Cancer and Metastasis Reviews

, Volume 34, Issue 1, pp 83–96 | Cite as

Proteomics of ovarian cancer: functional insights and clinical applications

Article

Abstract

In the past decade, there has been an increasing interest in applying proteomics to assist in understanding the pathogenesis of ovarian cancer, elucidating the mechanism of drug resistance, and in the development of biomarkers for early detection of ovarian cancer. Although ovarian cancer is a spectrum of different diseases, the strategies for diagnosis and treatment with surgery and adjuvant therapy are similar across ovarian cancer types, increasing the general applicability of discoveries made through proteomics research. While proteomic experiments face many difficulties which slow the pace of clinical applications, recent advances in proteomic technology contribute significantly to the identification of aberrant proteins and networks which can serve as targets for biomarker development and individualized therapies. This review provides a summary of the literature on proteomics’ contributions to ovarian cancer research and highlights the current issues, future directions, and challenges. We propose that protein-level characterization of primary lesion in ovarian cancer can decipher the mystery of this disease, improve diagnostic tools, and lead to more effective screening programs.

Keywords

Ovarian cancer Proteomics Biomarker Drug resistance Subtypes 

References

  1. 1.
    Siegel, R., Ma, J., Zou, Z., & Jemal, A. (2014). Cancer statistics, 2014. CA: A Cancer Journal for Clinicians, 64(1), 9–29. doi:10.3322/caac.21208.Google Scholar
  2. 2.
    Schaner, M. E., Ross, D. T., Ciaravino, G., Sorlie, T., Troyanskaya, O., Diehn, M., et al. (2003). Gene expression patterns in ovarian carcinomas. Molecular Biology of the Cell, 14(11), 4376–4386. doi:10.1091/mbc.E03-05-0279.PubMedPubMedCentralCrossRefGoogle Scholar
  3. 3.
    Kobel, M., Kalloger, S. E., Boyd, N., McKinney, S., Mehl, E., Palmer, C., et al. (2008). Ovarian carcinoma subtypes are different diseases: implications for biomarker studies. PLoS Medicine, 5(12), e232. doi:10.1371/journal.pmed.0050232.PubMedPubMedCentralCrossRefGoogle Scholar
  4. 4.
    Singer, G., Kurman, R. J., Chang, H. W., Cho, S. K., & Shih Ie, M. (2002). Diverse tumorigenic pathways in ovarian serous carcinoma. American Journal of Pathology, 160(4), 1223–1228.PubMedPubMedCentralCrossRefGoogle Scholar
  5. 5.
    Kurman, R. J., & Shih Ie, M. (2011). Molecular pathogenesis and extraovarian origin of epithelial ovarian cancer—shifting the paradigm. Human Pathology, 42(7), 918–931. doi:10.1016/j.humpath.2011.03.003.PubMedPubMedCentralCrossRefGoogle Scholar
  6. 6.
    Integrated genomic analyses of ovarian carcinoma (2011). Nature, 474(7353), 609–615. doi:10.1038/nature10166.
  7. 7.
    Landen, C. N., Jr., Birrer, M. J., & Sood, A. K. (2008). Early events in the pathogenesis of epithelial ovarian cancer. Journal of Clinical Oncology, 26(6), 995–1005. doi:10.1200/jco.2006.07.9970.PubMedCrossRefGoogle Scholar
  8. 8.
    Dubeau, L. (1999). The cell of origin of ovarian epithelial tumors and the ovarian surface epithelium dogma: does the emperor have no clothes? Gynecologic Oncology, 72(3), 437–442. doi:10.1006/gyno.1998.5275.PubMedCrossRefGoogle Scholar
  9. 9.
    Dubeau, L. (2008). The cell of origin of ovarian epithelial tumours. Lancet Oncology, 9(12), 1191–1197. doi:10.1016/s1470-2045(08)70308-5.PubMedCrossRefGoogle Scholar
  10. 10.
    Kurman, R. J., & Shih Ie, M. (2010). The origin and pathogenesis of epithelial ovarian cancer: a proposed unifying theory. American Journal of Surgical Pathology, 34(3), 433–443. doi:10.1097/PAS.0b013e3181cf3d79.PubMedPubMedCentralCrossRefGoogle Scholar
  11. 11.
    Kindelberger, D. W., Lee, Y., Miron, A., Hirsch, M. S., Feltmate, C., Medeiros, F., et al. (2007). Intraepithelial carcinoma of the fimbria and pelvic serous carcinoma: evidence for a causal relationship. American Journal of Surgical Pathology, 31(2), 161–169. doi:10.1097/01.pas.0000213335.40358.47.PubMedCrossRefGoogle Scholar
  12. 12.
    Crum, C. P., Drapkin, R., Kindelberger, D., Medeiros, F., Miron, A., & Lee, Y. (2007). Lessons from BRCA: the tubal fimbria emerges as an origin for pelvic serous cancer. Clinical Medicine & Research, 5(1), 35–44. doi:10.3121/cmr.2007.702.CrossRefGoogle Scholar
  13. 13.
    Nik, N. N., Vang, R., Shih Ie, M., & Kurman, R. J. (2014). Origin and pathogenesis of pelvic (ovarian, tubal, and primary peritoneal) serous carcinoma. Annual Review of Pathology, 9, 27–45. doi:10.1146/annurev-pathol-020712-163949.PubMedCrossRefGoogle Scholar
  14. 14.
    Schwanhausser, B., Busse, D., Li, N., Dittmar, G., Schuchhardt, J., Wolf, J., et al. (2011). Global quantification of mammalian gene expression control. Nature, 473(7347), 337–342. doi:10.1038/nature10098.PubMedCrossRefGoogle Scholar
  15. 15.
    Wu, L., Candille, S. I., Choi, Y., Xie, D., Jiang, L., Li-Pook-Than, J., et al. (2013). Variation and genetic control of protein abundance in humans. Nature, 499(7456), 79–82. doi:10.1038/nature12223.PubMedPubMedCentralCrossRefGoogle Scholar
  16. 16.
    Ellis, M. J., Gillette, M., Carr, S. A., Paulovich, A. G., Smith, R. D., Rodland, K. K., et al. (2013). Connecting genomic alterations to cancer biology with proteomics: the NCI Clinical Proteomic Tumor Analysis Consortium. Cancer Discovery, 3(10), 1108–1112. doi:10.1158/2159-8290.cd-13-0219.PubMedPubMedCentralCrossRefGoogle Scholar
  17. 17.
    Zhang, B., Wang, J., Wang, X., Zhu, J., Liu, Q., Shi, Z., et al. (2014). Proteogenomic characterization of human colon and rectal cancer. Nature, 513(7518), 382–387. doi:10.1038/nature13438.PubMedPubMedCentralCrossRefGoogle Scholar
  18. 18.
