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1 Departments of Medical Oncology and 2 Biostatistical Sciences, Dana-Farber Cancer Institute, Boston, Massachusetts; 3 Departments of Medicine and 4 Pathology, Harvard Medical School, Boston, Massachusetts; 5 Departments of Biostatistics and 6 Environmental Health, Harvard School of Public Health, Boston, Massachusetts; 7 Department of Laboratory Medicine, University of California, San Francisco, California, 8 Hamon Center for Therapeutic Oncology Research, and 9 Departments of Internal Medicine and Pharmacology, University of Texas Southwestern Medical Center, Dallas, Texas
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ABSTRACT |
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 Top ABSTRACT INTRODUCTIONMATERIALS AND METHODSArray CGH AnalysisQuantitative Real-Time PCRRESULTSDISCUSSIONREFERENCES |
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INTRODUCTION |
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSArray CGH AnalysisQuantitative Real-Time PCRRESULTSDISCUSSIONREFERENCES |
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Comparative genomic hybridization (CGH) is a method to detect chromosomal copy number by comparing hybridization intensity of a tumor and a normal control DNA sample (7) . Array-based CGH makes it possible to scan the genome for copy number with high resolution by hybridizing to arrayed genomic DNA or cDNA clones (8, 9, 10) . To increase sensitivity and specificity, the hybridization of genomic representations to CGH arrays has been developed (11) ; this is particularly useful for oligonucleotide arrays. Lucito et al. (12) have developed recently a new representational oligonucleotide microarray that could achieve an average resolution of 30 kb across the genome. However, currently available array CGH methods cannot simultaneously detect chromosomal loss of heterozygosity (LOH). To combine the detection of cancer copy number with cancer-specific LOH in the same experiments, we have developed an analytical method to detect DNA copy number changes by hybridization of representations of genomic DNA to commercially available single nucleotide polymorphism (SNP) arrays.
SNPs are the most frequent form of DNA variation present in the human genome, where >2 million SNPs have been identified by public efforts.10 Because of their abundance, even spacing, and stability across the genome, SNPs offer significant diagnostic potential for human diseases including cancers, compared with other polymorphisms such as fragment length polymorphisms and microsatellite markers. Moreover, scoring of SNPs is easily automated, e.g., high-density oligonucleotide arrays have been used for large-scale high-throughput SNP analysis (13) .
We and others have demonstrated previously that SNP arrays covering 1,494 SNP loci (HuSNP; Affymetrix) could accurately measure genome-wide LOH (14, 15, 16, 17, 18, 19) . LOH calls by SNP arrays were consistent with analysis using simple sequence length polymorphisms and CGH (15) . Furthermore, our group has demonstrated that hierarchical clustering based on genome-wide LOH patterns can distinguish different types of tumor cells based on their shared LOH (20) . A high-density SNP array has been generated recently that can analyze >10,000 SNP loci using a genome representation approach (21) . The XbaI mapping array is highly robust and reproducible with call rate accuracy well in excess of 99% (21) . We have shown that LOH analysis with this high-density array shares high concordance with microsatellite methods, and permits us to detect smaller regions of LOH that are missed by HuSNP array and microsatellite mapping (20) .
In the present study, we demonstrate the utility of 10K SNP arrays for characterizing DNA copy number changes including amplification and homozygous deletion from a subset of lung and breast carcinoma cell lines and lung tumors.
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MATERIALS AND METHODS |
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSArray CGH AnalysisQuantitative Real-Time PCRRESULTSDISCUSSIONREFERENCES |
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XbaI Mapping Array Hybridization
XbaI mapping array 130 (Affymetrix, Inc., Santa Clara, CA) was used in this study. This array covers 10,043 SNP loci distributed on all of the human chromosomes except Y chromosome, resulting in a resolution close to 300 kb. The analyses were performed according to previously described methods (21)
and the manufacturer’s instructions. In brief, 250 ng of genomic DNA is digested with XbaI restriction enzyme, ligated to an adaptor, and amplified by PCR. The resulting amplicons are fragmented, labeled with biotinylated dideoxy ATP using terminal deoxynucleotidyl transferase, and hybridized to the array. Hybridization is detected by incubation with streptavidin-phycoerythrin conjugates, followed by scanning the array for phycoerythrin fluorescence and quantitation using the MAS 5.0 software.
Imaging and Data Analysis
Normalization of Arrays and Model-Based Signal Values.
The median perfect match and mismatch probe intensities of the arrays range from 110 to 329, indicating the need for normalization to compare the signals across different arrays. We used the invariant set normalization method (22)
to normalize all arrays at the probe intensity level to a baseline array "HCC1937 BL." This method adaptively selects probes that have similar ranks (thus more likely to belong to SNPs that have the same copy numbers) between one array and the baseline array to determine the normalization function.
