Identification of serum proteome signatures of locally advanced and metastatic gastric cancer: a pilot study
© Abramowicz et al. 2015
Received: 13 June 2015
Accepted: 10 September 2015
Published: 17 September 2015
The gastric cancer is one of the most common and mortal cancer worldwide. The initial asymptomatic development and further nonspecific symptoms result in diagnosis at the advanced stage with poor prognosis. Yet, no clinically useful biomarkers are available for this malignancy, and invasive gastrointestinal endoscopy remains the only reliable option at the moment. Hence, there is a need for discovery of clinically useful noninvasive diagnostic and/or prognostic tool as an alternative (or complement) for current diagnostic tools. Here we aimed to search for serum proteins characteristic for local and invasive gastric cancer.
Pre-treatment blood samples were collected from patients with diagnosed gastric adenocarcinoma at the different stage of disease: 35 patients with locally advanced cancer and 18 patients with metastatic cancer; 50 healthy donors were also included as a control group. The low-molecular-weight fraction of serum proteome (i.e., endogenous peptidome) was profiled by the MALDI-ToF mass spectrometry, and the whole proteome components were identified and quantified by the LC–MS/MS shotgun approach.
Multicomponent peptidome signatures were revealed that allowed good discrimination between healthy controls and cancer patients, as well as between patients with locally advanced and metastatic cancer. Moreover, a LC–MS/MS approach revealed 49 serum proteins with different abundances between healthy donors and cancer patients (predominantly proteins associated with inflammation and acute phase response). Furthermore, 19 serum proteins with different abundances between patients with locally advanced and metastatic cancer were identified (including proteins associated with cytokine/chemokine response and metabolism of nucleic acids). However, neither peptidome profiling nor shotgun proteomics approach allowed detecting serum components discriminating between two subgroups of patients with local disease who either developed or did not develop metastases during follow-up.
The molecular differences between locally advanced and metastatic gastric cancer, as well as more obvious differences between healthy individuals and cancer patients, have marked reflection at the level of serum proteome. However, we have no evidence that features of pre-treatment serum proteome could predict a risk of cancer dissemination in patients treated due to local disease. Nevertheless, presented data confirmed potential applicability of a serum proteome signature-based biomarker in diagnostics of gastric cancer.
Gastric cancer is the fourth most common cancer and the second leading cause of cancer-related death worldwide. This cancer especially afflicts populations of East Asia, Eastern Europe, and parts of Central and South America, and the morbidity rate is twice higher for men than for women . The malignancy is associated with nonspecific symptoms or even asymptomatic development in its early stages, which often results in diagnosis at advanced stages. Stage of gastric cancer strongly correlates with poor prognosis. According to the National Cancer Data Base report, 5-year survival rate for stage IA was 78 % and it dropped substantially at each stage to about 7 % for patients diagnosed with stage IIIB or stage IV disease . Thus, early diagnosis of gastric cancer might radically increase efficacy of treatment and improve prognosis for this fatal illness. At present the most efficient diagnostic tool for detection of gastric cancer remains a gastrointestinal endoscopy, yet this invasive technique is not suitable for large-scale screening. Unfortunately, there is no alternative non-invasive biomarkers available, because the commonly used gastrointestinal tumor markers like CEA, CA 19-9 or CA 72-4 are insufficient for early diagnosis of this cancer due to their low sensitivity and specificity (20–30 %) [3–5]. Majority of gastric cancer cases (above 90 %) are classified as adenocarcinomas. More recently four molecular subtypes of gastric adenocarcinoma were distinguished based on genomic profiling delivered thanks to the Cancer Genome Atlas project . However, the knowledge on molecular heterogeneity and biology of this cancer, including its development and mechanisms of progression, remains rather limited yet. Hence, an urgent need for identification of clinically relevant biomarkers relates not only the early diagnosis but also prognosis and prediction of treatment outcome.
Clinical proteomics is an important approach to discovery of biomarkers of gastric cancer . It is generally accepted that blood proteome is a promising source of novel biomarkers of this cancer, including particularly valuable markers for early detection of the disease and monitoring of response to the treatment . Mass spectrometry-based profiling of the low-molecular-weight fraction of serum proteome, so called endogenous peptidome, revealed multi-peptide signatures with potential applicability in classification and diagnosis of different cancer types [9–13]. A few works have been published that explored MALDI/SELDI-based profiling of serum/plasma petidome for diagnosis of gastric cancer, which proposed peptide signatures that allowed discriminating healthy donors and patients with gastric cancer, or signatures associated with a course of a disease [14–22]. Several components of such signatures were further identified as fragments of KNG1 , APOC1 and APOA2 , SAA , TBB5 and TYB4  or FIBA [23, 24]. More recently, a panel of biomarkers composed of serum proteins pre-selected based on preclinical mouse model (afamin, clusterin, VDBP and haptoglobin) has been validated to discriminate between gastric cancer patients and patient with benign gastric diseases . Nevertheless, none of proposed serum proteome signatures of gastric cancer has been widely accepted and applied in clinical practice yet.
