| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
Molecular Biology, Pathobiology, and Genetics |
1 Departments of Hematology/Oncology, 2 Cell Therapy/Transplantation Medicine, and 3 Regeneration Medicine for Hematopoiesis, Graduate School of Medicine, University of Tokyo, Tokyo, Japan; 4 Affymetrix, Inc., Santa Clara, California; and 5 Core Research for Evolutional Science and Technology of Japan Science and Technology Corporation, Saitama, Japan
Requests for reprints: Seishi Ogawa, Department of Regeneration Medicine for Hematopoiesis, Graduate School of Medicine, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan. Phone: 813-3815-5411, ext. 35609; Fax: 813-5804-6261; E-mail: sogawa-tky{at}umin.ac.jp.
 |
Abstract |
|---|
 Top Abstract IntroductionMaterials and MethodsResultsDiscussionReferences |
|---|
|
Introduction |
|---|
|
TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
|---|
The initial approach to genome-wide detection of copy number changes was comparative genomic hybridization (CGH; ref. 6). This approach first enabled the exploration of genetic alterations in cancers across the human genome at 
20 Mb resolution, and was later improved to less than 1 Mb resolution by replacing target metaphase spreads with a large number of discrete genomic or cDNA clones in arrays (array-based CGH; refs. 1–3). Further increase in resolution was obtained by arrays consisting of 32,433 BAC clones spanning the entire human genome, resulting in comprehensive analyses of cancer genomes at less than 100 kb resolution (7).
Another recently described approach to genome-wide copy number detection is the use of synthetic high-density oligonucleotide microarrays (8–15). These commercially available microarrays, designed to genotype 10,000, 50,000, or 100,000 single-nucleotide polymorphisms (SNP) in human genomic DNA, provide attractive alternatives to BAC-array CGH (16–19). The Affymetrix GeneChip Mapping 100K high-density oligonucleotide arrays contain 116,204 SNPs with a mean spacing of 23.6 kb, and a potential to provide the highest resolution of copy number detection currently available (19). These arrays provide clear technical advantages including robust single-primer assay methodology, accurate and reproducible genotyping, and copy-neutral loss of heterozygosity (LOH) detection compared with array-CGH, karyotyping, and other oligonucleotide CGH arrays. Furthermore, these arrays are manufactured under stringent quality control procedures. However, there are a limited number of algorithms/software available and they suffer from certain limitations which may result in false-positive and false-negative estimations. For example, the software dChipSNP6 requires the use of a paired-normal sample, which is often unavailable, to perform the analysis (10, 20). Furthermore, dChipSNP can currently only process the Mapping 10K array and not the 50K or 100K arrays. Another alternative software/algorithm for detecting copy number changes from the Mapping 100K arrays is Chromosome Copy Number Analysis Tool (CNAT).7 CNAT uses a set of 110 normal reference individuals and thus overcomes the paired-normal requirement but does not account for experimental variation in the samples being compared (9).
Here we present an improved algorithm for copy number detection using Affymetrix GeneChip Mapping 100K arrays. This algorithm improves signal-to-noise (S/N) ratios by reducing variation in the raw signal ratios and optimizes the selection of the reference. In addition, availability of accurate genotyping information further enables LOH inference and allele-based copy number analysis. This combination of >100,000 SNP markers and a robust copy number algorithm results in a powerful system for high-resolution analysis of copy number alterations, which is unattainable by other current methods.
|
Materials and Methods |
|---|
|
TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
|---|
Affymetrix platform. Array experiments were done according to the standard protocols for Affymetrix GeneChip Mapping 100K arrays (Affymetrix, Inc., Santa Clara, CA). Briefly, total genomic DNA was digested with a restriction enzyme (XbaI or HindIII), ligated to an appropriate adapter for each enzyme, and subjected to PCR amplification using a single primer. After digestion with DNase I, the PCR products were labeled with a biotinylated nucleotide analogue using terminal deoxynucleotidyl transferase and hybridized to the microarray. Hybridized probes were captured by streptavidin-phycoerythrin conjugates and the array was scanned and genotypes called as described (18).
