Virtual CGH: Prediction of Novel Regions of Chromosomal Alterations in Tumor from Gene Expression Profiling

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1 Virtual CGH: Prediction of ovel egions of Chromosomal Alterations in Tumor from Gene Expression Profiling Huimin Geng 1,3, Javeed qbal 1, Xutao Deng 2, Wing C. Chan 1 and Hesham Ali 3 1 Department of Pathology and Microbiology, niversity of ebraska Medical Center, Omaha, E Cedars-Sinai esearch nstitute, Cedars-Sinai Medical Center, Los Angeles, CA Department of Computer Science, niversity of ebraska at Omaha, Omaha, E {huimingeng, jiqbal, jchan}@unmc.edu, dengx@cshs.org, hali@mail.unomaha.edu Abstract The identification of genetic alterations using array Comparative Genomic Hybridization (CGH would provide important insights into the mechanisms of tumorogenesis. High resolution array CGH are very expensive and the experiment require a separate sample, therefore, we developed a computational method for predicting gains and losses of genomic DA segments based on ma expression profiles of tumor cell lines, and called this method a Virtual CGH (vcgh predictor. VCGH is performed through a novel algorithm in which each chromosomal segment is evaluated by the gene transcriptional profiles. The calculation yields a log of odds (LOD score for each chromosomal segment and this likelihood-based score is used to predict the correlation between ma expression patterns and DA copy number alterations. By aligning all regions of gains and losses from multiple cell lines we can identify minimal common regions of gains and losses which may contain potential oncogenes or tumor suppressors. This method can be used to screen transcriptional profiles of other malignancies for the identification of DA segmental loss or gain. 1. ntroduction Tumors often accumulate a large series of genomic imbalances, which either lead to activation of oncogenes or deletion of tumor suppressors [1-3]. An oncogene can cause the transformation of normal cells into cancerous tumor cells. A tumor suppressor gene is involved in slowing down cell growth and if inactivated, allows growth to progress without control. Comparative Genomic Hybridization (CGH is a molecular cytogenetic technique used to identify DA copy number changes and has been widely used in defining the putative chromosomal regions involved in tumor progression [4-7]. CGH relies on the hybridization of tumor DA to whole genome arrays and image extraction through high resolution scanners. Several methods have been developed to select regions of gains and losses from the experimental array CGH data [8-16]. The other microarray-based genome-wide technique called Gene Expression Profiling (GEP has been widely used to identify transcriptional signatures essential for tumor development based on ma level changes compared to normal DA [17]. Many studies have reported a number of genes that are differentially expressed between cancer and healthy patients or between cancer subtypes [18]. Both CGH and GEP techniques are based on hybridization of cancer versus normal DA or A and are highly expensive and require preparation of two different samples (A and DA which can be of a limitation in tumor biopsies. The ma transcript changes in cancer patients detected by GEP have been found to correlate to corresponding DA copy number alterations detected by CGH [10, 19, 20]. Gene dosage has a direct effect on the level of transcriptome and therefore GEP can be indirectly used to measure the genomic abnormalities in tumors. Compared with CGH, GEP is a fundamental technology. t accumulates relatively large amount of data. Also, the high-density arrays available from Affymetrix make it possible to measure the genomic changes indirectly due to their whole genome coverage. Therefore, it is advantageous to use GEP data which measures gene expression on the level of transcriptome to predict CGH which measures the alterations of DA copy numbers on the genomic level. This prediction will reduce the need for extra samples and additional costs which could be otherwise consumed by a separate CGH experiment, and may help to reveal and /07 $ EEE 1

