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news.Applied Biosystems, an Applera Corporation business has expanded its collaboration with Stanford University and University of Miami to continue the study of genetic biomarkers associated with treatment response and survival in the most common form of non-Hodgkin's lymphoma.
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The collaborators published their initial findings in April 2004 in a New England Journal of Medicine article. While the previous study was based on samples from patients receiving standard chemotherapy, the second retrospective study will seek to further validate the predictive value of the six genes in a larger study using samples from patients who were treated with a combination of standard chemotherapy and Genentech and Biogen Idec’s Rituxan (rituximab) therapy, and whose outcomes are already known.
The researchers also will begin a prospective study to follow patients from diagnosis in order to evaluate if the six genes can predict whether or not patients respond to the combination of chemotherapy and Rituxan, and whether the biomarkers correctly identify those less likely to survive.
While Rituxan is not currently indicated for diffuse large B cell lymphoma (DLBCL), it is being investigated for the treatment of DLBCL due to its success in treating follicular lymphoma and recent results that it may prolong survival in elderly patients.
“By expanding our knowledge about the genetic biomarkers associated with diffuse large B cell lymphoma, we ultimately hope to provide clinicians with better tools to make treatment decisions,” said Dr Ronald Levy, professor of medicine and lead author, Stanford University Medical Center.
In the initial DLBCL study, the researchers used a combination of microarrays, bioinformatics, real-time polymerase chain reaction (PCR) and TaqMan gene expression assays to narrow candidate genes to the six deemed most predictive of survival. The current study will employ the Applied Biosystems TaqMan low density array, a microfluidic card for real-time PCR that can screen hundreds to thousands of samples across a set of genes in order to determine which genes are expressed.