    Chevalier, F. (2010). Highlights on the capacities of “gel-based” proteomics. Proteome Science, 8, 23. doi:10.1186/1477-5956-8-23.PubMedPubMedCentralCrossRefGoogle Scholar
  19. 19.
    Spurrier, B., Ramalingam, S., & Nishizuka, S. (2008). Reverse-phase protein lysate microarrays for cell signaling analysis. Nature Protocols, 3(11), 1796–1808. doi:10.1038/nprot.2008.179.PubMedCrossRefGoogle Scholar
  20. 20.
    Aebersold, R., & Mann, M. (2003). Mass spectrometry-based proteomics. Nature, 422(6928), 198–207. doi:10.1038/nature01511.PubMedCrossRefGoogle Scholar
  21. 21.
    Yates, J. R., Ruse, C. I., & Nakorchevsky, A. (2009). Proteomics by mass spectrometry: approaches, advances, and applications. Annual Review of Biomedical Engineering, 11, 49–79. doi:10.1146/annurev-bioeng-061008-124934.PubMedCrossRefGoogle Scholar
  22. 22.
    Bantscheff, M., Lemeer, S., Savitski, M. M., & Kuster, B. (2012). Quantitative mass spectrometry in proteomics: critical review update from 2007 to the present. Analytical and Bioanalytical Chemistry, 404(4), 939–965. doi:10.1007/s00216-012-6203-4.PubMedCrossRefGoogle Scholar
  23. 23.
    Gustafsson, O. J., Arentz, G., & Hoffmann, P. (2014). Proteomic developments in the analysis of formalin-fixed tissue. Biochimica et Biophysica Acta. doi:10.1016/j.bbapap.2014.10.003.PubMedCentralGoogle Scholar
  24. 24.
    Lawrie, L. C., Curran, S., McLeod, H. L., Fothergill, J. E., & Murray, G. I. (2001). Application of laser capture microdissection and proteomics in colon cancer. Molecular Pathology, 54(4), 253–258.PubMedPubMedCentralCrossRefGoogle Scholar
  25. 25.
    Espina, V., Wulfkuhle, J. D., Calvert, V. S., VanMeter, A., Zhou, W., Coukos, G., et al. (2006). Laser-capture microdissection. Nature Protocols, 1(2), 586–603. doi:10.1038/nprot.2006.85.PubMedCrossRefGoogle Scholar
  26. 26.
    Fowler, C. B., O’Leary, T. J., & Mason, J. T. (2013). Toward improving the proteomic analysis of formalin-fixed, paraffin-embedded tissue. Expert Review of Proteomics, 10(4), 389–400. doi:10.1586/14789450.2013.820531.PubMedCrossRefGoogle Scholar
  27. 27.
    Konstantinopoulos, P. A., Spentzos, D., & Cannistra, S. A. (2008). Gene-expression profiling in epithelial ovarian cancer. Nature Clinical Practice Oncology, 5(10), 577–587. doi:10.1038/ncponc1178.PubMedCrossRefGoogle Scholar
  28. 28.
    Zorn, K. K., Bonome, T., Gangi, L., Chandramouli, G. V., Awtrey, C. S., Gardner, G. J., et al. (2005). Gene expression profiles of serous, endometrioid, and clear cell subtypes of ovarian and endometrial cancer. Clinical Cancer Research, 11(18), 6422–6430. doi:10.1158/1078-0432.ccr-05-0508.PubMedCrossRefGoogle Scholar
  29. 29.
    Bonome, T., Lee, J. Y., Park, D. C., Radonovich, M., Pise-Masison, C., Brady, J., et al. (2005). Expression profiling of serous low malignant potential, low-grade, and high-grade tumors of the ovary. Cancer Research, 65(22), 10602–10612. doi:10.1158/0008-5472.can-05-2240.PubMedCrossRefGoogle Scholar
  30. 30.
    Morita, A., Miyagi, E., Yasumitsu, H., Kawasaki, H., Hirano, H., & Hirahara, F. (2006). Proteomic search for potential diagnostic markers and therapeutic targets for ovarian clear cell adenocarcinoma. Proteomics, 6(21), 5880–5890. doi:10.1002/pmic.200500708.PubMedCrossRefGoogle Scholar
  31. 31.
    Longuespee, R., Gagnon, H., Boyon, C., Strupat, K., Dauly, C., Kerdraon, O., et al. (2013). Proteomic analyses of serous and endometrioid epithelial ovarian cancers—cases studies—molecular insights of a possible histological etiology of serous ovarian cancer. Proteomics - Clinical Applications, 7(5–6), 337–354. doi:10.1002/prca.201200079.PubMedCrossRefGoogle Scholar
  32. 32.
    Jia, L., Zhang, H., Qu, X., Deng, B., & Kong, B. (2012). Proteomic analysis reflects different histologic subtypes of epithelial ovarian cancer. Medical Hypotheses, 78(3), 407–409. doi:10.1016/j.mehy.2011.11.017.PubMedCrossRefGoogle Scholar
  33. 33.
    Wiegand, K. C., Hennessy, B. T., Leung, S., Wang, Y., Ju, Z., McGahren, M., et al. (2014). A functional proteogenomic analysis of endometrioid and clear cell carcinomas using reverse phase protein array and mutation analysis: protein expression is histotype-specific and loss of ARID1A/BAF250a is associated with AKT phosphorylation. BMC Cancer, 14, 120. doi:10.1186/1471-2407-14-120.PubMedPubMedCentralCrossRefGoogle Scholar
  34. 34.
    An, H. J., Kim, D. S., Park, Y. K., Kim, S. K., Choi, Y. P., Kang, S., et al. (2006). Comparative proteomics of ovarian epithelial tumors. Journal of Proteome Research, 5(5), 1082–1090. doi:10.1021/pr050461p.PubMedCrossRefGoogle Scholar
  35. 35.
    Sereni, M. I., Baldelli, E., Gambara, G., Zanotti, L., Bandiera, E., Bignotti, E., et al. (2014). Functional characterization of epithelial ovarian cancer histotypes by drug target-based protein signaling activation mapping: implications for personalized cancer therapy. Proteomics. doi:10.1002/pmic.201400214.Google Scholar
  36. 36.
    Toyama, A., Suzuki, A., Shimada, T., Aoki, C., Aoki, Y., Umino, Y., et al. (2012). Proteomic characterization of ovarian cancers identifying annexin-A4, phosphoserine aminotransferase, cellular retinoic acid-binding protein 2, and serpin B5 as histology-specific biomarkers. Cancer Science, 103(4), 747–755. doi:10.1111/j.1349-7006.2012.02224.x.PubMedCrossRefGoogle Scholar
  37. 37.
