Breast Cancer Research and Treatment

, Volume 29, Issue 2, pp 179–187 | Cite as

S-phase determination of immunoselected cytokeratin-containing breast cancer cells improves the prediction of recurrence

  • Sten Wingren
  • Olle Stål
  • John Carstensen
  • Xiao-Feng Sun
  • Bo Nordenskjöld


Estimation of S-phase fraction in breast carcinomas with single parameter flow cytometry may include errors due to dilution of cancer cells by host cells. Use of tissue specific markers may to some extent correct for the effect of dilution. S-phase fraction was estimated by flow cytometry with and without immunoselection in 80 DNA-euploid breast carcinomas in stage I-II. The tumor tissue was mechanically disintegrated and fixed in ethanol. A primary antibody, specific for cytokeratins 8 and 18, was added before incubation with the secondary FITC-conjugated antibody. S-phase fraction was calculated for both the gated (cytokeratin-positive) and the ungated cell population. An increasing proportion of tetraploid cells compared to diploid cells was found when the immunoselection method was used. The gated population tended to have a higher S-phase fraction than the ungated population. In univariate analysis S-phase fraction estimated from both ungated and gated cell populations yielded significant information for predicting recurrence when stratified for tumor size and nodal status. In bivariate analysis S-phase fraction of the gated population contributed prognostic information in addition to S-phase fraction of the ungated population when both variables were included in the analysis. Our conclusion is that S-phase fraction calculated from cytokeratin-positive cells provides prognostic information in addition to ungated S-phase values in DNA euploid breast carcinomas.

Key words

breast cancer CAM 5.2 antibody cytokeratin flow cytometry prognosis S-phase fraction 


