MALDI-TOF-MS analysis in discovery and identification of serum proteomic patterns of ovarian cancer

Blood samples were collected from 89 patients operated in Gynecologic Oncology Department of Poznan University of Medical Sciences, Poland, on the day before surgery, between August 2014 and December 2015. Blood samples were incubated for 30 min at room temperature for clotting and centrifuged for 15 min at 4000 rpm. The resulting sera were isolated and stored at −80 °C until analysis. All serum samples were handled using the same laboratory equipment and stored in the same type of plastic vials and boxes. Based on histopathological result the patients were divided into two groups: OC (including borderline ovarian tumors) (N = 44) and no pathology of the ovaries - further referred to as “control group” (N = 45). The control group consisted of patients operated (hysterectomy with bilateral salpingoophorectomy) due to reasons other than ovarian tumors and in which the final histopathological examination confirmed no existing ovarian pathology. All participants were after overnight fasting. The patients were selected according to the following exclusion criteria: other than epithelial OC, other cancers currently or in anamnesis, chronic metabolic diseases (diabetes, dyslipidemia), previous or current cancer treatment (radiotherapy, chemotherapy, hormonal therapy), relevant concomitant medication (anti-diabetic agents, statins, hormonal replacement therapy, oral contraception. Additionally two markers, CA124 and HE4, were measured in the OC group with an electrochemiluminescence immunoassay (Roche Diagnostics, Indianapolis, IN, USA). Detailed characterization of the studied groups, including demographic and clinical profiles, is presented in Table 1 and Additional file 1 Table S1. The project was approved by the Bioethics Committee of Poznan University of Medical Sciences, Poland (Decision No. 165/16).

Each sample was diluted in 0.1% trifluoroacetic acid (TFA) in water (1:5). In order to desalt and concentrate the samples, solid phase extraction method based on ZipTip C18 pipette tips was used according to the manufacturer’s protocol (Millipore, Bedford, MA, USA). The tips were conditioned with acetonitrile (ACN) and 0.1% TFA. The prepared samples were loaded onto the tips and the peptides were bound. After washing with 0.1% TFA, sample fractions were eluted using 50% ACN solution in 0.1% TFA.

Each eluent sample was mixed with matrix solution of α-cyano-4-hydroxycinnamic acid (0.3 g/L HCCA in a solution containing 2:1 ethanol:acetone, v/v) at the ratio of 1:10. One microliter of the sample/matrix solution was spotted onto the MALDI target (AnchorChip 800 μm, Bruker Daltonics, Bremen, Germany) and left to crystallize at room temperature. The samples from both study groups were analyzed in a random order and the disease status of the women was blinded to minimize variability and systematic errors. UltrafleXtreme MALDI-TOF/TOF mass spectrometer (Bruker Daltonics, Bremen, Germany) was used to perform MS analyses in the linear positive mode. Positively charged ions were detected in the m/z range of 1000–10,000 Da and 2000 shots were accumulated per one spectrum. The MS spectra were externally calibrated with the mixture of Peptide Calibration Standard and Protein Calibration Standard I at the ratio of 1:5. The average mass deviation was less than 100 ppm. The matrix suppression mass cut off was m/z 700 Da. The following ion source parameters were used: ion source 1, 25.09 kV; ion source 2, 23.80 kV. Other settings for MALDI-TOF MS analysis were as follows: pulsed ion extraction, 260 ns and lens, 6.40 kV. FlexControl 3.4 software (Bruker Daltonics, Bremen, Germany) was applied for the acquisition and processing of the spectra. Each sample was analyzed in three repetitions. Inter-day and intra-day reproducibility of the applied procedure was evaluated in our previous study [22].