    Zhu, Y., Wu, R., Sangha, N., Yoo, C., Cho, K. R., Shedden, K. A., et al. (2006). Classifications of ovarian cancer tissues by proteomic patterns. Proteomics, 6(21), 5846–5856. doi:10.1002/pmic.200600165.PubMedCrossRefGoogle Scholar
  38. 38.
    Tian, Y., Yao, Z., Roden, R. B., & Zhang, H. (2011). Identification of glycoproteins associated with different histological subtypes of ovarian tumors using quantitative glycoproteomics. Proteomics, 11(24), 4677–4687. doi:10.1002/pmic.201000811.PubMedPubMedCentralCrossRefGoogle Scholar
  39. 39.
    Mann, M., & Jensen, O. N. (2003). Proteomic analysis of post-translational modifications. Nature Biotechnology, 21(3), 255–261. doi:10.1038/nbt0303-255.PubMedCrossRefGoogle Scholar
  40. 40.
    Blom, N., Sicheritz-Ponten, T., Gupta, R., Gammeltoft, S., & Brunak, S. (2004). Prediction of post-translational glycosylation and phosphorylation of proteins from the amino acid sequence. Proteomics, 4(6), 1633–1649. doi:10.1002/pmic.200300771.PubMedCrossRefGoogle Scholar
  41. 41.
    Abbott, K. L., Lim, J. M., Wells, L., Benigno, B. B., McDonald, J. F., & Pierce, M. (2010). Identification of candidate biomarkers with cancer-specific glycosylation in the tissue and serum of endometrioid ovarian cancer patients by glycoproteomic analysis. Proteomics, 10(3), 470–481. doi:10.1002/pmic.200900537.PubMedCrossRefGoogle Scholar
  42. 42.
    Shetty, V., Hafner, J., Shah, P., Nickens, Z., & Philip, R. (2012). Investigation of ovarian cancer associated sialylation changes in N-linked glycopeptides by quantitative proteomics. Clinical Proteomics, 9(1), 10. doi:10.1186/1559-0275-9-10.PubMedPubMedCentralCrossRefGoogle Scholar
  43. 43.
    Kuzmanov, U., Musrap, N., Kosanam, H., Smith, C. R., Batruch, I., Dimitromanolakis, A., et al. (2013). Glycoproteomic identification of potential glycoprotein biomarkers in ovarian cancer proximal fluids. Clinical Chemistry and Laboratory Medicine, 51(7), 1467–1476. doi:10.1515/cclm-2012-0642.PubMedCrossRefGoogle Scholar
  44. 44.
    Fila, J., & Honys, D. (2012). Enrichment techniques employed in phosphoproteomics. Amino Acids, 43(3), 1025–1047. doi:10.1007/s00726-011-1111-z.PubMedPubMedCentralCrossRefGoogle Scholar
  45. 45.
    Longuespee, R., Boyon, C., Desmons, A., Vinatier, D., Leblanc, E., Farre, I., et al. (2012). Ovarian cancer molecular pathology. Cancer Metastasis Reviews, 31(3–4), 713–732. doi:10.1007/s10555-012-9383-7.PubMedCrossRefGoogle Scholar
  46. 46.
    Cohen, J. G., White, M., Cruz, A., & Farias-Eisner, R. (2014). In 2014, can we do better than CA125 in the early detection of ovarian cancer? World Journal of Biological Chemistry, 5(3), 286–300. doi:10.4331/wjbc.v5.i3.286.PubMedPubMedCentralCrossRefGoogle Scholar
  47. 47.
    Yin, B. W., & Lloyd, K. O. (2001). Molecular cloning of the CA125 ovarian cancer antigen: identification as a new mucin, MUC16. Journal of Biological Chemistry, 276(29), 27371–27375. doi:10.1074/jbc.M103554200.PubMedCrossRefGoogle Scholar
  48. 48.
    Bast, R. C., Jr., Urban, N., Shridhar, V., Smith, D., Zhang, Z., Skates, S., et al. (2002). Early detection of ovarian cancer: promise and reality. Cancer Treatment and Research, 107, 61–97.PubMedGoogle Scholar
  49. 49.
    Fleming, N. D., Cass, I., Walsh, C. S., Karlan, B. Y., & Li, A. J. (2011). CA125 surveillance increases optimal resectability at secondary cytoreductive surgery for recurrent epithelial ovarian cancer. Gynecologic Oncology, 121(2), 249–252. doi:10.1016/j.ygyno.2011.01.014.PubMedCrossRefGoogle Scholar
  50. 50.
    Bast, R. C., Jr., Badgwell, D., Lu, Z., Marquez, R., Rosen, D., Liu, J., et al. (2005). New tumor markers: CA125 and beyond. International Journal of Gynecological Cancer, 15(Suppl 3), 274–281. doi:10.1111/j.1525-1438.2005.00441.x.PubMedCrossRefGoogle Scholar
  51. 51.
    Drapkin, R., von Horsten, H. H., Lin, Y., Mok, S. C., Crum, C. P., Welch, W. R., et al. (2005). Human epididymis protein 4 (HE4) is a secreted glycoprotein that is overexpressed by serous and endometrioid ovarian carcinomas. Cancer Research, 65(6), 2162–2169. doi:10.1158/0008-5472.can-04-3924.PubMedCrossRefGoogle Scholar
  52. 52.
    Hellstrom, I., Raycraft, J., Hayden-Ledbetter, M., Ledbetter, J. A., Schummer, M., McIntosh, M., et al. (2003). The HE4 (WFDC2) protein is a biomarker for ovarian carcinoma. Cancer Research, 63(13), 3695–3700.PubMedGoogle Scholar
  53. 53.
    Moore, R. G., Miller, M. C., Steinhoff, M. M., Skates, S. J., Lu, K. H., Lambert-Messerlian, G., et al. (2012). Serum HE4 levels are less frequently elevated than CA125 in women with benign gynecologic disorders. American Journal of Obstetrics and Gynecology, 206(4), 351 e351–358. doi:10.1016/j.ajog.2011.12.029.Google Scholar
  54. 54.
    Montagnana, M., Danese, E., Giudici, S., Franchi, M., Guidi, G. C., Plebani, M., et al. (2011). HE4 in ovarian cancer: from discovery to clinical application. Advances in Clinical Chemistry, 55, 1–20.PubMedCrossRefGoogle Scholar
  55. 55.
