Background Recurrent non-random genomic alterations are the hallmarks of cancer and the characterization of these imbalances is critical to our understanding of tumorigenesis and cancer progression.
Background
Recurrent non-random genomic alterations are the hallmarks of cancer and the characterization of these imbalances is critical to our understanding of tumorigenesis and cancer progression.Results We performed array-comparative genomic hybridization (A-CGH) on cDNA microarrays containing 42,000 elements in neuroblastoma (NB). We found that only two chromosomes (2p and 12q) had gene amplifications and all were in the  MYCN  amplified samples. There were 6 independent non-contiguous amplicons (10.4–69.4 Mb) on chromosome 2, and the largest contiguous region was 1.7 Mb bounded by  NAG  and an EST (clone: 757451); the smallest region was 27 Kb including an EST (clone: 241343),  NCYM , and  MYCN . Using a probabilistic approach to identify single copy number changes, we systemically investigated the genomic alterations occurring in Stage 1 and Stage 4 NBs with and without  MYCN  amplification (stage 1-, 4-, and 4+). We have not found genomic alterations universally present in all (100%) three subgroups of NBs. However we identified both common and unique patterns of genomic imbalance in NB including gain of 7q32, 17q21, 17q23-24 and loss of 3p21 were common to all three categories. Finally we confirm that the most frequent specific changes in Stage 4+ tumors were the loss of 1p36 with gain of 2p24-25 and they had fewer genomic alterations compared to either stage 1 or 4-, indicating that for this subgroup of poor risk NB requires a smaller number of genomic changes are required to develop the malignant phenotype.
Results
We performed array-comparative genomic hybridization (A-CGH) on cDNA microarrays containing 42,000 elements in neuroblastoma (NB). We found that only two chromosomes (2p and 12q) had gene amplifications and all were in the  MYCN  amplified samples. There were 6 independent non-contiguous amplicons (10.4–69.4 Mb) on chromosome 2, and the largest contiguous region was 1.7 Mb bounded by  NAG  and an EST (clone: 757451); the smallest region was 27 Kb including an EST (clone: 241343),  NCYM , and  MYCN . Using a probabilistic approach to identify single copy number changes, we systemically investigated the genomic alterations occurring in Stage 1 and Stage 4 NBs with and without  MYCN  amplification (stage 1-, 4-, and 4+). We have not found genomic alterations universally present in all (100%) three subgroups of NBs. However we identified both common and unique patterns of genomic imbalance in NB including gain of 7q32, 17q21, 17q23-24 and loss of 3p21 were common to all three categories. Finally we confirm that the most frequent specific changes in Stage 4+ tumors were the loss of 1p36 with gain of 2p24-25 and they had fewer genomic alterations compared to either stage 1 or 4-, indicating that for this subgroup of poor risk NB requires a smaller number of genomic changes are required to develop the malignant phenotype.MYCNNAGNCYMMYCNMYCNConclusions cDNA A-CGH analysis is an efficient method for the detection and characterization of amplicons. Furthermore we were able to detect single copy number changes using our probabilistic approach and identified genomic alterations specific to stage and  MYCN  amplification.
Conclusions
cDNA A-CGH analysis is an efficient method for the detection and characterization of amplicons. Furthermore we were able to detect single copy number changes using our probabilistic approach and identified genomic alterations specific to stage and  MYCN  amplification.MYCN

Background Neuroblastoma (NB) is one of the most common pediatric solid tumors, and accounts for 7–10% of all childhood cancers. The prognosis of patients with NB varies according to the stage, age and  MYCN  amplification status. Stage 1 disease is essentially curable, whereas patients with stage 4 disease, in particular those with  MYCN  amplification, remain largely incurable despite advances in cancer therapeutics [ 1 ]. Genomic alterations in NB have been investigated by cytogenetic, and molecular methods including spectral karyotyping and metaphase comparative genomic hybridization (M-CGH) [ 2 - 6 ]. Based on these studies several genomic alterations have been reported to correlate with prognosis including amplification of the  MYCN  oncogene (found in 30% of NB) [ 1 , 7 ], gains of 17q (>50%) and loss of 1p36 (30–35%) [ 1 , 8 , 9 ]. Other recurrent changes including losses of 3p, 4p, 9p, 11q, and 14q, as well as frequent gain of chromosome 7 have also been suggested to have relevance to the development and progression of these tumors [ 9 ]. Recently array-based CGH (A-CGH) on BAC and cDNA microarrays has been used to investigate the genomic alterations with high resolution [ 10 - 14 ]. cDNA A-CGH has been successfully utilized to detect amplification and to investigate the direct effects of genomic changes over gene expression level by using the same microarray for both A-CGH and gene expression analysis [ 14 - 16 ]. In this study, we applied A-CGH, on cDNA microarrays containing 42,000 elements, to systematically identify common aberrant genomic alterations in NB of various stages. We have applied a probabilistic approach to detect single-copy losses and gains of chromosomal regions. Our study has three principal aims: 1) Detection and high resolution mapping of amplicons in NB. 2) Detection of low copy number genomic alterations using a probabilistic approach. 3) Establishing a map of genomic imbalances in NB profiling samples with good (stage 1) and poor (stage 4 with or without  MYCN  amplification) prognosis.
Background
Neuroblastoma (NB) is one of the most common pediatric solid tumors, and accounts for 7–10% of all childhood cancers. The prognosis of patients with NB varies according to the stage, age and  MYCN  amplification status. Stage 1 disease is essentially curable, whereas patients with stage 4 disease, in particular those with  MYCN  amplification, remain largely incurable despite advances in cancer therapeutics [ 1 ]. Genomic alterations in NB have been investigated by cytogenetic, and molecular methods including spectral karyotyping and metaphase comparative genomic hybridization (M-CGH) [ 2 - 6 ]. Based on these studies several genomic alterations have been reported to correlate with prognosis including amplification of the  MYCN  oncogene (found in 30% of NB) [ 1 , 7 ], gains of 17q (>50%) and loss of 1p36 (30–35%) [ 1 , 8 , 9 ]. Other recurrent changes including losses of 3p, 4p, 9p, 11q, and 14q, as well as frequent gain of chromosome 7 have also been suggested to have relevance to the development and progression of these tumors [ 9 ].MYCNMYCNMYCNRecently array-based CGH (A-CGH) on BAC and cDNA microarrays has been used to investigate the genomic alterations with high resolution [ 10 - 14 ]. cDNA A-CGH has been successfully utilized to detect amplification and to investigate the direct effects of genomic changes over gene expression level by using the same microarray for both A-CGH and gene expression analysis [ 14 - 16 ]. In this study, we applied A-CGH, on cDNA microarrays containing 42,000 elements, to systematically identify common aberrant genomic alterations in NB of various stages. We have applied a probabilistic approach to detect single-copy losses and gains of chromosomal regions. Our study has three principal aims: 1) Detection and high resolution mapping of amplicons in NB. 2) Detection of low copy number genomic alterations using a probabilistic approach. 3) Establishing a map of genomic imbalances in NB profiling samples with good (stage 1) and poor (stage 4 with or without  MYCN  amplification) prognosis.MYCNResults Amplicon Mapping by A-CGH Totally around 24,000 qualified array cDNA clones were applied for data analysis in 12 NB cell lines and 32 NB primary tumor samples (see Table  1  for sample information). Fig.  1  shows the number of clones as well as the average spacing for each chromosome. We first determined the sensitivity of A-CGH to detect the copy number of highly amplified genes. We here chose  MYCN  since it is the most commonly amplified gene in NB and correlates with the biological behavior of these tumors. Fig.  2A  shows the linear regression plot of the  MYCN  amplification results from A-CGH and Quantitative-PCR (Q-PCR). We found that the slope of the fitting line was 0.35, and therefore an observed ratio of 2 by A-CGH corresponds to Q-PCR ratio of ~6. In order to identify the amplified regions, we initially selected genes with A-CGH ratio ≥2 for at least two contiguous clones in genome sequence order. Only two chromosomes (2p and 12q) showed amplifications by this criterion exclusively in the  MYCN  amplified samples. Focusing on 2p (Fig.  2B ), we found 6 independent non-contiguous amplicons (10.4–69.4 Mb). For the  MYCN  amplicon, the largest contiguous region was 1.7 Mb and bounded by  NAG  and an EST (clone: 757451) in three tumor samples, whilst the smallest region was 27 Kb including an EST (clone: 241343),  NCYM , and  MYCN . We identified 9 previously reported co-amplified genes ( HPCAL1 ,  ODC1 ,  NSE1 ,  NAG ,  DDX1 ,  NCYM ,  POMC ,  DNMT3A ,  ALK, MEIS1, TEM8 ) [ 16 , 20 - 27 ], and detected the novel amplification of several known genes ( NCOA1 ,  ADCY3 ,  PPP1CB ,  CGI-127 ,  LBH ,  CAPN13 ,  GalNac-T10 ,  EHD3 ,  XDH ,  SRD5A2 ,  CGI-27 ,  AMP18 ) and ESTs. Three of the cell lines (CHP134, IMR-5 and IMR-32) contained two amplicons in 2p13-15. The first (66.6–67.6 Mb) included previously reported amplified gene  MEIS1 , and the size of the second amplicon was 0.3 Mb (69.1–69.4 Mb), which was bounded by  LOC200504  and  TEM8 . In addition to chromosome 2p, we identified another amplicon on 12q14-q15 in a single tumor (NB21); bounded by  PRO2268  (68.9 Mb) and  RAB3IP  (69.9 Mb) containing one previously reported amplified gene ( MDM2 ) [ 28 ] as well as several novel amplifications ( CPM ,  CPSF6 ,  LYZ ,  GAS41 ,  SNT-1 ,  CCT2 ,  VMD2L3 ,  and RAB3IP ) (Fig.  2C ). We verified the amplification of  NSE1 ,  NAG ,  DDX1 ,  MYCN  and  TEM8  by Q-PCR (data not shown). Simultaneous gene expression profiling by using the same cDNA arrays for all samples showed that 47% of the amplified genes correlate with gene expression (using a correlation coefficient cutoff 0.5; data not shown). Table 1 Summary of Neuroblastoma Information Sample No Sample Label Age (yr.mo) Sex Diagnosis MYCN Source C1 CHP-134B 1.1 M 4 + NCI C2 GI-LI-N 1.11 M 4 + ICLC C3 IMR-32 1.1 M 4 + ATCC C4 IMR-5 ND ND ND + ICLC C5 LAN-1 2 M 4 + ICLC C6 LAN-5 0.4 M ND + NCI C7 SK-N-BE(2) 2.2 M 4 + ATCC C8 SK-N-DZ 2 F ND + ATCC C9 SMS-KCNR 1.2 M 4 + NCI C10 SH-SY5Y 4 F 4 - ATCC C11 SK-N-AS 8 F 4 - ATCC C12 SK-N-FI 11 M ND - ATCC C13 SK-N-SH 4 F 4 - ATCC T1 NB19 12.9 F 1 - DZNSG T2 NB20 1.3 M 1 - DZNSG T3 NB229 0.3 M 1 - CHTN T4 NB248 0.6 M 1 - CHW T5 NB29 0.3 M 1 - DZNSG T6 NB33 1.5 F 1 - DZNSG T7 NB34 1.2 M 1 - DZNSG T8 NB43 1.1 F 1 - DZNSG T9 NB44 1.6 M 1 - DZNSG T10 NB5 0.3 M 1 - DZNSG T11 NB7 1.3 F 1 - DZNSG T12 NB9 1.1 M 1 - DZNSG T13 NB16 3.11 F 4 - DZNSG T14 NB205 3.11 F 4 - CHTN T15 NB217 2 M 4 - CHTN T16 NB24 0.7 M 4 - DZNSG T17 NB246 3.7 M 4 - CHW T18 NB247 1.5 M 4 - CHW T19 NB26 1 M 4 - DZNSG T20 NB30 0.11 F 4 - DZNSG T21 NB31 1.4 F 4 - DZNSG T22 NB32 1.2 M 4 - DZNSG T23 NB35 2.7 F 4 - DZNSG T24 NB8 4.7 M 4 - DZNSG T25 NB14 0.11 M 4 + DZNSG T26 NB21 5.3 M 4 + DZNSG T27 NB249 0.8 M 4 + CHW T28 NB251 0.9 F 4 + CHW T29 NB252 0.10 F 4 + CHW T30 NB266 2 F 4 + CHW T31 NB27 10.6 M 4 + DZNSG T32 NB28 1.8 F 4 + DZNSG Abbreviations used are ND: not determined ATCC: American Type Culture Collection, CHTN: Cooperative Human Tissue Network CHW: The Children's Hospital at Westmead, Australia, DZNSG: German Cancer Research Center, Heidelberg, ICLC: Interlab Cell Line Collection, NCI: National Cancer Institute, NIH.  1 Naming convention "C" denotes cell lines, "T" tumors, such that C1 is cell line number 1. Figure 1 Distribution of cDNA clones in our microarray.  