For more than 20 years, precision cancer therapy has focused on pharmacogenomics, where gene biomarkers found in a patients’ tumor determine the approach to treatment. As the summation of physiologic and environmental influences, metabolites that make up the metabolome can similarly define a metabolic phenotype that can guide cancer treatment.
Cancer cells reprogram their metabolism to survive and proliferate. In doing so, they release metabolites into biological fluids, such as blood and urine, that can act as signals of tumor and treatment status. Furthermore, unlike genetic biomarkers, metabolic biomarkers can change during drug therapy, providing evidence of treatment effectiveness or resistance. Rather than assessing treatment effectiveness months after therapy, this method can allow clinicians to determine therapeutic response in real-time and early during treatment. Other advantages of profiling metabolic biomarkers include non-invasive sampling, high throughput analysis, and routine assessment throughout the duration of therapy. This new and emerging field of using metabolic biomarkers to predict or determine drug effectiveness has been coined pharmacometabolomics.
Characterizing tumor metabolic biomarkers presents many challenges. Various factors can affect blood and urine metabolic profiles, and it can be costly and time consuming to discern tumor specific metabolite production in large patient studies. To accelerate this process, the Velenosi lab uses a cutting-edge approach by employing highly controlled patient derived xenograft models, which involves implanting a human tumor into a mouse model, to identify and characterize metabolic biomarkers of drug effectiveness.
Ultimately, the Velenosi Lab aims to characterize metabolite biomarkers that will support precision care for patients with the potential ability to anticipate treatment needs ahead of cancer progression.
Characterizing metabolic biomarkers of drug response using patient-derived xenograft cancer models
Metabolites directly reflect genetic, physiological, and environmental changes and can provide a reliable readout for therapeutic effectiveness. In the assessment of cancer, metabolites excreted from tumors can be measured by routine non-invasive sampling of plasma and urine. However, various factors can affect the human plasma and urine metabolome, and it can be difficult to discern the underlying mechanisms of tumor metabolite production in clinical studies. The Velenosi lab uses highly controlled patient derived xenograft models to identify and characterize metabolic biomarkers of drug effectiveness. The goal of this approach is to understand the mechanisms of metabolic biomarker production and response to drug treatment in highly translational cancer models.
Developing computational methods to characterize lipid structures in lipidomics data
Lipids are a major component of cellular function and metabolism. The ability to evaluate all lipids in their entirety can provide fundamental insights into biological systems. Lipidomics allows for large-scale analysis of all lipids in a biological matrix. Lipids are structurally diverse with an estimated >40,000 compounds and therefore, determining the structure of all lipids in a lipidomics dataset presents a significant challenge. This project aims to develop computational methods towards the goal of identifying all lipids in a biological sample.