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Frauenheilkunde up2date 2011; 5(5): 277-297
DOI: 10.1055/s-0031-1283735
Allgemeine Gynäkologie und gynäkologische Onkologie
© Georg Thieme Verlag KG Stuttgart · New York

Neue prognostische und prädiktive Faktoren beim Mammakarzinom

M. Schmidt
,
H. Kölbl
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Publication Date:
18 October 2011 (online)

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Kernaussagen

Für Patientinnen mit Mammakarzinom haben Fortschritte bei der Früherkennung und der postoperative Einsatz medikamentöser adjuvanter Therapien das Überleben in der nodal-positiven sowie in der nodal-negativen Situation verbessert.

Für die Auswahl der geeigneten adjuvanten Therapiestrategie muss eine individuelle Risikoabschätzung erfolgen. Wichtigster pathologischer Risikofaktor ist dabei der axilläre Nodalstatus. Für nodal-negative Patientinnen gibt es zur Risikoklassifikation jedoch ebenfalls zusätzliche prognostische und prädiktive Faktoren sowie Analysealgorithmen zur Risikoberechnung. Letztere umfassen das internetbasierte System Adjuvant! und Multiparameter-Genexpressionsanalysen (70-Gene-Signatur, Recurrence-Score).

Prognostische Faktoren schätzen die Prognose ohne spezielle Berücksichtigung einer adjuvanten medikamentösen Therapie ab, während prädiktive Faktoren dazu dienen, die Prognose unter Berücksichtigung einer speziellen adjuvanten Therapie abzuschätzen. Zu den derzeit am besten untersuchten molekularen Tumormarkern gehören ER, Ki-67, HER-2, TOP2A, EpCAM, uPA / PAI-1. Sie haben z. T. prognostische, aber z. T. auch prädiktive Bedeutung für die Entscheidung der geeigneten adjuvanten Therapie.

Die derzeit laufende MINDACT-Studie untersucht und vergleicht prospektiv das Risikoprofil sowohl durch klinisch-pathologische Risikoabschätzungen mit Adjuvant! als auch durch Analysen der 70-Gene-Signatur.

 

  • Literatur

  • 1 Peto R, Boreham J, Clarke M et al. UK and USA breast cancer deaths down 25 % in year 2000 at ages 20–69 years. Lancet 2000; 355: 1822
    Google Scholar
  • 2 Berry DA, Cronin KA, Plevritis SK et al. Effect of screening and adjuvant therapy on mortality from breast cancer. N Engl J Med 2005; 353: 1784-1792
    CrossrefGoogle Scholar
  • 3 Early Breast Cancer Trialists’ Collaborative Group. Effects of chemotherapy and hormonal therapy for early breast cancer on recurrence and 15-year survival: an overview of the randomised trials. Lancet 2005; 365: 1687-1717
    CrossrefGoogle Scholar
  • 4 Goldhirsch A, Ingle JN, Gelber RD et al. Thresholds for therapies: highlights of the St Gallen International Expert Consensus on the primary therapy of early breast cancer 2009. Ann Oncol 2009; 20: 1319-1329
    CrossrefGoogle Scholar
  • 5 Fisher ER, Costantino J, Fisher B et al. Pathologic findings from the National Surgical Adjuvant Breast Project (Protocol 4). Discriminants for 15-year survival. National Surgical Adjuvant Breast and Bowel Project Investigators. Cancer 1993; 71: 2141-2150
    Google Scholar
  • 6 Chia SK, Speers CH, Bryce CJ et al. Ten-year outcomes in a population-based cohort of node-negative, lymphatic, and vascular invasion-negative early breast cancers without adjuvant systemic therapies. J Clin Oncol 2004; 22: 1630-1637
    CrossrefGoogle Scholar
  • 7 Goldhirsch A, Coates AS, Gelber RD et al. First – select the target: better choice of adjuvant treatments for breast cancer patients. Ann Oncol 2006; 17: 1772-1776
