Abstract
Inappropriate growth regulation is a defining feature of malignant neoplasia which, to a great extent, becomes manifest as abnormal proliferation of neoplastic populations. In recent years, it has become clear that cell growth and proliferation are controlled by an elaborate homeostatic mechanism which is balanced by numerous intracellular and extracellular signals. It is hardly surprising then that oncogenes and tumor suppressor genes which mediate this process have been found to have great relevance in neoplastic transformation and progression, reflecting clinical behavior. Recent molecular genetic advances, however, reflect numerous traditional observations which elegantly documented the critical importance of abnormal growth regulation and cell cycling (1). Prognosis in many clinical tumor systems is strikingly correlated to arguably pedestrian estimates of proliferation such as mitotic counts on histologic tissue sections and empirical doubling times extrapolated from serial radiographic measurements (2,3).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Malaise EP, Chavaudra N, Charbit A, Tubiana M: Relationship between the growth rate of human metastases, survival and pathologic type. Eur. J. Cancer 10:305–312, 1974.
Pearlman AW: Breast Cancer. Influence of growth rate on prognosis and treatment evaluation. A study on mastectomy scar recurrences. Cancer 38: 1826–1833, 1976.
Tubiana M, Koscielny S: Cell kinetics, growth rate and the natural history of breast cancer. Eur. J. Cancer Clin. Oncol. 24:9–14, 1988.
McDivitt RW, Stone KR, Craig B, et al: A proposed classification of breast cancer based on kinetic information. Derived from a comparison of risk factors in 168 primary operable breast cancers. Cancer 57:269–276, 1986.
Visscher DW, Sarkar FH, Crissman JD: Clinical significance of pathologic, cytometric and molecular parameters in carcinoma of the breast. In: Advances in Pathology and Laboratory Medicine, RS Weinstein (ed), Mosby, Vol. 5, 1992.
Baisch H, Beck HP, Christensen IJ, et al: A comparison of mathematical methods for the analysis of DNA histograms obtained by flow cytometry. Cell Tissue Kinet. 15:235–249, 1982.
Meyer JS, Coplin MD: Thymidine labeling index, flow cytometric S-phase measurement, and DNA index in human tumors. Am. J. Clin. Pathol. 89:589–595, 1988.
Waldman FM, Chew K, Ljung BM, et al: A comparison between Bromodeoxyuridine and 3H thymidine labeling in human breast tumors. Modern Pathology 4:718–722, 1991.
McDivitt RW, Stone KR, Meyer JS: A method of dissociation of viable human breast cancer cells that produces flow cytometric kinetic information similar to that obtained by thymidine labeling. Cancer Res. 44:2628–2633, 1984.
Garcia RL, Coltrera MD, Gown AM: Analysis of proliferative grade using anti-PCNA/cyclin monoclonal antibodies in fixed, embedded tissues. Am. J. Pathol. 134:733–739, 1989.
Sahin AA, Ro JY, El-Naggar AK, et al: Tumor proliferative fraction in solid malignant neoplasms. A comparative study of Ki-67 immunostaining and flow cytometric determinations. Am. J. Clin. Pathol. 96:512–519, 1991.
Dawson AE, Norton JA, Weinberg DS: Comparative assessment of proliferation and DNA content in breast carcinoma by image analysis and flow cytometry. Am. J. Pathol. 136:1115–1124, 1990.
Vielh P, Chevillard S, Mosseri V, et al: Ki67 index and S-phase fraction in human breast carcinomas. Am. J. Clin. Pathol. 94:681–686, 1990.
McDivitt RW, Stone KR, Craig RB, Meyer JS: A comparison of human breast cancer cell kinetics measured by flow cytometry and thymidine labeling. Lab. Invest. 52:287–291, 1985.
Isola JJ, Helin HJ, Helle MJ, Kallioniemi OP: Evaluation of cell proliferation in breast carcinoma. Comparison of Ki-67 immunohistochemical study, DNA flow cytometric analysis, and mitotic count. Cancer 65:1180–1184, 1990.
Rabinovitch PS: Practical considerations for DNA content and cell cycle analysis. In: Clinical Flow Cytometry, KD Bauer, RE Duque, TV Sharkey (eds.), Williams and Wilkins, Baltimore, pp. 117–142, 1990.
Bagwell CB: Theoretical aspects of flow cytometry data analysis. In: Clinical Flow Cytometry, KD Bauer, RE Duque, TV Sharkey (eds.), Williams and Wilkins, Baltimore, pp. 42–62, 1990.
Beerman H, Smit VT, Kluin PM, et al: Flow cytometric analysis of DNA stemline heterogeneity in primary and metastatic breast cancer. Cytometry 12: 147–154, 1991.
Olszewski W, Darzynkiewicz Z, Rosen PP, et al: Flow cytometry of breast carcinoma III. Possible altered kinetics in axillary lymph node metastases. Analy. Ouant. Cytol. 4:275–278, 1982.
Nicholson GL: Tumor cell instability, diversification, and progression to metastatic phenotype: From oncogene to oncofetal expression. Cancer Res. 47:1473–1487, 1987.
Meyer JS, Wittliff JL: Regional heterogeneity in breast carcinoma: Thymidine labelling index, steroid hormone receptors, DNA ploidy. Intl. J. Cancer 47:213–220, 1991.
Kute TE, Gregory B, Galleshaw J, et al: How reproducible are flow cytometry data from paraffin-embedded blocks? Cytometry 9:494–498, 1988.
Feichter G, Czech W, Haag D, et al: Comparison of S-phase fractions measured by flow cytometry and autoradiography in human transplant tumors. Cytometry 9:605–611, 1988.
Weaver DL, Bagwell CB, Hitchcox SA, et al: Improved flow cytometric determination of proliferative activity (S-phase fraction) from paraffin-embedded tissue. Am. J. Clin. Pathol. 94:576–584, 1990.
Rabinovitch PS, Barlow W, Visakorpi T, et al: Improving the prognostic strength of S and G2 estimates by optimizing DNA histogram analysis in breast cancer. Cytometry (suppl.) 6:82, 1993.
Haag D, Feichter G, Goerttler K, Kaufmann M: Influence of systematic errors on the evaluation of the S-phase portions from DNA distributions of solid tumors as shown for 328 breast carcinomas. Cytometry 8:377–385, 1987.
Visscher DW, Zarbo RJ, Jacobsen G, et al: Multiparametric deoxyribonucleic acid and cell cycle analysis of breast carcinomas by flow cytometry. Clinicopathologic correlations. Lab. Invest. 62: 370–378, 1990.
Seamer LC, Bunt JC, Dressler LG, Revels A: A comparison of pulse gating and mathematical modeling methods of reducing the contribution of aggregates to flow cytometric DNA single parameter histograms. Cytometry (suppl.) 6:44, 1993.
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1996 Kluwer Academic Publishers
About this chapter
Cite this chapter
Visscher, D.W., Wykes, S.M., Crissman, J.D. (1996). Flow Cytometric Proliferative Fraction Analysis in Solid Tumors. In: Valeriote, F.A., Nakeff, A., Valdivieso, M. (eds) Basic and Clinical Applications of Flow Cytometry. Developments in Oncology, vol 77. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-1253-6_3
Download citation
DOI: https://doi.org/10.1007/978-1-4613-1253-6_3
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4612-8534-2
Online ISBN: 978-1-4613-1253-6
eBook Packages: Springer Book Archive