Skip to main content

Advertisement

SpringerLink
Log in

Expression profiling identifies genes that predict recurrence of breast cancer after adjuvant CMF-based chemotherapy

  • Preclinical study
  • Published:
Breast Cancer Research and Treatment Aims and scope Submit manuscript

Abstract

Cyclophosphamide, methotrexate and 5-fluorouracile (CMF)-based chemotherapy for adjuvant treatment of breast cancer reduces the risk of relapse. In this exploratory study, we tested the feasibility of identifying molecular markers of recurrence in CMF-treated patients. Using Affymetrix U133A GeneChips, RNA samples from 19 patients with primary breast cancer who had been uniformly treated with adjuvant CMF chemotherapy were analyzed. Two supervised class prediction approaches were used to identify gene markers that can best discriminate between patients who would experience relapse and patients who would remain disease-free. An additional independent validation set of 51 patients and 21 genes were analyzed by quantitative RT-PCR. Applying different algorithms to evaluate our microarray data, we identified two gene expression signatures of 21 and 12 genes containing eight overlapping genes, that predict recurrence in 19 cases with high accuracy (94%). Quantitative RT-PCR demonstrated that six genes from the combined signatures (CXCL9, ITSN2, GNAI2, H2AFX, INDO, and MGC10986) were significantly differentially expressed in the recurrence versus the non-recurrence group of the 19 cases and the independent breast cancer patient cohort (n = 51) treated with CMF. High expression levels of CXCL9, ITSN2, and GNAI2 were associated with prolonged disease-free survival (DFS) (P = 0.029, 0.018 and 0.032, respectively). When patients were stratified by combined CXCL9/ITSN2 or CXCL9/FLJ22028 tumor levels, they exhibited significantly different disease-free survival curves (P = 0.0073 and P = 0.005, respectively). Finally, the CXCL9/ITSN2 and CXCL9/FLJ22028 ratio was an independent prognostic factor (P = 0.034 and P = 0.003, respectively) for DFS by multivariate Cox analysis in the 70-patient cohort. Our data highlight the feasibility of a prognostic assay that is applicable to therapeutic decision-making for breast cancer. Whether the biomarker profile is chemotherapy-specific or whether it is a more general indicator of bad prognosis of breast cancer patients remains to be explored.

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

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Eifel P, Axelson JA, Costa J, Crowley J, Curran WJ Jr, Deshler A et al (2001) National Institutes of Health Consensus Development Conference Statement: adjuvant therapy for breast cancer, November 1–3, 2000. J Natl Cancer Inst 93:979–989. doi:10.1093/jnci/93.13.979

    Article  CAS  PubMed  Google Scholar 

  2. Early Breast Cancer Trialists’ Collaborative Group (1992) Systemic treatment of early breast cancer by hormonal, cytotoxic, or immune therapy. 133 randomised trials involving 31,000 recurrences and 24,000 deaths among 75,000 women. Lancet 339:1–15

    Google Scholar 

  3. Early Breast Cancer Trialists’ Collaborative Group (1998) Polychemotherapy for early breast cancer: an overview of the randomised trials. Lancet 352:930–942. doi:10.1016/S0140-6736(98)03301-7

    Article  Google Scholar 

  4. Bonadonna G, Valagussa P, Moliterni A, Zambetti M, Brambilla C (1995) Adjuvant cyclophosphamide, methotrexate, and fluorouracil in node-positive breast cancer: the results of 20 years of follow-up. N Engl J Med 332:901–906. doi:10.1056/NEJM199504063321401

    Article  CAS  PubMed  Google Scholar 

  5. Levine MN, Bramwell VH, Pritchard KI, Norris BD, Shepherd LE, Abu-Zahra H et al (1998) Randomized trial of intensive cyclophosphamide, epirubicin, and fluorouracil chemotherapy compared with cyclophosphamide, methotrexate, and fluorouracil in premenopausal women with node-positive breast cancer. National Cancer Institute of Canada Clinical Trials Group. J Clin Oncol 16:2651–2658

