Breast Cancer Research and Treatment

, Volume 129, Issue 2, pp 451–458 | Cite as

Artificial neural network analysis of circulating tumor cells in metastatic breast cancer patients

  • Antonio Giordano
  • Mario Giuliano
  • Michelino De Laurentiis
  • Antonio Eleuteri
  • Francesco Iorio
  • Roberto Tagliaferri
  • Gabriel N. Hortobagyi
  • Lajos Pusztai
  • Sabino De Placido
  • Kenneth Hess
  • Massimo Cristofanilli
  • James M. Reuben
Clinical trial

Abstract

A cut-off of 5 circulating tumor cells (CTCs) per 7.5 ml of blood in metastatic breast cancer (MBC) patients is highly predictive of outcome. We analyzed the relationship between CTCs as a continuous variable and overall survival in immunohistochemically defined primary tumor molecular subtypes using an artificial neural network (ANN) prognostic tool to determine the shape of the relationship between risk of death and CTC count and to predict individual survival. We analyzed a training dataset of 311 of 517 (60%) consecutive MBC patients who had been treated at MD Anderson Cancer Center from September 2004 to 2009 and who had undergone pre-therapy CTC counts (CellSearch®). Age; estrogen, progesterone receptor, and HER2 status; visceral metastasis; metastatic disease sites; therapy type and line; and CTCs as a continuous value were evaluated using ANN. A model with parameter estimates obtained from the training data was tested in a validation set of the remaining 206 (40%) patients. The model estimates were accurate, with good discrimination and calibration. Risk of death, as estimated by ANN, linearly increased with increasing CTC count in all molecular tumor subtypes but was higher in ER+ and triple-negative MBC than in HER2+. The probabilities of survival for the four subtypes with 0 CTC were as follows: ER+/HER2− 0.947, ER+/HER2+ 0.959, ER−/HER2+ 0.902, and ER-/HER2− 0.875. For patients with 200 CTCs, they were ER+/HER2− 0.439, ER+/HER2+ 0.621, ER−/HER2+ 0.307, ER−/HER2− 0.130. In this large study, ANN revealed a linear increase of risk of death in MBC patients with increasing CTC counts in all tumor subtypes. CTCs’ prognostic effect was less evident in HER2+ MBC patients treated with targeted therapy. This study may support the concept that the number of CTCs, along with the biologic characteristics, needs to be carefully taken into account in future analysis.

Keywords

Circulating tumor cells Metastatic breast cancer Artificial neural network HER2 Prognosis 