    CrossrefGoogle Scholar
  • 8 Ravdin PM, Siminoff LA, Davis GJ et al. Computer program to assist in making decisions about adjuvant therapy for women with early breast cancer. J Clin Oncol 2001; 19: 980-991
    Google Scholar
  • 9 Olivotto IA, Bajdik CD, Ravdin PM et al. Population-based validation of the prognostic model ADJUVANT! for early breast cancer. J Clin Oncol 2005; 23: 2716-2725
    CrossrefPubMedGoogle Scholar
  • 10 Mook S, Schmidt MK, Rutgers EJ et al. Calibration and discriminatory accuracy of prognosis calculation for breast cancer with the online Adjuvant! program: a hospital-based retrospective cohort study. Lancet Oncol 2009; 10: 1070-1076
    CrossrefGoogle Scholar
  • 11 Ozanne EM, Braithwaite D, Sepucha K et al. Sensitivity to input variability of the Adjuvant! Online breast cancer prognostic model. J Clin Oncol 2009; 27: 214-219
    CrossrefGoogle Scholar
  • 12 Schmidt M, Victor A, Bratzel D et al. Long-term outcome prediction by clinicopathological risk classification algorithms in node-negative breast cancer – comparison between Adjuvant! St Gallen, and a novel risk algorithm used in the prospective randomized Node-Negative-Breast Cancer-3 (NNBC-3) trial. Ann Oncol 2009; 20: 258-264
    CrossrefGoogle Scholar
  • 13 Jung S, Han W, Lee JW et al. Ki-67 expression gives additional prognostic information on St  Gallen 2007 and Adjuvant! Online risk categories in early breast cancer. Ann Surg Oncol 2009; 16: 1112-1121
    CrossrefGoogle Scholar
  • 14 Viale G, Regan MM, Mastropasqua MG et al. Predictive value of tumor Ki-67 expression in two randomized trials of adjuvant chemoendocrine therapy for node-negative breast cancer. J Natl Cancer Inst 2008; 100: 207-212
    CrossrefPubMedGoogle Scholar
  • 15 Urruticoechea A, Smith IE, Dowsett M. Proliferation marker Ki-67 in early breast cancer. J Clin Oncol 2005; 23: 7212-7220
    CrossrefGoogle Scholar
  • 16 Petit T, Wilt M, Velten M et al. Comparative value of tumour grade, hormonal receptors, Ki-67, HER-2 and topoisomerase II alpha status as predictive markers in breast cancer patients treated with neoadjuvant anthracycline-based chemotherapy. Eur J Cancer 2004; 40: 205-211
    CrossrefGoogle Scholar
  • 17 Chang J, Powles TJ, Allred DC et al. Prediction of clinical outcome from primary tamoxifen by expression of biologic markers in breast cancer patients. Clin Cancer Res 2000; 6: 616-621
    Google Scholar
  • 18 Viale G, Giobbie-Hurder A, Regan MM et al. Prognostic and predictive value of centrally reviewed Ki-67 labeling index in postmenopausal women with endocrine-responsive breast cancer: results from Breast International Group Trial 1–98 comparing adjuvant tamoxifen with letrozole. J Clin Oncol 2008; 26: 5569-5575
    CrossrefGoogle Scholar
  • 19 Hammond MEH, Hayes DF, Dowsett M et al. American Society of Clinical Oncology / College Of American Pathologists guideline recommendations for immunohistochemical testing of estrogen and progesterone receptors in breast cancer. J Clin Oncol 2010; 28: 2784-2795
    CrossrefPubMedGoogle Scholar
  • 20 Wolff AC, Hammond MEH, Schwartz JN et al. American Society of Clinical Oncology / College of American Pathologists guideline recommendations for human epidermal growth factor receptor 2 testing in breast cancer. J Clin Oncol 2007; 25: 118-145
    Google Scholar
