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.

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Melanoma expression analysis with Big Data technologies

10.7287/peerj.preprints.3260v1 ◽

2017 ◽

Author(s):

Alicia Fernandez-Rovira ◽

Rocio Lavado ◽

Miguel Ángel Berciano Guerrero ◽

Ismael Navas-Delgado ◽

José F Aldana-Montes

Keyword(s):

Gene Expression ◽

Big Data ◽

Immune System ◽

Clinical Study ◽

Tumor Regression ◽

Scale Up ◽

Clinical Information ◽

Initial Study ◽

Expression Platform ◽

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.

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Computational Strategies for Scalable Genomics Analysis

Genes ◽

10.3390/genes10121017 ◽

2019 ◽

Vol 10 (12) ◽

pp. 1017

Author(s):

Lizhen Shi ◽

Zhong Wang

Keyword(s):

Big Data ◽

Computer Science ◽

Scale Up ◽

Next Generation Dna Sequencing ◽

Significant Challenge ◽

Sequencing Technologies ◽

Software Algorithms ◽

Big Data Technologies ◽

Pros And Cons ◽

Computing Infrastructure

The revolution in next-generation DNA sequencing technologies is leading to explosive data growth in genomics, posing a significant challenge to the computing infrastructure and software algorithms for genomics analysis. Various big data technologies have been explored to scale up/out current bioinformatics solutions to mine the big genomics data. In this review, we survey some of these exciting developments in the applications of parallel distributed computing and special hardware to genomics. We comment on the pros and cons of each strategy in the context of ease of development, robustness, scalability, and efficiency. Although this review is written for an audience from the genomics and bioinformatics fields, it may also be informative for the audience of computer science with interests in genomics applications.

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Big data in yeast systems biology

FEMS Yeast Research ◽

10.1093/femsyr/foz070 ◽

2019 ◽

Vol 19 (7) ◽

Cited By ~ 1

Author(s):

Rosemary Yu ◽

Jens Nielsen

Keyword(s):

Gene Expression ◽

Big Data ◽

Systems Biology ◽

Model Organism ◽

Cell Factory ◽

Yeast Saccharomyces Cerevisiae ◽

Complex Interactions ◽

Biological Insight ◽

Big Data Technologies ◽

Gene Expression Dynamics

ABSTRACT Systems biology uses computational and mathematical modeling to study complex interactions in a biological system. The yeast Saccharomyces cerevisiae, which has served as both an important model organism and cell factory, has pioneered both the early development of such models and modeling concepts, and the more recent integration of multi-omics big data in these models to elucidate fundamental principles of biology. Here, we review the advancement of big data technologies to gain biological insight in three aspects of yeast systems biology: gene expression dynamics, cellular metabolism and the regulation network between gene expression and metabolism. The role of big data and complementary modeling approaches, including the expansion of genome-scale metabolic models and machine learning methodologies, are discussed as key drivers in the rapid advancement of yeast systems biology.

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