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methods
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DNA was isolated from the breast cancer cell-line SKBR3 (obtained from ATCC) or from FFPE tumor tissue with at least 70% tumor cells as described before [>>6<<]. Two micrograms of total genomic DNA were labeled with ULS-Cy5 according to the manufacturers' instructions (Kreatech Biotechnology, Amsterdam).
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Corning CodeLink® slides containing the human 3.5 k BAC/PAC genomic clone set in triplicate were used as before [>>6<<]. As optimization target, we used CGH profiles of 6 FFPE tumors containing at least 70% tumor cells and the SKBR3 cell-line profile, obtained by the manual hybridization method described before [6]. Automated hybridizations were done in
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As optimization target, we used CGH profiles of 6 FFPE tumors containing at least 70% tumor cells and the SKBR3 cell-line profile, obtained by the manual hybridization method described before [>>6<<]. Automated hybridizations were done in 63.5 × 21 mm chambers in a Tecan HS4800 Pro™ hybridization station, which uses liquid agitation during hybridization. Experiments involving human tissues were conducted with permission of our
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Data processing included signal intensity measurement in ImaGene Software followed by median pintip (c.q. subarray) normalization and plotting in custom Matlab code as before [>>6<<].
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For each CGH profile, we calculated the variance across all log2 ratios relative to the ratios of the underlying true ploidy levels as estimated by CGH-segmentation [>>16<<], secondly, we defined the dynamic range as the difference between the minimum log2 ratio and the maximum log2 ratio calculated by CGH-segmentation [16], and the average of all the standard deviations of the triplicate spot measurements
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to the ratios of the underlying true ploidy levels as estimated by CGH-segmentation [16], secondly, we defined the dynamic range as the difference between the minimum log2 ratio and the maximum log2 ratio calculated by CGH-segmentation [>>16<<], and the average of all the standard deviations of the triplicate spot measurements of each probe was used as a third statistic.
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Array CGH has become a successful and valuable tool for the analysis of chromosome copy-number alterations including the detection of sub-megabase alterations and has been applied to e.g. cell-lines, (tumor) tissues, and lymphocytes [>>1<<-5]. The power of aCGH technology to detect low-level copy number changes is critically dependent on DNA quality (e.g. DNA fragmentation and cross-links) and sample heterogeneity.
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Therefore, selection of DNA of sufficient quality, especially when using FFPE material, is of great importance for aCGH [>>6<<]. Furthermore, whole genome amplification may be performed when insufficient DNA is available from a sample [7-9]. In addition to sample quality, enzymatic labeling protocols decrease average DNA size further which results in increased
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Furthermore, whole genome amplification may be performed when insufficient DNA is available from a sample [>>7<<-9]. In addition to sample quality, enzymatic labeling protocols decrease average DNA size further which results in increased noise due to non-specific binding [10], especially when the average PCR length of the sample template drops below
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In addition to sample quality, enzymatic labeling protocols decrease average DNA size further which results in increased noise due to non-specific binding [>>10<<], especially when the average PCR length of the sample template drops below 200 bp [6].
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to sample quality, enzymatic labeling protocols decrease average DNA size further which results in increased noise due to non-specific binding [10], especially when the average PCR length of the sample template drops below 200 bp [>>6<<]. As an alternative, chemical labeling protocols with cyanin cis-platinum-labeled DNA resulted in good aCGH results [11], also for FFPE archival samples [6].
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As an alternative, chemical labeling protocols with cyanin cis-platinum-labeled DNA resulted in good aCGH results [>>11<<], also for FFPE archival samples [6].
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As an alternative, chemical labeling protocols with cyanin cis-platinum-labeled DNA resulted in good aCGH results [11], also for FFPE archival samples [>>6<<]. One of the challenges of aCGH is its lower hybridization signal-intensity compared with metaphase-CGH. Based on literature and our previous experiments we hypothesized that the hybridization improves with increasing effective
n2:mentions
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There have been earlier reports on array CGH of FFPE material [>>6<<-8,12-14] and other reports on automated hybridization [15].
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There have been earlier reports on array CGH of FFPE material [6-8,>>12<<-14] and other reports on automated hybridization [15].