    Moore, R. G., Miller, M. C., Disilvestro, P., Landrum, L. M., Gajewski, W., Ball, J. J., et al. (2011). Evaluation of the diagnostic accuracy of the risk of ovarian malignancy algorithm in women with a pelvic mass. Obstetrics and Gynecology, 118(2 Pt 1), 280–288. doi:10.1097/AOG.0b013e318224fce2.PubMedPubMedCentralCrossRefGoogle Scholar
  56. 56.
    Nolen, B., Velikokhatnaya, L., Marrangoni, A., De Geest, K., Lomakin, A., Bast, R. C., Jr., et al. (2010). Serum biomarker panels for the discrimination of benign from malignant cases in patients with an adnexal mass. Gynecologic Oncology, 117(3), 440–445. doi:10.1016/j.ygyno.2010.02.005.PubMedPubMedCentralCrossRefGoogle Scholar
  57. 57.
    Escudero, J. M., Auge, J. M., Filella, X., Torne, A., Pahisa, J., & Molina, R. (2011). Comparison of serum human epididymis protein 4 with cancer antigen 125 as a tumor marker in patients with malignant and nonmalignant diseases. Clinical Chemistry, 57(11), 1534–1544. doi:10.1373/clinchem.2010.157073.PubMedCrossRefGoogle Scholar
  58. 58.
    Van Gorp, T., Cadron, I., Despierre, E., Daemen, A., Leunen, K., Amant, F., et al. (2011). HE4 and CA125 as a diagnostic test in ovarian cancer: prospective validation of the Risk of Ovarian Malignancy Algorithm. British Journal of Cancer, 104(5), 863–870. doi:10.1038/sj.bjc.6606092.PubMedPubMedCentralCrossRefGoogle Scholar
  59. 59.
    Montagnana, M., Danese, E., Ruzzenente, O., Bresciani, V., Nuzzo, T., Gelati, M., et al. (2011). The ROMA (Risk of Ovarian Malignancy Algorithm) for estimating the risk of epithelial ovarian cancer in women presenting with pelvic mass: is it really useful? Clinical Chemistry and Laboratory Medicine, 49(3), 521–525. doi:10.1515/cclm.2011.075.PubMedCrossRefGoogle Scholar
  60. 60.
    Moore, R. G., McMeekin, D. S., Brown, A. K., DiSilvestro, P., Miller, M. C., Allard, W. J., et al. (2009). A novel multiple marker bioassay utilizing HE4 and CA125 for the prediction of ovarian cancer in patients with a pelvic mass. Gynecologic Oncology, 112(1), 40–46. doi:10.1016/j.ygyno.2008.08.031.PubMedPubMedCentralCrossRefGoogle Scholar
  61. 61.
    Rodland, K. D., & Maihle, N. J. (2010). Searching for a system: the quest for ovarian cancer biomarkers. Cancer Biomarkers, 8(4–5), 223–230. doi:10.3233/cbm-2011-0216.PubMedGoogle Scholar
  62. 62.
    Zhang, B., Cai, F. F., & Zhong, X. Y. (2011). An overview of biomarkers for the ovarian cancer diagnosis. European Journal of Obstetrics, Gynecology, and Reproductive Biology, 158(2), 119–123. doi:10.1016/j.ejogrb.2011.04.023.PubMedCrossRefGoogle Scholar
  63. 63.
    Zhang, Z., Bast, R. C., Jr., Yu, Y., Li, J., Sokoll, L. J., Rai, A. J., et al. (2004). Three biomarkers identified from serum proteomic analysis for the detection of early stage ovarian cancer. Cancer Research, 64(16), 5882–5890. doi:10.1158/0008-5472.can-04-0746.PubMedCrossRefGoogle Scholar
  64. 64.
    Ueland, F. R., Desimone, C. P., Seamon, L. G., Miller, R. A., Goodrich, S., Podzielinski, I., et al. (2011). Effectiveness of a multivariate index assay in the preoperative assessment of ovarian tumors. Obstetrics and Gynecology, 117(6), 1289–1297. doi:10.1097/AOG.0b013e31821b5118.PubMedCrossRefGoogle Scholar
  65. 65.
    Vernooij, F., Heintz, P., Witteveen, E., & van der Graaf, Y. (2007). The outcomes of ovarian cancer treatment are better when provided by gynecologic oncologists and in specialized hospitals: a systematic review. Gynecologic Oncology, 105(3), 801–812. doi:10.1016/j.ygyno.2007.02.030.PubMedCrossRefGoogle Scholar
  66. 66.
    Wegdam, W., Argmann, C. A., Kramer, G., Vissers, J. P., Buist, M. R., Kenter, G. G., et al. (2014). Label-free LC-MSe in tissue and Serum reveals protein networks underlying differences between benign and malignant serous ovarian tumors. PLoS One, 9(9), e108046. doi:10.1371/journal.pone.0108046.PubMedPubMedCentralCrossRefGoogle Scholar
  67. 67.
    Kozak, K. R., Su, F., Whitelegge, J. P., Faull, K., Reddy, S., & Farias-Eisner, R. (2005). Characterization of serum biomarkers for detection of early stage ovarian cancer. Proteomics, 5(17), 4589–4596. doi:10.1002/pmic.200500093.PubMedCrossRefGoogle Scholar
  68. 68.
    Ahmed, N., Oliva, K. T., Barker, G., Hoffmann, P., Reeve, S., Smith, I. A., et al. (2005). Proteomic tracking of serum protein isoforms as screening biomarkers of ovarian cancer. Proteomics, 5(17), 4625–4636. doi:10.1002/pmic.200401321.PubMedCrossRefGoogle Scholar
  69. 69.
    Pan, S., Chen, R., Aebersold, R., & Brentnall, T. A. (2011). Mass spectrometry based glycoproteomics—from a proteomics perspective. Molecular and Cellular Proteomics, 10(1), R110 003251. doi:10.1074/mcp.R110.003251.PubMedPubMedCentralCrossRefGoogle Scholar
  70. 70.
    Faca, V. M., Ventura, A. P., Fitzgibbon, M. P., Pereira-Faca, S. R., Pitteri, S. J., Green, A. E., et al. (2008). Proteomic analysis of ovarian cancer cells reveals dynamic processes of protein secretion and shedding of extra-cellular domains. PLoS One, 3(6), e2425. doi:10.1371/journal.pone.0002425.PubMedPubMedCentralCrossRefGoogle Scholar
  71. 71.
    Gunawardana, C. G., Kuk, C., Smith, C. R., Batruch, I., Soosaipillai, A., & Diamandis, E. P. (2009). Comprehensive analysis of conditioned media from ovarian cancer cell lines identifies novel candidate markers of epithelial ovarian cancer. Journal of Proteome Research, 8(10), 4705–4713. doi:10.1021/pr900411g.PubMedCrossRefGoogle Scholar
  72. 72.
