- Open Access
A genome-wide map of aberrantly expressed chromosomal islands in colorectal cancer
- Eike Staub†1, 11Email author,
- Jörn Gröne†2,
- Detlev Mennerich5, 8,
- Stefan Röpcke1, 11,
- Irina Klamann4,
- Bernd Hinzmann3,
- Esmeralda Castanos-Velez3,
- Benno Mann7,
- Christian Pilarsky6,
- Thomas Brümmendorf8, 9,
- Birgit Weber8, 10,
- Heinz-Johannes Buhr2 and
- André Rosenthal3
© Staub et al; licensee BioMed Central Ltd. 2006
- Received: 05 September 2006
- Accepted: 18 September 2006
- Published: 18 September 2006
Cancer development is accompanied by genetic phenomena like deletion and amplification of chromosome parts or alterations of chromatin structure. It is expected that these mechanisms have a strong effect on regional gene expression.
We investigated genome-wide gene expression in colorectal carcinoma (CRC) and normal epithelial tissues from 25 patients using oligonucleotide arrays. This allowed us to identify 81 distinct chromosomal islands with aberrant gene expression. Of these, 38 islands show a gain in expression and 43 a loss of expression. In total, 7.892 genes (25.3% of all human genes) are located in aberrantly expressed islands. Many chromosomal regions that are linked to hereditary colorectal cancer show deregulated expression. Also, many known tumor genes localize to chromosomal islands of misregulated expression in CRC.
An extensive comparison with published CGH data suggests that chromosomal regions known for frequent deletions in colon cancer tend to show reduced expression. In contrast, regions that are often amplified in colorectal tumors exhibit heterogeneous expression patterns: even show a decrease of mRNA expression. Because for several islands of deregulated expression chromosomal aberrations have never been observed, we speculate that additional mechanisms (like abnormal states of regional chromatin) also have a substantial impact on the formation of co-expression islands in colorectal carcinoma.
- Chromosomal Region
- Adenomatous Polyposis Coli
- Pleomorphic Adenoma
- Laser Capture Microdissection
- Hereditary Colorectal Cancer
DNA microarrays have become a standard tool for the analysis of mRNA expression levels in colorectal cancer cells. Most studies focus on the identification of differentially expressed genes in tissues at different tumor stages or on the identification of new tumor subclasses and their diagnostic gene expression signatures [1–6]. In contrast, much less is known about the influence of chromosomal neighborhood on gene expression in tumors.
In tumors different genetic mechanisms are known to affect gene expression in wider chromosomal regions. Chromosomal aberrations, like homozygous and heterozygous deletions or amplifications, alter the DNA copy number of large genomic regions or even whole chromosome arms, leading to inactivation of tumor suppressor genes [7, 8] or to activation of oncogenes. Another genetic phenomenon that is assumed to have drastic effects on gene expression in cancer cells is the aberrant alteration of chromatin structure. Methylation of genomic DNA, histone acetylation, and histone methylation are assumed to have a large impact on the accessibility of DNA for transcription initiation . Such epigenetic mechanisms can affect large genomic regions by possibly either silencing or activating large arrays of genes. However, the regulatory mechanisms governing chromatin assembly and disassembly are only beginning to emerge. So far, due to methodological limitations it has not been possible to study the role of such phenomena for gene expression in cancer cells on a genome-wide scale. Nevertheless, evidence from single-gene focused studies suggests that chromatin regulation does play an important role in tumorigenesis [10, 11].
Regardless of which mechanism leads to coordinated expression in chromosomal domains, solely the knowledge about such domains is of considerable importance. Such knowledge could guide further studies that aim to differentiate between those differentially expressed genes that cause tumorigenesis and are the primary targets of regional genomic aberrations and those that are rather the outcome than the cause of tumor development. The rationale for the existence of such piggy-back genes is the following. The silencing of genes at close distance to a known tumor suppressor gene (TSGs) would in many cases just be a side effect of TSG silencing. A similar reasoning applies to oncogenes that can be activated by increased expression: genes that are co-amplified could also be expressed at higher levels although they do not contribute to tumorigenesis. Typical searches for differentially expressed genes by microarrays usually ignore such piggy-back effects. This may lead to the identification of large numbers of differentially expressed genes (DEGs), of which only a smaller fraction is causative for tumor development.