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  1. 1.
    Seidman H, Gelb SK, Silverberg E, La Verda N, Lubera JA: Survival experience in the breast cancer detection demonstration project. CA 37: 258–290, 1987Google Scholar
  2. 2.
    Fagerberg G, Baldetorp L, Gröntoft O, Lundström B, Månsson JC, Nordenskjöld B: Effects of repeated mammography screening on breast cancer stage distribution. Acta Radiol Oncol 24: 465–472, 1985Google Scholar
  3. 3.
    Miller AB: The role of early diagnosis and screening in oncology. Diagn Oncol 1: 19–27, 1991Google Scholar
  4. 4.
    Klintenberg C, Stål O, Nordenskjöld B, Wallgren A, Arvidsson S, Skoog L: Proliferative index, cytosol estrogen receptor and axillary node status as prognostic predictors in human mammary carcinoma. Breast Cancer Res Treat 7: 99–106, 1986Google Scholar
  5. 5.
    Kallioniemi OP, Hietanen T, Mattila J, Lehtinen M, Lauslahti K, Koivula T: Aneuploid DNA content and high S-phase fraction of tumor cells are related to poor prognosis in patients with primary breast cancer. Eur J Cancer Clin Oncol 23: 277–282, 1987Google Scholar
  6. 6.
    Clark G, Dressler L, Owens M, Pounds G, Oldaker T, McGuire W: Prediction of relapse or survival in patients with node negative breast cancer by DNA flow cytometry. New Engl J Med 320: 627–633, 1989Google Scholar
  7. 7.
    Lewis E: Prognostic significance of flow cytometric DNA analysis in node-negative breast cancer patients. Cancer 65: 2315–2320, 1990Google Scholar
  8. 8.
    Stål O, Wingren S, Carstensen J, Rutqvist L-E, Skoog L, Klintenberg C, Nordenskjöld B: Prognostic value of DNA ploidy and S-phase fraction in relation to estrogen receptor content and clinicopathological variables in primary breast cancer. Eur J Cancer Clin Oncol 25: 301–309, 1989Google Scholar
  9. 9.
    Wingren S, Rosenberg P, Andersson C, Risberg B, Carstensen J, Nordenskjöld B: Measurements of S-phase fraction in immunoselected endometrial carcinoma cells. Diagn Oncol 2: 69–73, 1992Google Scholar
  10. 10.
    Moll R, Franke W, Schiller D, Geiger B, Krepler R: The catalog of human cytokeratins: Patterns of expression in normal epithelia, tumors and cultured cells. Cells 31: 11–24, 1982Google Scholar
  11. 11.
    Ferrero F, Spyratos F, Le Doussal V, Desplaces A, Rouesse J: Flow cytometric analysis of DNA content and keratins by using CK7, CK8, CK18, CK19 and KL1 monoclonal antibodies in benign and malignant human breast tumors. Cytometry 11: 716–724, 1990Google Scholar
  12. 12.
    Ramaekers FCS, Puts JJG, Moesker O, Kant A, Huysmans A, Haag D, Jap PHK, Herman CJ, Vooijs GP: Antibodies to intermediate filament proteins in the immunohistochemical identification of human tumors: an overview. Histochemical Journal 15: 691–713, 1983Google Scholar
  13. 13.
    Zarbo R, Visscher D, Crissman J: Two-color multiparametric method for flow cytometric DNA analysis of carcinomas using staining for cytokeratin and leukocyte-common antigen. Anal Quant Cytol Histol 11: 391–402, 1989Google Scholar
  14. 14.
    Oud P, Henderik J, Beck H, Veldhuizen J, Vooijs P, Herman C, Ramaekers F: Flow cytometric analysis and sorting of human endometrial cells after immunocytochemical labeling for cytokeratin using a monoclonal antibody. Cytometry 6: 159–164, 1985Google Scholar
  15. 15.
    Visscher D, Zarbo R, Jacobsen G, Kambouris A, Talpos G, Sakr W, Crissman J: Multiparametric deoxyribonucleic acid and cell cycle analysis of breast carcinomas by flow cytometry. Lab Invest 62: 370–378, 1990Google Scholar
  16. 16.
    Vindelöv L, Christensen IB, Nissen N: A detergent-trypsin method for the preparation of nuclei for flow cytometric DNA analysis. Cytometry 3: 323–327, 1983Google Scholar
  17. 17.
    Mygind H, Nielsen B, Moe D, Clausen H, Dabelsteen E, Prætorius Clasen P: Antikeratin antibodies in routine diagnostic pathology. Acta Path Micro Immun Scand 96: 1009–1022, 1988Google Scholar
  18. 18.
    Makin CA, Bobrow LG, Bodmer WF: Monoclonal antibody to cytokeratin for use in routine histopathology. J Clin Pathol 37: 975–983, 1984Google Scholar
  19. 19.
    Baisch H, Beck H-P, Christensen IJ, Hartmann NR, Fried J, Dean PN, Gray JW, Jett JH, Johnston DA, White RA, Nicolini C, Zeitz S, Watson JV: Comparison of evaluation methods for DNA histograms measured by flow cytometry. Flow Cytometry 4: 152–155, 1980Google Scholar
  20. 20.
    Bagwell BC, Mayo SW, Whetstone SD, Hitchcox SA, Baker DR, Herbert DJ, Weaver DL, Jones MA, Lovett EJ: DNA histogram debris theory and compensation. Cytometry 12: 107–118, 1991Google Scholar
  21. 21.
    Breslow NE, Day NE: Statistical methods in cancer research. Volume 1: The analysis of case control studies. Lyon: International Agency for Research on Cancer, 1980Google Scholar
  22. 22.
    Wetzels R, Kuijpers H, Lane B, Leigh I, Troyanovsky S, Holland R, van Haeist U, Ramaekers F: Basal cell-specific and hyperproliferation-related keratins in human breast cancer. Am J Pathol 138: 751–763, 1991Google Scholar
  23. 23.
    Gould V, Koukoulis J, Jansson D, Nagle R, Franke W, Moll R: Coexpression patterns of vimentin and glial filament protein with cytokeratins in the normal, hyperplastic, and neoplastic breast. Am J Pathol 137: 1143–1155, 1990Google Scholar
  24. 24.
    Battifora H: Diagnostic uses of antibodies to keratin: Review and immunohistochemical comparison of seven monoclonal and three polyclonal antibodies. Prog Surg Pathol 8: 1–15, 1988Google Scholar
  25. 25.
    Looijenga LHJ, Oosterhuis JW, Ramaekers F, de Jong B, Dam A, Beck JLM, Sleijfer DT, Schraffordt Koops H: Dual parameter flow cytometry for deoxyribonucleic acid and intermediate filament proteins of residual mature teratoma. Lab Invest 64: 113–117, 1991Google Scholar
  26. 26.
    Altman D: Categorising continuous variables. Br J Cancer 64: 975, 1991Google Scholar

Copyright information

© Kluwer Academic Publishers 1993

Authors and Affiliations

  • Sten Wingren
    • 1
  • Olle Stål
    • 1
  • John Carstensen
    • 1
  • Xiao-Feng Sun
    • 1
  • Bo Nordenskjöld
    • 1
  1. 1.Department of Oncology, Faculty of Health SciencesLinköping UniversityLinköpingSweden

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