After normalization, we used a model-based method (23) to obtain the signal values for each SNP in each array. Because the probe response patterns of the three genotypes (AA, BB, and AB) are dissimilar, we defined the new perfect match probe intensity as pmA + pmB and the new mismatch probe intensity as mmA + mmB for each probe quartet of a probe set. This transformation makes the probe intensity pattern and magnitude of a probe set comparable across the genotypes. Then the perfect match/mismatch difference model was applied on the transformed probe-level data to compute model-based signal values. The model-based method weighs probes by their sensitivity and consistency when computing signal values, and image artifacts are also identified and eliminated by the outlier detection algorithm in this step.
Observed and Inferred DNA Copy Number.
For each SNP, the signal values of all of the normal cell lines were averaged to obtain the mean signal of 2 copy (male X chromosomes are multiplied by 2 before averaging), and the observed copy number is defined as (observed signal/mean signal of two copy) * 2, and visualized either log 2 ratio displayed in blue to white then to red color scale (Fig. 6B)
or white (0 copy) to red color scales. In general, we assume a diploid genome in the absence of specific average DNA content data, but experimental values for mean copy number, derived from flow cytometry, can be substituted for the 2-copy assumption and will give more reliable results. To infer the DNA copy number from the raw signal data, we used the Hidden Markov Model (HMM; Ref. 24
). First, we specify that for each SNP the observed signal values are random values drawn from a t distribution with parameters determined by the underlying real copy number (Fold*2) and the estimated mean signals and their SDs in the normal samples: (Signal – Mean * Fold / Std *Fold)
t(40). These distributions give the "emission probabilities" of the HMM. Secondly, we assume that the copy number changes are caused by genetic recombination events: for a particular sample, the larger the genetic distance between the two markers, the more likely it is that recombination (thus a copy number change) will happen within the interval. The Haldane’s map function. 
= 1/2 (1 – e–2d; Ref. 25
) is used to convert the genetic distance d between two SNP markers to the probability (2) that the copy number of the second marker will return to the background distribution of copy numbers in this sample and thus independent from the copy number of the first marker. These probabilities are used as the "transition probabilities" of HMM that determine how d, the real copy number of one marker, provides information of the real copy number of the adjacent marker. Thirdly, we estimate the background distribution of copy numbers in each sample in two rounds. The proportion of chromosome regions that have a particular copy number is set to fixed values in the first round [0.9 for 2 copy, 0.1/(N–1) for copy 0 to N except 2, where N is the maximal allowed copy number in inference]. The HMM is run as described below and then the inferred copy numbers are used to re-estimate the sample-specific background distribution of the copy numbers. After this, the HMM model is rerun to obtain the final results. These background distributions are used as the "initial probabilities" of HMM specifying the likelihood of observing a particular copy number at the beginning of the p-arm and also used together with the "transition probabilities" to determine the dependency of the copy number values of two adjacent markers as described above.
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The above described analysis methods are implemented in the dChip software Version 1.3 (26) , which is freely available to academic users.
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Array CGH Analysis |
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSArray CGH AnalysisQuantitative Real-Time PCRRESULTSDISCUSSIONREFERENCES |
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Quantitative Real-Time PCR |
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSArray CGH AnalysisQuantitative Real-Time PCRRESULTSDISCUSSIONREFERENCES |
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RESULTS |
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSArray CGH AnalysisQuantitative Real-Time PCRRESULTSDISCUSSIONREFERENCES |
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. The log 2 ratios of the raw signal for all 9684 of the SNPs located on autosomes were distributed normally with 50% of the absolute magnitude of the log 2 ratios <0.21 and 75% of the absolute ratios <0.37 (Fig. 1A)
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This correlation suggests that a quantitative model could be developed to predict chromosomal copy number based on SNP array hybridization intensity. To do so, we have implemented a novel analytical method to infer the copy number of each SNP based on a hidden Markov model within the dChipSNP computational platform (see details in "Materials and Methods").
The observed copy number ratios for X-chromosome SNPs (scatter plots) and the inferred copy numbers (black bars) correlated with the known copy number for each of the known karyotypes (Fig. 1, B–F)
. The XO genomic DNA showed a decreased X-chromosome hybridization signal compared with XX controls but little or no change in the raw and inferred autosomal signals (Fig. 1B)
. The experimental XX cell line DNA showed a constant inferred copy number of 2 for both the autosomes and the X chromosome (Fig. 1C)
, whereas the X-chromosomal signal was increased in samples with 3, 4, or 5 copies of the X chromosome, as reflected by inferred copy numbers of 3 for 3X, 4 for 4X, and 5 for 5X (Fig. 1, D–F)
. Overall, the inferred DNA copy number for the autosomes was accurately predicted as diploid for 99.2% of SNPs. This result suggests that the inferred copy number calculations based on SNP array hybridization intensity approximate closely to the actual copy number.