Though some experimental data recently became available linking microarray expression with DNA copy number analyses in some solid tumors [12–16] the knowledge about the existence of genomic islands of coordinated expression in colorectal carcinoma (CRC) is still limited. During the preparation of this manuscript a first assessment of chromosomal expression patterns in CRC in conjunction with genome-wide DNA copy number analyses became available . Tsafrir et al. described a correlation of gene copy number and expression for both, deleted and amplified genes. They claimed that the described alterations become more frequent as the tumors progress from benign to metastatic forms, highlighting the need for a more precise characterization of regions of coordinate expression and gene copy number change. In addition to this most recent work, a substantial body of literature on chromosomal aberrations in CRC has accumulated [7, 15, 18–25] that could help to interpret findings on islands of coordinated chromosomal expression.
The need for a more precise definition of chromosomal regions of altered gene expression prompted us to find a new approach to investigate chromosomal co-expression domains in CRC. The focus of our study was the identification of up- or down-regulated gene expression in primary colon carcinoma cells compared to normal colon epithelia of the same patient. By using laser capture microdissection (LCM) we aimed to investigate transcript abundance in relatively pure cell populations, trying to minimize the influence of contaminating stroma tissue or infiltrating peripheral blood cells on expression measurements. The use of Affymetrix DNA microarray technology allowed us to simultaneously assess mRNA levels of all known human genes using only small amounts of cells obtained by LCM. Finally, we developed a new bioinformatic approach to identify regions of chromosomal deregulation which enabled the most precise survey of chromosomal expression domains in colon cancer available today. In particular, we were interested in the question whether our data correlated with the data of Tsafrir et al. who performed genome scale arrayCGH and chip-based expression analyses on a different set of colorectal cancer patients . In contrast to Tsafrir et al. we put more emphasis on the identification of precise boundaries of expression domains and therefore we consider our work as complementary to their pioneering study.
Evaluation of data set quality by tissue-wise hierarchical clustering
Global search for chromosomal islands with up- or down-regulation
Individual chromosomal islands of up- or down-regulation.
potential tumor genes, hereditary CRC, known chromosomal imbalances
amplification of 1p36.33-p32 in CRC  // deletion of 13p36.3 in 25% of neuroblastomas and 87% of cell lines  // loss of expression and genomic deletion on 1p 
E2F2 // ID3 // loss of 1p36.1 in CRC [22, 25] // loss of expression and genomic deletion on 1p 
LCK at 1p35.1 // hereditary CRC at 1p35 (OMIM 114500) // loss of expression and genomic deletion on 1p 
1q32 amplification involving MDM4 and CNTN2 in malignant gliomas 
hereditary CRC at 2p25 (OMIM 114500)
83% loss of 2p11 in mantle cell lymphoma  // loss in mantle cell lymphoma 
hereditary HNPCC3 at 22q31-q33 (OMIM 600258) // familial breast cancer at 2q (OMIM 114480)
familial breast cancer at 2q (OMIM 114480)
familial breast cancer at 2q (OMIM 114480)
amplification of 3p25.2 in CRC  // RAF1 at 3p25.2 // FBLN1 at 3p25.2
hereditary HNPCC2 at 3p21.3 (OMIM 609310) // RASFF1
frequent 3q11.2-q13.1 amplifications in cervix carcinomas 
deletions of 4p14 in CRC  // SLIT2 at 4p15 is inactivated by hypermethylation in gliomas  // SLIT2 suppresses tumor growth  // loss of expression and genomic deletion on 1p 
global loss of expression and genomic deletion on 4 
transition of follicular B cell lymphoma to diffuse large cell lymphoma accompanied by 4q21-q23 deletions // global loss of expression and genomic deletion on 4 
deletion of 4q34-q35 in colorectal cancer cell lines  // CASP3 at 4q34.3 // global loss of expression and genomic deletion on 4 
hereditary colorectal adenoma and carcinoma 1 (CRAC1) (OMIM 601228) at 15q15.3-q221 // APC at 5q21 // loss of expression and genomic deletion on 5q 