Total 23975 unique UniGene clusters remained from the initial 42591 clones after quality filtering. Number of clones in each chromosome was represented in gray bar on the left side. Average spacing (chromosome size/number of clones in the chromosome) was represented in black dot on the right side. Figure 2 Amplifications in  MYCN  amplified samples .  A.  Regression analysis of  MYCN  ratio (sample  vs . normal) obtained from A-CGH and real-time Q-PCR in all of neuroblastoma samples including 13 cell lines and 32 primary tumors. The slope of the regression line is 0.35 indicating that an observed A-CGH ratio of 2 is equivalent to a Q-PCR ratio of 5.7.  B.  Independent amplicons in chromosome 2p. All amplified genes are listed under each amplicon in genome order. Map position, genome sequence position (Mb) and samples containing the specific amplicon are listed for each amplicon. The percentage of the  MYCN  amplified samples harboring these amplicons are shown in brackets following the gene name for all clones present in our microarray (gray), the remainder of the clones are predicted genes found in the NCBI database   that are mapped between the boundaries of the amplicon. Amplicons were selected based on the criteria of A-CGH ratio ≥2 for at least two contiguous clones in genome sequence order. In cases where a single clone has a ratio <2 but the ratio of its adjacent clones is greater than 2, that single clone was still considered as a part of amplicon. *: previously reported amplification.  C.  Amplicon in chromosome 12q in tumor NB21. Detection of low-level DNA copy number alterations To test the sensitivity of A-CGH to detect single copy number changes, we performed A-CGH with DNA from cell lines containing different numbers of X chromosomes (1–5 copies) [ 12 ] and compared them to a sample with 2 copies of X chromosomes. The observed mean fluorescence ratio of all clones across X chromosome was calculated (Fig.  3A ). For single copy deletions we observed an A-CGH ratio 0.9 (expected 0.5). The regression slope was 0.3, similar to that for the  MYCN  above (Fig  2A ). The underestimation of the expected ratio by A-CGH demonstrated that it is difficult to detect single-copy changes using pre-set threshold-based approaches. Figure 3 Sensitivity of A-CGH to detect the low-level copy number alteration .  A.  Measurement of X-chromosomal copy number. A-CGH was performed to analyze the copy number of genes in the X-chromosome. Female DNA (XX) was used as the reference DNA. Male DNA (XY), female DNA (XX), and DNA samples containing different number of X-chromosome (XXX, XXXX, XXXXX) were used as test DNA, with an expected ratio of test/reference of 0.5, 1.0, 1.5, 2.0, and 2.5 respectively for X-chromosome. Mean fluorescence ratios (±SEM) of autosomal DNAs (blue diamonds) and X-chromosomal DNAs (red circles) from each experiment are shown. The slope of the regression line is 0.3.  B.  Visualization of p-values derived from the topological statistics as described in the Methods along the X-chromosome from samples containing different X-chromosomal copy numbers. Each column represents a different experiment; and each row represents the p value for the alteration at a given SW-locus (a sliding window of 40 adjacent clones, details in Methods), ordered by genome map position from Xpter to Xqter. Red represents gain and green loss. The intensity of the color shows the level of
significance according to the p-value shown in the color scale. In order to increase the sensitivity for detecting low copy number changes, we applied a probabilistic approach utilizing t-statistics and the local genomic sequence mapping information of each of the cDNA clones on our arrays. To validate our method, we re-analyzed the A-CGH data generated from the cell lines containing 1–5 copies of the X chromosome as described above, and we were able to detect a single copy loss and gain of X chromosome where the expected ratio was 0.5 and 1.5 respectively (Fig.  3B ). In addition, we used the reported results from the literature as an independent validation. The cell line SK-N-AS is deleted within 1p36.2-p36.3, which has been investigated by FISH and southern blot analysis [ 29 , 30 ]. The proximal SK-N-AS deletion breakpoint was mapped to between  NPPA  and  PLOD , while the distal breakpoint is proximal of  CDC2L1 . The deletion detected by our method is bordered by  KIAA0495  and  CTNNBIP1 , which is within the region reported. They are in a very good accordance. In addition, we also compared the 17q gain results for four NB cell lines (CHP134, IMR-5, SMS-KCNR, and SK-N-AS) with the results in literature by FISH and Q-PCR [ 31 ]. Our results confirmed the gains in 17q for all 4 cell lines and the loss in 17p in SK-N-AS detected by FISH. We next analyzed the A-CGH data using this method to detect genome-wide alterations of DNA copy number in our NB samples. Using this t-statistics, we identified DNA copy number alterations that involved the majority of the chromosomes in both primary tumors and cell lines (Fig.  4 ). We confirmed previously reported genomic changes, including gains of whole chromosome 1, 2, 6, 7, 8, 12, 13, 17, 18 and 22, and losses of 3, 4, 9, 11, and 14; partial gains of 1q, 2p, 11p, 12q and 17q; partial losses of 1p, 3p, 4p, 9p, 11q and 14q [ 9 , 32 ]. The most common changes were losses on chromosome 1p, 4 and 11q; gains on 2p, 7, and 17q. Figure 4 Genome-wide analysis of DNA copy number alteration by A-CGH.  Samples were grouped based on sample type,  MYCN  amplification status and tumor stage. Each column represents a different sample; and each row represents a p-value of a given SW-locus using a sliding window of 40 adjacent clones, ordered in genome order across the whole genome. Black triangles on the right side of image represent centromere positions. Cell-: cell line without  MYCN  amplification; Cell+: cell line with  MYCN  amplification; Stage 4-: tumor in stage 4 without  MYCN  amplification; Stage 4+: tumor in stage 4 with  MYCN  amplification. On the right is shown an enlarged view of the region around the  MYCN  gene (2p24) for the amplified NB samples. Stage specific genomic alterations In order to identify the recurrent regions of genomic alterations that are specific to stage and  MYCN  amplification status, we partitioned the tumors into three subgroups (stage 1, stage 4  MYCN  not amplified (4-) and amplified (4+)), and analyzed the frequency of genomic changes at each SW-locus (as defined in Methods) for each subgroup. Since cell lines may contain tissue culture related genomic alterations, we only used primary NB tumor samples for this analysis. The frequency of alterations for a given SW-locus was estimated using the average probability ( P ) value as described in the Methods. Fig.  5  shows the graphic depiction of the  P  associated with each SW-locus for all possible pair-wise comparisons among the three subgroups for gains or losses: Stage 1  vs . 4-, 4+  vs . 4- and 1  vs . 4+. As expected, since the majority of the loci show no change, they were plotted to values of co-ordinates around (0.5, 0.5) (shown in black). Regions altered in both classes with similar frequency are plotted close to the y = x diagonal, whilst off-diagonal points represented regions primarily altered in one or the other of the classes (termed differential imbalance). Loci that plot around (0,0) reflect loci altered in both groups (termed common regions). The colored points were selected by using our criterion for "common" and "specific" SW-locus (see Methods). In summary, we found alterations that were common to all three subgroups, which included gain of 7q32, 17q21, 17q23-24 and loss of 3p21. We also found genomic imbalances that were specific for each of the subgroups and common regions of gain for stage 1 and 4- tumors. Of note there were no shared alterations of 4+ with 1 or 4- besides the regions common to all. A detailed description and map positions for all these recurrent regions are provided in the Table  2 , and a graphic representation of these imbalances is shown in Fig.  5 , where we will discuss in detail in the discussion section. Figure 5 Genomic alterations specific to stage and  MYCN  status.  Shown is the graphic depiction of the average p-values ( P ) of genomic alteration of each SW-locus (using a sliding window of 20 adjacent clones) within each tumor subgroup. All possible pair-wise comparisons among the three subgroups for gains or losses (stage 1 vs 4-, 4+ vs 4- and 1 vs. 4+) are shown. The frequency of alteration is estimated by  P  such that  P  = 0.15 is equivalent to a frequency of 70% and the lower the  P  the higher the frequency (details in Methods). Different colors were used to represent different clusters. Magenta: loci common to all groups; cyan: common to 1 and 4-; blue: 1 specific; green: 4- specific; red: 4+ specific, and black: all remaining loci. Colored dots were enlarged for easier visualization. Table 2 Recurrent regions related to MYCN status and stage Imbalance Chr. Cytoband Start (Mb) End (Mb) Clone No. Common to 1, 4-, & 4+ gain 7 7q32 133.19 137.05 26 gain 17 17q21 42.13 53.53 157 gain 17 17q23 55.41 61.8 99 gain 17 17q24 65.59 73.44 70 loss 3 3p21 45.72 46.77 13 Shared by 1 & 4- gain 7 7p14 32.41 32.83 11 gain 7 7q11 69.89 72.34 28 gain 7 7q36 148.74 152.97 39 gain 17 17q12 35.45 36.41 15 gain 17 17q21 39.56 40.03 16 Specific to 1 gain 2 2p22 32.23 33.33 12 gain 17 17p13 2.12 2.59 11 gain 17 17p13 5.67 7.17 22 gain 17 17p13 8.23 9.42 17 loss 8 8p12 38.32 39.84 12 loss 8 8q22-23 109.54 116.64 13 loss 11 11q12 60.87 63.09 29 loss 14 14q12 28.94 31.32 11 loss 14 14q23 58.04 58.76 12 loss 19 19q13 43.4 44.05 13 Specific to 4- gain 7 7p15 24.26 25.89 12 gain 7 7q34 139.53 141.16 22 loss 11 11q21 96.25 96.83 11 loss 11 11q22 108.12 110.64 11 loss 11 11q23 114.31 120.39 51 loss 11 11q24 125.28 126.76 20 loss 11 11q25 131.98 134.08 19 Specific to 4+ gain 2 2p24-25 10.97 20.08 38 loss 1 1p36 6.16 13.42 78 loss 1 1p36 15.22 16.13 20
Results
