Skip to main content
SpringerLink
Log in

Histopathological markers of treatment response and recurrence risk in ovarian cancers and borderline tumors

Histopathologische Marker für Therapieansprechen und Rezidivrisiko von Ovarialkarzinomen und Borderline-Tumoren

  • Hauptreferate: Aktuelle Habilitationen
  • Published:
Der Pathologe Aims and scope Submit manuscript

Abstract

Histopathology plays an important role in defining response to treatment for different tumor types. Histopathologic response criteria are currently used as reference standard in various types of cancer, including breast cancer, gastroesophageal cancer, and bone tumors. Since there were no generally accepted response criteria established for ovarian cancer, a systematic analysis of various features of tumor regression was performed. Patient survival served as the reference standard to validate the histopathologic features of tumor regression. In contrast to ovarian cancer, borderline ovarian tumors are epithelial ovarian neoplasms characterized by up-regulated cellular proliferation and cytologic atypia but without destructive stromal invasion. While borderline ovarian tumors generally have an excellent prognosis with a 5‑year survival of > 95%, recurrences and malignant transformation occur in a small percentage of patients. Nevertheless, the identification of patients at increased risk for recurrence remains difficult. The aim of studying histopathological markers in ovarian cancers and borderline tumors was to evaluate whether histopathologic features including molecular pathologic alterations can predict patient outcome, particularly the risk of recurrence of serous and mucinous borderline tumors.

Zusammenfassung

Die Histopathologie spielt bei der Bestimmung des Therapieansprechens verschiedener Tumoren eine wichtige Rolle. Während die histopathologische Tumorregression als Goldstandard zur Evaluierung des Therapieansprechens für mehrere solide Tumoren, einschließlich Mamma- und Magenkarzinome sowie Osteosarkome, etabliert ist, existieren dazu keine gesicherten Kriterien. Diese Arbeit untersuchte histopathologische Regressionskriterien mit dem Gesamtüberleben der Patientinnen als Referenzstandard für Therapieansprechen. Im Gegensatz zu Ovarialkarzinomen weisen Borderline-Tumoren des Ovars generell eine sehr gute Prognose mit einem 5‑Jahres-Überleben von > 95% auf. Dennoch kommt es in einem kleinen Prozentsatz zu Rezidiven, teilweise auch mit maligner Transformation. Verlässliche Parameter zur Vorhersage des Rezidivrisikos fehlen weitgehend und könnten zu einer individualisierten Therapieentscheidung beitragen. Das Ziel der Arbeit war es zu evaluieren, ob histologische und molekularpathologische Kriterien zu einer verbesserten Vorhersage der Prognose und insbesondere des Rezidivrisikos von serösen und muzinösen Borderline-Tumoren beitragen.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Avril S et al (2012) Histopathologic features of ovarian borderline tumors are not predictive of clinical outcome. Gynecol Oncol 127(3):516–524

    Article  PubMed  Google Scholar 

  2. Sassen S et al (2007) Histopathologic assessment of tumor regression after neoadjuvant chemotherapy in advanced-stage ovarian cancer. Hum Pathol 38(6):926–934

    Article  CAS  PubMed  Google Scholar 

  3. Avril-Sassen S et al (2009) Characterisation of microRNA expression in post-natal mouse mammary gland development. BMC Genomics 10:548

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  4. Raychaudhuri M et al (2012) Intratumoral Heterogeneity of MicroRNA Expression in Breast Cancer. J Mol Diagn 14(4):376–384

    Article  CAS  PubMed  Google Scholar 

  5. Malinowsky K et al (2012) Common protein biomarkers assessed by reverse phase protein arrays show considerable intratumoral heterogeneity in breast cancer tissues. PLOS ONE 7(7):e40285

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Mittermeyer G et al (2013) Variation in cell signaling protein expression may introduce sampling bias in primary epithelial ovarian cancer. PLOS ONE 8(10):e77825

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Becker K et al (2003) Histomorphology and grading of regression in gastric carcinoma treated with neoadjuvant chemotherapy. Cancer 98(7):1521–1530

    Article  PubMed  Google Scholar 

  8. Karamitopoulou E et al (2014) Assessment of tumor regression of esophageal adenocarcinomas after neoadjuvant chemotherapy: comparison of 2 commonly used scoring approaches. Am J Surg Pathol 38(11):1551–1556