Compensation of raw signal ratios and construction of the best-fit reference. Sum of signals from the 10 perfect match probes for the A allele (PA) and those for the B allele (PB) is normalized for each SNP. The mean signal intensity of all autosomal SNPs becomes the same for the two arrays being compared. Relative copy number at the ith SNP locus between the two samples is estimated from the log 2 ratio of the normalized signals of the ith SNP in sample 1 (Sisample1) and sample 2 (Sisample2), 
i1,2 = log2(Sisample1 / Sisample2). For the purpose of compensation for different PCR conditions, it is convenient to write the observed i1,2 as the sum of two components (see Supplementary Methods),
 |
i1,2 represents the corrected copy number and p(x) represents PCR amplification kinetics. We empirically showed that p(x) can be written as
 |
i1,2, which shows a lower SD than original i1,2. For a given sample 
, the averaged best-fit m references, Si,mREF, is calculated as 1 / m(
j2cj, x Si), where j (1, 2,..., m) represents the m reference samples in which the SD of ci,j takes the lowest values. The log 2 ratios of Si for this reference, ci, REF, are evaluated for their variance and give a better inference of copy number at the ith locus. Following the first round normalization, compensations, and optimization of references thus far described, the second-round adjustment is done for the analysis of tumor samples having complex chromosomal abnormalities for accurate calculation of the regression curves and correct assignment of the ploidy, because the mean log 2 ratio does not conform to that for the diploid SNPs and determination of the regression curves using all autosomal SNPs is confounded by a large number of nondiploid SNPs. In the latter process, we set chromosome numbers in accordance with the ploidy information referred by previous literature or determined by other methods, including fluorescence in situ hybridization, then iteratively performed the second-round normalization, compensations, and selection of optimal references exclusively using only such SNPs that belong to the diploid region for determination of the coefficients required for this step.
Allele-based analysis using a paired normal sample. When a paired normal sample is used for the allele-based copy number estimate, analysis is confined to those SNPs of which genotype in the reference is AB (heterozygous) and signal sums of PAs (ASi) and PBs (BSi) for the ith SNP are taken separately both in the tumor and the reference. The log 2 ratios of normalized values, ASi and BSi, are similarly compensated for the different experimental conditions to calculate the corrected log 2 ratios, citumor, REF (A) for PAs and citumor, REF(B) for PBs, where the coefficients used for the normalization and compensation procedure are determined using all the SNPs in a region specified as diploid as is the case with analysis using references not derived from the same individual. For the purpose of allele-based copy number analysis, the corrected log 2 ratios for the heterozygous SNPs are separated in two groups,
 |
 |
|
, is calculated, where k is the number of terms to be averaged and arbitrarily set to 3 to 10, according to the SD values after compensations of log 2 ratios and optimization of nonreferences for diploid SNPs. In the alternative analysis using a hidden Markov model (21), the inference of copy number is more efficient and automated, in which a real state of the copy number sequence (a hidden state) along a chromosome is inferred from the observed sequence citest, REF as the state of maximum likelihood that is calculated from the state transition load and the probability of the hidden state to "emit" the observed sequence of log 2 ratios, using the Viterbi algorithm. We assumed that copy number change (state transition) is the result of a genetic recombination event between the two adjacent SNP loci, and Kosambi's map function (1/2)tanh(2
) is used to transform the genomic distance, or recombination fraction between the two SNPs () to state "transition probability," where is expressed in cM units; for simplicity, 1 cM should be 1 Mbp. The observed log 2 ratio is assumed to follow the normal distribution according to real copy number states, which gives the "emission probability." The variables of normal distribution were empirically determined from the experimental data (Supplementary Methods). Comparison and confirmation of the results. CNAT was executed following the instructions of the suppliers. For proper comparison with our system, log 2–converted values of the Genome Smoothed Analysis Copy Number (GSA_CN) were compared. FISH analyses were done as previously described (13). Probe information used in FISH analysis is supplied in Supplementary Table 1. PCR primers were designed to amplify several adjacent fragments that are within and outside of the homozygously deleted regions in tumor samples. Primers and PCR conditions are provided in Supplementary Table 2.