loss of expression and genomic deletion on 5q 
amplification of 5q32-q34 in prostate cancer  // PDGFRB at 5q32 // loss of expression and genomic deletion on 5q 
amplification of 5q32-q34 in prostate cancer  // loss of expression and genomic deletion on 5q 
amplification of 6p25 in 24% of mantle cell lymphomas  // amplification of 6p25 in 75% of prostate cancers 
most frequent amplification of 6p22.3 in bladder cancer arrayCGH study 
CDKN1A at 6p21.2 // PIM1 at 6p21.2
amplification of 6q23-q24 assosicated with short survival 
hereditary HNPCC4 at 7p22 (gene PMS2) // gain of expression and genomic amplification of 7 
amplification of 7p21 in mantle cell lymphomas  // amplification of 7p21 in osteosarcoma  // gain of expression and genomic amplification of 7 
gain of expression and genomic amplification of 7 
amplification of 7q11.1-q12 in metastatic CRC  // gain of expression and genomic amplification of 7 
prostate cancer aggressiveness linked to 7q32-q33  // gain of expression and genomic amplification of 7 
amplifications of 8q11-q24 in metastasing CRC  // LYN at 8q12.1 // MOS at 8q12.1 // familial breast cancer at 8q11 (OMIM 114480) // amplifications at 8q in CRC [18, 21, 23, 25] // gain of expression and genomic amplification of 8q 
amplifications of 8q11-q24 in metastasing CRC  // MYC at 8q24.21 // PVT1 at 8q24.21 // amplification of 8q23-q24 in prostate cancer // gain of expression and genomic amplification of 8q 
loss of 9p21 in CRC  // TUBE1 at 9p21 // CDKN2A alias p16INK4A at ??? // frequent deletion of 9p21 in prostate cancer  // deletion of 9p21.3 in bladder cancer 
frequent LOH at 9p13-p21 in melanoma 
loss of 9q21-q22 in mantle cell lymphoma 
ABL1 protooncogene at 9q34.12
frequent LOH of 10p15 in gastric cancer  // telomerase repressor at 10q15.1  // deletion of 10p14 in mantle cell lymphoma [47, 59] // OPTN at 10p14 
RET at 10q11.21 // LOH in prostate cancer at 10q11.21 
hereditary CRC at 11p15.5 / HRAS at 11p15.5 // 11p15.5 methylation-dependent expression silencing and imprinting in phaeochromocytomas 
CTSD (Cathepsin D) at 11p15.5 // familial breast cancer at 11p15.5 (OMIM 114480)
WT1 at 11p13
BCL1 at 11q13.3 (anti-apoptotic, amplified in breast cancer) // CCND1 at 11q13.3 (amplified in breast cancer ) // FGF3 at 11q13
frequent loss of 11q23.3-q25 in neuroblastoma  // loss of 11q23 in 33% of 73 tumor types 
CDKN1B (alias p27Kip1) at 12p13.2
familial breast cancer at 12p12.1 (OMIM 114480)
MDM2 at 12q15 // validated up-regulation of GPR49 at 12q21.1
loss of 12q24 in pancreas tumors 
RB1 at 13q14.2 // ARLT1 at 13q14 // gain of expression and genomic amplification of 13q 
14q22-q23 losses in 25% of tumor types 
hereditary CRC at 14q24.3 (OMIM 114500) // loss of 14q24-31 in CRC metastases  // FOS at 14q24.3 // hereditary HNPCC7 at 14q24.3 (gene MLH3) (OMIM) // poor prognosis when 14q24-q31 is lost in renal cell carcinoma  // loss of expression and genomic DNA of 14q 
14q32 is a tumor suppressive region in esophagal cancer  // loss of expression and genomic DNA of 14q 
association between loss of 15q21.1-q22.2 and survival in hepatocellular carcinoma  // allelic imbalance at 15q21.1 in breast cancer metastases  // loss of expression and genomic DNA of 15q 
loss of expression and genomic DNA of 15q 
CDH1 (E-Cadherin) at 16q22.1
loss of 17p13.2 in CRC  // DHX33 at 17p13.2
Near TP53 at 17p13.1 .// hereditary CRC at 17p11.2 (OMIM 114500) // hereditary CRC at 17p13.1 (OMIM 114500) // // familial breast cancer at 17p13 (OMIM 114480) // loss of 17p12 in CRC  // ELAC2 at 17p11.2
NME1 (NME23) at 17q21.33 // familial breast cancer at 17q22-q23 (OMIM 114480) //
loss of expression and genomic DNA of 18 
DCC at 18q21.3 (OMIM 120470) // loss of 18q21.1 in CRC cell lines  // SMAD2 // SMAD4 mutations in CRC  // loss of expression and genomic DNA of 18 
NIFIE14 at 19q13.12
AKT2 (breast carcinoma at 19q13.2 // TGFB1 at 19q13.2 // proapototic Bax at 19q13.33
SRC at 20q11.23 (overexpressed in breast carcinoma) // gain of expression and genomic DNA of 20q 
gain of expression and genomic DNA of 20q 
ETS2 at 21q22.2
COL18A1 (Endostatin) at 21q22.3
familial breast cancer at 22q12.1 (OMIM 114480)
hereditary CRC at 22q13 (OMIM 114500)
Statistics on expression imbalances across human chromosomes.
