features an interactive database that can provide insight into the role of disease-associated mutations in cancer and prioritizes mutations that may be responsive to drug therapies.
The lead author of the study, Feixiong Cheng, PhD, assistant staff in Cleveland Clinic’s Genomic Medicine Institute said, “Although advances in sequencing technology have bestowed a wealth of cancer genomic data, the capabilities to bridge the translational gap between large-scale genomic studies and clinical decision making were lacking. MPM is a powerful tool that will aid in the identification of novel functional mutations/genes, drug targets and biomarkers for cancer, thus accelerating the progress towards cancer precision medicine.”
To develop the comprehensive cancer mutation database, the researchers integrated nearly 500,000 mutations from over 10,800 tumor exomes (the protein-coding part of the genome) across 33 cancer types. The researchers mapped systemically the mutations to over 94,500 protein-protein interactions (PPIs) and over 311,000 functional protein sites (where proteins physically bind with one another). They also incorporated patient survival and drug response data.
As this platform analyzes the relationships between genetic mutations, proteins, PPIs, protein functional sites and drugs, the users can easily search for clinically actionable mutations. Three interactive visualization tools that provide two- and three- dimensional views of disease-associated mutations and their associated survival and drug responses is provided by the database.
Earlier studies have linked disease pathogenesis and progression to mutations/variations that disrupt the human interactome, the complex network of proteins and PPIs that influence cellular function. Mutations can alter PPIs and disturb the network by changing the normal function of a protein.
PPI-altering mutations are also related with drug sensitivity or resistance as well as poor survival rate in cancer patients. MPM can lead to new insights in cancer genomics and treatments and ultimately help realize the goal of personalized care for cancer.
Dr Cheng added, “Our Nature Genetics study also demonstrates the effects of mutations/variations in other diseases. As a next step, we are developing new artificial intelligence algorithms to translate these genomic medicine findings into human genome-informed drug target identification and precision medicine drug discovery for other complex diseases, including heart disease and Alzheimer’s disease.”