A team of researchers have developed a personalized genomic medicine platform, My Personal Mutanome, that will help accelerate genome-informed cancer drug discovery, according to a study published in Genome Biology.
Cancer Genome Databases
There are already several data and web sources of cancer genomes/exosomes that facilitate cancer research and drug discovery. For instance, The Cancer Genome Atlas program has characterized genomes/exosomes of over 11,000 patients across 33 cancer types. Typical computational approaches are only able to identify a small portion of pathogenic variants with enough confidence for clinical decision-making. These databases are often difficult to navigate and provide little understanding of the genotype-phenotype interaction. Identification and prioritisation of genetic variants can help us understand their roles in tumorigenesis and disease progression, the discovery of new biomarkers, and relevant drug targets.
My Personal Mutanome (MPM)
The MPM platform features an interactive database that catalogues disease-associated mutations in cancer and prioritizes mutations that may be receptive to drug therapies. Using clinical data, the researchers in this study integrated almost 500,000 mutations from over 10,800 tumour exosomes across 33 cancer types to develop a comprehensive cancer mutation database. The mutations were mapped to over 94,500 protein-protein interactions (PPIs) and over 311,000 functional protein sites. The researchers then incorporated patient survival and drug response data. Overall, 8884 survival results and over 1 million drug responses were obtained for the mapped mutations.
Previous studies have linked cancer pathogenesis and progression to mutations or changes in the human interactome, the network of PPIs that influences cellular function. Notably, a study published in Nature Genetics found that disease-associated mutations were highly enriched where PPIs occurred. The study also demonstrated that PPI-altering mutations significantly correlated with drug sensitivity and survival rate.
Collectively, MPM offers network-based diagnosis and pharmacogenomics approaches to understand genotype-phenotype relationships and therapeutic responses. It goes beyond typical computation approaches by providing an all-in-one interface that allows users to search, view results, and visualise mutations in the same web page, thereby improving ease of navigation through the database.
The lead author from this study has stated that research is underway to develop new artificial intelligence algorithms to translate these findings into human genome-informed drug identification and precision medicine drug discovery for other complex diseases such as heart disease and Alzheimer’s disease.
MPM provides an easy-to-navigate genomic database that enables a better understanding of mutations affecting PPIs, which may provide new insights into cancer genomics and the potential for personalised cancer care.
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