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Cancer Res. Author manuscript; available in PMC Jul 15, 2009.
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PMCID: PMC2613025

Genomic alterations indicate tumor origin and varied metastatic potential of disseminated cells from prostate-cancer patients


Disseminated epithelial cells can be isolated from the bone marrow of a far greater fraction of prostate-cancer patients than the fraction of patients who progress to metastatic disease. To provide a better understanding of these cells, we have characterized their genomic alterations. We first present an array comparative genomic hybridization method capable of detecting genomic changes in the small number of disseminated cells (10-20) that can typically be obtained from bone-marrow aspirates of prostate-cancer patients. We show multiple regions of copy-number change, including alterations common in prostate cancer, such as 8p loss, 8q gain, and gain encompassing the androgen-receptor gene on Xq, in the disseminated cell pools from 11 metastatic patients. We found fewer and less striking genomic alterations in the 48 pools of disseminated cells from patients with organ-confined disease. However, we identify changes shared by these samples with their corresponding primary tumors and prostate-cancer alterations reported in the literature, evidence that these cells, like those in advanced disease, are disseminated tumor cells (DTCs). We also demonstrate that DTCs from patients with advanced and localized disease share several abnormalities, including losses containing cell-adhesion genes and alterations reported to associate with progressive disease. These shared alterations might confer the capability to disseminate or establish secondary disease. Overall, the spectrum of genomic deviations is evidence for metastatic capacity in advanced-disease DTCs and variation in that capacity in DTCs from localized disease. Our analysis lays the foundation for elucidation of the relationship between DTC genomic alterations and progressive prostate cancer.

Keywords: Disseminated tumor cells, disseminated cells, prostate cancer, array CGH, genomic alterations


The metastatic potential of a tumor is presumably contained within cells that are able to escape and disseminate from the primary lesion. Consequently, the identification of cytokeratin-positive cells (i.e., cells of epithelial origin) in the peripheral circulation and bone marrow of patients with clinically localized adenocarcinoma of the prostate has generated much interest. These cells are hypothesized to originate from the primary tumor and represent a very early stage in the metastatic process (1). The presence of these putative disseminated tumor cells (DTCs) in the bone marrow is particularly noteworthy, since bone metastases are a well known feature of lethal metastatic prostate cancer (2). It seems likely that these cells are the source of metastatic disease in those patients who develop secondary tumors years after resection of the primary tumor. Thus, DTCs have become a key target for risk-association studies and experiments characterizing molecular features that underlie the phenotypic traits responsible for survival, dormancy, and ultimately proliferation in environments distinct from their origin.

There is a compelling need for improved ways to evaluate patients with clinically-localized prostate cancer for their risk of developing metastasis. Currently, risk is estimated by the pathological features of the primary tumor (e.g., Gleason Score and tumor stage) and PSA levels in peripheral blood (3, 4). These predictive features are used to stratify patients into low-and high-risk groups. However, up to 50% of patients classified as high risk for progression do not develop metastasis, and ~10% of patients classified as low risk for progression subsequently develop secondary disease (5). Thus, the need to more accurately distinguish those men who would benefit from aggressive adjuvant treatment regimes from those who could be safely treated by active surveillance is a powerful motivation to improve upon the ability to predict outcome.

To date, some studies have found a positive correlation with the presence of DTCs and risk of relapse following primary therapies for localized prostate cancer (6-10), while others have not (11-16). Among the latter studies, Pfitzenmaier et al. (2007) found that 75% of pre-surgery patients without clinical evidence of metastatic spread, and only 11% of normal controls, harbored disseminated epithelial cells in their bone marrow. This frequency far exceeds the expected frequency of biochemical recurrence or metastasis. These results strongly suggest that the mere presence of DTCs is not indicative of risk of disease progression. Why do so many patients possess DTCs, if escape from the primary tumor is a rate-limiting step in the metastatic process? Despite the low incidence in normal controls, it is possible that the disseminated cells are not actually tumor cells. Characterization of the molecular features of disseminated cells might resolve the question of their tumor origin and provide a better understanding of their relationship with disease progression.

Low-resolution and low-throughput methods such as fluorescence in situ hybridization (FISH) (17) or conventional comparative genomic hybridization (CGH) (18) have provided some evidence that DTCs from epithelial cancers possess genomic abnormalities. However, the molecular characterization of DTCs by high-resolution techniques has been hampered by the small number of DTCs obtainable: typically fewer than 50 epithelial cells from bone-marrow aspirates are available for analysis. To date, high-resolution genomic analysis was only possible by first generating cell lines from DTCs (19).

