New platform may accelerate genomic medicine research, drug therapies

A research team, headed by Cleveland Clinic, has designed a novel personalized genomic medicine platform that would help speed up genomic medicine studies as well as genome-informed drug discovery. The results of the new study have been recently published in the Genome Biology journal.

The personalized genomic medicine platform, called My Personal Mutanome (MPM), includes an interactive database that offers a better understanding of the role played by disease-associated mutations in cancer and also prioritizes mutations that are likely to respond to drug treatments.

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.”

Feixiong Cheng, PhD, Study Lead Author and Assistant Staff, Genomic Medicine Institute, Cleveland Clinic

To design the complete cancer mutation database, the researchers used clinical data and incorporated almost 500,000 mutations from more than 10,800 tumor exomes (that is, the protein-coding section of the genome) across 33 types of cancer).

The researchers then methodically plotted the mutations to more than 311,000 functional protein sites (in which proteins physically attach to each other) and more than 94,500 protein-protein interactions (PPIs), and ultimately integrated the data relating to patient survival and drug response.

The new platform examines the link between PPIs, proteins, protein functional sites, genetic mutations, and medications to allow users to easily look for clinically actionable mutations.

Three interactive visualization tools are integrated into the MPM database and these tools offer two-dimensional and three-dimensional views of disease-associated mutations as well as their related survival and response to drugs.

Based on the results obtained from another research work published in the Nature Genetics journal, an association between Cleveland Clinic and many other institutions, the researchers were able to design the novel platform.

Prior studies have associated the progression and pathogenesis of diseases to variations/mutations that disturb the human interactome—the complex network of PPIs and proteins that govern the function of cells. Mutations can disturb this network by altering PPIs (edgetic effect) or by altering the regular function of a protein (nodetic effect).

Most importantly, in the Nature Genetics analysis, headed by Brigham & Women’s Hospital and Harvard Medical School, the team discovered that wherever the PPIs occurred, disease-associated mutations were extremely enriched. The researchers also showed that PPI-altering mutations considerably correlate with drug resistance or sensitivity and also correlate with poor survival rate in cancer patients.

On the whole, the MPM database allows a better interpretation of mutations at the human interactome network scale, which may shed more light on cancer genomics and therapies and eventually help realize the objective of personalized cancer care.

Every year, the researchers will update the MPM database to offer physicians and researchers the most comprehensive data available.

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.”

Feixiong Cheng, PhD, Study Lead Author and Assistant Staff, Genomic Medicine Institute, Cleveland Clinic

Source:
Journal reference:

Zhou, Y., et al. (2021) My personal mutanome: a computational genomic medicine platform for searching network perturbing alleles linking genotype to phenotype. Genome Biology. doi.org/10.1186/s13059-021-02269-3.

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