SNP microarray analyses reveal copy number alterations and progressive genome reorganization during tumor development in SVT/t driven mice breast cancer
© Standfuß et al.; licensee BioMed Central Ltd. 2012
Received: 28 February 2012
Accepted: 8 August 2012
Published: 31 August 2012
Tumor development is known to be a stepwise process involving dynamic changes that affect cellular integrity and cellular behavior. This complex interaction between genomic organization and gene, as well as protein expression is not yet fully understood. Tumor characterization by gene expression analyses is not sufficient, since expression levels are only available as a snapshot of the cell status. So far, research has mainly focused on gene expression profiling or alterations in oncogenes, even though DNA microarray platforms would allow for high-throughput analyses of copy number alterations (CNAs).
We analyzed DNA from mouse mammary gland epithelial cells using the Affymetrix Mouse Diversity Genotyping array (MOUSEDIVm520650) and calculated the CNAs. Segmental copy number alterations were computed based on the probeset CNAs using the circular binary segmentation algorithm. Motif search was performed in breakpoint regions (inter-segment regions) with the MEME suite to identify common motif sequences.
Here we present a four stage mouse model addressing copy number alterations in tumorigenesis. No considerable changes in CNA were identified for non-transgenic mice, but a stepwise increase in CNA was found during tumor development. The segmental copy number alteration revealed informative chromosomal fragmentation patterns. In inter-segment regions (hypothetical breakpoint sides) unique motifs were found.
Our analyses suggest genome reorganization as a stepwise process that involves amplifications and deletions of chromosomal regions. We conclude from distinctive fragmentation patterns that conserved as well as individual breakpoints exist which promote tumorigenesis.
KeywordsBreast cancer Genome reorganization Copy number alteration CNV fragile sites Cancer genomics Tumorigenesis
Cancer is known to be a disease involving dynamic changes affecting cellular integrity and cellular behavior . To date, research has been focused on discovering gene expression profiles, alterations in oncogenes or tumor-suppressors, and genetic mutations; but since tumorigenesis is a complex multistep process, the transformation of a normal cell into a malignant tumor is not completely understood. It has been well known for decades that alternative pathways in cell transformation (e.g. changes in cell cycle, signal transduction, metabolism, immune response) via a stepwise progression to final malignant tumors exist [1–4].
In fact, genomic DNA is more stable than mRNA or proteins . As a consequence of this, the focus on gene expression profiles may not completely reveal all genetic mechanisms of tumor development and progression. The alteration of chromosomal copy numbers is known to be a key genetic event in many well-studied diseases , such as Jacobsen syndrome , HIV acquisition and progression , systematic autoimmune diseases [8, 9] and cancer phenotypes . In normal human organisms more than 3% of the genome is known to be affected by copy number alterations (CNAs, also known as copy number variations - CNV) [11, 12], whereas in mice the estimates differ from 3%  to 10.7% . Significant efforts have been made to study CNAs in various organisms. Single nucleotide polymorphism (SNP) oligonucleotide microarrays and array comparative genomic hybridization (aCGH) allow for high-throughput analyses of CNAs. This enables the study of complex genomes and genetic events at a high resolution. Several studies have addressed CNAs in individuals from different mouse strains: Henrichsen et al.  and Cahan et al.  studied the impact of CNAs on the transcriptome, Cutler et al.  analyzed the gene content of inbred mouse strains, Graubert et al.  studied segmental DNA copy number alterations. Agam et al.  compared the CNAs found in the four mentioned studies with their own data and found significant differences. They show that 1.3% to 88.7% of the detected deletions and 2.1% to 100% of the gains are replicated from one study to the following ones. They infer that the reproducibility of these experiments depend on the array platform, the CNA detection algorithm and the protocols for platform design and hybridization. Moreover, microarray experiments in humans have revealed a connection between high amplified genes and gene expressions , and CNAs affecting well-characterized regions harboring tumor-suppressor genes in breast cancer and lung carcinoma . Therefore, the development of highly reliable and high-resolution genetic analysis approaches as presented by Hannemann et al. , is of high therapeutical relevance. To investigate the impact of CNAs on gene expression, several studies used network-based approaches [20–23]. For example, the study of Jörnsten et al.  used a global model of CNA-driven transcription to model mRNA expressions with the help of CNAs.
In the current study, we investigated the CNAs in a four stage tumorigenesis model. This model included copy number analyses in non-transgenic NMRI mice (normal; stage 1 in Figure 1) and in transgenic SVT/t mice: non-malignant hyperplastic mammary glands and breast cancers, as well as breast cancer derived cell lines (stages 2-4 in Figure 1, respectively). The WAP-SVT/t hybrid gene construct consists of the Wap (Whey acidic protein) promoter fused to the SV40 early coding region . The WAP-SVT/t expression is selectively activated in breast tissue during pregnancy and continues after weaning. All female mice developed breast cancer after the first lactation period. We have established the 762TuD breast cancer cell line (termed sens. cell line) from a WAP SVT/t tumor, which has switched off SVT/t expression during the cultivation process and developed a p53 hotspot mutation (G242). The 762TuD cells are immortalized, malignant transformed and highly aneuploid. Additionally, we established a drug resistant 762TuD cancer cell line (termed res. cell line). The karyograms (via mFISH) of these two cell lines (named SVTneg1) are published (, page 91). We focused our research on copy number analyses to compare the genomic alterations that occur during tumorigenesis. We addressed the question, whether common predisposed chromosomal breakpoints could be seen to promote malignant transformation. We can report a characteristic increase of copy number alterations from stage one to four (see Figure 1) in our model. Furthermore, we have identified continuous regions of copy number alteration (chromosomal segments) and found characteristic fragmentations. CNAs were compared on both the SNP probeset level and the level of continuous CNA regions (segments). Motif search was performed in hypothetical DNA breakpoint regions to find common motifs that may be coincident with a DNA break. The results of our model were compared to a model of PIK3CA-driven mammary tumors presented by Liu et al. .
