Imaging Mass Spectrometry Is an Accurate Tool in Differentiating Clear Cell Renal Cell Carcinoma and Chromophobe Renal Cell Carcinoma: A Proof-of-concept Study

Abstract

Clear cell renal cell carcinoma (ccRCC) and chromophobe renal cell carcinoma (chRCC) are relatively common tumors that can have significant risk for mortality. Treatment and prognostication in renal cell carcinoma (RCC) are dependent upon correct histologic typing. ccRCC and chRCC are generally straightforward to diagnose based on histomorphology alone. However, high-grade ccRCC and chRCC can sometimes resemble each other morphologically, particularly in small biopsies. Multiple immunostains and/or colloidal iron stain are sometimes required to differentiate the two. Imaging mass spectrometry (IMS) allows simultaneous spatial mapping of thousands of biomarkers, using formalin-fixed paraffin-embedded tissue sections. In this study, we evaluate the ability of IMS to differentiate between World Health Organization/International Society for Urological Pathology grade 3 ccRCC and chRCC. IMS spectra from a training set of 14 ccRCC and 13 chRCC were evaluated via support vector machine algorithm with a linear kernel for machine learning, building a classification model. The classification model was applied to a separate validation set of 6 ccRCC and 6 chRCC, with 19 to 20, 150-μm diameter tumor foci in each case sampled by IMS. Most evaluated tumor foci were classified correctly as ccRCC versus chRCC (99% accuracy, kappa=0.98), demonstrating that IMS is an accurate tool in differentiating high-grade ccRCC and chRCC.

Using the Synergy between HPLC-MS and MALDI-MS Imaging to Explore the Lipidomics of Clear Cell Renal Cell Carcinoma

“…That is why several mass spectrometry studies have been published aiming at the classification of samples into tumoral and nontumoral, or into ccRCC and chromophobe renal cell carcinoma (ChRCC). For that purpose, the use of the peptidic and/or lipidic signature has been explored, both in fresh tissue and in paraffin-embedded tissue. − However, most of those studies report a very limited number of markers, which in some cases do not lead to the correct classification of samples. In any case, there is a general consensus on the fact that important metabolic changes are produced in the context of ccRCC. ,, …”

Laser induced fluorescence spectroscopy analysis of kidney tissues: A pilot study for the identification of renal cell carcinoma

“…The treatment's success largely depends upon the identification of the phenotype and the corresponding treatment strategy [11]. Sometimes high-grade ccRCC looks similar with chRCC morphologically [39]. Figure 7 shows the fluorescence spectra of ccRCC and chRCC, even a cursory examination of the fluorescence spectra of these two sub-types show that they can easily be discriminated by their respective spectra.…”

The synergism of spatial metabolomics and morphometry improves machine learning‐based renal tumour subtype classification

“… 6 , 7 , 8 , 9 , 10 This potential has also been successfully applied for biomarker discovery and machine learning‐based renal tumour subtyping using unique molecular data. 11 , 12 , 13 , 14 , 15 …”