To additionally validate our ability to measure DNA copy changes from autosomes, we measured two otherwise diploid cell lines containing cytogenetically mapped partial or whole-chromosome copy number gains or losses. SNP array hybridization analysis shows both decreased raw SNP hybridization ratios (black dots) and an inferred copy number (black bar) of 1 for the entire chromosome 21 (Fig. 2A
, red arrow), which is lost in the GM01201 cell line. This analysis also shows increased hybridization intensities and a copy number gain to 3 copies within chromosome 9p (Fig. 2B
, red arrow) for which the GM03236 cell line is triploid. The inferred copy number analysis showed that 96.7% and 99.9% of the SNP loci from the two cell lines were predicted as two copies, in these otherwise diploid cell lines.
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Quantitative analysis of SNP array data from the cancer cell line samples revealed a variety of candidate copy number alterations, including both low-level and high-level amplifications, as well as hemizygous and homozygous deletions. Copy number analyses were similar regardless of whether the reference sample was paired normal DNA or pooled normal DNA (data not shown). Raw data are available at our website.13
An example of cancer-specific amplification is shown for the male small cell lung cancer cell line, H2171 (Fig. 3)
. A genome-wide view reveals a variety of large regions with triploid or haploid DNA content, including a single-copy X chromosome as expected and several regions of high-copy amplification (Fig. 3A)
. These include inferred copy numbers of 
7 in a 1.7–2.6 megabase region of chromosome 8q12 and in a 1.1–2.1 megabase region of chromosome 8q24 encompassing the MYC locus (Fig. 3B
; Table 1
), a 1.7–2.1 megabase region of chromosome 11q14 (Fig. 3C
; Table 1
), and a 1.9–3.2 megabase region of chromosome 12p11 (Fig. 3D
; Table 1
).
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7 (Table 1)
. Two regions, one encompassing MYC (NCI-H1395 and NCI-H2171) and another encompassing the BCAS1 gene (BT-474 and MCF7) were subject to predicted high-level amplification in more than one sample. Cancer cell-specific chromosome amplifications also included some other known regions of amplification, including regions of 11q13.3 (CCND1), 19q12-q13.12 (CCNE1), and 12q14.1-q15 (DYRK2; Table 1
). Moreover, SNP array quantitation of DNA from the BT-474 and UACC-812 breast cancer cell lines could detect recurrent amplifications in chromosomes 17 and 20. For example, the ERRB2 amplicon is seen to be highly amplified in UACC-812 (data not shown).
Additionally, we were able to detect several novel amplicons, including amplification of the NOV gene in NCI-H1395 cell DNA and a large amplicon in UACC- 812 cells from 13q14.2 to 13q31.3 with copy number as high as 11 (Table 1)
. The resolution of amplification detection will depend on the density of the SNP array used, but we have identified high-copy amplifications of <500 kb in maximum size and confirmed these amplifications by quantitative real-time PCR (Table 1
; Supplementary Table 1).
The copy numbers of the predicted amplified regions were validated by quantitative real-time PCR (Table 1)
. The magnitude of the amplification was generally underestimated by the SNP array hybridization intensity. The SNP array inferred copy number of the tested regions ranged from 7 to 12, whereas the quantitative PCR-derived copy number ranged from 5.15 to 60.63. This general underestimation most likely reflects the saturation of the SNP arrays at high copy number, but it is conceivable that local copy number variations between the SNP locus and the quantitative PCR locus may also contribute to the discrepancy. Additionally, we have additionally confirmed that 1.5–3-fold (3–6 copy) changes in copy number could be predicted with reasonable accuracy (Supplementary Fig. 2). For example, we evaluated amplification of the MYC locus in several samples with lower predicted copy numbers for the region. Samples with predicted copy numbers of 3 had a mean of 3.04 with a SD of 0.59 by quantitative PCR, whereas samples with SNP array predicted copy numbers of 4 had a mean of 4.80 with a SD of 1.38 by quantitative PCR.
SNP Array Identification of Homozygous Deletions.
SNP array analysis is able to detect homozygous deletions in cancer cell line DNA in genome-wide scans. Two examples of homozygous deletions from a breast cancer cell line, HCC38, are shown in Fig. 4
. Our criteria for homozygous deletion require the presence of at least 2 SNPs that cover an area of >1 kb in addition to an inferred copy number of 0; these eliminate candidate regions that may be caused by XbaI polymorphism together with LOH. The HCC38 cell line contains three regions with an inferred copy number of 0 (Fig. 4A)
, including larger regions on chromosome 3p12 (Fig. 4B
; Table 1
) and chromosome 9p21 (Fig. 4C
; Table 1
), as well as one small region, that do not meet the criteria. The chromosome 3p12 deletion has been described previously (28)
, whereas the 9p21.3-p21.1 deletion, encompassing the CDKN2A locus, has not been reported previously. The median observed copy number for the two regions of homozygous deletions predicted in HCC38 (Fig. 4, B and C)
was 0.0082 (log 2 ratio of –7.6 compared with diploid) and the 95th percentile of the observed copy number was 0.46 (log 2 ratio of –2.2 compared with diploid).