Given the proposed role that DTCs play in systemic disease and the paucity of genomic analyses of these cells, we have used array CGH to produce high-resolution genomic profiles of cells expressing epithelial markers isolated from the bone marrow of prostate-cancer patients. We categorize our samples by the status of the patients from which they were acquired. Thus, our samples of disseminated cells obtained from patients with organ-confined (localized) disease are designated as LocDCs. Our samples of disseminated cells from patients with metastatic (advanced) disease are referred to as AdvDCs. Our study of genomic changes in LocDCs and AdvDCs lays the groundwork for a better understanding of the role these cells play in carcinogenesis.


Sample acquisition

All samples were collected from patients undergoing treatment at the University of Washington Medical Center or the VA Puget Sound Health Care System after informed consent was obtained. A total of 59 prostate-cancer patients (11 patients with advanced disease and 48 patients with localized disease), participated in this study. Supplemental Table 1 gives the Gleason grade and TNM stage for 55 of the patients and chemical castration status for all advanced patients. Clinical data on 4 localized patients was unavailable. The mean (SD) age of these patients was 60.2 (7.4) years. Our use of human samples for this study was approved by the Institutional Review Boards of the participating institutions.

Just prior to radical prostatectomy, bone-marrow aspirates were collected from the upper iliac crest of all patients with localized disease. Bone-marrow aspirates were collected from advanced patients prior to any treatment for four patients and during the course of treatment for seven patients. Pools of 10-20 epithelial cells were isolated and collected into 10 μl H20 based on expression of EpCAM (CD326; a pan-epithelial cell antigen) from the aspirates of each of 58 patients and from three biological replicates from the aspirate of one metastatic patient, as described previously (14). Twenty of the 59 samples (14 from localized- and 6 from advanced-disease patients) were collected using 1:20 dilutions of both an anti-EpCAM(CD326) antibody conjugated with a fluorescein isothiocyanate isomer (Dako, Carpinteria, CA) and an anti-CD45 antibody conjugated to R-phycoerythrin (Dako) to label normal hematopoietic cells. This dual detection system was used to exclude normal lymphocytes from disseminated-epithelial-cell pools and to collect samples of normal-cell samples. These samples were analyzed by array CGH and used to define thresholds of loss and gain in disseminated-cell arrays (see Supplemental Methods and Results) and in proof-of-principle tests (see Results).

Prostate-tissue samples containing tumor were collected from the patients with localized disease following radical prostatectomy and were embedded in freezing media (Tissue-Tek OCT Compound, Sakura Finetek, Torrance, CA) and stored in liquid nitrogen. For nine patients from whom LocDCs were collected, the entire prostate was histologically evaluated (see Supplemental Table 1 for matched pairs). The area selected for molecular analysis was from the tumor, which was not multifocal in any of the cases. Tumor cells were isolated by laser-capture microdissection (LCM) from 5-μm thick tissue sections as described previously (20). Two to four thousand cells were collected from each patient and from the normal stroma of five patients. Pathologist (L.D.T.) review of all LCM images confirmed collection of tumor cells. Arrays of stromal-cell samples were used to define thresholds of copy-number change in arrays of primary tumor (see Supplemental Methods and Results).

All other methods are described in Supplemental Methods and Results.


Array CGH can be made compatible with small numbers of cells

To analyze samples containing only 10-20 cells by array CGH, we first amplified the genomic DNA using a ligation-mediated PCR method. This approach, which we term rare-cell genomic amplification (RCGA), was developed by Klein and coworkers to analyze the genome of a single cell by conventional CGH (18). Our CGH platform was a bacterial artificial chromosome (BAC) array, which contains ~4200 BAC clones, each with a known location in the publicly available assembly of the human genome (Build 35), and provides a resolution of ~0.4 Mbp across the euchromatic portions of the genome (21). For all small-cell-number arrays, the reference is normal female genomic DNA amplified by RCGA from an amount of DNA roughly equivalent to the amount in the test sample.

We conducted tests to establish that RCGA and array CGH when applied to small numbers of cells give (1) consistently low levels of experimental noise, (2) acceptable dynamic range for detecting chromosomal alterations, and (3) reproducibility across replicates. First, we amplified nine samples each composed of 10-20 normal CD45-positive bone marrow-derived cells (from patients with localized prostate cancer) by RCGA and analyzed them by array CGH. We used these normal-cell array results to define thresholds for calling loss and gain (see Supplementary Methods). Applying these thresholds back to the normal-cell arrays, we observed the expected X- and Y-chromosome shifts for all these comparisons of male test to female reference DNA. On average, only 1% (40/4096) and 0.6% (23/4096) of autosomal BACs were encompassed by segments designated as lost and gained, respectively. No site was called a deviation in more than two normal-cell arrays, and only four loci encompassing a total of 28 BACs were called deviant in two of the nine (~20%) normal-cell arrays. Thus, we report below only deviations detected in >20% of LocDC or AdvDC samples in order to minimize the risk of reporting false-positive deviant loci.

