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Cancer Genomics

Cancer Genomics

The aim is to develop software techniques and tools enabling conversion of genetic profiles of tissue into clinically useful knowledge assisting oncologists and pathologists.

The project is developed in close collaboration with the Peter MacCallum Cancer Centre  (PMCC). It aims to address knowledge extraction challenges arising from a series of cancer genomic datasets generated at the PMCC. In particular, we build software tools which can utilise molecular tissue markers for cancer diagnosis, detection of site of the origin, prediction of response to treatment, understanding/discovery of specific molecular processes and discovery of novel treatments.

What will this research achieve?

Cancer genomics aims to alter the way that people with cancer are diagnosed, staged, and treated. The field is currently in transition: major sites world-wide, including the PMCC, are now generating high-quality datasets involving hundreds of samples with detailed clinical information that have been analysed on complex microarrays (30,000+ elements). This includes arrays that generate different types of information such as gene expression, gene copy number and chemical modification of the DNA (methylation). Such datasets provide an excellent opportunity to develop and apply innovative approaches to data analysis and mining.

What are the key features?

The project involves three streams of activity:

1. Discovery and development of IT methodology for discerning of biologically meaningful and medically useful knowledge from microarray profiles of tissue, with a focus on cancer diagnosis and treatment:

  • Learning from a very small number of samples (VSS-learning)
  • Kernel machines and regularisation techniques
  •  Learning from structured data
  •  Feature Selection.

2. Development of practical algorithms and development of software tools for operational testing, demonstration and operational implementation of the discovered solutions.

3. Application of the results to cancer research and clinical oncology. 

Research team

Staff

Graduate Students

  • Benjamin Goudey   
  • Geoff McIntyre  
  • Fan Shi
  • Gerard Wong   

Associated Graduate Students 

  • Gad Abraham
  • Raj Gere
  • Shanika Kuruppu       

Research Highlights 

Research Publications (click here and search for CG)

Best representative publication: Pretreatment gene expression profiles can be used to predict response to neoadjuvant chemoradiotherapy in esophageal cancer.

 

Future Directions

Both the Cancer Genomics project and the Mapping Genetic Components in Crops project have been completed but form the basis for a proposal for a broader project called Diagnostic Genomics, currently under review within NICTA. The scope of the proposed project is outlined here.