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Interactive Platform Helps To Identify Druggable Cancer Mutations

Interactive Platform Helps To Identify Druggable Cancer Mutations

Interactive Platform Helps To Identify Druggable Cancer Mutations

Interactive Platform Helps To Identify Druggable Cancer Mutations

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A Cleveland Clinic-led team of researchers has developed a personalized genomic medicine platform that will help advance genomic medicine research and genome-informed drug discovery, according to new study results published recently in Genome Biology.

Known as My Personal Mutanome (MPM), the platform features an interactive database that provides insight into the role of somatic mutations (acquired mutations that cannot be passed to offspring) in cancer and prioritizes mutations that may be responsive to drug therapies.

“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,” said Feixiong Cheng, PhD, assistant staff in the Genomic Medicine Institute, and the study’s lead author. “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.”

Constructing a comprehensive cancer mutation database

Using clinical data, the researchers integrated nearly 500,000 mutations from over 10,800 tumor exomes (the protein-coding part of the genome) across 33 cancer types into the platform. They then systematically mapped the mutations to over 94,500 protein-protein interactions (PPIs) and over 311,000 functional protein sites (where proteins physically bind with one another) and incorporated patient survival and drug response data.

In practice, the platform analyzes the relationships between genetic mutations, proteins, PPIs, protein functional sites and drugs to help users easily search for clinically actionable mutations. The MPM database features three interactive visualization tools that provide two- and three- dimensional views of somatic mutations and their associated survival and drug responses.

Mutations affecting human interactome associated with cancer

“The results from another study published in Nature Genetics, which was a collaboration between Cleveland Clinic and several other institutions, motivated us to develop the mutanome platform,” said Dr. Cheng.

Previous studies have linked disease pathogenesis and progression to mutations/variations that perturb the human interactome, or the complex network of proteins and PPIs that influence cellular function. Mutations can disrupt the network by directly changing the normal function of a protein (nodetic effect) or by altering PPIs (edgetic effect).

Notably, in the study led by Joseph Loscalzo, MD, PhD, MA, chair of the Department of Medicine at Brigham & Women's Hospital, Harvard Medical School, they found that somatic mutations were highly enriched where PPIs occurred. They also demonstrated PPI-perturbing mutations to be significantly correlated with drug sensitivity or resistance as well as poor survival rate in cancer patients.

“Our Nature Genetics findings, along with previous research, provide proof-of-concept of both nodetic and edgetic effects of somatic mutations in cancer,” explained Dr. Cheng. “What we learned from that study inspired us to develop a systems biology tool that, by mapping mutations to PPI interfaces and protein functional sites and integrating survival and drug response data, identifies cancer-driving and actionable mutations to guide personalized treatment and drug discovery.”

Next steps

Collectively, MPM enables better understanding of mutations at the human interactome network level, which may lead to new insights in cancer genomics and treatments and ultimately help realize the goal of personalized care for cancer. The team will update MPM annually to provide researchers and physicians the most complete data available.

“Our Nature Genetics study also demonstrates the nodetic and edgetic effects of mutations/variations in other diseases,” added Dr. Cheng. “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 (i.e., protein-protein inhibitors) for other complex diseases, including heart disease and Alzheimer’s disease.”

Reference: Zhou Y, Zhao J, Fang J, et al. My personal mutanome: a computational genomic medicine platform for searching network perturbing alleles linking genotype to phenotype. Genome Biol. 2021. doi:10.1186/s13059-021-02269-3

This article has been republished from the following materials. Note: material may have been edited for length and content. For further information, please contact the cited source.