With mutations. Even though, CNAs profiles combined

the advent of high-throughput techniques and coupled with large-scale consortia
projects, an unprecedented amount of biological data in cancer has been
generated. This huge amount of data coupled with the abundance of clinical and
phenotypic attributes are a potential source for getting novel insights into the
biology driving tumorigenesis and cancer malignancy. However, a prevailing
limitation for research groups is bringing their data together in order to find
significant associations across different sources. Therefore, the challenge
remains in the effective mining of ‘big data’ for new biological findings,
which highlight the need to develop novel and user-friendly bioinformatic tools
that allow researchers to perform the comprehensive integration of different
and large data-types sets from distinct platforms.

the past twenty years, cancer genome
research has been revolutionized by array
comparative genomic hybridization (aCGH) technologies which have allowed the
detection of copy number aberrations (CNAs) with high resolution. CNAs
contribute to the initiation and progression of human cancers. Their
characterization has enabled the discovery of cancer-causing genes and the
development of diagnostic, prognostic and therapeutic strategies (Beroukhim R.,
et al 2010). Recurrent CNAs across tumors are by themselves essential informative
surrogates of driver mutations. Even though, CNAs profiles combined with
further high-throughput data can be a much more straightforward approach to
define the key pathways of a tumor and can have more decisive clinical

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arrayMap platform (www.arraymap.org) provides a flexible tool for
meta-analysis and systems level data integration of oncogenomic CNA data. Unlike other resources for CNAs datasets,
which are restricted to specific aCGH, single institutions or diseases,
arrayMap is targeted to all CNA data, independently of the platform or type of
cancer. Besides, it provides a global collection of publicly accessible oncogenomic array data which allow further
comparison with other cancer datasets (Cai, H., Kumar, N.,
and Baudis,
M. 2012). In addition, far from serving
just as a raw data repositories such as NCBI’s Gene Expression Omnibus (GEO) or
Ensembl ArrayExpress (AE), users’ samples can be visualized and analyzed via an
intuitive interface and computational tools are provided for biostatistical

we illustrate how the suite provided by ArrayMap can be enriched and used to
comprehensively integrate and analyze
results from genomics and digital pathology with its associated clinical features
to make the transition from data to actionable and transitional knowledge. In
addition, we will describe how complex data can be
simplified into a single metadata file for its integrated analysis in
this database.

represents the most common embryonal malignancy affecting young children. This
solid tumor is characterized by its complexity in terms of clinical
presentation, course, prognosis and genetic heterogeneity (10.1038/nrc3526). Despite
the large and heterogeneous data generated by the different omic techniques and
research groups, the findings have not been funneled
in the development of targeted therapies against the different disease
subgroups, especially for high-risk (HR) patients. When it comes to treatment
options, tumor genetic heterogeneity has not been taking into account and
patients with well-known distinct clinical and biological characteristics
considered as an undivided group of NB (doi:10.1080/23723556.2015.1079671.).
This particularity contributes to making
the treatment of patients with high-risk (HR) NB very unfavorable, leading to
an incurable disease with overall survival rates of less than 40%
(doi:10.1002/cncr.29706.). Therefore, it is imperative to integrate this
complex genomic and biological data to best refine patient stratification and
to allow a better classification of tumors and a tailored and more effective
therapy for HR NB patients.

the present study, we characterized the genomic imbalances and morphometric
alterations in highly malignant or aggressive tumors. As recently described,
not solely genetic alterations contribute to cancer progression but also the
tumor microenvironment (TME) plays a key role (10.1016/j.canlet.2015.11.017).
We focused on the contribution of the immune cells, the extracellular matrix
(ECM) and the vascularization associated with NB. These data will be together to
detect potential biomarkers or driving factors that define the switch from
favorable to aggressive HR disease.



the exemplified application in NB, this methodology can be broadly applicable to
other types of cancer and customized with the variables or data that each
research group is analyzing. We firmly
believe that data integration despite being a computational challenge is most
certainly an opportunity and hope that this article could help other
researchers to identify disease-relevant biological interactions that were
undetectable using traditional single-omic analysis approaches.



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