Phospo-proteomic profiling of Castration Resistant Prostate Cancer
The integration of diverse ‘omic’ datasets will increase our understanding of the key signaling pathways that drive disease. Here, we used clinical tissue cohorts corresponding to lethal metastatic castration resistant prostate cancer (CRPC) obtained at rapid autopsy to integrate mutational, transcriptomic, and phosphoproteomic datasets for pathway analysis. Using Tied Diffusion through Interacting Events (TieDIE), we integrated differentially expressed transcriptional master regulators, functionally mutated genes, and differentially ‘activated’ kinases in CRPC tissues to synthesize a robust signaling network consisting of pathways with known and novel gene interactions. For 6 individual CRPC patients for which we had transcriptomic and phosphoproteomic data we observed distinct pathway activation states for each patient profile. In one patient, the activated pathways were strikingly similar to a prostate cancer cell line, 22Rv1, providing us with a good pre-clinical model to test targeted, combination therapies. In all, synthesis of multiple ‘omic’ datasets revealed a plethora of pathway information suitable for targeted therapies in lethal prostate cancer.
Sample Processing Protocol
Patient samples were obtained from the University of California–Los Angeles (UCLA) Translational Pathology Core Laboratory, which is authorized by the UCLA Institutional Review Board to distribute anonymized tissues to researchers. Cancer and benign areas were clearly marked on the frozen section slides, and prostate tissue containing the cancer region was separated from the benign area before collecting for phosphoproteomic analyses. The Rapid Autopsy program at the University of Michigan provided frozen tissues for phosphotyrosine, phosphoserine, and phosphothreonine peptide analysis. Sections were stained with hematoxylin and eosin for representative histology. For tissue lysis, greater than 350 mg of frozen tumor mass was homogenized and sonicated in urea lysis buffer (20 mM Hepes pH 8.0, 9 M urea, 2.5 mM sodium pyrophosphate, 1.0 mM beta-glycerophosphate, 1% N-octyl glycoside, 2 mM sodium orthovanadate). Total protein was measured using the bicinchoninic acid (BCA) Protein Assay Kit (Thermo Scientific/Pierce), and 25 mg of total protein was used for phosphoproteomic analysis. Proteins were reduced with DTT, alkylated with iodoacetamide and then digested with trypsin. Phospho-Tyrosine Enrichment Enrichment of phospho-tyrosine peptides was done by resuspending lyophilized peptides in 100 mM Tris-HCl and the pH was adjusted to 7.4. Phospho-tyrosine peptides were immunoprecipitated with 4G10 antibody conjugated to agarose (Millipore, Billerica, MA) overnight at 4 °C. The following day, agarose beads were washed with 50 mM Tris-HCl, pH 7.4, three times followed by two washes with 25 mM NH4HCO3. Phosphopeptides were eluted with 0.1% trifluoroaceric acid (TFA) for 15 min at 37 °C, and concentrated by vacuum centrifugation. Phosphotyrosine peptides were further enriched using TiO2 (PolyLC) for 45 min mixing constantly at room temperature. TiO2 material was then washed with 45% acetonitrile, 0.1% TFA and the peptides were eluted with 3% NH3 in water. NH3 and water were removed by vacuum centrifugation and the peptides concentrated and desalted using ZipTip C18 (Millipore). Phospho-Serine/Threonine Enrichment Lyophilized peptides were resuspended in 5 mM KH2PO4 (pH 2.65), 5 mM KCl, and 30% acetonitrile. Peptides were fractionated by strong cation exchange (SCX) chromatography using solid-phase extraction cartridges containing PolySULFOETHYL A (Poly LC, Columbia, MD). Collection of phosphopeptides started as soon as the SCX cartridge was loaded with peptides and continued throughout an initial wash with resuspension buffer (fraction Load and Wash; LW). The following fraction was collected using a concentration of 70mM KCl in resuspension buffer. Afterward, acetonitrile was evaporated by vacuum centrifugation and salts were removed by solid-phase extraction with C18 cartridges and eluted in 50% acetonitrile, 0.1% TFA. Lactic acid was then added to a final concentration of 150 mg/mL to decrease the binding of acidic unphosphorylated peptides in the next step. Phosphopeptides were enriched using TiO2 (PolyLC) for 45 min mixing constantly at room temperature. TiO2 material was then washed with 45% acetonitrile, 0.1% TFA and the peptides were eluted with 3% NH3 in water. NH3 and water were removed by vacuum centrifugation and the peptides concentrated and desalted using MonoTip C18 (GL Sciences, Torrance, CA). Liquid chromatography tandem mass spectrometry (LC-MS/MS) was performed using a Q-Exactive mass spectrometer (Thermo Scientific) coupled to an EASY-nLC 1000 (Thermo Scientific). The desalted peptide mixture was fractionated online using EASY-spray columns (25cm x 75 μm ID, PepMap RSLC C18 2μm). The gradient was delivered by an easy-nLC 1000 ultra high-pressure liquid chromatography (UHPLC) system (Thermo Scientific). MS/MS spectra were collected on a Q-Exactive mass spectrometer (Thermo Scientific).
Data Processing Protocol
Samples were run in technical duplicates, and raw MS files were analyzed using MaxQuant version 18.104.22.168. MS/MS fragmentation spectra were searched using ANDROMEDA against the Uniprot human reference proteome database with canonical and isoform sequences (downloaded January 2012 from uniprot.org). N-terminal acetylation, oxidized methionine, and phosphorylated serine, threonine, or tyrosine were set as variable modifications, and carbamidomethyl cysteine (*C) was set as a fixed modification. The false discovery rate was set to 1% using a composite target‐reversed decoy database search strategy. Group-specific parameters included max missed cleavages of 2 and label free quantitation (LFQ) with an LFQ minimum ratio count of 1. Global parameters included match between runs with a match time window and alignment time window of 5 and 20 minutes, respectively, and match unidentified features selected. Quantitative, label-free phosphopeptide data from MaxQuant were log10 transformed and missing data were imputed using random values generated from a normal distribution centered on the 1% quantile and the median standard deviation of all phosphopeptides. After missing value imputation, phosphopeptides were quantile normalized. For clustering, phosphopeptide data was filtered using an FDR-corrected ANOVA p-value of 0.01.
Nicholas Graham, University of Southern California
Thomas Graeber, David Geffen School of Medicine, University of California, Los Angeles, CA 90095 Department of Molecular and Medical Pharmacology, Jonsson Comprehensive Cancer Center, Institute for Molecular Medicine, California NanoSystems Institute ( lab head )
Drake JM, Paull EO, Graham NA, Lee JK, Smith BA, Titz B, Stoyanova T, Faltermeier CM, Uzunangelov V, Carlin DE, Fleming DT, Wong CK, Newton Y, Sudha S, Vashisht AA, Huang J, Wohlschlegel JA, Graeber TG, Witte ON, Stuart JM. Phosphoproteome Integration Reveals Patient-Specific Networks in Prostate Cancer. Cell. 2016 Aug 11;166(4):1041-54 PubMed: 27499020