Genetic Variants Related to Longer Telomere Length are Associated with Increased Risk of Renal Cell Carcinoma.

BACKGROUND: Relative telomere length in peripheral blood leukocytes has been evaluated as a potential biomarker for renal cell carcinoma (RCC) risk in several studies, with conflicting findings. OBJECTIVE: We performed an analysis of genetic variants associated with leukocyte telomere length to assess the relationship between telomere length and RCC risk using Mendelian randomization, an approach unaffected by biases from temporal variability and reverse causation that might have affected earlier investigations. DESIGN, SETTING, AND PARTICIPANTS: Genotypes from nine telomere length-associated variants for 10 784 cases and 20 406 cancer-free controls from six genome-wide association studies (GWAS) of RCC were aggregated into a weighted genetic risk score (GRS) predictive of leukocyte telomere length. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Odds ratios (ORs) relating the GRS and RCC risk were computed in individual GWAS datasets and combined by meta-analysis. RESULTS AND LIMITATIONS: Longer genetically inferred telomere length was associated with an increased risk of RCC (OR=2.07 per predicted kilobase increase, 95% confidence interval [CI]:=1.70-2.53, p0.5) with GWAS-identified RCC risk variants (rs10936599 and rs9420907) from the telomere length GRS; despite this exclusion, a statistically significant association between the GRS and RCC risk persisted (OR=1.73, 95% CI=1.36-2.21, p<0.0001). Exploratory analyses for individual histologic subtypes suggested comparable associations with the telomere length GRS for clear cell (N=5573, OR=1.93, 95% CI=1.50-2.49, p<0.0001), papillary (N=573, OR=1.96, 95% CI=1.01-3.81, p=0.046), and chromophobe RCC (N=203, OR=2.37, 95% CI=0.78-7.17, p=0.13). CONCLUSIONS: Our investigation adds to the growing body of evidence indicating some aspect of longer telomere length is important for RCC risk. PATIENT SUMMARY: Telomeres are segments of DNA at chromosome ends that maintain chromosomal stability. Our study investigated the relationship between genetic variants associated with telomere length and renal cell carcinoma risk. We found evidence suggesting individuals with inherited predisposition to longer telomere length are at increased risk of developing renal cell carcinoma

DEVELOPPEMENT GALENIQUE DE COMPRIMES ORODISPERSIBLES A VISEE COMPLEMENTS ALIMENTAIRES

CLERMONT FD-BCIU-Santé (631132104) / SudocLYON1-BU Santé (693882101) / SudocSudocFranceF

Copy number variations alter methylation and parallel IGF2 overexpression in adrenal tumors

Overexpression of insulin growth factor 2 (IGF2) is a hallmark of adrenocortical carcinomas and pheochromocytomas. Previous studies investigating the IGF2/H19 locus have mainly focused on a single molecular level such as genomic alterations or altered DNA methylation levels and the causal changes underlying IGF2 overexpression are still not fully established. In the current study, we analyzed 62 tumors of the adrenal gland from patients with Conn's adenoma (CA, n=12), pheochromocytomas (PCC, n=10), adrenocortical benign tumors (ACBT, n=20), and adrenocortical carcinomas (ACC, n=20). Gene expression, somatic copy number variation of chr11p15.5, and DNA methylation status of three differential methylated regions of the IGF2/H19 locus including the H19 imprinting control region were integratively analyzed. IGF2 overexpression was found in 85% of the ACCs and 100% of the PCCs compared to 23% observed in CAs and ACBTs. Copy number aberrations of chr11p15.5 were abundant in both PCCs and ACCs but while PCCs retained a diploid state, ACCs were frequently tetraploid (7/19). Loss of either a single allele or loss of two alleles of the same parental origin in tetraploid samples resulted in a uniparental disomy-like genotype. These copy number changes correlated with hypermethylation of the H19 ICR suggesting that the lost alleles were the unmethylated maternal alleles. Our data provide conclusive evidence that loss of the maternal allele correlates with IGF2 overexpression in adrenal tumors and that hypermethylation of the H19 ICR is a consequence thereof

Prediction of Breast Cancer Treatment\textendashInduced Fatigue by Machine Learning Using Genome-Wide Association Data

