Perioperative activation of disseminated tumor cells in bone marrow of patients with prostate cancer.

PURPOSE: The outcome of prostate cancer is highly unpredictable. To assess the dynamics of systemic disease and to identify patients at high risk for early relapse we followed the fate of disseminated tumor cells in bone marrow for up to 10 years and genetically analyzed such cells isolated at various stages of disease. PATIENTS AND METHODS: Nine hundred bone marrow aspirates from 384 patients were stained using the monoclonal antibody A45-B/B3 directed against cytokeratins 8, 18, and 19. Log-rank statistics and Cox regression analysis were applied to determine the prognostic impact of positive cells detected before surgery (244 patients) and postoperatively (214 patients). Samples from primary tumors (n = 55) and single disseminated tumor cells (n = 100) were analyzed by comparative genomic hybridization. RESULTS: Detection of cytokeratin-positive cells before surgery was the strongest independent risk factor for metastasis within 48 months (P < .001; relative risk [RR], 5.5; 95% CI, 2.4 to 12.9). In contrast, cytokeratin-positive cells detected 6 months to 10 years after radical prostatectomy were consistently present in bone marrow with a prevalence of approximately 20% but had no influence on disease outcome. Characteristic genotypes of cytokeratin-positive cells were selected at manifestation of metastasis. CONCLUSION: Cytokeratin-positive cells in the bone marrow of prostate cancer patients are only prognostically relevant when detected before surgery. Because we could not identify significant genetic differences between pre- and postoperatively isolated tumor cells before manifestation of metastasis, we postulate the existence of perioperative stimuli that activate disseminated tumor cells. Patients with cytokeratin-positive cells in bone marrow before surgery may therefore benefit from adjuvant therapies.

High-resolution array comparative genomic hybridization of single micrometastatic tumor cells.

Only few selected cancer cells drive tumor progression and are responsible for therapy resistance. Their specific genomic characteristics, however, are largely unknown because high-resolution genome analysis is currently limited to DNA pooled from many cells. Here, we describe a protocol for array comparative genomic hybridization (array CGH), which enables the detection of DNA copy number changes in single cells. Combining a PCR-based whole genome amplification method with arrays of highly purified BAC clones we could accurately determine known chromosomal changes such as trisomy 21 in single leukocytes as well as complex genomic imbalances of single cell line cells. In single T47D cells aberrant regions as small as 1-2 Mb were identified in most cases when compared to non-amplified DNA from 10(6) cells. Most importantly, in single micrometastatic cancer cells isolated from bone marrow of breast cancer patients, we retrieved and confirmed amplifications as small as 4.4 and 5 Mb. Thus, high-resolution genome analysis of single metastatic precursor cells is now possible and may be used for the identification of novel therapy target genes.

The RIN: an RNA integrity number for assigning integrity values to RNA measurements.

BACKGROUND: The integrity of RNA molecules is of paramount importance for experiments that try to reflect the snapshot of gene expression at the moment of RNA extraction. Until recently, there has been no reliable standard for estimating the integrity of RNA samples and the ratio of 28S:18S ribosomal RNA, the common measure for this purpose, has been shown to be inconsistent. The advent of microcapillary electrophoretic RNA separation provides the basis for an automated high-throughput approach, in order to estimate the integrity of RNA samples in an unambiguous way. METHODS: A method is introduced that automatically selects features from signal measurements and constructs regression models based on a Bayesian learning technique. Feature spaces of different dimensionality are compared in the Bayesian framework, which allows selecting a final feature combination corresponding to models with high posterior probability. RESULTS: This approach is applied to a large collection of electrophoretic RNA measurements recorded with an Agilent 2100 bioanalyzer to extract an algorithm that describes RNA integrity. The resulting algorithm is a user-independent, automated and reliable procedure for standardization of RNA quality control that allows the calculation of an RNA integrity number (RIN). CONCLUSION: Our results show the importance of taking characteristics of several regions of the recorded electropherogram into account in order to get a robust and reliable prediction of RNA integrity, especially if compared to traditional methods.

From latent disseminated cells to overt metastasis: genetic analysis of systemic breast cancer progression.

According to the present view, metastasis marks the end in a sequence of genomic changes underlying the progression of an epithelial cell to a lethal cancer. Here, we aimed to find out at what stage of tumor development transformed cells leave the primary tumor and whether a defined genotype corresponds to metastatic disease. To this end, we isolated single disseminated cancer cells from bone marrow of breast cancer patients and performed single-cell comparative genomic hybridization. We analyzed disseminated tumor cells from patients after curative resection of the primary tumor (stage M0), as presumptive progenitors of manifest metastasis, and from patients with manifest metastasis (stage M1). Their genomic data were compared with those from microdissected areas of matched primary tumors. Disseminated cells from M0-stage patients displayed significantly fewer chromosomal aberrations than primary tumors or cells from M1-stage patients (P < 0.008 and P < 0.0001, respectively), and their aberrations appeared to be randomly generated. In contrast, primary tumors and M1 cells harbored different and characteristic chromosomal imbalances. Moreover, applying machine-learning methods for the classification of the genotypes, we could correctly identify the presence or absence of metastatic disease in a patient on the basis of a single-cell genome. We suggest that in breast cancer, tumor cells may disseminate in a far less progressed genomic state than previously thought, and that they acquire genomic aberrations typical of metastatic cells thereafter. Thus, our data challenge the widely held view that the precursors of metastasis are derived from the most advanced clone within the primary tumor.