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Mass Spectrometry Imaging enables Discrimination of Renal Oncocytoma from Renal Cell Cancer Subtypes and Normal Kidney Tissues.

Precise diagnosis and subtyping of kidney tumors are imperative to optimize and personalize treatment decision for patients. Patients with the most common benign renal tumor, renal oncocytomas (RO), may be over-treated with surgical resection due to limited pre-operative diagnostic methods that can accurately identify the benign condition with certainty. In this study, desorption electrospray ionization (DESI) mass spectrometry imaging was applied to study the metabolic and lipid profiles of various types of renal tissues, including normal kidney, RO, and renal cell carcinomas (RCC). A total of 73,992 mass spectra from 71 patient samples were obtained and used to build predictive models using the least absolute shrinkage and selection operator (lasso). Overall accuracies of 99.47% per-pixel and 100% per-patient for prediction of the three tissue types was achieved. In particular, RO and chromophobe renal cell carcinoma (chRCC), which present the most significant morphologic overlap and are sometimes indistinguishable using histology alone, were also investigated and the predictive models built yielded 100% accuracy in discriminating these tumor types. Discrimination of three subtypes of RCC was also achieved based on DESI-MS imaging data. Importantly, several small metabolites and lipids species were identified as characteristic of individual tissue types and chemically characterized using tandem MS and high mass accuracy measurements. Collectively, our study shows that the metabolic data acquired by DESI-MS imaging in conjunction with statistical modeling allows discrimination of renal tumors, and thus has the potential to be used in the clinical setting to improve treatment of kidney tumor patients.