Development Of Statistical Methodologies And Application To Clinical Cancer Studies
Funder
National Health and Medical Research Council
Funding Amount
$428,065.00
Summary
Integrating different layers of information coming from the recent ‘-omics’ technologies can help improving the treatment and the prevention of complex diseases. In particular, the identification of molecular markers of different types can be used for better diagnostics and prognosis in cancer and immune diseases. This project will develop innovative statistical solutions to handle and make sense of the vast amount of biological data that are routinely generated in the laboratories.
Statistical Analyses Of Breast Cancer Risks For Australian BRCA1 And BRCA2 Mutation Carriers
Funder
National Health and Medical Research Council
Funding Amount
$424,628.00
Summary
About 10 years ago two genes, called BRCA1 and BRCA2, were discovered. The normal function of these genes is to prevent breast and other cancers from developing. All people have two copies of each gene, one inherited from their mother and one from their father. Women who have inherited a fault in one copy are at increased risk of breast and ovarian cancer. There has been considerable controversy about what their actual cancer risks are, especially about how those risks might depend on their age. ....About 10 years ago two genes, called BRCA1 and BRCA2, were discovered. The normal function of these genes is to prevent breast and other cancers from developing. All people have two copies of each gene, one inherited from their mother and one from their father. Women who have inherited a fault in one copy are at increased risk of breast and ovarian cancer. There has been considerable controversy about what their actual cancer risks are, especially about how those risks might depend on their age. We have already conducted studies on this and have developed the necessary statistical methods to address these issues by analysing data from the families in which there are faulty genes. In this study we propose to use two large Australian studies, one of families with multiple-cases of breast cancer (Kathleen Cuningham Consortium for Research on Familial Breast Cancer; kConFab) and the other of the families of women with breast cancer chosen, irrespective of their family cancer histories, through the Victorian and NSW Cancer Registries (Australian Breast Cancer Family Study; ABCFS). A large amount of work has already been conducted to identify these families and test them for faults in BRCA1 and BRCA2. There are over 350 families who carry faults, making this one of the largest studies of its type in the world. We will check the cancer histories of these families and determine which members have, or are likely to have, inherited a faulty gene. We will then estimate the breast and ovarian cancer risks accurately, and with much more precision, than has been done previously. We will also use these large datasets to develop a simple method to identify which Australian women are most likely to carry a fault in BRCA1 or BRCA2, based on their personal and family cancer histories. This study will assist genetic counsellors inform Australian women who consider mutation testing for BRCA1 and BRCA2 about their cancer risks, and help make breast cancer genetics more cost effective.Read moreRead less
The Stemformatics gene expression compendium: development of multivariate statistical approaches for cross platform analyses. Scientific data is gathered in many different forms, but there are significant gaps in our ability to analyse multiple datasets when generated on different pieces of equipment. This project will study three typical research questions in stem cell biology to develop new analytical approaches to help solve this major data gap.
I am a bioinformatician conducting methodological research in statistical functional genomics. I use designed experiments involving highthroughput gene expression technologies to make inferences about gene function and to make discoveries of medical signi
Vertically integrated statistical modelling in multi-layered omics studies. This project will develop an adaptive statistical modelling framework that uses information from many omics data to discover a collection of stable and clinically significant biomarkers. Results will enable researchers to better understand the underlying biological system of complex diseases such as cancer, Alzheimer and diabetes.