Copy Number Analyzer for Affymetrix GeneChip mapping 100K arrays. Copy Number Analyzer for Affymetrix GeneChip Mapping 100K arrays (CNAG) ver 1.0 is the implementation of the set of algorithms described above that is written in C++ for Microsoft Windows. All the examples of copy number analysis with Affymetrix GeneChip Mapping 100K arrays presented in this article were done using CNAG. All data used in this article and CNAG can be downloaded.8
|
Results |
|---|
|
TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
|---|
|
Optimization of reference selection. Although correcting for the fragment length and GC content dramatically reduces SD values, there was still considerable variation among different samples. Therefore, we explored the effects of using different reference sets. The type of reference set used largely depends on the following: (a) cases where tumor and normal DNA are available from the same patient (paired normal samples) and (b) cases where only the test or tumor sample is available. Use of a paired normal sample coupled with compensation for variability across experimental conditions generally gives lower SD values. However, the benefit of having a paired normal sample for a reference tends to be diminished when test and reference samples are processed separately (data not shown). The case when a paired normal sample for a reference is unavailable is more complicated. Although the array that shows the lowest SD value for the test sample is a candidate for the reference, we investigated choosing the average of multiple samples with the lowest SD values or best-fit samples as a reference.
To determine the effect of averaging multiple references on SD values, data from 96 normal samples were compared with the averages of varying numbers of the best-fit references, and SD values were calculated for each comparison (Fig. 1D). As the number of samples increases to five, the SD value gradually decreases to <0.15 for most normal samples and then reaches a plateau, suggesting that taking an average of at least five best-fit samples is sufficient to optimize the comparison (Fig. 2A).
|
Validation of new algorithm using normal and tumor samples. We evaluated the performance of this new algorithm by analyzing a variety of samples. Figure 2B shows a representative analysis of a glioma specimen, showing the dramatic reduction of baseline fluctuations or SD values when using this algorithm relative to raw log 2 ratios. The raw data (Fig. 2B-1) has an SD of 0.365, which is reduced to 0.222 after applying a single best-fit reference (Fig. 2B-2). A further improvement was seen when using multiple best-fit references, SD = 0.118 (Fig. 2B-3). Regions of homozygous and hemizygous deletion are indicated by small and large arrows, respectively. As tumor samples frequently exhibit complex chromosome abnormalities with extensive genetic imbalances (22), analysis of tumor specimens requires additional considerations in selection of references. SD values may be disproportionately inflated or overwhelmed by the effect of genetic abnormalities when all autosomal signals are included in the calculation of SD values. To circumvent this problem and to calculate proper SD values for reference selection, we adopted a two-step approach. We first made a tentative estimation of the copy number changes by using all autosomal signals and predicted the regions that are diploid by reference to other independent information (FISH, PCR, or results in the literature). In the second step, one of these diploid regions was used for normalization and for calculation of SD values to identify the best-fit references. This final step further improved the SD value, 0.114 (Fig. 2B-4).
Reductions of SD values in different tumor samples are summarized in Fig. 2C. The SD values from 33 tumor samples were calculated from raw data, as well as using the new algorithm with five best-fit references. The distribution of SD values is clearly decreased using the new algorithm. Figure 2C also illustrates the results using limited regional SNPs, local averaging of five or ten consecutive SNPs, and using a paired normal reference. After all corrections and optimizations of best-fit reference samples, a local mean procedure was applied which results in lower SD values relative to the paired normal reference sample, where no local mean was used. All of these analyses resulted in a dramatic reduction in the SD values relative to the raw data, which enables genome-wide copy number detection with a high degree of accuracy (Fig. 3).
|
2.0 (for trisomy) or more (for monosomy) in most cases.
Estimation of the resolution is directly proportional to the distribution of SNP markers. We determined the size of genomic alterations in CRL-5929 and CRL-5872 cell lines (Table 1). Deletions of less than 500 kb at 14q11.2 (T-cell receptor 
) and less than 393 kb at 7q34 (T-cell receptor ß) were observed in the immature T-cell line HPB-ALL; these were caused by a T-cell receptor rearrangement (23). The genomic regions comprising these deletions contain approximately 20 and 10 SNPs, respectively, from the Affymetrix GeneChip Mapping 100K arrays. As the number of SNP markers increases, the accuracy of size determinations increases and even smaller deletions are likely to be detected.
|
|
|
Discussion |
|---|
|
TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
|---|
Use of a paired normal reference further reduces SD values to 0.16 ± 0.03 (P < 0.05 for best-fit references), as comparison of signals is more accurately done between identical SNP loci. Use of a paired normal reference also enables allele-based analysis, which can unveil regions of copy neutral LOH in cancer cells. However, allele-based analysis only uses heterozygous SNPs, thus reducing the overall resolution by 20% compared with the non-allele-based analysis. Recently, large regions with successive homozygous SNPs are reportedly commonly seen in leukemia samples (25).
