Gastric cancer-specific protein profile identified using endoscopic biopsy samples via MALDI mass spectrometry

Abstract

To date, proteomic analyses on gastrointestinal cancer tissue samples have been performed using surgical specimens only, which are obtained after a diagnosis is made. To determine if a proteomic signature obtained from endoscopic biopsy samples could be found to assist with diagnosis, frozen endoscopic biopsy samples collected from 63 gastric cancer patients and 43 healthy volunteers were analyzed using matrix-assisted laser desorption/ionization (MALDI) mass spectrometry. A statistical classification model was developed to distinguish tumor from normal tissues using half the samples and validated with the other half. A protein profile was discovered consisting of 73 signals that could classify 32 cancer and 22 normal samples in the validation set with high predictive values (positive and negative predictive values for cancer, 96.8% and 91.3%; sensitivity, 93.8%; specificity, 95.5%). Signals overexpressed in tumors were identified as alpha-defensin-1, alpha-defensin-2, calgranulin A, and calgranulin B. A protein profile was also found to distinguish pathologic stage Ia (pT1N0M0) samples (n = 10) from more advanced stage (Ib or higher) tumors (n = 48). Thus, protein profiles obtained from endoscopic biopsy samples may be useful in assisting with the diagnosis of gastric cancer and, possibly, in identifying early stage disease.

Figure 1

General procedure for preparing sections for MALDI analysis. (A) Optical image of an H&E section from a cancer endoscopic biopsy tissue cut serially to the section shown in (C). The optical section was evaluated by a pathologist, and marked at discrete locations (enriched in tumor cells) with 200 μm diameter circles. (C) Optical image of the serial section with matrix applied at the locations marked in (A). A total of ~9 nL sinapinic acid was applied at each spot, resulting in dried matrix spots of ~200 μm diameter. (B) and (D) are magnified areas of the H&E stained section and MALDI section, respectively.

Figure 2

Representative average mass spectra from one normal patient (black) and one cancer patient (red). The area from m/z 3000–5500 has been expanded to highlight differences. The normal spectrum is an average of 8 independent measurements; the cancer spectrum is an average of 9 independent measurements.

Figure 3

Principal component analysis plot for 63 gastric cancer patient samples (red) and 43 samples from healthy volunteers (blue), which graphically represents Euclidean distances among samples. Each sphere represents a single sample, and samples whose protein expression profiles are very similar are shown close together.

Figure 4

Intensity profile for identified discriminatory signals among 63 cancer patient samples (red) and 43 samples from healthy volunteers (black). The false discovery rate (FDR) was estimated as the proportion of the signal with univariate P values less than or equal to the P value of each identified signal, using the method of Benjami and Hochberg.