DNA copy number abnormalities (amplifications and deletions) are characteristic of tumours,^1,2 and are found in association with developmental abnormalities and/or mental retardation.³ Several techniques have been developed for detecting CNA, including comparative genomic hybridisation (CGH), fluorescence in situ hybridisation (FISH), and loss of heterozygosity (LOH).^4–7 Recently, several groups have observed that chromosomal alterations can lead to regional gene expression biases in human tumours and tumour derived cell lines.^8,9,10 These studies suggested that a fraction of gene expression values (15–25%) are regulated in concordance with chromosomal DNA content. Statistical methods developed by our group and others have shown promising results for detecting CNA based on differential gene expression.^10,11 Crawley and colleagues used measures of gene expression bias to identify entire chromosomal arms showing aberrant expression.¹⁰ We recently found that a maximum likelihood statistical model could be used to localise the origin of chromosomal amplification within a chromosome that had already been identified as showing global expression abnormalities.¹¹ In the present study, we adapt that statistical approach to detect the origin of chromosomal deletion based on gene expression data. Using three cell lines with known chromosomal deletions as model system, we compared mRNA expression in those cells with that observed in diploid cell lines of matched tissue origin.

The deletion cells del(7)(GM03240,46,XY,del(7)(q34)), del(9)(GM00870,46,XX,del(9)(p21)), del(10)(GM03047, 46,XY, and del(10)(p11.2)), generated from patients with congenital anomalies and mental retardation, and normal control cells GM00302, GM04552, and GM05386, were obtained from Coriell Cell Repositories/NIGMS (http://locus.umdnj.edu/nigms/). Cells were grown under standard culture conditions (minimum essential medium Eagle-Earle BSS, 2× essential and non-essential amino acid and vitamin, with 2 mmol/l l-glutamine). Total RNA was isolated using a Qiagen RNeasy kit, and cRNA was synthesised, labelled, and fragmented, then hybridised to Affymetrix U133+ 2.0 GeneChip high density oligonucleotide arrays according to the manufacturer’s standard protocol. Paired comparison analyses were performed for deletion cells and their respective controls using the statistical expression algorithm of the Affymetrix Microarray Suite 5.0 software. Default settings were used to identify underexpressed transcripts (downregulated at p<0.002). The extent to which transcripts from a given chromosome were over-represented among the set of underexpressed genes was indicated by an odds ratio relative to the basal representation of genes from that chromosome in the entire Affymetrix sampling frame. Statistical significance of excess representation was evaluated using the χ² test, which produced a global test statistic indicating departure from expected incidence across all chromosomes (χ²with 23 df).¹²

To identify the specific chromosome showing significant CNA, the global test statistic was separated into constituent values for each chromosome (χ² with 1 df expressed as a % of the total χ² value with 23 df). For deletion cell lines, the diploid and the haploid (deleted) regions were analysed separately. The differentially expressed transcripts were mapped to their respective chromosomal locations. Genes located in the region where there was a deletion (single copy of the chromosomal region), were found to have a significantly higher prevalence in the underexpressed set than would be expected based on the prevalence of transcripts from that region in the entire set of transcripts assayed by the Affymetrix array (all p<0.00001, with odds ratios of 3.13, 2.10, and 3.54 for del(7), del(9), del(10) cells, respectively) (table 1). These data show that it is feasible to use microarray detection of differential mRNA expression to identify DNA copy number abnormalities.

To determine whether we could identify the boundaries of chromosomal deletion from underexpression data, we fitted a simple breakpoint statistical model to the data from chromosomes 7, 9, and 10 for the del(7), del(9) and del(10) cells respectively. A parameter θ was employed to indicate the chromosomal location at which the incidence of underexpression increases from the diploid base rate of β to an elevated rate of δβ in the deletion region. This statistical model expresses the probability of underexpression for each of N assayed transcripts as a function of the chromosomal location of its transcription start site and the origin of haploid DNA. (Pr(gene n is underexpressed) = δθ_nβ, with n = 1, 2, … N indexing the ordinal position of transcription start sites beginning with pter and ending at qter, θ indicating chromosomal location at which deletion begins, and the subscripts θn indicating the dependence of δ on both the location of the transcription start site and the origin of deletion of gene n). Transcripts originating outside of the deletion region (n<θ) are underexpressed at a base rate β (that is, δ_θn = 1), and transcripts originating within the deletion region (n>θ) are underexpressed at an altered rate δ_θnβ (δ_θn≠1). The model was fitted by maximum likelihood (binomial probability density), and the sampling distribution of θ was estimated by non-parametric bootstrapping (2000 resamplings of the ordered transcripts from chromosome 7, 9, and 10 present in the Affymetrix array).¹³ Analysis showed that, for del(10) cells, underexpressed genes increased from a base rate of 7.2% to 29.5% in the vicinity of locus 224 of the 1221 ordered loci on chromosome 10 (95% confidence interval 197 to 252, likelihood ratio χ² 91.1, p<0.000001). This corresponds to a location 28.1 Mb from chr10pter (fig 1B). This estimate of the breakpoint of deletion from underexpression analysis agrees closely with the previously documented breakpoint (10p12) by cytogenetic methods, which would correspond to a breakpoint at ordered locus 241 (30.7 Mb from 10pter). Similar results were observed for the del(7) and del(9) cell lines, in which the identified breakpoints also agreed closely with karyotypes (table 2). These findings suggest that changes in underexpression rates can be used to pinpoint the boundaries of chromosomal deletions.

