A quantitative proteomics-based signature of platinum sensitivity in ovarian cancer cell lines.

Although DNA encodes the molecular instructions that underlie the control of cell function, it is the proteins that are primarily responsible for implementing those instructions. Therefore quantitative analyses of the proteome would be expected to yield insights into important candidates for the detection and treatment of disease. We present an iTRAQ (isobaric tag for relative and absolute quantification)-based proteomic analysis of ten ovarian cancer cell lines and two normal ovarian surface epithelial cell lines. We profiled the abundance of 2659 cellular proteins of which 1273 were common to all 12 cell lines. Of the 1273, 75 proteins exhibited elevated expression and 164 proteins had diminished expression in the cancerous cells compared with the normal cell lines. The iTRAQ expression profiles allowed us to segregate cell lines based upon sensitivity and resistance to carboplatin. Importantly, we observed no substantial correlation between protein abundance and RNA expression or epigenetic DNA methylation data. Furthermore, we could not discriminate between sensitivity and resistance to carboplatin on the basis of RNA expression and DNA methylation data alone. The present study illustrates the importance of proteomics-based discovery for defining the basis for the carboplatin response in ovarian cancer and highlights candidate proteins, particularly involved in cellular redox regulation, homologous recombination and DNA damage repair, which otherwise could not have been predicted from whole genome and expression data sources alone.

[1]  Nevenka Dimitrova,et al.  Methylation detection oligonucleotide microarray analysis: a high-resolution method for detection of CpG island methylation , 2009, Nucleic acids research.

[2]  Biaoyang Lin,et al.  Proteins Associated with Cisplatin Resistance in Ovarian Cancer Cells Identified by Quantitative Proteomic Technology and Integrated with mRNA Expression Levels*S , 2006, Molecular & Cellular Proteomics.

[3]  Ruedi Aebersold,et al.  Protein expression changes in ovarian cancer during the transition from benign to malignant. , 2012, Journal of proteome research.

[4]  A. Mes-Masson,et al.  Comparative proteome analysis of human epithelial ovarian cancer , 2007, Proteome Science.

[5]  Y. Yoshioka,et al.  Annexin A4 is a possible biomarker for cisplatin susceptibility of malignant mesothelioma cells. , 2012, Biochemical and biophysical research communications.

[6]  Katrin Marcus,et al.  Quantitative Methods in Proteomics , 2012, Methods in Molecular Biology.

[7]  A. Skubitz,et al.  Quantitative proteomic analysis by iTRAQ® for the identification of candidate biomarkers in ovarian cancer serum , 2010, Proteome Science.

[8]  J. Peterson,et al.  Phospholipase A2-activating protein (PLAA) enhances cisplatin-induced apoptosis in HeLa cells. , 2009, Cellular signalling.

[9]  R. Xiang,et al.  Quantitative proteome analysis of ovarian cancer tissues using a iTRAQ approach , 2012, Journal of cellular biochemistry.

[10]  Marie Locard-Paulet,et al.  Analysing signalling networks by mass spectrometry , 2012, Amino Acids.

[11]  M. Dimopoulos,et al.  Replication Protein A in Nonearly Ovarian Adenocarcinomas: Correlation With MCM-2, MCM-5, Ki-67 Index and Prognostic Significance , 2012, International journal of gynecological pathology : official journal of the International Society of Gynecological Pathologists.

[12]  D. Ong,et al.  Retinoic acid biosynthesis by normal human breast epithelium is via aldehyde dehydrogenase 6, absent in MCF-7 cells. , 2001, Cancer research.

[13]  Ida Landini,et al.  Proteomic analysis of A2780/S ovarian cancer cell response to the cytotoxic organogold(III) compound Aubipy(c). , 2014, Journal of proteomics.

[14]  Michael Q. Zhang,et al.  Identification of Tumor Suppressors and Oncogenes from Genomic and Epigenetic Features in Ovarian Cancer , 2011, PloS one.

[15]  Akihiko Yoshida,et al.  Secernin-1 as a novel prognostic biomarker candidate of synovial sarcoma revealed by proteomics. , 2011, Journal of proteomics.

[16]  J. Stenvang,et al.  Silencing of microRNA families by seed-targeting tiny LNAs , 2011, Nature Genetics.

[17]  M. Tsao,et al.  Integrin-linked kinase inhibitor KP-392 demonstrates clinical benefits in an orthotopic human non-small cell lung cancer model. , 2006, Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer.