Amplicon Mapping by A-CGH Totally around 24,000 qualified array cDNA clones were applied for data analysis in 12 NB cell lines and 32 NB primary tumor samples (see Table  1  for sample information). Fig.  1  shows the number of clones as well as the average spacing for each chromosome. We first determined the sensitivity of A-CGH to detect the copy number of highly amplified genes. We here chose  MYCN  since it is the most commonly amplified gene in NB and correlates with the biological behavior of these tumors. Fig.  2A  shows the linear regression plot of the  MYCN  amplification results from A-CGH and Quantitative-PCR (Q-PCR). We found that the slope of the fitting line was 0.35, and therefore an observed ratio of 2 by A-CGH corresponds to Q-PCR ratio of ~6. In order to identify the amplified regions, we initially selected genes with A-CGH ratio ≥2 for at least two contiguous clones in genome sequence order. Only two chromosomes (2p and 12q) showed amplifications by this criterion exclusively in the  MYCN  amplified samples. Focusing on 2p (Fig.  2B ), we found 6 independent non-contiguous amplicons (10.4–69.4 Mb). For the  MYCN  amplicon, the largest contiguous region was 1.7 Mb and bounded by  NAG  and an EST (clone: 757451) in three tumor samples, whilst the smallest region was 27 Kb including an EST (clone: 241343),  NCYM , and  MYCN . We identified 9 previously reported co-amplified genes ( HPCAL1 ,  ODC1 ,  NSE1 ,  NAG ,  DDX1 ,  NCYM ,  POMC ,  DNMT3A ,  ALK, MEIS1, TEM8 ) [ 16 , 20 - 27 ], and detected the novel amplification of several known genes ( NCOA1 ,  ADCY3 ,  PPP1CB ,  CGI-127 ,  LBH ,  CAPN13 ,  GalNac-T10 ,  EHD3 ,  XDH ,  SRD5A2 ,  CGI-27 ,  AMP18 ) and ESTs. Three of the cell lines (CHP134, IMR-5 and IMR-32) contained two amplicons in 2p13-15. The first (66.6–67.6 Mb) included previously reported amplified gene  MEIS1 , and the size of the second amplicon was 0.3 Mb (69.1–69.4 Mb), which was bounded by  LOC200504  and  TEM8 . In addition to chromosome 2p, we identified another amplicon on 12q14-q15 in a single tumor (NB21); bounded by  PRO2268  (68.9 Mb) and  RAB3IP  (69.9 Mb) containing one previously reported amplified gene ( MDM2 ) [ 28 ] as well as several novel amplifications ( CPM ,  CPSF6 ,  LYZ ,  GAS41 ,  SNT-1 ,  CCT2 ,  VMD2L3 ,  and RAB3IP ) (Fig.  2C ). We verified the amplification of  NSE1 ,  NAG ,  DDX1 ,  MYCN  and  TEM8  by Q-PCR (data not shown). Simultaneous gene expression profiling by using the same cDNA arrays for all samples showed that 47% of the amplified genes correlate with gene expression (using a correlation coefficient cutoff 0.5; data not shown). Table 1 Summary of Neuroblastoma Information Sample No Sample Label Age (yr.mo) Sex Diagnosis MYCN Source C1 CHP-134B 1.1 M 4 + NCI C2 GI-LI-N 1.11 M 4 + ICLC C3 IMR-32 1.1 M 4 + ATCC C4 IMR-5 ND ND ND + ICLC C5 LAN-1 2 M 4 + ICLC C6 LAN-5 0.4 M ND + NCI C7 SK-N-BE(2) 2.2 M 4 + ATCC C8 SK-N-DZ 2 F ND + ATCC C9 SMS-KCNR 1.2 M 4 + NCI C10 SH-SY5Y 4 F 4 - ATCC C11 SK-N-AS 8 F 4 - ATCC C12 SK-N-FI 11 M ND - ATCC C13 SK-N-SH 4 F 4 - ATCC T1 NB19 12.9 F 1 - DZNSG T2 NB20 1.3 M 1 - DZNSG T3 NB229 0.3 M 1 - CHTN T4 NB248 0.6 M 1 - CHW T5 NB29 0.3 M 1 - DZNSG T6 NB33 1.5 F 1 - DZNSG T7 NB34 1.2 M 1 - DZNSG T8 NB43 1.1 F 1 - DZNSG T9 NB44 1.6 M 1 - DZNSG T10 NB5 0.3 M 1 - DZNSG T11 NB7 1.3 F 1 - DZNSG T12 NB9 1.1 M 1 - DZNSG T13 NB16 3.11 F 4 - DZNSG T14 NB205 3.11 F 4 - CHTN T15 NB217 2 M 4 - CHTN T16 NB24 0.7 M 4 - DZNSG T17 NB246 3.7 M 4 - CHW T18 NB247 1.5 M 4 - CHW T19 NB26 1 M 4 - DZNSG T20 NB30 0.11 F 4 - DZNSG T21 NB31 1.4 F 4 - DZNSG T22 NB32 1.2 M 4 - DZNSG T23 NB35 2.7 F 4 - DZNSG T24 NB8 4.7 M 4 - DZNSG T25 NB14 0.11 M 4 + DZNSG T26 NB21 5.3 M 4 + DZNSG T27 NB249 0.8 M 4 + CHW T28 NB251 0.9 F 4 + CHW T29 NB252 0.10 F 4 + CHW T30 NB266 2 F 4 + CHW T31 NB27 10.6 M 4 + DZNSG T32 NB28 1.8 F 4 + DZNSG Abbreviations used are ND: not determined ATCC: American Type Culture Collection, CHTN: Cooperative Human Tissue Network CHW: The Children's Hospital at Westmead, Australia, DZNSG: German Cancer Research Center, Heidelberg, ICLC: Interlab Cell Line Collection, NCI: National Cancer Institute, NIH.  1 Naming convention "C" denotes cell lines, "T" tumors, such that C1 is cell line number 1. Figure 1 Distribution of cDNA clones in our microarray.  Total 23975 unique UniGene clusters remained from the initial 42591 clones after quality filtering. Number of clones in each chromosome was represented in gray bar on the left side. Average spacing (chromosome size/number of clones in the chromosome) was represented in black dot on the right side. Figure 2 Amplifications in  MYCN  amplified samples .  A.  Regression analysis of  MYCN  ratio (sample  vs . normal) obtained from A-CGH and real-time Q-PCR in all of neuroblastoma samples including 13 cell lines and 32 primary tumors. The slope of the regression line is 0.35 indicating that an observed A-CGH ratio of 2 is equivalent to a Q-PCR ratio of 5.7.  B.  Independent amplicons in chromosome 2p. All amplified genes are listed under each amplicon in genome order. Map position, genome sequence position (Mb) and samples containing the specific amplicon are listed for each amplicon. The percentage of the  MYCN  amplified samples harboring these amplicons are shown in brackets following the gene name for all clones present in our microarray (gray), the remainder of the clones are predicted genes found in the NCBI database   that are mapped between the boundaries of the amplicon. Amplicons were selected based on the criteria of A-CGH ratio ≥2 for at least two contiguous clones in genome sequence order. In cases where a single clone has a ratio <2 but the ratio of its adjacent clones is greater than 2, that single clone was still considered as a part of amplicon. *: previously reported amplification.  C.  Amplicon in chromosome 12q in tumor NB21.
Amplicon Mapping by A-CGH
Totally around 24,000 qualified array cDNA clones were applied for data analysis in 12 NB cell lines and 32 NB primary tumor samples (see Table  1  for sample information). Fig.  1  shows the number of clones as well as the average spacing for each chromosome. We first determined the sensitivity of A-CGH to detect the copy number of highly amplified genes. We here chose  MYCN  since it is the most commonly amplified gene in NB and correlates with the biological behavior of these tumors. Fig.  2A  shows the linear regression plot of the  MYCN  amplification results from A-CGH and Quantitative-PCR (Q-PCR). We found that the slope of the fitting line was 0.35, and therefore an observed ratio of 2 by A-CGH corresponds to Q-PCR ratio of ~6. In order to identify the amplified regions, we initially selected genes with A-CGH ratio ≥2 for at least two contiguous clones in genome sequence order. Only two chromosomes (2p and 12q) showed amplifications by this criterion exclusively in the  MYCN  amplified samples. Focusing on 2p (Fig.  2B ), we found 6 independent non-contiguous amplicons (10.4–69.4 Mb). For the  MYCN  amplicon, the largest contiguous region was 1.7 Mb and bounded by  NAG  and an EST (clone: 757451) in three tumor samples, whilst the smallest region was 27 Kb including an EST (clone: 241343),  NCYM , and  MYCN . We identified 9 previously reported co-amplified genes ( HPCAL1 ,  ODC1 ,  NSE1 ,  NAG ,  DDX1 ,  NCYM ,  POMC ,  DNMT3A ,  ALK, MEIS1, TEM8 ) [ 16 , 20 - 27 ], and detected the novel amplification of several known genes ( NCOA1 ,  ADCY3 ,  PPP1CB ,  CGI-127 ,  LBH ,  CAPN13 ,  GalNac-T10 ,  EHD3 ,  XDH ,  SRD5A2 ,  CGI-27 ,  AMP18 ) and ESTs. Three of the cell lines (CHP134, IMR-5 and IMR-32) contained two amplicons in 2p13-15. The first (66.6–67.6 Mb) included previously reported amplified gene  MEIS1 , and the size of the second amplicon was 0.3 Mb (69.1–69.4 Mb), which was bounded by  LOC200504  and  TEM8 . In addition to chromosome 2p, we identified another amplicon on 12q14-q15 in a single tumor (NB21); bounded by  PRO2268  (68.9 Mb) and  RAB3IP  (69.9 Mb) containing one previously reported amplified gene ( MDM2 ) [ 28 ] as well as several novel amplifications ( CPM ,  CPSF6 ,  LYZ ,  GAS41 ,  SNT-1 ,  CCT2 ,  VMD2L3 ,  and RAB3IP ) (Fig.  2C ). We verified the amplification of  NSE1 ,  NAG ,  DDX1 ,  MYCN  and  TEM8  by Q-PCR (data not shown). Simultaneous gene expression profiling by using the same cDNA arrays for all samples showed that 47% of the amplified genes correlate with gene expression (using a correlation coefficient cutoff 0.5; data not shown).MYCNMYCNMYCNMYCNNAGNCYMMYCNHPCAL1ODC1NSE1NAGDDX1NCYMPOMCDNMT3AALK, MEIS1, TEM8NCOA1ADCY3PPP1CBCGI-127LBHCAPN13GalNac-T10EHD3XDHSRD5A2CGI-27AMP18MEIS1LOC200504TEM8PRO2268RAB3IPMDM2CPMCPSF6LYZGAS41SNT-1CCT2VMD2L3and RAB3IPNSE1NAGDDX1MYCNTEM8Summary of Neuroblastoma InformationAbbreviations used are ND: not determined ATCC: American Type Culture Collection, CHTN: Cooperative Human Tissue Network CHW: The Children's Hospital at Westmead, Australia, DZNSG: German Cancer Research Center, Heidelberg, ICLC: Interlab Cell Line Collection, NCI: National Cancer Institute, NIH.  1 Naming convention "C" denotes cell lines, "T" tumors, such that C1 is cell line number 1.Detection of low-level DNA copy number alterations To test the sensitivity of A-CGH to detect single copy number changes, we performed A-CGH with DNA from cell lines containing different numbers of X chromosomes (1–5 copies) [ 12 ] and compared them to a sample with 2 copies of X chromosomes. The observed mean fluorescence ratio of all clones across X chromosome was calculated (Fig.  3A ). For single copy deletions we observed an A-CGH ratio 0.9 (expected 0.5). The regression slope was 0.3, similar to that for the  MYCN  above (Fig  2A ). The underestimation of the expected ratio by A-CGH demonstrated that it is difficult to detect single-copy changes using pre-set threshold-based approaches. Figure 3 Sensitivity of A-CGH to detect the low-level copy number alteration .  A.  Measurement of X-chromosomal copy number. A-CGH was performed to analyze the copy number of genes in the X-chromosome. Female DNA (XX) was used as the reference DNA. Male DNA (XY), female DNA (XX), and DNA samples containing different number of X-chromosome (XXX, XXXX, XXXXX) were used as test DNA, with an expected ratio of test/reference of 0.5, 1.0, 1.5, 2.0, and 2.5 respectively for X-chromosome. Mean fluorescence ratios (±SEM) of autosomal DNAs (blue diamonds) and X-chromosomal DNAs (red circles) from each experiment are shown. The slope of the regression line is 0.3.  B.  Visualization of p-values derived from the topological statistics as described in the Methods along the X-chromosome from samples containing different X-chromosomal copy numbers. Each column represents a different experiment; and each row represents the p value for the alteration at a given SW-locus (a sliding window of 40 adjacent clones, details in Methods), ordered by genome map position from Xpter to Xqter. Red represents gain and green loss. The intensity of the color shows the level of
significance according to the p-value shown in the color scale. In order to increase the sensitivity for detecting low copy number changes, we applied a probabilistic approach utilizing t-statistics and the local genomic sequence mapping information of each of the cDNA clones on our arrays. To validate our method, we re-analyzed the A-CGH data generated from the cell lines containing 1–5 copies of the X chromosome as described above, and we were able to detect a single copy loss and gain of X chromosome where the expected ratio was 0.5 and 1.5 respectively (Fig.  3B ). In addition, we used the reported results from the literature as an independent validation. The cell line SK-N-AS is deleted within 1p36.2-p36.3, which has been investigated by FISH and southern blot analysis [ 29 , 30 ]. The proximal SK-N-AS deletion breakpoint was mapped to between  NPPA  and  PLOD , while the distal breakpoint is proximal of  CDC2L1 . The deletion detected by our method is bordered by  KIAA0495  and  CTNNBIP1 , which is within the region reported. They are in a very good accordance. In addition, we also compared the 17q gain results for four NB cell lines (CHP134, IMR-5, SMS-KCNR, and SK-N-AS) with the results in literature by FISH and Q-PCR [ 31 ]. Our results confirmed the gains in 17q for all 4 cell lines and the loss in 17p in SK-N-AS detected by FISH. We next analyzed the A-CGH data using this method to detect genome-wide alterations of DNA copy number in our NB samples. Using this t-statistics, we identified DNA copy number alterations that involved the majority of the chromosomes in both primary tumors and cell lines (Fig.  4 ). We confirmed previously reported genomic changes, including gains of whole chromosome 1, 2, 6, 7, 8, 12, 13, 17, 18 and 22, and losses of 3, 4, 9, 11, and 14; partial gains of 1q, 2p, 11p, 12q and 17q; partial losses of 1p, 3p, 4p, 9p, 11q and 14q [ 9 , 32 ]. The most common changes were losses on chromosome 1p, 4 and 11q; gains on 2p, 7, and 17q. Figure 4 Genome-wide analysis of DNA copy number alteration by A-CGH.  Samples were grouped based on sample type,  MYCN  amplification status and tumor stage. Each column represents a different sample; and each row represents a p-value of a given SW-locus using a sliding window of 40 adjacent clones, ordered in genome order across the whole genome. Black triangles on the right side of image represent centromere positions. Cell-: cell line without  MYCN  amplification; Cell+: cell line with  MYCN  amplification; Stage 4-: tumor in stage 4 without  MYCN  amplification; Stage 4+: tumor in stage 4 with  MYCN  amplification. On the right is shown an enlarged view of the region around the  MYCN  gene (2p24) for the amplified NB samples.
