Validation of Breast Cancer Margins by Tissue Spray Mass Spectrometry

Abstract

Current methods for the intraoperative determination of breast cancer margins commonly suffer from the insufficient accuracy, specificity and/or low speed of analysis, increasing the time and cost of operation as well the risk of cancer recurrence. The purpose of this study is to develop a method for the rapid and accurate determination of breast cancer margins using direct molecular profiling by mass spectrometry (MS). Direct molecular fingerprinting of tiny pieces of breast tissue (approximately 1 × 1 × 1 mm) is performed using a home-built tissue spray ionization source installed on a Maxis Impact quadrupole time-of-flight mass spectrometer (qTOF MS) (Bruker Daltonics, Hamburg, Germany). Statistical analysis of MS data from 50 samples of both normal and cancer tissue (from 25 patients) was performed using orthogonal projections onto latent structures discriminant analysis (OPLS-DA). Additionally, the results of OPLS classification of new 19 pieces of two tissue samples were compared with the results of histological analysis performed on the same tissues samples. The average time of analysis for one sample was about 5 min. Positive and negative ionization modes are used to provide complementary information and to find out the most informative method for a breast tissue classification. The analysis provides information on 11 lipid classes. OPLS-DA models are created for the classification of normal and cancer tissue based on the various datasets: All mass spectrometric peaks over 300 counts; peaks with a statistically significant difference of intensity determined by the Mann-Whitney U-test (p < 0.05); peaks identified as lipids; both identified and significantly different peaks. The highest values of Q2 have models built on all MS peaks and on significantly different peaks. While such models are useful for classification itself, they are of less value for building explanatory mechanisms of pathophysiology and providing a pathway analysis. Models based on identified peaks are preferable from this point of view. Results obtained by OPLS-DA classification of the tissue spray MS data of a new sample set (n = 19) revealed 100% sensitivity and specificity when compared to histological analysis, the "gold" standard for tissue classification. "All peaks" and "significantly different peaks" datasets in the positive ion mode were ideal for breast cancer tissue classification. Our results indicate the potential of tissue spray mass spectrometry for rapid, accurate and intraoperative diagnostics of breast cancer tissue as a means to reduce surgical intervention.

Keywords: breast cancer; direct mass spectrometry; discriminant model; lipidomics; molecular profiling; tissue spray.

Figure 1

Characteristic tissue spray spectral profiles of breast samples recorded on Maxis Impact MS in m/z 200 to 1000. (a) Positive ion mode normal tissue; (b) positive ion mode tumor tissue; (c) negative ion mode normal tissue; (d) negative ion mode tumor tissue.

Figure 2

Relative abundances (%) of lipids in normal (gray) and tumor (orange) tissues: (a) SM, (b) PC, (c) PI. Lipid annotation: PI, phosphatidylinositols; PC, phosphatidylcholines; SM, sphingomyelins. SM and PC are detected in the positive ion mode, PI—in the negative mode. Statistically significant differences according to U-test are indicated by an asterisk: *—p-value < 0.05; **—p-value < 0.01; ***—p-value < 0.001. Black dots correspond to outliers.

Figure 3

Validation of developed OPLS-DA models for tissue classification on a new set of samples (n = 19). (a,b)—The photo showing the pieces of two tissue samples that underwent both histological and tissue spray analysis. (c–f) The plot of tissue classification score vs. its spatial position for two samples for four types of datasets: all peaks, peaks with a statistically significant difference of intensity determined by the Mann–Whitney U-test, peaks identified as lipids, lipids with a statistically significant difference. Tissue spray MS is performed in the positive ion mode. The scores are obtained by unsupervised analysis of tissue spray mass spectra with the previously developed OPLS-DA models. The red line on the graph is determined by statistical model and separates the “normal region” from the “cancer region”.