Thousands of patients’ tumors have been sequenced in the past decade, yielding a rich source of data on the changes associated with the cancer development and treatment response. However, there are no validated methods that are used in the clinic to select the best therapy. Today, Mayo Clinic researchers report an omics-guided (comprehensive) drug prioritization method tailored to an individual cancer patient.
“To date, genomic sequencing data provided to clinicians includes information on a small set of gene alterations. Recommendations for therapy do not account for many other genomic and clinical factors that might dictate tumor response,” says Mayo researcher Krishna Rani Kalari, Ph.D. “Therefore, there is an urgent need for a comprehensive approach to integrate an individual’s clinical, germline and tumor genomic data to identify and select the best treatment for a patient.”
Dr. Rani Kalari, a computational biologist, and lead author of a Mayo Clinic led study, published in JCO Clinical Cancer Informatics showed that combining multiple sources of data to predict the most effective drug choices for patients with cancer is feasible. Read the rest of the article on the Individualized Medicine blog.
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