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Molecular Biology, Pathobiology, and Genetics |
1 Norris Cotton Cancer Center and 2 Department of Genetics, Dartmouth Medical School, Lebanon, New Hampshire; and 3 Molecular Cell Biology-R&D, Applied Biosystems, Foster City, California
Requests for reprints: Mark A. Israel, Norris Cotton Cancer Center, Dartmouth Medical School, Lebanon, NH 03755. Phone: 603-653-3611; Fax: 603-653-9003; E-mail: Mark.A.Israel{at}dartmouth.edu.
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Abstract |
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Introduction |
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The NCI-60 panel of human tumor cell lines provides unique opportunities for identifying the molecular and genetic underpinnings of neoplasia. This set of 59 tumor cell lines is derived from melanoma and from cancers of the gastrointestinal tract, kidney, ovary, breast, prostate, lung, and central nervous system (CNS) as well as from various leukemias. The NCI-60 cell lines have been extensively employed as experimental models of neoplastic disease and are annotated by multiple large-scale data sets, including results of pharmacologic studies that document the sensitivities of these cells to >100,000 different chemical compounds and chemotherapeutics (22–25). In addition, the National Cancer Institute's Developmental Therapeutics Program has assessed the molecular features of these cell lines related to cancer and chemotherapeutic sensitivity (24, 26, 27). To better understand how microRNAs might contribute to malignancy, we evaluated the expression patterns of 241 microRNAs in each of the 59 cell lines that comprise the NCI-60 panel and in a set of corresponding normal tissues. We found that the patterns of microRNA expression among these normal and neoplastic cells suggest the potential activities of specific microRNAs in contributing to the pathobiology of certain types of human tumors.
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Materials and Methods |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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Real-time quantification of 241 microRNAs using stem-loop real-time PCR. The expression profiles of 241 microRNAs were measured as described previously (28). This method uses stem-loop primers for reverse transcription followed by real-time PCR (TaqMan MicroRNA Assays; Applied Biosystems, Foster City, CA). RNA input was normalized using four endogenous controls: 18S rRNA, ß2M, glyceraldehyde-3-phosphate dehydrogenase, and ß-actin.
Data analyses. Statistical analyses including the leave-one-out sensitivity analyses, Student's t tests, and Spearman rank analysis were carried out using TM4MeV v4.0b software (Institute for Genomic Research, Rockville, MD), R statistical package4 and Microsoft Excel. Experimentally normalized 
Ct values for the microRNA profiles were used to evaluate the NCI-60 cell lines by agglomerative hierarchical clustering using average linkage and correlation similarity and verified for significance by multiscale bootstrap resampling analyses (29).
Target prediction for microRNAs up-regulated in CNS tumor–derived cell lines. Potential targets for microRNAs were predicted using MiRanda associated with the Sanger MIRBASE. Gene expression data describing glioblastomas and normal brain tissue (30) and NCI-60 cell lines (22) were extracted from the Stanford Microarray Database.
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Results |
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MicroRNA expression patterns characterize human cancer cell lines based on their tissue of origin. Experimentally normalized Ct values corresponding to the level of expression of 241 microRNAs were used to evaluate normal tissues and the NCI-60 cell lines by agglomerative hierarchical clustering. This analysis of patterns of microRNA expression revealed potential relationships among the normal tissues and the 59 cell lines. To assess the robustness of these relationships, we conducted a multiscale bootstrap resampling analysis of these same data. The resulting dendrogram describing these relationships had three main branches: one that held all the NCI-60 cell lines and two others which together held all the normal tissue samples (Fig. 1A
). This bootstrap analysis identified six statistically significant clusters. Two of the six clusters consisted entirely of normal tissues. One of these consisted of normal brain tissue and peripheral blood mononuclear cells, whereas the other cluster included samples of the normal tissues from small intestine, thymus, breast, prostate, kidney, lung, pancreas, spleen, ovary, liver, lymph node, and normal colon (Fig. 1A, black). The other four significant clusters consisted entirely of NCI-60 cell lines derived from hematologic, colon, melanoma, or CNS tumors (Fig. 1A).