2 confirm the links between DA and ma that may cause cancers. n this study, we focused on one type of cancers, atural Killer (K cell lymphoma. Eight K-cell lymphoma cell lines were profiled with GeneChips (plus 2 from Affymetrix. Then vcgh predictor was applied using expression levels and the chromosomal locations of the genes, to identify the chromosome regions with consistent patterns of over or under expression. Finally, some of these regions were validated with experimental array CGH using tiling arrays [4]. This paper is organized as follows. n section 2, we describe the vcgh method. n section 3, we apply this method to GEP data of eight cell lines of K-cell lymphoma and get the predicted genomic gain or loss regions. n section 4, we compare the prediction results with those from experimental array CGH. n section 5, we conclude that vcgh is a powerful tool that may significantly enhance the data analysis of cancer GEP. 2. Method Virtual CGH (vcgh is a novel algorithm that utilizes the changes in ma expression level to predict DA copy number alterations. The key of vcgh is to determine the magnitude of correlation between ma changes and DA copy number changes. Since the DA copy number changes are highly correlated to the spatial relationship of genes on the genome, in vcgh, we measure the correlation of ma level expression changes of a gene and its chromosomal location, and use a probabilistic model to estimate the likelihood of a chromosome segment being a genetically abnormal region. VCGH can be applied for two purposes. The first one is to predict DA copy number alterations using ma expression profiles. The second usage is to compare and combine vcgh results with experimental CGH data to study functional abnormalities Microarray data preprocessing First, we need to find differentially expressed genes between cancer and normal tissues and mark them as over-expressed or under-expressed. Fold change, t-test, SAM [21], or other methods can be applied for detecting genes that are differentially expressed. Then each gene is assigned a symbol,, or representing it is nduced, epressed, or nchanged in cancer compared with normal samples. The spatial order of the genes on each chromosome need be preserved. This data preprocessing transformed the original expression data into 22 symbolic vectors, each for one autosome. (We ignore X and Y chromosomes in this study Calculating LOD score n this step, we give a probability score to each chromosomal segment of a fixed size (e.g. six consecutive genes which shows the likelihood of this segment being a genomic gain or loss. ntuitively, if a chromosomal segment contains an enriched set of up or down genes, the correlation is high. This correlation is defined as a log of odds (LOD score which is computed from a probabilistic model illustrated below. Suppose now we focus on a chromosomal segment that contains n consecutive genes. We count the occurrences of each type of symbols and denote the counting results as n, n, and n, for the number of induced, repressed and unchanged genes, respectively. The likelihood of the observation of n, n, and n under certain hypothesis H is computed from the binomial distribution as in Eq. (1: n! ( nn Gain:, n n H,!( n n! n! n ( n Loss:, n H. n!( n! (1 where n n n, 1, and 1. L is the likelihood, H is the hypothesis, n is the number of genes, and is the parameter of the success probability in the binomial distribution. Hypothesis H is specified by parameter which is the underlying probability that certain symbol occurs. We can then use this basic probability model to evaluate the chromosomal segments of being abnormal regions. The likelihood of observing n, n, and n in a segment of n consecutive genes is computed under two different hypotheses: H o : The chromosomal segment does not belong to a DA gain/loss region. H 1 : The chromosomal segment belongs to a DA gain/loss region. H 0 implies that the observations of up-regulated and down-regulated genes do not correlate to their chromosomal spatial locations where the genomic gains and losses happen. On the other hand, H 1 spells out that the expression changes of genes do correlate to 2