Detection of low-level DNA copy number alterations
To test the sensitivity of A-CGH to detect single copy number changes, we performed A-CGH with DNA from cell lines containing different numbers of X chromosomes (1–5 copies) [ 12 ] and compared them to a sample with 2 copies of X chromosomes. The observed mean fluorescence ratio of all clones across X chromosome was calculated (Fig.  3A ). For single copy deletions we observed an A-CGH ratio 0.9 (expected 0.5). The regression slope was 0.3, similar to that for the  MYCN  above (Fig  2A ). The underestimation of the expected ratio by A-CGH demonstrated that it is difficult to detect single-copy changes using pre-set threshold-based approaches.MYCNIn order to increase the sensitivity for detecting low copy number changes, we applied a probabilistic approach utilizing t-statistics and the local genomic sequence mapping information of each of the cDNA clones on our arrays. To validate our method, we re-analyzed the A-CGH data generated from the cell lines containing 1–5 copies of the X chromosome as described above, and we were able to detect a single copy loss and gain of X chromosome where the expected ratio was 0.5 and 1.5 respectively (Fig.  3B ). In addition, we used the reported results from the literature as an independent validation. The cell line SK-N-AS is deleted within 1p36.2-p36.3, which has been investigated by FISH and southern blot analysis [ 29 , 30 ]. The proximal SK-N-AS deletion breakpoint was mapped to between  NPPA  and  PLOD , while the distal breakpoint is proximal of  CDC2L1 . The deletion detected by our method is bordered by  KIAA0495  and  CTNNBIP1 , which is within the region reported. They are in a very good accordance. In addition, we also compared the 17q gain results for four NB cell lines (CHP134, IMR-5, SMS-KCNR, and SK-N-AS) with the results in literature by FISH and Q-PCR [ 31 ]. Our results confirmed the gains in 17q for all 4 cell lines and the loss in 17p in SK-N-AS detected by FISH.NPPAPLODCDC2L1KIAA0495CTNNBIP1We next analyzed the A-CGH data using this method to detect genome-wide alterations of DNA copy number in our NB samples. Using this t-statistics, we identified DNA copy number alterations that involved the majority of the chromosomes in both primary tumors and cell lines (Fig.  4 ). We confirmed previously reported genomic changes, including gains of whole chromosome 1, 2, 6, 7, 8, 12, 13, 17, 18 and 22, and losses of 3, 4, 9, 11, and 14; partial gains of 1q, 2p, 11p, 12q and 17q; partial losses of 1p, 3p, 4p, 9p, 11q and 14q [ 9 , 32 ]. The most common changes were losses on chromosome 1p, 4 and 11q; gains on 2p, 7, and 17q.Stage specific genomic alterations In order to identify the recurrent regions of genomic alterations that are specific to stage and  MYCN  amplification status, we partitioned the tumors into three subgroups (stage 1, stage 4  MYCN  not amplified (4-) and amplified (4+)), and analyzed the frequency of genomic changes at each SW-locus (as defined in Methods) for each subgroup. Since cell lines may contain tissue culture related genomic alterations, we only used primary NB tumor samples for this analysis. The frequency of alterations for a given SW-locus was estimated using the average probability ( P ) value as described in the Methods. Fig.  5  shows the graphic depiction of the  P  associated with each SW-locus for all possible pair-wise comparisons among the three subgroups for gains or losses: Stage 1  vs . 4-, 4+  vs . 4- and 1  vs . 4+. As expected, since the majority of the loci show no change, they were plotted to values of co-ordinates around (0.5, 0.5) (shown in black). Regions altered in both classes with similar frequency are plotted close to the y = x diagonal, whilst off-diagonal points represented regions primarily altered in one or the other of the classes (termed differential imbalance). Loci that plot around (0,0) reflect loci altered in both groups (termed common regions). The colored points were selected by using our criterion for "common" and "specific" SW-locus (see Methods). In summary, we found alterations that were common to all three subgroups, which included gain of 7q32, 17q21, 17q23-24 and loss of 3p21. We also found genomic imbalances that were specific for each of the subgroups and common regions of gain for stage 1 and 4- tumors. Of note there were no shared alterations of 4+ with 1 or 4- besides the regions common to all. A detailed description and map positions for all these recurrent regions are provided in the Table  2 , and a graphic representation of these imbalances is shown in Fig.  5 , where we will discuss in detail in the discussion section. Figure 5 Genomic alterations specific to stage and  MYCN  status.  Shown is the graphic depiction of the average p-values ( P ) of genomic alteration of each SW-locus (using a sliding window of 20 adjacent clones) within each tumor subgroup. All possible pair-wise comparisons among the three subgroups for gains or losses (stage 1 vs 4-, 4+ vs 4- and 1 vs. 4+) are shown. The frequency of alteration is estimated by  P  such that  P  = 0.15 is equivalent to a frequency of 70% and the lower the  P  the higher the frequency (details in Methods). Different colors were used to represent different clusters. Magenta: loci common to all groups; cyan: common to 1 and 4-; blue: 1 specific; green: 4- specific; red: 4+ specific, and black: all remaining loci. Colored dots were enlarged for easier visualization. Table 2 Recurrent regions related to MYCN status and stage Imbalance Chr. Cytoband Start (Mb) End (Mb) Clone No. Common to 1, 4-, & 4+ gain 7 7q32 133.19 137.05 26 gain 17 17q21 42.13 53.53 157 gain 17 17q23 55.41 61.8 99 gain 17 17q24 65.59 73.44 70 loss 3 3p21 45.72 46.77 13 Shared by 1 & 4- gain 7 7p14 32.41 32.83 11 gain 7 7q11 69.89 72.34 28 gain 7 7q36 148.74 152.97 39 gain 17 17q12 35.45 36.41 15 gain 17 17q21 39.56 40.03 16 Specific to 1 gain 2 2p22 32.23 33.33 12 gain 17 17p13 2.12 2.59 11 gain 17 17p13 5.67 7.17 22 gain 17 17p13 8.23 9.42 17 loss 8 8p12 38.32 39.84 12 loss 8 8q22-23 109.54 116.64 13 loss 11 11q12 60.87 63.09 29 loss 14 14q12 28.94 31.32 11 loss 14 14q23 58.04 58.76 12 loss 19 19q13 43.4 44.05 13 Specific to 4- gain 7 7p15 24.26 25.89 12 gain 7 7q34 139.53 141.16 22 loss 11 11q21 96.25 96.83 11 loss 11 11q22 108.12 110.64 11 loss 11 11q23 114.31 120.39 51 loss 11 11q24 125.28 126.76 20 loss 11 11q25 131.98 134.08 19 Specific to 4+ gain 2 2p24-25 10.97 20.08 38 loss 1 1p36 6.16 13.42 78 loss 1 1p36 15.22 16.13 20
Stage specific genomic alterations
In order to identify the recurrent regions of genomic alterations that are specific to stage and  MYCN  amplification status, we partitioned the tumors into three subgroups (stage 1, stage 4  MYCN  not amplified (4-) and amplified (4+)), and analyzed the frequency of genomic changes at each SW-locus (as defined in Methods) for each subgroup. Since cell lines may contain tissue culture related genomic alterations, we only used primary NB tumor samples for this analysis. The frequency of alterations for a given SW-locus was estimated using the average probability ( P ) value as described in the Methods. Fig.  5  shows the graphic depiction of the  P  associated with each SW-locus for all possible pair-wise comparisons among the three subgroups for gains or losses: Stage 1  vs . 4-, 4+  vs . 4- and 1  vs . 4+. As expected, since the majority of the loci show no change, they were plotted to values of co-ordinates around (0.5, 0.5) (shown in black). Regions altered in both classes with similar frequency are plotted close to the y = x diagonal, whilst off-diagonal points represented regions primarily altered in one or the other of the classes (termed differential imbalance). Loci that plot around (0,0) reflect loci altered in both groups (termed common regions). The colored points were selected by using our criterion for "common" and "specific" SW-locus (see Methods). In summary, we found alterations that were common to all three subgroups, which included gain of 7q32, 17q21, 17q23-24 and loss of 3p21. We also found genomic imbalances that were specific for each of the subgroups and common regions of gain for stage 1 and 4- tumors. Of note there were no shared alterations of 4+ with 1 or 4- besides the regions common to all. A detailed description and map positions for all these recurrent regions are provided in the Table  2 , and a graphic representation of these imbalances is shown in Fig.  5 , where we will discuss in detail in the discussion section.MYCNMYCNvsvsvsRecurrent regions related to MYCN status and stageImbalanceChr.CytobandStart (Mb)End (Mb)Clone No.Common to 1, 4-, & 4+Shared by 1 & 4-Specific to 1Specific to 4-Specific to 4+Discussion Amplicons in Neuroblastoma In our study we found that unlike breast cancers [ 14 ] NBs do not have a wide variety of different amplicons or amplified genes. We identified 6 independent amplicons on 2p and one on 12q and precisely defined boundaries for all amplicons. Several genes related to angiogenesis and oncogenesis were in these novel amplified regions including  TEM8  (tumor-specific endothelial marker), mapped to 2p13.1, which has been shown to have elevated expression during tumor angiogenesis [ 33 ]. Indeed this gene was recently reported to be amplified in the cell line IMR-32 in accord with our data [ 27 ]. The gene is expressed in human endothelium and has been implicated in colorectal cancer. Our present study showed  TEM8  was amplified and over-expressed (data not shown) in several neuroblastoma cell lines. The significance of amplification of  TEM8  in neuroblastoma cell lines but not endothelial cells raises an intriguing possibility that these tumor cells themselves contribute to the angiogenic process and requires further investigation. We also identified amplification of  GAS41  (glioma amplified sequence) mapped to 12q14-q15 in one tumor sample.  GAS41 , a transcription factor ubiquitously expressed with the highest expression in human brain, was previously shown frequently amplified in human gliomas [ 34 ].  