Cancer of the kidney and renal pelvis is among the to ten cancers diagnosed in the US every year. In 2022, it is estimated that 79,000 Americans will be diagnosed with the disease, and approximately 14,000 are expected to die from it. The survival for those with kidney and renal pelvis cancer ranges widely based on the stage of disease at which a patient is diagnosed. The 5-year survival is over 90% for patients diagnosed when the cancer is still in its early stage but is less than 15% for those diagnosed in the disease’s late stage. In addition to estimating survival, the stage of disease is also used to guide treatment decisions. To determine the stage of the most common form of kidney and renal pelvis cancer, renal cell carcinoma, clinicians initially use computerized tomography (CT) scans or magnetic resonance imaging (MRIs). However, these tests can be expensive, prompting researchers supported by the Common Fund’s Metabolomics Program to explore alternative approaches to determining the stage of renal cell carcinomas.
A major goal of the Metabolomics program is to discover new ways to diagnose diseases by improving the analysis of metabolites, which are molecules produced from chemical reactions that occur in our cells. Since cancer alters the metabolites produced in our bodies, and because the kidneys produce urine, Dr. Arthur Edison and colleagues examined whether cancer-related changes to the metabolites found in urine could be used to detect the stage of renal cell carcinomas. Using machine learning, a type of artificial intelligence where computer algorithms use data to make predictions, the researchers analyzed samples from 70 patients with renal cell carcinoma. A panel of 24 metabolites were identified that could differentiate between early and late stage renal cell carcinoma with 87% accuracy. Though more research is needed to validate this work, these findings pave the way for the potential use of metabolites to determine the stage of renal cell carcinoma.
Reference
- Urine-Based Metabolomics and Machine Learning Reveals Metabolites Associated with Renal Cell Carcinoma Stage. O.O. Bifarin, D.A. Gaul, S. Sah, R.S. Arnold, K.O., V.A. Master, D.L. Roberts, S.H. Bergquist, J.A. Petros, A.S. Edison, F.M. Fernández. Cancers. 2021 Dec 13; 13(24):6253.