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The NCI-60 lines have also been characterized by others based on mRNA expression levels. As microRNAs can regulate mRNA levels, we explored whether the relationships between cell lines that we detected based on microRNA expression patterns (Supplementary Fig. S1) were similar to the relationships between these same cell lines based on mRNA expression patterns. We compared our hierarchical clustering of the NCI-60 microRNA profiles to the clustering analysis of mRNAs in these lines reported in the Stanford cDNA Microarray Database (Supplementary Fig. S1). A comparison of the dendrograms derived from these clustering analyses revealed similar patterns, in which cell lines segregated in a manner that reflected the tissue of origin of the tumors from which they were derived. Similar to the hierarchical clustering based on microRNA expression patterns (Fig. 1A), the clustering based on mRNA expression of cell lines derived from hematologic, melanoma, CNS, colon, and renal tumor tissues were found in independent terminal branches, and those derived from lung, ovarian, prostate, and breast tumors were distributed in multiple different terminal branches (Supplementary Fig. S1).
Expression of specific microRNAs is essential for the clustering of tumor cell lines based on their tissue of origin. The multiscale bootstrap analysis characterized the expression levels of 241 different microRNAs and identified four highly significant clusters of cell lines derived from hematologic, colon, melanoma, and CNS tumors, respectively (Fig. 1A). To identify those microRNAs in which the expression patterns most significantly distinguished these four cell line clusters, we did a comparative t test analysis. For each of the four clusters, the average Ct of each microRNA in the cell lines within the cluster was compared with the average Ct for that same microRNA in all of the NCI-60 cell lines not within that cluster. By this analysis, microRNAs were selected in which the average level differed significantly (P 
0.01) between a cluster and the rest of the NCI-60 cell lines (Supplementary Table S1). From this evaluation of all microRNAs in each of the four clusters, a total of 81 distinguishing microRNAs were identified for further analysis.
To determine if this set of 81 significant microRNAs was sufficient to define the four significant tumor cell clusters, we conducted a hierarchical cluster analysis of the NCI-60 cell lines using expression data from these 81 microRNAs only. The dendrogram describing the relationship of the cell lines in this analysis (Supplementary Fig. S1) contains distinct clusters of cell lines derived from hematologic, melanoma, CNS, colon, and renal tissues, whereas cell lines derived from lung, ovarian, prostate, and breast tumors were distributed in multiple different terminal branches. This pattern closely parallels the relationships we observed in our initial analysis of all 241 microRNAs (Fig. 1A). Moreover, multiscale bootstrap analysis of the dendrogram in Fig. 1B revealed the same four significant clusters of cell lines derived from hematologic, melanoma, CNS, and colon tumors as were detected in Fig. 1A. These findings confirm that this set of 81 microRNAs is sufficient to define the four clusters of tumor cell lines from hematologic, colon, melanoma, and CNS tissue.
To evaluate the relative contribution of each of the 81 microRNAs to the integrity of the tumor cell line clusters, we determined the effect of omitting each individual microRNA from the multiscale bootstrap analysis shown in Fig. 1B. For this evaluation, we first eliminated 39 microRNAs that displayed an expression pattern indistinguishable from the pattern of one of the other microRNAs, based on a Pearson coefficient of correlation of >0.8 at P 0.05 (Table 1
). The remaining 42 microRNAs (Table 1, boldface), each of which is expressed in a unique pattern, were evaluated by a leave-one-out sensitivity analysis. Of these 42 tested, two microRNAs (mir-375 and mir-211), upon being taken out of the analysis, had no effect; however, when each of the remaining 40 microRNAs were individually removed from the multiscale bootstrap analysis of all the NCI-60 cell lines, the confidence interval for one or more clusters dropped below 95% (corresponding to P 0.05) or cluster membership changed by two or more cell lines (Table 1). Thus, among the core set of 81 distinguishing microRNAs that were identified based on t test analyses (Supplementary Table S1), the 40 microRNAs (and the candidates showing correlated expression patterns) play a significant role in maintaining one or more of the four significant NCI-60 cell line clusters in Fig. 1A.
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To assess the expression patterns of the microRNAs identified as being differentially expressed in a specific cell line cluster, we created heat maps in which the expressions of microRNA were found to be significantly different in one cell line cluster when compared with all other cell lines (Fig. 2A–D ). In these heat maps, the cell lines of the NCI-60 panel were grouped by tissue of origin and aligned across the top of Fig. 2, and microRNAs identified by t test analysis as being differential expressed were displayed on the vertical axis. Indeed, for each of the cell line clusters derived from hematologic, CNS, colon, and melanoma tumors (Fig. 1A), the subset of microRNAs key for the establishment of each cluster (Table 1) displays a pattern of expression apparently unique to that cluster (Fig. 2).
