Pharmacoproteomic characterisation of human colon and rectal cancer - CRC65 Full Proteomes
Most molecular cancer therapies act on protein targets but data on the proteome status of patients and cellular models are only beginning to emerge. Here, we profiled the proteomes of 65 colorectal cancer (CRC) cell lines to a depth of >10,000 proteins using mass spectrometry. Integration with proteomes of 90 CRC patients, as well as transcriptomes of 145 cell lines and 89 patients defined integrated CRC subtypes, highlighting cell lines representative of each tumour subtype. Modelling the responses of 52 CRC cell lines to 577 drugs as a function of proteome profiles enabled predicting drug sensitivity for cell lines and patients. Among many novel associations, MERTK was identified as a predictive marker for resistance towards MEK1/2 inhibitors and immunohistochemistry of 1,000 CRC tumours confirmed MERTK as a prognostic survival marker. We provide the proteomic and pharmacological data to the community to e.g. facilitate the design of innovative prospective clinical trials.
Sample Processing Protocol
Analytical Sample Protocol - Alkylation: Chloroacetamide, Fractionation: hSAX, Fractions: 24, Proteolysis: Trypsin, Starting Amount: 300 ug; Chromatography Protocol - Column Length: 40 cm, Column Type: C18, Gradient Length: 110 min, Injected: 5 uL, Inside Diameter: 75 um, Particle Size: 5 um; Mass Spectrometry Protocol - Dissociation: HCD, Instrument: Thermo LTQ Orbitrap Velos, MS1 Resolution: 30000, Precursors: Top 10.
Data Processing Protocol
MaxQuant v.184.108.40.206 was used to search our Full Proteome raw data, as well as the raw data from the original CPTAC publication on human colon and rectal cancer (Zhang et al., 2014) against UniProtKB (v25.11.2015; 92,011 sequences), concatenated with a list of common contaminants supplied by MaxQuant (245 sequences) in two separate runs with identical settings. Therefore, some data used in this publication were generated by the Clinical Proteomic Tumor Analysis Consortium (NCI/NIH). We set the digestion mode to fully tryptic, allowing for cleavage before proline (Trypsin/P) and a maximum of two missed cleavages. Carbamidomethylation of cysteines was set as a fixed modification and oxidation of methionines, as well as acetylation of protein N-termini were set as variable modifications, allowing for a maximum number of 5 modifications per peptide. Candidate peptides were required to have a length of at least 7 amino acids, with a maximum peptide mass of 4,600 Da. The fragment ion tolerance was set to 20 ppm for FTMS and 0.4 Da for ITMS spectra, respectively. A first search with a precursor ion tolerance of 20 ppm was used to recalibrate raw data based on all peptide-spectrum-matches (PSMs) without filtering using hard score cut-offs. After recalibration, the data were searched with a precursor ion tolerance of 4.5 ppm, while chimeric spectra were searched a second time using MaxQuant’s “Second peptides” option to identify co-fragmented peptide precursors. We used “Match between runs” with an alignment time window of 30 min and a match time window of 1.1 min to transfer identifications between raw files of the same and neighbouring fractions (± 1 fraction). Using the classical target-decoy approach with a concatenated database of reversed peptide sequences, data were filtered using a PSM and protein false discovery rate (FDR) of 1%. Protein groups were required to have at least one unique or razor peptide, with each razor peptide being used only once during the calculation of the protein FDR. No score cut-offs were applied in addition to the target-decoy FDR. We used unique and razor peptides for quantification, discarding the unmodified counterparts of peptides harbouring oxidated methionines and acetylated protein N-termini.
Frejno M, Zenezini Chiozzi R, Wilhelm M, Koch H, Zheng R, Klaeger S, Ruprecht B, Meng C, Kramer K, Jarzab A, Heinzlmeir S, Johnstone E, Domingo E, Kerr D, Jesinghaus M, Slotta-Huspenina J, Weichert W, Knapp S, Feller SM, Kuster B. Pharmacoproteomic characterisation of human colon and rectal cancer. Mol Syst Biol. 2017 13(11):951 PubMed: 29101300