Abstract Background We aimed at predicting fatigue after breast cancer treatment using machine learning on clinical covariates and germline genome-wide data. Methods We accessed germline genome-wide data of 2799 early-stage breast cancer patients from the Cancer Toxicity study (NCT01993498). The primary endpoint was defined as scoring zero at diagnosis and higher than quartile 3 at 1\,year after primary treatment completion on European Organization for Research and Treatment of Cancer quality-of-life questionnaires for Overall Fatigue and on the multidimensional questionnaire for Physical, Emotional, and Cognitive fatigue. First, we tested univariate associations of each endpoint with clinical variables and genome-wide variants. Then, using preselected clinical (false discovery rate &lt; 0.05) and genomic (P\,&lt;\,.001) variables, a multivariable preconditioned random-forest regression model was built and validated on a hold-out subset to predict fatigue. Gene set enrichment analysis identified key biological correlates (MetaCore). All statistical tests were 2-sided. Results Statistically significant clinical associations were found only with Emotional and Cognitive Fatigue, including receipt of chemotherapy, anxiety, and pain. Some single nucleotide polymorphisms had some degree of association (P\,&lt;\,.001) with the different fatigue endpoints, although there were no genome-wide statistically significant (P\,&lt;\,5.00 \texttimes 10-8) associations. Only for Cognitive Fatigue, the predictive ability of the genomic multivariable model was statistically significantly better than random (area under the curve = 0.59, P\,=\,.01) and marginally improved with clinical variables (area under the curve\,=\,0.60, P\,=\,.005). Single nucleotide polymorphisms found to be associated (P\,&lt;\,.001) with Cognitive Fatigue belonged to genes linked to inflammation (false discovery rate adjusted P\,=\,.03), cognitive disorders (P\,=\,1.51 \texttimes 10-12), and synaptic transmission (P\,=\,6.28 \texttimes 10-8). Conclusions Genomic analyses in this large cohort of breast cancer survivors suggest a possible genetic role for severe Cognitive Fatigue that warrants further exploration

Prediction of Breast Cancer Treatment-Induced Fatigue by Machine Learning Using&nbsp;Genome-Wide Association Data.

BackgroundWe aimed at predicting fatigue after breast cancer treatment using machine learning on clinical covariates and germline genome-wide data.MethodsWe accessed germline genome-wide data of 2799 early-stage breast cancer patients from the Cancer Toxicity study (NCT01993498). The primary endpoint was defined as scoring zero at diagnosis and higher than quartile 3 at 1 year after primary treatment completion on European Organization for Research and Treatment of Cancer quality-of-life questionnaires for Overall Fatigue and on the multidimensional questionnaire for Physical, Emotional, and Cognitive fatigue. First, we tested univariate associations of each endpoint with clinical variables and genome-wide variants. Then, using preselected clinical (false discovery rate &lt; 0.05) and genomic (P &lt; .001) variables, a multivariable preconditioned random-forest regression model was built and validated on a hold-out subset to predict fatigue. Gene set enrichment analysis identified key biological correlates (MetaCore). All statistical tests were 2-sided.ResultsStatistically significant clinical associations were found only with Emotional and Cognitive Fatigue, including receipt of chemotherapy, anxiety, and pain. Some single nucleotide polymorphisms had some degree of association (P &lt; .001) with the different fatigue endpoints, although there were no genome-wide statistically significant (P &lt; 5.00 × 10-8) associations. Only for Cognitive Fatigue, the predictive ability of the genomic multivariable model was statistically significantly better than random (area under the curve = 0.59, P = .01) and marginally improved with clinical variables (area under the curve = 0.60, P = .005). Single nucleotide polymorphisms found to be associated (P &lt; .001) with Cognitive Fatigue belonged to genes linked to inflammation (false discovery rate adjusted P = .03), cognitive disorders (P = 1.51 × 10-12), and synaptic transmission (P = 6.28 × 10-8).ConclusionsGenomic analyses in this large cohort of breast cancer survivors suggest a possible genetic role for severe Cognitive Fatigue that warrants further exploration

Outcomes of patients with cancer and sarcoid-like granulomatosis associated with immune checkpoint inhibitors: A case–control study

International audienc

Epigenetic Variation in Tree Evolution: a case study in black poplar (Populus nigra)

How perennial organisms adapt to environments is a key question in biology. To address this question, we investigated ten natural black poplar ( Populus nigra ) populations from Western Europe, a keystone forest tree of riparian ecosystems. We assessed the role of (epi)genetic regulation in driving tree species evolution and adaptation over several millions of years (macro-evolution) up to a few generations (micro-evolution). At the macro-evolution scale, polar experienced differential structural (gene loss) and regulation (expression and methylation) reprogramming between sister genomic compartments inherited from polyploidization events. More interestingly, at the micro-evolution scale, both genetic and epigenetic variations differentiate populations from different geographic origins, targeting specifically genes involved in disease resistance, immune response, hormonal and stress response that can be considered as key functions of local adaptation of long lifespan species. Moreover, genes involved in cambium formation, an important functional trait for forest trees, as well as basal functions for cell survival are constitutively expressed though methylation control. These results highlight DNA methylation as a marker of population differentiation, evolutionary adaptation to diverse ecological environments and ultimately opening the need to take epigenetic marks into account in breeding strategies, especially for woody plants