Breast Cancer Research and Treatment

, Volume 122, Issue 1, pp 211–217 | Cite as

Modeling the relationship between circulating tumour cells number and prognosis of metastatic breast cancer

  • Edoardo Botteri
  • Maria Teresa Sandri
  • Vincenzo Bagnardi
  • Elisabetta Munzone
  • Laura Zorzino
  • Nicole Rotmensz
  • Chiara Casadio
  • Maria Cristina Cassatella
  • Angela Esposito
  • Giuseppe Curigliano
  • Michela Salvatici
  • Elena Verri
  • Laura Adamoli
  • Aron Goldhirsch
  • Franco Nolè
Epidemiology

Abstract

Circulating tumor cell (CTC) count has been shown to be an independent predictor of progression in metastatic breast, prostate, and colorectal cancer. A cutpoint is generally used to identify favorable and unfavorable response groups. In this study, we propose an approach in which the number of CTCs is analyzed as a continuous predictor, to detect the shape of the relationship between CTCs and prognosis of metastatic breast cancer. We evaluated the association of baseline CTC with progression-free survival (PFS) and overall survival (OS) in a series of 80 patients treated for advanced breast cancer at the European Institute of Oncology, Milan. The association between CTCs and prognosis was analyzed with standard categorical survival analysis and spline regression models. At baseline, median age was 55 years; 33 patients were newly diagnosed with metastatic breast cancer (41%), while 28 (35%) and 19 (24%) were pretreated with one and two previous chemotherapy lines, respectively. After a median follow-up of 28 months, 76 disease progressions and 44 deaths were observed. Kaplan–Meier curves showed a clear association between CTCs and PFS (P-value 0.03) and OS (P-value < 0.01). Patients with no CTC at baseline had a significantly better prognosis. When analyzing the CTCs as a continuous variable, we found an increase in risk with increasing number of CTCs, for both PFS and OS. The increase rate lessened after approximately 5 CTCs. CTCs represent a robust prognostic factor in the metastatic breast cancer setting. A nonlinear increase in risk of both progression and death with increasing number of CTCs was observed, with a lessening increase after approximately 5 CTCs. If distinct prognostic groups are to be identified, women with no CTC could plausibly represent a distinct favorable one.