Figure 2C illustrates that when a paired normal reference is unavailable, a best-fit reference can be used, resulting in SD reduction to 0.18 ± 0.03, which is only slightly higher than those obtained using a paired normal reference. In addition, the existence of copy neutral LOH can be also predicted from an unusually long tract of homozygous SNP calls in tumor samples (Fig. 5, bottom). Thus, when available, a paired normal reference is the ideal reference, but the average of the best-fit references is an excellent alternative that works satisfactorily in most cases, and at a lower cost.
The advantage of the copy number analysis using GeneChip Mapping 100K arrays over conventional BAC-array CGH lies in its extremely high resolution and availability of genotype information, enabling high-density LOH analysis. Although the number of BAC clones on a single array can vary from 3,000 to 32,000, there exists a clear limitation to their resolution because small deletions or gains relative to the size of the average BAC clones can easily escape detection in CGH analysis (Fig. 4B). Additional limitations include (a) requirement of large amounts of BAC genomic DNA for generating spotted arrays, a process which is difficult to quality control, and (b) lack of additional genotyping information indispensable for LOH detections. All of these challenges are overcome by Affymetrix GeneChip Mapping 100K arrays. These arrays are manufactured under stringent quality control procedures; the assay requires only 250 ng of starting genomic DNA per array and provides genotyping information at >99.5% accuracy. Together, these features comprise a comprehensive approach for copy number assessment at a resolution currently not achievable by other means.
This system also has limitations. For instance, polymorphism of primer locus or restriction site would affect the signal intensity that leads to misjudge of copy numbers. Although we can exclude these artifacts when these changes strides over different restriction fragments, validations using other methods are required to validate changes that are confined in a single fragment. It is sometimes difficult to distinguish deletions from large-scale copy number polymorphisms when best-fit references are used. In analyzing disease samples mixed with normal tissues, the amplitude of copy number changes diminishes according to the rate of normal fraction contamination. Whereas extensive SNP selection of the Mapping 100K arrays using >300 individuals likely resulted in the exclusion of SNPs on fragments with restriction site polymorphisms (via Hardy-Weinberg disequilibrium), we cannot exclude the possibility that rare mutations in restriction enzyme sites might affect amplification of affected fragments. Even if this did occur, it is highly unlikely to result in an erroneous copy number estimation because of the low likelihood that the mutation would affect more than one or two adjacent genomic fragments.
In conclusion, our improved copy number detection algorithm, combined with Affymetrix GeneChip Mapping 100K arrays, provides a powerful tool for high-resolution analysis of copy number alterations or variations across the human genome.
|
Acknowledgments |
|---|
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.
|
Footnotes |
|---|
6 http://www.biostat.harvard.edu/complab/dchip/snp/ 
7 http://www.affymetrix.com/support/developer/tools/affytools.affx
9 http://www.path.cam.ac.uk/~pawefish/LungCellLineDescriptions/NCI-H2171.html.
Received 2/14/05. Revised 4/11/05. Accepted 4/29/05.
