Melanoma expression analysis with Big Data technologies

Melanoma 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.Universidad 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

Genomic Relationships, Novel Loci, and Pleiotropic Mechanisms across Eight Psychiatric Disorders

Genetic influences on psychiatric disorders transcend diagnostic boundaries, suggesting substantial pleiotropy of contributing loci. However, the nature and mechanisms of these pleiotropic effects remain unclear. We performed analyses of 232,964 cases and 494,162 controls from genome-wide studies of anorexia nervosa, attention-deficit/hyper-activity disorder, autism spectrum disorder, bipolar disorder, major depression, obsessive-compulsive disorder, schizophrenia, and Tourette syndrome. Genetic correlation analyses revealed a meaningful structure within the eight disorders, identifying three groups of inter-related disorders. Meta-analysis across these eight disorders detected 109 loci associated with at least two psychiatric disorders, including 23 loci with pleiotropic effects on four or more disorders and 11 loci with antagonistic effects on multiple disorders. The pleiotropic loci are located within genes that show heightened expression in the brain throughout the lifespan, beginning prenatally in the second trimester, and play prominent roles in neurodevelopmental processes. These findings have important implications for psychiatric nosology, drug development, and risk prediction.Peer reviewe