References

  1. 1.
    Cristofanilli M, Budd GT, Ellis MJ et al (2004) Circulating tumor cells, disease progression, and survival in metastatic breast cancer. N Engl J Med 351:781–791PubMedCrossRefGoogle Scholar
  2. 2.
    Cristofanilli M, Hayes DF, Budd GT et al (2005) Circulating tumor cells: a novel prognostic factor for newly diagnosed metastatic breast cancer. J Clin Oncol 23:1420–1430PubMedCrossRefGoogle Scholar
  3. 3.
    Bauernhofer T, Zenahlik S, Hofmann G et al (2005) Association of disease progression and poor overall survival with detection of circulating tumor cells in peripheral blood of patients with metastatic breast cancer. Oncol Rep 13:179–184PubMedGoogle Scholar
  4. 4.
    Hayes DF, Cristofanilli M, Budd GT et al (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 12:4218–4224PubMedCrossRefGoogle Scholar
  5. 5.
    Budd GT, Cristofanilli M, Ellis MJ et al (2006) Circulating tumor cells versus imaging–predicting overall survival in metastatic breast cancer. Clin Cancer Res 12:6403–6409PubMedCrossRefGoogle Scholar
  6. 6.
    Riethdorf S, Fritsche H, Muller V et al (2007) Detection of circulating tumor cells in peripheral blood of patients with metastatic breast cancer: a validation study of the CellSearch system. Clin Cancer Res 13:920–928PubMedCrossRefGoogle Scholar
  7. 7.
    Dawood S, Broglio K, Valero V et al (2008) Circulating tumor cells in metastatic breast cancer: from prognostic stratification to modification of the staging system? Cancer 113:2422–2430PubMedCrossRefGoogle Scholar
  8. 8.
    De Giorgi U, Valero V, Rohren E et al (2009) Circulating tumor cells and [18F]fluorodeoxyglucose positron emission tomography/computed tomography for outcome prediction in metastatic breast cancer. J Clin Oncol 27:3303–3311PubMedCrossRefGoogle Scholar
  9. 9.
    Bidard FC, Mathiot C, Degeorges A et al (2010) Clinical value of circulating endothelial cells and circulating tumor cells in metastatic breast cancer patients treated first line with bevacizumab and chemotherapy. Ann Oncol 21(9):1765–1771PubMedCrossRefGoogle Scholar
  10. 10.
    De Giorgi U, Valero V, Rohren E et al (2010) Circulating tumor cells and bone metastases as detected by FDG-PET/CT in patients with metastatic breast cancer. Ann Oncol 21:33–39PubMedCrossRefGoogle Scholar
  11. 11.
    Royston P, Altman DG, Sauerbrei W (2006) Dichotomizing continuous predictors in multiple regression: a bad idea. Stat Med 25:127–141PubMedCrossRefGoogle Scholar
  12. 12.
    Botteri E, Sandri MT, Bagnardi V et al (2010) Modeling the relationship between circulating tumour cells number and prognosis of metastatic breast cancer. Breast Cancer Res Treat 122:211–217PubMedCrossRefGoogle Scholar
  13. 13.
    Biganzoli E, Boracchi P, Mariani L, Marubini E (1998) Feed forward neural networks for the analysis of censored survival data: a partial logistic regression approach. Stat Med 17:1169–1186PubMedCrossRefGoogle Scholar
  14. 14.
    Schwarzer G, Vach W, Schumacher M (2000) On the misuses of artificial neural networks for prognostic and diagnostic classification in oncology. Stat Med 19:541–561PubMedCrossRefGoogle Scholar
  15. 15.
    Eleuteri A, Tagliaferri R, Milano L, De PS, De LM (2003) A novel neural network-based survival analysis model. Neural Netw 16:855–864PubMedCrossRefGoogle Scholar
  16. 16.
    Ripley RM, Harris AL, Tarassenko L (2004) Non-linear survival analysis using neural networks. Stat Med 23:825–842PubMedCrossRefGoogle Scholar
  17. 17.
    Eleuteri A, Aung MS, Taktak AF, Damato B, Lisboa PJ (2007) Continuous and discrete time survival analysis: neural network approaches. Conf Proc IEEE Eng Med Biol Soc 2007:5420–5423PubMedGoogle Scholar
  18. 18.
    Damato B, Eleuteri A, Fisher AC, Coupland SE, Taktak AF (2008) Artificial neural networks estimating survival probability after treatment of choroidal melanoma. Ophthalmology 115:1598–1607PubMedCrossRefGoogle Scholar
  19. 19.
    Harrell FE Jr, Califf RM, Pryor DB, Lee KL, Rosati RA (1982) Evaluating the yield of medical tests. JAMA 247:2543–2546PubMedCrossRefGoogle Scholar
  20. 20.
    Tibbe AG, Miller MC, Terstappen LW (2007) Statistical considerations for enumeration of circulating tumor cells. Cytometry A 71:154–162PubMedGoogle Scholar
  21. 21.
    Fehm T, Sauerbrei W (2010) Information from CTC measurements for metastatic breast cancer prognosis-we should do more than selecting an “optimal cut point”. Breast Cancer Res Treat 122:219–220PubMedCrossRefGoogle Scholar
  22. 22.
    Fehm T, Muller V, Aktas B et al (2010) HER2 status of circulating tumor cells in patients with metastatic breast cancer: a prospective, multicenter trial. Breast Cancer Res Treat 124(2):403–412PubMedCrossRefGoogle Scholar
  23. 23.
    Riethdorf S, Muller V, Zhang L et al (2010) Detection and HER2 expression of circulating tumor cells: prospective monitoring in breast cancer patients treated in the neoadjuvant GeparQuattro trial. Clin Cancer Res 16:2634–2645PubMedCrossRefGoogle Scholar
  24. 24.
    Flores LM, Kindelberger DW, Ligon AH et al (2010) Improving the yield of circulating tumour cells facilitates molecular characterisation and recognition of discordant HER2 amplification in breast cancer. Br J Cancer 102:1495–1502PubMedCrossRefGoogle Scholar
  25. 25.
    Pestrin M, Bessi S, Galardi F et al (2009) Correlation of HER2 status between primary tumors and corresponding circulating tumor cells in advanced breast cancer patients. Breast Cancer Res Treat 118:523–530PubMedCrossRefGoogle Scholar
  26. 26.
    Meng S, Tripathy D, Shete S et al (2004) HER-2 gene amplification can be acquired as breast cancer progresses. Proc Natl Acad Sci USA 101:9393–9398PubMedCrossRefGoogle Scholar
  27. 27.
    Fehm T, Hoffmann O, Aktas B et al (2009) Detection and characterization of circulating tumor cells in blood of primary breast cancer patients by RT-PCR and comparison to status of bone marrow disseminated cells. Breast Cancer Res 11:R59PubMedCrossRefGoogle Scholar
  28. 28.
    Kennecke H, Yerushalmi R, Woods R et al (2010) Metastatic behavior of breast cancer subtypes. J Clin Oncol 28:3271–3277PubMedCrossRefGoogle Scholar
  29. 29.
    Dawood S, Broglio K, Buzdar AU, Hortobagyi GN, Giordano SH (2010) Prognosis of women with metastatic breast cancer by HER2 status and trastuzumab treatment: an institutional-based review. J Clin Oncol 28:92–98PubMedCrossRefGoogle Scholar
  30. 30.
    Marty M, Cognetti F, Maraninchi D et al (2005) Randomized phase II trial of the efficacy and safety of trastuzumab combined with docetaxel in patients with human epidermal growth factor receptor 2-positive metastatic breast cancer administered as first-line treatment: the M77001 study group. J Clin Oncol 23:4265–4274PubMedCrossRefGoogle Scholar
  31. 31.
    Ferretti G, Felici A, Papaldo P, Fabi A, Cognetti F (2007) HER2/neu role in breast cancer: from a prognostic foe to a predictive friend. Curr Opin Obstet Gynecol 19:56–62PubMedCrossRefGoogle Scholar
  32. 32.
    Bozionellou V, Mavroudis D, Perraki M et al (2004) Trastuzumab administration can effectively target chemotherapy-resistant cytokeratin-19 messenger RNA-positive tumor cells in the peripheral blood and bone marrow of patients with breast cancer. Clin Cancer Res 10:8185–8194PubMedCrossRefGoogle Scholar
  33. 33.
    Rosner B (2006) Fundamentals of biostatistics, 6th edn. Thomson/Brooks Cole, Belmont, p 782Google Scholar