|
References |
|---|
|
TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
|---|
This article has been cited by other articles:
|
|
 |
|
  J. Yu, H. Deshmukh, R. J. Gutmann, R. J. Emnett, F. J. Rodriguez, M. A. Watson, R. Nagarajan, and D. H. Gutmann Alterations of BRAF and HIPK2 loci predominate in sporadic pilocytic astrocytoma Neurology, November 10, 2009; 73(19): 1526 - 1531. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 T. Isoda, A. M. Ford, D. Tomizawa, F. W. van Delft, D. G. De Castro, N. Mitsuiki, J. Score, T. Taki, T. Morio, M. Takagi, et al. From the Cover: Immunologically silent cancer clone transmission from mother to offspring PNAS, October 20, 2009; 106(42): 17882 - 17885. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 L. Wan, K. Sun, Q. Ding, Y. Cui, M. Li, Y. Wen, R. C. Elston, M. Qian, and W. J Fu Hybridization modeling of oligonucleotide SNP arrays for accurate DNA copy number estimation Nucleic Acids Res., September 1, 2009; 37(17): e117 - e117. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 T. Akagi, L.-Y. Shih, S. Ogawa, J. Gerss, S. R. Moore, R. Schreck, N. Kawamata, D.-C. Liang, M. Sanada, Y. Nannya, et al. Single nucleotide polymorphism genomic arrays analysis of t(8;21) acute myeloid leukemia cells Haematologica, September 1, 2009; 94(9): 1301 - 1306. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
S. Bicciato, R. Spinelli, M. Zampieri, E. Mangano, F. Ferrari, L. Beltrame, I. Cifola, C. Peano, A. Solari, and C. Battaglia A computational procedure to identify significant overlap of differentially expressed and genomic imbalanced regions in cancer datasets Nucleic Acids Res., August 1, 2009; 37(15): 5057 - 5070. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
T. LaFramboise Single nucleotide polymorphism arrays: a decade of biological, computational and technological advances Nucleic Acids Res., July 1, 2009; (2009) gkp552v1. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 R. Pique-Regi, A. Ortega, and S. Asgharzadeh Joint estimation of copy number variation and reference intensities on multiple DNA arrays using GADA Bioinformatics, May 15, 2009; 25(10): 1223 - 1230. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 K. W. Littink, M. M. van Genderen, R. W. J. Collin, S. Roosing, A. P. M. de Brouwer, F. C. C. Riemslag, H. Venselaar, A. A. H. J. Thiadens, C. B. Hoyng, K. Rohrschneider, et al. A Novel Homozygous Nonsense Mutation in CABP4 Causes Congenital Cone-Rod Synaptic Disorder Invest. Ophthalmol. Vis. Sci., May 1, 2009; 50(5): 2344 - 2350. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 D. Yin, S. Ogawa, N. Kawamata, P. Tunici, G. Finocchiaro, M. Eoli, C. Ruckert, T. Huynh, G. Liu, M. Kato, et al. High-Resolution Genomic Copy Number Profiling of Glioblastoma Multiforme by Single Nucleotide Polymorphism DNA Microarray Mol. Cancer Res., May 1, 2009; 7(5): 665 - 677. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
D. Nowak, E. Le Toriellec, M.-H. Stern, N. Kawamata, T. Akagi, M. J. Dyer, W.-K. Hofmann, S. Ogawa, and H. P. Koeffler Molecular allelokaryotyping of T-cell prolymphocytic leukemia cells with high density single nucleotide polymorphism arrays identifies novel common genomic lesions and acquired uniparental disomy Haematologica, April 1, 2009; 94(4): 518 - 527. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 T. Akagi, L.-Y. Shih, M. Kato, N. Kawamata, G. Yamamoto, M. Sanada, R. Okamoto, C. W. Miller, D.-C. Liang, S. Ogawa, et al. Hidden abnormalities and novel classification of t(15;17) acute promyelocytic leukemia (APL) based on genomic alterations Blood, February 19, 2009; 113(8): 1741 - 1748. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