3 their chromosomal locations where the genomic gains and losses happen. For null hypothesis H 0, the 0 s can be estimated by normal/normal hybridization for the chromosomal segments. For alternative hypothesis H 1, the 1 s can be estimated by cancer/normal hybridization for the corresponding chromosomal segments. n case normal/normal hybridization is not available, we can estimate 0 s (under H 0 by using cancer/normal hybridization, but using the whole genome instead of a segment, to estimate the background occurrence frequencies. The parameters 0 s (for hypotheses H 0 and 1 s (for hypothesis H 1 are estimated using the maximum likelihood estimator as in Eq. (2: ˆ, '', '', '', or '', 0, ˆ n, '', '', '', or '', 1, n (2 where is the total number of genes across all chromosomes; is the number of genes in certain gene categories across all chromosomes ( '', '', '', or '' representing induced, repressed, induced + unchanged, or repressed + unchanged, respectively; n is the total number of genes within a specific segment, and n is the number of genes in certain gene categories in the specific segment ( has the same meaning as mentioned in. The LOD score is the log base 10 of the likelihood ratio under the hypotheses of H 1 and H 0. Having the parameters estimated from the observations from the real GEP data, we could now calculate the LOD score of a segment of being a gain or loss region as follows: ( nn, n n H1 1, 1, ( nn, n n H0 0, 0, n ( n, n H1 1, 1, n ( n, n H0 0, 0, Gain: LOD log log Loss: LOD log log For example, we use a DA segment of a total of ten genes containing eight induced genes, one reduced gene and one unchanged gene to illustrate the LOD score computation for the gain. Suppose from the background (a total of genes from the whole genome, the parameters under H 0 have been estimated as: (3 ˆ ˆ ˆ 0, 0.1, 0, 0.1, 0, 0.8. Since n = 8, n = 1, n = 1 and n = 10, we have the parameters under H 1 be estimated as: ˆ ˆ n ˆ 1, 0.8, n 1, 0.1, 1, 0.1. n n n ( nn 8 2 1, 1, So, LODgain log10 log ( nn , 0, A LOD score of 5.9 means that the probability that H 1 is true (that this segment is a genomic gain is times more likely than it is that H 0 is true (that this segment is not a genomic gain. 3. Virtual CGH prediction from GEP 3.1. Gene expression profiling The vcgh predictor is applied to GEP on one normal K-cell sample from peripheral blood of healthy donors as reference and eight well characterized K-cell lymphoma cell lines. The sample preparation, A isolation and cda synthesis was performed strictly according to the Affymetrix protocol. The hybridizations from each sample were performed on Gene-Chip 133 plus 2 (Affymetrix, nc. All arrays were scanned using the Gene-chip scanner (Affymetrix and analyzed by the Affymetrix GeneChip Operating Software Data processing We uploaded all the gene expression data in BB- ArrayTool ( ArrayTools.html. The threshold of the intensity at the minimum value was set as 10 if the intensity was below the minimum, and normalization was applied using median over entire array. All the information regarding the probe sets, gene names, chromosomal and cytoband locations was downloaded using the BB-ArrayTools. The parameters under H 0 were estimated by using all spots/genes across the whole genome as indicated in Eq. (2. Figure 1 shows, under H 0, the log 2 fold difference between tumor and normal samples has normal distribution with the mean of and the standard deviation (s.d. of When the threshold was set at two fold, we get 3

4 ˆ ˆ ( , ; ˆ ( , , 0, ˆ 0, 0, For H 1, we split one file containing the expression values of all genes over 22 chromosomes to 22 files, each for one chromosome, and ordered those genes in a spatial order on the genome. The gene names and corresponding start and stop base pair positions were downloaded from CB Homo sapiens Genome Build We screened the neighboring genes along a chromosome by moving the window from the pter to the qter of each chromosome. For each segment of the chromosome, we calculated its LOD scores for both gain and loss as in Eq. (3 and draw graphics of a chromosomal position versus the likelihood that a segment (of a fixed window size, which is centered at that chromosomal position, is a genetic alterative region Abnormal regions identified by vcgh n our gene expression analysis, the transcription levels of genes in the abnormal regions identified by vcgh were > +2 for gains and < -2 for loss as compared to normal K sample profile. We set up the window size as six genes and calculate the LOD scores for each segment in each chromosome, for each of the eight cell lines. f a segment shows consistent gene expression either low or high along the regional cluster in all cell lines, then this segment is more likely to contain critical genes (oncogenes or tumor suppressors for the development of K-cell lymphoma. According to vcgh, we identified several chromosomes showing frequent gene expression alterations, such as chromosomes 1, 7, 13, 17 and 18. We show chromosomes 1, 13 and 18 as illustration in Figure 2 and Figure 3. n Figure 2, we graphically display the LOD scores of gain and loss for chromosomes 1 and 13, with loss on the left side of a chromosomal ideogram and gain on the right side. For example, the regions in chromosome 1 with increased expression in a set of consecutive genes were 1q41, 7q21.1-q22.1 and 17p13, and the regions with decreased expression were 1p36.1, 7p22.1, 17p13 and 18p11.2, in concordance with previously published results using conventional or BAC array CGH. Figure 3 was for the further comparison of the vcgh and the experimental CGH using losses of chromosome 18 as an example. The data is unpublished and will appear in another biological paper together with a detailed discussion of the biological meanings of those genomic alteration regions. Fig. 1. Histogram of log2 ratio of tumor over normal samples under H 0. t is a normal distribution (mean = and s.d. =

