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1 Génotypes et Phénotypes Tumoraux, EMI229 INSERM/Université Montpellier I, Montpellier, France; 2 ERM 206 INSERM/Université Aix-Marseille 2, Parc Scientifique de Luminy, Marseille cedex, France; and 3 IGBMC, U596 INSERM/Université Louis Pasteur, Parc d’Innovation, Illkirch cedex, France
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ABSTRACT |
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INTRODUCTION |
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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CGH, loss of heterozygosity, and molecular genetics data, taken together, show that chromosome 17 is rearranged in at least 30% of breast tumors (3 , 4) . Short and long arms differ in the type of events they harbor. Chromosome 17p is principally involved in losses, some of them possibly focal, whereas CGH on 17q shows complex combinations of overlapping gains and losses. Most recent efforts have focused on two regions of gains considered to be the principal events: 17q12-q21 corresponding to the amplification of ERBB2 and collinear genes, and a large region at 17q23 (5 , 6) . A number of new candidate oncogenes have been identified, among which GRB7 and TOP2A at 17q21 or RP6SKB1, TBX2, PPM1D, and MUL at 17q23 have drawn most attention (6, 7, 8, 9, 10) . Furthermore, DNA microarray studies have revealed additional candidates, with some located outside current regions of gains, thus suggesting the existence of additional amplicons on 17q (8 , 9) .
Our previous loss of heterozygosity mapping data pointed to the existence at 17q of at least five regions of imbalance (of which two corresponded to DNA amplification; ref. 11 ). This is likely to be a minimal estimate, when taking into account similar data from the literature. This view was reinforced by fluorescence in situ hybridization studies performed in our laboratory4 and confirmed by array-CGH (8 , 9) . Moreover, the observation of complex combinations of gains and losses within 40 to 50 Mb at 17q in individual breast tumors prompted us to further investigate these extensive rearrangements.
Our goal was to define with greater accuracy regions of copy number losses and/or gains on chromosome 17 and determine their boundaries. To do this, we applied the recently developed CGH on genomic arrays approach. We also sought to gain better insight on the genes involved and wanted to verify the existence of recurrent sites of rearrangements on chromosome 17. We built a genomic array covering chromosome 17 at a mean density of 1 clone per 500 Kb and used it to characterize patterns of gains and losses in 30 breast cancer cell lines and 22 primary breast tumors. Expression profiles of genomically typed tumors or cell lines were established using custom-made cDNA arrays comprising 376 expressed sequence tag sequences corresponding to 358 known genes mapping at 17q. This enabled the definition of regions of recurrent gains and losses. These were correlated with recurrent changes in expression levels that confirmed previously proposed candidates and identified novel genes. Furthermore, it appeared that individual tumors or cell lines could bear highly complex patterns of anomalies, cumulating in several amplification peaks and concomitant interstitial losses. Finally, because studied tumors and cell lines recurrently showed abrupt ruptures at the boundaries of some amplicons, we propose the existence of recurrent breakpoint sites.
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MATERIALS AND METHODS |
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Classical Comparative Genomic Hybridization.
Normal metaphase chromosomes were prepared from umbilical cord blood according to standard cytogenetic protocols. Hybridizations were done on Vysis (Downers Grove, IL) normal human metaphases. Genomic DNA labeling and CGH reaction were performed as described by Courjal and Theillet (3)
. CGH images were captured on a Zeiss (Le Pecq, France) epifluorescence microscope equipped with a JAI (Glostrup, Denmark) charge-coupled device camera run by Metasystems (Altlussheim, Germany) image analysis software. CGH analysis was done using ISIS 4.4 software (Metasystems).
Genomic Arrays.
