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dc.contributor.authorAramburu Siso, Ander
dc.contributor.authorZudaire Ripa, Isabel
dc.contributor.authorPajares Villandiego, María J.
dc.contributor.authorAgorreta Arrazubi, Jackeline
dc.contributor.authorOrta Ruiz, Alberto
dc.contributor.authorLozano Escario, María D.
dc.contributor.authorGúrpide Ayarra, Alfonso
dc.contributor.authorGómez Román, José Javier 
dc.contributor.authorMartínez Climent, José A.
dc.contributor.authorJassem, Jacek
dc.contributor.authorSkrzypski, Marcin
dc.contributor.authorSuraokar, Milind
dc.contributor.authorBehrens, Carmen
dc.contributor.authorWistuba, Ignacio I.
dc.contributor.authorPío Osés, Rubén
dc.contributor.authorRubio Díaz-Cordovés, Ángel
dc.contributor.authorMontuenga Badía, Luis M.
dc.contributor.otherUniversidad de Cantabriaes_ES
dc.description.abstractBACKGROUND: The development of a more refined prognostic methodology for early non-small cell lung cancer (NSCLC) is an unmet clinical need. An accurate prognostic tool might help to select patients at early stages for adjuvant therapies. RESULTS: A new integrated bioinformatics searching strategy, that combines gene copy number alterations and expression, together with clinical parameters was applied to derive two prognostic genomic signatures. The proposed methodology combines data from patients with and without clinical data with a priori information on the ability of a gene to be a prognostic marker. Two initial candidate sets of 513 and 150 genes for lung adenocarcinoma (ADC) and squamous cell carcinoma (SCC), respectively, were generated by identifying genes which have both: a) significant correlation between copy number and gene expression, and b) significant prognostic value at the gene expression level in external databases. From these candidates, two panels of 7 (ADC) and 5 (SCC) genes were further identified via semi-supervised learning. These panels, together with clinical data (stage, age and sex), were used to construct the ADC and SCC hazard scores combining clinical and genomic data. The signatures were validated in two independent datasets (n = 73 for ADC, n = 97 for SCC), confirming that the prognostic value of both clinical-genomic models is robust, statistically significant (P = 0.008 for ADC and P = 0.019 for SCC) and outperforms both the clinical models (P = 0.060 for ADC and P = 0.121 for SCC) and the genomic models applied separately (P = 0.350 for ADC and P = 0.269 for SCC). CONCLUSION: The present work provides a methodology to generate a robust signature using copy number data that can be potentially used to any cancer. Using it, we found new prognostic scores based on tumor DNA that, jointly with clinical information, are able to predict overall survival (OS) in patients with early-stage ADC and SCC.es_ES
dc.format.extent10 p.es_ES
dc.publisherBioMed Centrales_ES
dc.rightsAtribución 3.0 España*
dc.sourceBMC Genomics. 2015 Oct 6;16(1):752es_ES
dc.subject.otherEarly stage lung canceres_ES
dc.subject.otherCopy number profilinges_ES
dc.subject.otherGene filteringes_ES
dc.subject.otherSemi-supervised learninges_ES
dc.titleCombined clinical and genomic signatures for the prognosis of early stage non-small cell lung cancer based on gene copy number alterationses_ES

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Atribución 3.0 EspañaExcept where otherwise noted, this item's license is described as Atribución 3.0 España