| Metadata |
| datasetIdentifier | PASS00864 |
| datasetType | SWATH |
| submitter | Olga Schubert <olga.schubert@gmail.com> |
| submitter_organization | ETHZ |
| lab_head_full_name | Pavel Bouchal |
| lab_head_email | bouchal@chemi.muni.cz |
| lab_head_organization | Masaryk University |
| lab_head_country | Czech Republic |
| datasetTag | BreastCancerSWATH |
| datasetTitle | Breast cancer classification based on proteotypes obtained by SWATH mass spectrometry |
| publicReleaseDate | 2019-07-23 00:00:00 |
| finalizedDate | 2019-07-24 21:40:23 |
| summary | Accurate classification of breast tumors is vital for patient management decisions and enables more precise cancer treatment. Here, we present a quantitative proteotyping approach based on sequential windowed acquisition of all theoretical fragment ion spectra (SWATH) mass spectrometry and establish key proteins for breast tumor classification. The study is based on 96 tissue samples representing five conventional breast cancer subtypes. SWATH proteotype patterns largely recapitulate these subtypes; however, they also reveal varying heterogeneity within the conventional subtypes, with triple negative tumors being the most heterogeneous. Proteins that contribute most strongly to the proteotypebased classification include INPP4B, CDK1, and ERBB2 and are associated with estrogen receptor (ER) status, tumor grade status, and HER2 status. Although these three key proteins exhibit high levels of correlation with transcript levels (R > 0.67), general correlation did not exceed R = 0.29, indicating the value of protein-level measurements of disease regulated genes. Overall, this study highlights how cancer tissue proteotyping can lead to more accurate patient stratification. |
| contributors | Pavel Bouchal, Olga T. Schubert, Jakub Faktor, Lenka Capkova, Hana Imrichova, Karolina Zoufalova, Vendula Paralova, Roman Hrstka, Yansheng Liu, H. Alexander Ebhardt, Eva Budinska, Rudolf Nenutil and Ruedi Aebersold |
| publication | Bouchal, P. et al. (2019). Breast Cancer Classification Based on Proteotypes Obtained by SWATH Mass Spectrometry. Cell Reports 28, 832–843.e837. PubMed ID: 31315058 |
| growth | Informed patient consent forms along with tissue procurement procedures were approved by the ethics committee of the Masaryk Memorial Cancer Institute (MMCI). Breast cancer tissues were frozen in liquid nitrogen within 20 minutes after surgical removal and stored at -180°C in the tissue bank at MMCI. A set of 96 preoperatively untreated breast carcinomas of 11-20 mm maximum diameter (pT1c) was selected for the study. It consisted of 48 estrogen receptor (ER) positive, progesterone receptor (PR) positive, HER2 negative, grade 1 (luminal A-like (LA)) tumors; 16 ER positive, PR positive or negative, HER2 negative, grade 3 (luminal B-like (LB) tumors); 8 ER positive, PR positive or negative, HER2 positive, grade 3 (luminal B-like HER2 positive (LBH) tumors); and 16 ER negative, PR negative, HER2 negative, grade 3 (triple negative-like (TN) tumors). Half of the tumors in each group were lymph node positive and half were lymph node negative at the time of diagnosis. |
| treatment | na |
| extraction | Frozen breast cancer tissue (approx. 20 mm3) was homogenized in 150 μl lysis buffer (6 M guanidine hydrochloride; 0.1 M Na-phosphate buffer, pH 6.6; 1% Triton X-100) in MM301 mechanic homogenizer (Retsch, Germany) using a metal ball for 2 × 2 min at 20 s-1, needle-sonicated (Bandelin 2200 Ultrasonic homogenizer, Bandelin, Germany; 30 × 0.1 s pulses at 50 W) and kept on ice for 1 h. After centrifugation, protein concentration was measured in the supernatant using RC-DC assay (Bio-Rad, USA). |
| separation | na |
| digestion | The lysate (volume corresponding to 60 μg of protein) was digested using a filter aided sample preparation protocol with modifications. Briefly, aliquots of the lysate were mixed with 200 μl 8 M urea in 0.5 M triethylammonium bicarbonate (TEAB) pH 8.5 on Vivacon 500 filter device, cut-off 10K (Sartorius Stedim Biotech GmbH, Germany). The device was centrifuged at 14 000 × g at 20°C for 20 min (all of the following centrifugation steps were performed applying the same conditions). Subsequently, 100 μl 5 mM tris(2-carboxyethyl)phosphine in 8 M urea, 0.5 M TEAB, pH 8.5 was added to the filter, proteins were reduced at 37°C for 60 min at 600 rpm and centrifuged. Next, 100 μl 10 mM S-methyl methanethiosulfonate