Keywords

Circulating tumor cells Metastatic breast cancer Prognosis 

References

  1. 1.
    Ries LA, MP Eisner, CL Kosary et al Seer cancer statistics review, 1975-2002. http://seer.cancer.gov/csr/1975_2002/
  2. 2.
    American Cancer Society (2008) Cancer facts and figures, 2008. American Cancer Society, OaklandGoogle Scholar
  3. 3.
    Cristofanilli M, Hayes DF, Budd GT, Ellis MJ, Stopeck A, Reuben JM, Doyle GV, Matera J, Allard WJ, Miller MC, Fritsche HA, Hortobagyi GN, Terstappen LW (2005) Circulating tumor cells: a novel prognostic factor for newly diagnosed metastatic breast cancer. J Clin Oncol 23:1420–1430CrossRefPubMedGoogle Scholar
  4. 4.
    Nole F, Munzone E, Zorzino L, Minchella I, Salvatici M, Botteri E, Medici M, Verri E, Adamoli L, Rotmensz N, Goldhirsch A, Sandri MT (2008) Variation of circulating tumor cell levels during treatment of metastatic breast cancer: prognostic and therapeutic implications. Ann Oncol 19:891–897CrossRefPubMedGoogle Scholar
  5. 5.
    Hayes DF, Cristofanilli M, Budd GT, Ellis MJ, Stopeck A, Miller MC, Matera J, Allard WJ, Doyle GV, Terstappen LW (2006) Circulating tumor cells at each follow-up time point during therapy of metastatic breast cancer patients predict progression-free and overall survival. Clin Cancer Res 12:4218–4224CrossRefPubMedGoogle Scholar
  6. 6.
    Budd GT, Cristofanilli M, Ellis MJ, Stopeck A, Borden E, Miller MC, Matera J, Repollet M, Doyle GV, Terstappen LW, Hayes DF (2006) Circulating tumor cells versus imaging—predicting overall survival in metastatic breast cancer. Clin Cancer Res 12:6403–6409CrossRefPubMedGoogle Scholar
  7. 7.
    de Bono JS, Scher HI, Montgomery RB, Parker C, Miller MC, Tissing H, Doyle GV, Terstappen LW, Pienta KJ, Raghavan D (2008) Circulating tumor cells predict survival benefit from treatment in metastatic castration-resistant prostate cancer. Clin Cancer Res 14:6302–6309CrossRefPubMedGoogle Scholar
  8. 8.
    Scher HI, Jia X, de Bono JS, Fleisher M, Pienta KJ, Raghavan D, Heller G (2009) Circulating tumour cells as prognostic markers in progressive, castration-resistant prostate cancer: a reanalysis of IMMC38 trial data. Lancet Oncol 10:233–239CrossRefPubMedGoogle Scholar
  9. 9.
    Shaffer DR, Leversha MA, Danila DC, Lin O, Gonzalez-Espinoza R, Gu B, Anand A, Smith K, Maslak P, Doyle GV, Terstappen LW, Lilja H, Heller G, Fleisher M, Scher HI (2007) Circulating tumor cell analysis in patients with progressive castration-resistant prostate cancer. Clin Cancer Res 13:2023–2029CrossRefPubMedGoogle Scholar
  10. 10.
    Danila DC, Heller G, Gignac GA, Gonzalez-Espinoza R, Anand A, Tanaka E, Lilja H, Schwartz L, Larson S, Fleisher M, Scher HI (2007) Circulating tumor cell number and prognosis in progressive castration-resistant prostate cancer. Clin Cancer Res 13:7053–7058CrossRefPubMedGoogle Scholar
  11. 11.
    Cohen SJ, Punt CJ, Iannotti N, Saidman BH, Sabbath KD, Gabrail NY, Picus J, Morse MA, Mitchell E, Miller MC, Doyle GV, Tissing H, Terstappen LW, Meropol NJ (2009) Prognostic significance of circulating tumor cells in patients with metastatic colorectal cancer. Ann Oncol 20(7):1223–1229CrossRefPubMedGoogle Scholar
  12. 12.
    Cohen SJ, Punt CJ, Iannotti N, Saidman BH, Sabbath KD, Gabrail NY, Picus J, Morse M, Mitchell E, Miller MC, Doyle GV, Tissing H, Terstappen LW, Meropol NJ (2008) Relationship of circulating tumor cells to tumor response, progression-free survival, and overall survival in patients with metastatic colorectal cancer. J Clin Oncol 26:3213–3221CrossRefPubMedGoogle Scholar
  13. 13.
    Cristofanilli M, Budd GT, Ellis MJ, Stopeck A, Matera J, Miller MC, Reuben JM, Doyle GV, Allard WJ, Terstappen LW, Hayes DF (2004) Circulating tumor cells, disease progression, and survival in metastatic breast cancer. N Engl J Med 351:781–791CrossRefPubMedGoogle Scholar
  14. 14.
    De Giorgi U, Valero V, Rohren E, Dawood S, Ueno NT, Miller MC, Doyle GV, Jackson S, Andreopoulou E, Handy BC, Reuben JM, Fritsche HA, Macapinlac HA, Hortobagyi GN, Cristofanilli M (2009) Circulating tumor cells and [18F]fluorodeoxyglucose positron emission tomography/computed tomography for outcome prediction in metastatic breast cancer. J Clin Oncol 27(20):3303–3311CrossRefPubMedGoogle Scholar
  15. 15.
