The correlation between the mutation of protein kinase genes and the clinical characteristics of breast cancer progression

Poster


Birney, Genevieve, Liu, Guang B. and Daggard, Grant. 2010. "The correlation between the mutation of protein kinase genes and the clinical characteristics of breast cancer progression." ASMR Queensland Postgraduate Student Conference 2010. St Lucia, Australia 02 Jun 2010 St. Lucia, Australia.
Paper/Presentation Title

The correlation between the mutation of protein kinase genes and the clinical characteristics of breast cancer progression

Presentation TypePoster
AuthorsBirney, Genevieve (Author), Liu, Guang B. (Author) and Daggard, Grant (Author)
Year2010
Place of PublicationSt. Lucia, Australia
Web Address (URL) of Paperhttp://www.asmr.org.au/MRWQld.html
Conference/EventASMR Queensland Postgraduate Student Conference 2010
Event Details
ASMR Queensland Postgraduate Student Conference 2010
Event Date
02 Jun 2010
Event Location
St Lucia, Australia
Abstract

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.

Keywordsbreast cancer; protein kinase genes; mutation; correlation
ANZSRC Field of Research 2020310114. Systems biology
310299. Bioinformatics and computational biology not elsewhere classified
310505. Gene expression (incl. microarray and other genome-wide approaches)
Public Notes

Only abstracts published in the conference proceedings, as supplied here.

Byline AffiliationsDepartment of Biological and Physical Sciences
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Hu, Hong, Li, Jiuyong, Plank, Ashley, Wang, Hua and Daggard, Grant. 2006. "A comparative study of classification methods for microarray data analysis." Christen, Peter, Kennedy, Paul J., Li, Jiuyong, Simoff, Simeon J. and Williams, Graham J. (ed.) 5th Australasian Conference on Data Mining and Analystics (AusDM 2006). Sydney, Australia 29 - 30 Nov 2006 Canberra, Australia.
Codon usage analysis of prokaryotic mechanosensation genes
Liu, Guang B., Chen, Rong, Martinac, Boris and Zhao, Kong-Nan. 2006. "Codon usage analysis of prokaryotic mechanosensation genes." Gaeta, Bruno (ed.) Bioinformatics Australia 2006. Sydney, Australia 21 - 22 Nov 2006 Australia.
A metabolite-deficient mutant of Bordetella pertussis that induces secretory IgA and cell-mediated immunity in mice vaccinated by the intranasal route
Mukkur, T. K. S., Cornford-Nairn, Renee A., Rossetti, T., Kershaw, S., Fry, Scott Robert, Xu, Youhong and Daggard, Grant A.. 2005. "A metabolite-deficient mutant of Bordetella pertussis that induces secretory IgA and cell-mediated immunity in mice vaccinated by the intranasal route." Fang, Ferric C. (ed.) 105th American Society for Microbiology: General Meeting. Atlanta, Georgia 05 - 09 Jun 2005 Washington.
Preparation of total RNA from a very small wheat embryo suitable for differential display
Chen, Yihua, Weining, Song and Daggard, Grant. 2003. "Preparation of total RNA from a very small wheat embryo suitable for differential display ." Annals of Applied Biology. 143 (2), pp. 261-264. https://doi.org/10.1111/j.1744-7348.2003.tb00293.x
Targetting AFLP-DNA markers to specific traits and chromosome regions
Campbell, A. W., Daggard, G., Békés, F., Pedler, A., Sutherland, M. W and Appels, R.. 2001. "Targetting AFLP-DNA markers to specific traits and chromosome regions ." Australian Journal of Agricultural Research. 52 (11-12), pp. 1153-1160. https://doi.org/10.1071/ar01028
Enhanced shoot regeneration in nine Australian wheat cultivars by spermidine and water stress treatments
Khanna, Harjeet K. and Daggard, Grant E.. 2001. "Enhanced shoot regeneration in nine Australian wheat cultivars by spermidine and water stress treatments ." Australian Journal of Plant Physiology. 28 (12), pp. 1243-1247. https://doi.org/10.1071/pp01115