The chromosome 17 genomic array consisted of 107 Roswell Park Cancer Institute (RPCI)-bacterial artificial chromosome (BAC) and P1 artificial chromosome (PAC) clones from the set of cytogenetically mapped clones reported previously,5
20 BACs selected using sequence data, and 46 BAC and PAC clones corresponding to genetic markers and known genes. A large majority of RPCI-1,3, 5 PAC clones and RPCI-11 BAC clones were obtained from the Children’s Hospital Oakland Research Institute (Oakland, CA). Nine clones (CTD-2251J22, RP11-455O6, RP11-300G13, RP11-319A23, RP11-379P18, RP11-387C17, RP11-399J11, RP11-469C13, and RP11-489G5) were obtained from Research Genetics (Huntsville, AL). Clones corresponding to genetic markers were isolated from the Down to The Well human BAC library of GenomeSystems Inc. (St. Louis, MO). Clones D152 and PO135 were isolated from the RZPD Human Chromosome-sorted Cosmid Library of chromosome 17 (Berlin, Germany). Clones 56K13 and 201L4 were obtained by screening the HGMP Human PAC Library of the United Kingdom HGMP Resource Centre (Cambridge, United Kingdom). Cosmid clones Neu1 and Neu4 and P1 clone 610 were provided by Dr. A. Kallioniemi (Bethesda, MD). Clone P1.9 was from Dr. D. Viskochil (Salt Lake City, UT). See the list of clones in Supplementary Table S1.
Array-Comparative Genomic Hybridization Conditions.
We isolated BAC, PAC, and cosmid DNA using Nucleobond BAC100 from Macherey-Nagel (Hoerdt, France). We carried out degenerated nucleotide primer (DOP)-polymerase chain reaction (PCR) amplification on 10 ng of prepared DNA in a final reaction volume of 100 µL. Primer sequences and the DOP-PCR protocol used are available on the Sanger Center web site (12)
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We performed it with slight modifications: second-round DOP-PCR primer was not amino-linked in our experiments. Purification of PCR products was done using Nucleofast 96 PCR plates (Macherey-Nagel). Purified PCR products were resuspended in double-distilled H2O at 2 µg/µL. An aliquot was run on an agarose gel to ascertain even distribution of product in all wells. Prior spotting products were diluted 1:1 in spotting solution (Amersham Biosciences, Orsay, France) and spotted in quadriplicate onto Corning GapsII slides (Schiphol-Rijk, the Netherlands) using a Lucidea array spotter IV (Amersham Biosciences).
Hybridization to Microarrays and Image and Data Analysis.
Genomic DNA was digested by NdeII according to the supplier’s recommendations (Roche Diagnostics, Meylan, France). Three hundred nanograms of digested genomic DNA were labeled by random priming in a 50-µL reaction containing 0.02 mmol/L dATP, 0.02 mmol/L dGTP, 0.02 mmol/L dTTP, 0.05 mmol/L dCTP, 0.04 mmol/L Cy3-dCTP or Cy5-dCTP; 25 units of Klenow fragment (50 units/µL; New England Biolabs, Ozyme, Saint Quentin Yvelines, France), 10 mmol/L ß-mercaptoethanol, 5 mmol/L MgCl2, 50 mmol/L Tris-HCl (pH 6.8), and 300 µg/mL random octamers. The reaction was incubated at 37°C for 20 hours and stopped by adding 2.5 µL of 0.5 mol/L EDTA (pH 8). The reaction product size was about 100 bp. We purified labeled products using microcon 30 filters (Amicon, Millipore, Molsheim, France). Abundance of the labeled DNA was checked using a spectrophotometer, and incorporation of dyes was calculated using Molecular Probes software.7
A mixture of 700 pmol of Cy5-labeled probes and 700 pmol of Cy3-labeled probes was ethanol precipitated in the presence of 250 to 300 µg of human Cot-1 DNA (Roche Diagnostics) and 100 µg of herring sperm DNA (Promega, Charbonnières, France). The pellet was dried and resuspended in 280 µL of Hybrisol VII (Appligene Oncor, Qbiogen, Illkirch, France). The probes were denatured at 80°C for 10 minutes, and repetitive sequences were blocked by preannealing at 37°C for 90 minutes. Slides were blocked for 20 minutes at 42°C in saturation buffer (1% bovine serum albumin, 0.2% SDS, and 5x SSC), washed in 2x SSC and 0.2% SDS and then in 2x SSC, and dehydrated in an ethanol series. A 8.8-cm2 open hybridization chamber (Gene Frame, Abgene, Courtaboeuf, France) was fixed on the slide, and the 280-µL preannealed mix was applied and hybridized in a humid chamber at 37°C on a rocking table for 16 hours. After hybridization, slides were washed in 2x SSC and 0.1% SDS (pH 7) at 55°C for 5 minutes and in 1x SSC and 0.1% SDS (pH 7) at 55°C for 5 minutes, followed by three washes in 0.1x SSC for 30 seconds at room temperature, and briefly rinsed in water. Slides were dried by spinning for 5 minutes at 1,000 rpm and stored at room temperature until scanned. Arrays were scanned by a GenIII Array Scanner (Amersham Biosciences). Images were analyzed by ARRAY-VISION 6.0 software (Amersham Biosciences). Spots were defined by use of the automatic grid feature of the software and manually adjusted when necessary. Fluorescence intensities of all spots were then calculated after subtraction of local background. These data were then analyzed using a custom-made MS-Excel VBA script. Cy3 and Cy5 global intensities were normalized with the entire set of spots on the array, the Cy3/Cy5 ratios were calculated, the median values of replicate spots were calculated, and these values were used to define the selection threshold for individual spots (only replicates showing <15% of deviation from the median were kept), with representation of profiles with log 2 ratios on the Y axis and Mb position of clones8
along the chromosome on the X axis. For each sample, at least two experiments were performed (Cy3/Cy5 and Cy5/Cy3), and the final profile corresponds to the mean of two experiments.
