Project PXD011315

PRIDE Assigned Tags:
Biomedical Dataset

Summary

Title

Proteome Analysis of Non-Small Cell Lung Cancer Cell Line Secretomes and Patient Sputum Reveals Biofluid Biomarker Candidates for Cisplatin Response Prediction

Description

We profiled the secretomes of 6 NSCLC cell lines with varying IC50-values for cisplatin, using label-free GeLC-MS/MS-based proteomics. Out of a total dataset of 2618 proteins, 304 proteins showed significant differences in expression levels between cisplatin sensitive and insensitive cell lines. Functional data mining revealed that the secretion of typically extracellular factors was associated with a higher sensitivity towards cisplatin, while cisplatin insensitivity correlated with increased secretion of theoretically intra-cellular proteins. Stringent statistical analysis and quantitative filtering yielded 58 biomarker candidates, 34 of which could be detected in clinical biofluids of lung cancer patients such as sputum using label-free LC-MS/MS-based proteomics. To assess performance of these biofluid biomarker candidates, we correlated protein expression with patient survival using a publically available clinical gene expression data set (GSE14814). We thus identified 3 top candidates with potential predictive value in determining cisplatin response (UGGT1, COL6A1 and MAP4) for future development as non-invasive biomarkers to guide treatment decisions.

Sample Processing Protocol

Non-small cell lung cancer cell lines SW1573, H2228, H522, H1650, H1975 and H1703 (American Type Culture Collection, ATCC® ™, Rockville, MD) were cultured in RPMI-1640 or DMEM (SW1573 only) medium containing 10% fetal bovine serum (FCS). Collection of cell line secretomes Conditioned medium (referred to as ‘secretomes’ here) from cell lines was collected and processed as described before ( J. Proteome Res. 2010, 9 (4), 1913–1922). Collection of the soluble fraction of patient sputum Sputum was collected in 5 ml PBS (RT) and stored at -20˚C. After thawing at room temperature, 1M DTT was added to a final concentration of 8 mM, followed by a 30 minute incubation on a roller bench. To collect the cell pellet, the sputum was centrifuged for 5 minutes at 500 x g. Supernatant was carefully transferred to a 15 ml tube and centrifuged for 15 minutes at maximum speed. To remove the last particles, the supernatant was filtered through a 0.45µm syringe filters. The supernatant was concentrated to 200µl using a Amicon 3kDa cut-off filters. 50µl 4xNuPage non-reducing sample buffer and 20 µl 1M DTT was added to the concentrated supernatant and mixed by pipetting up and down. The sample was incubated for 3 minutes at 99˚C and stored at -80˚C. Cell lysis and digestion and electrophoresis were performed as in ( J. Proteome Res. 2010, 9 (4), 1913–1922). LC-MS/MS NanoLC-MS/MS analysis for cell line secretomes: peptides were separated by an Ultimate 3000 nanoLC system (Dionex LC-Packings, Amsterdam, The Netherlands) equipped with a 20 cm × 75 μm ID fused silica column custom packed with 3 μm 120 Å ReproSil Pur C18 aqua (Dr Maisch GMBH, Ammerbuch-Entringen, Germany). After injection, peptides were trapped at 6 μl/min on a 1 cm × 100 μm ID trap column packed with 5 μm 120 Å ReproSil C18 aqua at 2% buffer B (buffer A: 0.05% formic acid in MQ; buffer B: 80% acetonitrile + 0.05% formic acid in MQ) and separated at 300 nl/min in a 10–40% buffer B gradient in 60 min (90 min.inject-to-inject). Eluting peptides were ionized at 1.7 kV in a Nanomate Triversa Chip-based nanospray source using a Triversa LC coupler (Advion, Ithaca, NJ). Intact peptide mass spectra and fragmentation spectra were acquired on a LTQ-FT hybridmass spectrometer (Thermo Fisher, Bremen, Germany). Intact masses were measured at resolution 50,000 in the ICR cell using a target value of 1 × E6 charges. In parallel, following an FT pre-scan, the top 5 peptide signals (charge-states 2+ and higher) were submitted to MS/MS in the linear ion trap (3 amu isolation width, 30 ms activation, 35% normalized activation energy, Q value of 0.25 and a threshold of 5000 counts). Dynamic exclusion was applied with a repeat count of 1 and an exclusion time of 30 s. For nanoLC-MS/MS analysis of patient sputum samples, peptides were separated by an Ultimate 3000 nanoLC-MS/MS system (Dionex LC-Packings, Amsterdam, The Netherlands) equipped with a 22 cm × 75 μm ID fused silica column custom packed with 1.9 μm 120 Å ReproSil Pur C18 aqua (Dr Maisch GMBH, Ammerbuch-Entringen, Germany). After injection, peptides were trapped at 6 μl/min on a 10 mm × 100 μm ID trap column packed with 5 μm 120 Å ReproSil Pur C18 aqua at 2% buffer B (buffer A: 0.5% acetic acid (Fischer Scientific), buffer B: 80% ACN, 0.5% acetic acid) and separated at 300 nl/min in a 10–40% buffer B gradient in 60 min. (90 min. inject-to-inject) at 50°C using a column heater (PST,Phoenix Az). Eluting peptides were ionized at a potential of +2 kV into a Q Exactive mass spectrometer (Thermo Fisher, Bremen,Germany). Intact masses were measured from 350-1400 m/z at resolution 70.000 (at m/z 200) in the orbitrap using an AGC target value of 3 × E6 charges. The top 10 peptide signals (charge-states 2+ and higher) were submitted to MS/MS in the HCD (higher-energy collision) cell (4 amu isolation width, 25% normalized collision energy). MS/MS spectra were acquired at resolution 17.500 (at m/z 200) in the orbitrap using an AGC target value of 1 × E5 charges and an underfill ratio of 0.1%. Dynamic exclusion was applied with a repeat count of 1 and an exclusion time of 30 s.