    Article  PubMed  Google Scholar 

  9. Salzer-Kuntschik M, Brand G, Delling G (1983) Determination of the degree of morphological regression following chemotherapy in malignant bone tumors. Pathologe 4(3):135–141

    CAS  PubMed  Google Scholar 

  10. Schmidt T et al (2014) Prognostic value of histopathological regression in 850 neoadjuvantly treated oesophagogastric adenocarcinomas. Br J Cancer 110(7):1712–1720

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Sinn HP et al (1994) Histologic regression of breast cancer after primary (neoadjuvant) chemotherapy. Geburtshilfe Frauenheilkd 54(10):552–558

    Article  CAS  PubMed  Google Scholar 

  12. Fisher ER et al (2002) Pathobiology of preoperative chemotherapy: findings from the National Surgical Adjuvant Breast and Bowel (NSABP) protocol B‑18. Cancer 95(4):681–695

    Article  PubMed  Google Scholar 

  13. Symmans WF et al (2007) Measurement of residual breast cancer burden to predict survival after neoadjuvant chemotherapy. J Clin Oncol 25(28):4414–4422

    Article  PubMed  Google Scholar 

  14. Junker K et al (2001) Grading of tumor regression in non-small cell lung cancer: morphology and prognosis. Chest 120(5):1584–1591

    Article  CAS  PubMed  Google Scholar 

  15. McCluggage WG et al (2002) Morphological effects of chemotherapy on ovarian carcinoma. J Clin Pathol 55(1):27–31

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Moreno A et al (2002) Pathologic changes related to CMF primary chemotherapy in breast cancer. Pathological evaluation of response predicts clinical outcome. Breast Cancer Res Treat 75(2):119–125

    Article  CAS  PubMed  Google Scholar 

  17. Heintz AP et al (2006) Carcinoma of the ovary. FIGO 26th annual report on the results of treatment in gynecological cancer. Int J Gynaecol Obstet 95(Suppl 1):S161–92

    Article  PubMed  Google Scholar 

  18. Deutsche Krebsgesellschaft, AWMF (2013) S3-Leitlinie Diagnostik, Therapie und Nachsorge maligner Ovarialtumoren, Version 1.0. AWMF Registrierungsnummer: 032–035OL. http://leitlinienprogramm-onkologie.de/Leitlinien.7.0.html. Accessed January 2015

    Google Scholar 

  19. Cannistra SA (2004) Cancer of the ovary. N Engl J Med 351(24):2519–2529

    Article  CAS  PubMed  Google Scholar 

  20. Silverberg SG et al (2004) Borderline ovarian tumors: key points and workshop summary. Hum Pathol 35(8):910–917

    Article  PubMed  Google Scholar 

  21. Seidman JD, Kurman RJ (2000) Ovarian serous borderline tumors: a critical review of the literature with emphasis on prognostic indicators. Hum Pathol 31(5):539–557

    Article  CAS  PubMed  Google Scholar 

  22. Shih Ie M, Kurman RJ (2004) Ovarian tumorigenesis: a proposed model based on morphological and molecular genetic analysis. Am J Pathol 164(5):1511–1518

    Article  PubMed  Google Scholar 

  23. Singer G et al (2005) Patterns of p53 mutations separate ovarian serous borderline tumors and low- and high-grade carcinomas and provide support for a new model of ovarian carcinogenesis: a mutational analysis with immunohistochemical correlation. Am J Surg Pathol 29(2):218–224

    Article  PubMed  Google Scholar 

  24. Kurman RJ, Shih Ie M (2008) Pathogenesis of ovarian cancer: lessons from morphology and molecular biology and their clinical implications. Int J Gynecol Pathol 27(2):151–160

    PubMed  PubMed Central  Google Scholar 

  25. Kurman RJ, Shih Ie M (2010) The origin and pathogenesis of epithelial ovarian cancer: a proposed unifying theory. Am J Surg Pathol 34(3):433–443

    Article  PubMed  PubMed Central  Google Scholar 

  26. Seidman JD et al (2004) Borderline ovarian tumors: diverse contemporary viewpoints on terminology and diagnostic criteria with illustrative images. Hum Pathol 35(8):918–933