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; one region, the CDKN2A locus on chromosome 9p21 was identified in two samples. Thirteeen of the 15 candidates could be confirmed by quantitative and non-quantitative PCR analysis. These included previously unreported candidate regions in chromosomes 4q35.1–4q35.2, 10q21.3, 14q23.2, and Xq21.32-Xq21.33, as well as previously described regions in 3p12, 3p14, and the CDKN2A locus on 9p21 (Table 2)
. Candidate homozygous deletion regions from primary tumors could not be validated in this study (Table 2)
, consistent with the need for purified samples for homozygous deletion detection (see Fig. 7
, below). The smallest region of homozygous deletion that was confirmed by quantitative real-time PCR was a maximum of 502 kb in size (Table 2)
.
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. All types of arrays readily detected two broad amplicons within 17q12 and 17q22–23 (Fig. 5, D–F)
. Similarly, an amplicon located in 20q13 and a region of single-copy loss located in 20q12, confirmed previously by BAC array CGH and cDNA array CGH (8
, 9)
, were also observable in all three of the arrays (Fig. 5, G–I)
. In addition, we compared copy number data between SNP array and BAC array for a breast cancer cell line, HCC1937, with multiple copy number changes (29)
. Both SNP and BAC arrays detected a remarkably similar pattern of copy number changes (data not shown). In conclusion, we find that SNP arrays, cDNA arrays, and BAC arrays detect generally the same types of copy number variation in the same locations.
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, right lower diagram), the other LOH mechanisms do not lead to DNA copy number changes (Fig. 6A
, right upper diagram).
Because high-density SNP arrays can efficiently detect both copy number changes and LOH, we reasoned that we could discriminate between underlying LOH mechanisms by analyzing copy number changes. We observed that some LOH regions do not exhibit copy number changes. For example, in HCC1599 and HCC1187 cell lines, the entire chromosome 13 and 9, respectively, undergo LOH as detected by genotyping analysis, but there is no change in copy number (Fig. 6B and 6E)
, suggesting that these LOH events could be caused by copy-neutral events such as mitotic nondisjunction followed by duplication of one parental chromosome. In contrast, chromosome 13pter-q22 undergoes LOH in HCC2218, and copy number analysis indicates loss of one copy (Fig. 6C)
, whereas the remainder of chromosome 13 shows retention of heterozygosity and a diploid copy number, suggesting that this LOH event might be caused by hemizygous deletion.
Similarly, all of chromosome 9 undergoes LOH in NCI-H1648, but copy number analysis suggests that the LOH on 9p is due to hemizygous deletion, whereas the LOH on 9q is due to a copy-neutral mechanism (Fig. 6D)
. Each of the copy number values of the regions of LOH or retention, described above, was confirmed by quantitative real-time PCR of selected loci (Table 3)
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and 2)
, and the corresponding B-lymphoblast "normal control" cell lines, HCC1143 BL and HCC38 BL. DNA from each tumor cell line was mixed with matched normal DNA at ratios of 0, 60, 70, 80, 90, and 100% tumor cell line DNA. Copy number amplifications could still be detected in mixtures containing 60% tumor DNA (Fig. 7, A and B)
. However, the accuracy of scoring homozygous deletions with dChipSNP drops off steeply with decreasing purity of tumor where the deletion of 9p21 in HCC38 cell line is detectable with tumor DNA purity of 90% (Fig. 7C)
, whereas the deletion of chromosome 3p12 is detectable only at 100% tumor purity (Fig. 7D)
. These results suggest that, unlike amplification, detection of homozygous deletion is considerably dependent on tumor purity.
Next, we tested our copy number analysis approach on five primary lung tumor samples. We detected one chromosomal amplification in a primary lung tumor, which was subsequently confirmed by real-time quantitative PCR (Table 1)
, whereas two homozygous deletions detected in these tumors appear to be false positive (Table 2)
. These results suggest that whole genome amplification of tumor sample dissected from laser capture microdissection will be the best approach to isolate DNA from primary tumor samples.14
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DISCUSSION |
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSArray CGH AnalysisQuantitative Real-Time PCRRESULTSDISCUSSIONREFERENCES |
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This study represents the first application of SNP arrays in genome-wide screening for DNA copy number changes in human cancers. Comparison with BAC and cDNA array analysis shows that the three platforms give generally comparable results. The noise of individual measurements is generally lower using BAC arrays, but the possible density of markers is greater with SNP or other representational oligonucleotide arrays (arrays representing 120,000 SNPs have now been generated). Furthermore, the SNP array approach offers the unique possibility to analyze copy number and LOH simultaneously using the same platform. Thus, this makes it possible to distinguish copy- reducing from copy-neutral genetic mechanisms underlying LOH events.
As part of this work, we have developed the signal analysis module in the dChipSNP platform, which is highly automated and freely available to the scientific community, for copy number analyses and for correlating copy changes readily with cytoband and gene information.15 These analytic methods could also be adapted to other copy number platforms. Upon further refinement, the SNP array methods should also permit analysis of allele-specific amplification.