Results and discussion
Furthermore, the results presented in this work were compared to the data of six recurrent tumor samples published by Liu and coworkers .
SNP copy number alteration
We analyzed 584,729 SNPs for each of the eight samples with the Affymetrix Mouse Diversity Genotyping array (see Yang et al. 2009  for additional information), and calculated SNP copy number alterations (CNA) during tumorigenesis, which are indicators for chromosomal aberrations . We added up the signal intensities for SNP alleles and compared the total intensities of all samples against a reference data set (mean signal intensity of both normal samples). For each SNP, CNA was computed by log2-ratios and all values in the range of −0.2<= × <=0.2 were considered as unchanged which corresponds to a fold change between 0.87 and 1.15. This indicates that not more than 30% of the cells carry the CNA. Compared to normal tissues a significant increase in the number of CNA was detected in the tumors (Welch two sample two-sided t-Test p = 8.43 ∗ 10−8). For visualization, log2-ratio copy number values of the SNPs were ranged into five groups to compare changes in different samples (see Figure 3A). We categorized the CNAs to unchanged (-0.2 <= × <= 0.2), slightly increased (0.2 < × < 0.6, orange), slightly decreased (-0.6 < × < -0.2, light blue), highly increased (× >= 0.6, red) and highly decreased (-0.6 < =× , dark blue). 96% of the SNP signal intensities were found to be unchanged in both normal samples (Normal1 and Normal2) (depicted in Figure 3A). These findings are in concordance with previously published studies [13, 14]. In comparison, 10% of all SNP probeset intensities in the Transgenic2 sample show an increase in copy numbers (CNs), and 10% are decreased compared to the normal samples; in Transgenic1 the number of SNPs with a decrease in CNA is even higher (up to 15%). A further increase in CNA could be observed in both tumor samples. Here, approximately 21% of all SNPs show a decrease and an additional 21% show an increase in CN. The percentages in CN changes indicate a progressive increase of CNA from normal to transgenic and then tumor within our model. The highest percentage of CNA could be found in both cell line samples with a total change of 46.5% (sensitive cell line) and 45% (resistant cell line) of all SNP copy number values. Interestingly, comparable cell lines equally exhibit the most differentially expressed genes . This reveals that considerable aberrations take place during cell cultivation.
For comparison we analyzed recently published data from Liu and colleagues , who have established a PIK3CA-driven breast cancer model conditionally expressing PIK3CA. CN analyses were carried out for six recurrent tumor samples with the Affymetrix Mouse Diversity Genotyping Array. A total change in CNA of about 26% can be identified in tumors RCT-D782 and RCT-D419; 16% to 21% of all SNPs in the remaining tumors show a copy number alteration. This is comparable to the changes detected in our transgenic samples. In fact, less changes in SNP copy numbers were found in the recurrent tumor samples than in both WAP-SVT/t tumor samples in our study. This may be explained by the differences in tumor development which became obvious in the mean latency of the tumor survival data: seven month for the PIK3CA-tumors in contrast to only three months in the WAP-SVT/t mice (see Kaplan-Meier survival curve in Figure 1F and supplemental Figure S1, Additional file 2).
Detection of continuous CNA regions
The individual CNA of a single SNP may not be relevant or error-prone, hence we focused our research on genome reorganization. We addressed the purpose of continuous CNA detection on chromosomal regions and named these regions “chromosomal segments” (segCNA). The chromosomal segmentation of adjacent SNPs with similar log2-ratio values was calculated using the circular binary segmentation algorithm (CBS algorithm) introduced by Olshen et al. . In both normal samples a similar number of about 70 distinct segments was detected. The number of calculated segments for the transgenic samples differed from 760 (Transgenic1) to 292 (Transgenic2) segments (see Table S2 in Additional file 3). A comparable difference in the number of segments was found in both cell line samples with 705 (sensitive cell line) and 354 (resistant cell line) segments calculated. In the tumors the number of segments in both samples also differ remarkable, by a factor of 7. 1,241 delimited segments were calculated in the Tumor1 sample whereas only 184 segments were found in the Tumor2 sample. This indicates an individual development of DNA reorganization for each sample during tumorigenesis. Although the SNP copy number alterations between both tumor samples were comparable, significant changes in chromosomal segmentation were found (see Tumor1 and Tumor2 in Table S2, Additional file 3). This can be explained by the CBS algorithm . Only adjacent SNPs with a concordant signal intensity occur in contiguous regions of the chromosome. In contrast, the number of segments found in all six recurrent tumor samples differ from 31 segments in RCT-D782 to 85 segments in RCT-E472. Corresponding to our tumor samples we found two groups with significant differences in number of segments: group 1 having 31 to 42 segments in each sample, and group 2 having 68 to 85 segments per sample. This underlines the differences in both models and the individual development found for the copy number alteration analysis of individual SNPs. Two of the recurrent tumors (RCT-D782 and RCT-E565) of group 1 were found to retain a high abundance of active p-AKT and phospho-S6 ribosomal protein (p-S6RP); whereas two tumors (RCT-E472 and RCT-C658) of group 2 show a low abundance . Although differences in segmentation were detected in both WAP-SVT/t tumor samples, about 9% of the calculated breakpoints in Tumor2 were also found in Tumor1 (see most inner circular track in Figure S4, Additional file 4). This indicates that even though the segmentation pattern may be different for each sample, they may share a common set of chromosomal breakpoints inducing similar reorganization patterns.