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Figure 1

 Mapping the boundaries of chromosomal deletion by differential expression. (A) Underexpressed transcripts in del(10) cells were identified using Affymetrix Microarray Suite 5.0, with decreased transcription declared when change in p value was <0.002. The transcripts were ordered according to sequence on chromosome 10, with red bars indicating the transcription start site of genes identified as significantly underexpressed in del(10) cells relative to a tissue matched normal control cell. (B) As detailed in the text, a single breakpoint model allowing differential density of underexpression was fitted by maximum likelihood. The log likelihood associated with breakpoints at each ordinal position on chromosome 10 was plotted (black line) with the maximum likelihood value serving as the estimated origin of CNA. Grey lines map the ordinal positions of each assayed transcript to its chromosomal location. Significant change in the prevalence of underexpressed transcripts was identified at ordered transcript 224, 28.1 Mb from 10pter, agreeing with the previously defined origin of deletion by cytogenetic analyses. (C) To determine whether deletion extended to the p terminus, transcripts 1–223 were re-scanned, and a second significant change in the prevalence of underexpressed transcripts was identified at ordered transcript 85, 12.2 Mb from 10pter. (D) No significant change in the prevalence of underexpressed transcripts was identified in the region ranging from ordered transcript 224 to 10qter. Together with the results from (B), these data indicate a single partial deletion of chromosome 10p spanning the region 10p14 to 10p12. (E) Subtelomere FISH verified results from the maximum likelihood expression based analysis by confirming that the 10p deletion was interstitial with the intact subtelomere regions. Probes used are: 10ptel006 (10pter probe, green); 10qtel24 (10qter probe, red); PML (15q22 probe, aqua) and AFMA224XHI (15qter probe, yellow). Two normal signals for both 10p and 10q subtelomeres were clearly identified. Inset: G banded chromosome 10 of del(10) cell showing the deletion of p arm of the chromosome 10.

To determine whether the analysed chromosomes might contain novel abnormalities not previously detected, we applied the same maximum likelihood breakpoint analysis to each of the subregions defined by the results of the initial breakpoint analysis. For example, the initial analysis of chromosome 7 identified a breakpoint at ordered locus 1354 of the total 1493 chromosome 7 transcripts present in the Affymetrix sampling frame (table 2). In subsequent analyses, we scanned one fragment spanning ordered loci 1–1353 and another fragment spanning loci 1354–1493. Analyses of fragment data from chromosome 7 of del(7) cells and chromosome 9 of del(9) cells failed to suggest any further non-homogeneity in differential expression rates. However, analysis of the pter fragment of chromosome 10 from del(10) cells revealed a significant decrease in the incidence of downregulated genes in the vicinity of ordered locus 85 (out of the 223 total loci spanning 10pter-10p12) (fig 1C). The change in incidence was highly significant (χ²(1) = 36.16, p<0.0001), with the prevalence of downregulated genes increasing from 7.1% in the telomere–proximal region to 43.5% in the centromere–proximal region (odds ratio 10.13). These results suggested that del(10) cells retain normal diploid gene expression in the region 10pter-10p14, and that chromosomal deletion may be limited to the region 10p14-10p12. This hypothesis contradicts with the karyotype provided by the cell vendor, which indicates a complete deletion of 10pter-10p12. To resolve the contradiction, we carried out FISH as described previously, with subtelomere probes specific to 10p and 10q.¹⁴ As shown in fig 1E, del(10) cells clearly maintain two subtelomeres on chromosome 10 (both pter and qter). Probes for chromosome 15 were used as internal control. Thus the statistical analysis of differential expression data can identify and map the boundaries of discrete chromosomal deletions.

In summary, our data clearly show that genes from deleted chromosome regions are substantially over-represented (χ², p<0.000001) in the underexpressed subset for all three deletion cell lines. Furthermore, recursive application of a statistical breakpoint analysis can generate a high resolution mapping of the bounds of localised chromosomal deletions not previously recognised. This successive decomposition of heterogeneity in differential gene expression is reminiscent of the binary recursive partitioning strategies employed in non-parametric regression¹⁵ and could conceivably be applied to mapping other types of CNA (such as localised amplification). Expression based detection of DNA copy number abnormalities may thus provide a complementary approach to well established genomic and cytogenetic methods such as CGH and FISH, which directly measure changes in genomic DNA content. The present method is novel in using indirect functional data (transcription) to infer the underlying causative genomic changes. This approach is likely to be most useful when DNA based data are not available (for example, attempts to extract genomic information from archived expression data from clinical tumour samples), or when analysts seek to generate hypotheses about structural bases for differential gene expression in microarray data. The resolution of this method depends inherently on the density of genes in different chromosomal locations, and the specific set of genes represented on a particular microarray platform. Given the variability in these values, it is difficult to specify the resolution of the present technique in DNA base terms. However, given the magnitude of expression changes observed here, the present technique should be able to localise CNAs to contiguous regions spanning as few as 40 genes. These data show that statistical analysis of differential expression data can accurately identify the origin of CNAs in well defined model systems (see also Zhou et al¹¹), but further experimental and statistical studies will be required to evaluate the feasibility of this approach for identifying CNAs in clinical tumour samples. However, the present results suggest that expression based analysis of chromosomal abnormalities could provide a novel means for defining pathogenic structural abnormalities in cases where DNA data are not directly available.