  • 21 Perou CM, Sørlie T, Eisen MB et al. Molecular portraits of human breast tumours. Nature 2000; 406: 747-752
    CrossrefGoogle Scholar
  • 22 Schmidt M, Böhm D, von Törne C et al. The humoral immune system has a key prognostic impact in node-negative breast cancer. Cancer Res 2008; 68: 5405-5413
    CrossrefPubMedGoogle Scholar
  • 23 Colleoni M, Viale G, Zahrieh D et al. Chemotherapy is more effective in patients with breast cancer not expressing steroid hormone receptors: a study of preoperative treatment. Clin Cancer Res 2004; 10: 6622-6628
    CrossrefPubMedGoogle Scholar
  • 24 Slamon DJ, Clark GM, Wong SG et al. Human breast cancer: correlation of relapse and survival with amplification of the HER-2 / neu oncogene. Science 1987; 235: 177-182
    CrossrefGoogle Scholar
  • 25 Ross JS, Slodkowska EA, Symmans WF et al. The HER-2 receptor and breast cancer: ten years of targeted anti-HER-2 therapy and personalized medicine. Oncologist 2009; 14: 320-368
    CrossrefGoogle Scholar
  • 26 Schmidt M, Lewark B, Kohlschmidt N et al. Long-term prognostic significance of HER-2 / neu in untreated node-negative breast cancer depends on the method of testing. Breast Cancer Res 2005; 7: R256-R266
    CrossrefGoogle Scholar
  • 27 Petry IB, Fieber E, Schmidt M et al. ERBB2 induces an antiapoptotic expression pattern of Bcl-2 family members in node-negative breast cancer. Clin Cancer Res 2010; 16: 451-460
    CrossrefGoogle Scholar
  • 28 de Laurentiis M, Arpino G, Massarelli E et al. A meta-analysis on the interaction between HER-2 expression and response to endocrine treatment in advanced breast cancer. Clin Cancer Res 2005; 11: 4741-4748
    CrossrefGoogle Scholar
  • 29 Pritchard KI, Shepherd LE, O‘Malley FP et al. HER2 and responsiveness of breast cancer to adjuvant chemotherapy. N Engl J Med 2006; 354: 2103-2111
    CrossrefGoogle Scholar
  • 30 Hayes DF, Thor AD, Dressler LG et al. HER2 and response to paclitaxel in node-positive breast cancer. N Engl J Med 2007; 357: 1496-1506
    CrossrefPubMedGoogle Scholar
  • 31 Slamon DJ, Leyland-Jones B, Shak S et al. Use of chemotherapy plus a monoclonal antibody against HER2 for metastatic breast cancer that overexpresses HER2. N Engl J Med 2001; 344: 783-792
    CrossrefGoogle Scholar
  • 32 Dawood S, Broglio K, Buzdar AU et al. Prognosis of women with metastatic breast cancer by HER2 status and trastuzumab treatment: an institutional-based review. J Clin Oncol 2010; 28: 92-98
    CrossrefGoogle Scholar
  • 33 Joensuu H, Kellokumpu-Lehtinen P, Bono P et al. Adjuvant docetaxel or vinorelbine with or without trastuzumab for breast cancer. N Engl J Med 2006; 354: 809-820
    CrossrefGoogle Scholar
  • 34 Litvinov SV, Velders MP, Bakker HA et al. Ep-CAM: a human epithelial antigen is a homophilic cell-cell adhesion molecule. J Cell Biol 1994; 125: 437-446
    CrossrefGoogle Scholar
  • 35 Trzpis M, McLaughlin PMJ, Leij LMFH de et al. Epithelial cell adhesion molecule: more than a carcinoma marker and adhesion molecule. Am J Pathol 2007; 171: 386-395
    CrossrefGoogle Scholar
  • 36 Gastl G, Spizzo G, Obrist P et al. Ep-CAM overexpression in breast cancer as a predictor of survival. Lancet 2000; 356: 1981-1982
    CrossrefGoogle Scholar
  • 37 Schmidt M, Hasenclever D, Schaeffer M et al. Prognostic effect of epithelial cell adhesion molecule overexpression in untreated node-negative breast cancer. Clin Cancer Res 2008; 14: 5849-5855
    CrossrefGoogle Scholar
  • 38 Schmidt M, Petry IB, Böhm D et al. Ep-CAM RNA expression predicts metastasis-free survival in three cohorts of untreated node-negative breast cancer. Breast Cancer Res Treat 2010; 125: 637-646
    Google Scholar
  • 39 Scheulen ME, Dittrich C, Schmidt M et al. An open-label, randomized phase II study of adecatumumab, a fully human anti-EpCAM antibody, as monotherapy in patients with metastatic breast cancer. Ann Oncol 2010; 21: 275-282
    CrossrefGoogle Scholar
  • 40 Reis-Filho JS, Tutt ANJ. Triple negative tumours: a critical review. Histopathol 2008; 52: 108-118
    Google Scholar
  • 41 Rakha EA, El-Sayed ME, Green AR et al. Prognostic markers in triple-negative breast cancer. Cancer 2007; 109: 25-32
    CrossrefGoogle Scholar
  • 42 Dent R, Trudeau M, Pritchard KI et al. Triple-negative breast cancer: clinical features and patterns of recurrence. Clin Cancer Res 2007; 13: 4429-4434
    CrossrefPubMedGoogle Scholar
  • 43 Liedtke C, Mazouni C, Hess KR et al. Response to neoadjuvant therapy and long-term survival in patients with triple-negative breast cancer. J Clin Oncol 2008; 26: 1275-1281
    CrossrefGoogle Scholar
  • 44 Dent R, Hanna WM, Trudeau M et al. Pattern of metastatic spread in triple-negative breast cancer. Breast Cancer Res Treat 2009; 115: 423-428
    CrossrefGoogle Scholar
  • 45 Rakha EA, Tan DSP, Foulkes WD et al. Are triple-negative tumours and basal-like breast cancer synonymous?. Breast Cancer Res 2007; 9: 404-404 author reply 405
    CrossrefGoogle Scholar
  • 46 Kreike B, van Kouwenhove M, Horlings H et al. Gene expression profiling and histopathological characterization of triple-negative/basal-like breast carcinomas. Breast Cancer Res 2007; 9: R65
    CrossrefGoogle Scholar
  • 47 Rakha EA, Elsheikh SE, Aleskandarany MA et al. Triple-negative breast cancer: distinguishing between basal and nonbasal subtypes. Clin Cancer Res 2009; 15: 2302-2310
    CrossrefGoogle Scholar
  • 48 O‘Shaughnessy J, Osborne C, Pippen JE et al. Iniparib plus chemotherapy in metastatic triple-negative breast cancer. N Engl J Med 2011; 364: 205-214
    CrossrefGoogle Scholar
  • 49 Bartlett JMS, Munro A, Cameron DA et al. Type 1 receptor tyrosine kinase profiles identify patients with enhanced benefit from anthracyclines in the BR9601 adjuvant breast cancer chemotherapy trial. J Clin Oncol 2008; 26: 5027-5035
    CrossrefGoogle Scholar
  • 50 Knoop AS, Knudsen H, Balslev E et al. Retrospective analysis of topoisomerase IIa amplifications and deletions as predictive markers in primary breast cancer patients randomly assigned to cyclophosphamide, methotrexate, and fluorouracil or cyclophosphamide, epirubicin, and fluorouracil: Danish Breast Cancer Cooperative Group. J Clin Oncol 2005; 23: 7483-7490
    CrossrefGoogle Scholar
  • 51 Bartlett JMS, Munro AF, Dunn JA et al. Predictive markers of anthracycline benefit: a prospectively planned analysis of the UK National Epirubicin Adjuvant Trial (NEAT / BR9601). Lancet Oncol 2010; 11: 266-274
    CrossrefGoogle Scholar
  • 52 Brase JC, Schmidt M, Fischbach T et al. ERBB2 and TOP2A in breast cancer: a comprehensive analysis of gene amplification, RNA levels, and protein expression and their influence on prognosis and prediction. Clin Cancer Res 2010; 16: 2391-2401
    CrossrefGoogle Scholar
  • 53 McShane LM, Altman DG, Sauerbrei W et al. Reporting recommendations for tumor marker prognostic studies. J Clin Oncol 2005; 23: 9067-9072
    CrossrefGoogle Scholar
  • 54 Hayes DF, Bast RC, Desch CE et al. Tumor marker utility grading system: a framework to evaluate clinical utility of tumor markers. J Natl Cancer Inst 1996; 88: 1456-1466
    CrossrefGoogle Scholar
  • 55 Simon RM, Paik S, Hayes DF. Use of archived specimens in evaluation of prognostic and predictive biomarkers. J Natl Cancer Inst 2009; 101: 1446-1452
    CrossrefPubMedGoogle Scholar
  • 56 Harris L, Fritsche H, Mennel R et al. American Society of Clinical Oncology 2007 update of recommendations for the use of tumor markers in breast cancer. J Clin Oncol 2007; 25: 5287-5312
    CrossrefGoogle Scholar
  • 57 Jänicke F, Schmitt M, Pache L et al. Urokinase (uPA) and its inhibitor PAI-1 are strong and independent prognostic factors in node-negative breast cancer. Breast Cancer Res Treat 1993; 24: 195-208
    CrossrefGoogle Scholar
  • 58 Look MP, van Putten WLJ, Duffy MJ et al. Pooled analysis of prognostic impact of urokinase-type plasminogen activator and its inhibitor PAI-1 in 8377 breast cancer patients. J Natl Cancer Inst 2002; 94: 116-128
    CrossrefGoogle Scholar
  • 59 Thomssen C, Harbeck N, Dittmer J et al. Feasibility of measuring the prognostic factors uPA and PAI-1 in core needle biopsy breast cancer specimens. J Natl Cancer Inst 2009; 101: 1028-1029
    CrossrefGoogle Scholar
  • 60 Sørlie T, Perou CM, Tibshirani R et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci USA 2001; 98: 10869-10874
    CrossrefGoogle Scholar
  • 61 Smid M, Wang Y, Zhang Y et al. Subtypes of breast cancer show preferential site of relapse. Cancer Res 2008; 68: 3108-3114
    CrossrefGoogle Scholar
  • 62 Rouzier R, Perou CM, Symmans WF et al. Breast cancer molecular subtypes respond differently to preoperative chemotherapy. Clin Cancer Res 2005; 11: 5678-5685
    CrossrefGoogle Scholar
  • 63 Parker JS, Mullins M, Cheang MCU et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. J Clin Oncol 2009; 27: 1160-1167
    CrossrefGoogle Scholar
  • 64 Hu Z, Fan C, Oh DS et al. The molecular portraits of breast tumors are conserved across microarray platforms. BMC Genomics 2006; 7: 96
    CrossrefGoogle Scholar
  • 65 van Veer LJ, Dai H, van de Vijver MJ et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature 2002; 415: 530-536
    CrossrefGoogle Scholar
  • 66 van de Vijver MJ, He YD, Veer van‘t LJ et al. A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 2002; 347: 1999-2009
    CrossrefGoogle Scholar
  • 67 Buyse M, Loi S, Veer van‘t L et al. Validation and clinical utility of a 70-gene prognostic signature for women with node-negative breast cancer. J Natl Cancer Inst 2006; 98: 1183-1192
    CrossrefGoogle Scholar
  • 68 Mook S, Schmidt MK, Weigelt B et al. The 70-gene prognosis signature predicts early metastasis in breast cancer patients between 55 and 70 years of age. Ann Oncol 2010; 21: 717-722
    CrossrefGoogle Scholar
  • 69 Mook S, Schmidt MK, Viale G et al. The 70-gene prognosis-signature predicts disease outcome in breast cancer patients with 1–3 positive lymph nodes in an independent validation study. Breast Cancer Res Treat 2009; 116: 295-302
    CrossrefGoogle Scholar
  • 70 Cardoso F, Veer van‘t L, Rutgers E et al. Clinical application of the 70-gene profile: the MINDACT trial. J Clin Oncol 2008; 26: 729-735
    CrossrefGoogle Scholar
  • 71 Paik S, Shak S, Tang G et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med 2004; 351: 2817-2826