Y. Jiang, A. Dunbar, L. P. Gondek, S. Mohan, M. Rataul, C. O'Keefe, M. Sekeres, Y. Saunthararajah, and J. P. Maciejewski Aberrant DNA methylation is a dominant mechanism in MDS progression to AML Blood, February 5, 2009; 113(6): 1315 - 1325. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 E. F. Attiyeh, S. J. Diskin, M. A. Attiyeh, Y. P. Mosse, C. Hou, E. M. Jackson, C. Kim, J. Glessner, H. Hakonarson, J. A. Biegel, et al. Genomic copy number determination in cancer cells from single nucleotide polymorphism microarrays based on quantitative genotyping corrected for aneuploidy Genome Res., February 1, 2009; 19(2): 276 - 283. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
T. Akagi, S. Ogawa, M. Dugas, N. Kawamata, G. Yamamoto, Y. Nannya, M. Sanada, C. W. Miller, A. Yung, S. Schnittger, et al. Frequent genomic abnormalities in acute myeloid leukemia/myelodysplastic syndrome with normal karyotype Haematologica, February 1, 2009; 94(2): 213 - 223. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 D L Bruno, D Ganesamoorthy, J Schoumans, A Bankier, D Coman, M Delatycki, R J M Gardner, M Hunter, P A James, P Kannu, et al. Detection of cryptic pathogenic copy number variations and constitutional loss of heterozygosity using high resolution SNP microarray analysis in 117 patients referred for cytogenetic analysis and impact on clinical practice J. Med. Genet., February 1, 2009; 46(2): 123 - 131. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
S. Sulong, A. V. Moorman, J. A. E. Irving, J. C. Strefford, Z. J. Konn, M. C. Case, L. Minto, K. E. Barber, H. Parker, S. L. Wright, et al. A comprehensive analysis of the CDKN2A gene in childhood acute lymphoblastic leukemia reveals genomic deletion, copy number neutral loss of heterozygosity, and association with specific cytogenetic subgroups Blood, January 1, 2009; 113(1): 100 - 107. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 A. S. Bassett, C. R. Marshall, A. C. Lionel, E. W.C. Chow, and S. W. Scherer Copy number variations and risk for schizophrenia in 22q11.2 deletion syndrome Hum. Mol. Genet., December 15, 2008; 17(24): 4045 - 4053. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 W. E. Corver, A. Middeldorp, N. T. ter Haar, E. S. Jordanova, M. van Puijenbroek, R. van Eijk, C. J. Cornelisse, G. J. Fleuren, H. Morreau, J. Oosting, et al. Genome-wide Allelic State Analysis on Flow-Sorted Tumor Fractions Provides an Accurate Measure of Chromosomal Aberrations Cancer Res., December 15, 2008; 68(24): 10333 - 10340. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
A. J. Dunbar, L. P. Gondek, C. L. O'Keefe, H. Makishima, M. S. Rataul, H. Szpurka, M. A. Sekeres, X. F. Wang, M. A. McDevitt, and J. P. Maciejewski 250K Single Nucleotide Polymorphism Array Karyotyping Identifies Acquired Uniparental Disomy and Homozygous Mutations, Including Novel Missense Substitutions of c-Cbl, in Myeloid Malignancies Cancer Res., December 15, 2008; 68(24): 10349 - 10357. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 M. J.H. Coenen, M. Ploeg, M. M.V.A.P. Schijvenaars, E. B. Cornel, H. F.M. Karthaus, H. Scheffer, J. A. Witjes, B. Franke, and L. A.L.M. Kiemeney Allelic Imbalance Analysis Using a Single-Nucleotide Polymorphism Microarray for the Detection of Bladder Cancer Recurrence Clin. Cancer Res., December 15, 2008; 14(24): 8198 - 8204. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
K. J. Duffy, J. Littrell, A. Locke, S. L. Sherman, and M. Olivier A novel procedure for genotyping of single nucleotide polymorphisms in trisomy with genomic DNA and the invader assay Nucleic Acids Res., December 1, 2008; 36(22): e145 - e145. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
C. S. Walsh, S. Ogawa, D. R. Scoles, C. W. Miller, N. Kawamata, S. A. Narod, H. P. Koeffler, and B. Y. Karlan Genome-Wide Loss of Heterozygosity and Uniparental Disomy in BRCA1/2-Associated Ovarian Carcinomas Clin. Cancer Res., December 1, 2008; 14(23): 7645 - 7651. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
S. J. Diskin, M. Li, C. Hou, S. Yang, J. Glessner, H. Hakonarson, M. Bucan, J. M. Maris, and K. Wang Adjustment of genomic waves in signal intensities from whole-genome SNP genotyping platforms Nucleic Acids Res., November 1, 2008; 36(19): e126 - e126. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
