Dataset Information

A comprehensive analysis of cancer-driving mutations and genes in kidney cancer.

ABSTRACT: An accumulation of driver mutations is important for cancer formation and progression, and leads to the disruption of genes and signaling pathways. The identification of driver mutations and genes has been the subject of numerous previous studies. The present study was performed to identify cancer-driving mutations and genes in renal cell carcinoma (RCC), prioritizing noncoding variants with a high functional impact, in order to analyze the most informative features. Sorting Intolerant From Tolerant (SIFT), Polymorphism Phenotyping version 2 (Polyphen2) and MutationAssessor were applied to predict deleterious mutations in the coding genome. OncodriveFM and OncodriveCLUST were used to detect potential driver genes and signaling pathways. The functional impact of noncoding variants was evaluated using Combined Annotation Dependent Depletion, FunSeq2 and Genome-Wide Annotation of Variants. Noncoding features were analyzed with respect to their enrichment of high-scoring variants. A total of 1,327 coding mutations in clear cell RCC, 258 in chromophobe RCC and 1,186 in papillary RCC were predicted to be deleterious by all three of MutationAssessor, Polyphen2 and SIFT. In total, 77 genes were positively selected by OncodriveFM and 1 by OncodriveCLUST, 45 of which were recurrently mutated genes. In addition, 10 signaling pathways were recurrently mutated and had a high functional impact bias (FM bias), and 31 novel signaling pathways with high FM bias were identified. Furthermore, noncoding regulatory features and conserved regions contained numerous high-scoring variants, and expression, replication time, GC content and recombination rate were positively correlated with the densities of high-scoring variants. In conclusion, the present study identified a list of cancer-driving genes and signaling pathways, features like regulatory elements, conserved regions, replication time, expression, GC content and recombination rate are major factors that affect the distribution of high-scoring non-coding mutations in kidney cancer.

SUBMITTER: Long C

PROVIDER: S-EPMC5403472 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A comprehensive analysis of cancer-driving mutations and genes in kidney cancer.

Long Chengmei C Jian Jinbo J Li Xinchang X Wang Gongxian G Wang Jingen J

Oncology letters 20170207 4

An accumulation of driver mutations is important for cancer formation and progression, and leads to the disruption of genes and signaling pathways. The identification of driver mutations and genes has been the subject of numerous previous studies. The present study was performed to identify cancer-driving mutations and genes in renal cell carcinoma (RCC), prioritizing noncoding variants with a high functional impact, in order to analyze the most informative features. Sorting Intolerant From Tole ...[more]

PMID: 28454375

Similar Datasets

Project description:BackgroundNext generation sequencing (NGS) has been a handy tool in clinical practice, mainly due to its efficiency and cost-effectiveness. It has been widely used in genetic diagnosis of several inherited diseases, and, in clinical oncology, it may enhance the discovery of new susceptibility genes and enable individualized care of cancer patients. In this context, we explored a pan-cancer panel in the investigation of germline variants in Brazilian patients presenting clinical criteria for hereditary cancer syndromes or familial history.MethodsSeventy-one individuals diagnosed or with familial history of hereditary cancer syndromes were submitted to custom pan-cancer panel including 16 high and moderate penetrance genes previously associated with hereditary cancer syndromes (APC, BRCA1, BRCA2, CDH1, CDKN2A, CHEK2, MSH2, MSH6, MUTYH, PTEN, RB1, RET, TP53, VHL, XPA and XPC). All pathogenic variants were validated by Sanger sequencing.ResultsWe identified a total of eight pathogenic variants among 12 of 71 individuals (16.9%). Among the mutation-positive subjects, 50% were diagnosed with breast cancer and had mutations in BRCA1, CDH1 and MUTYH. Notably, 33.3% were individuals diagnosed with polyposis or who had family cases and harbored pathogenic mutations in APC and MUTYH. The remaining individuals (16.7%) were gastric cancer patients with pathogenic variants in CDH1 and MSH2. Overall, 54 (76.05%) individuals presented at least one variant uncertain significance (VUS), totalizing 81 VUS. Of these, seven were predicted to have disease-causing potential.ConclusionOverall, analysis of all these genes in NGS-panel allowed the identification not only of pathogenic variants related to hereditary cancer syndromes but also of some VUS that need further clinical and molecular investigations. The results obtained in this study had a significant impact on patients and their relatives since it allowed genetic counselling and personalized management decisions.

Project description:Antistaphylococcal penicillins such as oxacillin are the key antibiotics in the treatment of invasive methicillin-susceptible Staphylococcus aureus (MSSA) infections; however, mec gene-independent resistance adaptation can cause treatment failure. Despite its clinical relevance, the basis of this phenomenon remains poorly understood. Here, we investigated the genomic adaptation to oxacillin at an unprecedented scale using a large collection of 503 clinical mec-negative isolates and 30 in vitro-adapted isolates from independent oxacillin exposures. By combining comparative genomics, evolutionary convergence, and genome-wide association analysis, we found 21 genetic loci associated with low-level oxacillin resistance, underscoring the polygenic nature of this phenotype. Evidence of adaptation was particularly strong for the c-di-AMP signal transduction pathways (gdpP and dacA) and in the clpXP chaperone-protease complex. The role of mutations in gdpP in conferring low-level oxacillin resistance was confirmed by allele-swapping experiments. We found that resistance to oxacillin emerges at high frequency in vitro (median, 2.9?×?10-6; interquartile range [IQR], 1.9?×?10-6 to 3.9?×?10-6), which is consistent with a recurrent minimum inhibitory concentration (MIC) increase across the global phylogeny of clinical isolates. Nevertheless, adaptation in clinical isolates appears sporadically, with no stably adapted lineages, suggesting a high fitness cost of resistance, confirmed by growth assessment of mutants in rich media. Our data provide a broader understanding of the emergence and dynamics of oxacillin resistance adaptation in S. aureus and a framework for future surveillance of this clinically important phenomenon.IMPORTANCE The majority of Staphylococcus aureus strains causing human disease are methicillin-susceptible (MSSA) and can be treated with antistaphylococcal penicillins (such as oxacillin). While acquisition of the mec gene represents the main resistance mechanism to oxacillin, S. aureus can acquire low-level resistance through adaptive mutations in other genes. In this study, we used genomic approaches to understand the basis of S. aureus adaption to oxacillin and its dynamic at the population level. By combining a genome analysis of clinical isolates from persistent MSSA infections, in vitro selection of oxacillin resistance, and genome-wide association analysis on a large collection of isolates, we identified 21 genes linked to secondary oxacillin resistance. Adaptive mutations in these genes were easy to select when S. aureus was exposed to oxacillin, but they also came at a substantial cost in terms of bacterial fitness, suggesting that this phenotype emerges preferentially in the setting of sustained antibiotic exposure.

Dataset Information

A comprehensive analysis of cancer-driving mutations and genes in kidney cancer.

Publications

A comprehensive analysis of cancer-driving mutations and genes in kidney cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets