Geburtshilfe Frauenheilkd 2012; 72(9): 819-832
DOI: 10.1055/s-0032-1315340
Review
GebFra Science
Georg Thieme Verlag KG Stuttgart · New York

Biomarkers in Breast Cancer – An Update

Biomarker beim Mammakarzinom – ein Update
M. Schmidt
1   Klinik für Geburtshilfe und Frauenkrankheiten, Universitätsmedizin Mainz, Mainz
,
P. A. Fasching
2   Frauenklinik, Universitätsklinikum Erlangen, Erlangen
,
M. W. Beckmann
2   Frauenklinik, Universitätsklinikum Erlangen, Erlangen
,
H. Kölbl
1   Klinik für Geburtshilfe und Frauenkrankheiten, Universitätsmedizin Mainz, Mainz
› Author Affiliations
Further Information

Publication History

received 08 August 2012

accepted 08 August 2012

Publication Date:
27 September 2012 (online)

   Permissions and Reprints

Abstract

The therapy of choice for breast cancer patients requiring adjuvant chemo- or radiotherapy is increasingly guided by the principle of weighing the individual effectiveness of the therapy against the associated side effects. This has only been made possible by the discovery and validation of modern biomarkers. In the last decades and in the last few years some biomarkers have been integrated in clinical practice and a number have been included in modern study concepts. The importance of biomarkers lies not merely in their prognostic value indicating the future course of disease but also in their use to predict patient response to therapy. Due to the many subgroups, mathematical models and computer-assisted analysis are increasingly being used to assess the prognostic information obtained from established clinical and histopathological factors. In addition to describing some recent computer programmes this overview will focus on established molecular markers which have already been extensively validated in clinical practice and on new molecular markers identified by genome-wide studies.

Zusammenfassung

Die Therapiewahl für die Mammakarzinompatientin in der adjuvanten Situation folgt immer mehr dem Prinzip, die individuelle Therapieeffektivität und die Nebenwirkungen gegeneinander abzuwägen. Die Entdeckung und Validierung moderner Biomarker ermöglicht erst dieses Vorgehen. In den letzten Jahrzehnten und insbesondere in den letzten Jahren konnten einige Biomarker in die klinische Praxis und in moderne Studienkonzepte integriert werden. Nicht nur der Vorhersage der Prognose kommt hierbei eine besondere Bedeutung zu, sondern auch der Vorhersage des Therapieansprechens durch Prädiktivfaktoren. Die Nutzung der prognostischen Information aus etablierten, klinischen und histopathologischen Faktoren erfolgt aufgrund der Vielzahl von Untergruppen mehr und mehr in Form von mathematischen Modellen und computergestützter Auswertung. Neben der Darstellung aktueller Programme soll in dieser Übersichtsarbeit des Weiteren der Fokus auf etablierten, molekularen Markern, die bereits eine umfassende klinische Validierung vorweisen können, und neuen molekularen Markern liegen, die durch genomweite Ansätze identifiziert wurden.

Key words

biomarker - Her2/neu (human epidermal growth factor receptor) - breast cancer

Schlüsselwörter

Biomarker - Her-2/neu - Mammakarzinom

Supporting Information

 

  • References

  • 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
    Search in 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
    CrossrefSearch in Google Scholar
  • 3 Fasching PA, Fehm T, Janni W et al. Breast cancer therapy – a state of the art review. Geburtsh Frauenheilk 2010; 70: 875-886
    Thieme ConnectSearch in Google Scholar
  • 4 Kummel S, Kolberg HC, Luftner D et al. Breast cancer 2011 – new aspects. Geburtsh Frauenheilk 2011; 71: 939-953
    Thieme ConnectSearch in Google Scholar
  • 5 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
    CrossrefSearch in Google Scholar
  • 6 Liedtke C, Wolf MK, Kiesel L. New concepts for targeted systemic therapy in breast cancer. Geburtsh Frauenheilk 2010; 70: 625-633
    Thieme ConnectSearch in Google Scholar
  • 7 Peto R, Davies C, Godwin J et al. Comparisons between different polychemotherapy regimens for early breast cancer: meta-analyses of long-term outcome among 100 000 women in 123 randomised trials. Lancet 2012; 379: 432-444
    CrossrefPubMedSearch in Google Scholar
  • 8 Goldhirsch A, Wood WC, Coates AS et al. Strategies for subtypes – dealing with the diversity of breast cancer: highlights of the St. Gallen international expert consensus on the primary therapy of early breast cancer 2011. Ann Oncol 2011; 22: 1736-1747
    CrossrefSearch in Google Scholar
  • 9 Untch M, Gerber B, Mobus V et al. Zurich Consensus: statement of German experts on St. Gallen conference 2011 on breast cancer (Zurich 2011). Geburtsh Frauenheilk 2011; 71: 381-390
    Thieme ConnectSearch in Google Scholar
  • 10 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
    Search in Google Scholar
  • 11 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
    CrossrefPubMedSearch in Google Scholar
  • 12 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
    CrossrefSearch in Google Scholar
  • 13 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
    CrossrefSearch in Google Scholar
  • 14 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
    CrossrefSearch in Google Scholar
  • 15 Wishart GC, Bajdik CD, Dicks E et al. PREDICT Plus: development and validation of a prognostic model for early breast cancer that includes HER2. Br J Cancer 2012;
    Search in Google Scholar
  • 16 Wishart GC, Bajdik CD, Azzato EM et al. A population-based validation of the prognostic model PREDICT for early breast cancer. Eur J Surg Oncol 2011; 37: 411-417
    CrossrefSearch in Google Scholar
  • 17 Wishart GC, Azzato EM, Greenberg DC et al. PREDICT: a new UK prognostic model that predicts survival following surgery for invasive breast cancer. Breast Cancer Res 2010; 12: R1
    CrossrefSearch in Google Scholar
  • 18 Jung SY, 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
    CrossrefSearch in Google Scholar
  • 19 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
    CrossrefPubMedSearch in Google Scholar
  • 20 Urruticoechea A, Smith IE, Dowsett M. Proliferation marker Ki-67 in early breast cancer. J Clin Oncol 2005; 23: 7212-7220
    CrossrefSearch in Google Scholar
  • 21 Cheang MC, Chia SK, Voduc D et al. Ki67 index, HER2 status, and prognosis of patients with luminal B breast cancer. J Natl Cancer Inst 2009; 101: 736-750
    CrossrefSearch in Google Scholar
  • 22 Hammond ME, 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
    CrossrefPubMedSearch in Google Scholar
  • 23 Wolff AC, Hammond ME, 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
    Search in Google Scholar
  • 24 Dowsett M, Nielsen TO, AʼHern R et al. Assessment of Ki67 in breast cancer: recommendations from the International Ki67 in Breast Cancer working group. J Natl Cancer Inst 2011; 103: 1656-1664
    CrossrefPubMedSearch in Google Scholar
  • 25 McShane LM, Altman DG, Sauerbrei W et al. Reporting recommendations for tumor marker prognostic studies. J Clin Oncol 2005; 23: 9067-9072
    CrossrefSearch in Google Scholar
  • 26 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
    CrossrefSearch in Google Scholar
  • 27 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
    CrossrefPubMedSearch in Google Scholar
  • 28 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
    CrossrefSearch in Google Scholar
  • 29 Janicke 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
    CrossrefSearch in Google Scholar
  • 30 Look MP, van Putten WL, 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
    CrossrefSearch in Google Scholar
  • 31 Janicke F, Prechtl A, Thomssen C et al. Randomized adjuvant chemotherapy trial in high-risk, lymph node-negative breast cancer patients identified by urokinase-type plasminogen activator and plasminogen activator inhibitor type 1. J Natl Cancer Inst 2001; 93: 913-920
    CrossrefSearch in Google Scholar
  • 32 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
    CrossrefSearch in Google Scholar
  • 33 Strehl JD, Wachter DL, Fasching PA et al. Invasive breast cancer: recognition of molecular subtypes. Breast Care (Basel) 2011; 6: 258-264
    Search in Google Scholar
  • 34 Sorlie 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
    CrossrefSearch in Google Scholar
  • 35 Smid M, Wang Y, Zhang Y et al. Subtypes of breast cancer show preferential site of relapse. Cancer Res 2008; 68: 3108-3114
    CrossrefSearch in Google Scholar
  • 36 Rouzier R, Perou CM, Symmans WF et al. Breast cancer molecular subtypes respond differently to preoperative chemotherapy. Clin Cancer Res 2005; 11: 5678-5685
    CrossrefSearch in Google Scholar
  • 37 Parker JS, Mullins M, Cheang MC et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. J Clin Oncol 2009; 27: 1160-1167
    CrossrefSearch in Google Scholar