Complementary DNA Array Construction and Analysis.
Preparation and hybridization of cDNA arrays were as described previously (13)
. Of the 720 cDNAs spotted, 376 corresponded to 358 known genes positioned on chromosome 17 (Supplementary Table S2).8
Hybridization signals were quantified using HDG Analyzer software (Genomic Solutions, Ann Arbor, MI) by integrating all spot pixel signal intensities and removing spot background values determined in the neighboring area.
Expression values for each sample were normalized according to the median expression levels in all samples (tumors and cell lines). This was done to favor the selection of expression differences related to quantitative genomic anomalies. Using an adaptation of the Spline function proposed by Cole (14) , the variance was adjusted to be constant in the whole dataset (for low and high expression levels). Then a confidence interval determining genes that showed nonsignificant variation was defined. Its bandwidth was adjusted to fit the SD in the dataset. It encompassed 68.3% of the spots on the array. The distance separating the limit of the confidence interval from its orthogonal projection on the first diagonal was defined as the basic unit of expression variation. Thus, within the confidence interval, all values equaled |1|. This defined the baseline, and genes with values > 1 (spots above the first diagonal) were considered overexpressed, and genes with values < –1 (spots below the first diagonal) were considered underexpressed.
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RESULTS |
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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To address this in greater detail, we built a genomic array covering chromosome 17 with 173 genomic clones (BAC, PAC, and cosmids). The average density was 1 target per 0.5 Mb. Coverage was not even throughout the chromosome, with a higher density at 17q12-q21 and 17q23-q25 and lower density on 17p with 1 target per 1 Mb (Supplementary Fig. S1). Clones selected on the array contained 191 genes identified according to the June 2002 human genome sequence freeze.8 To determine the threshold for gains and losses and test for variability, four normal/normal hybridizations were performed, and SD was determined (Supplementary Fig. S2).
Array-CGH data of both cell lines and tumors showed complex profiles on chromosome 17 (complete dataset is in Supplementary Figs. S4 and S5), especially for the long arm, which showed combinations of gains and intervening losses (Fig. 1A and B)
. This elevated complexity prompted a two-level analysis of genomic profiles. First we wanted to define consensus regions that we would subsequently use as a basis for a comparison of genomic and expression profiles. Compilation of data from primary tumors and cell lines allowed us to define segments according to the main type of event observed (gain or loss). To do this, losses or gains were scored for each target clone along the chromosome, and ruptures in their frequency curve defined the boundaries of different segments (Fig. 2A and B)
. Thirteen segments were defined. These were distributed as four segments showing mainly losses (17p, 17q11.2, 17q21, and 17q24), six segments showing mainly gains (one at 17q12 and five in the 17q22-q25 interval), and three segments involved in either gains or losses (17q21.3, 17q22, and 17q25). Second, we searched for the smallest regions of overlap (SRO). To be considered, they had to occur in at least three tumors or cell lines. Accordingly, 18 SRO of gains and 16 SRO of losses were defined (Fig. 2C)
. Finally, we noted the existence of sharp transitions bordering gains or losses of elevated amplitude (Fig. 1)
. We identified 14 transition sites; interestingly, these tended to cluster within narrow intervals. One striking example is a transition downstream of ERBB2-GRB7 observed 15 times within an interval of 0.2 Mb (Fig. 1
; Supplementary Table S3).