We next tested the ability of RCGA and array CGH to detect deviations in small numbers of cells of the LNCaP prostate-cancer cell line, which has well characterized genomic abnormalities (22). Four samples each composed of 10 or 20 cells collected by micropipetting, and one sample comprised of 200 ng of bulk genomic DNA purified from cells of the same passage, were independently amplified by RCGA for array CGH analysis. The analysis of bulk LNCaP DNA identified 13 sites of deviation, all previously reported for this cell line (22). Seven of the 13 (54%) deviant loci observed in the bulk sample were identified in all of the samples of 10-20 cells, and 11 of 13 (85%) were observed in at least one of the arrays from the 10- or 20-cell pools. The two deviations observed in the bulk sample but not in the small-cell-number samples each encompassed only two BACs. Thus, short deviations might be missed in arrays of DNA amplified from a small number of cells.

We found 8.5 additional deviant loci, on average, per array of 10-20 LNCaP cells. We selected five of these deviations to verify by FISH, using one of the BACs encompassed by each deviation as FISH probe. FISH demonstrated a heterogeneous mix of cells with and without the deviation of interest for three of the five copy-number alterations assessed (data not shown). Thus, random selection from a heterogeneous population contributes to the deviations detected by array CGH in small samples of LNCaP cells.

We next demonstrated the reproducibility of our approach using three biological replicates composed of 20 DTCs each collected from a single patient with metastatic prostate cancer. Each 20-cell sample was independently isolated from bone marrow, amplified, labeled, and analyzed by array CGH. Figure 1A shows the high correlation between the normalized log2-ratio values of each possible pair of replicates. Numerous sites of copy-number change spanning many Mbp of genomic material were identified for this patient’s samples. Figure 1B shows the considerable number of consistent deviant segments for the three replicates. Of the 3,077 BACs encompassed by alterations in one or more of these arrays, 72% (2219/3077) were identified in all three cell samples, and 88% (2716/3077) were deviant in two or more samples.

Figure 1
Reproducibility of array CGH on RCGA-amplified samples from 10-20 cells. Three pools of 20 AdvDCs were collected independently and amplified independently. Arrays for the three biological replicates were run in parallel against the same female reference. ...

The numerous genomic alterations in AdvDCs include those associated with prostate cancer and disease progression

High-level amplifications as well as loss or gain of many segments encompassing several Mbp were detected in all 11 AdvDC samples. On average, each AdvDC array detected 13 gains and 14 losses. All chromosomes showed a deviation in at least one of the AdvDC samples. Figure 2 summarizes the deviations observed in the AdvDC samples. The list of copy-number changes observed in three or more samples (≥27%) is extensive (Supplemental Table 2).

Figure 2
Frequency of deviations in AdvDCs (n = 11). The frequency of AdvDC samples with a deviation (y-axis) is plotted at the genomic position of the midpoint of each BAC encompassed by the deviation. Blue presents the frequency of gains, and red represents ...

The minimally overlapping regions (MORs) of copy-number change for deviations observed in at least four samples (36%) are given in Table 1. This list of MORs includes many chromosomal locations reported in the literature as frequent alterations for primary or metastatic prostate tumors, including loss on 8p, 10q, 13q, and 16q, and gain on 8q and Xq. Our results help narrow in on the regions of interest for these prostate-cancer related alterations. We show five discrete MORs on 10q, three on 13q, three on 16q, and four on Xq.

Table 1
Locations of minimally overlapping regions (MORs) of loss (A) and gain (B) detected in at least four of the 11 AdvDC samples.

Genomic alterations support the tumor origin of LocDCs

Based on the lack of clear associations between the mere detection of DTCs in men with clinically localized prostate cancer and subsequent relapse, an important question is if what we call DTCs are in fact tumor cells. To address this question, we compared the LocDC array results with the results from the primary tumor and AdvDC arrays. First, we found that the log2 ratios of the deviant autosomal segments for the LocDCs have a significantly lower dynamic range than those of the primary tumors and AdvDCs (Student’s t-test p < 0.05). The median log2 ratio of gained segments in LocDCs was 0.13 (SD = 0.07), in primary tumors 0.21 (SD = 0.11), and in AdvDCs 0.15 (SD = 0.15). The median log2 ratio of segments classified as loss for LOCDCs was -0.15 (SD = 0.10), for primary tumors -0.17 (SD = 0.09), and for AdvDCs -0.17 (SD = 0.15). Second, we found fewer deviations, on average, in LocDCs (avg. = 14) than in primary tumors (avg. = 43) or AdvDCs (avg. = 27).

We determined that normal-cell contamination is unlikely to be responsible for the diminished dynamic range in LocDC arrays. The normalized log2-ratio values for LocDCs collected using a marker (CD45) to exclude normal hematopoietic cells were not significantly different than those from LocDCs collected without this marker (p=0.1362). Moreover, AdvDCs were collected by the same methods as LocDCs, yet showed no evidence of normal-cell contamination.

