Four groups of type 2 diabetes contribute to the etiological and clinical heterogeneity in newly diagnosed individuals: An IMI DIRECT study

The presentation and underlying pathophysiology of type 2 diabetes (T2D) is complex and heterogeneous. Recent studies attempted to stratify T2D into distinct subgroups using data-driven approaches, but their clinical utility may be limited if categorical representations of complex phenotypes are suboptimal. We apply a soft-clustering (archetype) method to characterize newly diagnosed T2D based on 32 clinical variables. We assign quantitative clustering scores for individuals and investigate the associations with glycemic deterioration, genetic risk scores, circulating omics biomarkers, and phenotypic stability over 36 months. Four archetype profiles represent dysfunction patterns across combinations of T2D etiological processes and correlate with multiple circulating biomarkers. One archetype associated with obesity, insulin resistance, dyslipidemia, and impaired β cell glucose sensitivity corresponds with the fastest disease progression and highest demand for anti-diabetic treatment. We demonstrate that clinical heterogeneity in T2D can be mapped to heterogeneity in individual etiological processes, providing a potential route to personalized treatments

Radiosensitivity in breast cancer assessed by the Comet and micronucleus assays

Spontaneous and radiation-induced genetic instability of peripheral blood mononuclear cells derived from unselected breast cancer (BC) patients (n=50) was examined using the single-cell gel electrophoresis (Comet) assay and a modified G2 micronucleus (MN) test. Cells from apparently healthy donors (n=16) and from cancer patients (n=9) with an adverse early skin reaction to radiotherapy (RT) served as references. Nonirradiated cells from the three tested groups exhibited similar baseline levels of DNA fragmentation assessed by the Comet assay. Likewise, the Comet analysis of in vitro irradiated (5 Gy) cells did not reveal any significant differences among the three groups with respect to the initial and residual DNA fragmentation, as well as the DNA repair kinetics. The G2 MN test showed that cells from cancer patients with an adverse skin reaction to RT displayed increased frequencies of both spontaneous and radiation-induced MN compared to healthy control or the group of unselected BC patients. Two patients from the latter group developed an increased early skin reaction to RT, which was associated with an increased initial DNA fragmentation in vitro only in one of them. Cells from the other BC patient exhibited a striking slope in the dose–response curve detected by the G2 MN test. We also found that previous RT strongly increased both spontaneous and in vitro radiation-induced MN levels, and to a lesser extent, the radiation-induced DNA damage assessed by the Comet assay. These data suggest that clinical radiation may provoke genetic instability and/or induce persistent DNA damage in normal cells of cancer patients, thus leading to increased levels of MN induction and DNA fragmentation after irradiation in vitro. Therefore, care has to be taken when blood samples collected postradiotherapeutically are used to assess the radiosensitivity of cancer patients

Genome-wide association meta-analysis identifies pleiotropic risk loci for aerodigestive squamous cell cancers

Squamous cell carcinomas (SqCC) of the aerodigestive tract have similar etiological risk factors. Although genetic risk variants for individual cancers have been identified, an agnostic, genome-wide search for shared genetic susceptibility has not been performed. To identify novel and pleotropic SqCC risk variants, we performed a meta-analysis of GWAS data on lung SqCC (LuSqCC), oro/pharyngeal SqCC (OSqCC), laryngeal SqCC (LaSqCC) and esophageal SqCC (ESqCC) cancers, totaling 13,887 cases and 61,961 controls of European ancestry. We identified one novel genome-wide significant (Pmeta<5x10-8) aerodigestive SqCC susceptibility loci in the 2q33.1 region (rs56321285, TMEM273). Additionally, three previously unknown loci reached suggestive significance (Pmeta<5x10-7): 1q32.1 (rs12133735, near MDM4), 5q31.2 (rs13181561, TMEM173) and 19p13.11 (rs61494113, ABHD8). Multiple previously identified loci for aerodigestive SqCC also showed evidence of pleiotropy in at least another SqCC site, these include: 4q23 (ADH1B), 6p21.33 (STK19), 6p21.32 (HLA-DQB1), 9p21.33 (CDKN2B-AS1) and 13q13.1(BRCA2). Gene-based association and gene set enrichment identified a set of 48 SqCC-related genes to DNA damage and epigenetic regulation pathways. Our study highlights the importance of cross-cancer analyses to identify pleiotropic risk loci of histology-related cancers arising at distinct anatomical sites

Four groups of type 2 diabetes contribute to the etiological and clinical heterogeneity in newly diagnosed individuals: An IMI DIRECT study