    Article  PubMed  Google Scholar 

  27. Alvarez-Garcia I, Miska EA (2005) MicroRNA functions in animal development and human disease. Development 132(21):4653–4662

    Article  CAS  PubMed  Google Scholar 

  28. Farazi TA et al (2011) miRNAs in human cancer. J Pathol 223(2):102–115

    Article  CAS  PubMed  Google Scholar 

  29. Ma L, Weinberg RA (2008) MicroRNAs in malignant progression. Cell Cycle 7(5):570–572

    Article  CAS  PubMed  Google Scholar 

  30. Sassen S, Miska EA, Caldas C (2008) MicroRNA: implications for cancer. Virchows Arch 452(1):1–10

    Article  CAS  PubMed  Google Scholar 

  31. Doleshal M et al (2008) Evaluation and validation of total RNA extraction methods for microRNA expression analyses in formalin-fixed, paraffin-embedded tissues. J Mol Diagn 10(3):203–211

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Hasemeier B et al (2008) Reliable microRNA profiling in routinely processed formalin-fixed paraffin-embedded breast cancer specimens using fluorescence labelled bead technology. BMC Biotechnol 8:90

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. Siebolts U et al (2009) Tissues from routine pathology archives are suitable for microRNA analyses by quantitative PCR. J Clin Pathol 62(1):84–88

    Article  CAS  PubMed  Google Scholar 

  34. Lu J et al (2005) MicroRNA expression profiles classify human cancers. Nature 435(7043):834–838

    Article  CAS  PubMed  Google Scholar 

  35. Rosenfeld N et al (2008) MicroRNAs accurately identify cancer tissue origin. Nat Biotechnol 26(4):462–469

    Article  CAS  PubMed  Google Scholar 

  36. Slack FJ, Weidhaas JB (2008) MicroRNA in cancer prognosis. N Engl J Med 359(25):2720–2722

    Article  CAS  PubMed  Google Scholar 

  37. Tavazoie SF et al (2008) Endogenous human microRNAs that suppress breast cancer metastasis. Nature 451(7175):147–152

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Yanaihara N et al (2006) Unique microRNA molecular profiles in lung cancer diagnosis and prognosis. Cancer Cell 9(3):189–198

    Article  CAS  PubMed  Google Scholar 

  39. Iorio MV et al (2005) MicroRNA gene expression deregulation in human breast cancer. Cancer Res 65(16):7065–7070

    Article  CAS  PubMed  Google Scholar 

  40. Blenkiron C et al (2007) MicroRNA expression profiling of human breast cancer identifies new markers of tumour subtype. Genome Biol 8(10):R214

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Andorfer CA et al (2011) MicroRNA signatures: clinical biomarkers for the diagnosis and treatment of breast cancer. Trends Mol Med 17(6):313–319

    Article  CAS  PubMed  Google Scholar 

  42. Esteva FJ, Hortobagyi GN (2004) Prognostic molecular markers in early breast cancer. Breast Cancer Res 6(3):109–118

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Grubb RL et al (2003) Signal pathway profiling of prostate cancer using reverse phase protein arrays. Proteomics 3(11):2142–2146

    Article  CAS  PubMed  Google Scholar 

  44. Paweletz CP et al (2001) Reverse phase protein microarrays which capture disease progression show activation of pro-survival pathways at the cancer invasion front. Oncogene 20(16):1981–1989

    Article  CAS  PubMed  Google Scholar 

  45. Espina V et al (2003) Protein microarrays: molecular profiling technologies for clinical specimens. Proteomics 3(11):2091–2100

    Article  CAS  PubMed  Google Scholar 

  46. Liotta LA et al (2003) Protein microarrays: meeting analytical challenges for clinical applications. Cancer Cell 3(4):317–325

    Article  CAS  PubMed  Google Scholar 

  47. Wulfkuhle JD et al (2006) Technology insight: pharmacoproteomics for cancer – promises of patient-tailored medicine using protein microarrays. Nat Clin Pract Oncol 3(5):256–268

    Article  CAS  PubMed  Google Scholar 

  48. Berg D et al (2011) Use of formalin-fixed and paraffin-embedded tissues for diagnosis and therapy in routine clinical settings. Methods Mol Biol 785:109–122