    Zhang, Y., Xu, B., Liu, Y., Yao, H., Lu, N., Li, B., et al. (2012). The ovarian cancer-derived secretory/releasing proteome: a repertoire of tumor markers. Proteomics, 12(11), 1883–1891. doi:10.1002/pmic.201100654.PubMedCrossRefGoogle Scholar
  73. 73.
    Gortzak-Uzan, L., Ignatchenko, A., Evangelou, A. I., Agochiya, M., Brown, K. A., St Onge, P., et al. (2008). A proteome resource of ovarian cancer ascites: integrated proteomic and bioinformatic analyses to identify putative biomarkers. Journal of Proteome Research, 7(1), 339–351. doi:10.1021/pr0703223.PubMedCrossRefGoogle Scholar
  74. 74.
    Kuk, C., Kulasingam, V., Gunawardana, C. G., Smith, C. R., Batruch, I., & Diamandis, E. P. (2009). Mining the ovarian cancer ascites proteome for potential ovarian cancer biomarkers. Molecular and Cellular Proteomics, 8(4), 661–669. doi:10.1074/mcp. M800313-MCP200.PubMedPubMedCentralCrossRefGoogle Scholar
  75. 75.
    Kristjansdottir, B., Levan, K., Partheen, K., Carlsohn, E., & Sundfeldt, K. (2013). Potential tumor biomarkers identified in ovarian cyst fluid by quantitative proteomic analysis, iTRAQ. Clinical Proteomics, 10(1), 4. doi:10.1186/1559-0275-10-4.PubMedPubMedCentralCrossRefGoogle Scholar
  76. 76.
    Davidson, B., Espina, V., Steinberg, S. M., Florenes, V. A., Liotta, L. A., Kristensen, G. B., et al. (2006). Proteomic analysis of malignant ovarian cancer effusions as a tool for biologic and prognostic profiling. Clinical Cancer Research, 12(3 Pt 1), 791–799. doi:10.1158/1078-0432.ccr-05-2516.PubMedCrossRefGoogle Scholar
  77. 77.
    Petri, A. L., Simonsen, A. H., Yip, T. T., Hogdall, E., Fung, E. T., Lundvall, L., et al. (2009). Three new potential ovarian cancer biomarkers detected in human urine with equalizer bead technology. Acta Obstetricia et Gynecologica Scandinavica, 88(1), 18–26. doi:10.1080/00016340802443830.PubMedCrossRefGoogle Scholar
  78. 78.
    Moyer, V. A. (2012). Screening for ovarian cancer: U.S. Preventive Services Task Force reaffirmation recommendation statement. Annals of Internal Medicine, 157(12), 900–904. doi:10.7326/0003-4819-157-11-201212040-00539.PubMedCrossRefGoogle Scholar
  79. 79.
    American College of, O., & Gynecologists Committee on Gynecologic, P. (2011). Committee Opinion No. 477: the role of the obstetrician–gynecologist in the early detection of epithelial ovarian cancer. Obstetrics and Gynecology, 117(3), 742–746. doi:10.1097/AOG.0b013e31821477db.CrossRefGoogle Scholar
  80. 80.
    Buys, S. S., Partridge, E., Black, A., Johnson, C. C., Lamerato, L., Isaacs, C., et al. (2011). Effect of screening on ovarian cancer mortality: the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Randomized Controlled Trial. JAMA, 305(22), 2295–2303. doi:10.1001/jama.2011.766.PubMedCrossRefGoogle Scholar
  81. 81.
    Kobayashi, H., Yamada, Y., Sado, T., Sakata, M., Yoshida, S., Kawaguchi, R., et al. (2008). A randomized study of screening for ovarian cancer: a multicenter study in Japan. International Journal of Gynecological Cancer, 18(3), 414–420. doi:10.1111/j.1525-1438.2007.01035.x.PubMedCrossRefGoogle Scholar
  82. 82.
    Menon, U., Gentry-Maharaj, A., Hallett, R., Ryan, A., Burnell, M., Sharma, A., et al. (2009). Sensitivity and specificity of multimodal and ultrasound screening for ovarian cancer, and stage distribution of detected cancers: results of the prevalence screen of the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS). Lancet Oncology, 10(4), 327–340. doi:10.1016/s1470-2045(09)70026-9.PubMedCrossRefGoogle Scholar
  83. 83.
    Ozols, R. F., Bundy, B. N., Greer, B. E., Fowler, J. M., Clarke-Pearson, D., Burger, R. A., et al. (2003). Phase III trial of carboplatin and paclitaxel compared with cisplatin and paclitaxel in patients with optimally resected stage III ovarian cancer: a Gynecologic Oncology Group study. Journal of Clinical Oncology, 21(17), 3194–3200. doi:10.1200/jco.2003.02.153.PubMedCrossRefGoogle Scholar
  84. 84.
    Kim, A., Ueda, Y., Naka, T., & Enomoto, T. (2012). Therapeutic strategies in epithelial ovarian cancer. Journal of Experimental & Clinical Cancer Research, 31, 14. doi:10.1186/1756-9966-31-14.CrossRefGoogle Scholar
  85. 85.
    Fountain, J., Trimble, E., & Birrer, M. J. (2006). Summary and discussion of session recommendations. Gynecologic Oncology, 103(2 Suppl 1), S23–S25. doi:10.1016/j.ygyno.2006.08.022.PubMedGoogle Scholar
  86. 86.
    Hartmann, L. C., Lu, K. H., Linette, G. P., Cliby, W. A., Kalli, K. R., Gershenson, D., et al. (2005). Gene expression profiles predict early relapse in ovarian cancer after platinum–paclitaxel chemotherapy. Clinical Cancer Research, 11(6), 2149–2155. doi:10.1158/1078-0432.ccr-04-1673.PubMedCrossRefGoogle Scholar
  87. 87.
    Berchuck, A., Iversen, E. S., Lancaster, J. M., Dressman, H. K., West, M., Nevins, J. R., et al. (2004). Prediction of optimal versus suboptimal cytoreduction of advanced-stage serous ovarian cancer with the use of microarrays. American Journal of Obstetrics and Gynecology, 190(4), 910–925. doi:10.1016/j.ajog.2004.02.005.PubMedCrossRefGoogle Scholar
  88. 88.
    Spentzos, D., Levine, D. A., Ramoni, M. F., Joseph, M., Gu, X., Boyd, J., et al. (2004). Gene expression signature with independent prognostic significance in epithelial ovarian cancer. Journal of Clinical Oncology, 22(23), 4700–4710. doi:10.1200/jco.2004.04.070.PubMedCrossRefGoogle Scholar
  89. 89.
