Prognostic impact of alternative splicing-derived hMENA isoforms in resected, node-negative, non-small-cell lung cancer

摘要: // Emilio Bria 1,2,* , Francesca Di Modugno 3,* Isabella Sperduti 4 Pierluigi Iapicca 3 Paolo Visca 5 Gabriele Alessandrini 6 Barbara Antoniani Sara Pilotto 2 Vienna Ludovini 7 Jacopo Vannucci 8 Guido Bellezza 9 Angelo Sidoni Giampaolo Tortora Derek C. Radisky 10 Lucio Crino Francesco Cognetti 1 Facciolo Marcella Mottolese Michele Milella 1,* and Paola Nistico Department of Medical Oncology, Regina Elena National Cancer Institute, Rome, Italy Azienda Ospedaliera Universitaria Integrata, University Verona, Laboratory Immunology, Biostatistics Scientific Direction, Pathology, Thoracic Surgery, Perugia, Institute Pathological Anatomy Histology, Mayo Clinic Center, Jacksonville, FL, USA * These authors contributed equally to this work Correspondence: Mottolese, email: Keywords : Lung cancer; Splicing; Biomarkers Received July 08, 2014 Accepted October 21, Published Abstract Risk assessment treatment choice remain a challenge in early non-small-cell lung cancer (NSCLC). Alternative splicing is an emerging source for diagnostic, prognostic therapeutic tools. Here, we investigated the value actin cytoskeleton regulator hMENA its isoforms, 11a hMENAΔv6, NSCLC. The epithelial isoform was expressed NSCLC lines expressing E-CADHERIN alternatively with hMENAΔv6. Enforced expression hMENAΔv6 or increased decreased invasive ability A549 cells, respectively. evaluated 248 node-negative High pan-hMENA low were only independent predictors shorter disease-free cancer-specific survival, predictor overall at multivariate analysis. Patients pan-hMENA/high fared significantly better ( P ≤0.0015) than any other subgroup. Such hybrid variable incorporated T-size number resected lymph nodes into 3-class-risk stratification model, which strikingly discriminated between different risks relapse, cancer-related death, death. model externally validated dataset 133 patients. Relative splice isoforms powerful factor NSCLC, complementing clinical parameters accurately predict individual patient risk.