    Article  CAS  PubMed  Google Scholar 

  49. Wolff C et al (2011) Producing reverse phase protein microarrays from formalin-fixed tissues. Methods Mol Biol 785:123–140

    Article  CAS  PubMed  Google Scholar 

  50. Murphy CG, Modi S (2009) HER2 breast cancer therapies: a review. Biologics 3:289–301

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Berx G, Van Roy F (2001) The E‑cadherin/catenin complex: an important gatekeeper in breast cancer tumorigenesis and malignant progression. Breast Cancer Res 3(5):289–293

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Look MP et al (2002) Pooled analysis of prognostic impact of urokinase-type plasminogen activator and its inhibitor PAI-1 in 8377 breast cancer patients. J Natl Cancer Inst 94(2):116–128

    Article  CAS  PubMed  Google Scholar 

  53. Schmitt M et al (2008) Assessment of urokinase-type plasminogen activator and its inhibitor PAI-1 in breast cancer tissue: historical aspects and future prospects. Breast Care (Basel) 3(s2):3–10

    Google Scholar 

  54. Dean E, El-Helw L, Hasan J (2010) Targeted therapies in epithelial ovarian cancer. Cancers (Basel) 2(1):88–113

    Article  CAS  Google Scholar 

  55. Moroney JW et al (2011) A phase I trial of liposomal doxorubicin, bevacizumab, and temsirolimus in patients with advanced gynecologic and breast malignancies. Clin Cancer Res 17(21):6840–6846

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Dean E et al (2010) Targeted therapies in epithelial ovarian cancer. Cancers (Basel) 2(L):88–113

    Article  CAS  Google Scholar 

  57. Crum CP (2009) Intercepting pelvic cancer in the distal fallopian tube: theories and realities. Mol Oncol 3(2):165–170

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Gross AL et al (2010) Precursor lesions of high-grade serous ovarian carcinoma: morphological and molecular characteristics. J Oncol. https://doi.org/10.1155/2010/126295

    PubMed  PubMed Central  Google Scholar 

  59. Honkoop AH et al (1997) Effects of chemotherapy on pathologic and biologic characteristics of locally advanced breast cancer. Am J Clin Pathol 107(2):211–218

    Article  CAS  PubMed  Google Scholar 

  60. Mandard AM et al (1994) Pathologic assessment of tumor regression after preoperative chemoradiotherapy of esophageal carcinoma. Clinicopathologic correlations. Cancer 73(11):2680–2686

    Article  CAS  PubMed  Google Scholar 

  61. Junker K (2004) Therapy-induced morphological changes in lung cancer. Pathologe 25(6):475–480

    Article  CAS  PubMed  Google Scholar 

  62. du Bois A et al (2013) Borderline tumours of the ovary: A cohort study of the Arbeitsgmeinschaft Gynakologische Onkologie (AGO) Study Group. Eur J Cancer 49(8):1905–1914

    Article  PubMed  Google Scholar 

  63. Cadron I et al (2007) Management of borderline ovarian neoplasms. J Clin Oncol 25(20):2928–2937

    Article  PubMed  Google Scholar 

  64. Cusido M et al (2007) Results of the national survey of borderline ovarian tumors in Spain. Gynecol Oncol 104(3):617–622

    Article  PubMed  Google Scholar 

  65. Du Bois A et al (2009) Borderlinetumoren des Ovars – eine systematische Übersicht. Borderline Tumors of the Ovary – A Systematic Review. Geburtshilfe Frauenheilkd 69:1–27

    Article  Google Scholar 

  66. Shih KK et al (2011) Accuracy of frozen section diagnosis of ovarian borderline tumor. Gynecol Oncol 123(3):517–521

    Article  CAS  PubMed  Google Scholar 

  67. Burks RT, Sherman ME, Kurman RJ (1996) Micropapillary serous carcinoma of the ovary. A distinctive low-grade carcinoma related to serous borderline tumors. Am J Surg Pathol 20(11):1319–1330

    Article  CAS  PubMed  Google Scholar 

  68. Seidman JD, Kurman RJ (1996) Subclassification of serous borderline tumors of the ovary into benign and malignant types. A clinicopathologic study of 65 advanced stage cases. Am J Surg Pathol 20(11):1331–1345