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. Mean expression levels were calculated for each gene within each group (no CNC, gained, and lost) for each segment. Next, expression for each gene of the gained or lost group was normalized according to that of the no CNC group, and the expression difference d was calculated (Fig. 3)
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. Genes retained in this restricted screen included a number of previously identified genes (LASP1, RPL19, ERBB2, GRB7, HOXB7, NDP52, RPS6KB1, GRB2, and BIRC5), as well as a number of novel candidates (TOM1L1, COX11, ZNF161, FLJ20062, SMARCD2, LLGL2, SMT3H2, CDK3, and SECTM1). In addition to overexpressed genes, we noted 32 genes showing reduced expression levels in segments of gains. This unexpected pattern will be worth exploring in order to determine whether this apparent paradox is related to any function detrimental to cancer growth. Remarkably, the retinoid receptor homologue NR1D1 and CBX1 (human orthologue of the chromo-domain protein HP1), which both act as transcriptional repressors, were members of this group (Supplementary Table S6).
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, we selected a subset of 32 genes (Table 2)
. Disregarding the 9 genes alternatively overexpressed when gained or underexpressed when lost, there remained 23 genes whose expression was consistently reduced as a consequence of a genomic loss. Whether this is related to the inactivation of a tumor suppressor gene remains to be determined.
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DISCUSSION |
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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We present here a comprehensive study on copy number aberrations on chromosome 17 and their consequences at the RNA expression level in breast cancer. At the genomic level, our data clearly showed that this chromosome was severely rearranged in breast cancer, and anomalies were found throughout the entire length of chromosome 17. We applied a two-level definition for regions of anomalies. Compiling data obtained on 22 tumors and 30 cell lines, we defined 13 consensus segments according to the main anomaly observed. We then searched for the SRO among the consensus regions limited by transition sites. These regions represented events that could occur independently and may thus be a more accurate representation of core events. In total, 17 SRO of gains and 16 SRO of losses were defined. Interestingly, 10 of 17 SRO of gains could be involved in high-level amplification, and 7 of 16 regions of losses showed events of high amplitude. This strongly suggested that these events resulted from a positive selection. Moreover, a number of these events of elevated amplitude were bordered by sharp transitions, and these breakpoints tended to cluster in narrow intervals (0.2–2 Mb). We identified 14 such sites, and array-CGH profiles suggested the occurrence of multiple breaks within a single tumor. This was supported by fluorescence in situ hybridization data showing multiple clusters of amplified chromosome 17 sequences dispersed at several chromosomal locations (11) . These breakpoints could correspond to chromosomal fragile sites and play an active part in the occurrence of CNCs at 17q in breast tumors. Indeed, it is well established that unrepaired double-strand breaks are initiating events for DNA amplification (19) . Sites of rupture related to regions of chromosomal fragility are apparently essential for DNA amplification to occur (20) . It is noteworthy that the rupture site we mapped at 17q21.2 (38 Mb) colocalized with the t(15;17)(q22;q12-q21) translocation breakpoint cluster stereotypical of acute promyelocytic leukemia (21) . It would be interesting to verify whether some of these rupture sites on 17q correspond to recurrent breast cancer-specific translocation breakpoints such as the recently characterized breakpoint at 8p12-p21 (22) .
Copy number variations are expected to affect RNA expression levels. This is well accepted for DNA amplification, which was shown to arise as a selective mechanism for increased expression of one or more target genes (23) . Some authors have proposed to extend this model to lower level CNCs, such as those resulting from aneuploidy (24) . From the studies of Virtaneva et al. (25) , a trisomy of chromosome 8 in acute myeloid leukemia apparently results in a global expression increase of genes on this chromosome, and other reports on tumors with more deeply affected karyotypes also suggest global modifications in expression concordant with chromosomal dosage (26) . However, the selective advantage of such unstable events may be questionable because aneuploidy is a byproduct of mitotic instability in tumor cells (27) and is therefore prone to undergo rapid changes, as recently shown by us (13) .
Overall, our data clearly indicate that CNCs, as gains or losses, are associated with important modifications in RNA expression levels. Five to fifty percent of genes in an amplified segment showed increased expression, whereas up to 30% of genes in a region of loss presented reduced RNA levels. A search for the most consistent expression changes led to the selection of 85 genes gained and overexpressed and 67 genes underexpressed in conjunction with a genomic loss. We observed 19 genes that showed both overexpression when gained and underexpression when lost. A number of these genes were either proven oncogenes or strong candidates. This finding emphasizes the strong influence of genomic dosage on expression levels. Transcription levels appeared to be almost mechanically adjusted according to copy numbers, and for such genes, DNA amplification could be the most efficient mechanism to select for increased RNA expression.