    Potti, A., Dressman, H. K., Bild, A., Riedel, R. F., Chan, G., Sayer, R., et al. (2006). Genomic signatures to guide the use of chemotherapeutics. Nature Medicine, 12(11), 1294–1300. doi:10.1038/nm1491.PubMedCrossRefGoogle Scholar
  90. 90.
    Agarwal, R., & Kaye, S. B. (2003). Ovarian cancer: strategies for overcoming resistance to chemotherapy. Nature Reviews Cancer, 3(7), 502–516. doi:10.1038/nrc1123.PubMedCrossRefGoogle Scholar
  91. 91.
    Li, S.-L., Ye, F., Cai, W.-J., Hu, H.-D., Hu, P., Ren, H., et al. (2010). Quantitative proteome analysis of multidrug resistance in human ovarian cancer cell line. Journal of Cellular Biochemistry, 109(4), 625–633. doi:10.1002/jcb.22413.PubMedGoogle Scholar
  92. 92.
    Yan, X. D., Pan, L. Y., Yuan, Y., Lang, J. H., & Mao, N. (2007). Identification of platinum-resistance associated proteins through proteomic analysis of human ovarian cancer cells and their platinum-resistant sublines. Journal of Proteome Research, 6(2), 772–780. doi:10.1021/pr060402r.PubMedCrossRefGoogle Scholar
  93. 93.
    Di Michele, M., Marcone, S., Cicchillitti, L., Della Corte, A., Ferlini, C., Scambia, G., et al. (2010). Glycoproteomics of paclitaxel resistance in human epithelial ovarian cancer cell lines: towards the identification of putative biomarkers. Journal of Proteomics, 73(5), 879–898. doi:10.1016/j.jprot.2009.11.012.PubMedCrossRefGoogle Scholar
  94. 94.
    Dai, Z., Yin, J., He, H., Li, W., Hou, C., Qian, X., et al. (2010). Mitochondrial comparative proteomics of human ovarian cancer cells and their platinum-resistant sublines. Proteomics, 10(21), 3789–3799. doi:10.1002/pmic.200900685.PubMedCrossRefGoogle Scholar
  95. 95.
    Lee, D. H., Chung, K., Song, J. A., Kim, T. H., Kang, H., Huh, J. H., et al. (2010). Proteomic identification of paclitaxel-resistance associated hnRNP A2 and GDI 2 proteins in human ovarian cancer cells. Journal of Proteome Research, 9(11), 5668–5676. doi:10.1021/pr100478u.PubMedCrossRefGoogle Scholar
  96. 96.
    Chappell, N. P., Teng, P. N., Hood, B. L., Wang, G., Darcy, K. M., Hamilton, C. A., et al. (2012). Mitochondrial proteomic analysis of cisplatin resistance in ovarian cancer. Journal of Proteome Research, 11(9), 4605–4614. doi:10.1021/pr300403d.PubMedCrossRefGoogle Scholar
  97. 97.
    Chen, X., Wei, S., Ma, Y., Lu, J., Niu, G., Xue, Y., et al. (2014). Quantitative proteomics analysis identifies mitochondria as therapeutic targets of multidrug-resistance in ovarian cancer. Theranostics, 4(12), 1164–1175. doi:10.7150/thno.8502.PubMedPubMedCentralCrossRefGoogle Scholar
  98. 98.
    Cicchillitti, L., Di Michele, M., Urbani, A., Ferlini, C., Donat, M. B., Scambia, G., et al. (2009). Comparative proteomic analysis of paclitaxel sensitive A2780 epithelial ovarian cancer cell line and its resistant counterpart A2780TC1 by 2D-DIGE: the role of ERp57. Journal of Proteome Research, 8(4), 1902–1912.PubMedCrossRefGoogle Scholar
  99. 99.
    Le Moguen, K., Lincet, H., Marcelo, P., Lemoisson, E., Heutte, N., Duval, M., et al. (2007). A proteomic kinetic analysis of IGROV1 ovarian carcinoma cell line response to cisplatin treatment. Proteomics, 7(22), 4090–4101. doi:10.1002/pmic.200700231.PubMedCrossRefGoogle Scholar
  100. 100.
    Yang, J. Y., Yoshihara, K., Tanaka, K., Hatae, M., Masuzaki, H., Itamochi, H., et al. (2013). Predicting time to ovarian carcinoma recurrence using protein markers. Journal of Clinical Investigation, 123(9), 3740–3750. doi:10.1172/jci68509.PubMedPubMedCentralCrossRefGoogle Scholar
  101. 101.
    Lee, J. M., Hays, J. L., Annunziata, C. M., Noonan, A. M., Minasian, L., Zujewski, J. A., et al. (2014). Phase I/Ib study of olaparib and carboplatin in BRCA1 or BRCA2 mutation-associated breast or ovarian cancer with biomarker analyses. Journal of the National Cancer Institute, 106(6), dju089. doi:10.1093/jnci/dju089.PubMedPubMedCentralCrossRefGoogle Scholar
  102. 102.
    Carey, M. S., Agarwal, R., Gilks, B., Swenerton, K., Kalloger, S., Santos, J., et al. (2010). Functional proteomic analysis of advanced serous ovarian cancer using reverse phase protein array: TGF–beta pathway signaling indicates response to primary chemotherapy. Clinical Cancer Research, 16(10), 2852–2860. doi:10.1158/1078-0432.ccr-09-2502.PubMedPubMedCentralCrossRefGoogle Scholar
  103. 103.
    Picotti, P., Lam, H., Campbell, D., Deutsch, E. W., Mirzaei, H., Ranish, J., et al. (2008). A database of mass spectrometric assays for the yeast proteome. Nature Methods, 5(11), 913–914. doi:10.1038/nmeth1108-913.PubMedPubMedCentralCrossRefGoogle Scholar
  104. 104.
    Chen, Y., Gruidl, M., Remily-Wood, E., Liu, R. Z., Eschrich, S., Lloyd, M., et al. (2010). Quantification of beta-catenin signaling components in colon cancer cell lines, tissue sections, and microdissected tumor cells using reaction monitoring mass spectrometry. Journal of Proteome Research, 9(8), 4215–4227. doi:10.1021/pr1005197.PubMedPubMedCentralCrossRefGoogle Scholar
  105. 105.
    Schrader, M., Schulz-Knappe, P., & Fricker, L. D. (2014). Historical perspective of peptidomics. EuPA Open Proteomics, 3, 171–182. doi:10.1016/j.euprot.2014.02.014.CrossRefGoogle Scholar
  106. 106.
    Villanueva, J., Shaffer, D. R., Philip, J., Chaparro, C. A., Erdjument-Bromage, H., Olshen, A. B., et al. (2006). Differential exoprotease activities confer tumor-specific serum peptidome patterns. Journal of Clinical Investigation, 116(1), 271–284. doi:10.1172/jci26022.PubMedPubMedCentralCrossRefGoogle Scholar
  107. 107.