Copyright information

© Springer Science+Business Media, LLC. 2011

Authors and Affiliations

  • Antonio Giordano
    • 1
    • 7
  • Mario Giuliano
    • 2
    • 7
  • Michelino De Laurentiis
    • 3
  • Antonio Eleuteri
    • 4
  • Francesco Iorio
    • 5
  • Roberto Tagliaferri
    • 5
  • Gabriel N. Hortobagyi
    • 6
  • Lajos Pusztai
    • 6
  • Sabino De Placido
    • 7
  • Kenneth Hess
    • 8
  • Massimo Cristofanilli
    • 9
  • James M. Reuben
    • 1
  1. 1.Department of HematopathologyThe University of Texas MD Anderson Cancer CenterHoustonUSA
  2. 2.Breast Center, Baylor College of MedicineHoustonUSA
  3. 3.Department of Breast OncologyNational Cancer Institute “Fondazione Pascale”NaplesItaly
  4. 4.Department of Medical Physics and Clinical EngineeringRoyal Liverpool University HospitalLiverpoolUK
  5. 5.Department of Mathematics and InformaticsUniversity of SalernoFiscianoItaly
  6. 6.Department of Breast Medical OncologyThe University of Texas MD Anderson Cancer CenterHoustonUSA
  7. 7.Department of Endocrinology and Molecular and Clinical OncologyUniversity of Naples Federico IINaplesItaly
  8. 8.Department of BiostatisticsThe University of Texas MD Anderson Cancer CenterHoustonUSA
  9. 9.Department of Medical OncologyFox Chase Cancer CenterPhiladelphiaUSA

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