The U.K.-based University of Leeds has announced that it will use big data to help match patients with certain types of blood cancers to the best treatments.
The new resource, funded by the charity Leukaemia & Lymphoma Research, will store cancer cell samples and anonymous medical records of patients with non-Hodgkin lymphoma blood cancers in the Yorkshire region, allowing doctors access to richly detailed information about similar previous patients when treating new cases.
Non-Hodgkin lymphoma is diagnosed in about 10,000 people a year in the U.K., making it the sixth most common form of cancer, and normally appears as a solid tumor in glands called lymph nodes.
One of the key challenges in treating it is its diversity. Non-Hodgkin lymphoma can be divided into up to 40 different diseases, each of which is treated differently. Even within these subcategories, there can be very significant differences between individual patients, with different genetic faults in an individual's lymphoma cells dictating whether certain drugs will be effective or not.
The database and data mining techniques developed by researchers at the University of Leeds' Faculty of Biological Sciences are expected to revolutionize treatment, officials say. Doctors will be able to cast aside traditional disease categories, which were defined when scientists couldn't look at cells at a molecular level, and search the database for previous patients whose lymphoma has similarities at a biological level to newly diagnosed patients. Knowing which of the various possible treatments were most successful in similar patients in the past will help guide treatment for current patients.
"It is increasingly clear that cancer in general and lymphoma in particular is a highly variable disease,” David Westhead, professor of Bioinformatics at the University of Leeds, said in a statement. “Individuals previously diagnosed in the same broad categories may have diseases that are quite different when you look at the fundamental biology of their cancers. This database enables us to take a step towards more individualized treatment."
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