Integration, analysis and meta-mining of data to aid disease diagnosis, prognosis and treatment response
Marketa Zvelebil1, Costas Mitsopoulos2
1Breakthrough Breast Cancer Research, London, UK, 2Institute for Cancer Research, London, UK
Proffered paper presentation
Background
In systems biology, biologically relevant quantitative modelling of
physiological processes requires the integration of experimental data from
diverse sources. Recent developments in high-throughput methodologies enable
analysis on a previously unprecedented scale, causing a deluge of data in
public databases. Effective integration and analysis of this data can lead to
better understanding and treatment of breast cancer with the minimum of side effects.However, a
key requirement of this analysis is the understanding
of the networks of interactions between cellular components.
Methods
A major step in building this understanding
is the combinatorial analysis of data from different experimental sources. This
requires not only a wide range of experimental approaches, but also a central,
integrated database and associated analysis and data-mining tools. For example,
microarray technology has enabled the development of a range of genome-scale
analyses, such as analysis of DNA copy number (aCGH), promoter methylation, SNP
genotyping and gene expression. We have
developed an online database with associated analysis and data-mining tools to
facilitate integration of RNAi, aCGH and gene expression data with networks and
pathways.We can also cross-correlate SNP
genotyping, methylation, drugs/ligand and protein structure data as well as
Next Generation Sequencing to facilitate the detailed mapping of key pathways
involved in breast cancer.
Results
Combinatorial
analysis and integration with network and pathway information is rapidly
emerging as a powerful technique to identify the most biologically significant
results from these large, noisy datasets.
Conclusion
This integration and mathematical modelling
approach should aid disease diagnosis, prognosis and treatment response.
