Maria Fontanazza

April 1, 2009

2 Min Read
Breast Cancer Tool Assesses Options

R&D DIGEST


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A circle network shows the dynamic structure of the human interactome. It is overlayed on raw microarray data with an image of a human breast cancer tumor. (Photo courtesy of LORI WATERS/WATERS BIOMEDICAL)

Within five years, Canadian researchers hope novel technology will help doctors choose the best treatment for breast cancer patients. Called DyNeMo, the tool can predict a patient's chances of beating the cancer with more than 80% accuracy.


The DyNeMo technology, which stands for dynamic network modularity, analyzes protein networks in cancer cells. “This technique has proven effective at predicting breast cancer patient outcomes based on differences in protein interaction network modularity from patient tumor samples,” says Ian Taylor, a PhD candidate at the University of Toronto who developed the technology and its intellectual framework.
Taylor is the lead author of a study involving the technology that was recently published online in the February 2009 issue of Nature Biotechnology. His team includes researchers from Mount Sinai Hospital (Toronto) and coauthors from the University of Toronto and the University of London. Part of their analysis compares the network of proteins in tumor cells of patients who survived breast cancer with patients who succumb to the cancer.
The software-based system requires microarray and protein interaction data from patient samples. With different study goals in mind, researchers are currently applying the tool to patients with osteosarcoma, inflammatory bowel syndrome, and retinoblastoma. Objectives include predicting metastasis or an individual's response to drugs, and identifying protein networks deregulated in a disease.
The DyNeMo technology hasn't been commercialized, but the researchers are actively seeking a company that can deliver the product to a wide portion of the market. Taylor, who serves as the chief scientific officer, hopes the partner can expand DyNeMo's application to other indications, along with predicting patient response to drugs both during clinical trials and once drugs are on the market.
Copyright ©2009 Medical Device & Diagnostic Industry

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