    Lopez, M. F., Mikulskis, A., Kuzdzal, S., Golenko, E., Petricoin, E. F., 3rd, Liotta, L. A., et al. (2007). A novel, high-throughput workflow for discovery and identification of serum carrier protein-bound peptide biomarker candidates in ovarian cancer samples. Clinical Chemistry, 53(6), 1067–1074. doi:10.1373/clinchem.2006.080721.PubMedCrossRefGoogle Scholar
  108. 108.
    Fredolini, C., Meani, F., Luchini, A., Zhou, W., Russo, P., Ross, M., et al. (2010). Investigation of the ovarian and prostate cancer peptidome for candidate early detection markers using a novel nanoparticle biomarker capture technology. The AAPS Journal, 12(4), 504–518. doi:10.1208/s12248-010-9211-3.PubMedPubMedCentralCrossRefGoogle Scholar
  109. 109.
    Xu, Z., Wu, C., Xie, F., Slysz, G. W., Tolic, N., Monroe, M. E., et al. (2014). Comprehensive quantitative analysis of ovarian and breast cancer tumor peptidomes. Journal of Proteome Research. doi:10.1021/pr500840w.Google Scholar
  110. 110.
    Pan, B. T., & Johnstone, R. M. (1983). Fate of the transferrin receptor during maturation of sheep reticulocytes in vitro: selective externalization of the receptor. Cell, 33(3), 967–978.PubMedCrossRefGoogle Scholar
  111. 111.
    Ciechanover, A., Schwartz, A. L., Dautry-Varsat, A., & Lodish, H. F. (1983). Kinetics of internalization and recycling of transferrin and the transferrin receptor in a human hepatoma cell line. Effect of lysosomotropic agents. Journal of Biological Chemistry, 258(16), 9681–9689.PubMedGoogle Scholar
  112. 112.
    E. L. A, S., Mager, I., Breakefield, X. O., & Wood, M. J. (2013). Extracellular vesicles: biology and emerging therapeutic opportunities. Nature Reviews Drug Discovery, 12(5), 347–357. doi:10.1038/nrd3978.CrossRefGoogle Scholar
  113. 113.
    Azmi, A. S., Bao, B., & Sarkar, F. H. (2013). Exosomes in cancer development, metastasis, and drug resistance: a comprehensive review. Cancer Metastasis Reviews, 32(3–4), 623–642. doi:10.1007/s10555-013-9441-9.PubMedCrossRefGoogle Scholar
  114. 114.
    Chairoungdua, A., Smith, D. L., Pochard, P., Hull, M., & Caplan, M. J. (2010). Exosome release of beta-catenin: a novel mechanism that antagonizes Wnt signaling. Journal of Cell Biology, 190(6), 1079–1091. doi:10.1083/jcb.201002049.PubMedPubMedCentralCrossRefGoogle Scholar
  115. 115.
    Cho, J. A., Park, H., Lim, E. H., & Lee, K. W. (2012). Exosomes from breast cancer cells can convert adipose tissue-derived mesenchymal stem cells into myofibroblast-like cells. International Journal of Oncology, 40(1), 130–138. doi:10.3892/ijo.2011.1193.PubMedGoogle Scholar
  116. 116.
    Shedden, K., Xie, X. T., Chandaroy, P., Chang, Y. T., & Rosania, G. R. (2003). Expulsion of small molecules in vesicles shed by cancer cells: association with gene expression and chemosensitivity profiles. Cancer Research, 63(15), 4331–4337.PubMedGoogle Scholar
  117. 117.
    Safaei, R., Larson, B. J., Cheng, T. C., Gibson, M. A., Otani, S., Naerdemann, W., et al. (2005). Abnormal lysosomal trafficking and enhanced exosomal export of cisplatin in drug-resistant human ovarian carcinoma cells. Molecular Cancer Therapeutics, 4(10), 1595–1604. doi:10.1158/1535-7163.mct-05-0102.PubMedCrossRefGoogle Scholar
  118. 118.
    Beach, A., Zhang, H. G., Ratajczak, M. Z., & Kakar, S. S. (2014). Exosomes: an overview of biogenesis, composition and role in ovarian cancer. Journal of Ovarian Research, 7(1), 14. doi:10.1186/1757-2215-7-14.PubMedPubMedCentralCrossRefGoogle Scholar
  119. 119.
    Sinha, A., Ignatchenko, V., Ignatchenko, A., Mejia-Guerrero, S., & Kislinger, T. (2014). In-depth proteomic analyses of ovarian cancer cell line exosomes reveals differential enrichment of functional categories compared to the NCI 60 proteome. Biochemical and Biophysical Research Communications, 445(4), 694–701. doi:10.1016/j.bbrc.2013.12.070.PubMedCrossRefGoogle Scholar
  120. 120.
    Pisitkun, T., Gandolfo, M. T., Das, S., Knepper, M. A., & Bagnasco, S. M. (2012). Application of systems biology principles to protein biomarker discovery: urinary exosomal proteome in renal transplantation. Proteomics - Clinical Applications, 6(5–6), 268–278. doi:10.1002/prca.201100108.PubMedCrossRefGoogle Scholar
  121. 121.
    Kalra, H., Adda, C. G., Liem, M., Ang, C. S., Mechler, A., Simpson, R. J., et al. (2013). Comparative proteomics evaluation of plasma exosome isolation techniques and assessment of the stability of exosomes in normal human blood plasma. Proteomics, 13(22), 3354–3364. doi:10.1002/pmic.201300282.PubMedCrossRefGoogle Scholar
  122. 122.
    Zubiri, I., Vivanco, F., & Alvarez-Llamas, G. (2013). Proteomic analysis of urinary exosomes in cardiovascular and associated kidney diseases by two-dimensional electrophoresis and LC-MS/MS. Methods in Molecular Biology, 1000, 209–220. doi:10.1007/978-1-62703-405-0_16.PubMedCrossRefGoogle Scholar
  123. 123.
    Street, J. M., Barran, P. E., Mackay, C. L., Weidt, S., Balmforth, C., Walsh, T. S., et al. (2012). Identification and proteomic profiling of exosomes in human cerebrospinal fluid. Journal of Translational Medicine, 10, 5. doi:10.1186/1479-5876-10-5.PubMedPubMedCentralCrossRefGoogle Scholar
  124. 124.
    Xiao, H., & Wong, D. T. (2012). Proteomic analysis of microvesicles in human saliva by gel electrophoresis with liquid chromatography-mass spectrometry. Analitica Chimica Acta, 723, 61–67. doi:10.1016/j.aca.2012.02.018.CrossRefGoogle Scholar
  125. 125.