Percentage of segment CN
As shown in Figure 3B the percentage of changed segment copy number (segCN) values in the tumor samples is remarkably higher than in the normal and the transgenic samples (by more than 50%). Interestingly, the amount of segments with a decrease in segCN is higher (value < -0.2) than those with an increase. This implies that deletion events are more frequent than amplifications (see Figure 3B and Table S3B in Additional file 5). The apparent increase in segCN of about 26% in Tumor2 is due to the small total number of 176 segments, compared to 1241 segments in Tumor1. The percentage of segmental copy number alteration of all recurrent tumor samples (published by Liu et al. ) is smaller than in the WAP-SVT/t tumor samples mentioned previously. Again, two groups can be identified. A variation in segCN was found for 13% to 20% of all segments in one group (RCT-D782, RCT-D565, RCT-D419), and in 33% to 35% of all segments in another. Moreover, as indicated by the different numbers of amplification and deletion events (see Figure 3B), it is obvious that tumor samples are heterogeneous.
Comparison of CN studies
In comparing different CNA studies, one find only a weak overlap of segmental positions, segment length and copy number values . Agam et al.  found 1,477 loss events and 499 gain events across seven mouse strains. 21 candidate regions of high-level DNA amplification were found in different carcinoma samples by Zhao et al. in 2004 . Egan et al.  analyzed different mouse strains by tiling array CGH experiments and identified 38 CNAs for multiple probes and 23 segmental CNAs. Not only different segmentation algorithms and differences in probe hybridization, but also different types of microarray designs (aCGH, oligonucleotide) and different platforms may cause the problems. In their study Agam et al.  referred to the overlap of two sets of CNA between technical replicates. This overlap was compared to the overlap of CNAs called in animals of the same strain. Using the same algorithm and platform, they could show that more consistent results were produced by technical replicates rather than by biological ones.
Segmentation and gene expression
Correlation of Gene expression and SNP copy number
Number of genes
Number of SNPs with
Correlation of Gene expression and segment copy number
Number of genes
Number of segments with
We reviewed the previously described CN amplification by qPCR analyses for the unamplified region (chr6:17.3MB-17.5MB, log2-ratio = 0.3 in Tumor1) including parts of the Met gene and the amplified region (chr6:17.5MB-18.14MB, log2-ratio = 1.75). One primer pair was located within the unamplified segment, two pairs within the amplified segment. In Figure S2 (see Additional file 7) the results of qPCR analyses of Normal1, Normal2, Tumor1 and Tumor2 are shown. Only in Tumor1 an amplification was found, for both primer pairs up to six-fold within the region expected to be amplified. Compared to the normal samples and the Tumor2 sample, a slight amplification was detected within the region expected to be unamplified. This is reflected in the small log2-ratio change of 0.3 (1.23-fold) detected. Both results are in accordance with the calculated segment intensity values from our CNA data (see Figure 5).
