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dc.contributor.authorFernandez-Rovira, Alicia
dc.contributor.authorLavado-Valenzuela, Rocio
dc.contributor.authorBerciano-Guerrero, Miguel Angel
dc.contributor.authorNavas-Delgado, Ismael 
dc.contributor.authorAldana-Montes, Jose Francisco 
dc.date.accessioned2017-10-13T09:23:51Z
dc.date.available2017-10-13T09:23:51Z
dc.date.created2017-10-13
dc.date.issued2017-09-26
dc.identifier.citationFernandez-Rovira A, Lavado-Valenzuela R, Berciano Guerrero MÁ, Navas-Delgado I, Aldana-Montes JF. (2017) Melanoma expression analysis with Big Data technologies. PeerJ Preprints 5:e3260v2 https://doi.org/10.7287/peerj.preprints.3260v2es_ES
dc.identifier.urihttp://hdl.handle.net/10630/14629
dc.description.abstractMelanoma is a highly immunogenic tumor. Therefore, in recent years physicians have incorporated drugs that alter the immune system into their therapeutic arsenal against this disease, revolutionizing in the treatment of patients in an advanced stage of the disease. This has led us to explore and deepen our knowledge of the immunology surrounding melanoma, in order to optimize its approach. At present, immunotherapy for metastatic melanoma is based on stimulating an individual’s own immune system through the use of specific monoclonal antibodies. The use of immunotherapy has meant that many of patients with melanoma have survived and therefore it constitutes a present and future treatment in this field. At the same time, drugs have been developed targeting specific mutations, specifically BRAF, resulting in large responses in tumor regression (set up in this clinical study to 18 months), as well as a higher percentage of long-term survivors. The analysis of the gene expression changes and their correlation with clinical changes can be developed using the tools provided by those companies which currently provide gene expression platforms. The gene expression platform used in this clinical study is NanoString, which provides nCounter. However, nCounter has some limitations as the type of analysis is restricted to a predefined set, and the introduction of clinical features is a complex task. This paper presents an approach to collect the clinical information using a structured database and a Web user interface to introduce this information, including the results of the gene expression measurements, to go a step further than the nCounter tool. As part of this work, we present an initial analysis of changes in the gene expression of a set of patients before and after targeted therapy. This analysis has been carried out using Big Data technologies (Apache Spark) with the final goal being to scale up to large numbers of patients, even though this initial study has a limited number of enrolled patients (12 in the first analysis). This is not a Big Data problem, but the underlaying study aims at targeting 20 patients per year just in Málaga, and this could be extended to be used to analyze the 3.600 patients diagnosed with melanoma per year.es_ES
dc.description.sponsorshipUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. This work was funded in part by Grants TIN2014-58304-R (Ministerio de Ciencia e Innovación) and P11-TIC-7529 and P12-TIC-1519 (Plan Andaluz de Investigación, Desarrollo e Innovación). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.es_ES
dc.language.isoenges_ES
dc.publisherPeerJes_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectCancer - Proceso de datos - Congresoses_ES
dc.subject.otherMelanonaes_ES
dc.subject.otherBig Dataes_ES
dc.titleMelanoma expression analysis with Big Data technologieses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.centroE.T.S.I. Informáticaes_ES
dc.relation.eventtitleNETTAB 2017es_ES
dc.relation.eventplacePalermo, Italiaes_ES
dc.relation.eventdate16/10/2017es_ES
dc.identifier.orcidhttp://orcid.org/0000-0001-7819-5416es_ES
dc.cclicenseby-nc-ndes_ES


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