in 8 M urea and 0.5 M TEAB, pH 8.5 were added to the filter, cysteine groups of peptides were alkylated at 20°C for 10 min and centrifuged. The resulting concentrate was diluted with 100 μl 8 M urea in 0.5 M TEAB, pH 8.5 and concentrated again. This step was repeated twice. The concentrate was subjected to proteolytic digestion by adding 100 μl 0.5 M TEAB, pH 8.5 containing trypsin (TPCK treated, AB Sciex, USA) reconstituted in water (trypsin to protein weight ratio 1:30) and by incubating at 37°C for 16 h. The digests were collected by centrifugation into clean tubes, dried in a vacuum concentrator and C18 desalted using 0.1% trifluoracetic acid as an ion pairing reagent. Eleven retention time anchor peptides (iRT peptides, Biognosys AG, Zurich, Switzerland) were added into each sample at a ratio of 1:40 v/v. For SWATH-MS analysis, equal amounts of samples (estimated to be 1.33 µg protein) were injected in single technical replicates. |
| acquisition | SWATH-MS datasets of the individual patients were acquired on a TripleTOF 5600+ mass spectrometer (SCIEX, Canada) interfaced to an Eksigent Ekspert nanoLC 400 system (SCIEX, Canada). Prior to separation, the peptides were concentrated on a C18 PepMap100 pre-column (Thermo Fisher Scientific, USA; particle size 5 mm, 100 Å pore size, 300 mm x 5 mm). After 10 min washing with a solvent consisting of 2% acetonitrile and 0.05% (v/v) trifluoroacetic acid, the peptides were eluted from a capillary column (75 mm 3 250 mm, X-Bridge BEH C18 130 Å, particle size 2.5 mm, Waters, USA) using 2% mobile phase B for 10 min (mobile phase A was composed of 0.1% (v/v) formic acid in water, mobile phase B of 0.1% (v/v) formic acid in acetonitrile) followed by gradient elution from 2% to 40% mobile phase B in the next 120 min at a flow rate of 300 nl/min. Output of the separation column was directly coupled to nano-electrospray source. Using an isolation width of 9.7 m/z (containing 1 m/z for the window overlap), a set of 69 overlapping SWATH windows was constructed covering the precursor mass range of 400-1000 m/z. The effective isolation windows can be considered as 400.5-408.2 (first narrower window), 408.2-416.9, 416.9-425.6 etc. SWATH MS2 spectra were collected from 360 to 1460 m/z. The collision energy was optimized for each window according to the calculation for a charge 2+ ion centered upon the window with a spread of 15 eV. An accumulation time (dwell time) of 50 ms was used for all fragment ion scans in high-sensitivity mode, and for each SWATH cycle a survey scan was also acquired for 50 ms, resulting in a duty cycle of 3.5 s and a typical LC peak width of ~30 s. Compared to the above conditions, for the analysis of pooled samples the parameters were changes as follows: (i) chromatographic separation of peptides was performed on 20-cm emitter (75 mm inner diameter, #PF360-75-10-N-5, New Objective, USA) packed in-house with C18 resin (Magic C18 AQ 3 mm diameter, 200 Å pore size, Michrom BioResources, USA); (ii) a linear gradient from 2%–30% solvent B (98% ACN/0.1% FA) was run over 120 min at a flow rate of 300 nl/min; (iii) because of the increased sample complexity due to the pooling strategy, a set of 64 SWATH windows (containing 1 m/z for the window overlap) with variable width optimized for human samples was used to cover the precursor mass range of 400-1200 m/z. |
| informatics | The SWATH-MS data was analyzed using OpenSWATH with the following parameters: Chromatograms were extracted with 0.05 Th around the expected mass of the fragment ions and with an extraction window of ± 5 min around the expected retention time. The best models to separate true from false positives (per run) were determined by pyProphet with 10 cross-validations. The runs were subsequently aligned with a target FDR of 0.01 for aligned features. Background signals were extracted for features that could not be confidently identified. To reduce the size of the output data and remove low-quality features, two filtering steps were introduced: (i) keep only the 10 most intense peptide features per protein and (ii) of these, keep only features that were identified with an FDR < 0.01 in at least four samples over all runs, corresponding to the smallest tumor group in the dataset defined by a combination of subtype and lymph node status. |
| instruments | TripleTOF 5600+ |
| species | Human |
| massModifications | static: C+45.987721 |