Motif search and repeats
GO term predictions
BP - positive regulation of transcription
from RNA polymerase II promoter
BP - transcription
BP - negative regulation of transcription
from RNA polymerase II promoter complex
MF - transcription activator activity
MF - transcription factor activity
MF - sequence-specific DNA binding
BP - transcription
BP - inner ear morphogenesis
BP - proximal/distal pattern formation
MF - sequence-specific DNA binding
MF - transcription factor activity
BP - positive regulation of transcription from
RNA polymerase II promoter
MF - calcium ion binding
MF - receptor binding
BP - axon guidance
BP - positive regulation of immune response
BP - defense response
In this work we study the CNAs of a four stage tumorigenesis model. Our model includes copy number analyses in a normal, in a transgenic, and in a tumor phenotype as well as in tumor-derived cell lines. We analyzed the copy number (CN) of mouse mammary gland epithelial cells and compared their gene expression to the copy number alterations detected. Here, we demonstrated a stepwise increase in fragmentation of mouse chromosomes during tumorigenesis with non-random fragmentation patterns within each stage of our model. Nearly 10% of all breakpoints detected in the Tumor2 sample were found to be common with the Tumor1 sample. This indicates that individual breakpoints as well as common breakpoint patterns contribute to tumor progression. Further analyses will have to confirm the impact of these common breakpoints on tumorigenesis. The distinctive fragmentation showing a stepwise increase of copy numbers suggest predisposed or conserved breakpoints which promote oncogenesis. The limitation of this work was the small number of samples for the comparison of copy numbers and gene expression, making it hard to determine the exact correlation between them, also making the determination of conserved or common breakpoints within one stage difficult. Therefore, further experiments on a larger number of samples will be undertaken to find a subset of breakpoints or chromosomal regions common within a stage. Animal models provide a reliable basis for further experiments. Samples from transgenic SVT/t mice during the first lactation period are comparable to early tumor stages in human breast cancer . A goal of this work was to discriminate between early and late genomic changes in tumor development. The profound identification of early stages in breast cancer would be helpful for diagnosis and could influence the therapeutic decisions. Further, we might detect a chronology in genomic reorganization during tumorigenesis. Nevertheless, a large number of experiments is necessary if one is to study the impact of CNAs and breakpoints on gene expression differences during tumor development. The six motifs identified in inter-segment regions (ISRs) show a significant appearance in more than 40 different ISRs. Two of these six motifs were found to have no GO term associations, but they match known motifs from the UniProbe database. Two other motifs found within the ISRs match no known motifs of the UniProbe, but an association to biological processes and molecular functions could be predicted. Further analyses have to be made, analyzing the exact function of these motifs in ISRs and their effect on CN and chromosomal breakpoints.
Mammary gland tissue samples from six NMRI mice were analyzed. Two samples originated from normal non-transgenic mice, and four from WAP-SVT/t transgenic mice (see Figure 1). The two transgenic samples were derived from WAP-SVT/t mice on the first day of lactation. Moreover, two breast cancer samples originated from WAP-SVT/t mice that had developed cancer after the first lactation period. Additionally, two samples were derived from the 762TuD cell lines as described in the work of Klein et al. . The cytosine arabinoside sensitive sample SVTneg1 (CAs) was in passage 111 and the cytosine arabinoside resistant sample SVTneg1 (CAr) was in passage 23 when DNA was taken for analyses. The data have been deposited in GEO database  and are accessible through GEO Series accession number GSE35873 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE35873). The induction of tumor formation by SV40-T-antigen synthesis was tested in a previous work by Santarelli et al. .
For further comparison, six recurrent tumor samples of PIK3CA-driven mammary tumors provided by Liu et al.  were used for the analyses (data available at NCBI GEO database , accession number GSE27691).
Mouse SNP analyses
DNA was extracted using Purelink Genomic DNA Kit (K1820, Invitrogen) in accordance with the manufacturer’s protocol. The genotyping analyses were carried out at Atlas Biolabs GmbH (Berlin, Germany) using Affymetrix Mouse Diversity Array (MOUSEDIVm520650) [see supplement for protocol, Additional file 8. The array design was described by Yang et al. in 2009 . Normalization and allele summarization were performed with the BRLMM-P algorithm provided by the Affymetrix Power Tools Software Package (version 126.96.36.199). To compare the total signal intensity distributions of all samples, intensities of both alleles for each SNP were added up. Copy number alteration (CNA) for each SNP was computed as log2-ratios of each sample and a reference dataset. The reference for each SNP was calculated as the mean signal intensity of both normal samples (Normal1 and Normal2). In the case of the six recurrent tumor samples the ratio was computed using the normal sample provided by Liu et. al. .
Segmentation analyses and motif finding
All statistical analyses were performed using R (version 2.14). Differences in copy number (CN) and segmentation of each chromosome were calculated with the DNAcopy package (version 1.28.0) of Bioconductor (version 2.9) , using log2-ratio values. The DNAcopy package implements the circular binary segmentation algorithm introduced by Olshen et al. . Continuous CNA regions (segments) were predicted finding a ’change-point’ between two groups of SNP intensity values according to their common distribution function. The parameters of the significance level α and the standard deviation SD were tested to assess the number of resulting segments (data not shown). Here the parameter settings of α = 0.001, SD = 0.5 and “sd.undo” were used. Motif search was performed in inter-segment regions (ISR) of the Tumor1 sample using the MEME Suite . To enhance the significance, only inter-segment regions of two adjacent segments with a difference in segment mean of at least 0.8 were analyzed. The MEME parameters were set to a minimum motif width of 15 bp and a maximum width of 40 bp. Motifs found within the ISR were annotated using GOMO  and compared to known motifs of the UniProbe database  using TomTom .
Gene expression analyses
RNA was extracted from frozen tissue segments with RNAzol (PeQLab, Biotechnology GmbH) in accordance with the manufacturer’s protocol. RNA was hybridized to Affymetrix’s Mouse Expression Set 430 A; chips were scanned with the GeneChip Scanner 3000 and VSN normalization was applied to the gene expression data for normalization. Gene expression data (published by Klein et al. ) can be found on NCBI Gene Expression Omnibus database  (GEO series accession number GSE6772; see Additional file 1 for sample accession numbers). Differentially expressed genes were determined based on the false discovery rate adjusted p-value (FDR p-value), using the limma package  (version 3.10.1) of Bioconductor. For comparison of the gene expression and the copy number variation, the 500 top-ranked differentially expressed genes between the two normal and the two tumor samples were computed. It was analyzed whether an increase or decrease of a gene CN influences the gene expression.