Despite the less striking amplitude and number of deviations in LocDCs, we provide evidence that they are tumor cells. We exclude experimental noise as being responsible for the deviations observed in LocDCs. We compared LocDC array results to the normal-cell arrays (produced from the nine samples of 10-20 normal CD45+ cells collected and amplified in parallel with the LocDC samples). The deviant segment values for gains and losses of autosomal material in the LocDC and normal-cell arrays were not significantly different (Student’s t-test p = 0.16 and 0.09, respectively). However, the number of alterations and the amount of genomic material altered were significantly greater in LocDC arrays than those found in the arrays of 10-20 normal cells. LocDC samples showed an average of 14 deviations (3.5-fold more than normal cells, p = 0.005) and ~320 Mbp of altered genomic material (7.8-fold more than normal cells, p = 0.039).

Seventeen loci showed copy-number deviation in >20% of LocDCs (Table 2, Figure 3). Thirteen of these loci are likely to be real chromosomal deviations because they have no overlap with deviant segments in normal-cell arrays. Losses involving 13q22 and 19q12 seen in >20% of the LocDCs encompass significantly more BACs than overlapping deviations registered in the normal-cell arrays (18 vs. 9, and 9 vs. 2 BACs respectively) and are thus also candidate tumor-related changes. We discount two deviations (gain in 4p16 and 11q13) as probable experimental artifact, because they appeared in ~20% of normal-cell arrays.

Figure 3
Comparison of copy-number changes observed in LocDC samples, matching primary tumors and AdvDC samples. A. Percent of deviations observed across the genome in LocDCs (dots) overlaid on the frequencies for primary tumors (bars). B. Percent of deviations ...
Table 2
Losses (A) and gains (B) observed in >20% of LocDC arrays and the percentage of primary-tumor and AdvDC samples showing an overlapping alteration. A range of percentages is given for most deviations, since not all BACs were encompassed by deviation ...

A comparison of the genomic profiles of LocDC and primary tumors indicates several overlapping regions of deviation (Figure 3A and Table 2) supporting their common origin. Nine of the 15 (60%) sites of genomic change detected in >20% of the LocDC samples (excluding the two sites discounted as possible artifact) were also detected as deviant, in the same direction, in >20% of the primary tumor arrays. Moreover, 12 of the 15 loci showing change in >20% of the LocDC samples correspond to previously reported deviations in prostate cancer (Table 2, (23-28)), providing additional evidence that the LocDCs are prostate-tumor cells.

Direct comparison of concordant sites in the nine matched samples of LocDCs and primary tumors provides additional evidence that LocDCs are tumor cells. A concordant deviation was defined as one in which >30% of BACs in one sample’s deviation were encompassed by the other sample’s deviation, and vice versa. There were a total of 29 concordant sites of deviation across the nine sample pairs (Table 3), an average of 3.2 concordant loci per pair. For 24 of the concordant sites, >50% of BACs were shared. Only one pair lacked concordant alterations. Using simulated data sets, we found that three of the matched pairs have significantly more concordant changes than the number expected if the deviant segments were randomly distributed across the two genomes.

Table 3
The number of deviations observed in each matching pair of LocDC and primary tumor samples. The sample pairs with a significant number of concordant deviations (p < 0.05) are in bold. The column “concordant” gives the number of ...

The degree of concordance we observe is unlikely to be affected by the different amplification schemes used to amplify LocDCs (RCGA) and primary tumors (WGA2) (see Supplemental Methods and Results for detailed comparisons of RGCA and WGA2). In brief, arrays produced from LNCaP DNA amplified by these two methods showed significant concordance in their sites of genomic change (p<0.0001) and no significant difference in the dynamic range of the deviant segments (p=0.4820).

Frequent LocDC alterations are often detected in AdvDCs but not vice versa

We compared the array-CGH profiles of LocDCs and AdvDCs to look for changes that might illuminate the shared or divergent biology of these cell types. Ten (67%) of the 15 deviations observed in >20% of the LocDC samples (excluding those discounted as artifacts) were also observed in >20% of the AdvDC samples (Table 2). This set of loci includes loss at 8p23, one of the putative tumor suppressor containing regions on 8p (29), which was observed in 27% (3/11) of AdvDC samples and 23% (11/48) of LocDC samples. Two of the four most prevalent changes seen in the AdvDC samples were seen in >20% of the LocDC samples. Loss at 10q26 was observed in 55% (6/11) of AdvDC samples and 25% (12/48) of LocDC samples. Loss at 16q21 was also observed in 55% (5/11) of AdvDC samples and in 31% (15/48) of the LocDC samples.

