SARS-CoV-2 complete genome sequencing from the Italian Campania region using a highly automated next generation sequencing system

Since the first complete genome sequencing of SARS-CoV-2 in December 2019, more than 550,000 genomes have been submitted into the GISAID database. Sequencing of the SARS-CoV-2 genome might allow identification of variants with increased contagiousness, different clinical patterns and/or different response to vaccines. A highly automated next generation sequencing (NGS)-based method might facilitate an active genomic surveillance of the virus

Simultaneous detection of lung fusions using a multiplex RT-PCR next generation sequencing-based approach:A multi-institutional research study

Contains fulltext : 195300.pdf (publisher's version ) (Open Access

Optimizing response to gefitinib in the treatment of non-small-cell lung cancer

Pietro Carotenuto1, Cristin Roma1, Anna Maria Rachiglio1, Raffaella Pasquale1, Renato Franco2, Giuseppe Antinolfi3, Francovito Piantedosi4, Alfonso Illiano5, Gerardo Botti2, Alessandro Morabito6, Nicola Normanno7, Antonella De Luca71Pharmacogenomic Laboratory, CROM – Centro Ricerche Oncologiche di Mercogliano, Avellino, Italy; 2Surgical Pathology Unit, INT Fondazione "G. Pascale", Naples, Italy; 3Surgical Pathology Unit, Monaldi Hospital, Naples, Italy; 4Pneumoncology DH Unit, Monaldi Hospital, Naples, Italy; 5Pneumoncology Unit, Monaldi Hospital, Naples, Italy; 6Medical Oncology, Thoracic Department, INT Fondazione "G. Pascale", Naples, Italy; 7Cell Biology and Biotherapy Unit, INT Fondazione "G. Pascale", Naples, ItalyAbstract: The epidermal growth factor receptor (EGFR) is expressed in the majority of non-small-cell lung cancer (NSCLC). However, only a restricted subgroup of NSCLC patients respond to treatment with the EGFR tyrosine kinase inhibitor (EGFR TKI) gefitinib. Clinical trials have demonstrated that patients carrying activating mutations of the EGFR significantly benefit from treatment with gefitinib. In particular, mutations of the EGFR TK domain have been shown to increase the sensitivity of the EGFR to exogenous growth factors and, at the same time, to EGFR TKIs such as gefitinib. EGFR mutations are more frequent in patients with particular clinical and pathological features such as female sex, nonsmoker status, adenocarcinoma histology, and East Asian ethnicity. A close correlation was found between EGFR mutations and response to gefitinib in NSCLC patients. More importantly, randomized Phase III studies have shown the superiority of gefitinib compared with chemotherapy in EGFR mutant patients in the first-line setting. In addition, gefitinib showed a good toxicity profile with an incidence of adverse events that was significantly lower compared with chemotherapy. Therefore, gefitinib is a major breakthrough for the management of EGFR mutant NSCLC patients and represents the first step toward personalized treatment of NSCLC.Keywords: gefitinib, EGFR, NSCLC, EGFR mutation

A multiple network-based bioinformatics pipeline for the study of molecular mechanisms in oncological diseases for personalized medicine

Assessment of genetic mutations is an essential element in the modern era of personalized cancer treatment. Our strategy is focused on 'multiple network analysis' in which we try to improve cancer diagnostics by using biological networks. Genetic alterations in some important hubs or in driver genes such as BRAF and TP53 play a critical role in regulating many important molecular processes. Most of the studies are focused on the analysis of the effects of single mutations, while tumors often carry mutations of multiple driver genes. The aim of this work is to define an innovative bioinformatics pipeline focused on the design and analysis of networks (such as biomedical and molecular networks), in order to: (1) improve the disease diagnosis; (2) identify the patients that could better respond to a given drug treatment; and (3) predict what are the primary and secondary effects of gene mutations involved in human diseases

A multiple network-based bioinformatics pipeline for the study of molecular mechanisms in oncological diseases for personalized medicine

Assessment of genetic mutations is an essential element in the modern era of personalized cancer treatment. Our strategy is focused on 'multiple network analysis' in which we try to improve cancer diagnostics by using biological networks. Genetic alterations in some important hubs or in driver genes such as BRAF and TP53 play a critical role in regulating many important molecular processes. Most of the studies are focused on the analysis of the effects of single mutations, while tumors often carry mutations of multiple driver genes. The aim of this work is to define an innovative bioinformatics pipeline focused on the design and analysis of networks (such as biomedical and molecular networks), in order to: (1) improve the disease diagnosis; (2) identify the patients that could better respond to a given drug treatment; and (3) predict what are the primary and secondary effects of gene mutations involved in human diseases

FGFR Fusions in Cancer: From Diagnostic Approaches to Therapeutic Intervention

Fibroblast growth factor receptors (FGFRs) are tyrosine kinase receptors involved in many biological processes. Deregulated FGFR signaling plays an important role in tumor development and progression in different cancer types. FGFR genomic alterations, including FGFR gene fusions that originate by chromosomal rearrangements, represent a promising therapeutic target. Next-generation-sequencing (NGS) approaches have significantly improved the discovery of FGFR gene fusions and their detection in clinical samples. A variety of FGFR inhibitors have been developed, and several studies are trying to evaluate the efficacy of these agents in molecularly selected patients carrying FGFR genomic alterations. In this review, we describe the most frequent FGFR aberrations in human cancer. We also discuss the different approaches employed for the detection of FGFR fusions and the potential role of these genomic alterations as prognostic/predictive biomarkers