Abstract
Recent advances in multicolor flow cytometry have made possible the simultaneous analysis of up to 12 distinct cell surface molecules. This technical advance increases sensitivity and specificity for more accurate diagnosis and classification of various malignancies. In turn, this allows for a better assessment of disease prognosis and the establishment of a treatment regimen. In addition, multicolor flow cytometry provides a sensitive technique for investigating the occurrence of minimal residual disease. Studies of leukemias and lymphomas are particularly suitable for investigation by flow cytometry. Studies on solid tumors such as breast cancer, or on tumor infiltrating immune cells, are also possible if appropriate processing techniques are used to generate single-cell suspensions without altering cell surface receptor expression. In addition to analytical immunophenotyping, isolation of even very small (malignant) cell populations by multicolor flow cytometry provides a highly pure source for DNA and RNA analyses. Such combined sorting and molecular approaches provide a comprehensive basis for studying the molecular mechanisms underlying malignant transformation. Importantly, they also enable distinct diagnoses to be made when cell surface marker immunophenotyping alone is inconclusive. To exemplify the importance of combining various immunophenotyping tools, such as multicolor flow cytometry and gene expression analyses, with the classical tools of histopathology and differential blood count, we provide data on the analysis of a mouse model of human chronic B-cell leukemia (B-CLL): the New Zealand Black mouse. We relied for this study on gene expression databases generated by the National Cancer Institute for various human B-cell malignancies. B-CLL is one of the most common forms of leukemia in the Western world. No therapies currently exist against this usually slow-progressing but always fatal malignancy, making the development of a good animal model an important task. The data show that although the New Zealand Black mouse shows a spontaneous expansion of a CD5+ B-cell population (a hallmark of human B-CLL), it does not fit the classification of B-CLL by either phenotypic or genetic analysis criteria. In conclusion, increasing the number of simultaneous measurements for immunophenotyping and cell sorting by flow cytometry and combining it with comprehensive gene expression studies supports and expands the power of classical analysis tools.
Highlights
The remarkable generation of scores of increasingly sophisticated mouse models of mammary cancer over the past two decades has provided tremendous insights into molecular derangements that can lead to cancer
We report that somatic mutations of p53 in mouse mammary epithelial cells lead to ERα-positive and ERαnegative tumors. p53 inactivation in pre-pubertal/pubertal mice, but not in adult mice, leads to the development of ERα-positive tumors, suggesting that developmental stages influence the availability of ERα-positive tumor origin cells
Genetic alterations commonly observed in human breast cancer including c-myc amplification and Her2/Neu/erbB2 activation were seen in these mouse tumors
Summary
Transgenic Oncogenesis Group, Laboratory of Cell Regulation and Carcinogenesis, National Cancer Institute, Bethesda, Maryland, USA. The remarkable generation of scores of increasingly sophisticated mouse models of mammary cancer over the past two decades has provided tremendous insights into molecular derangements that can lead to cancer. The relationships of these models to human breast cancer, remain problematic. P53 inactivation in pre-pubertal/pubertal mice, but not in adult mice, leads to the development of ERα-positive tumors, suggesting that developmental stages influence the availability of ERα-positive tumor origin cells. These tumors have a high rate of metastasis that is independent of tumor latency. Since it is feasible to isolate ERα-positive epithelial cells from normal mammary glands and tumors, molecular mechanisms underlying ERα-positive and ERα-negative mammary carcinogenesis can be systematically addressed using this model
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