[18]  J. Inazawa,et al.  γ-Glutamylcyclotransferase as a novel immunohistochemical biomarker for the malignancy of esophageal squamous tumors. , 2014, Human pathology.

[19]  Albert Sickmann,et al.  Robust Workflow for iTRAQ-Based Peptide and Protein Quantification , 2012, Quantitative Methods in Proteomics.

[20]  Terence P. Speed,et al.  A comparison of normalization methods for high density oligonucleotide array data based on variance and bias , 2003, Bioinform..

[21]  L. Galluzzi,et al.  Molecular mechanisms of cisplatin resistance , 2012, Oncogene.

[22]  T. Chou,et al.  Galectin-1 Promotes Lung Cancer Progression and Chemoresistance by Upregulating p38 MAPK, ERK, and Cyclooxygenase-2 , 2012, Clinical Cancer Research.

[23]  A. Modesti,et al.  Proteomic analysis of ovarian cancer cell responses to cytotoxic gold compounds. , 2012, Metallomics.

[24]  Vinay Varadan,et al.  DNA methylation patterns in luminal breast cancers differ from non‐luminal subtypes and can identify relapse risk independent of other clinical variables , 2011, Molecular oncology.

[25]  S. Cannistra,et al.  Gene-expression profiling in epithelial ovarian cancer , 2008, Nature Clinical Practice Oncology.

[26]  K. Parker,et al.  Multiplexed Protein Quantitation in Saccharomyces cerevisiae Using Amine-reactive Isobaric Tagging Reagents*S , 2004, Molecular & Cellular Proteomics.

[27]  F. Pontén,et al.  Expression of the RNA-binding protein RBM3 is associated with a favourable prognosis and cisplatin sensitivity in epithelial ovarian cancer , 2010, Journal of Translational Medicine.

[28]  H. Hornshøj,et al.  Transcriptomic and proteomic profiling of two porcine tissues using high-throughput technologies , 2009, BMC Genomics.

[29]  Songying Zhang,et al.  Calcium-binding protein S100P and cancer: mechanisms and clinical relevance , 2011, Journal of Cancer Research and Clinical Oncology.

[30]  C. Smythe,et al.  Purification and identification of secernin, a novel cytosolic protein that regulates exocytosis in mast cells. , 2002, Molecular biology of the cell.

[31]  D. Fishman,et al.  Periostin secreted by epithelial ovarian carcinoma is a ligand for alpha(V)beta(3) and alpha(V)beta(5) integrins and promotes cell motility. , 2002, Cancer research.

[32]  S. Feller Crk family adaptors–signalling complex formation and biological roles , 2001, Oncogene.

[33]  R. Bast,et al.  Minireview: human ovarian cancer: biology, current management, and paths to personalizing therapy. , 2012, Endocrinology.

[34]  A. Belmaaza,et al.  EMSY overexpression disrupts the BRCA2/RAD51 pathway in the DNA-damage response: implications for chromosomal instability/recombination syndromes as checkpoint diseases , 2011, Molecular Genetics and Genomics.

[35]  R. Lucito,et al.  Protein-tyrosine Phosphatase 1B Antagonized Signaling by Insulin-like Growth Factor-1 Receptor and Kinase BRK/PTK6 in Ovarian Cancer Cells*♦ , 2013, The Journal of Biological Chemistry.

[36]  M. Mann,et al.  Decoding signalling networks by mass spectrometry-based proteomics , 2010, Nature Reviews Molecular Cell Biology.

[37]  Yunbao Pan,et al.  Suppression of Jab1/CSN5 induces radiation- and chemo-sensitivity in nasopharyngeal carcinoma through changes to the DNA damage and repair pathways , 2012, Oncogene.

[38]  Min Liu,et al.  S100P sensitizes ovarian cancer cells to carboplatin and paclitaxel in vitro. , 2008, Cancer letters.

[39]  Yuan Tian,et al.  Identification of glycoproteins associated with different histological subtypes of ovarian tumors using quantitative glycoproteomics , 2011, Proteomics.

[40]  Richard Simon,et al.  Roadmap for developing and validating therapeutically relevant genomic classifiers. , 2005, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[41]  Andrew Emili,et al.  Integrating gene and protein expression data: pattern analysis and profile mining. , 2005, Methods.

[42]  C. Wolf,et al.  Glutathione and glutathione-dependent enzymes in ovarian adenocarcinoma cell lines derived from a patient before and after the onset of drug resistance: intrinsic differences and cell cycle effects. , 1988, Carcinogenesis.