Genes In Regions With Expression Imbalance
Genes In Regions With Expression Gain
Genes In Regions With Expression Loss
Individual chromosomal islands with gain of expression
Individual chromosomal islands with loss of expression
Expression in Islands frequently deleted in CRC.
fraction of patients with deletions
congruent with our expression data
[15, 17, 18]
[17, 18, 23, 25]
Global analysis of chromosomal regions with expression gain or loss
We found that 25% of the genes lie in regions that are affected by expression imbalance in colon cancer. This does not mean that 25% of the genes are misregulated as many genes that fall into these regions are not expressed at all in tumors and in normal epithelium of the colon. Additionally, we note that these numbers are probably an upper limit because the sliding window approach probably included several genes in close proximity to the boundaries of misexpressed regions. Nevertheless, the number of regions of imbalanced expression is remarkable and suggest that there is extensive regulation in CRC at the genomic level. Recently, Nakao et al. estimated from genome-wide array CGH data that ~17% of the human genome is affected by DNA copy number changes in CRC . Prior to a more detailed analysis of individual regions in this study, this suggested that not all regional expression changes in CRC will be explainable by DNA copy number aberrations.
There are only slightly more genes with expression loss than regions with expression gain. One can argue that a tumor ought to show a higher frequency of expression loss than expression gain. Reasons are that there should be a tendency to lose tumor suppressor genes selectively and to lose non-essential genes (genomic ballast) as a side effect. If transcription would be a process that is predominantly driven by positive regulation of transcriptional activators, one would assume that any partial genome loss results in a slow down of transcription. In the light of these considerations, an equally high number of regions with expression gain can be interpreted in two ways. Either positive selection drives expression gain of some regions in cancer cells, or a default phenotype of transcription suppression dominates in normal cells which is relaxed during tumor cell development.
Gene expression in chromosomal regions with frequent DNA copy number changes in CRC
Most studies reported frequent gains of chromosome 7, 8q, 13q, 20q and losses of 4 and 18q in CRC [18, 19, 21–25]. These broadly-defined alterations are in perfect agreement with chromosome-specific trends in our expression data, especially the exclusive presence of domains of expression gain on 8, 13 and 20 and the exclusive presence of domains of expression loss on chromosome 4 and 18 (see Table 2 and Figures 21, 22, 23, 24, 25, 26). There is a single discrepancy for chromosome 7: region 7q11-7q12 has been reported as amplified in CRC, but its expression is significantly down-regulated in our tumor samples.
Expression in Islands frequently amplified in CRC.
fraction of patients
congruence with our expression data
[17, 18, 25]
down at 12p13.31-12p13-2
down at 15q21.1-15q22.31
down at 16p12.1-16p11.2
However, there are also many regions of frequent deletions that did not show alterations in expression or that were even down-regulated (7q11.2-7q12, 9q34, 12p13.1-13.2, 15q22-15q23, 16p12-16p11, 22q11; compare Tables 3 and 4). One possible explanation is that these down-regulated regions are not amplified in our tumor samples. An alternative explanation is that the influence of chromosomal amplification on transcription levels can be either positive or negative. It is possible that amplification of a particular genomic region disrupts transcription of amplified genes by a yet unknown mechanism, e.g. by induction of chromatin-based silencing, or by separation of essential enhancer regions from transcription starts.
Platzer et al. found amplifications in 7p, 8q, 13q, 20q in 26%–43% of their CRC patients and revealed by microarray-based expression analysis that only 81 of 2146 genes in amplified regions show over-expression (3.8%) whereas 164 of 2146 genes show under-expression (7.7%). Using a different approach (microdissection, oligo arrays, analysis aimed at the identification of single chromosomal expression domains and not at the location of all differentially expressed genes in chromosomes) we found several smaller up-regulated regions and no regions of down-regulation in the same chromosomal regions. Therefore, our data partly contradicts the findings of Platzer et al. which state that in these frequently amplified regions gene expression is rather down-regulated. However, other misregulated expression domains (see above) of our study confirmed the general notion by Platzer et al. that frequently amplified regions in CRC can also exhibit down-regulation of transcript levels.