Here we aimed to characterize proteome features of pre-treatment serum associated with risk of metastasis of gastric cancer. Two types of proteomic analyses were performed: (1) the low-molecular-weight fraction of serum proteome was profiled by the MALDI-ToF mass spectrometry, (2) the whole proteome components were identified and quantified by LC–MS/MS after digestion with trypsin (a “shotgun proteomics” approach). Groups of previously untreated patients with locally advanced gastric cancer and metastatic disease were enrolled to this study (a matched group of healthy individuals was analyzed as a reference); such comprehensive proteomic analysis was performed in a group of Caucasians patients with gastric cancer for the first time. Serum proteome signature that differentiated between patients with locally advanced cancer and metastatic cancer was detected in this pilot study, yet features specific for patients with locally advanced disease at time of diagnosis who eventually developed metastases were not observed at this level.
Characteristics of patient groups
Characteristics of donor groups enrolled into the study
Cancer patients (all cases)
Patients with locally advanced cancer
Patients with metastatic cancer
28–60 (median 50)
34–74 (median 59)
36–70 (median 59)
34–73 (median 58)
35–74 (median 60)
15 (28 %)
3 (16 %)
6 (37 %)
6 (33 %)
31 (59 %)
13 (68 %)
9 (57 %)
9 (50 %)
7 (13 %)
3 (16 %)
1 (6 %)
3 (17 %)
16 (30 %)
10 (53 %)
3 (19 %)
3 (17 %)
29 (55 %)
8 (42 %)
11 (69 %)
10 (56 %)
8 (15 %)
1 (5 %)
2 (12 %)
5 (27 %)
50 (94 %)
19 (100 %)
16 (100 %)
15 (83 %)
3 (6 %)
0 (0 %)
0 (0 %)
3 (17 %)
20 (38 %)
13 (68 %)
5 (31 %)
2 (11 %)
33 (62 %)
6 (32 %)
11 (69 %)
16 (89 %)
35 (66 %)
19 (100 %)
16 (100 %)
0 (0 %)
18 (34 %)
0 (0 %)
0 (0 %)
18 (100 %)
Preparation of serum samples
Pre-treatment blood was collected into a 5 ml Vacutainer Tube (Becton–Dickinson), incubated for 30 min at room temperature to allow clotting and then centrifuged at 1000g for 10 min to remove the clot. The serum was aliquoted and stored at −70 °C until use.
Profiling of the low-molecular-weight fraction of serum proteome
Before analysis samples were diluted 1:5 with buffer containing 20 % acetonitrile (ACN) and 25 mM ammonium bicarbonate, and then filtered by centrifugation through Amicon Ultra units (50 kDa cut-off) for removing the abundant high-molecular weight proteins, particularly albumin. Immediately before analysis samples were desalted and concentrated by loading onto ZipTip C18 microcolumns (EMD Millipore), and then eluted with 1 μl of matrix solution (saturated solution of alpha-cyano-4-hydroxy-cinnamic acid in 30 % ACN/H2O and 0.1 % TFA) directly onto the 800 μm AnchorChip™ (Bruker Daltonics) plate. The analysis was done by using an UltrafleXtreme MALDI-ToF mass spectrometer (Bruker Daltonics); the analyzer worked in the linear mode, and positive ions were recorded in the mass range between 1000 and 12,000 Da. The samples were spotted in duplicate and for each spot two spectra were acquired. Mass calibration was performed after every four samples using Protein Calibration Standard I (Bruker Daltonics). Randomization in blocks was used in spectra registration to avoid a possible batch effect. Afterwards the raw data was exported to TXT files and the spectral components were preprocessed using bioinformatics algorithms created in our group, which included alignment, detection and removal of outlier profiles by Dixon’s Q test (single spectra were removed from about 5 % of samples), averaging of technical repeats, baseline removal and normalization of the total ion current. The spectra smoothing, peak picking, binning and statistical analysis were performed using Spectrolyzer software (version 22.214.171.12490, MedicWave).