    Article  CAS  PubMed  Google Scholar 

  69. Prat J, De Nictolis M (2002) Serous borderline tumors of the ovary: a long-term follow-up study of 137 cases, including 18 with a micropapillary pattern and 20 with microinvasion. Am J Surg Pathol 26(9):1111–1128

    Article  PubMed  Google Scholar 

  70. Ronnett BM et al (2004) Mucinous borderline ovarian tumors: points of general agreement and persistent controversies regarding nomenclature, diagnostic criteria, and behavior. Hum Pathol 35(8):949–960

    Article  PubMed  Google Scholar 

  71. Park JY et al (2011) Micropapillary pattern in serous borderline ovarian tumors: does it matter? Gynecol Oncol 123(3):511–516

    Article  PubMed  Google Scholar 

  72. Eichhorn JH et al (1999) Ovarian serous borderline tumors with micropapillary and cribriform patterns: a study of 40 cases and comparison with 44 cases without these patterns. Am J Surg Pathol 23(4):397–409

    Article  CAS  PubMed  Google Scholar 

  73. Bell DA et al (2004) Serous borderline (low malignant potential, atypical proliferative) ovarian tumors: workshop perspectives. Hum Pathol 35(8):934–948

    Article  PubMed  Google Scholar 

  74. Morice P et al (2003) Prognostic factors for patients with advanced stage serous borderline tumours of the ovary. Ann Oncol 14(4):592–598

    Article  CAS  PubMed  Google Scholar 

  75. Gershenson DM et al (1998) Ovarian serous borderline tumors with invasive peritoneal implants. Cancer 82(6):1096–1103

    Article  CAS  PubMed  Google Scholar 

  76. Kommission Ovar der AGO, Schmalfeldt B, Pfisterer J (eds) (2007) Interdisziplinäre S2 k‑Leitlinie für die Diagnostik und Therapie maligner Ovarialtumoren. W. Zuckschwerdt, München

    Google Scholar 

  77. Shih KK et al (2011) Risk factors for recurrence of ovarian borderline tumors. Gynecol Oncol 120(3):480–484

    Article  CAS  PubMed  Google Scholar 

  78. Daniel CW, Smith GH (1999) The mammary gland: a model for development. J Mammary Gland Biol Neoplasia 4(1):3–8

    Article  CAS  PubMed  Google Scholar 

  79. Hennighausen L, Robinson GW (2005) Information networks in the mammary gland. Nat Rev Mol Cell Biol 6(9):715–725

    Article  CAS  PubMed  Google Scholar 

  80. Caldas C, Brenton JD (2005) Sizing up miRNAs as cancer genes. Nat Med 11(7):712–714

    Article  CAS  PubMed  Google Scholar 

  81. Greene SB et al (2010) A putative role for microRNA-205 in mammary epithelial cell progenitors. J Cell Sci 123(Pt 4):606–618

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Ibarra I et al (2007) A role for microRNAs in maintenance of mouse mammary epithelial progenitor cells. Genes Dev 21(24):3238–3243

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Sempere LF et al (2007) Altered MicroRNA expression confined to specific epithelial cell subpopulations in breast cancer. Cancer Res 67(24):11612–11620

    Article  CAS  PubMed  Google Scholar 

  84. Farh KK et al (2005) The widespread impact of mammalian MicroRNAs on mRNA repression and evolution. Science 310(5755):1817–1821

    Article  CAS  PubMed  Google Scholar 

  85. Lim LP et al (2005) Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs. Nature 433(7027):769–773

    Article  CAS  PubMed  Google Scholar 

  86. Mattie MD et al (2006) Optimized high-throughput microRNA expression profiling provides novel biomarker assessment of clinical prostate and breast cancer biopsies. Mol Cancer 5:24

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  87. Lowery AJ et al (2009) MicroRNA signatures predict oestrogen receptor, progesterone receptor and HER2/neu receptor status in breast cancer. Breast Cancer Res 11(3):R27

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  88. Volinia S et al (2006) A microRNA expression signature of human solid tumors defines cancer gene targets. Proc Natl Acad Sci USA 103(7):2257–2261

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Farazi TA et al (2011) MicroRNA sequence and expression analysis in breast tumors by deep sequencing. Cancer Res 71(13):4443. https://doi.org/10.1158/0008-5472.CAN-11-0608