    Royston P, Altman DG, Sauerbrei W (2006) Dichotomizing continuous predictors in multiple regression: a bad idea. Stat Med 25:127–141CrossRefPubMedGoogle Scholar
  16. 16.
    Altman DG, Lausen B, Sauerbrei W, Schumacher M (1994) Dangers of using optimal cutpoints in the evaluation of prognostic factors. J Natl Cancer Inst 86:829–835CrossRefPubMedGoogle Scholar
  17. 17.
    Austin PC, Brunner LJ (2004) Inflation of the type I error rate when a continuous confounding variable is categorized in logistic regression analyses. Stat Med 23:1159–1178CrossRefPubMedGoogle Scholar
  18. 18.
    Faraggi D, Simon R (1996) A simulation study of cross-validation for selecting an optimal cutpoint in univariate survival analysis. Stat Med 15:2203–2213CrossRefPubMedGoogle Scholar
  19. 19.
    Durrleman S, Simon R (1989) Flexible regression models with cubic splines. Stat Med 8:551–561CrossRefPubMedGoogle Scholar
  20. 20.
    Allard WJ, Matera J, Miller MC, Repollet M, Connelly MC, Rao C, Tibbe AG, Uhr JW, Terstappen LW (2004) Tumor cells circulate in the peripheral blood of all major carcinomas but not in healthy subjects or patients with nonmalignant diseases. Clin Cancer Res 10:6897–6904CrossRefPubMedGoogle Scholar
  21. 21.
    MacCallum RC, Zhang S, Preacher KJ, Rucker DD (2002) On the practice of dichotomization of quantitative variables. Psychol Methods 7:19–40CrossRefPubMedGoogle Scholar
  22. 22.
    Leeflang MM, Moons KG, Reitsma JB, Zwinderman AH (2008) Bias in sensitivity and specificity caused by data-driven selection of optimal cutoff values: mechanisms, magnitude, and solutions. Clin Chem 54:729–737CrossRefPubMedGoogle Scholar
  23. 23.
    Greenland S (1995) Avoiding power loss associated with categorization and ordinal scores in dose-response and trend analysis. Epidemiology 6:450–454CrossRefPubMedGoogle Scholar
  24. 24.
    Royston P (2000) A strategy for modelling the effect of a continuous covariate in medicine and epidemiology. Stat Med 19:1831–1847CrossRefPubMedGoogle Scholar
  25. 25.
    Greenland S (1995) Dose-response and trend analysis in epidemiology: alternatives to categorical analysis. Epidemiology 6:356–365CrossRefPubMedGoogle Scholar
  26. 26.
    Royston P, Reitz M, Atzpodien J (2006) An approach to estimating prognosis using fractional polynomials in metastatic renal carcinoma. Br J Cancer 94:1785–1788CrossRefPubMedGoogle Scholar
  27. 27.
    Sauerbrei W, Royston P, Binder H (2007) Selection of important variables and determination of functional form for continuous predictors in multivariable model building. Stat Med 26:5512–5528CrossRefPubMedGoogle Scholar
  28. 28.
    Bagnardi V, Zambon A, Quatto P, Corrao G (2004) Flexible meta-regression functions for modeling aggregate dose-response data, with an application to alcohol and mortality. Am J Epidemiol 159:1077–1086CrossRefPubMedGoogle Scholar
  29. 29.
    Tibbe AG, Miller MC, Terstappen LW (2007) Statistical considerations for enumeration of circulating tumor cells. Cytometry A 71:154–162PubMedGoogle Scholar
  30. 30.
    d’Onofrio A (2009) Fractal growth of tumors and other cellular populations: linking the mechanistic to the phenomenological modeling and vice versa. Chaos Solitons Fractals 41:875–880CrossRefGoogle Scholar
  31. 31.
    Wheldon TE (1988) Mathematical model in cancer research. A. Hilger, BristolGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC. 2010

Authors and Affiliations

  • Edoardo Botteri
    • 1
  • Maria Teresa Sandri
    • 2
  • Vincenzo Bagnardi
    • 1
    • 3
  • Elisabetta Munzone
    • 4
  • Laura Zorzino
    • 2
  • Nicole Rotmensz
    • 1
  • Chiara Casadio
    • 5
  • Maria Cristina Cassatella
    • 2
  • Angela Esposito
    • 4
  • Giuseppe Curigliano
    • 4
  • Michela Salvatici
    • 2
  • Elena Verri
    • 4
  • Laura Adamoli
    • 4
  • Aron Goldhirsch
    • 4
  • Franco Nolè
    • 4
  1. 1.Division of Epidemiology and BiostatisticsEuropean Institute of OncologyMilanItaly
  2. 2.Unit of Laboratory MedicineEuropean Institute of OncologyMilanItaly
  3. 3.Department of StatisticsUniversity of Milan-BicoccaMilanItaly
  4. 4.Division of Medical OncologyEuropean Institute of OncologyMilanItaly
  5. 5.Division of PathologyEuropean Institute of OncologyMilanItaly

Personalised recommendations