Breast Cancer Research and Treatment

, Volume 132, Issue 1, pp 317–326 | Cite as

Prognostic role of CA15.3 in 7942 patients with operable breast cancer

  • M. T. Sandri
  • M. Salvatici
  • E. Botteri
  • R. Passerini
  • L. Zorzino
  • N. Rotmensz
  • A. Luini
  • C. Mauro
  • V. Bagnardi
  • M. C. Cassatella
  • F. Bottari
  • C. Casadio
  • M. Colleoni


To assess the prognostic value of presurgical CA15.3 in a large cohort of patients with early breast cancer. A total of 7.942 consecutive patients with breast cancer operated at the European Institute of Oncology between 1998 and 2005 and with presurgical values of CA 15.3 available were included. We explored patterns of recurrence by baseline CA 15.3 values. Mean CA15.3 was 17.0 U/ml. CA15.3 was associated with age, tumor size, nodal involvement, Ki-67 labeling index, grade, HER2 expression, molecular subtype, and perivascular invasion. CA15.3 was independently associated with distant metastases [HR > 20 U/ml vs. ≤ 20 U/ml: 1.34 (95% CI 1.15–1.56)] and death [HR > 20 U/ml vs. ≤ 20 U/ml: 1.30 (95% CI 1.11–1.53)]. When considering CA15.3 as continuous variable, we observed a constant risk of metastasis and death from the lowest values to about 15–20 U/ml, and then a significantly increasing risk with increasing values of CA15.3. Finally, CA15.3 provided significant additional information to the common prognostic factors to predict the occurrence of metastases (C-index P value 0.04). In patients with operable breast cancer, presurgical CA15.3 value is an independent prognostic factor for metastases and deaths. CA15.3 provides additional information to the common prognostic factors and should be considered in the adjuvant therapeutic algorithm.


Biomarker CA15.3 Early breast cancer Prognostic value Tumor marker Tumor subtypes 





