Natuer Subjournal: A new platform for predicting new and specific metabolic targets for ovarian cancer

Researchers at the University of Michigan Rogel Cancer Center have developed a computing platform that can predict new and specific metabolic targets for ovarian cancer, meaning there is an opportunity to develop personalized therapies for patients, based on the genetic makeup
of their tumors.
The study was published in the journal Nature Metabolism
.

Cancer mutations occur frequently in ovarian cancer, giving cells a growth advantage, which increases the aggressiveness
of the disease.
But sometimes the deletion of certain genes occurs with these mutations, making cells vulnerable to treatment
.
However, cancer cells grow so well because similar genes can compensate for the loss of this function and continue to drive tumor formation
.

Dr.
Deepak Nagrath, an associate professor of biomedical engineering, led the study, and he hopes to learn more about these compensatory genes
involved in metabolism.
"When a gene is deleted, the metabolic gene that allows cancer cells to grow is also deleted
.
The theory holds that the metabolism of cancer cells develops fragility
due to specific genetic changes.
”

When genes that regulate metabolic function are removed, cancer cells essentially reconnect their metabolism to come up with a backup plan
.
By integrating methods of complex metabolic modeling, machine learning, and optimization theory in cell lines and mouse models, the team discovered the unexpected function
of an ovarian cancer enzyme, MTHFD2.
This is specific to mitochondrially damaged ovarian cancer cells due to the common deletion
of UQCR11.
This leads to a serious imbalance
of the basic metabolite NAD+ in the mitochondria.

The algorithm predicted that MTHFD2 unexpectedly reversed its role
in providing NAD+ in cells.
This creates a weakness that can selectively kill cancer cells while minimizing
the impact on healthy cells.

Dr Abhinav Achreja, a researcher and first author of the study, said: "Personalized therapies like this are becoming a growing possibility
to improve the effectiveness of first-line cancer treatments.
There are several ways to discover personalized targets for cancer, and some platforms are based on big data analytics to predict targets
.
Our platform makes predictions by considering metabolic function and mechanisms, increasing the chances of
success when translated into the clinic.
”

DOI: "Metabolic Secondary Lethal Target Recognition Reveals Para-Dependence of MTHFD2 in Ovarian Cancer", Natural Metabolism
.
10.
1038/s42255-022-00636-3

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