Given the remarkable degree of genomic change in AdvDCs and the more limited deviations in LocDCs, it is not surprising that the majority of deviations that we observed in >20% of AdvDC samples were deviant in fewer than 20% of LocDC samples (Figure 3B and Supplemental Table 2). For example, losses that overlap with 8p12-21 and 8p22 were observed in four of the eleven (36%) AdvDC samples, five of the nine (56%) primary tumor samples, but only one of the 48 (2%) LocDC samples. Two deviations with considerable frequency differences was a gain of most of 8q (36% of AdvDCs and 10% of LocDCs) and gain of 1q32 (45% of AdvDCs and 8% of LocDCs).

Regions of frequent chromosomal alteration in DTCs and primary tumors are enriched for genes of involved in specific biological processes

We sought to determine if the chromosomal regions altered in DTCs and primary tumors impact genes of particular biological processes, based on gene ontology (GO) enrichment analysis. We considered all regions altered in >20% of the LocDC, AdvDC, or primary tumor samples (Supplemental Table 3), identified the genes corresponding to sites of loss or gain, and used hypergeometric tests (30) to determine if any GO categories were enriched (at a significance level of p < 0.001) in those gene sets. The enriched categories are given in Supplemental Table 4 (losses) and Table 5 (gains). We also include a second p-value, the SimPValue, which is an indicator of those GO categories that might be enriched due to gene clustering. Thus, categories with a SimPValue > 0.05 should be considered with caution.

The notable GO categories significantly enriched in alterations for >20% of the LocDC, AdvDC, or primary tumor samples with a SimPValue < 0.05 are as follows. Of the four categories enriched in the LocDC regions of gain, one was vitamin transport. This particular GO enrichment included FOLR1, a gene whose transcript and protein product are increased in specific epithelial tumors and are positively associated with tumor stage and grade (31). DNA metabolic process and chromatin modification were two of the six categories enriched in primary tumor gains, and cell-cell signaling was one of the 12 categories enriched in primary tumors losses. Various immune related processes made up the six categories enriched in the AdvDC regions of gain. Only electron transport was an enriched category in AdvDC losses (SimPValue = 0.001). One of the electron transport genes in AdvDC regions of loss was the tumor suppressor WWOX (32).

A few enriched gene categories with SimPValue > 0.05 represent biological processes that are sufficiently compelling to note here. Two categories related to adhesion (cell-cell adhesion (SimPValue = 0.051) and homophilic cell adhesion (SimPValue = 0.054)) were enriched in LocDC regions of loss. Transcription, DNA packaging, chromatin assembly, and protein-DNA complex assembly were GO categories enriched in the primary tumor gains. Eight categories related to regulation of transcription, metabolic, cellular, and biological processes were enriched in AdvDC losses.


To the best of our knowledge, this study represents the highest resolution genome-wide analysis of changes performed directly on prostate-cancer DTCs. The genome analysis of a small number of cells was made possible by combining RCGA, a whole-genome amplification scheme developed by Klein et al. (18), and array profiling by CGH. The use of a spotted DNA array as a CGH platform detects loss and gain at a high resolution and provides a direct link to the sequence of the human genome, a considerable improvement over conventional methods. Our proof-of-principle studies show that it is possible to produce genomic profiles of a small number of cells that exhibit relatively low levels of experimental noise, highly concordant results between bulk DNA and samples of a small number of cells, and excellent reproducibility across biological replicates.

We demonstrate that LocDCs have real genomic deviations and are composed of tumor cells, even though the genomic deviations in the LocDC samples were generally fewer in number and showed lower dynamic range than those in primary tumors and AdvDCs. First, we see significantly more deviations in the LocDC arrays than in the arrays of a comparably small number of normal cells, arguing against experimental noise as the source of alterations in LocDCs. Second, 80% of the deviant sites detected in >20% of the LocDC samples were previously reported in the literature as altered for prostate tumors. Third, we show significant concordance between a third of the matched pairs of LocDCs and primary tumors and that 60% (9/15) of the deviations observed in >20% of the all the LocDC samples were identified in our primary tumor samples.

AdvDCs are unmistakably tumor cells, given their numerous genomic alterations. Moreover, AdvDCs frequently possess deviations known to associate with a progressed disease state; deviations that were infrequently observed in LocDCs. A notable example is gain of 8q (36% of AdvDCs and 10% of LocDCs), an alteration associated with advanced disease and poor outcome in patients with organ-confined disease (23, 33, 34). Regions containing the metastasis-suppressor genes MKK4 (17p12) and KAI1 (11p11.2) were each deleted in ~30% of AdvDC samples but very few of the LocDCs. One-third of our AdvDC samples and none of the LocDC samples had a high-level amplification of the region on chromosome X encompassing the androgen receptor gene, a well characterized alteration specific to advanced disease (35, 36). Gain of 1q32 was one of the most common alterations found in AdvDCs (45%), uncommon in LocDCs (8%), and absent in our set of primary tumors. Notably, 50% of the metastatic tumors analyzed by array CGH in a parallel study also have a gain of 1q32 (Holcomb et al., in preparation)19, indicating that this region is likely to be associated with advanced disease. Will the small number of patients with LocDCs possessing metastases-associated alterations represent a significant proportion of progressors? Long-term follow-up studies are underway to answer this question.