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Heyn H et al (2011) MicroRNA miR-335 is crucial for the BRCA1 regulatory cascade in breast cancer development. Int J Cancer 129(12):2797. https://doi.org/10.1002/ijc.25962

    Article  CAS  PubMed  Google Scholar 

  91. Camps C et al (2008) hsa-miR-210 Is induced by hypoxia and is an independent prognostic factor in breast cancer. Clin Cancer Res 14(5):1340–1348

    Article  CAS  PubMed  Google Scholar 

  92. Ma L, Teruya-Feldstein J, Weinberg RA (2007) Tumour invasion and metastasis initiated by microRNA-10b in breast cancer. Nature 449(7163):682. https://doi.org/10.1038/nature06174 (Erratum in Nature. 2008 Sep 11;455(7210):256)

    Article  CAS  PubMed  Google Scholar 

  93. Nassar A et al (2010) Intratumoral heterogeneity of immunohistochemical marker expression in breast carcinoma: a tissue microarray-based study. Appl Immunohistochem Mol Morphol 18(5):433–441

    CAS  PubMed  Google Scholar 

  94. Brunelli M et al (2009) Genotypic intratumoral heterogeneity in breast carcinoma with HER2/neu amplification: evaluation according to ASCO/CAP criteria. Am J Clin Pathol 131(5):678–682

    Article  PubMed  Google Scholar 

  95. Shin SJ et al (2006) Intratumoral heterogeneity of her-2/neu in invasive mammary carcinomas using fluorescence in-situ hybridization and tissue microarray. Int J Surg Pathol 14(4):279–284

    Article  PubMed  Google Scholar 

  96. Fujii H et al (1996) Genetic divergence in the clonal evolution of breast cancer. Cancer Res 56(7):1493–1497

    CAS  PubMed  Google Scholar 

  97. Shen CY et al (2000) Genome-wide search for loss of heterozygosity using laser capture microdissected tissue of breast carcinoma: an implication for mutator phenotype and breast cancer pathogenesis. Cancer Res 60(14):3884–3892

    CAS  PubMed  Google Scholar 

  98. Glockner S et al (2002) Marked intratumoral heterogeneity of c‑myc and cyclinD1 but not of c‑erbB2 amplification in breast cancer. Lab Invest 82(10):1419–1426

    Article  PubMed  CAS  Google Scholar 

  99. Lonn U et al (1994) Intratumoral heterogeneity for amplified genes in human breast carcinoma. Int J Cancer 58(1):40–45

    Article  CAS  PubMed  Google Scholar 

  100. Hall M et al (2013) Targeted anti-vascular therapies for ovarian cancer: current evidence. Br J Cancer 108(2):250–258

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Wolff C et al (2011) Signalling networks associated with urokinase-type plasminogen activator (uPA) and its inhibitor PAI-1 in breast cancer tissues: new insights from protein microarray analysis. J Pathol 223(1):54–63

    Article  CAS  PubMed  Google Scholar 

  102. Becker KF et al (2007) Quantitative protein analysis from formalin-fixed tissues: implications for translational clinical research and nanoscale molecular diagnosis. J Pathol 211(3):370–378

    Article  CAS  PubMed  Google Scholar 

  103. Berg D et al (2011) Protein microarray-based comparison of HER2, estrogen receptor, and progesterone receptor status in core biopsies and surgical specimens from FFPE breast cancer tissues. Appl Immunohistochem Mol Morphol 19(4):300–305

    Article  CAS  PubMed  Google Scholar 

  104. Hennessy BT et al (2010) A technical assessment of the utility of reverse phase protein arrays for the study of the functional proteome in non-microdissected human breast cancers. Clin Proteomics 6(4):129–151

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Burger RA et al (2007) Phase II trial of bevacizumab in persistent or recurrent epithelial ovarian cancer or primary peritoneal cancer: a Gynecologic Oncology Group Study. J Clin Oncol 25(33):5165–5171

    Article  CAS  PubMed  Google Scholar 

  106. Cannistra SA et al (2007) Phase II study of bevacizumab in patients with platinum-resistant ovarian cancer or peritoneal serous cancer. J Clin Oncol 25(33):5180–5186