    Admyre, C., Johansson, S. M., Qazi, K. R., Filen, J. J., Lahesmaa, R., Norman, M., et al. (2007). Exosomes with immune modulatory features are present in human breast milk. Journal of Immunology, 179(3), 1969–1978.CrossRefGoogle Scholar
  126. 126.
    Liang, B., Peng, P., Chen, S., Li, L., Zhang, M., Cao, D., et al. (2013). Characterization and proteomic analysis of ovarian cancer-derived exosomes. Journal of Proteomics, 80, 171–182. doi:10.1016/j.jprot.2012.12.029.PubMedCrossRefGoogle Scholar
  127. 127.
    Hortin, G. L., & Sviridov, D. (2010). The dynamic range problem in the analysis of the plasma proteome. Journal of Proteomics, 73(3), 629–636. doi:10.1016/j.jprot.2009.07.001.PubMedCrossRefGoogle Scholar
  128. 128.
    Fernando, S. A., & Wilson, G. S. (1992). Studies of the ‘hook’ effect in the one-step sandwich immunoassay. Journal of Immunological Methods, 151(1–2), 47–66.PubMedCrossRefGoogle Scholar
  129. 129.
    Ioannidis, J. P. (2013). Biomarker failures. Clinical Chemistry, 59(1), 202–204. doi:10.1373/clinchem.2012.185801.PubMedCrossRefGoogle Scholar
  130. 130.
    Gagne, J. P., Ethier, C., Gagne, P., Mercier, G., Bonicalzi, M. E., Mes-Masson, A. M., et al. (2007). Comparative proteome analysis of human epithelial ovarian cancer. Proteome Science, 5, 16. doi:10.1186/1477-5956-5-16.PubMedPubMedCentralCrossRefGoogle Scholar
  131. 131.
    Domcke, S., Sinha, R., Levine, D. A., Sander, C., & Schultz, N. (2013). Evaluating cell lines as tumour models by comparison of genomic profiles. Nature Communications, 4, 2126. doi:10.1038/ncomms3126.PubMedPubMedCentralCrossRefGoogle Scholar
  132. 132.
    Wei, B. R., Hoover, S. B., Ross, M. M., Zhou, W., Meani, F., Edwards, J. B., et al. (2009). Serum S100A6 concentration predicts peritoneal tumor burden in mice with epithelial ovarian cancer and is associated with advanced stage in patients. PLoS One, 4(10), e7670. doi:10.1371/journal.pone.0007670.PubMedPubMedCentralCrossRefGoogle Scholar
  133. 133.
    He, Y., Wu, X., Liu, X., Yan, G., & Xu, C. (2010). LC-MS/MS analysis of ovarian cancer metastasis-related proteins using a nude mouse model: 14-3-3 zeta as a candidate biomarker. Journal of Proteome Research, 9(12), 6180–6190. doi:10.1021/pr100822v.PubMedCrossRefGoogle Scholar
  134. 134.
    Pitteri, S. J., JeBailey, L., Faca, V. M., Thorpe, J. D., Silva, M. A., Ireton, R. C., et al. (2009). Integrated proteomic analysis of human cancer cells and plasma from tumor bearing mice for ovarian cancer biomarker discovery. PLoS One, 4(11), e7916. doi:10.1371/journal.pone.0007916.PubMedPubMedCentralCrossRefGoogle Scholar
  135. 135.
    Tang, H. Y., Beer, L. A., Chang-Wong, T., Hammond, R., Gimotty, P., Coukos, G., et al. (2012). A xenograft mouse model coupled with in-depth plasma proteome analysis facilitates identification of novel serum biomarkers for human ovarian cancer. Journal of Proteome Research, 11(2), 678–691. doi:10.1021/pr200603h.PubMedPubMedCentralCrossRefGoogle Scholar
  136. 136.
    Parker, W. H., Broder, M. S., Chang, E., Feskanich, D., Farquhar, C., Liu, Z., et al. (2009). Ovarian conservation at the time of hysterectomy and long-term health outcomes in the nurses’ health study. Obstetrics and Gynecology, 113(5), 1027–1037. doi:10.1097/AOG.0b013e3181a11c64.PubMedPubMedCentralCrossRefGoogle Scholar
  137. 137.
    Harsha, H. C., & Pandey, A. (2010). Phosphoproteomics in cancer. Molecular Oncology, 4(6), 482–495. doi:10.1016/j.molonc.2010.09.004.PubMedPubMedCentralCrossRefGoogle Scholar
  138. 138.
    Naumann, R. W., & Coleman, R. L. (2011). Management strategies for recurrent platinum-resistant ovarian cancer. Drugs, 71(11), 1397–1412. doi:10.2165/11591720-000000000-00000.PubMedCrossRefGoogle Scholar
  139. 139.
    Yap, T. A., Carden, C. P., & Kaye, S. B. (2009). Beyond chemotherapy: targeted therapies in ovarian cancer. Nature Reviews Cancer, 9(3), 167–181. doi:10.1038/nrc2583.PubMedCrossRefGoogle Scholar
  140. 140.
    Lee, J. M., Ledermann, J. A., & Kohn, E. C. (2014). PARP inhibitors for BRCA1/2 mutation-associated and BRCA-like malignancies. Annals of Oncology, 25(1), 32–40. doi:10.1093/annonc/mdt384.PubMedPubMedCentralCrossRefGoogle Scholar
  141. 141.
    Farmer, H., McCabe, N., Lord, C. J., Tutt, A. N., Johnson, D. A., Richardson, T. B., et al. (2005). Targeting the DNA repair defect in BRCA mutant cells as a therapeutic strategy. Nature, 434(7035), 917–921. doi:10.1038/nature03445.PubMedCrossRefGoogle Scholar
  142. 142.
    Dunn, L., & Demichele, A. (2009). Genomic predictors of outcome and treatment response in breast cancer. Molecular Diagnosis & Therapy, 13(2), 73–90. doi:10.2165/01250444-200913020-00002.CrossRefGoogle Scholar
  143. 143.
    Yuan, Y., Van Allen, E. M., Omberg, L., Wagle, N., Amin-Mansour, A., Sokolov, A., et al. (2014). Assessing the clinical utility of cancer genomic and proteomic data across tumor types. [Computational Biology]. Nature Biotechnology, 32(7), 644–652. doi:10.1038/nbt.2940.PubMedPubMedCentralCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York (outside the USA) 2015

Authors and Affiliations

  1. 1.Egybiotech for Research and BiotechnologyAlexandriaEgypt
  2. 2.Biological Sciences DivisionPacific Northwest National LaboratoryRichlandUSA

Personalised recommendations