Quantitative real-time polymerase chain reaction
DNA samples from two non-transgenic NMRI mice on the first day of lactation and two WAP-SVT/t tumor samples were used for quantitative PCR analysis. Quantitative real-time polymerase chain reaction (qPCR) was performed on optical grade PCR plate (BioRad Laboratories, Munich, Germany) using a BioRad iQ iCycler Detection System (BioRad Laboratories). All qPCRs were performed in triplicate in a total volume of 20 μl, containing 15 ng of gDNA sample, 20 nmol of each primer, and 10 μl of SensiFAST SYBR Lo-ROX Kit (Bioline, Luckenwalde, Germany). Baseline setting, Ct values and efficiency of PCR reactions were determined with the help of LinRegPCR version 12.16 [48, 49]. Relative quantities of the gene to be studied were normalized to glyceraldehyde 3-phosphate dehydrogenase quantities. Each experiment was carried out in triplicate. The following primers were used for qPCR analysis: for the unamplified region Met_ua_s 5’-TGCTTGGTGACTTTGGTGTGGT-3’ and Met_ua1_as 5’-AGCAGGCAGAAATGCGTGAAAGT-3’; for the amplified region Met_am_1_s 5’-ACGTGGAGTTCAGCAGCAATCTGT-3’ and Met_am1_as 5’-TGGCTTGGGATTAGGGCTGTTCT-3’ as well as Met_am2_s 5’-CCTCCAGCACGGGATTCAACCA-3’ and Met_am2_as 5’-TGACTACATGCCGCGCCTAAC-3’.
We analyzed the time it took for tumors to develop in 64 female mice. Time was measured from first day of mating until the finding of a tumor; after a tumor was found the mice were euthanized. The Kaplan-Meier survival curve was computed using the R package Survival (version 2.36-10).
Array annotations and genomic information
SNP array annotations of release 31 were downloaded from Affymetrix’s website and used for SNP copy number analyses and segmentation analyses. Mouse DNA sequences were downloaded from Ensembl  (release 65, Mouse Genome Assembly NCBI m37).
All animal experiments were carried out in accordance with the protocols of the animal care committee of the Senate of Berlin.
Array comparative genomic hybridization
Copy number alterations
Segmental copy number alteration
Single nucleotide polymorphism.
We gratefully thank Nathalie Tafelmacher for proofreading and Beata Schmid for additional help. The work was thankfully supported by the Technical University of Applied Sciences Wildau.
- Hanahan D, Weinberg RA: The hallmarks of cancer. Cell. 2000, 100: 57-70. 10.1016/S0092-8674(00)81683-9.View ArticlePubMedGoogle Scholar
- Foulds L: The experimental study of tumor progression: a review. Cancer Res. 1954, 14: 327-339.PubMedGoogle Scholar
- Klein A, Guhl E, Zollinger R, Tzeng Y, Wessel R, Hummel M, Graessmann M, Graessmann A: Gene expressionprofiling: cell cycle deregulation and aneuploidy do not cause breast cancer formation in WAP-SVT/t transgenic animals. J Mol Med (Berl). 2005, 83: 362-376. 10.1007/s00109-004-0625-1.View ArticleGoogle Scholar
- Osborne C, Wilson P, Tripathyand D: Oncogenes and tumor suppressor genes in breast cancer: potential diagnostic and therapeutic applications. Oncologist. 2004, 9: 361-377. 10.1634/theoncologist.9-4-361.View ArticlePubMedGoogle Scholar
- Bergamaschi A, Kim YH, Wang P, Sørlie T, Hernandez-Boussard T, Lonning PE, Tibshirani R, Børresen-Dale A, Pollack JR: Distinct patterns of DNA copy number alteration are associated with different clinicopathological features and gene-expression subtypes of breast cancer. Genes Chromosomes Cancer. 2006, 45: 1033-1040. 10.1002/gcc.20366.View ArticlePubMedGoogle Scholar
- Sutherland GR, Baker E, Richards RI: Fragile sites still breaking. Trends Genet. 1998, 14: 501-506. 10.1016/S0168-9525(98)01628-X.View ArticlePubMedGoogle Scholar
- Gonzalez E, Kulkarni H, Bolivar H, Mangano A, Sanchez R, Catano G, Nibbs RJ, Freedman BI, Quinones MP, Bamshad MJ, Murthy KK, Rovin BH, Bradley W, Clark RA, Anderson SA, O’Connell RJ, Agan BK, Ahuja SS, Bologna R, Sen L, Dolan MJ, Ahuja SK: The influence of CCL3L1 gene-containing segmental duplications on HIV-1/AIDS susceptibility. Science. 2005, 307: 1434-1440. 10.1126/science.1101160.View ArticlePubMedGoogle Scholar