In contrast, 32 genes showed reduced expression in conjunction with genomic gains. This suggests a down-regulation of these genes when amplified, at variance with collinear genes, which were selected for increased expression. It will be interesting to see whether this effectively corresponds to transcriptional repression, thus favoring an interpretation that these genes could act as tumor suppressors.
Further work based on the analysis of a large set of breast tumors will be needed to validate the relative significance of the different candidates and, eventually, to evaluate their interplay. In this respect, we were motivated to determine whether genes mapping in different amplification cores presented coordinated expression profiles, thus suggesting coselection processes. Therefore, we analyzed expression profiling data by hierarchical clustering and searched for groups of recurrently coclustering genes. Three clusters grouping genes located in different regions of gains at 17q were identified. Cluster 2, for example, grouped genes at 17q12 (PSMD11, PSMB3, RPL19, and TAF2N), 17q23 (TOM1L1), and 17q25 (SECTM1 and TBCD).
The diversity of functions among these genes was striking and covered almost every area of cell physiology and metabolism, including transcription (ZNF161 and SMARCD2), DNA replication (CDC6), recombination (RAD51 and TOP2A), chromatin remodeling (CBX1 and HBOA), protein catabolism (PSMB1 and SMT3H2), vesicular trafficking (TOM1L1), RNA translation (RPL19 and RPS6KB1), and respiratory chain (COX11). COX11 encodes for an enzyme located at the mitochondrial inner membrane (28) , and its amplification/overexpression could be related to the selection of PHB in our list of 85 amplified/overexpressed genes. Indeed, PHB codes for prohibitin and was originally proposed as a tumor suppressor. However, its role is unclear because it is presented either as a nuclear protein interacting with pRB (29) or as a chaperone stabilizing respiratory complexes at the mitochondrial inner membrane. Interestingly, PHB is up-regulated in case of mitochondrial stress (30) .
Chromosome 17 is commonly and intensely rearranged in a number of human malignancies. Our work and published data show that a large number of genes can be involved. The prevalent involvement of chromosome 17 in cancer is puzzling and suggests that it harbors genes instrumental to the cancer process. Additionally, the presence of a number of chromosomal fragility sites could be a synergistic element. Chromosomal breaks will favor DNA copy number aberrations and modify expression profiles. This will in turn result in accelerated cell proliferation and bypass of cell cycle checkpoints, which will eventually end up in additional genetic aberrations. Similarly, it can easily be envisioned that deregulated expression of genes such as RAD51, which is instrumental for homologous recombination-mediated DNA repair, or HBOA, which affects chromatin conformation, will have profound consequences on genomic integrity and thus worsen the cancer phenotype.
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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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.
Note: Supplementary data for this article can be found at Cancer Research Online (http://cancerres.aacrjournals.org).
Requests for reprints: Charles Theillet, EMI 229 INSERM, Centre de Recherche, CRLC Val d’Aurelle 34298 Montpellier cedex 5, France. Phone: 33-467-613-766; Fax: 33-467-613-041; E-mail: theillet{at}valdorel.fnclcc.fr
4 B. Orsetti, unpublished observations. 
5 http://www.ncbi.nlm.nih.gov/genome/cyto/hbrc.shtml.
6 http://www.sanger.ac.uk/HGP/methods/cytogenetics/DOPPCR.shtml.
7 http://www.probes.com/resources/calc/basedyeratio.html.
8 http://genome.ucsc.edu, June 2002 freeze.
Received 3/ 2/04. Revised 5/26/04. Accepted 7/19/04.
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1.5 for overexpression and d 
–1.5 for reduced expression in at least 20% of the tumors or cell lines showing CNCs. Significant expression differences are represented as black bars. Gray bars correspond to nonsignificant differences. The number of samples used to calculate the mean expression value in a segment is indicated on each graph for normal and altered samples (G, gains; L, loss, N, no CNC). SRO are depicted as gray (gains) or black (losses) horizontal bars at the bottom of the graph. A, expression differences in conjunction with gains in segment 3. B, expression differences in conjunction with gains in segment 6. C, expression differences in conjunction with losses in segment 4.