Aberrantly expressed chromosomal islands linked to hereditary cancer
Roughly 5% of all colorectal carcinomas are hereditary non-polyposis colorectal cancers (HNPCCs). In HNPCC, histologically verified colorectal carcinoma is found in at least three relatives from two or more successive generations. In at least one patient, the age of onset should be less than 50 years. Seven chromosomal regions have been linked to HNPCC. More than half of these HNPCC regions show misregulated expression in our patients. Three regions show down-regulation (3p21.3, 2q31-q33 comprising PMS1, 14q24.3 comprising MLH3), one region shows up-regulation (7p22 comprising PMS2), and three regions do not show significant changes in expression (2p22.p21 comprising MSH2, 2p16 comprising MSH6, 3p22 comprising TGFBR2). Eleven further chromosomal regions are linked to hereditary colorectal carcinoma under a common entry in OMIM (14500). More than 50% of these regions show significant expression changes in our data. Five regions show down-regulation (1p35, 14q24.3, 17p11.2, 17p13.1, 22q13), one region shows up-regulation (2p25), and five regions do not show significant expression changes in our data (3q26.3, 8p22-p21.3, 11p11.2, 15q15, 17q24). In combination, these findings strongly suggest that expression changes in regions linked to hereditary CRC play a role in CRC development.
Congruence of our study with the genome-wide copy number and expression analysis of Tsafrir et al
A particular focus of our study was on the congruence of our data with that of Tsafrir et al. . These authors described 11 alterations of whole chromosomes or chromosome arms. Using our approach based solely on expression data we found precisely defined region of coordinated up-regulation in all four regions of gene expression and gDNA copy number gain that they reported (+7, +8q, +13q, +20q). For six of seven aberrations (-1p, -4, -5q, -14q, -15q, -18) we discovered smaller expression islands of coordinated down-regulation. We were not able to reproduce the finding of expression loss on 8p. In summary, this large congruence of our results with that of Tsafrir et al. can be regarded as an external validation of our results. The comparison illustrates the power of our data analysis approach which allows to define expression islands on a single-gene resolution. Most importantly it confirms our confidence in the use of the chip platform (Affymetrix U133A) that was used in both studies and apparently can lead to largely congruent results in different patient cohorts and laboratories.
Roughly a quarter of all human genes is located in islands of misregulated gene expression in colorectal cancer. There are only slightly more down-regulated than up-regulated genes. Chromosomal regions that are linked to hereditary colorectal cancer often exhibit deregulated expression, suggesting that they are implicated in spontaneous CRC not only through collection of mutations. Thus, genes in these chromosomal hotspots may be systematically tested in patients with sporadic CRC for molecular lesions and for transcriptional silencing.
Chromosomal regions that are frequently deleted in CRC very often comprise islands in which we found reduced expression. Although many regions that are known to be amplified in colorectal tumors show a gain of expression, there are also a considerable number of amplified islands that show no alterations or even down-regulation. Comparison of published CGH studies with our expression data suggests that amplified or deleted chromosomal regions are responsible for many islands with aberrant expression. However, we suggest that it is necessary to invoke other mechanism like epigenetic regulation of chromatin or disruption of enhancer actions to explain the remaining expression imbalances.
25 colorectal cancer patients undergoing elective standard oncological resection at the department of surgery, Charité, Campus Benjamin Franklin, Berlin, Germany were prospectively recruited for this study. The study was approved by the local ethical committee and informed consent was obtained from all patients. Rectal cancer patients receiving neo-adjuvant radiochemotherapy were excluded from this study.