- Yang Y, Chung EK, Wu YL, Savelli SL, Nagaraja HN, Zhou B, Hebert M, Jones KN, Shu Y, Kitzmiller K, Blanchong CA, McBride KL, Higgins GC, Rennebohm RM, Rice RR, Hackshaw KV, Roubey RA, Grossman JM, Tsao BP, Birmingham DJ, Rovin BH, Hebert LA, Yu CY: Gene copy-number variation and associated polymorphisms of complement component C4 in human systemic lupus erythematosus (SLE): low copy number is a risk factor for and high copy number is a protective factor against SLE susceptibility in European Americans. Am J Hum Genet. 2007, 80: 1037-1054. 10.1086/518257.View ArticlePubMedPubMed CentralGoogle Scholar
- Fanciulli M, Petretto E, Aitman T: Gene copy number variation and common human disease. Clin Genet. 2010, 77: 201-203. 10.1111/j.1399-0004.2009.01342.x.View ArticlePubMedGoogle Scholar
- Hannemann J, Meyer-Staeckling S, Kemming D, Alpers I, Joosse SA, Pospisil H, Kurtz S, Görndt J, Püschel K, Riethdorf S, Pantel K, Brandt B: Quantitative high-resolution genomic analysis of single cancer cells. PLoS ONE. 2011, 6: e26362-10.1371/journal.pone.0026362.View ArticlePubMedPubMed CentralGoogle Scholar
- Conrad D, Pinto D, Redon R, Gokcumen O, Zhang Y, Aerts J, Andrews T, Barnes C, Campbell P, Fitzgerald T, Hu M, Ihm C, Kristiansson K, Macarthur D, Macdonald J, Onyiah I, Pang A, Robson S, Stirrups K, Valsesia A, Walter K, Wei J, Consortium WTCC, Tyler-Smith C, Carter N, Lee C, Scherer S, Hurles M, Feuk L: Origins and functional impact of copy number variation in the human genome. Nature. 2010, 464: 704-712. 10.1038/nature08516.View ArticlePubMedGoogle Scholar
- Redon R, Ishikawa S, Fitch K, Feuk L, Perry G, Andrews T, Fiegler H, Shapero M, Carson A, Chen W, Cho E, Dallaire S, Freeman J, Gratacòs M, Huang J, Kalaitzopoulos D, Komura D, MacDonald J, Marshall C, Mei R, Montgomery L, Nishimura K, Okamura K, Shen F, Somerville M, Tchinda J, Valsesia A, Woodwark C, Yang F, González J, et al: Global variation in copy number in the human genome. Nature. 2006, 444: 444-454. 10.1038/nature05329.View ArticlePubMedPubMed CentralGoogle Scholar
- Cahan P, Li Y, Izumi M, Graubert TA: The impact of copy number variation on local gene expression in mouse hematopoietic stem and progenitor cells. Nat Genet. 2009, 14: 430-437.View ArticleGoogle Scholar
- Henrichsen C, Vinckenbosch N, Zöllner S, Chaignat E, Pradervand S, Schütz F, Ruedi M, Kaessmann H, Reymond A: Segmental copy number variation shapes tissue transcriptomes. Nat Genet. 2009, 41: 424-429. 10.1038/ng.345.View ArticlePubMedGoogle Scholar
- Cutler G, Marshall LA, Chin N, Baribault H, Kassner PD: Significant gene content variation characterizes the genomes of inbred mouse strains. Genome Res. 2007, 17: 1743-1754. 10.1101/gr.6754607.View ArticlePubMedPubMed CentralGoogle Scholar
- Graubert TA, Cahan P, Edwin D, Selzer R, Richmond T: A high-resolution map of segmental DNA copy number variation in the mouse genome. PLoS Genet. 2007, 3: e3-10.1371/journal.pgen.0030003.View ArticlePubMedPubMed CentralGoogle Scholar
- Agam A, Yalcin B, Bhomra A, Cubin M, Webber C: Elusive copy number variation in the mouse Genome. PLoS ONE. 2010, 5: e12839-10.1371/journal.pone.0012839.View ArticlePubMedPubMed CentralGoogle Scholar
- Pollack JR, Sørlie T, Perou CM, Rees CA, Jeffrey SS, Lonning PE, Tibshirani R, Botstein D, Børresen-Dale A, Brown PO: Microarray analysis reveals a major direct role of DNA copy number alteration in the transcriptional program of human breast tumors. Proc Natl Acad Sci USA. 2002, 99: 12963-12968. 10.1073/pnas.162471999.View ArticlePubMedPubMed CentralGoogle Scholar
- Zhao X, Li C, Paez JG, Chin K, Jänne PA, Chen T, Girard L, Minna J, Christiani D, Leo C, Gray JW, Sellers WR, Meyerson M: An integrated view of copy number and Allelic alterations in the cancer genome using single nucleotide Polymorphism arrays. Cancer Res. 2004, 64: 3060-3071. 10.1158/0008-5472.CAN-03-3308.View ArticlePubMedGoogle Scholar
- Jörnsten R, Abenius T, Kling T, Schmidt L, Johansson E, Nordling T, Nordlander B, Sander C, Gennemark P, Funa K, Nilsson B, Lindahl L, Nelander S: Network modeling of the transcriptional effects of copy number aberrations in glioblastoma. Mol Syst Biol. 2011, 7: 486-View ArticlePubMedPubMed CentralGoogle Scholar