ALK  (anaplastic lymphoma kinase) receptor, an oncogene and reported highly expressed in neuroblastoma [ 26 ], was identified to be amplified in two of our tumor samples. In addition to these genes, most of those newly identified amplified genes have not been implicated previously in neuroblastoma tumorigenesis and progression; therefore a further characterization of these genes might provide the biological insights to neuroblastoma biology. Interestingly, all amplicons occurred in  MYCN  amplified samples, and we have not found a single amplicon in  MYCN  single copy samples. Additionally, using our search criterion (A-CGH ratio >2 corresponding to copy number >6 see above) we found no evidence of amplifications in other chromosomal regions. This was in conflict with a study by Satito-Ohara et al. who found evidence of high level gains 9 NB cell lines and amplification as evidenced by a homogeneous staining region (HSR) in one line [ 35 ]. This difference could be explained by potential artifacts that arise in cell lines in tissue culture or be as a result of under detection by our study because of the relatively small number of tumor samples in our study, and would require confirmation in a larger sample set. Detection of low level of genomic changes In this study, we have applied a t-statistics-based method to explore genomic alterations in cancer from data generated by A-CGH on a cDNA microarray platform. Our method efficiently dealt with the low sensitivity of cDNA microarrays to detect low copy changes. The microarrays we utilized contain 42,000 clones, containing around 24,000 unique UniGene clusters with an average coverage of one cluster every 125 Kb. Underestimation of DNA copy number ratios by cDNA A-CGH data made it difficult to detect low level of gains and losses using ratio threshold based approaches, which was addressed in our study and previous reports [ 14 - 16 ]. The algorithm we have implemented included an efficient noise reduction strategy by combining ratios within a sliding window of clones as has been previously described [ 13 , 14 ]. However the incorporation of t-statistics demonstrated several advantages over the sole reliance of moving average for detecting genomic changes. It provides confidence levels for detecting genomic changes at each DNA location (SW-locus) in terms of p-values. This has theoretical advantages over the original raw ratio, because it incorporates an estimate of possible statistical errors in the analysis by giving a p-value attached to each genomic change within a SW-locus. By this method we were able to detect 1.5 fold changes of gene copy number as shown in our X-chromosome validation experiment. However, all these advantages are traded for a loss of resolution: genomic imbalances much smaller than the window-size cannot be detected and the boundaries of instable regions are blurred. Therefore we should choose the smallest window that has the desired level of statistical significance. The effective resolution can be obtained by analyzing the correlation of overlapping sliding windows. The integrated autocorrelation time is an estimator of the minimal distance for windows to be effectively uncorrelated [ 36 ] even when they overlap. For the sliding window t-test in our algorithm this distance can be calculated to be half the window size w, thus the number (N) of total unique Unigene clusters is reduced to 2N/w for the effectively independent measurements of the DNA copy number. Our results indicate that a window size 20 is needed in order to detect the lowest possible DNA change (one copy change) with reasonable statistical significance. According to the discussion above, this window size reduces the approximately 24,000 quality filtered unique Unigene clusters to 2 * 24,000/20 = 2400 independent estimates. This resolution is comparable to typical BAC arrays. For the stronger signals, less noise reduction is required. To detect 2-copy number DNA changes, only a small window size 5 is needed, therefore the resolution will be 4 fold higher. Although cDNA A-CGH is known not as sensitive as BAC A-CGH for the detection of low level of DNA copy number changes, currently we are able to obtain the comparable detection by using the probabilistic approach. In addition, with cDNA array it is possible to identify genomic amplification at the gene level and investigate the direct effect of gene copy number change over gene expression level in parallel, which will be addressed in future studies.
Discussion
Amplicons in Neuroblastoma In our study we found that unlike breast cancers [ 14 ] NBs do not have a wide variety of different amplicons or amplified genes. We identified 6 independent amplicons on 2p and one on 12q and precisely defined boundaries for all amplicons. Several genes related to angiogenesis and oncogenesis were in these novel amplified regions including  TEM8  (tumor-specific endothelial marker), mapped to 2p13.1, which has been shown to have elevated expression during tumor angiogenesis [ 33 ]. Indeed this gene was recently reported to be amplified in the cell line IMR-32 in accord with our data [ 27 ]. The gene is expressed in human endothelium and has been implicated in colorectal cancer. Our present study showed  TEM8  was amplified and over-expressed (data not shown) in several neuroblastoma cell lines. The significance of amplification of  TEM8  in neuroblastoma cell lines but not endothelial cells raises an intriguing possibility that these tumor cells themselves contribute to the angiogenic process and requires further investigation. We also identified amplification of  GAS41  (glioma amplified sequence) mapped to 12q14-q15 in one tumor sample.  GAS41 , a transcription factor ubiquitously expressed with the highest expression in human brain, was previously shown frequently amplified in human gliomas [ 34 ].  ALK  (anaplastic lymphoma kinase) receptor, an oncogene and reported highly expressed in neuroblastoma [ 26 ], was identified to be amplified in two of our tumor samples. In addition to these genes, most of those newly identified amplified genes have not been implicated previously in neuroblastoma tumorigenesis and progression; therefore a further characterization of these genes might provide the biological insights to neuroblastoma biology. Interestingly, all amplicons occurred in  MYCN  amplified samples, and we have not found a single amplicon in  MYCN  single copy samples. Additionally, using our search criterion (A-CGH ratio >2 corresponding to copy number >6 see above) we found no evidence of amplifications in other chromosomal regions. This was in conflict with a study by Satito-Ohara et al. who found evidence of high level gains 9 NB cell lines and amplification as evidenced by a homogeneous staining region (HSR) in one line [ 35 ]. This difference could be explained by potential artifacts that arise in cell lines in tissue culture or be as a result of under detection by our study because of the relatively small number of tumor samples in our study, and would require confirmation in a larger sample set.
Amplicons in Neuroblastoma
In our study we found that unlike breast cancers [ 14 ] NBs do not have a wide variety of different amplicons or amplified genes. We identified 6 independent amplicons on 2p and one on 12q and precisely defined boundaries for all amplicons. Several genes related to angiogenesis and oncogenesis were in these novel amplified regions including  TEM8  (tumor-specific endothelial marker), mapped to 2p13.1, which has been shown to have elevated expression during tumor angiogenesis [ 33 ]. Indeed this gene was recently reported to be amplified in the cell line IMR-32 in accord with our data [ 27 ]. The gene is expressed in human endothelium and has been implicated in colorectal cancer. Our present study showed  TEM8  was amplified and over-expressed (data not shown) in several neuroblastoma cell lines. The significance of amplification of  TEM8  in neuroblastoma cell lines but not endothelial cells raises an intriguing possibility that these tumor cells themselves contribute to the angiogenic process and requires further investigation. We also identified amplification of  GAS41  (glioma amplified sequence) mapped to 12q14-q15 in one tumor sample.  GAS41 , a transcription factor ubiquitously expressed with the highest expression in human brain, was previously shown frequently amplified in human gliomas [ 34 ].  ALK  (anaplastic lymphoma kinase) receptor, an oncogene and reported highly expressed in neuroblastoma [ 26 ], was identified to be amplified in two of our tumor samples. In addition to these genes, most of those newly identified amplified genes have not been implicated previously in neuroblastoma tumorigenesis and progression; therefore a further characterization of these genes might provide the biological insights to neuroblastoma biology. Interestingly, all amplicons occurred in  MYCN  amplified samples, and we have not found a single amplicon in  MYCN  single copy samples.TEM8TEM8TEM8GAS41GAS41ALKMYCNMYCNAdditionally, using our search criterion (A-CGH ratio >2 corresponding to copy number >6 see above) we found no evidence of amplifications in other chromosomal regions. This was in conflict with a study by Satito-Ohara et al. who found evidence of high level gains 9 NB cell lines and amplification as evidenced by a homogeneous staining region (HSR) in one line [ 35 ]. This difference could be explained by potential artifacts that arise in cell lines in tissue culture or be as a result of under detection by our study because of the relatively small number of tumor samples in our study, and would require confirmation in a larger sample set.Detection of low level of genomic changes In this study, we have applied a t-statistics-based method to explore genomic alterations in cancer from data generated by A-CGH on a cDNA microarray platform. Our method efficiently dealt with the low sensitivity of cDNA microarrays to detect low copy changes. The microarrays we utilized contain 42,000 clones, containing around 24,000 unique UniGene clusters with an average coverage of one cluster every 125 Kb. Underestimation of DNA copy number ratios by cDNA A-CGH data made it difficult to detect low level of gains and losses using ratio threshold based approaches, which was addressed in our study and previous reports [ 14 - 16 ]. The algorithm we have implemented included an efficient noise reduction strategy by combining ratios within a sliding window of clones as has been previously described [ 13 , 14 ]. However the incorporation of t-statistics demonstrated several advantages over the sole reliance of moving average for detecting genomic changes. It provides confidence levels for detecting genomic changes at each DNA location (SW-locus) in terms of p-values. This has theoretical advantages over the original raw ratio, because it incorporates an estimate of possible statistical errors in the analysis by giving a p-value attached to each genomic change within a SW-locus. By this method we were able to detect 1.5 fold changes of gene copy number as shown in our X-chromosome validation experiment. However, all these advantages are traded for a loss of resolution: genomic imbalances much smaller than the window-size cannot be detected and the boundaries of instable regions are blurred. Therefore we should choose the smallest window that has the desired level of statistical significance. The effective resolution can be obtained by analyzing the correlation of overlapping sliding windows. The integrated autocorrelation time is an estimator of the minimal distance for windows to be effectively uncorrelated [ 36 ] even when they overlap. For the sliding window t-test in our algorithm this distance can be calculated to be half the window size w, thus the number (N) of total unique Unigene clusters is reduced to 2N/w for the effectively independent measurements of the DNA copy number. Our results indicate that a window size 20 is needed in order to detect the lowest possible DNA change (one copy change) with reasonable statistical significance. According to the discussion above, this window size reduces the approximately 24,000 quality filtered unique Unigene clusters to 2 * 24,000/20 = 2400 independent estimates. This resolution is comparable to typical BAC arrays. For the stronger signals, less noise reduction is required. To detect 2-copy number DNA changes, only a small window size 5 is needed, therefore the resolution will be 4 fold higher. Although cDNA A-CGH is known not as sensitive as BAC A-CGH for the detection of low level of DNA copy number changes, currently we are able to obtain the comparable detection by using the probabilistic approach. In addition, with cDNA array it is possible to identify genomic amplification at the gene level and investigate the direct effect of gene copy number change over gene expression level in parallel, which will be addressed in future studies.