The presentation and underlying pathophysiology of type 2 diabetes (T2D) is complex and heterogeneous. Recent studies attempted to stratify T2D into distinct subgroups using data-driven approaches, but their clinical utility may be limited if categorical representations of complex phenotypes are suboptimal. We apply a soft-clustering (archetype) method to characterize newly diagnosed T2D based on 32 clinical variables. We assign quantitative clustering scores for individuals and investigate the associations with glycemic deterioration, genetic risk scores, circulating omics biomarkers, and phenotypic stability over 36 months. Four archetype profiles represent dysfunction patterns across combinations of T2D etiological processes and correlate with multiple circulating biomarkers. One archetype associated with obesity, insulin resistance, dyslipidemia, and impaired β cell glucose sensitivity corresponds with the fastest disease progression and highest demand for anti-diabetic treatment. We demonstrate that clinical heterogeneity in T2D can be mapped to heterogeneity in individual etiological processes, providing a potential route to personalized treatments

Pleiotropy of genetic variants on obesity and smoking phenotypes: Results from the Oncoarray Project of The International Lung Cancer Consortium

Obesity and cigarette smoking are correlated through complex relationships. Common genetic causes may contribute to these correlations. In this study, we selected 241 loci potentially associated with body mass index (BMI) based on the Genetic Investigation of ANthropometric Traits (GIANT) consortium data and calculated a BMI genetic risk score (BMI-GRS) for 17,037 individuals of European descent from the Oncoarray Project of the International Lung Cancer Consortium (ILCCO). Smokers had a significantly higher BMI-GRS than never-smokers (p = 0.016 and 0.010 before and after adjustment for BMI, respectively). The BMI-GRS was also positively correlated with pack-years of smoking (p<0.001) in smokers. Based on causal network inference analyses, seven and five of 241 SNPs were classified to pleiotropic models for BMI/smoking status and BMI/pack-years, respectively. Among them, three and four SNPs associated with smoking status and pack-years (p<0.05), respectively, were followed up in the ever-smoking data of the Tobacco, Alcohol and Genetics (TAG) consortium. Among these seven candidate SNPs, one SNP (rs11030104, BDNF) achieved statistical significance after Bonferroni correction for multiple testing, and three suggestive SNPs (rs13021737, TMEM18; rs11583200, ELAVL4; and rs6990042, SGCZ) achieved a nominal statistical significance. Our results suggest that there is a common genetic component between BMI and smoking, and pleiotropy analysis can be useful to identify novel genetic loci of complex phenotypes

One step ATRP initiator immobilization on surfaces leading to gradient-grafted polymer brushes

Published: April 30, 2014A method is described that allows potentially any surface to be functionalized covalently with atom transfer radical polymerization (ATRP) initiators derived from ethyl-2-bromoisobutyrl bromide in a single step. In addition, the initiator surface density was variable and tunable such that the thickness of polymer chain grafted from the surface varied greatly on the surfaces providing examples, across the surface of a substrate, of increased chain stretching due to the entropic nature of crowded polymer chains leading toward polymer brushes. An initiator gradient of increasing surface density was deposited by plasma copolymerization of an ATRP initiator (ethyl 2-bromoisobutyrate) and a non-ATRP reactive diluent molecule (ethanol). The deposited plasma polymer retained its chemical ability to surface-initiate polymerization reactions as exemplified by N,N'-dimethyl acrylamide and poly(ethylene glycol) methyl ether methacrylate polymerizations, illustrating linear and bottle-brush-like chains, respectively. A large variation in graft thickness was observed from the low to high chain-density side suggesting that chains were forced to stretch away from the surface interface--a consequence of entropic effects resulting from increased surface crowding. The tert-butyl bromide group of ethyl 2-bromoisobutyrate is a commonly used initiator in ATRP, so a method for covalent linkage to any substrate in a single step desirably simplifies the multistep surface activation procedures currently used.Bryan R. Coad, Katie E. Styan, and Laurence Meaghe

Iam hiQ—a novel pair of accuracy indices for imputed genotypes

Background Imputation of untyped markers is a standard tool in genome-wide association studies to close the gap between directly genotyped and other known DNA variants. However, high accuracy with which genotypes are imputed is fundamental. Several accuracy measures have been proposed and some are implemented in imputation software, unfortunately diversely across platforms. In the present paper, we introduce Iam hiQ, an independent pair of accuracy measures that can be applied to dosage files, the output of all imputation software. Iam (imputation accuracy measure) quantifies the average amount of individual-specific versus population-specific genotype information in a linear manner. hiQ (heterogeneity in quantities of dosages) addresses the inter-individual heterogeneity between dosages of a marker across the sample at hand. Results Applying both measures to a large case–control sample of the International Lung Cancer Consortium (ILCCO), comprising 27,065 individuals, we found meaningful thresholds for Iam and hiQ suitable to classify markers of poor accuracy. We demonstrate how Manhattan-like plots and moving averages of Iam and hiQ can be useful to identify regions enriched with less accurate imputed markers, whereas these regions would by missed when applying the accuracy measure info (implemented in IMPUTE2). Conclusion We recommend using Iam hiQ additional to other accuracy scores for variant filtering before stepping into the analysis of imputed GWAS data