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Other microRNAs provide potential signature patterns of expression in each of the cell line clusters examined. In the six CNS tumor–derived lines, a total of 52 microRNAs were expressed at significantly different levels when compared with their level of expression in the other NCI-60 lines. Of these 52 microRNAs, only 6 were expressed at higher levels, whereas 46 microRNAs were down-regulated (Fig. 2B). Among the seven colon tumor-derived lines, 30 microRNAs were expressed at significantly different levels. Of these, 5 were down-regulated, whereas the remaining 25 were up-regulated when their expression levels were compared with the other NCI-60 lines (Fig. 2C). In the eight melanoma lines, only 15 microRNAs were expressed significantly differently; 4 were up-regulated and 11 down-regulated when compared with levels in the other NCI-60 lines (Fig. 2D). Therefore, the down-regulation of specific microRNAs together with the up-regulation of other microRNAs formed a distinctive signature for each of the four cell line clusters examined (Fig. 2A–D).
MicroRNAs as candidate tumor suppressors or oncogenes. Cancer is a collection of heterogeneous genetic diseases that arise in association with the accumulation of mutations that activate proto-oncogenes and inactivate tumor suppressor genes. There is considerable literature relevant to the emerging role of microRNAs in tumor development, and others have identified a number of microRNAs as candidate oncogenes and tumor suppressor genes (31–34). We sought to identify microRNAs in which the expression level in specific tumor cell lines was either significantly increased or decreased from that observed in a corresponding normal tissue and therefore suggested that their function was either enhanced or diminished in association with tumorigenesis. We examined the microRNAs expressed in three clusters of cell lines (hematologic, CNS, and colon) as each of these clusters expressed a significantly distinct pattern of microRNAs (Fig. 2), and in each case, there was a sufficient number of normal tissue controls for comparison in a t test analysis.
One hundred and forty-five microRNAs were significantly increased or decreased at least 2-fold in one or more cell line clusters compared with their corresponding normal tissues (Supplementary Table S2). Seventy microRNAs were decreased in all three clusters or in two of the three clusters, compared with their respective normal tissues, and could act as tumor suppressors in more than one type of tumor-derived cell line (Supplementary Table S2). Alternatively, reduction of microRNAs in multiple cell line clusters could reflect other differences between a cultured tumor cell line and a normal tissue specimen, such as changes resulting from the in vitro culture of tumor cells. However, if a microRNA is down-regulated in cell lines derived from just a single tumor type, it is less likely that this reduction would result from nonspecific changes associated with in vitro growth conditions. Accordingly, in Table 2A and B , we present only those microRNAs that were expressed at a significantly different level in only one of the three clusters we examined. In the hematologic, colon, and CNS tumor–derived cell lines, 4, 7, and 59 potential tumor suppressors, respectively, were identified that satisfied this criterion of tumor cell type specificity (Table 2A). Of these potential tumor suppressor microRNAs, 15 microRNAs (1 in the hematologic, 1 in the colon, and 13 in the CNS tumor–derived cell lines) are of particular interest as they did not display cell type–specific expression among the normal hematologic, colon, and CNS tissue samples (Table 2A). This finding suggests that these microRNAs do not seem to be simply markers of hematologic, colonic, or CNS differentiation, providing additional evidence of their potential role in oncogenesis.
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We sought to ascertain whether an association between microRNA expression and copy number changes in the DNA encoding the set of 81 microRNAs that define the four highly significant clusters of tumor cell lines derived from hematologic, colon, melanoma, and CNS tissues (Fig. 1B) could be identified. We used spectral karyotyping data5 combined with single nucleotide polymorphism (SNP) data6 to calculate the SNP copy number in the specific regions that carry the 81 microRNAs. If a microRNA fell within the 50 kb range of a known SNP copy number (calculations described in Supplementary Table S4A), it was included in our analysis. Based on this criteria, we identified 59 microRNAs in which the SNP copy number could be evaluated and compared that to the level of their corresponding RNA copy number in the 27 cell lines that make up the four distinct clusters. Of these 59, we found 3 microRNAs in which the expression levels were moderately correlated (P 0.05) with the DNA copy number of the region in which they are located (Supplementary Table S4A). Mir-182, mir-192, and mir-31 had a Spearman r value of 0.482295482, 0.425824176, and 0.402625153, respectively (Supplementary Table S4B). All three of these microRNAs were highly expressed in cell lines that had excess copies of the region of the genome in which they were located, suggesting that gene amplification might contribute to the high levels of expression we observed.