Many tumor suppressor and oncogene loci have been identified by pinpointing recurrently deleted or amplified chromosomal regions. CGH, fluorescence in situ hybridization, and other techniques have revealed many recurrent copy number changes in a variety of tumors. In this study, we have identified many known regions, such as homozygous deletion of chromosome 9p21 and amplification of chromosomes 8q24 and 17q21. These regions harbor well-characterized TSGs such as CDKN2A as well as oncogenes such as MYC and ERBB2, which are implicated in lung and breast tumorigenesis. In addition, we discovered several novel homozygous deletions and high-level amplifications (Tables 1
and 2)
. Although the interpretation of these regions must be cautious given the presence of genome instability in cancer cell lines, the surveying of additional cancer specimens will help to address their significance.
The high density of SNP arrays may also make possible the characterization of haplotype structures to analyze cancer predisposition. Furthermore, the detection of single-copy changes with SNP arrays suggest that these arrays could be used to study other genetic diseases in addition to cancers, such as Down, Prader Willi, Angelman, and cri du chat syndromes. The SNP arrays may find application as diagnostic as well as research reagents in this area.
In conclusion, we have demonstrated that SNP array hybridization is a highly efficient method for evaluating genome-wide copy number changes. Whereas the novel deleted and amplified regions discovered in this study may already be significant, application of the SNP array approach to large cancer data sets should prove highly fruitful in discovering cancer-specific genomic alterations.
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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Note: Supplementary data for this article can be found at Cancer Research Online (http://cancerres.aacrjournals.org).
The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
Requests for reprints: M. Meyerson, Department of Medical Oncology, Dana-Farber Cancer Institute, Mayer 430, 44 Binney Street, Boston, MA 02115. Phone: (617) 632-4768; Fax: (617) 632-5998; E-mail: matthew_meyerson{at}dfci.harvard.edu
10 Internet address: http://www.ncbi.nlm.nih.gov/SNP/. 
11 Internet address: http://ml.ucsf.edu/cores/arrays_bac.asp.
12 Internet address: http://www-genome.wi.mit.edu/cgi-bin/primer/primer3_www.cgi.
13 Internet address: http://research.dfci.harvard.edu/meyersonlab/snp/snp.htm.
14 G. J. Paez, R. Beroukhim, J. Lee, et al. Genome coverage and sequence fidelity of f29 polymerase based multiple strand displacement whole genome amplification, submitted for publication.
15 Internet address: http://www.dchip.org.
Received 10/21/03. Revised 1/27/04. Accepted 2/17/04.
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REFERENCES |
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSArray CGH AnalysisQuantitative Real-Time PCRRESULTSDISCUSSIONREFERENCES |
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T. J. Pugh, A. D. Delaney, N. Farnoud, S. Flibotte, M. Griffith, H. I. Li, H. Qian, P. Farinha, R. D. Gascoyne, and M. A. Marra Impact of whole genome amplification on analysis of copy number variants Nucleic Acids Res., August 1, 2008; 36(13): e80 - e80. [Abstract] [Full Text] [PDF]
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C. O'Brien, G. Cavet, A. Pandita, X. Hu, L. Haydu, S. Mohan, K. Toy, C. S. Rivers, Z. Modrusan, L. C. Amler, et al. Functional Genomics Identifies ABCC3 as a Mediator of Taxane Resistance in HER2-Amplified Breast Cancer Cancer Res., July 1, 2008; 68(13): 5380 - 5389. [Abstract] [Full Text] [PDF]
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P. Cahan, L. E. Godfrey, P. S. Eis, T. A. Richmond, R. R. Selzer, M. Brent, H. L. McLeod, T. J. Ley, and T. A. Graubert wuHMM: a robust algorithm to detect DNA copy number variation using long oligonucleotide microarray data Nucleic Acids Res., April 1, 2008; 36(7): e41 - e41. [Abstract] [Full Text] [PDF]
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P. Ouillette, H. Erba, L. Kujawski, M. Kaminski, K. Shedden, and S. N. Malek Integrated Genomic Profiling of Chronic Lymphocytic Leukemia Identifies Subtypes of Deletion 13q14 Cancer Res., February 15, 2008; 68(4): 1012 - 1021. [Abstract] [Full Text] [PDF]