    CrossrefPubMedGoogle Scholar
  • 72 Paik S, Tang G, Shak S et al. Gene expression and benefit of chemotherapy in women with node-negative, estrogen receptor-positive breast cancer. J Clin Oncol 2006; 24: 3726-3734
    CrossrefPubMedGoogle Scholar
  • 73 Albain KS, Barlow WE, Shak S et al. Prognostic and predictive value of the 21-gene recurrence score assay in postmenopausal women with node-positive, oestrogen-receptor-positive breast cancer on chemotherapy: a retrospective analysis of a randomised trial. Lancet Oncol 2010; 11: 55-65
    CrossrefGoogle Scholar
  • 74 Dowsett M, Cuzick J, Wale C et al. Prediction of risk of distant recurrence using the 21-gene recurrence score in node-negative and node-positive postmenopausal patients with breast cancer treated with anastrozole or tamoxifen: a TransATAC study. J Clin Oncol 2010; 28: 1829-1834
    CrossrefPubMedGoogle Scholar
  • 75 Tang G, Shak S, Paik S et al. Comparison of the prognostic and predictive utilities of the 21-gene Recurrence Score assay and Adjuvant! for women with node-negative, ER-positive breast cancer: results from NSABP B-14 and NSABP B-20. Breast Cancer Res Treat 2011; 127: 133-142
    CrossrefGoogle Scholar
  • 76 Sparano JA, Paik S. Development of the 21-gene assay and its application in clinical practice and clinical trials. J Clin Oncol 2008; 26: 721-728
    CrossrefPubMedGoogle Scholar
  • 77 Sotiriou C, Pusztai L. Gene-expression signatures in breast cancer. N Engl J Med 2009; 360: 790-800
    CrossrefGoogle Scholar
  • 78 Fan C, Oh DS, Wessels L et al. Concordance among gene-expression-based predictors for breast cancer. N Engl J Med 2006; 355: 560-569
    CrossrefGoogle Scholar
  • 79 Calabrò A, Beissbarth T, Kuner R et al. Effects of infiltrating lymphocytes and estrogen receptor on gene expression and prognosis in breast cancer. Breast Cancer Res Treat 2009; 116: 69-77
    CrossrefGoogle Scholar
  • 80 Rody A, Holtrich U, Pusztai L et al. T-cell metagene predicts a favorable prognosis in estrogen receptor-negative and HER2-positive breast cancers. Breast Cancer Res 2009; 11: R15
    CrossrefGoogle Scholar
  • 81 Teschendorff AE, Miremadi A, Pinder SE et al. An immune response gene expression module identifies a good prognosis subtype in estrogen receptor negative breast cancer. Genome Biol 2008; 8: R157
    Google Scholar
  • 82 Alexe G, Dalgin GS, Scanfeld D et al. High expression of lymphocyte-associated genes in node-negative HER2+ breast cancers correlates with lower recurrence rates. Cancer Res 2007; 67: 10669-10676
    CrossrefGoogle Scholar
  • 83 Denkert C, Loibl S, Noske A et al. Tumor-associated lymphocytes as an independent predictor of response to neoadjuvant chemotherapy in breast cancer. J Clin Oncol 2010; 28: 105-113
    CrossrefGoogle Scholar
  • 84 Bianchini G, Qi Y, Alvarez RH et al. Molecular anatomy of breast cancer stroma and its prognostic value in estrogen receptor-positive and -negative cancers. J Clin Oncol 2010; 28: 4316-4323
    CrossrefGoogle Scholar
  • 85 Schmidt M, Petry IB, Boehm D et al. Immunoglobulin Kappa C has independent prognostic significance in node-negative breast cancer. Cancer Res 2009; 69suppl: 729-729
    Google Scholar
  • 86 Schmidt M, Hengstler JG, von Törne C et al. Coordinates in the universe of node-negative breast cancer revisited. Cancer Res 2009; 69: 2695-2698
    CrossrefGoogle Scholar