Cancer Weakness Discovered: New Method Pushes Cancer Cells Into Remission

The most successful targets for precision medicine can be found by using algorithms created by University of Michigan researchers. These algorithms successfully identify the weakest targets in ovarian cancer cells—genes these cells depend on to live in the human body.

Cancer cells delete DNA when they go to the dark side, so a team of doctors and engineers targeted the ‘backup plans’ that run essential cell functions.

Researchers at the University of Michigan and Indiana University have discovered a cancer weakness. They found that the way that tumor cells enable their uncontrolled growth is also a weakness that can be harnessed to treat cancer. 

Their machine-learning algorithm can identify backup genes that only tumor cells use, allowing drugs to precisely target cancer.

The researchers used mice to demonstrate their innovative precision medicine approach to treating ovarian cancer. Furthermore, the cellular behavior that reveals these vulnerabilities is common in most cancers, implying that the algorithms might generate superior treatment plans for a variety of cancers.

Abhinav Achreja, Ph.D., Research Fellow at the University of Michigan Biomedical Engineering and Deepak Nagrath, Ph.D. Associate Professor of Biomedical Engineering work on ovarian cancer cell research in the bio-engineering lab at the North Campus Research Center (NCRC). Credit: Marcin Szczepanski/Lead Multimedia Storyteller, University of Michigan College of Engineering

“This could revolutionize the precision medicine field because the drug targeting will only affect and kill cancer cells and spare the normal cells,” said Deepak Nagrath, a U-M associate professor of biomedical engineering and senior author of the study published in Nature Metabolism. “Most cancer drugs affect normal tissues and cells. However, our strategy allows specific targeting of cancer cells.”

This method is known as collateral lethality, and it involves leveraging information acquired from genes that cancer cells discard to identify weaknesses. The human body is equipped with a variety of defenses against cancer. Cancer cells used to have suppressor genes that prevented them from spreading. Those cells, however, have a clever strategy for dealing with this; they simply delete a portion of their DNA that contains those suppressor genes.

In doing so, the cells typically lose other genes that are necessary for survival. To avoid death, the cells find a paralog—a gene that can serve a similar function. Usually, there are one or, possibly, two genes that can step in and perform the same function to keep the cell alive.

What if you could identify the right paralog and target it in a way that shuts down its vital function for the cell?

“When a direct replacement for the deleted metabolic gene is not available, our algorithms use a mathematical model of the cancer cells’ metabolism to predict the paralogous metabolic pathway they might use,” said Abhinav Achreja, a U-M research fellow in biomedical engineering and lead author on the research paper. “These metabolic pathways are important to the cancer cells and can be targeted selectively.”

Attacking metabolic pathways essentially shuts down the cell’s energy source. In examining ovarian cancer cells, U-M’s team zeroed in on one gene, UQCR11, that was often deleted along with a suppressor gene. UQCR11 plays a vital role in cell respiration—how cells break down glucose for energy in order to survive.

Disturbances in this process can lead to a major imbalance of an important metabolite, NAD+, in the mitochondria, where respiration takes place. Despite all odds, ovarian cancer cells continue to thrive by relying on their backup plan.

U-M’s algorithm correctly sorted through multiple options and successfully predicted a cell missing UQCR11 would turn to the gene MTHFD2 as its backup supplier of NAD+.

Researchers at the Indiana University School of Medicine helped validate the findings in the lab. This team, led by professor of medicine Xiongbin Lu, developed genetically modified cell and animal models of ovarian cancers with the deletions. Six out of six mice tested showed complete cancer remission.

Reference: “Metabolic collateral lethal target identification reveals MTHFD2 paralogue dependency in ovarian cancer” by Abhinav Achreja, Tao Yu, Anjali Mittal, Srinadh Choppara, Olamide Animasahun, Minal Nenwani, Fulei Wuchu, Noah Meurs, Aradhana Mohan, Jin Heon Jeon, Itisam Sarangi, Anusha Jayaraman, Sarah Owen, Reva Kulkarni, Michele Cusato, Frank Weinberg, Hye Kyong Kweon, Chitra Subramanian, Max S. Wicha, Sofia D. Merajver, Sunitha Nagrath, Kathleen R. Cho, Analisa DiFeo, Xiongbin Lu and Deepak Nagrath, 21 September 2022, Nature Metabolism.
DOI: 10.1038/s42255-022-00636-3

The study was funded by the National Cancer Institute, the Office of the Director for the National Institutes of Health, the University of Michigan Precision Health Scholars Award, and the Forbes Scholar Award from the Forbes Institute of Cancer Discovery.