Changes that we frequently observed in both AdvDCs and LocDCs might reflect their common origin or shared state of dissemination. Arguing for the latter, both DTC types showed frequent losses in 8p23, 10q, 13q, and 16q, alterations that have been frequently identified in prostate cancer (33). Deviations found in both types of DTCs, but that were absent from our primary tumor samples might be alterations related to dissemination. These sites include losses in 10q25.1, 13q22.2-32.1, 13q32-34, and 16q21. Interestingly, the loss in 16q21 encompasses two members of the cadherin family of adhesion genes (CDH8 and CDH11). Disruption of the cadherin pathway is implicated in tumor invasiveness and disease progression for a number of carcinomas (37). Analysis of a much larger set of matched primary and DTC samples will be needed to confirm what alterations are in fact specifically associated with dissemination.

The reduced amplitude of the genomic changes in LocDCs compared to AdvDCs and primary tumors could have several explanations. Copy-number changes can be dampened in the array-CGH readout by normal-cell contamination, abnormal ploidy, and heterogeneity. As we have shown, normal-cell contamination of our DTC samples is unlikely. Extra ploidy is also unlikely to be a factor in the LocDCs, as it is well established that most early prostate cancers, from which the LocDCs are presumably disseminated, are approximately diploid (38).

Genomic heterogeneity of LocDCs, as is seen in prostate tumors (39), is likely to contribute to a reduction in amplitude of LocDC alterations. Moreover, early dissemination might contribute additional heterogeneity to LocDCs relative to what might be found in the tumor itself. Inclusion of cells that migrated before acquiring multiple genome changes in the pool of LocDCs will dilute and reduce the overall number of detected alterations relative to the primary tumor at the time of resection, which we observe. Early dissemination of LocDCs might also explain their notably infrequent possession of 8p12-22 loss, the most common copy-number alteration reported in prostate cancer (35). Loss in this region was seen in only one of our 48 LocDC samples, in contrast with 54% of our primary tumors and 55% of our AdvDC samples.

How do our LocDC findings fit the current models of metastasis? The two predominant models are that (1) rare, highly metastatic cells within the primary tumor give rise to metastases, or (2) particular tumors, not rare cells, are prone to metastasis. In either case, the implication is that cells leave the primary tumor in possession of the molecular alterations necessary for secondary growth and survival. However, it is hard to imagine that the cells that generate secondary disease in patients many years after the removal of their primary tumor possess full metastatic potential (40). Theories of tumor-cell dormancy have been proposed recently (41-43) suggesting that DTCs from primary tumors might represent a dormant population of cells that can, through selection and mutation over time, acquire key molecular changes that will permit metastatic growth (41). Dormancy is supported by our GO analyses, which showed an enrichment of genes related to proliferation in sites of frequent alteration in our primary tumor and AdvDC samples, but not in our LocDCs, indicating that growth is not a prevailing theme in LocDC alterations. Thus, additional genomic hits would seem to be required, if LocDCs are to become overt metastases.

The high fraction of prostate cancer patients with localized disease in whom DTCs can be detected, and the relatively low level of genomic disorder shown here for such cells are persuasive evidence that LocDCs have not, in the majority of cases, reached full metastatic potential. If a subset of these cells can produce clinically significant metastases, then it seems likely, from the large number of studies showing gross chromosomal deviations in metastatic tumors, that these cells must eventually undergo additional genomic events. However, a subset of the genomic abnormalities that we identified in LocDCs may prove to be harbingers of progressive disease. Follow-up clinical studies of the patient population studied here along with additional molecular characterization of LocDCs could provide insight into the metastatic potential of DTCs.

Supplementary Material



The authors gratefully acknowledge the support and technical expertise of members of the Trask, Vessella, and Nelson laboratories, especially Roger Coleman, Ilsa Coleman, Edward Arfman, Christine Wang, Bryce Lakely, and Mengchu Wu.


19Holcomb IN, Grove DI, Young JM, Kim TS, Gifford D, Hsu L, Nelson PS, Vessella RL, Trask BJ. High-resolution analysis of metastatic tumors from prostate cancer. Manuscript in preparation 2008.