    CAS  PubMed  Google Scholar 

  6. Goldhirsch A, Glick JH, Gelber RD, Coates AS, Thurlimann B, Senn HJ (2005) Meeting highlights: international expert consensus on the primary therapy of early breast cancer 2005. Ann Oncol 16:1569–1583. doi:10.1093/annonc/mdi326

    Article  CAS  PubMed  Google Scholar 

  7. Pritchard KI, Shepherd LE, O’Malley FP, Andrulis IL, Tu D, Bramwell VH et al (2006) HER2 and responsiveness of breast cancer to adjuvant chemotherapy. N Engl J Med 354:2103–2111. doi:10.1056/NEJMoa054504

    Article  CAS  PubMed  Google Scholar 

  8. Askmalm MS, Carstensen J, Nordenskjold B, Olsson B, Rutqvist LE, Skoog L et al (2004) Mutation and accumulation of p53 related to results of adjuvant therapy of postmenopausal breast cancer patients. Acta Oncol 43:235–244. doi:10.1080/02841860410029474

    Article  CAS  PubMed  Google Scholar 

  9. Andersson J, Larsson L, Klaar S, Holmberg L, Nilsson J, Inganas M et al (2005) Worse survival for TP53 (p53)-mutated breast cancer patients receiving adjuvant CMF. Ann Oncol 16:743–748. doi:10.1093/annonc/mdi150

    Article  CAS  PubMed  Google Scholar 

  10. Paik S, Shak S, Tang G, Kim C, Baker J, Cronin M et al (2004) A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med 351:2817–2826. doi:10.1056/NEJMoa041588

    Article  CAS  PubMed  Google Scholar 

  11. Elston CW, Ellis IO (1991) Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experience from a large study with long-term follow-up. Histopathology 19:403–410. doi:10.1111/j.1365-2559.1991.tb00229.x

    Article  CAS  PubMed  Google Scholar 

  12. Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP et al (1999) Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286:531–537. doi:10.1126/science.286.5439.531

    Article  CAS  PubMed  Google Scholar 

  13. Tamayo P, Slonim D, Mesirov J, Zhu Q, Kitareewan S, Dmitrovsky E et al (1999) Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc Natl Acad Sci USA 96:2907–2912. doi:10.1073/pnas.96.6.2907

    Article  CAS  PubMed  Google Scholar 

  14. van‘t Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, Mao M et al (2002) Gene expression profiling predicts clinical outcome of breast cancer. Nature 415:530–536. doi:10.1038/415530a

    Article  Google Scholar 

  15. van de Vijver MJ, He YD, van’t Veer LJ, Dai H, Hart AA, Voskuil DW et al (2002) A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 347:1999–2009. doi:10.1056/NEJMoa021967

    Article  PubMed  Google Scholar 

  16. Hwang D, Schmitt WA, Stephanopoulos G (2002) Determination of minimum sample size and discriminatory expression patterns in microarray data. Bioinformatics 18:1184–119317

    Article  CAS  PubMed  Google Scholar 

  17. Mukherjee S, Tamayo P, Rogers S, Rifkin R, Engle A, Campbell C et al (2003) Estimating dataset size requirements for classifying DNA microarray data. J Comput Biol 10:119–142

    Article  CAS  PubMed  Google Scholar 

  18. Ayers M, Symmans WF, Stec J, Damokosh AI, Clark E, Hess K et al (2004) Gene expression profiles predict complete pathologic response to neoadjuvant paclitaxel and fluorouracil, doxorubicin, and cyclophosphamide chemotherapy in breast cancer. J Clin Oncol 22:2284–2293. doi:10.1200/JCO.2004.05.166

    Article  CAS  PubMed  Google Scholar 

  19. Datta S, Datta S (2003) Comparisons and validation of statistical clustering techniques for microarray gene expression data. Bioinformatics 19:459–466. doi:10.1093/bioinformatics/btg025

    Article  CAS  PubMed  Google Scholar 

  20. Hilsenbeck SG, Friedrichs WE, Schiff R, O’Connell P, Hansen RK, Osborne CK et al (1999) Statistical analysis of array expression data as applied to the problem of tamoxifen resistance. J Natl Cancer Inst 91:453–459. doi:10.1093/jnci/91.5.453

