Identification of diagnostic metabolic signatures in clear cell renal cell carcinoma using mass spectrometry imaging

Abstract

Clear cell renal cell carcinoma (ccRCC) is the most common and lethal subtype of kidney cancer. Intraoperative frozen section (IFS) analysis is used to confirm the diagnosis during partial nephrectomy. However, surgical margin evaluation using IFS analysis is time consuming and unreliable, leading to relatively low utilization. In our study, we demonstrated the use of desorption electrospray ionization mass spectrometry imaging (DESI-MSI) as a molecular diagnostic and prognostic tool for ccRCC. DESI-MSI was conducted on fresh-frozen 23 normal tumor paired nephrectomy specimens of ccRCC. An independent validation cohort of 17 normal tumor pairs was analyzed. DESI-MSI provides two-dimensional molecular images of tissues with mass spectra representing small metabolites, fatty acids and lipids. These tissues were subjected to histopathologic evaluation. A set of metabolites that distinguish ccRCC from normal kidney were identified by performing least absolute shrinkage and selection operator (Lasso) and log-ratio Lasso analysis. Lasso analysis with leave-one-patient-out cross-validation selected 57 peaks from over 27,000 metabolic features across 37,608 pixels obtained using DESI-MSI of ccRCC and normal tissues. Baseline Lasso of metabolites predicted the class of each tissue to be normal or cancerous tissue with an accuracy of 94 and 76%, respectively. Combining the baseline Lasso with the ratio of glucose to arachidonic acid could potentially reduce scan time and improve accuracy to identify normal (82%) and ccRCC (88%) tissue. DESI-MSI allows rapid detection of metabolites associated with normal and ccRCC with high accuracy. As this technology advances, it could be used for rapid intraoperative assessment of surgical margin status.

Keywords: clear cell renal cell carcinoma; electrospray ionization; histopathology; metabolome; nephrectomy; surgical margins.

Figure 1:

The comparison between MS profiles of normal versus ccRCC tissue imaged at m/z 50–200. Inset shows the hematoxylin and eosin (H&E) images of ccRCC and normal tissue and the respective heatmaps of the tissue plotted with respect to glucose. Peaks marked with an asterisk arise from background.

Figure 2:

Comparison between MS profiles of normal versus ccRCC tissue imaged at m/z 200–1000. Inset shows the H&E images of ccRCC and normal tissue and the respective heatmaps of the tissue plotted with respect to arachidonic acid.

Figure 3.

Comparison of relative intensity of ratio of glucose to arachidonic acid in normal tissue (0) and cancerous tissue (1). Each data point represents the average relative intensity of the ratio from all pixels in a tissue.

Figure 4:

Sensitivity analysis to evaluate model accuracy, true positive, and true negative rates for various pixel to the tissue thresholds. This threshold (cutpoint) refers to the percentage of pixels required to be cancer positive for the overall tissue to be labeled as cancerous.

Figure 5.

Comparison of three models using area under the curve (AUC) and receiving operator characteristic (ROC) curve at the pixel level. AUC metrics are 0.814, 0.714, and 0.842 for baseline, log-ratio, and combined models, respectively.