- Valsesia A, Rimoldi D, Martinet D, Ibberson M, Benaglio P, Quadroni M, Waridel P, Gaillard M, Pidoux M, Rapin B, Rivolta C, Xenarios I, Simpson AJG, Antonarakis SE, Beckmann JS, Jongeneel CV, Iseli C, Stevenson BJ: Network-guided analysis of genes with altered somatic copy number and gene expression reveals pathways commonly Perturbed in Metastatic Melanoma. PLoS ONE. 2011, 6: e18369-10.1371/journal.pone.0018369.View ArticlePubMedPubMed CentralGoogle Scholar
- Mileykoa Y, Johb RI, Weitza JS: Small-scale copy number variation and large-scale changes in gene expression. Nat Genet. 2008, 105: 16659-16664.Google Scholar
- Ahn S, Wang RT, Park CC, Lin A, Leahy RM, Lange K, Smith DJ: Directed mammalian gene regulatory networks using expression and comparative genomic hybridization microarray data from radiation hybrids. PLoS Comput Biol. 2009, 5: e1000407-10.1371/journal.pcbi.1000407.View ArticlePubMedPubMed CentralGoogle Scholar
- Klein A, Li N, Nicholson J, McCormack A, Graessmann A, Duesberg P: Transgenic oncogenes induce oncogene-independent cancers with individual karyotypes and phenotypes. Cancer Genet Cytogenet. 2010, 200 (2): 79-99. 10.1016/j.cancergencyto.2010.04.008.View ArticlePubMedGoogle Scholar
- Liu P, Cheng H, Santiago S, Raeder M, Zhang F, Isabella A, Yang J, Semaan D, Chen C, Fox E, Gray N, Monahan J, Schlegel R, Beroukhim R, Mills G, Zhao J: Oncogenic PIK3CA-driven mammary tumors frequently recur via PI3K pathway-dependent and PI3K pathway-independent mechanisms. Nat Med. 2011, 17: 1116-1120. 10.1038/nm.2402.View ArticlePubMedPubMed CentralGoogle Scholar
- Olshen A, Venkatraman E, Lucito R, Wigler M: Circular binary segmentation for the analysis of array-based DNA copy number data. Biostatistics. 2004, 5: 557-572. 10.1093/biostatistics/kxh008.View ArticlePubMedGoogle Scholar
- Yang H, Ding Y, Hutchins L, Szatkiewicz J, Bell T, Paigen B, Graber J, de Villena, GA C: A customized and versatile high-density genotyping array for the mouse. Nat Methods. 2009, 6: 663-666. 10.1038/nmeth.1359.View ArticlePubMedPubMed CentralGoogle Scholar
- Egan C, Sridhar S, Wigler M, IM H: Recurrent DNA copy number variation in the laboratory mouse. Nat Genet. 2007, 39: 1384-1389. 10.1038/ng.2007.19.View ArticlePubMedGoogle Scholar
- Lee H, Kong SW, J PP: Integrative analysis reveals the direct and indirect interactions between DNA copy number aberrations and gene expression changes. Bioinformatics. 2008, 24: 889-896. 10.1093/bioinformatics/btn034.View ArticlePubMedPubMed CentralGoogle Scholar
- Gherardi E, Birchmeier W, Birchmeier C, Vande Woude: Targeting MET in cancer: rationale and progress. Nat Rev Cancer. 2012, 12: 89-103. 10.1038/nrc3205.View ArticlePubMedGoogle Scholar
- Graveel C, DeGroot J, Su Y, Koeman J, Dykema K, Leung S, Snider J, Davies S, Swiatek P, Cottingham S, Watson M, Ellis M, Sigler R, Furge K, GF VW: Met induces diverse mammary carcinomas in mice and is associated with human basal breast cancer. Proc Natl Acad Sci USA. 2009, 106: 12909-12914. 10.1073/pnas.0810403106.View ArticlePubMedPubMed CentralGoogle Scholar
- Ponzo M, M P: The Met receptor tyrosine kinase and basal breast cancer. Cell Cycle. 2010, 9: 1043-1050. 10.4161/cc.9.6.11033.View ArticlePubMedGoogle Scholar
- Bailey T, Boden M, Buske F, Frith M, Grant C, Clementi L, Ren J, Li W, WS N: MEME SUITE: tools for motif discovery and searching. Nucleic Acids Res. 2009, 37 (suppl 2): W202-W208.View ArticlePubMedPubMed CentralGoogle Scholar
- Puttagunta R, Gordon L, Meyer G, Kapfhamer D, Lamerdin J, Kantheti P, Portman K, Chung W, Jenne D, Olsen A, Burmeister M: Comparative maps of human 19p13.3 and mouse chromosome 10 allow identification of sequences at evolutionary breakpoints. Genome Res. 2000, 10: 1369-1380. 10.1101/gr.145200.View ArticlePubMedPubMed CentralGoogle Scholar
- Kehrer-Sawatzki H, Sandig C, Chuzhanova N, Goidts V, Szamalek JM, Tänzer S, Müller S, Platzer M, Cooper DN, Hameister H: Breakpoint analysis of the pericentric inversion distinguishing human chromosome 4 from the homologous chromosome in the chimpanzee (Pan troglodytes). Human Mutation. 2005, 25: 45-55. 10.1002/humu.20116.View ArticlePubMedGoogle Scholar