  1. 1.
    Sturgeon C (2002) Practice guidelines for tumor marker use in the clinic. Clin Chem 48:1151–1159PubMedGoogle Scholar
  2. 2.
    Hilkens J, Buijs F, Hilgers J et al (1984) Monoclonal antibodies against human milk fat globule membranes detecting differentiation antigens of mammary gland and its tumor. Int J Cancer 34:197–206PubMedCrossRefGoogle Scholar
  3. 3.
    Wesseling J, van der Valk SW, Hilkens J (1996) A mechanism for inhibition of E-cadherin-mediated cell–cell adhesion by the membrane associated mucin episialian/MUC1. Mol Biol Cell 7:565–577PubMedGoogle Scholar
  4. 4.
    Fung PYS, Longenecker BM (1991) Specific immunosuppressive activity of epiglycanin, a mucin like glycoprotein secreted by a murine mammary adenocarcinoma (TA3-HA). Cancer Res 51:1170–1176PubMedGoogle Scholar
  5. 5.
    Gimmi CD, Morrison BW, Mainprice BA et al (1996) Breast cancer associated antigen, DF3/MUC1, induces apoptosis of activated human T cells. Nat Med 2:1369–1370CrossRefGoogle Scholar
  6. 6.
    Agrawal B, Gendler SJ, Longenecker BM (1998) The biological role of mucins in cellular interactions and immune regulation: prospects for cancer immunotherapy. Mol Med Today 4:397–403PubMedCrossRefGoogle Scholar
  7. 7.
    Lacunza E, Baudis M, Colussi AG, Segal-Eiras A, Croce MV, Abba MC (2010) MUC1 oncogene amplification correlates with protein overexpression in invasive breast carcinoma cells. Cancer Genet Cytogenet 201:102–110PubMedCrossRefGoogle Scholar
  8. 8.
    Duffy MJ (1999) CA 15–3 and related mucins as circulating markers in breast cancer. Ann Clin Biochem 36:579–586PubMedGoogle Scholar
  9. 9.
    Duffy MJ, Shering S, Sherry F et al (2000) CA 15–3: a prognostic marker in breast cancer. Int J Biol Markers 15:330–333PubMedGoogle Scholar
  10. 10.
    Harris L, Fritsche H, Mennel R et al (2007) American society of clinical oncology 2007 update of recommendations for the use of tumor markers in breast cancer. J Clin Oncol 25:5287–5312PubMedCrossRefGoogle Scholar
  11. 11.
    McLaughlin R, McGrath J, Grimes, Given HF (2000) The prognostic value of the tumor marker CA 15–3 at initial diagnosis of patients with breast cancer. Int J Biol Markers 15:340–342PubMedGoogle Scholar
  12. 12.
    Canizares F, Sola J, Perez M et al (2001) Preoperative values of CA15.3 and CEA as prognostic factors in breast cancer: a multivariate analysis. Tumor Biol 22:273–281CrossRefGoogle Scholar
  13. 13.
    Gion M, Boracchi P, Dittardi R et al (2002) Prognostic role of serum CA 15-3 in 362 node-negative breast cancers. An old player for a new game. Eur J Cancer 38:1181–1188PubMedCrossRefGoogle Scholar
  14. 14.
    Ebeling FG, Stieber P, Untch M et al (2002) Serum CEA and CA 15–3 as a prognostic factors in primary breast cancer. Br J Cancer 86:1217–1222PubMedCrossRefGoogle Scholar
  15. 15.
    Kumpulainen EJ, Keskikuru RJ, Johansson RT (2002) Serum tumor marker CA 15–3 and stage are the two most powerful predictors of survival in primary breast cancer. Breast Cancer Res Treat 76:95–102PubMedCrossRefGoogle Scholar
  16. 16.
    Molina R, Filella X, Alicarte J et al (2003) Prospective evaluation of CEA and CA15.3 in patients with locoregionale breast cancer. Anticancer Res 23:1035–1042PubMedGoogle Scholar
  17. 17.
    Duffy MJ, Duggan C, Keane R et al (2004) High preoperative CA 15–3 concentrations predict adverse outcome in node-negative and node-positive breast cancer: study of 600 patients with histologically confirmed breast cancer. Clin Chem 50:559–563PubMedCrossRefGoogle Scholar
  18. 18.
    Martin A, Corte MD, Alvarez AM et al (2006) Prognostic value of pre-operative serum CA15.3 levels in breast cancer. Anticancer Res 26:3965–3972PubMedGoogle Scholar
  19. 19.
    Velaiutham S, Taib NA, Ng KL, Yoong BK, Yip CH (2008) Does the pre-operative value of serum CA15.3 correlate with survival in breast cancer? Asian Pac J Cancer Prev 9:445–448PubMedGoogle Scholar
  20. 20.
    Park BW, Oh JW, Kim JH et al (2008) Preoperative CA 15–3 and CEA serum levels as predictor for breast cancer outcomes. Ann of Oncol 19:675–681CrossRefGoogle Scholar
  21. 21.
    Goldhirsch A, Wood WC, Coates AS et al (2011) 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 22:1736–1747PubMedCrossRefGoogle Scholar
  22. 22.
    Cheang MC, Chia SK, Voduc D et al (2009) Ki67 index, HER2 status, and prognosis of patients with luminal B breast cancer. J Natl Cancer Inst 101:736–750PubMedCrossRefGoogle Scholar
  23. 23.
    Viale G, Regan MM, Maiorano E et al (2007) Adjuvant letrozole versus tamoxifen according to centrally-assessed ERBB2 status for postmenopausal women with endocrine-responsive early breast cancer: supplementary results from the BIG 1–98 randomised trial. J Clin Oncol 25:3846–3852PubMedCrossRefGoogle Scholar
  24. 24.
    Viale G, Giobbie-Hurder A, Regan MM et al (2008) 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 26:5569–5575PubMedCrossRefGoogle Scholar
  25. 25.
    Rasmussen BB, Regan MM, Lykkesfeldt AE et al (2008) Adjuvant letrozole versus tamoxifen according to centrally-assessed ERBB2 status for postmenopausal women with endocrine-responsive early breast cancer: supplementary results from the BIG 1–98 randomised trial. Lancet Oncol 9:23–28PubMedCrossRefGoogle Scholar
  26. 26.
    Gray RJ (1998) A class of K-sample tests for comparing the cumulative incidence of a competing risk. Ann Statist 16:1141–1154CrossRefGoogle Scholar
  27. 27.
    Durrleman S, Simon R (1989) Flexible regression models with cubic splines. Stat Med 8:551–561PubMedCrossRefGoogle Scholar
  28. 28.
    Antolini L, Nam B-H, D’Agostino RB (2004) Inference on correlated discrimination measures in survival analysis: a nonparametric approach. Comm Statist Theory Methods 33:2117–2135CrossRefGoogle Scholar
  29. 29.
    Davis BW, Gelber RD, Goldhirsch A et al (1986) Prognostic significance of tumor grade in clinical trials of adjuvant therapy for breast cancer with axillary lymph node metastasis. Cancer 58:2662–2670PubMedCrossRefGoogle Scholar
  30. 30.
    Layfield LJ, Goldstein N, Perkinson KR, Proia AD (2003) Interlaboratory variation in results from immunohistochemical assessment of estrogen receptor status. Breast J 9:257–259PubMedCrossRefGoogle Scholar
  31. 31.
    Diaz LK, Sneige N (2005) Estrogen receptor analysis for breast cancer: current issues and keys to increasing testing accuracy. Adv Anat Pathol 12:10–19PubMedCrossRefGoogle Scholar
  32. 32.
    Perez EA, Suman VJ, Davidson NE et al (2006) HER2 testing by local, central, and reference laboratories in specimens from the North Central Cancer Treatment Group N9831 intergroup adjuvant trial. J Clin Oncol 24:3032–3038PubMedCrossRefGoogle Scholar
  33. 33.
    Mukhopadhyay P, Chakraborty S, Ponnusamy MP et al (2011) Mucins in the pathogenesis of breast cancer: implications in diagnosis, prognosis and therapy. Biochim Biophys Acta 1815:224–240PubMedGoogle Scholar
  34. 34.
    Perou C, Sorlie T, Elsen MB et al (2000) Molecular portraits of human breast tumours. Nature 406:747–752PubMedCrossRefGoogle Scholar
  35. 35.
    Sorlie T, Perou AM, Tibshirani R et al (2001) Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci USA 98:10869–10874PubMedCrossRefGoogle Scholar
  36. 36.
    Sotiriou C, Pusztai L (2009) Gene-expression signatures in breast cancer. N Engl J Med 360:790–800PubMedCrossRefGoogle Scholar
  37. 37.
    Viale G, Rotmensz N, Maisonneuve P et al (2009) Invasive ductal carcinoma of the breast with the “triple-negative” phenotype: prognostic implications of EGFR immunoreactivity. Breast Cancer Res Treat 116:317–328PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC. 2011

