CLIM: A Data Science Approach to Identifying “Bystander” Genes in Cancer Precision Medicine
A recent paper, “Metabolic collateral lethal target identification reveals MTHFD2 paralogue dependency in ovarian cancer,” published in Nature Metabolism, details a new data science platform funded by NCI.
Deepak Nagrath, Ph.D., associate professor of biomedical engineering at the University of Michigan, and Xiongbin Lu, Ph.D., a professor at Indiana University school of medicine, led the platform's development. Ultimately, this new approach could help inform how clinicians target gene therapy in precision medicine.
Using a person’s genetic makeup to target treatment is a cornerstone of precision medicine. Unfortunately, this technology isn’t always an exact science. Targeting a suspect gene or group of genes doesn’t necessarily eliminate the disease or may only work in some people but not in others.
To better understand how a given gene mutation leads to disease, the researchers used a metabolic systems biology approach. They coupled transcriptomic data and machine learning with sophisticated analysis (i.e., genome-scale metabolic flux analysis and state-of-the-art metabolic isotope tracing studies) to identify “passenger genes” (i.e., collateral lethal genes). Those genes are linked to cancer risk but don’t have a direct impact on genetic mutations. Instead, such “bystander” genes may influence critical metabolic pathways that ultimately impact how a person responds (or doesn’t respond) to treatment.
According to the authors, their platform, called CLIM or collateral lethal gene identification via metabolic fluxes, “offers an end-to-end precision medicine platform that mines clinically relevant chromosomal alterations underlying metabolic vulnerability.”
Using CLIM, the researchers were able to successfully identify a collateral lethal gene (i.e., MTHFD2) in ovarian cancers in cases where the essential gene (i.e., UQCR11) was lost as part of a larger mutation commonly found in patients.
Dr. Nagrath said, “Data from The Cancer Genome Atlas Program were crucial in validating and reconstructing the metabolic model used in this research and will continue to be essential for future discoveries and improvements to the platform.” In particular, to further advance CLIM, his team will be looking into the diversity (i.e., heterogeneity) of certain tumor cells (i.e., stromal cells). Those cells influence immunity, inflammation, and other vital functions that impact how cancer develops and grows.
This type of modeling has a promising future. According to Dr. Nagrath, his research team is now collaborating with clinicians at University of Michigan Rogel Cancer Center to unravel new metabolically collateral lethal targets in other cancers, with the hope of advancing precision medicine for treating a variety of cancer types.