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Identification of Novel Somatic TP53 Mutations in Patients with High-Grade Serous Ovarian Cancer (HGSOC) Using Next-Generation Sequencing (NGS).

Marica Garziera | Erika Cecchin | Vincenzo Canzonieri | Roberto Sorio | Giorgio Giorda | Simona Scalone | Elena De Mattia | Rossana Roncato | Sara Gagno | Elena Poletto | Loredana Romanato | Franca Sartor | Jerry Polesel | Giuseppe Toffoli
International journal of molecular sciences | 2018

Somatic mutations in TP53 are a hallmark of high-grade serous ovarian cancer (HGSOC), although their prognostic and predictive value as markers is not well defined. Next-generation sequencing (NGS) can identify novel mutations with high sensitivity, that may be repurposed as potential druggable anti-cancer targets and aid in therapeutic decisions. Here, a commercial NGS cancer panel comprising 26 genes, including TP53, was used to identify new genetic markers of platinum resistance and patient prognosis in a retrospective set of patients diagnosed with epithelial ovarian cancer. Six novel TP53 somatic mutations in untreated tumors from six distinct patients diagnosed with HGSOC were identified: TP53 c.728_739delTGGGCGGCATGA (p.Met243_Met247del, in-frame insertion or deletion (INDEL); TP53 c.795_809delGGGACGGAACAGCTT (p.Gly266_Phe270del, in-frame INDEL); TP53 c.826_827GC>AT (p.Ala276Ile, missense); TP53 c.1022insT (p.Arg342Profs*5, frameshift INDEL); TP53 c.1180delT (p.Ter394Aspfs*28, frameshift INDEL); and TP53 c.573insT (p.Gln192Serfs*17, frameshift INDEL). Novel TP53 variants were validated by classical sequencing methods and their impact on protein expression in tumors explored by immunohistochemistry. Further insights into the potential functional effect of the mutations were obtained by different in silico approaches, bioinformatics tools, and structural modeling. This discovery of previously unreported TP53 somatic mutations provides an opportunity to translate NGS technology into personalized medicine and identify new potential targets for therapeutic applications.

Pubmed ID: 29783665

Associated grants

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