Detection of low level of genomic changes
In this study, we have applied a t-statistics-based method to explore genomic alterations in cancer from data generated by A-CGH on a cDNA microarray platform. Our method efficiently dealt with the low sensitivity of cDNA microarrays to detect low copy changes. The microarrays we utilized contain 42,000 clones, containing around 24,000 unique UniGene clusters with an average coverage of one cluster every 125 Kb. Underestimation of DNA copy number ratios by cDNA A-CGH data made it difficult to detect low level of gains and losses using ratio threshold based approaches, which was addressed in our study and previous reports [ 14 - 16 ]. The algorithm we have implemented included an efficient noise reduction strategy by combining ratios within a sliding window of clones as has been previously described [ 13 , 14 ]. However the incorporation of t-statistics demonstrated several advantages over the sole reliance of moving average for detecting genomic changes. It provides confidence levels for detecting genomic changes at each DNA location (SW-locus) in terms of p-values. This has theoretical advantages over the original raw ratio, because it incorporates an estimate of possible statistical errors in the analysis by giving a p-value attached to each genomic change within a SW-locus. By this method we were able to detect 1.5 fold changes of gene copy number as shown in our X-chromosome validation experiment.However, all these advantages are traded for a loss of resolution: genomic imbalances much smaller than the window-size cannot be detected and the boundaries of instable regions are blurred. Therefore we should choose the smallest window that has the desired level of statistical significance. The effective resolution can be obtained by analyzing the correlation of overlapping sliding windows. The integrated autocorrelation time is an estimator of the minimal distance for windows to be effectively uncorrelated [ 36 ] even when they overlap. For the sliding window t-test in our algorithm this distance can be calculated to be half the window size w, thus the number (N) of total unique Unigene clusters is reduced to 2N/w for the effectively independent measurements of the DNA copy number. Our results indicate that a window size 20 is needed in order to detect the lowest possible DNA change (one copy change) with reasonable statistical significance. According to the discussion above, this window size reduces the approximately 24,000 quality filtered unique Unigene clusters to 2 * 24,000/20 = 2400 independent estimates. This resolution is comparable to typical BAC arrays. For the stronger signals, less noise reduction is required. To detect 2-copy number DNA changes, only a small window size 5 is needed, therefore the resolution will be 4 fold higher. Although cDNA A-CGH is known not as sensitive as BAC A-CGH for the detection of low level of DNA copy number changes, currently we are able to obtain the comparable detection by using the probabilistic approach. In addition, with cDNA array it is possible to identify genomic amplification at the gene level and investigate the direct effect of gene copy number change over gene expression level in parallel, which will be addressed in future studies.Conclusions In this study we explored the genomic alterations in NB from the data generated by A-CGH on a cDNA microarray platform. We have not found genomic alterations universally present in  all  (100%) three subgroups of NBs, although such a region would be interesting since it may harbor specific genes that are uniquely responsible for NB tumorigenesis. We therefore focused on commonly altered regions where >70% of tumors showed changes in a given region, for our three different subgroups (Fig.  5  and Table  2 ). We found only a few of imbalances occurring in all three subgroups, of which gain of 17q21-24 and loss of 3p21 have been previously described in NB biology [ 8 , 37 ]. Apart from these regions stage 4+ tumors did not have any other regions that commonly change with the other two stages, whereas stages 1 and 4- had several common alterations. Stage 4- tumors demonstrated several unique changes of which losses in 11q has been previously described in  MYCN  single copy NB [ 38 ] and acts as a possible marker of unfavorable phenotype independent of  MYCN  amplification [ 39 ]. Remarkably stage 4+ disease appears to have very few genomic alterations when compared with Stage 1 and 4- implying that  MYCN  amplification is sufficient to drive these tumors to an aggressive phenotype, and although other genomic changes occur, including loss of 1p36 as shown by us and others [ 40 ], it does not require extensive changes. This is in agreement with the murine  MYCN  transgenic model of NB where the  MYCN  transgene itself is enough for tumor development, but these tumors develop additional genomic changes characteristic of NB [ 41 ]. Based on these results we found that cDNA A-CGH analysis is an efficient method for the detection and characterization of amplicons. We confirmed the previously reported amplified genes and also identified novel amplifications in neuroblastoma. Furthermore our probabilistic approach allows the detection of single copy number changes from cDNA A-CGH and can be applied to other CGH platforms including BAC or oligonucleotides based arrays.
Conclusions
In this study we explored the genomic alterations in NB from the data generated by A-CGH on a cDNA microarray platform. We have not found genomic alterations universally present in  all  (100%) three subgroups of NBs, although such a region would be interesting since it may harbor specific genes that are uniquely responsible for NB tumorigenesis. We therefore focused on commonly altered regions where >70% of tumors showed changes in a given region, for our three different subgroups (Fig.  5  and Table  2 ). We found only a few of imbalances occurring in all three subgroups, of which gain of 17q21-24 and loss of 3p21 have been previously described in NB biology [ 8 , 37 ]. Apart from these regions stage 4+ tumors did not have any other regions that commonly change with the other two stages, whereas stages 1 and 4- had several common alterations. Stage 4- tumors demonstrated several unique changes of which losses in 11q has been previously described in  MYCN  single copy NB [ 38 ] and acts as a possible marker of unfavorable phenotype independent of  MYCN  amplification [ 39 ]. Remarkably stage 4+ disease appears to have very few genomic alterations when compared with Stage 1 and 4- implying that  MYCN  amplification is sufficient to drive these tumors to an aggressive phenotype, and although other genomic changes occur, including loss of 1p36 as shown by us and others [ 40 ], it does not require extensive changes. This is in agreement with the murine  MYCN  transgenic model of NB where the  MYCN  transgene itself is enough for tumor development, but these tumors develop additional genomic changes characteristic of NB [ 41 ].allMYCNMYCNMYCNMYCNMYCNBased on these results we found that cDNA A-CGH analysis is an efficient method for the detection and characterization of amplicons. We confirmed the previously reported amplified genes and also identified novel amplifications in neuroblastoma. Furthermore our probabilistic approach allows the detection of single copy number changes from cDNA A-CGH and can be applied to other CGH platforms including BAC or oligonucleotides based arrays.Methods Tumors, cell lines, and genomic DNA Thirty-two snap frozen neuroblastoma specimens were obtained from 12 patients with stage 1, and 20 patients with stage 4 of which 12 were  MYCN -amplified and 8 were  MYCN  single-copy tumors. The original histological diagnoses were made at tertiary hospitals with extensive experience in diagnosis and management of neuroblastoma. Additionally, 12 neuroblastoma cell lines including 8  MYCN -amplified and 4  MYCN  single copy samples were used in the study. Details of individual sample are summarized in Table  1 . The conditions for cell cultures were done as described previously [ 17 ]. High molecular weight genomic DNA was extracted from interphase of a Trizol preparation for RNA extraction according to the manufacturer's instructions (Invitrogen, Gaithersburg, MD). Genomic DNA was treated with RNase A and protease (Qiagen, Valencia, CA), and purified by phenol/chloroform extraction followed by ethanol precipitation. We obtained normal genomic DNA samples (male, female or 1:1 mixture of male and female) from Promega, and genomic DNA samples containing the different numbers of X chromosomes (XXX, XXXX, and XXXXX) from the NIGMS  . Microarray experiments Preparation of glass cDNA microarrays was performed according to a previously published protocol [ 18 ]. Image analysis was performed using DeArray software [ 19 ]. The cDNA library containing 42,000 clones was obtained from Research Genetics (Huntsville, AL) and clones were printed on two microscope glass slides as a set. Approximately 50% of the cDNAs on the microarrays were either known genes or similar to known genes in other organisms, whereas the remainders were anonymous ESTs. For A-CGH experiments on cDNA microarrays, 20 μg of genomic DNA from neuroblastoma tumor or cell line samples were sonicated and purified with QIAquick PCR purification column (Qiagen, Valencia, CA). Three micrograms of sonicated DNA were labeled with aminoallyl-dUTP (Sigma) in a 25-μl reaction, including random hexamer (0.24 μg/μl, Roche), dATP, dCTP and dGTP (125 μM each), dTTP (25 μM), aminoallyl-dUTP (100 μM) and high concentration of Klenow fragment (2.5 U/μl, NEB). The labeling reaction was purified with QIAquick PCR purification column. Cy3 and Cy5 dyes were coupled to the reference DNA (1:1 mixture of normal male and female DNA) and sample DNA respectively. Cy3- and Cy5-labeled probes were then combined along with human Cot-1 DNA (50 μg, Invitrogen) and yeast tRNA (100 μg, Invitrogen). The mixture was concentrated and re-suspended in 32 μl of hybridization buffer (50% formamide, 10% dextran sulfate, 4 × SSC, and 2% SDS). The hybridization mix was first heated at 75°C for 10 min, then at 37°C for an hour, and finally loaded to the pre-hybridized array. The hybridization was performed at 37°C overnight. The washing procedure was performed as described previously [ 17 ]. Real time quantitative PCR Real time PCR was carried out using SYBR Green PCR core reagents according to the manufacturer's instructions (Applied Biosystems, Foster City, CA). Each DNA sample was analyzed in triplicate using the ABI PRISM 7000 Sequence Detector. For quantitative PCR, 10 ng of genomic DNA was used for SYBR green PCR assay. Serial dilutions of neuroblastoma cell line CHP134 DNA were used as templates for a standard curve, and the normal genomic DNA was used as a calibrator. The normalization was performed as described using  BCMA  and  SDC4  as reference genes [ 20 ]. Data analysis Fluorescence ratios were normalized for each microarray by setting the average log ratio for each subarray elements equal to zero (commonly referred to as "pin-normalization"). The data was quality-filtered by removing those clones with quality lower than 0.5 in more than 20% of all the samples [ 19 ]. For the clones that passed this filter, if the quality for a specific sample is lower than 0.5, then its fluorescence ratio is replaced by the average ratio value of all other samples with the good quality. The clones were finally assigned to UniGene Cluster (Build 154 September 2002). For the UniGene clusters represented by multiple clones, mean fluorescence ratios of those clones are used. After these processes we had 23975 unique UniGene clusters remaining from the initial 42591 clones. Map positions for the cluster were assigned by Blat searches against the "Golden Path" genome assembly ( ; June, 2002 Freeze). Throughout this publication, all