LC–MS/MS analysis of serum proteome components
Serum samples were reduced with 5 mM dithiothreitol for 5 min at 95 °C, then alkylated with 10 mM iodoacetamide for 20 min in darkness at room temperature, and afterwards digested overnight at 37 °C with trypsin (Promega). The analysis was performed on Dionex UltiMate 3000 RSLC nanoLC System connected to Q Exactive Orbitrap mass spectrometer (Thermo Fisher Scientific); each sample was analyzed separately. Tryptic peptides (2.5 µg of peptides) were separated on reverse phase Acclaim PepMap RSLC nanoViper C18 column (75 µm × 25 cm, 2 µm granulation) using the acetonitrile gradient (from 4 to 60 %, in 0.1 % formic acid) at 30 °C and a flow rate of 250 nL/min (for 230 min). The spectrometer was operating in the data-dependent MS/MS mode with survey scans acquired at a resolution of 70,000 at m/z 200 Da in MS mode and 17,500 at m/z 200 Da in MS2 mode, respectively. The spectra were recorded in the scan m/z range 300–2000 in the positive ion mode. Higher energy collisional dissociation (HCD) ion fragmentation was performed with normalized collision energies set to 25. Protein identification was performed using Swiss-Prot human database with a precision tolerance 10 ppm for peptide masses and 0.05 Da for fragment ion masses. The abundances of identified proteins were estimated using MaxQuant 126.96.36.199 software.
Statistical and bioinformatics analyses
For each component of MALDI mass profiles the comparison between groups of donors was performed using the Student’s t test after logarithmic transformation of data. Multi-component classifiers were built and tested with the SVM-based approach using Spectrolyzer software (version 188.8.131.5290, MedicWave). Significance of differences in abundances of proteins quantified by LC–MS/MS were assessed using the t test or the Mann–Whitney test depending on normality of data (type of distribution was estimated using the Shapiro–Wilk test, the Lilliefors test and the F test for homogeneity of variances), and the Nemenyi test for pairwise comparisons. In general, p = 0.05 was selected as a statistical significance threshold except for MALDI profiling where the Bonferroni correction for multiple testing was applied. The Empirical Proteomic Ontology Knowledge Base (EPO-KB), which annotates registered m/z values to known peptide/proteins , was employed to assign hypothetical identification of the spectra components (0.5 % mass accuracy limit was allowed). List of genes corresponding to identified proteins was annotated at GO terms using gProfiler (http://biit.cs.ut.ee/gprofiler/); the significance of the term over-representation was assessed using the hypergeometric distribution test. In order to visualize functional relationships between identified proteins corresponding genes were annotated at the GeneMANIA Cytoscape plugin for pathway interaction networks (http://pages.genemania.org/plugin/).
Numbers of serum peptidome components with abundances different between compared groups of individuals
Control vs. cancer (all cases)
Locally advanced vs. metastatic cancer
Local/no spread vs. metastatic cancer
Local/spread vs. metastatic cancer
Local/no spread vs. local/spread cancer
50 vs. 53
35 vs. 18
19 vs. 18
16 vs. 18
19 vs. 16
p < 0.05
p < 0.05/Bonferroni
Differentiating serum proteins
Protein full name
Alpha-1-acid glycoprotein 1
Alpha-1-acid glycoprotein 2
Complement C1s subcomponent
Carbonic anhydrase 1
Monocyte antigen CD14
Complement factor B
Complement component C6
Complement component C8 gamma
Complement component C9
Platelet basic protein
Complement factor H-related prot. 1
Glutathione peroxidase 3
Hemoglobin subunit alpha
Hemoglobin subunit beta
Hemoglobin subunit delta
Hepatocyte growth factor activator
Hepatocyte growth factor-like prot.
Plasma protease C1 inhibitor
Inter-alpha-trypsin inhib. heavy ch. 1
Inter-alpha-trypsin inhib. heavy ch. 3
Platelet factor 4
Serum paraoxonase/arylesterase 1
Vitamin K-dependent protein Z
Serum amyloid A-1 protein
Serum amyloid A-2 protein
Sex hormone-binding globulin
Vitamin D-binding protein
The last decade has abounded in the publications that reported the MALDI/SELDI-based profiling of the serum peptidome as a promising tool for the effective identification of gastric cancer patients [14–22]. Four different diagnostic classifiers were built on the basis of tri-peptide combination by Ebert et al.  (m/z 3946, 3503, 15,958), Su et al.  (m/z 1468, 3935, 7560), Liu et al.  (m/z 5906.4, 6632.9, 8704.3) and Fan et al.  (m/z 1867, 2701, 2094). Although the discriminatory peaks were not consistent among those studies, probably because of the diverse methodology of sample preparation (especially highly abundant proteins removal), measurement and/or data processing , there were some common features of proposed signatures. For example, the m/z peak at 1466–1468 Da identified as fibrinopeptide A (FIBA) was reported as a candidate biomarker in three reports [16, 23, 24]. Moreover, increased level of another fragment of FIBA (approx. weight 5904–5906 Da) was observed in serum of patients with gastric cancer , but also in serum of patients with ovarian, hepatocellular and urothelial cancers [27–29]. In our study we have detected over 100 components of serum peptidome that differentiate compared groups of gastric cancer patients and healthy volunteers. This included several components that putatively corresponded to fragments of FIBA, exemplified by 1469 and 5904 Da components upregulated in blood of patients with metastatic cancer. Thus, our results clearly confirmed and extended previous reports indicating that multipeptide signatures based on features of endogenous serum peptidome could be used for classification of patients with gastric cancer and differentiation of patients with metastatic disease.