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W. M. Lin, A. C. Baker, R. Beroukhim, W. Winckler, W. Feng, J. M. Marmion, E. Laine, H. Greulich, H. Tseng, C. Gates, et al. Modeling Genomic Diversity and Tumor Dependency in Malignant Melanoma Cancer Res., February 1, 2008; 68(3): 664 - 673. [Abstract] [Full Text] [PDF]
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R. Pique-Regi, J. Monso-Varona, A. Ortega, R. C. Seeger, T. J. Triche, and S. Asgharzadeh Sparse representation and Bayesian detection of genome copy number alterations from microarray data Bioinformatics, February 1, 2008; 24(3): 309 - 318. [Abstract] [Full Text] [PDF]
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C. Saddler, P. Ouillette, L. Kujawski, S. Shangary, M. Talpaz, M. Kaminski, H. Erba, K. Shedden, S. Wang, and S. N. Malek Comprehensive biomarker and genomic analysis identifies p53 status as the major determinant of response to MDM2 inhibitors in chronic lymphocytic leukemia Blood, February 1, 2008; 111(3): 1584 - 1593. [Abstract] [Full Text] [PDF]
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L. M. Hansford, A. E. McKee, L. Zhang, R. E. George, J. T. Gerstle, P. S. Thorner, K. M. Smith, A. T. Look, H. Yeger, F. D. Miller, et al. Neuroblastoma Cells Isolated from Bone Marrow Metastases Contain a Naturally Enriched Tumor-Initiating Cell Cancer Res., December 1, 2007; 67(23): 11234 - 11243. [Abstract] [Full Text] [PDF]
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C. Spittle, M. R. Ward, K. L. Nathanson, P. A. Gimotty, E. Rappaport, M. S. Brose, A. Medina, R. Letrero, M. Herlyn, and R. H. Edwards Application of a BRAF Pyrosequencing Assay for Mutation Detection and Copy Number Analysis in Malignant Melanoma J. Mol. Diagn., September 1, 2007; 9(4): 464 - 471. [Abstract] [Full Text] [PDF]
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C. W. Ross, P. D. Ouillette, C. M. Saddler, K. A. Shedden, and S. N. Malek Comprehensive Analysis of Copy Number and Allele Status Identifies Multiple Chromosome Defects Underlying Follicular Lymphoma Pathogenesis Clin. Cancer Res., August 15, 2007; 13(16): 4777 - 4785. [Abstract] [Full Text] [PDF]
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M. Laakso, S. Tuupanen, A. Karhu, R. Lehtonen, L. A. Aaltonen, and S. Hautaniemi Computational identification of candidate loci for recessively inherited mutation using high-throughput SNP arrays Bioinformatics, August 1, 2007; 23(15): 1952 - 1961. [Abstract] [Full Text] [PDF]
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C. M. Heaphy, W. C. Hines, K. S. Butler, C. M. Haaland, G. Heywood, E. G. Fischer, M. Bisoffi, and J. K. Griffith Assessment of the Frequency of Allelic Imbalance in Human Tissue Using a Multiplex Polymerase Chain Reaction System J. Mol. Diagn., April 1, 2007; 9(2): 266 - 271. [Abstract] [Full Text] [PDF]
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S. Colella, C. Yau, J. M. Taylor, G. Mirza, H. Butler, P. Clouston, A. S. Bassett, A. Seller, C. C. Holmes, and J. Ragoussis QuantiSNP: an Objective Bayes Hidden-Markov Model to detect and accurately map copy number variation using SNP genotyping data Nucleic Acids Res., March 27, 2007; (2007) gkm076v3. [Abstract] [Full Text] [PDF]
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M. Stark and N. Hayward Genome-Wide Loss of Heterozygosity and Copy Number Analysis in Melanoma Using High-Density Single-Nucleotide Polymorphism Arrays Cancer Res., March 15, 2007; 67(6): 2632 - 2642. [Abstract] [Full Text] [PDF]
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J. Oosting, E. H. Lips, R. van Eijk, P. H.C. Eilers, K. Szuhai, C. Wijmenga, H. Morreau, and T. van Wezel High-resolution copy number analysis of paraffin-embedded archival tissue using SNP BeadArrays Genome Res., March 1, 2007; 17(3): 368 - 376. [Abstract] [Full Text] [PDF]
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D. Pfeifer, M. Pantic, I. Skatulla, J. Rawluk, C. Kreutz, U. M. Martens, P. Fisch, J. Timmer, and H. Veelken Genome-wide analysis of DNA copy number changes and LOH in CLL using high-density SNP arrays Blood, February 1, 2007; 109(3): 1202 - 1210. [Abstract] [Full Text] [PDF]
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S. K Gruvberger-Saal, H. E Cunliffe, K. M Carr, and I. A Hedenfalk Microarrays in breast cancer research and clinical practice - the future lies ahead Endocr. Relat. Cancer, December 1, 2006; 13(4): 1017 - 1031. [Abstract] [Full Text] [PDF]