The sample was prepared with ZipTip technique. The obtained eluent was further subjected to nano-LC separation using: nanoflow HPLC set (EASY-nano LC II, Bruker Daltonics, Germany) and fraction collector (Proteineer-fc II, Bruker Daltonics, Germany). The nano-LC system consisted of a trap column, NS-MP-10 BioSphere C18, (20 mm × 100 μm I.D., particle size 5 μm, pore size 120 Å) (NanoSeparations, Nieuwkoop, the Netherlands) and Thermo Scientific Acclaim PepMap 100 column C18 (150 mm × 75 μm I.D., particle size 3 μm, pore size 100 Å) (Thermo Scientific: Sunnyvale, CA, USA). Linear gradient was 2%–50% of ACN during 96 min. Two mobile phases were used: mobile phase A (0.05% TFA in water) and mobile phase B (0.05% TFA 90% ACN). The volume of injected sample eluent was 4 μL. The separation was performed with a flow rate 300 nL/min. A total of 384 fractions, 80 nL each, were obtained. Each eluent was automatically mixed with 420 nL of matrix solution that was prepared by mixing 36 μL of HCCA saturated solution of 0.1% TFA and ACN (90:10 v/v), 784 μL ACN and 0.1% TFA (95:5 v/v), 8 μL of 10% TFA and 8 μL of 100 mM ammonium phosphate monobasic and spotted onto the MALDI target (AnchorChip 800 μm) using a fraction collector. The system was controlled by HyStar 3.2 software (Bruker Daltonics, Germany). MALDI-TOF/TOF mass spectrometer (UltrafleXtreme, Bruker Daltonics, Germany) operated in a reflector mode was used in further analysis of the sample. The MS spectra were externally calibrated using Peptide Calibration Standard mixture (Bruker Daltonics, Germany). A list of precursor peaks was obtained using WARP-LC software (Bruker Daltonics, Germany). The chosen discriminative m/z were analyzed with MS/MS mode for protein identification. The parameters for MS and MS/MS mode were described in our previous study [22]. FlexControl 3.4 software (Bruker Daltonics, Germany) was applied for the acquisition of spectra. Processing and evaluation of the data was achieved using FlexAnalysis 3.4 (Bruker Daltonics, Germany). BioTools 3.2 (Bruker Daltonics, Germany) was used to perform protein database searches. Proteins were identified using the SwissProt database and Mascot 2.4.1 search engine (Matrix Science, London, UK) with taxonomical restriction to “Homo sapiens”. The following general protein search parameters were used: precursor-ion mass tolerance ±50 ppm; fragment-ion mass tolerance ±0.7 Da; no enzyme; monoisotopic mass; peptide charge +1.

Data analysis of each spectrum was performed with ClinProTools version 3.0 software (Bruker Daltonics, Germany). In order to let the software group all analyzed sample replicates into one biological replicate, spectra grouping function was applied. This option provided improved measurement quality. Before any analysis or spectra processing, the multiple measurements were averaged. Further steps were processed upon one averaged spectrum per sample. Comparison of the obtained data was achieved through a standard workflow. Each spectrum was first normalized to the total ion current (TIC) and recalibrated with the prominent common m/z values. “Top hat” baseline subtraction with the minimum baseline width set to 10% was used to remove broad structures. Spectra were also smoothed and processed in the mass range of 1000–10,000 Da. The signal-to-noise ratio was greater than or equal to 5. Peak picking and average peak calculation procedures were used. A total average spectrum was calculated from the preprocessed spectra. Averaging of the spectra allowed us to improve the signal to noise during peak picking procedure. Due to average peak list calculation, small peaks that might be missed on a single spectrum, were included in the overall profile. All reproducible peaks were detected according to this procedure.

Comparisons between patients with OC and healthy individuals were evaluated with Wilcoxon test. Statistical significance was attained when p-value was ≤0.02. All p-values were internally corrected with the Benjamini-Hochberg algorithm. Evaluation of the discrimination ability of each peak was achieved by calculating receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) (Fig. 1). Chemometric algorithms: supervised neural network (SNN), genetic algorithm (GA), and quick classifier (QC) were used for model analysis and selection of peptide/protein peak clusters. Each model indicated a combination of the differentiating peaks. The studied groups were randomly subdivided into a training set (containing 33 ovarian cancer patients and 33 healthy controls) and a test set (containing 11 ovarian cancer patients and 12 healthy controls). The use of these two sets allowed for testing robustness of the obtained models. For the training set two parameters, 20% leave one out cross validation and recognition capability, were calculated. For the model with the best performance of these two indicators, an external validation using the test set was calculated. The values of sensitivity and specificity were used to define discriminative ability of the model. Peaks that indicated the best discrimination between the studied groups were further identified as fragments of defined proteins.