    Article  CAS  PubMed  Google Scholar 

  107. Machida S et al (2005) Inhibition of peritoneal dissemination of ovarian cancer by tyrosine kinase receptor inhibitor SU6668 (TSU-68). Int J Cancer 114(2):224–229

    Article  CAS  PubMed  Google Scholar 

  108. Xu L et al (2000) Inhibition of malignant ascites and growth of human ovarian carcinoma by oral administration of a potent inhibitor of the vascular endothelial growth factor receptor tyrosine kinases. Int J Oncol 16(3):445–454

    CAS  PubMed  Google Scholar 

  109. Lee S et al (2005) Activation of PI3K/Akt pathway by PTEN reduction and PIK3CA mRNA amplification contributes to cisplatin resistance in an ovarian cancer cell line. Gynecol Oncol 97(1):26–34

    Article  CAS  PubMed  Google Scholar 

  110. Peng DJ et al (2010) Role of the Akt/mTOR survival pathway in cisplatin resistance in ovarian cancer cells. Biochem Biophys Res Commun 394(3):600–605

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Yang X et al (2006) Akt-mediated cisplatin resistance in ovarian cancer: modulation of p53 action on caspase-dependent mitochondrial death pathway. Cancer Res 66(6):3126–3136

    Article  CAS  PubMed  Google Scholar 

  112. Alaiya AA et al (1999) Two-dimensional gel analysis of protein expression in ovarian tumors shows a low degree of intratumoral heterogeneity. Electrophoresis 20(4–5):1039–1046

    Article  CAS  PubMed  Google Scholar 

  113. Rubin SC et al (1991) Analysis of antigen expression at multiple tumor sites in epithelial ovarian cancer. Am J Obstet Gynecol 164(2):558–563

    Article  CAS  PubMed  Google Scholar 

  114. Kobel M et al (2011) Biomarker expression in pelvic high-grade serous carcinoma: comparison of ovarian and omental sites. Int J Gynecol Pathol 30(4):366–371

    Article  PubMed  Google Scholar 

  115. Abelson S et al (2012) Intratumoral heterogeneity in the self-renewal and tumorigenic differentiation of ovarian cancer. Stem Cells 30(3):415–424

    Article  CAS  PubMed  Google Scholar 

  116. Zborovskaya I et al (1999) Somatic genetic alterations (LOH) in benign, borderline and invasive ovarian tumours: intratumoral molecular heterogeneity. Int J Cancer 82(6):822–826

    Article  CAS  PubMed  Google Scholar 

  117. Gerlinger M et al (2012) Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med 366(10):883–892

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The studies summarized in this article were in part funded by Deutsche Forschungsgemeinschaft (DFG) Grant No SA1698/1–2 awarded to Stefanie Avril. The author would like to thank her mentors, Professors Heinz Hoefler, Markus Schwaiger, Marion Kiechle, Carlos Caldas, and Eric Miska, as well as collaborators and co-authors on the original publications of this work: Mithu Raychaudhuri and Theresa Buchner (heterogeneity of miRNAs in breast cancer), Karl-Friedrich Becker and Katharina Malinowsky (intratumoral heterogeneity of protein expression), Barbara Schmalfeldt and Holger Bronger (treatment response and recurrence risk in ovarian cancer and borderline tumors). Stefanie Avril is currently supported by the Clinical and Translational Science Collaborative of Cleveland (KL2TR000440) from the National Center for Advancing Translational Sciences (NCATS) component of the National Institutes of Health (NIH).

Author information

Authors and Affiliations

  1. Institute of Pathology, Technische Universität München, Munich, Germany

    S. Avril

  2. Department of Pathology, Case Western Reserve University, University Hospitals Cleveland Medical Center, 11100 Euclid Ave., 44106, Cleveland, OH, USA

    S. Avril

Authors
  1. S. Avril
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S. Avril.

Ethics declarations

Conflict of interest

S. Avril declares that she has no competing interests.

This article does not contain any studies with human participants or animals performed by any of the authors.

The supplement containing this article is not sponsored by industry

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Avril, S. Histopathological markers of treatment response and recurrence risk in ovarian cancers and borderline tumors. Pathologe 38 (Suppl 2), 180–191 (2017). https://doi.org/10.1007/s00292-017-0375-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00292-017-0375-9

Keywords

Schlüsselwörter

Search

Navigation