Machine learning creates opportunity for new personalized therapies

In cell-line and mouse models of ovarian cancer, researchers developed an interdisciplinary approach to identify metabolic vulnerabilities in certain genes that could be targeted to kill cancer cells.

10:27 AM

Author | Anna Megdell

ovarian cancer tumor under microscope lab note
National Cancer Institute

Researchers at the University of Michigan Rogel Cancer Center have developed a computational platform that can predict new and specific metabolic targets in ovarian cancer, suggesting opportunities to develop personalized therapies for patients that are informed by the genetic makeup of their tumors. The study appeared in Nature Metabolism.

Cancer mutations occur frequently in ovarian cancer, giving cells a growth advantage that contributes to the aggressiveness of the disease. But sometimes deletions of certain genes can occur alongside these mutations and make cells vulnerable to treatment. Still, cancer cells grow so well because paralog genes can compensate for this loss of function and continue to drive tumor formation.

Deepak Nagrath, Ph.D., associate professor of biomedical engineering who led this study, wanted to understand more about these compensatory genes as they relate to metabolism. "When a gene is deleted, metabolic genes, which allow the cancer cells to grow, are also deleted. The theory is that vulnerabilities emerge in the metabolism of cancer cells due to specific genetic alterations."

When genes that regulate metabolic function are deleted, cancer cells essentially rewire their metabolism to come up with a backup plan. Using a method that integrates complex metabolic modeling, machine learning and optimization theory in cell-line and mouse models, the team discovered an unexpected function of an ovarian cancer enzyme, MTHFD2. This was specific to ovarian cancer cells with an impairment to the mitochondria, due to a commonly occurring deletion of UQCR11. This led to a critical imbalance of an essential metabolite, NAD+, within the mitochondria.

The algorithm predicted that MTHFD2 surprisingly reversed its role to provide NAD+ in the cells. This created a vulnerability that could be targeted to selectively kill off the cancer cells while minimally affecting healthy cells.

"Personalized therapies like this are becoming an increasing possibility for improving efficacy of first-line cancer treatments," said research fellow and first author of this study Abhinav Achreja, Ph.D. "There are several approaches to discovering personalized targets for cancer, and several platforms predict targets based on big data analyses. Our platform makes predictions by considering the metabolic functionality and mechanism, increasing the chances of success when translating to the clinic."

Paper cited: "Metabolic collateral lethal target identification reveals MTHFD2 paralogue dependency in ovarian cancer," Nature Metabolism. DOI: 10.1038/s42255-022-00636-3

This research was supported by funding from the National Cancer Institute, the Office of the Director for the National Institutes of Health, the University of Michigan Precision Health Scholars Award, and Forbes Scholar Award from Forbes Institute of Cancer Discovery.


More Articles About: Lab Notes Ovarian Cancer All Research Topics Cancer Research Cancer: Cancer Types
Health Lab word mark overlaying blue cells
Health Lab

Explore a variety of healthcare news & stories by visiting the Health Lab home page for more articles.

Media Contact Public Relations

Department of Communication at Michigan Medicine

[email protected]

734-764-2220

Stay Informed

Want top health & research news weekly? Sign up for Health Lab’s newsletters today!

Subscribe
Featured News & Stories purple cells floating up close
Health Lab
Study links gene network and pancreatic beta cell defects to type 2 diabetes
Teams from Vanderbilt University Medical Center and the University of Michigan design a comprehensive study that integrates multiple analytic approaches that has linked a regulatory gene network and functional defects in insulin-producing pancreatic beta cells to type 2 diabetes.
smart watch on wrist
Health Lab
Clinical smart watch finds success at identifying atrial fibrillation
A Michigan Medicine research team developed a prescription wristwatch that continuously monitors the wearer’s heart rhythm and uses a unique algorithm to detect atrial fibrillation. The clinical-grade device, called the Verily Study Watch, proved very accurate at identifying atrial fibrillation in participants.
sketched out bacteria in a dish yellow and blue colors of U-M
Health Lab
This gross mixture has big benefits for the study of bacteria
Michigan Medicine researchers have found that growing bacteria on agar mixed with organs is an efficient and effective way to study infectious pathogens.
green blue map of michigan
Health Lab
How does exposure to ‘forever chemicals’ impact your cancer risk
Pearce, professor of epidemiology at the School of Public Health and co-lead of Rogel’s cancer control and population sciences program, reflects on the project and why bringing this study to Michigan is so critical.
person holding walker with nurse next to them closer up on hands lower body
Health Lab
Long COVID happens in nursing homes, too
Post-acute sequelae of Sars-COV2 (PASC, long COVID) caused a decrease in independence and cognitive ability after coronavirus infection in nursing home residents
expert at stand hearing in suit
Health Lab
Keep telehealth alive and well, experts tell Senate subcommittee
Telehealth coverage by Medicare is scheduled to expire at the end of 2024; experts told Senators what they think should happen to preserve it.