Potential targets of microRNAs that are up-regulated in CNS tumor–derived cell lines. Although determining the precise contribution of each microRNA in which the level of expression might be altered in a particular tumor type is beyond the scope of this investigation, we sought additional evidence supporting the likelihood that microRNAs expressed at aberrant levels were contributing to the malignant characteristics of the tumors from which the cell lines we examined were derived. We identified putative target genes that might be regulated by microRNAs highly expressed in CNS tumor–derived cell lines. We used the MiRanda target prediction algorithm associated with the Sanger MIRBASE to examine mirs-10a/b, mirs-196a/b, and mir-21 that are overexpressed in CNS tumor–derived cell lines. Six hundred and eighty-nine unique predicted target transcripts for these microRNAs were identified. Four hundred and seventy-four of these have UniGene IDs, and 388 have analyzable mRNA expression profiles in CNS tumors reported in a published microarray data set examining 3 normal brain and 29 glioblastoma specimens (30). We hypothesized that some of the transcripts might encode proteins involved in tumor-suppressive activities in glioblastomas, and hence, should have decreased expression in glioblastomas compared with normal brain. To determine whether the expression of these genes was significantly decreased in glioblastomas, a Student's t test was done on 252 genes for which there were data for at least two specimens of both glioblastoma tissue and normal brain. Among these, we found 23 genes with significantly decreased expressions in glioblastomas compared with normal brain (P 0.05; Table 3
; Supplementary Table S3). Many genes among these have no currently recognizable relationship to tumorigenesis, however, one extracellular matrix protein, SPOCK1 (35), and two transcription regulators, ZMYND11 (36) and RB1CC1 (ref. 37; Table 3), have known functions that could contribute to tumorigenesis.
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0.01). The heat maps shown in Fig. 3
describe the expression levels of microRNAs that had a significant correlation with doubling time. Two distinct subgroups emerged consisting of five microRNAs in which the expression levels tended to increase with increasing doubling time (Fig. 3A) and 16 microRNAs in which the expression levels tended to decrease with increasing doubling time (Fig. 3B). The microRNAs in which elevated expression correlates with longer doubling times could be interpreted as candidate antiproliferative microRNAs that act across a broad range of tissue types. Similarly, the microRNAs in which expression correlates with rapid cell cycling represent a set of potentially broadly acting enhancers of proliferation. |
Discussion |
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Agglomerative hierarchical clustering followed by multiscale bootstrap resampling analyses identified major features of the microRNA expression patterns in these cells. First, we observed that these patterns were distinctly different in normal and malignant tissues. Normal tissues clustered separately from the NCI-60 cell lines, and the cell lines segregated into major subclusters (Fig. 1A). Second, major subclusters reflected their tissue of origin, suggesting that the microRNA expression may signify the fundamental properties of these tumor lines. In vitro growth conditions including the inclusion of synthetic medium and fetal bovine serum may have an effect on microRNA expression patterns in these human tumor cell lines. However, it seems unlikely that the microRNA expression patterns of the NCI-60 cell lines are merely artifacts of in vitro culture conditions. If that were the case, similar changes in microRNA expression throughout the NCI-60 cell lines irrespective of their distinctive biological characteristics would have been observed. Finally, the expression patterns of a limited number of specific microRNAs (Table 1) were found to underlie the tissue-based clustering of some tumor cell lines. Leave-one-out sensitivity analysis showed that the expression of 81 specific microRNAs was sufficient for the tissue-specific clustering of cell lines derived from melanoma and tumors of hematologic, colonic, and CNS origin (Table 1; Fig. 1B). The molecular signatures encompassed by this relatively small number of microRNAs (Fig. 1B) may reflect the role of these microRNAs in mediating tissue-specific differentiation and/or tumorigenesis, and furthermore, suggest a molecular basis for the development of disease.
