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

2015; Impact Journals LLC; Volume: 6; Issue: 31 Linguagem: Inglês

10.18632/oncotarget.5147

1949-2553

Francesco Piva, Matteo Giulietti, Giulia Occhipinti, Matteo Santoni, Francesco Massari, Valeria Sotte, Roberto Iacovelli, Luciano Burattini, Daniele Santini, Rodolfo Montironi, Stefano Cascinu, Giovanni Principato,

Cancer Genomics and Diagnostics

// Francesco Piva 1, * , Matteo Giulietti 1, * , Giulia Occhipinti 1 , Matteo Santoni 2 , Francesco Massari 3 , Valeria Sotte 2 , Roberto Iacovelli 4 , Luciano Burattini 2 , Daniele Santini 5 , Rodolfo Montironi 6 , Stefano Cascinu 2 , Giovanni Principato 1 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 * These authors have contributed equally to this work Correspondence to: Francesco Piva, e-mail: f.piva@univpm.it Keywords: mutations, polymorphisms, predictions, RCC, computational Received: July 15, 2015 Accepted: August 12, 2015 Published: October 07, 2015 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.