In 1937, President Franklin Roosevelt signed the National Cancer Act, launching a nationwide effort to combat the disease. Eighty-seven years later, despite significant progress, cancer treatment often falls short, with 50 to 80 percent of patients not responding to treatment and more than 600,000 cancer deaths annually in the United States. What if clinicians could predict the success of any cancer treatment, ensuring each patient receives the most effective care? The challenge lies in the diverse nature of the disease.
There are hundreds of different types of cancers, characterized by the specific type of cell from which they originate. Even patients with the same cancer type require personalized treatments due to unique factors like genetic predisposition, lifestyle and immune response. The therapeutic outcomes -; from complete remission to resistance to treatment -; are unpredictable because cancer cells can develop resistance to drugs through genetic mutations, rendering therapy ineffective.
To tackle this complexity, a research team at the University of Alabama at Birmingham led by Anindya Dutta, Ph.D., professor and chair of the UAB Department of Genetics, sought to identify patterns within this apparent randomness.
Leveraging established cancer cell databases -; including the Genomics of Drug Sensitivity in Cancer, or GDSC, the Cancer Therapeutics Response Portal, or CTRP, and the Catalogue of Somatic Mutations in Cancers, or COSMIC -; the team investigated "whether g.