    Article  CAS  PubMed  Google Scholar 

  21. Huang E, Cheng SH, Dressman H, Pittman J, Tsou MH, Horng CF et al (2003) Gene expression predictors of breast cancer outcomes. Lancet 361:1590–1596. doi:10.1016/S0140-6736(03)13308-9

    Article  CAS  PubMed  Google Scholar 

  22. Sotiriou C, Neo SY, McShane LM, Korn EL, Long PM, Jazaeri A et al (2003) Breast cancer classification and prognosis based on gene expression profiles from a population-based study. Proc Natl Acad Sci USA 100:10393–10398. doi:10.1073/pnas.1732912100

    Article  CAS  PubMed  Google Scholar 

  23. Sgadari C, Farber JM, Angiolillo AL, Liao F, Teruya-Feldstein J, Burd PR et al (1997) Mig, the monokine induced by interferon-gamma, promotes tumor necrosis in vivo. Blood 89:2635–2643

    CAS  PubMed  Google Scholar 

  24. Dorsey R, Kundu N, Yang Q, Tannenbaum CS, Sun H, Hamilton TA et al (2002) Immunotherapy with interleukin-10 depends on the CXC chemokines inducible protein-10 and monokine induced by IFN-gamma. Cancer Res 62:2606–2610

    CAS  PubMed  Google Scholar 

  25. Kondo T, Ito F, Nakazawa H, Horita S, Osaka Y, Toma H (2004) High expression of chemokine gene as a favorable prognostic factor in renal cell carcinoma. J Urol 171:2171–2175. doi:10.1097/01.ju.0000127726.25609.87

    Article  CAS  PubMed  Google Scholar 

  26. Mochizuki N, Ohba Y, Kiyokawa E, Kurata T, Murakami T, Ozaki T et al (1999) Activation of the ERK/MAPK pathway by an isoform of rap1GAP associated with G alpha(i). Nature 400:891–894. doi:10.1038/23738

    Article  CAS  PubMed  Google Scholar 

  27. Pucharcos C, Estivill X, de la Luna S (2000) Intersectin 2, a new multimodular protein involved in clathrin-mediated endocytosis. FEBS Lett 478:43–51. doi:10.1016/S0014-5793(00)01793-2

    Article  CAS  PubMed  Google Scholar 

  28. Schafer DA (2002) Coupling actin dynamics and membrane dynamics during endocytosis. Curr Opin Cell Biol 14:76–81. doi:10.1016/S0955-0674(01)00297-6

    Article  CAS  PubMed  Google Scholar 

  29. Niméus-Malmström E, Ritz C, Edén P, Johnsson A, Ohlsson M, Strand C et al (2006) Gene expression profiles and conventional clinical markers to predict distant recurrences for premenopausal breast cancer patients after adjuvant chemotherapy. Eur J Cancer 42:2729–2737. doi:10.1016/j.ejca.2006.06.031

    Article  PubMed  Google Scholar 

  30. Paik S, Tang G, Shak S, Kim C, Baker J, Kim W et al (2006) Gene expression and benefit of chemotherapy in women with node-negative, estrogen receptor-positive breast cancer. J Clin Oncol 24:3726–3734. doi:10.1200/JCO.2005.04.7985

    Article  CAS  PubMed  Google Scholar 

  31. Martin M, Villar A, Sole-Calvo A, Gonzalez R, Massuti B, Lizon J et al (2003) Doxorubicin in combination with fluorouracil and cyclophosphamide (i.v. FAC regimen, day 1, 21) versus methotrexate in combination with fluorouracil and cyclophosphamide (i.v. CMF regimen, day 1, 21) as adjuvant chemotherapy for operable breast cancer: a study by the GEICAM group. Ann Oncol 14:833–842. doi:10.1093/annonc/mdg260

    Article  CAS  PubMed  Google Scholar 

  32. Bonadonna G, Zambetti M, Moliterni A, Gianni L, Valagussa P (2004) Clinical relevance of different sequencing of doxorubicin and cyclophosphamide, methotrexate, and fluorouracil in operable breast cancer. J Clin Oncol 22:1614–1620. doi:10.1200/JCO.2004.07.190

