Machine Learning-Enabled Renal Cell Carcinoma Status Prediction Using Multiplatform Urine-Based Metabolomics

• Published in: Journal of proteome research Vol. 20; no. 7; pp. 3629 - 3641
• Main Authors: Bifarin, Olatomiwa O, Gaul, David A, Sah, Samyukta, Arnold, Rebecca S, Ogan, Kenneth, Master, Viraj A, Roberts, David L, Bergquist, Sharon H, Petros, John A, Fernández, Facundo M, Edison, Arthur S
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 02.07.2021
• Subjects: Carcinoma, Renal Cell - diagnosis; Humans; Kidney Neoplasms - diagnostic imaging; Machine Learning; Mass Spectrometry; Metabolomics; liquid chromatography mass spectrometry; nuclear magnetic resonance spectroscopy; renal cell carcinoma; machine learning; metabolomics
• ISSN: 1535-3893; 1535-3907
• DOI: 10.1021/acs.jproteome.1c00213

Renal cell carcinoma (RCC) is diagnosed through expensive cross-sectional imaging, frequently followed by renal mass biopsy, which is not only invasive but also prone to sampling errors. Hence, there is a critical need for a noninvasive diagnostic assay. RCC exhibits altered cellular metabolism combined with the close proximity of the tumor(s) to the urine in the kidney, suggesting that urine metabolomic profiling is an excellent choice for assay development. Here, we acquired liquid chromatography–mass spectrometry (LC–MS) and nuclear magnetic resonance (NMR) data followed by the use of machine learning (ML) to discover candidate metabolomic panels for RCC. The study cohort consisted of 105 RCC patients and 179 controls separated into two subcohorts: the model cohort and the test cohort. Univariate, wrapper, and embedded methods were used to select discriminatory features using the model cohort. Three ML techniques, each with different induction biases, were used for training and hyperparameter tuning. Assessment of RCC status prediction was evaluated using the test cohort with the selected biomarkers and the optimally tuned ML algorithms. A seven-metabolite panel predicted RCC in the test cohort with 88% accuracy, 94% sensitivity, 85% specificity, and 0.98 AUC. Metabolomics Workbench Study IDs are ST001705 and ST001706.