Birney, Genevieve and Liu, Guang B. and Daggard, Grant (2010) The correlation between the mutation of protein kinase genes and the clinical characteristics of breast cancer progression. In: ASMR Queensland Postgraduate Student Conference 2010, 2 June 2010, St Lucia, Australia.
|HTML Citation||EndNote||MODS||Dublin Core||Reference Manager|
Full text available as:
|PDF (Accepted Version - Abstract) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader|
Official URL: http://www.asmr.org.au/MRWQld.html
It is accepted that breast cancer (BC) is a heterogeneous disease. In order to investigate BC as a group of disease sub-types, the varying clinical characteristics of BC patients must be considered. In this project a series of clinical, pathological, genetic and genomic data, retrieved from multiple data repositories, will be reviewed for selection in a large-scale meta-analysis and then categorised into 5 sub-groups (Luminal A, Luminal B, Basal, HER2 and Normal). The meta-analysis is primarily designed to ascertain if a correlation exists between the mutation of protein kinase (PK) genes and BC progression. As PK genes play important roles in regulating most cellular processes (e.g. cell proliferation, differentiation and apoptosis), it is no surprise that deregulated PK activity is a frequent cause of disease, and that PK genes are often oncogenes. The meta-analysis objectives are two-fold: 1. To conduct an integrative meta-analysis of the differential gene expression of the PK gene family between clinical categories of BC progression (low vs high proliferation; luminal vs basal tissue; and grade 1 vs grade 3 tumours). Results from the meta-analysis will generate a ranked list of PK gene expression profiles observed in BC progression. 2. Through the use of powerful bioinformatics tools and sequence analysis interfaces the ranked PK list will be used to direct investigations into the correlations between: codon usage bias; aberrant epigenetic factors; somatic mutations; and observed structural/functional changes of deregulated PK genes in different BC progression categories. To address these objectives a series of in silico bioinformatics experiments have been designed. A software program (MYGEO) has been specifically written for: multiple dataset download; calculation of p-values between BC progression groups; finding Q-values to control for the false discovery rate over multiple dataset comparisons; and to perform permutation testing on the ranked PK gene list; and 2D/3D sequence analysis functions for the analysis of structure/function relationships in significantly differentiated PK genes in BC progression. This project will benefit our understanding of the complex system of BC biology by identifying significantly deregulated PK genes in BC progression. The results will identify BC biomarkers and structural/functional locations within PK genes not yet elucidated, thus providing new directions for the development of PK inhibitors and improving the effectiveness of current BC treatment strategies.
|Item Type:||Conference or Workshop Item (Commonwealth Reporting Category E) (Poster)|
|Additional Information:||Only abstracts published in the conference proceedings, as supplied here.|
|Uncontrolled Keywords:||breast cancer; protein kinase genes; mutation; correlation|
|Fields of Research (FOR2008):||06 Biological Sciences > 0601 Biochemistry and Cell Biology > 060114 Systems Biology|
06 Biological Sciences > 0601 Biochemistry and Cell Biology > 060102 Bioinformatics
06 Biological Sciences > 0604 Genetics > 060405 Gene Expression (incl. Microarray and other genome-wide approaches)
|Socio-Economic Objective (SEO2008):||E Expanding Knowledge > 97 Expanding Knowledge > 970106 Expanding Knowledge in the Biological Sciences|
|Deposited On:||15 Nov 2010 15:26|
|Last Modified:||23 Jun 2011 14:23|
Archive Staff Only: edit this record