  • 38 Mackay A, Weigelt B, Grigoriadis A et al. Microarray-based class discovery for molecular classification of breast cancer: analysis of interobserver agreement. J Natl Cancer Inst 2011; 103: 662-673
    CrossrefSearch in Google Scholar
  • 39 Haibe-Kains B, Desmedt C, Loi S et al. A three-gene model to robustly identify breast cancer molecular subtypes. J Natl Cancer Inst 2012; 104: 311-325
    CrossrefSearch in Google Scholar
  • 40 vanʼt Veer LJ, Dai H, van de Vijver MJ et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature 2002; 415: 530-536
    CrossrefSearch in Google Scholar
  • 41 van de Vijver MJ, He YD, vanʼt Veer LJ et al. A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 2002; 347: 1999-2009
    CrossrefSearch in Google Scholar
  • 42 Buyse M, Loi S, vanʼt Veer 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
    CrossrefSearch in Google Scholar
  • 43 Cardoso F, vanʼt Veer L, Rutgers E et al. Clinical application of the 70-gene profile: the MINDACT trial. J Clin Oncol 2008; 26: 729-735
    CrossrefSearch in Google Scholar
  • 44 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
    CrossrefPubMedSearch in Google Scholar
  • 45 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
    CrossrefPubMedSearch in Google Scholar
  • 46 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
    CrossrefSearch in Google Scholar
  • 47 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
    CrossrefPubMedSearch in Google Scholar
  • 48 Filipits M, Rudas M, Jakesz R et al. A new molecular predictor of distant recurrence in ER-positive, HER2-negative breast cancer adds independent information to conventional clinical risk factors. Clin Cancer Res 2011; 17: 6012-6020
    CrossrefSearch in Google Scholar
  • 49 Sotiriou C, Pusztai L. Gene-expression signatures in breast cancer. N Engl J Med 2009; 360: 790-800
    CrossrefSearch in Google Scholar
  • 50 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
    CrossrefSearch in Google Scholar
  • 51 Reis-Filho JS, Pusztai L. Gene expression profiling in breast cancer: classification, prognostication, and prediction. Lancet 2011; 378: 1812-1823
    CrossrefSearch in Google Scholar
  • 52 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
    CrossrefSearch in Google Scholar
  • 53 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 2007; 8: R157
    CrossrefSearch in Google Scholar
  • 54 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
    CrossrefSearch in Google Scholar
  • 55 Schmidt M, Bohm D, von Torne C et al. The humoral immune system has a key prognostic impact in node-negative breast cancer. Cancer Res 2008; 68: 5405-5413
    CrossrefPubMedSearch in Google Scholar
  • 56 Schmidt M, Hellwig B, Hammad S et al. A comprehensive analysis of human gene expression profiles identifies stromal immunoglobulin kappa C as a compatible prognostic marker in human solid tumors. Clin Cancer Res 2012; 18: 2695-2703
    CrossrefSearch in Google Scholar
  • 57 Schmidt M, Hengstler JG, von Torne C et al. Coordinates in the universe of node-negative breast cancer revisited. Cancer Res 2009; 69: 2695-2698
    CrossrefSearch in Google Scholar
  • 58 Russnes HG, Navin N, Hicks J et al. Insight into the heterogeneity of breast cancer through next-generation sequencing. J Clin Invest 2011; 121: 3810-3818
    CrossrefSearch in Google Scholar
  • 59 Curtis C, Shah SP, Chin SF et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 2012; 486: 346-352
    Search in Google Scholar
  • 60 Perou CM, Sorlie T, Eisen MB et al. Molecular portraits of human breast tumours. Nature 2000; 406: 747-752
    CrossrefSearch in Google Scholar
  • 61 Early Breast Cancer Trialistsʼ 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
    CrossrefSearch in Google Scholar
  • 62 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
    CrossrefPubMedSearch in Google Scholar
  • 63 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
    CrossrefSearch in Google Scholar
  • 64 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
    CrossrefSearch in Google Scholar
  • 65 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
    Search in Google Scholar
  • 66 Pritchard KI, Shepherd LE, OʼMalley FP et al. HER2 and responsiveness of breast cancer to adjuvant chemotherapy. N Engl J Med 2006; 354: 2103-2111
    CrossrefSearch in Google Scholar
  • 67 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
    CrossrefPubMedSearch in Google Scholar
  • 68 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
    CrossrefSearch in Google Scholar
  • 69 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
    CrossrefSearch in Google Scholar
  • 70 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
    CrossrefSearch in Google Scholar
  • 71 Fasching PA, Heusinger K, Haeberle L et al. Ki67, chemotherapy response, and prognosis in breast cancer patients receiving neoadjuvant treatment. BMC cancer 2011; 11: 486
    CrossrefSearch in Google Scholar
  • 72 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
    Search in Google Scholar
  • 73 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
    CrossrefSearch in Google Scholar
  • 74 Bartlett JM, 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
    CrossrefSearch in Google Scholar
  • 75 Knoop AS, Knudsen H, Balslev E et al. Retrospective analysis of topoisomerase II a 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
    CrossrefSearch in Google Scholar
  • 76 Bartlett JM, 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
    CrossrefSearch in Google Scholar
  • 77 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
    CrossrefSearch in Google Scholar