5 a, chromosome 1 b, chromosome 13 Fig. 2. LOD scores of gain and loss for chromosomes 1 and 13. The left side of a chromosomal ideogram represents Loss and right side represents Gain. The series in different colors indicate different cell lines. 4. Experimental CGH validation To validate the proposed vcgh predictor, we compared the recurrent regions predicted from GEP to the ones identified by experimental array CGH. We aligned the gain and loss regions from eight cell lines for each chromosome and mapped the alignment results along with the chromosomal ideograms. There were good associations between the experimental CGH and vcgh. To graphically view the comparison between the two methodologies, we draw the gain and loss regions from vcgh and the experimental CGH along with the chromosomal ideogram together. The associations of the two on chromosome 18 are shown in Figure 3 as an illustration. Human physical map information used in the graph was obtained from Vysis nc. ( 5

6 VCGH Fig. 3. Comparison of results from vcgh and experimental CGH for losses on chromosome 18. On the right side, regions of losses are indicated by the lines on the left of the chromosomal ideogram obtained from experimental CGH, and the graphs on the left side show the predicted loss regions from vcgh. 5. Conclusions Whole genome array CGH can be used to identify and analyze the genetic alterations and hence provide important insights into the genetic basis of tumors. While noticing that high resolution array CGH are very expensive and require a separate sample, we developed a vcgh method for predicting gains and losses of DA segments based on GEP. n vcgh, we calculate the correlation between ma changes and DA copy number changes using LOD score, which is the likelihood that a chromosomal segment belongs to a genetic gain or loss region. Experimental CGH Employing the vcgh predictor to GEP of eight cell lines of K-cell lymphoma, we predicted several genomic alterative regions most likely happened for K-cell lymphoma. The revealed abnormal regions of genetic alterations may contain oncogenes and tumor suppressors important in the pathogenesis of K-cell lymphoma. The loss of chromosome pieces indicates that the tumor suppressor genes (common in lymphoid malignancies may be located in these regions and those suppressor genes may be associated with the development of K lymphoma/leukemia. The gain of chromosome pieces indicates that the tumor oncogenes, which cause the transformation of normal cells into cancerous tumor cells, may be located within these regions. We have also performed experimental array CGH on the same cell lines to validate our method and the results showed that the predicted gain or loss regions were in accordance with the recurrent regions identified by the experimental array CGH. 6. Future work n this paper, we build a computational model to give an indication from experimental GEP to experimental CGH: GEP Virtual CGH CGH. Based on this preliminary work, we would like to continue on the following further studies: Cross link GEP and CGH. Combination of genomic profiling by array CGH and transcriptional profiling by microarrays may narrow down the number of candidate genes which are most likely participating in the malignancies. We would also provide a web-based search for the genes in the recurrent regions and the description of these genes as well. Possible candidate genes will be listed and require further investigation. Build a Bayesian framework by combining vcgh and array CGH. Many types of cancer and inherited diseases are known to have genome abnormalities, which may have a direct or indirect impact on gene expression patterns in the affected genomic region. esearchers commonly use A gene expression analysis as the default approach to cluster and classify patient samples, but often lack information about the underlying DA that may also undergo dramatic copy number change. Therefore, an integrated tool using both genomic profiling and transcriptional profiling has the advantage in evaluating DA copy number and A gene expression at the same time and is important for understanding the underlying biological mechanisms. 6

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