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News

Queen's University in Canada implements Xceed's Ziplex tool for translational research

Xceed Molecular : 16 September, 2008  (Application Story)
Queen's University in Kingston, Ontario, Canada is implementing Xceed's Ziplex System as a critical tool to advance its translational research.
Harriet Feilotter, PhD, FCCMG, of the University's Department of Pathology and Molecular Medicine, is the principal investigator of a study that will use the Ziplex System to validate her team's gene expression signature to predict the clinical outcomes in follicular lymphoma.

The most common form of non-Hodgkin lymphoma, accounting for one in four of all cases, follicular lymphoma is a slow-growing cancer that arises from B-lymphocytes, a type of white blood cell, and mainly affects older adults - men and women in equal numbers. The mean age of diagnosis is between 50 and 60 years of age and because its symptoms are subtle in its earliest stages, the disease often can remain untreated for a long time before it becomes symptomatic. Although most patients have good outcomes and can live long and productive lives, in a substantial subset of patients, the disease can take a more aggressive course, resulting in shorter survival times.

'The work that Dr Feilotter and her team at Queen's University have been doing to develop novel diagnostic and prognostic tools in cancer shows great promise, and we are excited to be a part of it,' said Xceed president and chief executive officer, David Deems. 'Her follicular lymphoma research, which includes the use of both archival and fresh frozen tissues as sources of samples, has implications far beyond this particular research study. We look forward to a productive partnership.'

Dr Feilotter said, 'Our previous research identified a number of genes whose expression levels in primary follicular lymphoma were correlated with a five-year outcome. The Ziplex System will facilitate and streamline the critical next phase of our research to validate the signature. Currently there are no molecular diagnostic tools on the market that can predict which primary tumours will be aggressive. Having an easy way to accurately categorize an individual patient's risk will help clinicians determine the treatment plan that is most likely to result in a positive health outcome.'
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