1. Pantel K, Brakenhoff RH. Dissecting the metastatic cascade. Nat Rev Cancer. 2004;4:448–56. [PubMed]
2. Cone S. A case of carcinoma metastases in bone from a primary tumor of the prostate. Johns Hopkins Hospital Bulletin. 1898;9:114–8.
3. Partin AW, Kattan MW, Subong EN, et al. Combination of prostate-specific antigen, clinical stage, and Gleason score to predict pathological stage of localized prostate cancer. A multi-institutional update. Jama. 1997;277:1445–51. [PubMed]
4. Partin AW, Mangold LA, Lamm DM, Walsh PC, Epstein JI, Pearson JD. Contemporary update of prostate cancer staging nomograms (Partin Tables) for the new millennium. Urology. 2001;58:843–8. [PubMed]
5. Greenlee RT, Murray T, Bolden S, Wingo PA. Cancer statistics, 2000. CA Cancer J Clin. 2000;50:7–33. [PubMed]
6. Bianco FJ, Jr., Wood DP, Jr., de Oliveira J Gomes, Nemeth JA, Beaman AA, Cher ML. Proliferation of prostate cancer cells in the bone marrow predicts recurrence in patients with localized prostate cancer. Prostate. 2001;49:235–42. [PubMed]
7. Hofmann T, Riesenberg R, Buchner A, Zimmermann W, Hofstetter A, Oberneder R. Disseminated tumor cells in bone marrow of patients with transitional cell carcinoma: immunocytochemical detection and correlation with established prognostic indicators. J Urol. 2003;169:1303–7. [PubMed]
8. Lilleby W, Nesland JM, Fossa SD, Torlakovic G, Waehre H, Kvalheim G. The prognostic impact of cytokeratin-positive cells in bone marrow of patients with localized prostate cancer. Int J Cancer. 2003;103:91–6. [PubMed]
9. Moreno JG, Miller MC, Gross S, Allard WJ, Gomella LG, Terstappen LW. Circulating tumor cells predict survival in patients with metastatic prostate cancer. Urology. 2005;65:713–8. [PubMed]
10. Berg A, Berner A, Lilleby W, et al. Impact of disseminated tumor cells in bone marrow at diagnosis in patients with nonmetastatic prostate cancer treated by definitive radiotherapy. Int J Cancer. 2007;120:1603–9. [PubMed]
11. Ellis WJ, Vessella RL, Corey E, et al. The value of a reverse transcriptase polymerase chain reaction assay in preoperative staging and followup of patients with prostate cancer. J Urol. 1998;159:1134–8. [PubMed]
12. Ellis WJ, Pfitzenmaier J, Colli J, Arfman E, Lange PH, Vessella RL. Detection and isolation of prostate cancer cells from peripheral blood and bone marrow. Urology. 2003;61:277–81. [PubMed]
13. Gao CL, Maheshwari S, Dean RC, et al. Blinded evaluation of reverse transcriptase-polymerase chain reaction prostate-specific antigen peripheral blood assay for molecular staging of prostate cancer. Urology. 1999;53:714–21. [PubMed]
14. Pfitzenmaier J, Ellis WJ, Hawley S, et al. The detection and isolation of viable prostate-specific antigen positive epithelial cells by enrichment: a comparison to standard prostate-specific antigen reverse transcriptase polymerase chain reaction and its clinical relevance in prostate cancer. Urol Oncol. 2007;25:214–20. [PubMed]
15. Millon R, Jacqmin D, Muller D, Guillot J, Eber M, Abecassis J. Detection of prostate-specific antigen- or prostate-specific membrane antigen-positive circulating cells in prostatic cancer patients: clinical implications. Eur Urol. 1999;36:278–85. [PubMed]
16. Weckermann D, Muller P, Wawroschek F, Krawczak G, Riethmuller G, Schlimok G. Micrometastases of bone marrow in localized prostate cancer: correlation with established risk factors. J Clin Oncol. 1999;17:3438–43. [PubMed]
17. Mueller P, Carroll P, Bowers E, et al. Low frequency epithelial cells in bone marrow aspirates from prostate carcinoma patients are cytogenetically aberrant. Cancer. 1998;83:538–46. [PubMed]
18. Klein CA, Schmidt-Kittler O, Schardt JA, Pantel K, Speicher MR, Riethmuller G. Comparative genomic hybridization, loss of heterozygosity, and DNA sequence analysis of single cells. Proc Natl Acad Sci U S A. 1999;96:4494–9. [PMC free article] [PubMed]
19. Kraus J, Pantel K, Pinkel D, Albertson DG, Speicher MR. High-resolution genomic profiling of occult micrometastatic tumor cells. Genes Chromosomes Cancer. 2003;36:159–66. [PubMed]
20. Lin DW, Coleman IM, Hawley S, et al. Influence of surgical manipulation on prostate gene expression: implications for molecular correlates of treatment effects and disease prognosis. J Clin Oncol. 2006;24:3763–70. [PubMed]