Tissue samples and UV-laser microdissection
Transmural cancer specimens were snap frozen (liquid nitrogen) within 20 minutes following excision and stored at -80°C. All tissue samples were evaluated by a pathologist before and during laser micro-dissection to ensure an enrichment of vital tumor cells. Six-micron serial frozen sections were cut on a standard cryostat and mounted on RNase-free foil (2,5 μm) coated on glass slides followed by immediate fixation (70% ethanol for 30s), H&E staining, and ethanol dehydration (70%, 95% and finally 100% ethanol). After vacuum drying the membranes carrying the sections were manually turned and coated on new RNase free glass slides. Optically transparent CapSure LCM caps (ARCTURUS, CA) were placed on the foil over a selected field of cells. Vital colorectal epithelial carcinoma cells (> 90% proportion) from the invasion front were isolated using UV-LCM Systems from PALM (Microlaser Technologie, Germany) and SL (Microtest GmbH, Germany). After visual control of completeness of dissection the captured cells were immersed in denaturation buffer (GTC Extraction Buffer, 2% beta-mercaptoethanol, Promega, WI) and stored at -80°C.
mRNA-extraction, cRNA-preparation and -amplification
Poly(A)+ RNAs were isolated using PolyATtract 1000 kit (Promega, Heidelberg, Germany) according to the manufacturer's recommendations. For each sample the cDNA synthesis and repetitive in vitro transcription were performed three times, as described previously [38–40]. In brief, the total amount of prepared mRNA from one sample was used. First strand cDNA synthesis was initiated using the Affymetrix T7-oligo-dT promoter-primer combination. The second strand cDNA was synthesized by internal priming. In vitro transcription was performed using Ambion's Megascript kit (Ambion, Huntington, UK) as recommended by the manufacturer. From the generated cRNA a new first strand synthesis was initiated using 0.025 mM of a random hexamer as primer. After completion, the second strand synthesis was primed using the Affymetrix T7-oligo-dT promoter-primer combination at a concentration of 0.1 mM. A second in vitro transcription was performed and then the procedure was repeated one additional time. During the third in vitro transcription biotin-labeled nucleotides were incorporated into the cRNA as recommended by the Affymetrix protocol.
BIO+cRNAs were hybridized on Affymetrix Human Genome U133A and U133B GeneChips, that consist of 44.928 probe sets (Affymetrix, Santa Clara, CA). Fragmentation, preparation of hybridization cocktails, hybridization, washing, staining and scanning of Affymetrix GeneChip were performed according to the manufacturer's protocols.
Preprocessing of expression data
We used our own algorithm to condensate the probe level data provided by Affymetrix CEL-files per chip experiment: Background intensity was computed as the mean of the 2% darkest feature intensities. This background value was subtracted from each feature value. Subsequently, each feature value was divided by the median of all feature values. As a representative expression value (PMQ) for each probe set, the third quartile (75%) of all intensities of all perfect match oligonucleotides was used. Furthermore, to distinguish real expression signals from noise the Wilcoxon signed rank test was applied to each probe set. A probe set was called detectable if the result of the Wilcoxon signed rank test applied to its 11 probe pairs (perfect match versus mismatch oligonucleotide) had a significance level of p < 0.1 and relative expression value (PMQ) of > 4.0. We used these constraints for decision whether a gene is expressed or not due to validation results of several gene expression pattern by quantitative RT-PCR and/or Northern Blot analysis in our lab (data not shown).
For each patient and probeset an expression ratio was calculated according to the following rules: If expression was detectable in both the normal and tumor sample (Wilcoxon test p <= 0.10 and relative expression value PMQ >= 4), the ratio PMQ(T)/PMQ(N) is our expression ratio (hereafter called T/N). If expression was undetectable in either the normal or the tumor sample, the expression ratio was either set to T/N = 2 (normal absent) or to T/N = 0.5 (tumor absent). If expression was undetectable in both the normal and tumor sample, no expression ratio was calculated and we call the probe set not informative. For each probe set the number of cases which showed an up-regulation (T/N >= 2), a down-regulation (T/N <= 0.5) or the number of unchanged transcription levels (0.5 < T/N < 2) were counted. We filtered out those probe sets which are not informative in any patient, reducing the number of probe sets to 19404. To eliminate redundancy of probe sets with respect to genes, we kept only the most informative probe set of a single gene, i.e. the probe set which is informative in the highest number of matched sample pairs. Additionally, only probe sets that could unambiguously be linked to a particular genomic locus were considered (chromosome band and position; see Affymetrix U133A/B annotation files). Finally, the pre-processing resulted in a total number of 10.935 probe sets which were the basis of all further analyses.
Analysis of expression along chromosomes
In each graph of Figures 2, 3, 4, 5, we plotted the numbers of patient samples with tumor up/down regulation (percentage on informative cases) for all genes according to their position on the chromosome. In these plots, the smoothing of the curve is achieved by averaging over 50 consecutive genes.