    Article  CAS  PubMed  Google Scholar 

  33. Chang JC, Wooten EC, Tsimelzon A, Hilsenbeck SG, Gutierrez MC, Elledge R et al (2003) Gene expression profiling for the prediction of therapeutic response to docetaxel in patients with breast cancer. Lancet 362:362–369. doi:10.1016/S0140-6736(03)14023-8

    Article  CAS  PubMed  Google Scholar 

  34. Thuerigen O, Schneeweiss A, Toedt G, Warnat P, Hahn M, Kramer H et al (2006) Gene expression signature predicting pathologic complete response with gemcitabine, epirubicin, and docetaxel in primary breast cancer. J Clin Oncol 24:1839–1845. doi:10.1200/JCO.2005.04.7019

    Article  CAS  PubMed  Google Scholar 

  35. Rody A, Karn T, Gätje R, Kourtis K, Minckwitz G, Loibl S et al (2006) Gene expression profiles of breast cancer obtained from core cut biopsies before neoadjuvant docetaxel, adriamycin, and cyclophoshamide chemotherapy correlate with routine prognostic markers and could be used to identify predictive signatures. Zentralbl Gynakol 128:76–81. doi:10.1055/s-2006-921508

    Article  CAS  PubMed  Google Scholar 

  36. Rody A, Karn T, Solbach C, Gaetje R, Munnes M, Kissler S et al (2007) The erbB2+ cluster of the intrinsic gene set predicts tumor response of breast cancer patients receiving neoadjuvant chemotherapy with docetaxel, doxorubicin and cyclophosphamide within the GEPARTRIO trial. Breast 16:235–240. doi:10.1016/j.breast.2007.02.006

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

Supported by a grant to M. Kiechle and H. Hoefler from the BMBF (Federal Ministry of Education and Research), Germany, German National Genome Project (KR-S15T03).

Author information

Authors and Affiliations

  1. Institute of Pathology, Technical University Munich, Ismaningerstrasse 22, 81675, Munich, Germany

    Katja Specht, Joerg Naehrig, Rupert Langer & Heinz Hoefler

  2. Department of Obstetrics and Gynecology, Klinikum Rechts der Isar, Technical University Munich, 81675, Munich, Germany

    Nadia Harbeck, Katja Annecke, Manfred Schmitt & Marion Kiechle

  3. Institute of Pathology, GSF – National Research Centre for Environment and Health, 85764, Neuherberg, Germany

    Jan Smida, Ulrike Reich & Heinz Hoefler

  4. Institute of Medical Microbiology, Immunology and Hygiene, Technical University Munich, 81675, Munich, Germany

    Joerg Mages

  5. Institute of Medical Statistics and Epidemiology, Klinikum Rechts der Isar, Technical University Munich, 81675, Munich, Germany

    Raymonde Busch

  6. Institute of Medical Statistics, Biometry and Epidemiology, University Hospital Essen, 45122, Essen, Germany

    Elisabeth Kruse

  7. Institute of Cell Biology, IFZ, University Hospital Essen, 45122, Essen, Germany

    Ludger Klein-Hitpass

Authors
  1. Katja Specht
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nadia Harbeck
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jan Smida
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Katja Annecke
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ulrike Reich
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Joerg Naehrig
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Rupert Langer
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Joerg Mages
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Raymonde Busch
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Elisabeth Kruse
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Ludger Klein-Hitpass
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Manfred Schmitt
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Marion Kiechle
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Heinz Hoefler
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Katja Specht.

Electronic supplementary material

Below is the link to the electronic supplementary material.

(PDF 388 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Specht, K., Harbeck, N., Smida, J. et al. Expression profiling identifies genes that predict recurrence of breast cancer after adjuvant CMF-based chemotherapy. Breast Cancer Res Treat 118, 45–56 (2009). https://doi.org/10.1007/s10549-008-0207-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10549-008-0207-y

Keywords

Search

Navigation