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K.-K. Wong, Y. T.M. Tsang, Y.-M. Chang, J. Su, A. M. Di Francesco, D. Meco, R. Riccardi, L. Perlaky, R. C. Dauser, A. Adesina, et al. Genome-Wide Allelic Imbalance Analysis of Pediatric Gliomas by Single Nucleotide Polymorphic Allele Array Cancer Res., December 1, 2006; 66(23): 11172 - 11178. [Abstract] [Full Text] [PDF]
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D. Komura, F. Shen, S. Ishikawa, K. R. Fitch, W. Chen, J. Zhang, G. Liu, S. Ihara, H. Nakamura, M. E. Hurles, et al. Genome-wide detection of human copy number variations using high-density DNA oligonucleotide arrays Genome Res., December 1, 2006; 16(12): 1575 - 1584. [Abstract] [Full Text] [PDF]
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P. Lamy, C. L. Andersen, F. P. Wikman, and C. Wiuf Genotyping and annotation of Affymetrix SNP arrays Nucleic Acids Res., September 1, 2006; 34(14): e100 - e100. [Abstract] [Full Text] [PDF]
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D. A. Peiffer, J. M. Le, F. J. Steemers, W. Chang, T. Jenniges, F. Garcia, K. Haden, J. Li, C. A. Shaw, J. Belmont, et al. High-resolution genomic profiling of chromosomal aberrations using Infinium whole-genome genotyping Genome Res., September 1, 2006; 16(9): 1136 - 1148. [Abstract] [Full Text] [PDF]
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J. M. Amann, P. Chaurand, A. Gonzalez, J. A. Mobley, P. P. Massion, D. P. Carbone, and R. M. Caprioli Selective Profiling of Proteins in Lung Cancer Cells from Fine-Needle Aspirates by Matrix-Assisted Laser Desorption Ionization Time-of-Flight Mass Spectrometry Clin. Cancer Res., September 1, 2006; 12(17): 5142 - 5150. [Abstract] [Full Text] [PDF]
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R. K. Thomas, B. Weir, and M. Meyerson Genomic approaches to lung cancer. Clin. Cancer Res., July 15, 2006; 12(14): 4384s - 4391s. [Abstract] [Full Text] [PDF]
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A. Dutt and K.-K. Wong Mouse models of lung cancer. Clin. Cancer Res., July 15, 2006; 12(14): 4396s - 4402s. [Abstract] [Full Text] [PDF]
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J. T. Park, M. Li, K. Nakayama, T.-L. Mao, B. Davidson, Z. Zhang, R. J. Kurman, C. G. Eberhart, I.-M. Shih, and T.-L. Wang Notch3 gene amplification in ovarian cancer. Cancer Res., June 15, 2006; 66(12): 6312 - 6318. [Abstract] [Full Text] [PDF]
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E. Yuan, F. Haghighi, S. White, R. Costa, J. McMinn, K. Chun, M. Minden, and B. Tycko A single nucleotide polymorphism chip-based method for combined genetic and epigenetic profiling: validation in decitabine therapy and tumor/normal comparisons. Cancer Res., April 1, 2006; 66(7): 3443 - 3451. [Abstract] [Full Text] [PDF]
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C. A. Baron, C. G. Tepper, S. Y. Liu, R. R. Davis, N. J. Wang, N. C. Schanen, and J. P. Gregg Genomic and functional profiling of duplicated chromosome 15 cell lines reveal regulatory alterations in UBE3A-associated ubiquitin-proteasome pathway processes Hum. Mol. Genet., March 15, 2006; 15(6): 853 - 869. [Abstract] [Full Text] [PDF]
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B. Ylstra, P. van den IJssel, B. Carvalho, R. H. Brakenhoff, and G. A. Meijer BAC to the future! or oligonucleotides: a perspective for micro array comparative genomic hybridization (array CGH) Nucleic Acids Res., January 26, 2006; 34(2): 445 - 450. [Abstract] [Full Text] [PDF]
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 J. R. Downing and C. G. Mullighan Tumor-Specific Genetic Lesions and Their Influence on Therapy in Pediatric Acute Lymphoblastic Leukemia Hematology, January 1, 2006; 2006(1): 118 - 122. [Abstract] [Full Text] [PDF]
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Y. Wang, M. Moorhead, G. Karlin-Neumann, M. Falkowski, C. Chen, F. Siddiqui, R. W. Davis, T. D. Willis, and M. Faham Allele quantification using molecular inversion probes (MIP) Nucleic Acids Res., November 28, 2005; 33(21): e183 - e183. [Abstract] [Full Text] [PDF]
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E. H. Lips, J. W. F. Dierssen, R. van Eijk, J. Oosting, P. H.C. Eilers, R. A.E.M. Tollenaar, E. J. de Graaf, R. van't Slot, C. Wijmenga, H. Morreau, et al. Reliable High-Throughput Genotyping and Loss-of-Heterozygosity Detection in Formalin-Fixed, Paraffin-Embedded Tumors Using Single Nucleotide Polymorphism Arrays Cancer Res., November 15, 2005; 65(22): 10188 - 10191. [Abstract] [Full Text] [PDF]
