Shutting Down Backup Genes Could Provide Key to Targeted Cancer Therapies

In a recent study, researchers from the University of Michigan and Indiana University discovered that the mechanism responsible for tumor cells’ uncontrolled growth might be a weakness to exploit as an oncology target. With a machine-learning algorithm, the team could identify backup genes that only tumor cells use.

In a recent study, researchers from the University of Michigan and Indiana University discovered that the mechanism responsible for tumor cells’ uncontrolled growth might be a weakness to exploit as an oncology target. With a machine-learning algorithm, the team could identify backup genes that only tumor cells use.

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Targeting Paralogs as an Anticancer Approach

The novel approach is known as collateral lethality, which uses information gleaned from genes that cancer cells discard to target their weaknesses. The human body comes with many mechanisms designed to protect against cancer. For example, cancer cells themselves once contained suppressor genes that prevented their spread. However, once a portion of DNA that includes those suppressor genes is deleted, the growth of the cells loses control and becomes cancer.

In doing so, the cells typically lose other genes necessary for survival. The cells find a gene that can serve a similar function called a paralog to avoid death. Usually, there are one or, possibly, two genes that perform the same function to keep the cell alive. Hence, the scientists intended to identify the right paralog and target it to shut down its vital function for the cell.

In this study, researchers performed a new precision medicine approach to treat ovarian cancer in mice. The team uses a mathematical model of the cancer cells’ metabolism to predict the paralogous metabolic pathway they might use. Attacking metabolic pathways can shut down the cell’s energy source. 

The team targeted UQCR11, a gene often deleted along with a suppressor. UQCR11 plays a vital role in cell respiration. 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. The team’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+.

This Milestone’s Potential in Precision Medicine

The researchers confirmed this UQCR11–MTHFD2 collateral lethality in vivo, with MTHFD2 inhibition leading to the complete remission of UQCR11-deleted ovarian tumors. 

To sum up, the approach provides new insights into developing targeted therapy. Most cancer drugs usually affect normal tissues and cells too. In contrast, this strategy identifies genes that only tumor cells are using and thus can target cancer precisely. Moreover, the cellular behavior that exposes these vulnerabilities is common across most forms of cancer, meaning the algorithms could provide better treatment plans for a variety of malignancies.

Author

Nai Ye Yeat