Data Processing Protocol

Protein identification MS/MS spectra were searched against Swissprot reference proteome FASTA file (release September 2015, 42122 entries) for cell line secretome samples or Uniprot human reference proteome FASTA file (release January 2014, 61552 entries) for patient sputum samples using MaxQuant versions 1.5.2.8 or 1.4.1.2 31, respectively. Enzyme specificity was set to trypsin and up to two missed cleavages were allowed. Cysteine carboxamidomethylation (Cys, +57.021464 Da) was treated as fixed modification and methionine oxidation (Met, +15.994915 Da) and N-terminal acetylation (N-terminal, +42.010565 Da) as variable modifications. Peptide precursor ions were searched with a maximum mass deviation of 4.5 ppm and fragment ions with a maximum mass deviation of 20 ppm. Peptide and protein identifications were filtered at an FDR of 1% using the decoy database strategy. Proteins that could not be differentiated based on MS/MS spectra alone were grouped to protein groups (default MaxQuant settings). A protein was considered identified when at least 1 unique peptide was identified in one sample. Peptide identifications were propagated across samples using the match-between-runs option checked. Searches were performed with the label-free quantification option selected. Protein quantification was done using spectral counting. For each sample, the number of spectral counts identified per protein was normalized on the total number of spectral counts of all identified proteins in the sample. Normalization and statistical testing were performed in R. Differential statistical analysis of samples was performed on the normalized spectral counts per sample using Pearson correlation analysis to correlate protein expression with cisplatin IC50 values across all 6 cell lines, and using the beta-binominal test to compare protein expression between the 2 cell lines with highest cisplatin sensitivity and the 2 cell lines with lowest cisplatin sensitivity.

Contact

Sander Piersma, OncoProteomics Laboratory, dept of Medical Oncology, VUmc Medical Center, Amsterdam, The Netherlands
Connie Ramona Jimenez, Amsterdam UMC, Vrije Universiteit Amsterdam, Medical Oncology, Cancer Center Amsterdam, OncoProteomics Laboratory, Amsterdam, Netherlands ( lab head )

Submission Date

09/10/2018

Publication Date

15/02/2019

Tissue

Not available

Instrument

LTQ FT
Q Exactive

Software

Not available

Experiment Type

Shotgun proteomics

Publication

    Böttger F, Schaaij-Visser TB, de Reus I, Piersma SR, Pham TV, Nagel R, Brakenhoff RH, Thunnissen E, Smit EF, Jimenez CR. Proteome analysis of non-small cell lung cancer cell line secretomes and patient sputum reveals biofluid biomarker candidates for cisplatin response prediction. J Proteomics. 2019 196:106-119 PubMed: 30710758