Significant deviations from average expression in a particular chromosomal region is not sufficient to infer coordinated deregulation. This is because it does not allow to infer whether all genes of a region are actually de-regulated in the same subset of patients. They could also be de-regulated in different patients. Consider three genes G1, G2, G3 and their expression in patients A,B,C,D. Each gene is up-regulated in 50% of patients. If the genes are up-regulated in different patients (G1 is up-regulated in A/B, G2 is up-regulated in B/C, G3 is up-regulated in C/D), then one can not assume that there is a regional up-regulation in all patients. However, if the genes are up-regulated in the same patients (G1, G2 and G3 are all up-regulated in A and B), then it is fair to assume that they have undergone coordinated regional up-regulation. Chance effects more likely create non-coordinated up-regulation. To capture such a gene-versus-gene correlation structure, we performed the following for a given chromosome region:
For each pair of genes of a given chromosome region we count the number of their coordinated (simultaneous) up-regulations (based on the above computed fold changes) over the set of patients and the number of coordinated down-regulations, separately. These values can be represented in gray-scale plots: one gray scale plot for the coordinated up-regulation and a similar one for coordinated down-regulation. Both, horizontal and vertical axis comprise genes of the chromosome region in the right chromosomal order (see Figures 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32). The darkness of squares represents the number of coordinated up- or down-regulations, respectively. Coordinately up-regulated regions show up as squares with high "correlation" measures along the diagonal. Such resulting cross-comparison matrices can be visualized interactively for any chromosomal region on our supplementary website along with heat maps of expression intensities and are used in Figures 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32. Alternatively, we applied "correlation" measures like Pearson correlation coefficients on fold changes, mutual information, and set-theoretic coefficients like the Dice and Jaccard coefficients on binary patterns of up-regulation and down-regulation (only available on our website ).
Although this analysis is already instructive for the visual identification of general up/down-regulation of a particular region, it does not allow to infer the precise boundaries of deregulated regions. Several software packages for the analysis of array CGH data exist that have been announced to also be suited for the analysis of expression data [42–44]. In the following, we used the ChARM software package . ChARM can be used to infer intervals of variable size with significant positive or negative signal amplitudes in ordered data, such as log(intensity) values in array CGH data and mRNA expression data. We applied the ChARM algorithm on different data sets that harbor information about the numbers of patients with coordinated up- and down-regulation of expression for all genes on human autosomes and the X chromosome. For each chromosome six separate data sets were prepared, according to scanning window sizes of 5, 11, 21, 31, 41, 51. Within each window all possible gene pairs (excluding self comparisons) were considered. For each gene pair, the number of coordinated up-regulated (counted as +1) and down-regulated (counted as -1) was determined. For each window the sum of these gene pair-specific values divided by the total number of pairs gave the cumulative misregulation score (CMS). In a sliding window approach, each gene was associated with a CMS value. CMS values for genes at the edges of chromosomes were calculated with reduced window sizes. The main theoretical advantage of the use of CMS scores compared to raw up-regulation counts or averaged expression ratios is that it captures only information from co-regulated neighboring gene pairs: Noise signals fluctuate across genes and may more often lead to artificial assignment of high expression ratios between two genes. In contrast, real signals of regional up-/down-regulation lead to consistent changes in the same patients for two genes. For each window size, CMS data sets of each chromosome were subject to ChARM analysis. ChARM determines borders of regions with high signal amplitudes in ordered data, here regions of expression imbalances along a chromosome, by an expectation-maximization approach. In addition, ChARM provides different statistical estimates to judge the significance of expression deregulation in a particular chromosomal region . The identified deregulated regions were further evaluated manually using heat maps and the above mentioned gene-versus-gene "correlation" plots (see above, Figures 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32 and accompanying website).
Availability and requirements
Project name: Colorectal carcinoma comparative chromosomal gene expression analysis (CC-CCGEA) .
Project home page: http://ccgea.molgen.mpg.de/cgi-bin/ccgea/ccgea.pl
Operating system(s): all
Programming language: Perl-CGI
Licence: GNU GPL
Restrictions to use by non-academics: none
We especially acknowledge valuable contributions by Klaus Hermann who died of stomach cancer during this study. He has been a wonderful colleague for us and has put much effort into the implementation of the data preprocessing pipeline that was used in this study.
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