In the second part of our study serum samples were further analyzed using the shotgun LC–MS/MS approach, which is currently the gold standard for identification of proteins allowing label-free quantitation and providing large coverage of sample’s proteome . Among serum proteins with abundances significantly different between healthy individuals and patients with gastric cancer dominated those associated with immunity, inflammation and acute phase response, even though immunoglobulins were excluded from the analysis. Up-regulation of proteins involved in immunity and inflammation is a typical picture of serum proteome of cancer patients, especially in advanced cases, and increased levels of proteins like C-reactive protein, haptoglobin or serum amyloid have been previously reported for many different types of cancer [31–35]. Most recently, iTRAQ-based approach has been used to identify serum proteins differentiating healthy controls and patients with gastric adenocarcinoma in a small sample of Asian population . Upregulation of 48 proteins in samples of cancer patients was reported, that included several inflammation/acute phase-related proteins: A1AG1 (ORM1), A1AT (SERPINA1), AACT (SERPINA3), CO4B, CO9, HPT, ITIH4, LBP and SAA1, which upregulation was revealed also in our study. Moreover, other proteins revealed in both reports included upregulated A2GL (LRG1), ITIH3, ORM2 and SHGB, which collectively indicated very high conformity of serum proteome signature of gastric cancer that based on samples of unrelated Polish and India’s populations.
Moreover, our study revealed serum proteome components with levels discriminating patients with locally advanced gastric adenocarcinoma and with metastatic disease. Proteins upregulated in serum of patients with metastatic disease included C-reactive protein and IC1 (SERPING1), factors involved in immunity and inflammation, and angiogenin, protein involved in angiogenesis. Other proteins differentially expressed in blood of patients with local and metastatic cancer included molecules associated with response to cytokines/chemokines, leukocyte migration and blood coagulation as well as factors associated with extracellular transport, and metabolism of phosphorus and nucleic acids. Elevated level of CEA and CA 19-9 was previously associated with increased risk of gastric cancer metastases. However, increased level of such “classical” markers has been observed only in case of 30–50 % of patients with disseminated disease [3–5], thus there is an obvious space for new proteomics-based markers of metastatic gastric cancer. Furthermore, several patients enrolled into the study were diagnosed with locally advanced cancer, yet in some cases the disease was spread and distant metastases were detected during follow-up. However, comparison of pre-treatment serum samples collected in both subgroups of patients with local disease (who further either developed or did not distant metastases) revealed very few significant differences (similar results were delivered by LC–MS/MS-based analysis of complete proteome and MALDI-based profiling of endogenous peptidome). Hence, our data revealed serum proteome signature discriminating patients with locally advanced and metastatic gastric adenocarcinoma. However, our study did not reveal serum proteome components that could be used for prediction of risk of metastasis in patients diagnosed with local cancer.
Significant differences between patients with gastric cancer and healthy individuals as well as between patients with locally advanced and metastatic cancer have been detected at the level of serum proteome in pre-treatment blood samples. However, no evidence that features of pre-treatment serum proteome could predict a risk of cancer dissemination in patients treated due to local disease has been observed. Nevertheless, presented data confirmed potential applicability of biomarkers based on serum proteome signature in diagnostics of gastric cancer.
AA designed and performed experiments, drafted manuscript, AW performed MS analyses, AGK performed MS analyses, JP performed mathematical modeling and statistical analyses, PR performed MS analyses, PP collected and interpreted clinical data, ANK collected and interpreted clinical data, MP performed MS analyses, interpreted results, JW designed and interpreted clinical part of study, PW designed overall study, interpreted data and finalized the manuscript. All authors read and approved the final manuscript.
This work was supported by Polish National Science Centre, Grant No. N403 283140.
Compliance with ethical guidelines
Competing interests The authors declare that they have no competing interests.
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