Authors and Affiliations

  • M. T. Sandri
    • 1
  • M. Salvatici
    • 1
  • E. Botteri
    • 2
    • 3
  • R. Passerini
    • 1
  • L. Zorzino
    • 1
  • N. Rotmensz
    • 2
  • A. Luini
    • 4
  • C. Mauro
    • 1
  • V. Bagnardi
    • 2
    • 5
  • M. C. Cassatella
    • 1
  • F. Bottari
    • 1
  • C. Casadio
    • 6
  • M. Colleoni
    • 7
  1. 1.Division of Laboratory MedicineEuropean Institute of OncologyMilanItaly
  2. 2.Division of Epidemiology and BiostatisticsEuropean Institute of OncologyMilanItaly
  3. 3.Institute of Medical Statistics and Biometrics ‘G. A. Maccacaro’University of MilanMilanItaly
  4. 4.Division of SenologyEuropean Institute of OncologyMilanItaly
  5. 5.Department of StatisticsUniversity of Milan-BicoccaMilanItaly
  6. 6.Unit of Diagnostic Cytology, Division of Pathology and Laboratory MedicineEuropean Institute of OncologyMilanItaly
  7. 7.Research Unit in Medical Senology, Department of MedicineEuropean Institute of OncologyMilanItaly

Personalised recommendations