702 genotyped and imputed single nucleotide polymorphisms in 551 genes (flanking region +/- 250kb) related to DNA methylation, histone modification or chromatin remodeling based on GO and GeneCard databases. In order to allow variants to be associated with only a subset of traits we used subset based meta-analysis. False-discovery rate (FDR) corrected p-values (q-values) lower than 0.05 were considered significant. Results and Discussion 582 SNPs were significantly associated with risk of at least one cancer. We identified nine major regions that showed significant associations with more than one cancer type. Among the most interesting regions was the region around PHC3 (3q36), which showed associations with prostate and colorectal cancer and clear cell ovarian carcinomas. PHC3 is involved in chromatin remodeling and plays a role in epithelial neoplasms. Significant Odds ratios (ORs) ranged from 0.80 to 1.31. The number of risk and protective alleles in this region was similarly distributed (19 and 18, respectively). One of the strongest associations was observed for rs76925190 (intronic in PRKC1), which increased the risk of colorectal and prostate cancer (q-value 4.28*10-10). Variants in this region were previously associated with prostate cancer. Polymorphisms in the region (19q13) around BABAM1 (RISC and BRCA1 A complex member 1), were associated with lung, breast, ovarian and prostate cancer. BABAM1 is associated with the BRCA1-complex. Its function in histone modification and DNA repair emphasizes its importance in carcinogenesis. Significant ORs ranged from 0.88 to 1.14 with similar distribution of risk and protective alleles in this region (19 and 17, respectively). The strongest association was observed for rs4808076 (intronic in ANKLE1), which increased the risk of squamous lung, serous ovarian and ER- -breast cancer (q-value 2.40*10-6). Variants in this region were previously associated with risk of breast and ovarian cancer. Conclusions This study emphasizes the importance of variants in genes of epigenetic processes on cancer risk and further provides insights into novel, pleiotropic epigenetic mechanisms of cancer development. Citation Format: Dominique Scherer, Reka Toth, Linda Kelemen, Angela Risch, Aditi Hazra, Jean Pierre Issa, Victor Moreno, Rosalind A. Eeles, John Quackenbush, Ellen L. Goode, Shuji Ogino, Rayjean Hung, Cornelia M. Ulrich. Genetic variants in epigenetic pathways and risk of multiple cancer types in the GAME-ON consortium. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4612. doi:10.1158/1538-7445.AM2015-4612