- Ruiz-Herrera A, Castresana J, Robinson T: Is mammalian chromosomal evolution driven by regions of genome fragility?. Genome Biol. 2006, 7: R115-10.1186/gb-2006-7-12-r115.View ArticlePubMedPubMed CentralGoogle Scholar
- Schwartz M, Zlotorynski E, Kerem B: The molecular basis of common and rare fragile sites. Cancer Lett. 2006, 232: 13-26. 10.1016/j.canlet.2005.07.039.View ArticlePubMedGoogle Scholar
- Kato T, Inagaki H, Yamada K, Kogo H, Ohye T, Kowa H, Nagaoka K, Taniguchi M, Emanuel B, Kurahashi H: Genetic variation affects de novo translocation frequency. Science. 2006, 311: 971-10.1126/science.1121452.View ArticlePubMedPubMed CentralGoogle Scholar
- Kurahashi H, Shaikh T, BS E: Alu-mediated PCR artifacts and the constitutional t(11;22) breakpoint. Hum Mol Genet. 2000, 9: 2727-2732. 10.1093/hmg/9.18.2727.View ArticlePubMedGoogle Scholar
- Buske F, Bodén M, Bauer D, TL B: Assigning roles to DNA regulatory motifs using comparative genomics. Bioinformatics. 2010, 26: 860-866. 10.1093/bioinformatics/btq049.View ArticlePubMedPubMed CentralGoogle Scholar
- Gupta S, Stamatoyannopoulos J, Bailey T, Noble W: Quantifying similarity between motifs. Genome Biol. 2010, 8: R24-View ArticleGoogle Scholar
- Newburger D, Bulyk M: UniPROBE: an online database of protein binding microarray data on protein-DNA interaction. Nucleic Acids Res. 2009, 37 (suppl 1): D77-D82.View ArticlePubMedGoogle Scholar
- Klein A, Wessel R, Graessmann M, Jürgens M, Petersen I, Schmutzler R, Niederacher D, Arnold N, Meindl A, Scherneck S, Seitz S, Graessmann A: Comparison of gene expression data from human and mouse breast cancers: identification of a conserved breast tumor gene set. Int J Cancer. 2007, 121 (3): 683-688. 10.1002/ijc.22630.View ArticlePubMedGoogle Scholar
- Barrett T, Troup D, Wilhite S, Ledoux P, Evangelista C, Kim I, Tomashevsky M, Marshall K, Phillippy K, Sherman P, Muertter R, Holko M, Ayanbule O, Yefanov A, Soboleva A: NCBI GEO: archive for functional genomics data sets–10 years on. Nucl Acids Res. 2011, 39: D1005-D1010. 10.1093/nar/gkq1184.View ArticlePubMedGoogle Scholar
- Santarelli R, Tzeng Y, Zimmermann C, Guhl E, Graessmann A: SV40 T-antigen induces breast cancer formation with a high efficiency in lactating and virgin WAP-SV-T transgenic animals but with a low efficiency in ovariectomized animals. Oncogene. 1996, 12: 495-505.PubMedGoogle Scholar
- Gentleman R, Carey V, Bates D, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, Hornik K, Hothorn T, Huber W, Iacus S, Irizarry R, Leisch F, Li C, Maechler M, Rossini A, Sawitzki G, Smith C, Smyth G, Tierney L, Yang J, Zhang J: Bioconductor: Open software development for computational biology and bioinformatics. Genome Biol. 2004, 5: R80-10.1186/gb-2004-5-10-r80.View ArticlePubMedPubMed CentralGoogle Scholar
- Smyth GK: Limma: linear models for microarray data. Bioinformatics and Computational Biology Solutions using R and Bioconductor. Edited by: Gentleman R, Carey V, Dudoit S, Irizarry R, Huber W. 2005, New York: Springer, 397-420.View ArticleGoogle Scholar
- Ramakers C, Ruijter J, Deprez R, Moorman A: Assumption-free analysis of quantitative real-time polymerase chain reaction (PCR) data. Neurosci Lett. 2003, 339 (1): 62-66. 10.1016/S0304-3940(02)01423-4.View ArticlePubMedGoogle Scholar
- Ruijter J, Ramakers C, Hoogaars W, Karlen Y, Bakker O, van den Hoff M, Moorman A: Amplification efficiency: linking baseline and bias in the analysis of quantitative PCR data. Nucleic Acids Res. 2009, 37 (6): e45-10.1093/nar/gkp045.View ArticlePubMedPubMed CentralGoogle Scholar
- Kersey P, Staines D, Lawson D, Kulesha E, Derwent P, Humphrey J, Hughes D, Keenan S, Kerhornou A, Koscielny G, Langridge N, McDowall M, Megy K, Maheswari U, Nuhn M, Paulini M, Pedro H, Toneva I, Wilson D, Yates A, Birney E: Ensembl Genomes: an integrative resource for genome-scale data from non-vertebrate species. Nucleic Acids Res. 2012, 40 (1): D91-D97. 10.1093/nar/gkr895.View ArticlePubMedGoogle Scholar
- The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2407/12/380/prepub
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