genomic coordinates are given with the respect to this assembly. Finally the clusters were sorted according to their starting position of sequence on each individual chromosome. Detection of single copy changes To identify the alterations of copy number along the genome, we compared the distribution of the ratios in a sliding window of 20 clones in genomic order with a "null distribution" using a t-test. The p-value for genomic change (P gc ) obtained in this way is assigned to the center of the sliding window (referred to as the "SW-locus" throughout this manuscript). The t-test is valid in this instance because the observed distribution of P gc  for the loci in random order matches the expected theoretical distribution. The null-distribution used in t-test represents the unaltered part of genome. To identify the cDNA clones for the null-distribution, we start with the whole genome. The SW-loci corresponding to portions of the DNA that are amplified or deleted with a p-value smaller than 0.05 are removed recursively from the null dataset, until the null dataset is stable and there is no more amplified or deleted SW-locus in the null data set. Finally, the confidence of identified genomic alterations is visualized in a pseudo-color map in which color intensity represents the log of p-values (red for gain and green for loss). Estimation of frequency of genomic changes among the samples The probabilistic approach above provides P-values for the presence of genomic alteration in a given sample. In order to estimate the frequency of a genomic alteration we can set a threshold ratio and identify how many samples have a ratio value outside this threshold in the same genomic region. The disadvantage of this method is that different ratio thresholds will give different frequencies. We therefore applied another approach to avoid the use of ratio thresholds. To determine the frequency of loss or gain that correlate with the stage or  MYCN  amplification status, we first calculated the mean of the P gc  or  P , for each group. This value is proportional to the frequency f with change  = N with change /N total  of their occurrence, where N with change  is the number of samples in a subgroup with a given genomic imbalance and N total  is the overall number of samples in that subgroup. This is valid as follows. For all loci in which there are no genomic imbalances the observed P gc  will follow the flat theoretical distribution with a mean (expectation value) < P > = 0.5. Therefore, for those cases where we are sure of a genomic imbalance P gc  is close to 0 (for example p < 0.01), whereas for the samples in which there are no changes P gc  = 0.5. According to the formula:  P  = ((N no change  × 0.5) + (N with change  × 0))/N total  = (1 - f with change ) × 0.5, the lower the  P  the higher the frequency of a genomic change in that SW-locus. Thus the  P  can be used to determine the frequency of a given change e.g.  P  = 0.15 corresponds to a frequency of ~70% of the samples with that given change. Determination of recurrent regions We first define a SW-locus with  P  < 0.15 in a specific subgroup as altered. This threshold corresponds to roughly a fraction of >70% of all tumor samples harboring that alteration in each subgroup. We define an altered SW-locus as common in all tumors, if the SW-locus passes the threshold for each of the three subgroups. A SW-locus is called differential in one subgroup with respect to another subgroup, if the frequency of genomic change is at least 3 times higher in one subgroup as compared to another. A SW-locus is defined as specific if the locus in one subgroup is differential with respect to each of the other subgroups; a SW-locus is defined as shared in two groups, if in both groups it is differential with respect to the third subgroup.
Methods
Tumors, cell lines, and genomic DNA Thirty-two snap frozen neuroblastoma specimens were obtained from 12 patients with stage 1, and 20 patients with stage 4 of which 12 were  MYCN -amplified and 8 were  MYCN  single-copy tumors. The original histological diagnoses were made at tertiary hospitals with extensive experience in diagnosis and management of neuroblastoma. Additionally, 12 neuroblastoma cell lines including 8  MYCN -amplified and 4  MYCN  single copy samples were used in the study. Details of individual sample are summarized in Table  1 . The conditions for cell cultures were done as described previously [ 17 ]. High molecular weight genomic DNA was extracted from interphase of a Trizol preparation for RNA extraction according to the manufacturer's instructions (Invitrogen, Gaithersburg, MD). Genomic DNA was treated with RNase A and protease (Qiagen, Valencia, CA), and purified by phenol/chloroform extraction followed by ethanol precipitation. We obtained normal genomic DNA samples (male, female or 1:1 mixture of male and female) from Promega, and genomic DNA samples containing the different numbers of X chromosomes (XXX, XXXX, and XXXXX) from the NIGMS  .
Tumors, cell lines, and genomic DNA
Thirty-two snap frozen neuroblastoma specimens were obtained from 12 patients with stage 1, and 20 patients with stage 4 of which 12 were  MYCN -amplified and 8 were  MYCN  single-copy tumors. The original histological diagnoses were made at tertiary hospitals with extensive experience in diagnosis and management of neuroblastoma. Additionally, 12 neuroblastoma cell lines including 8  MYCN -amplified and 4  MYCN  single copy samples were used in the study. Details of individual sample are summarized in Table  1 . The conditions for cell cultures were done as described previously [ 17 ]. High molecular weight genomic DNA was extracted from interphase of a Trizol preparation for RNA extraction according to the manufacturer's instructions (Invitrogen, Gaithersburg, MD). Genomic DNA was treated with RNase A and protease (Qiagen, Valencia, CA), and purified by phenol/chloroform extraction followed by ethanol precipitation. We obtained normal genomic DNA samples (male, female or 1:1 mixture of male and female) from Promega, and genomic DNA samples containing the different numbers of X chromosomes (XXX, XXXX, and XXXXX) from the NIGMS  .MYCNMYCNMYCNMYCNMicroarray experiments Preparation of glass cDNA microarrays was performed according to a previously published protocol [ 18 ]. Image analysis was performed using DeArray software [ 19 ]. The cDNA library containing 42,000 clones was obtained from Research Genetics (Huntsville, AL) and clones were printed on two microscope glass slides as a set. Approximately 50% of the cDNAs on the microarrays were either known genes or similar to known genes in other organisms, whereas the remainders were anonymous ESTs. For A-CGH experiments on cDNA microarrays, 20 μg of genomic DNA from neuroblastoma tumor or cell line samples were sonicated and purified with QIAquick PCR purification column (Qiagen, Valencia, CA). Three micrograms of sonicated DNA were labeled with aminoallyl-dUTP (Sigma) in a 25-μl reaction, including random hexamer (0.24 μg/μl, Roche), dATP, dCTP and dGTP (125 μM each), dTTP (25 μM), aminoallyl-dUTP (100 μM) and high concentration of Klenow fragment (2.5 U/μl, NEB). The labeling reaction was purified with QIAquick PCR purification column. Cy3 and Cy5 dyes were coupled to the reference DNA (1:1 mixture of normal male and female DNA) and sample DNA respectively. Cy3- and Cy5-labeled probes were then combined along with human Cot-1 DNA (50 μg, Invitrogen) and yeast tRNA (100 μg, Invitrogen). The mixture was concentrated and re-suspended in 32 μl of hybridization buffer (50% formamide, 10% dextran sulfate, 4 × SSC, and 2% SDS). The hybridization mix was first heated at 75°C for 10 min, then at 37°C for an hour, and finally loaded to the pre-hybridized array. The hybridization was performed at 37°C overnight. The washing procedure was performed as described previously [ 17 ].
Microarray experiments
Preparation of glass cDNA microarrays was performed according to a previously published protocol [ 18 ]. Image analysis was performed using DeArray software [ 19 ]. The cDNA library containing 42,000 clones was obtained from Research Genetics (Huntsville, AL) and clones were printed on two microscope glass slides as a set. Approximately 50% of the cDNAs on the microarrays were either known genes or similar to known genes in other organisms, whereas the remainders were anonymous ESTs. For A-CGH experiments on cDNA microarrays, 20 μg of genomic DNA from neuroblastoma tumor or cell line samples were sonicated and purified with QIAquick PCR purification column (Qiagen, Valencia, CA). Three micrograms of sonicated DNA were labeled with aminoallyl-dUTP (Sigma) in a 25-μl reaction, including random hexamer (0.24 μg/μl, Roche), dATP, dCTP and dGTP (125 μM each), dTTP (25 μM), aminoallyl-dUTP (100 μM) and high concentration of Klenow fragment (2.5 U/μl, NEB). The labeling reaction was purified with QIAquick PCR purification column. Cy3 and Cy5 dyes were coupled to the reference DNA (1:1 mixture of normal male and female DNA) and sample DNA respectively. Cy3- and Cy5-labeled probes were then combined along with human Cot-1 DNA (50 μg, Invitrogen) and yeast tRNA (100 μg, Invitrogen). The mixture was concentrated and re-suspended in 32 μl of hybridization buffer (50% formamide, 10% dextran sulfate, 4 × SSC, and 2% SDS). The hybridization mix was first heated at 75°C for 10 min, then at 37°C for an hour, and finally loaded to the pre-hybridized array. The hybridization was performed at 37°C overnight. The washing procedure was performed as described previously [ 17 ].Real time quantitative PCR Real time PCR was carried out using SYBR Green PCR core reagents according to the manufacturer's instructions (Applied Biosystems, Foster City, CA). Each DNA sample was analyzed in triplicate using the ABI PRISM 7000 Sequence Detector. For quantitative PCR, 10 ng of genomic DNA was used for SYBR green PCR assay. Serial dilutions of neuroblastoma cell line CHP134 DNA were used as templates for a standard curve, and the normal genomic DNA was used as a calibrator. The normalization was performed as described using  BCMA  and  SDC4  as reference genes [ 20 ].
Real time quantitative PCR
Real time PCR was carried out using SYBR Green PCR core reagents according to the manufacturer's instructions (Applied Biosystems, Foster City, CA). Each DNA sample was analyzed in triplicate using the ABI PRISM 7000 Sequence Detector. For quantitative PCR, 10 ng of genomic DNA was used for SYBR green PCR assay. Serial dilutions of neuroblastoma cell line CHP134 DNA were used as templates for a standard curve, and the normal genomic DNA was used as a calibrator. The normalization was performed as described using  BCMA  and  SDC4  as reference genes [ 20 ].BCMASDC4Data analysis Fluorescence ratios were normalized for each microarray by setting the average log ratio for each subarray elements equal to zero (commonly referred to as "pin-normalization"). The data was quality-filtered by removing those clones with quality lower than 0.5 in more than 20% of all the samples [ 19 ]. For the clones that passed this filter, if the quality for a specific sample is lower than 0.5, then its fluorescence ratio is replaced by the average ratio value of all other samples with the good quality. The clones were finally assigned to UniGene Cluster (Build 154 September 2002). For the UniGene clusters represented by multiple clones, mean fluorescence ratios of those clones are used. After these processes we had 23975 unique UniGene clusters remaining from the initial 42591 clones. Map positions for the cluster were assigned by Blat searches against the "Golden Path" genome assembly ( ; June, 2002 Freeze). Throughout this publication, all genomic coordinates are given with the respect to this assembly. Finally the clusters were sorted according to their starting position of sequence on each individual chromosome.