Further support for the interpretation that microRNA expression patterns reflect bona fide properties of the cells of origin of the NCI-60 cell lines comes from our comparison of the expression patterns of microRNAs to the patterns of mRNA expression in the NCI-60 panel (Supplementary Fig. S1). Specifically, the microRNA and mRNA clustering patterns exhibited both, significantly clustering based largely on the tissue of origin (Supplementary Fig. S1). In particular, cell lines derived from hematologic, melanoma, CNS, colon, and renal tumor tissues were similarly clustered into independent terminal branches in both analyses (Fig. 1A; Supplementary Fig. S1). The observation that lung, ovarian, prostate, and breast tumors were distributed throughout many branches in the cluster analysis would seem to be inconsistent with this observation. However, these branches all emanated from the same main secondary branch, perhaps reflecting the likely epithelial tissues of origin.
The pattern of microRNA expression associated with hematologic tumors (Fig. 2A) could provide a signature to differentiate non–solid from solid tumors, as these cell lines are all found clustered on a single dendrogram branch that differentiates them from solid tumors (41). Of particular note is the polycistronic set of microRNAs consisting of mir-17, mir-18, mir-19a, mir-19b, and mir-92, which are overexpressed in leukemic lines compared with the other cell lines. These closely linked, coexpressed microRNAs lie within a region (C13orf25v2) of amplification in some leukemias and lymphomas (11, 12). The mir-17-92 cluster is up-regulated in chronic lymphocyte leukemias and chronic lymphocyte leukemia cell lines, as well as many different lymphoma cell lines. Also, overexpression of this cluster of microRNAs in human diffuse large B-cell lymphoma has been described (19). Recently, in a mouse B-cell lymphoma model, overexpression of the mir-17 cluster was found to cooperate with c-myc to accelerate tumor development (12). Our finding that mir-17-92 expression contributes to the signature of hematologic tumor cell lines provides evidence that microRNA expression patterns in NCI-60 cell lines reflects microRNA patterns observed in primary hematologic malignancies.
The overall reduction in the expression level of many microRNAs in cell lines compared with normal tissues (Fig. 1A) may reflect of the relatively undifferentiated characteristics of most tumor cells. The lowest levels of microRNA expression are seen in very poorly differentiated tumors (42). Also, in mammalian embryonic stem cells, most microRNAs are induced during cellular maturation in tissue-specific gene expression patterns and play key roles in the maintenance of cell lineage characteristics (43–45). Therefore, the patterns of microRNA expression retained by tumor cells could reflect microRNAs defining the developmental state of the cells in which the tumor originated as well as microRNAs functioning in transformation pathways.
Altered expression of microRNAs could have profound effects on the expression of proteins key for mediating transformation. MicroRNAs that are overexpressed in tumors might contribute to oncogenesis by diminishing the level of expression of targeted tumor suppressor genes. Conversely, microRNAs with diminished expressions in tumors might normally repress one or more oncogenes, such that reduction of the microRNA could lead to enhanced expression of one or more oncogenic mRNA. Accordingly, we identified microRNAs that might contribute to malignancy using the criterion that they were either significantly down-regulated (Table 2A) or significantly up-regulated (Table 2B) in just one of the three major tumor cell line clusters relative to the corresponding normal tissue, and that did not display tissue-specific expression in comparisons among the normal tissue samples. MicroRNAs that were up-regulated or down-regulated in more than one major tumor cell line clusters (Supplementary Table S2) were interpreted as less likely to be tumor-suppressive or oncogenic microRNAs, as their expression might have been altered as a consequence of in vitro culture. It is striking that a relatively large number of microRNAs (59) seemed to behave like potential tumor suppressors for CNS (Table 2A). This result suggests that the malignant behavior of CNS tumor cells could result from relatively complex changes in gene expression, compared with the colon or hematopoietic tumors. Alternatively, the widespread reduction of microRNAs in CNS tumor lines relative to normal brain tissue could reflect the widely recognized complexity of cell types that make up brain tissue and which would not be represented in the tumor line being examined. We also compared our data on these potentially tumor-suppressive or oncogenic microRNAs in cell lines to existing literature in tissue from specific tumor types [Table 2A and B; Supplementary Table S2; and reviewed in refs. (33, 46)].
