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      Computational analysis of the mutations in BAP1, PBRM1 and SETD2 genes reveals the impaired molecular processes in renal cell carcinoma

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          Abstract

          Clear cell Renal Cell Carcinoma (ccRCC) is due to loss of von Hippel–Lindau ( VHL) gene and at least one out of three chromatin regulating genes BRCA1-associated protein-1 ( BAP1), Polybromo-1 ( PBRM1) and Set domain-containing 2 (SETD2). More than 350, 700 and 500 mutations are known respectively for BAP1, PBRM1 and SETD2 genes. Each variation damages these genes with different severity levels. Unfortunately for most of these mutations the molecular effect is unknown, so precluding a severity classification. Moreover, the huge number of these gene mutations does not allow to perform experimental assays for each of them. By bioinformatic tools, we performed predictions of the molecular effects of all mutations lying in BAP1, PBRM1 and SETD2 genes. Our results allow to distinguish whether a mutation alters protein function directly or by splicing pattern destruction and how much severely. This classification could be useful to reveal correlation with patients’ outcome, to guide experiments, to select the variations that are worth to be included in translational/association studies, and to direct gene therapies.

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          BAP1 loss defines a new class of renal cell carcinoma

          The molecular pathogenesis of renal cell carcinoma (RCC) is poorly understood. Whole-genome and exome sequencing followed by innovative tumorgraft analyses (to accurately determine mutant allele ratios) identified several putative two-hit tumor suppressor genes including BAP1. BAP1, a nuclear deubiquitinase, is inactivated in 15% of clear-cell RCCs. BAP1 cofractionates with and binds to HCF-1 in tumorgrafts. Mutations disrupting the HCF-1 binding motif impair BAP1-mediated suppression of cell proliferation, but not H2AK119ub1 deubiquitination. BAP1 loss sensitizes RCC cells in vitro to genotoxic stress. Interestingly, BAP1 and PBRM1 mutations anticorrelate in tumors (P=3×10−5), and combined loss of BAP1 and PBRM1 in a few RCCs was associated with rhabdoid features (q=0.0007). BAP1 and PBRM1 regulate seemingly different gene expression programs, and BAP1 loss was associated with high tumor grade (q=0.0005). Our results establish the foundation for an integrated pathological and molecular genetic classification of RCC, paving the way for subtype-specific treatments exploiting genetic vulnerabilities.
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            Improved splice site detection in Genie.

            We present an improved splice site predictor for the genefinding program Genie. Genie is based on a generalized Hidden Markov Model (GHMM) that describes the grammar of a legal parse of a multi-exon gene in a DNA sequence. In Genie, probabilities are estimated for gene features by using dynamic programming to combine information from multiple content and signal sensors, including sensors that integrate matches to homologous sequences from a database. One of the hardest problems in genefinding is to determine the complete gene structure correctly. The splice site sensors are the key signal sensors that address this problem. We replaced the existing splice site sensors in Genie with two novel neural networks based on dinucleotide frequencies. Using these novel sensors, Genie shows significant improvements in the sensitivity and specificity of gene structure identification. Experimental results in tests using a standard set of annotated genes showed that Genie identified 86% of coding nucleotides correctly with a specificity of 85%, versus 80% and 84% in the older system. In further splice site experiments, we also looked at correlations between splice site scores and intron and exon lengths, as well as at the effect of distance to the nearest splice site on false positive rates.
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              PredictSNP: Robust and Accurate Consensus Classifier for Prediction of Disease-Related Mutations

              Single nucleotide variants represent a prevalent form of genetic variation. Mutations in the coding regions are frequently associated with the development of various genetic diseases. Computational tools for the prediction of the effects of mutations on protein function are very important for analysis of single nucleotide variants and their prioritization for experimental characterization. Many computational tools are already widely employed for this purpose. Unfortunately, their comparison and further improvement is hindered by large overlaps between the training datasets and benchmark datasets, which lead to biased and overly optimistic reported performances. In this study, we have constructed three independent datasets by removing all duplicities, inconsistencies and mutations previously used in the training of evaluated tools. The benchmark dataset containing over 43,000 mutations was employed for the unbiased evaluation of eight established prediction tools: MAPP, nsSNPAnalyzer, PANTHER, PhD-SNP, PolyPhen-1, PolyPhen-2, SIFT and SNAP. The six best performing tools were combined into a consensus classifier PredictSNP, resulting into significantly improved prediction performance, and at the same time returned results for all mutations, confirming that consensus prediction represents an accurate and robust alternative to the predictions delivered by individual tools. A user-friendly web interface enables easy access to all eight prediction tools, the consensus classifier PredictSNP and annotations from the Protein Mutant Database and the UniProt database. The web server and the datasets are freely available to the academic community at http://loschmidt.chemi.muni.cz/predictsnp.
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                Author and article information

                Journal
                Oncotarget
                Oncotarget
                ImpactJ
                Oncotarget
                Impact Journals LLC
                1949-2553
                13 October 2015
                7 October 2015
                : 6
                : 31
                : 32161-32168
                Affiliations
                1 Department of Specialistic Clinical and Odontostomatological Sciences, Polytechnic University of Marche Region, Ancona, Italy
                2 Department of Medical Oncology, AOU Ospedali Riuniti – Polytechnic University of the Marche Region, Ancona, Italy
                3 Department of Medical Oncology, University of Verona, Verona, Italy
                4 Medical Oncology Unit of Urogenital and Head & Neck Tumors, European Institute of Oncology, Milan, Italy
                5 Department of Medical Oncology, Campus Bio-Medico University of Rome, Rome, Italy
                6 Pathological Anatomy, Polytechnic University of the Marche Region School of Medicine United Hospitals, Ancona, Italy
                Author notes
                Correspondence to: Francesco Piva, f.piva@ 123456univpm.it
                Article
                10.18632/oncotarget.5147
                4741666
                26452128
                3d8ae707-71a9-4ee1-bbb1-76f710056dbf
                Copyright: © 2015 Piva et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 15 July 2015
                : 12 August 2015
                Categories
                Research Paper

                Oncology & Radiotherapy
                mutations,polymorphisms,predictions,rcc,computational
                Oncology & Radiotherapy
                mutations, polymorphisms, predictions, rcc, computational

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