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There have been earlier reports on array CGH of FFPE material [6-8,12-14] and other reports on automated hybridization [>>15<<]. This report however, is the first that combines automated hybridization of FFPE tumor material on a BAC array, using non-enzymatic labeling and provides a method without formamide in the post-hybridization washes.
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results and discussion
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Therefore, we chose to use the widely studied cell-line SKBR3 as a model for which chromosomal aberrations have been well documented [>>2<<,3,17], although the existence of minor sub-clone related alterations cannot be ruled out.
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Therefore, we chose to use the widely studied cell-line SKBR3 as a model for which chromosomal aberrations have been well documented [2,>>3<<,17], although the existence of minor sub-clone related alterations cannot be ruled out.
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In a previous study [>>6<<], we performed manual hybridizations of over one hundred BAC arrays that helped us to develop the quality criteria that were now used to optimize automated hybridization.
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We used SKBR3 as a model cell-line and compared its CGH profile with published [>>2<<,3,17] and our own manual hybridizations. Figure 1A represents the SKBR3 CGH profile published by Pollack et al., hybridized to a human cDNA micro array containing 6,691 different mapped human genes [2].
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We used SKBR3 as a model cell-line and compared its CGH profile with published [2,>>3<<,17] and our own manual hybridizations. Figure 1A represents the SKBR3 CGH profile published by Pollack et al., hybridized to a human cDNA micro array containing 6,691 different mapped human genes [2].
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Figure 1A represents the SKBR3 CGH profile published by Pollack et al., hybridized to a human cDNA micro array containing 6,691 different mapped human genes [>>2<<]. Figure 1B represents the SKBR3 CGH profile published by Shadeo and Lam., hybridized to a whole-genome tiling path BAC array containing 32,433 overlapping BAC-derived DNA segments [3]. Figure 1C represents the SKBR3 CGH profile published
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Figure 1B represents the SKBR3 CGH profile published by Shadeo and Lam., hybridized to a whole-genome tiling path BAC array containing 32,433 overlapping BAC-derived DNA segments [>>3<<]. Figure 1C represents the SKBR3 CGH profile published by Jong et al., hybridized to a human oligonucleotide array containing 28,830 unique genes [17]. Figure 1D represents our manually hybridized SKBR3 CGH profile. Depicted in figure 1E
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This figure summarizes all the breakpoints and estimated copy number levels as plotted in red in figure 1A–1F calculated by CGH-segmentation [>>16<<]. Breakpoint locations and calling of copy number levels (gain, unchanged, heterozygous loss, and homozygous loss) are provided as additional file 1 and 2. Although a lower density 3.5 k BAC array was used, figure 1G illustrates that
n2:mentions
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Although a lower density 3.5 k BAC array was used, figure 1G illustrates that nearly all aberrations and breakpoint in our results (figure 1D and 1E) are similar to the three published data sets [>>2<<,3,17]. We concluded that the dynamic range of both our manual and automated hybridization protocols are adequate to detect single copy number losses and gains. Reproducibility of this automated protocol is shown by replicate
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Although a lower density 3.5 k BAC array was used, figure 1G illustrates that nearly all aberrations and breakpoint in our results (figure 1D and 1E) are similar to the three published data sets [2,>>3<<,17]. We concluded that the dynamic range of both our manual and automated hybridization protocols are adequate to detect single copy number losses and gains. Reproducibility of this automated protocol is shown by replicate hybridizations
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To develop aCGH also as a diagnostic tool, it will be essential to validate its applicability on patient tumor samples and especially on archival FFPE tissue [>>18<<]. Extracted DNA from this material is often heavily cross-linked, heterogeneous (i.e. mix of cells of different genomic composition), fragmented, and rarely composed of 100% tumor cells.
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For this particular tumor, the maximal CGH-segmentation [>>16<<] value was used as dynamic range ("Max CGHseg", table 5), because the homozygous loss on chromosome 11 would have a disproportional contribution to its value (same tumor as in figure 3).
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Also the dynamic range kept at similar levels (again the highest ratio calculated by CGH-segmentation [>>16<<] was used because of the homologous loss in this tumor in chromosome 11 as depicted in figure 3).
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Figure 4 shows the CGH profiles of the FFPE tumors (averaged log2 ratios of the manual and the automated hybridization), with very similar breakpoint locations and copy number estimates [>>16<<] for each hybridization method.
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