Metabolomics opens window to better cancer treatments
Nothing in a flamingo’s genetic code says it should be pink. Instead, by munching on carotenoid-rich algae and shrimp from its environment, the flamingo takes on its signature pink hue. Like flamingos, humans are also products of our environment. Although genetics determine much of human biology, factors such as the food we eat, the medications we take, and the diseases we develop paint a more complete picture of human health.
The many factors that contribute to health in small and big ways may seem endless, but metabolites — the small molecules produced as byproducts when cells break down food, drugs, or other chemicals — give scientists a window on the molecular processes that govern human health. For example, the metabolites produced by healthy cells often differ from those released by cancer cells. Differences in individual metabolism can make certain drugs effective in some people, but ineffective in others.
Taking a systems approach, Stefano Tiziani, a chemical biologist at the University of Texas at Austin, studies the metabolome in the context of cancer to find more effective cancer treatments and to identify who may or may not benefit from chemotherapy treatment. particular. .
How can understanding the metabolome lead to the development of better cancer drugs?
An incredible example of how understanding the metabolome has led to successful chemotherapy therapy is amino acid depletion therapy for children with acute lymphoblastic leukemia (ALL). One of the drug treatments is the enzyme asparaginase, which simply depletes the concentration of asparagine and glutamine metabolites. Due to a change in their metabolism, all cancer cells are under great pressure to absorb these metabolites, so their depletion is part of the success of chemotherapy treatment.
In my research, we want to understand how the metabolic response of a person’s cells correlates with their response to chemotherapy treatment. By doing this we can potentially identify which patients are not responding to treatment and why they are not responding. From the metabolomic reading, we can propose a new treatment strategy for these patients. We also use metabolomics to identify chemotherapeutic agents that might be more effective in combination than when used alone.
How do you use metabolomics to identify new combination cancer treatments?
We have recently developed a new method to test the synergistic combination of two drugs for prostate cancer by assessing the metabolome (1). “Synergistic” is like the word “significant” in that it is a statistical term. We can’t just say that two drugs work well together; we have to quantify it. To do this, we have developed a new algorithm called Euclidean distance synergy quantification based on principal component analysis (PEDS).
Using metabolomics, Stefano Tiziani and his team have identified a new way to identify drugs that act synergistically to treat prostate cancer.
Credit: Stefano Tiziani
We realized that when the majority of people apply -omics analysis to questions of drug synergy, they determine the synergistic combination based on a variable such as cell survivability or apoptosis assays. It doesn’t really benefit the systems biology platform. Our PEDS algorithm can take hundreds of variables and assess overall metabolism to determine if two drugs or natural compounds synergize or antagonize each other. With this information, we can assess how this drug combination works at the molecular level. We can identify biomarkers associated with mitochondria such as those involved in oxidative phosphorylation, the TCA cycle and glycolysis for example. With this metabolic information, we can characterize the response of cells to treatment and assess whether the patient would successfully respond to treatment or not.
What was your reaction when you identified two well synergized drugs for the treatment of prostate cancer?
The two drugs – the glutaminase inhibitor CB-839 and the approved cancer drug docetaxel – came out synergistically in a totally unbiased way. They worked very well in vitro in the 3D model of prostate cancer and in mice, which we were very happy to see. The beauty of this combination is that it came from screening about 300 compounds, not a thousand compounds, because I run a lab, not a rig.
We partner with a number of hospitals and medical research institutes to test and analyze clinical trial data to generate new hypotheses for potential drug combinations. We are already working on leukemia and glioblastoma, and we are working on other drugs. If we can identify a new combination, we hope it will benefit the patient and be more selective and less toxic than current cancer treatments.
How much more complicated would it be to seek synergy between three drugs rather than two?
I think it is doable. The main challenge will be to optimize drug concentrations and ratios to be used. When we want to move from cell lines to animal models, we need to know the appropriate concentrations and ratios of drugs. Our article gives a good basis, so we would translate the algorithm from a two-drug combination to a three-drug combination to an “n” combination. The number of possibilities would increase considerably.
What excites you most about developing these metabolomics screens and algorithms?
One of the most exciting things about metabolomics is that we can collaborate with so many people – researchers working at the genomics, proteomics and epigenetic levels. We can use metabolomics not only for drug screening but also for phenotypic screening. For example, after someone has had a cup of coffee or eaten something rich in omega three fatty acids, metabolomics screening can identify how their metabolism changes. Think about it. One day, during a blood test, the doctor will evaluate not only 10 to 50 biomarkers, but potentially thousands. Can you imagine how much additional information will come from a simple test? We can potentially use these metabolic biomarkers to predict, for example, the risk of developing a disease and start preventive treatments earlier.
Lu, X. et al. Metabolomics-based phenotypic screens for drug synergy assessment by direct infusion mass spectrometry. iScience 25104221 (2022).