Gastric Cancer: Early detection of disease, relapse and prediction of extent of disease

Funding Activity

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Funded Activity Summary

Gastric cancer (GC) is the second commonest cause of cancer in the world. The mainstay of treatment for GC is surgical resection, but despite improvements in surgical interventions the mortality rate remains high. The 5 year survival rate of GC is about 30% over 5 years. Accurate staging is fundamental to the management of GC and current investigations are inadequate. It has become possible to measure the activity of thousands of genes to identify those genes that predict whether a patient will survive or succumb to their disease. We propose to use gene expression profiling to predict the risk of recurrence of gastric cancer in patients. We will examine over 270 tumours and use an independent group of patients to evaluate the test. We aim to develop a test that will help the clinician decide the type of surgical resection to perform or whether to give adjuvant chemotherapy. The test may also guide the use of more specific anticancer drugs. Early detection of GC is very important because patients with early stage GC have better outcome. We have already analysed over 60 GC tumours with microarrays and found genes that are specifically expressed by the tumours that are potential candidates as cancer markers. We plan to examine more cases of GC, both to find more genes and validate our candidate genes as tumour markers. We also want to look for patterns of proteins in blood of patients that identifies GC and use this pattern to follow patient progress to treatment.

Funded Activity Details

Start Date: 01-01-2004

End Date: 01-01-2006

Funding Scheme: NHMRC Project Grants

Funding Amount: $421,800.00

Funder: National Health and Medical Research Council

Research Topics

ANZSRC Field of Research (FoR)

Sport and exercise nutrition

ANZSRC Socio-Economic Objective (SEO)

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Other Keywords

Early detection | Gastric Cancer | Prediction of prognosis | Staging | Tumour marker | biomarker | genomics | microarray | proteomics