Our finding that a number of microRNAs that were highly expressed in selected cell lines exhibited an association between their expression levels and the copy number of adjacent SNPs (Supplementary Table S4) suggests that these microRNAs might be biologically selected during tumorigenesis because of the important functions they serve. Furthermore, the correlation of microRNA expression in malignant tissues and cancer-associated genomic regions (CAGR) identifies microRNAs possibly involved in cancer (47). We examined whether genes for microRNAs up-regulated or down-regulated in NCI-60 cell lines were also known to be located in CAGRs such as fragile sites, minimal regions of loss of heterozygosity, minimal regions of amplification, or common breakpoint regions. Among the candidate tumor-suppressive microRNAs we identified (Table 2A; Supplementary Table S2), mir-125b, mir-34a, mir-143, mir-145, mir-26a, mir-99a, let-7a, let-7d, let-7f, and let-7g were located in fragile sites and regions of loss of heterozygosity/amplification (47). One of the candidate oncogenic microRNAs that we identified (Table 2B), mir-21, was also located in a fragile site/region of amplification (47). Importantly, certain microRNAs that we have identified as potentially tumor-suppressive or oncogenic are located in CAGRs known to be important in various malignancies. For example, mir-125b and let-7a are located in regions known to be altered in lung, breast, ovarian, and cervical cancer, whereas mir-34a, mir-99a, and mir-26a are located in CAGRs involved in breast, lung, and epithelial/nasopharyngeal cancers. Additionally, mir-143 and mir-145 are located in CAGRs involved in the myelodysplastic syndrome and the oncogenic mir-21 in regions critical for neuroblastoma (47). Four of the five potentially oncogenic microRNAs we identified in the CNS tumor–derived cell lines (Table 2B) are located within HOX clusters, many of which are overexpressed in glioblastoma cell lines as well as in primary glioblastoma tumor tissue suggests their potential as transforming genes (48). Mir-10a and mir-196a are located within the HOX B cluster on 17q21, mir-196b is in the HOX C cluster on 12q13, and mir-10b is in the HOX D cluster on 2q31 (10), suggesting the potential involvement of these particular microRNAs in tumorigenesis.
Although a comprehensive analysis of the potential regulatory targets of microRNAs in NCI-60 cell lines is beyond the scope of this study, we found that a set of potential targets decreased in glioblastomas. SPOCK1 is a predicted target of mir-21 in which the level is increased 17.7-fold in CNS tumor–derived lines when compared with normal brain (Table 2B). SPOCK1 is a proteoglycan originally described as being expressed in neurons (35), but its expression is increased in reactive astrocytes, and it inhibits the activities of membrane-type matrix metalloproteinases and cathepsin L, which promote migration of neoplastic astrocytes (49). Mir-10a is up-regulated 64.9-fold in CNS tumor–derived lines compared with brain (Table 2B) and one of its potential targets is ZMYND11, a transcriptional suppressor of the adenovirus E1A protein (36). E1A interacts with tumor suppressor genes (such as RB1). RB1CC1, another known tumor-suppressor is up-regulated 23.2-fold in CNS tumor lines compared with brain, and is a predicted target of mir-10b (Table 2B). RB1CC1 is a key regulator of RB1 (37). Mutations of RB1CC1 occur in 20% of primary breast cancers (50).
The NCI-60 cell line panel has been studied for numerous characteristics related to malignancy including drug sensitivity, gene expression patterns, and molecular and biological alterations of importance for tumorigenesis. Our studies, revealing that microRNA expression correlates to cell proliferation indices, suggests specific microRNAs in which the biological activities may be of importance in regulating proliferation. Importantly, these findings indicate the feasibility of seeking correlations between microRNA expression and the numerous other characteristics of these cell lines reported in public databases. This should further elucidate the pathophysiologic roles of microRNAs in various types of cancers.
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Acknowledgments |
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The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
We thank Dr. Susan Holbeck for sending us frozen pellets of the NCI-60 panel of cell lines as well as for calculating the SNP copy number in the specific regions that carry the 81 core microRNAs. We are also grateful for the advice and help received from Dr. Eric Posmentier at Dartmouth College in correlating microRNA expression levels to DNA copy number, and for the excellent support in data analyses that we received from Alexis Meng and Carol S. Ringelberg at Dartmouth College.
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Footnotes |
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5 http://www.ncbi.nlm.nih.gov/sky/skyweb.cgi?formtype=submitters 
6 http://dtp.nci.nih.gov/mtargets/download.html
Received 7/31/06. Revised 11/14/06. Accepted 12/29/06.
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