21. Loo LW, Grove DI, Williams EM, et al. Array comparative genomic hybridization analysis of genomic alterations in breast cancer subtypes. Cancer Res. 2004;64:8541–9. [PubMed]
22. van Bokhoven A, Varella-Garcia M, Korch C, et al. Molecular characterization of human prostate carcinoma cell lines. Prostate. 2003;57:205–25. [PubMed]
23. Nupponen NN, Kakkola L, Koivisto P, Visakorpi T. Genetic alterations in hormonerefractory recurrent prostate carcinomas. Am J Pathol. 1998;153:141–8. [PMC free article] [PubMed]
24. Wolf M, Mousses S, Hautaniemi S, et al. High-resolution analysis of gene copy number alterations in human prostate cancer using CGH on cDNA microarrays: impact of copy number on gene expression. Neoplasia. 2004;6:240–7. [PMC free article] [PubMed]
25. Torring N, Borre M, Sorensen KD, Andersen CL, Wiuf C, Orntoft TF. Genome-wide analysis of allelic imbalance in prostate cancer using the Affymetrix 50K SNP mapping array. Br J Cancer. 2007;96:499–506. [PMC free article] [PubMed]
26. Pettus JA, Cowley BC, Maxwell T, et al. Multiple abnormalities detected by dye reversal genomic microarrays in prostate cancer: a much greater sensitivity than conventional cytogenetics. Cancer Genet Cytogenet. 2004;154:110–8. [PubMed]
27. Saramaki OR, Porkka KP, Vessella RL, Visakorpi T. Genetic aberrations in prostate cancer by microarray analysis. Int J Cancer. 2006;119:1322–9. [PubMed]
28. Sun J, Liu W, Adams TS, et al. DNA copy number alterations in prostate cancers: a combined analysis of published CGH studies. Prostate. 2007;67:692–700. [PubMed]
29. Perinchery G, Bukurov N, Nakajima K, et al. Loss of two new loci on chromosome 8 (8p23 and 8q12-13) in human prostate cancer. Int J Oncol. 1999;14:495–500. [PubMed]
30. Falcon S, Gentleman R. Using GOstats to test gene lists for GO term association. Bioinformatics. 2007;23:257–8. [PubMed]
31. Kelemen LE. The role of folate receptor alpha in cancer development, progression and treatment: cause, consequence or innocent bystander? Int J Cancer. 2006;119:243–50. [PubMed]
32. Hezova R, Ehrmann J, Kolar Z. WWOX, a new potential tumor suppressor gene. Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub. 2007;151:11–5. [PubMed]
33. Visakorpi T, Kallioniemi AH, Syvanen AC, et al. Genetic changes in primary and recurrent prostate cancer by comparative genomic hybridization. Cancer Res. 1995;55:342–7. [PubMed]
34. Steiner T, Junker K, Burkhardt F, Braunsdorf A, Janitzky V, Schubert J. Gain in chromosome 8q correlates with early progression in hormonal treated prostate cancer. Eur Urol. 2002;41:167–71. [PubMed]
35. Bubendorf L, Kononen J, Koivisto P, et al. Survey of gene amplifications during prostate cancer progression by high-throughout fluorescence in situ hybridization on tissue microarrays. Cancer Res. 1999;59:803–6. [PubMed]
36. Koivisto P, Kononen J, Palmberg C, et al. Androgen receptor gene amplification: a possible molecular mechanism for androgen deprivation therapy failure in prostate cancer. Cancer Res. 1997;57:314–9. [PubMed]
37. Takeichi M. Cadherins in cancer: implications for invasion and metastasis. Curr Opin Cell Biol. 1993;5:806–11. [PubMed]
38. Ozen M, Pathak S. Genetic alterations in human prostate cancer: a review of current literature. Anticancer Res. 2000;20:1905–12. [PubMed]
39. Beheshti B, Vukovic B, Marrano P, Squire JA, Park PC. Resolution of genotypic heterogeneity in prostate tumors using polymerase chain reaction and comparative genomic hybridization on microdissected carcinoma and prostatic intraepithelial neoplasia foci. Cancer Genet Cytogenet. 2002;137:15–22. [PubMed]
40. Pound CR, Partin AW, Eisenberger MA, Chan DW, Pearson JD, Walsh PC. Natural history of progression after PSA elevation following radical prostatectomy. Jama. 1999;281:1591–7. [PubMed]
41. Klein CA, Holzel D. Systemic cancer progression and tumor dormancy: mathematical models meet single cell genomics. Cell Cycle. 2006;5:1788–98. [PubMed]
42. Aguirre-Ghiso JA. Models, mechanisms and clinical evidence for cancer dormancy. Nat Rev Cancer. 2007;7:834–46. [PMC free article] [PubMed]
43. Vessella RL, Pantel K, Mohla S. Tumor cell dormancy: an NCI workshop report. Cancer Biol Ther. 2007;6:1496–504. [PubMed]
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