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H. Willenbrock and J. Fridlyand A comparison study: applying segmentation to array CGH data for downstream analyses Bioinformatics, November 15, 2005; 21(22): 4084 - 4091. [Abstract] [Full Text] [PDF]
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P. L.M. Dahia, K. Hao, J. Rogus, C. Colin, M. A.G. Pujana, K. Ross, D. Magoffin, N. Aronin, A. Cascon, C. Y. Hayashida, et al. Novel Pheochromocytoma Susceptibility Loci Identified by Integrative Genomics Cancer Res., November 1, 2005; 65(21): 9651 - 9658. [Abstract] [Full Text] [PDF]
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K. Zieger, L. Dyrskjot, C. Wiuf, J. L. Jensen, C. L. Andersen, K. M.-E. Jensen, and T. F. Orntoft Role of Activating Fibroblast Growth Factor Receptor 3 Mutations in the Development of Bladder Tumors Clin. Cancer Res., November 1, 2005; 11(21): 7709 - 7719. [Abstract] [Full Text] [PDF]
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T. Huang, B. Wu, P. Lizardi, and H. Zhao Detection of DNA copy number alterations using penalized least squares regression Bioinformatics, October 15, 2005; 21(20): 3811 - 3817. [Abstract] [Full Text] [PDF]
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L. J. Wirth, R. I. Haddad, N. I. Lindeman, X. Zhao, J. C. Lee, V. A. Joshi, C. M. Norris Jr, and M. R. Posner Phase I Study of Gefitinib Plus Celecoxib in Recurrent or Metastatic Squamous Cell Carcinoma of the Head and Neck J. Clin. Oncol., October 1, 2005; 23(28): 6976 - 6981. [Abstract] [Full Text] [PDF]
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Y. Nannya, M. Sanada, K. Nakazaki, N. Hosoya, L. Wang, A. Hangaishi, M. Kurokawa, S. Chiba, D. K. Bailey, G. C. Kennedy, et al. A Robust Algorithm for Copy Number Detection Using High-Density Oligonucleotide Single Nucleotide Polymorphism Genotyping Arrays Cancer Res., July 15, 2005; 65(14): 6071 - 6079. [Abstract] [Full Text] [PDF]
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X. Zhao, B. A. Weir, T. LaFramboise, M. Lin, R. Beroukhim, L. Garraway, J. Beheshti, J. C. Lee, K. Naoki, W. G. Richards, et al. Homozygous Deletions and Chromosome Amplifications in Human Lung Carcinomas Revealed by Single Nucleotide Polymorphism Array Analysis Cancer Res., July 1, 2005; 65(13): 5561 - 5570. [Abstract] [Full Text] [PDF]
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N. Hu, C. Wang, Y. Hu, H. H. Yang, C. Giffen, Z.-Z. Tang, X.-Y. Han, A. M. Goldstein, M. R. Emmert-Buck, K. H. Buetow, et al. Genome-Wide Association Study in Esophageal Cancer Using GeneChip Mapping 10K Array Cancer Res., April 1, 2005; 65(7): 2542 - 2546. [Abstract] [Full Text] [PDF]
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M. Raghavan, D. M. Lillington, S. Skoulakis, S. Debernardi, T. Chaplin, N. J. Foot, T. A. Lister, and B. D. Young Genome-Wide Single Nucleotide Polymorphism Analysis Reveals Frequent Partial Uniparental Disomy Due to Somatic Recombination in Acute Myeloid Leukemias Cancer Res., January 15, 2005; 65(2): 375 - 378. [Abstract] [Full Text] [PDF]
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J. Cardoso, L. Molenaar, R. X. de Menezes, C. Rosenberg, H. Morreau, G. Moslein, R. Fodde, and J. M. Boer Genomic profiling by DNA amplification of laser capture microdissected tissues and array CGH Nucleic Acids Res., October 28, 2004; 32(19): e146 - e146. [Abstract] [Full Text] [PDF]
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T. Tengs, T. LaFramboise, R. B. Den, D. N. Hayes, J. Zhang, S. DebRoy, R. C. Gentleman, K. O'Neill, B. Birren, and M. Meyerson Genomic representations using concatenates of Type IIB restriction endonuclease digestion fragments Nucleic Acids Res., August 25, 2004; 32(15): e121 - e121. [Abstract] [Full Text] [PDF]
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C. Brennan, Y. Zhang, C. Leo, B. Feng, C. Cauwels, A. J. Aguirre, M. Kim, A. Protopopov, and L. Chin High-Resolution Global Profiling of Genomic Alterations with Long Oligonucleotide Microarray Cancer Res., July 15, 2004; 64(14): 4744 - 4748. [Abstract] [Full Text] [PDF]
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J. G. Paez, M. Lin, R. Beroukhim, J. C. Lee, X. Zhao, D. J. Richter, S. Gabriel, P. Herman, H. Sasaki, D. Altshuler, et al. Genome coverage and sequence fidelity of {phi}29 polymerase-based multiple strand displacement whole genome amplification Nucleic Acids Res., May 18, 2004; 32(9): e71 - e71. [Abstract] [Full Text] [PDF]
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