Data analysis
Fluorescence ratios were normalized for each microarray by setting the average log ratio for each subarray elements equal to zero (commonly referred to as "pin-normalization"). The data was quality-filtered by removing those clones with quality lower than 0.5 in more than 20% of all the samples [ 19 ]. For the clones that passed this filter, if the quality for a specific sample is lower than 0.5, then its fluorescence ratio is replaced by the average ratio value of all other samples with the good quality. The clones were finally assigned to UniGene Cluster (Build 154 September 2002). For the UniGene clusters represented by multiple clones, mean fluorescence ratios of those clones are used. After these processes we had 23975 unique UniGene clusters remaining from the initial 42591 clones. Map positions for the cluster were assigned by Blat searches against the "Golden Path" genome assembly ( ; June, 2002 Freeze). Throughout this publication, all genomic coordinates are given with the respect to this assembly. Finally the clusters were sorted according to their starting position of sequence on each individual chromosome.Detection of single copy changes To identify the alterations of copy number along the genome, we compared the distribution of the ratios in a sliding window of 20 clones in genomic order with a "null distribution" using a t-test. The p-value for genomic change (P gc ) obtained in this way is assigned to the center of the sliding window (referred to as the "SW-locus" throughout this manuscript). The t-test is valid in this instance because the observed distribution of P gc  for the loci in random order matches the expected theoretical distribution. The null-distribution used in t-test represents the unaltered part of genome. To identify the cDNA clones for the null-distribution, we start with the whole genome. The SW-loci corresponding to portions of the DNA that are amplified or deleted with a p-value smaller than 0.05 are removed recursively from the null dataset, until the null dataset is stable and there is no more amplified or deleted SW-locus in the null data set. Finally, the confidence of identified genomic alterations is visualized in a pseudo-color map in which color intensity represents the log of p-values (red for gain and green for loss).
Detection of single copy changes
To identify the alterations of copy number along the genome, we compared the distribution of the ratios in a sliding window of 20 clones in genomic order with a "null distribution" using a t-test. The p-value for genomic change (P gc ) obtained in this way is assigned to the center of the sliding window (referred to as the "SW-locus" throughout this manuscript). The t-test is valid in this instance because the observed distribution of P gc  for the loci in random order matches the expected theoretical distribution. The null-distribution used in t-test represents the unaltered part of genome. To identify the cDNA clones for the null-distribution, we start with the whole genome. The SW-loci corresponding to portions of the DNA that are amplified or deleted with a p-value smaller than 0.05 are removed recursively from the null dataset, until the null dataset is stable and there is no more amplified or deleted SW-locus in the null data set. Finally, the confidence of identified genomic alterations is visualized in a pseudo-color map in which color intensity represents the log of p-values (red for gain and green for loss).Estimation of frequency of genomic changes among the samples The probabilistic approach above provides P-values for the presence of genomic alteration in a given sample. In order to estimate the frequency of a genomic alteration we can set a threshold ratio and identify how many samples have a ratio value outside this threshold in the same genomic region. The disadvantage of this method is that different ratio thresholds will give different frequencies. We therefore applied another approach to avoid the use of ratio thresholds. To determine the frequency of loss or gain that correlate with the stage or  MYCN  amplification status, we first calculated the mean of the P gc  or  P , for each group. This value is proportional to the frequency f with change  = N with change /N total  of their occurrence, where N with change  is the number of samples in a subgroup with a given genomic imbalance and N total  is the overall number of samples in that subgroup. This is valid as follows. For all loci in which there are no genomic imbalances the observed P gc  will follow the flat theoretical distribution with a mean (expectation value) < P > = 0.5. Therefore, for those cases where we are sure of a genomic imbalance P gc  is close to 0 (for example p < 0.01), whereas for the samples in which there are no changes P gc  = 0.5. According to the formula:  P  = ((N no change  × 0.5) + (N with change  × 0))/N total  = (1 - f with change ) × 0.5, the lower the  P  the higher the frequency of a genomic change in that SW-locus. Thus the  P  can be used to determine the frequency of a given change e.g.  P  = 0.15 corresponds to a frequency of ~70% of the samples with that given change.
Estimation of frequency of genomic changes among the samples
The probabilistic approach above provides P-values for the presence of genomic alteration in a given sample. In order to estimate the frequency of a genomic alteration we can set a threshold ratio and identify how many samples have a ratio value outside this threshold in the same genomic region. The disadvantage of this method is that different ratio thresholds will give different frequencies. We therefore applied another approach to avoid the use of ratio thresholds. To determine the frequency of loss or gain that correlate with the stage or  MYCN  amplification status, we first calculated the mean of the P gc  or  P , for each group. This value is proportional to the frequency f with change  = N with change /N total  of their occurrence, where N with change  is the number of samples in a subgroup with a given genomic imbalance and N total  is the overall number of samples in that subgroup. This is valid as follows. For all loci in which there are no genomic imbalances the observed P gc  will follow the flat theoretical distribution with a mean (expectation value) < P > = 0.5. Therefore, for those cases where we are sure of a genomic imbalance P gc  is close to 0 (for example p < 0.01), whereas for the samples in which there are no changes P gc  = 0.5. According to the formula:  P  = ((N no change  × 0.5) + (N with change  × 0))/N total  = (1 - f with change ) × 0.5, the lower the  P  the higher the frequency of a genomic change in that SW-locus. Thus the  P  can be used to determine the frequency of a given change e.g.  P  = 0.15 corresponds to a frequency of ~70% of the samples with that given change.MYCNDetermination of recurrent regions We first define a SW-locus with  P  < 0.15 in a specific subgroup as altered. This threshold corresponds to roughly a fraction of >70% of all tumor samples harboring that alteration in each subgroup. We define an altered SW-locus as common in all tumors, if the SW-locus passes the threshold for each of the three subgroups. A SW-locus is called differential in one subgroup with respect to another subgroup, if the frequency of genomic change is at least 3 times higher in one subgroup as compared to another. A SW-locus is defined as specific if the locus in one subgroup is differential with respect to each of the other subgroups; a SW-locus is defined as shared in two groups, if in both groups it is differential with respect to the third subgroup.
Determination of recurrent regions
We first define a SW-locus with  P  < 0.15 in a specific subgroup as altered. This threshold corresponds to roughly a fraction of >70% of all tumor samples harboring that alteration in each subgroup. We define an altered SW-locus as common in all tumors, if the SW-locus passes the threshold for each of the three subgroups. A SW-locus is called differential in one subgroup with respect to another subgroup, if the frequency of genomic change is at least 3 times higher in one subgroup as compared to another. A SW-locus is defined as specific if the locus in one subgroup is differential with respect to each of the other subgroups; a SW-locus is defined as shared in two groups, if in both groups it is differential with respect to the third subgroup.Abbreviations NB: neuroblastoma A-CGH: array-comparative genomic hybridization M-CGH: metaphase-comparative genomic hybridization Stage 1-: stage 1 without  MYCN  amplification Stage 4-: stage 4 without  MYCN  amplification Stage 4+: stage 4 with  MYCN  amplification BAC: Bacterial artificial chromosome
Abbreviations
NB: neuroblastomaA-CGH: array-comparative genomic hybridizationM-CGH: metaphase-comparative genomic hybridizationStage 1-: stage 1 without  MYCN  amplificationMYCNStage 4-: stage 4 without  MYCN  amplificationMYCNStage 4+: stage 4 with  MYCN  amplificationMYCNBAC: Bacterial artificial chromosomeAuthor's contributions QC carried out all experiments and participated in data analysis. SB performed array CGH data analysis and statistical analysis. QC and SB drafted the manuscript. JSW, CCW, NC and CS were involved in the microarray production and the manuscript edition. ALK and BG were involved in the data analysis and the manuscript edition. FW, FB, MS and DC provided the tumor samples and patient information and were also involved in the manuscript edition. JK principal investigator of the project, participated in its design and is the final editor of the manuscript. All authors read and approved the final manuscript.
Author's contributions
QC carried out all experiments and participated in data analysis. SB performed array CGH data analysis and statistical analysis. QC and SB drafted the manuscript. JSW, CCW, NC and CS were involved in the microarray production and the manuscript edition. ALK and BG were involved in the data analysis and the manuscript edition. FW, FB, MS and DC provided the tumor samples and patient information and were also involved in the manuscript edition. JK principal investigator of the project, participated in its design and is the final editor of the manuscript. All authors read and approved the final manuscript.


Nevirapine and efavirenz are the most commonly prescribed of the class of antiretroviral drugs called non-nucleoside reverse transcriptase inhibitors (NNRTIs). Efavirenz has the advantage of once-daily dosing. In a recent study called the 2NN study (Lancet 363: 1253–1263), it appeared to be only marginally superior to nevirapine in terms of clinical success and virological suppression. Van Leth and colleagues have now shown that while nevirapine and efavirenz both raise high-density lipoprotein (HDL) cholesterol (the “good” type of cholesterol), the overall lipid profile is better with nevirapine than with efavirenz.“These data suggest that nevirapine may be preferable to efavirenz in HIV-infected adults with other cardiovascular risk factors,” says the study's academic editor, Andrew Carr of St. Vincent's Hospital in Darlinghurst, Australia. “However, perceived cardiovascular risk is only one factor that would affect the choice between these two drugs.”Van Leth and colleagues prospectively analyzed the lipids of patients enrolled in the 2NN study, a randomized, open-label efficacy study that included adults with HIV who had never been on antiretroviral drugs. All patients were given stavudine and lamivudine and were then randomized into three treatment groups: nevirapine, efavirenz, or both.For the lipid analysis, which was preplanned, the researchers included only the nevirapine and efavirenz groups (417 and 289 patients, respectively). This was because the 2NN study showed that simultaneous use of nevirapine and efavirenz should be avoided—the combination is associated with increased toxicity without increased efficacy. The increase in HDL cholesterol was significantly higher with nevirapine than with efavirenz. There was a decrease in the ratio of total cholesterol to HDL cholesterol with nevirapine and an increase with efavirenz.The study does not prove, however, that the rise in HDL cholesterol seen with NNRTIs (especially nevirapine) actually leads to a reduction in coronary heart disease. “There are no vascular functional data,” says Carr, “or clinical vascular endpoint data that confirm that the statistically significant lipid differences observed are clinically significant.”The study was funded by Boehringer Ingelheim, the manufacturer of nevirapine. The authors clearly state that the company had “a nonbinding input on issues of study design and analyses” but it had “no influence on reporting of the data or the decision to publish.”Despite its limitations, van Leth and colleagues' study “moves clinicians and patients away from ‘one-size-fits-all’ antiretroviral therapy,” says Carr. “It takes us further along the path of choice of antiretroviral therapy being individualized according to other patient comorbidities and risk factors, as well as therapy simplicity and side effects.”