D A Koolen, A J Sharp, J A Hurst, H V Firth, S J L Knight, A Goldenberg, P Saugier-Veber, R Pfundt, L E L M Vissers, A Destree, et al. Clinical and molecular delineation of the 17q21.31 microdeletion syndrome J. Med. Genet., November 1, 2008; 45(11): 710 - 720. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 M. Nakashima, M. Tsuda, A. Kinoshita, T. Kishino, S. Kondo, O. Shimokawa, N. Niikawa, and K.-i. Yoshiura Precision of High-Throughput Single-Nucleotide Polymorphism Genotyping with Fingernail DNA: Comparison with Blood DNA Clin. Chem., October 1, 2008; 54(10): 1746 - 1748. [Full Text] [PDF]
|
|
|
|
|
|
P. E. Leone, B. A. Walker, M. W. Jenner, L. Chiecchio, G. Dagrada, R. K.M. Protheroe, D. C. Johnson, N. J. Dickens, J. L. Brito, M. Else, et al. Deletions of CDKN2C in Multiple Myeloma: Biological and Clinical Implications Clin. Cancer Res., October 1, 2008; 14(19): 6033 - 6041. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
L. Kujawski, P. Ouillette, H. Erba, C. Saddler, A. Jakubowiak, M. Kaminski, K. Shedden, and S. N. Malek Genomic complexity identifies patients with aggressive chronic lymphocytic leukemia Blood, September 1, 2008; 112(5): 1993 - 2003. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
N. Kawamata, S. Ogawa, M. Zimmermann, B. Niebuhr, C. Stocking, M. Sanada, K. Hemminki, G. Yamatomo, Y. Nannya, R. Koehler, et al. Cloning of genes involved in chromosomal translocations by high-resolution single nucleotide polymorphism genomic microarray PNAS, August 19, 2008; 105(33): 11921 - 11926. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. P. Maciejewski and G. J. Mufti Whole genome scanning as a cytogenetic tool in hematologic malignancies Blood, August 15, 2008; 112(4): 965 - 974. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
A. Shlien, U. Tabori, C. R. Marshall, M. Pienkowska, L. Feuk, A. Novokmet, S. Nanda, H. Druker, S. W. Scherer, and D. Malkin Excessive genomic DNA copy number variation in the Li-Fraumeni cancer predisposition syndrome PNAS, August 12, 2008; 105(32): 11264 - 11269. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
T. Hidaka, S. Nakahata, K. Hatakeyama, M. Hamasaki, K. Yamashita, T. Kohno, Y. Arai, T. Taki, K. Nishida, A. Okayama, et al. Down-regulation of TCF8 is involved in the leukemogenesis of adult T-cell leukemia/lymphoma Blood, July 15, 2008; 112(2): 383 - 393. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
L. Wang, C. Fidler, N. Nadig, A. Giagounidis, M. G. Della Porta, L. Malcovati, S. Killick, N. Gattermann, C. Aul, J. Boultwood, et al. Genome-wide analysis of copy number changes and loss of heterozygosity in myelodysplastic syndrome with del(5q) using high-density single nucleotide polymorphism arrays Haematologica, July 1, 2008; 93(7): 994 - 1000. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 C. S. Walsh, S. Ogawa, H. Karahashi, D. R. Scoles, J. C. Pavelka, H. Tran, C. W. Miller, N. Kawamata, C. Ginther, J. Dering, et al. ERCC5 Is a Novel Biomarker of Ovarian Cancer Prognosis J. Clin. Oncol., June 20, 2008; 26(18): 2952 - 2958. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
M. Tada, F. Kanai, Y. Tanaka, K. Tateishi, M. Ohta, Y. Asaoka, M. Seto, R. Muroyama, K. Fukai, F. Imazeki, et al. Down-Regulation of Hedgehog-Interacting Protein through Genetic and Epigenetic Alterations in Human Hepatocellular Carcinoma Clin. Cancer Res., June 15, 2008; 14(12): 3768 - 3776. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
I. Nielander, J. I. Martin-Subero, F. Wagner, M. Baudis, S. Gesk, L. Harder, D. Hasenclever, W. Klapper, M. Kreuz, C. Pott, et al. Recurrent loss of the Y chromosome and homozygous deletions within the pseudoautosomal region 1: association with male predominance in mantle cell lymphoma Haematologica, June 1, 2008; 93(6): 949 - 950. [Full Text] [PDF]
|
|
|
|
|
|
R. F. Thompson, M. Reimers, B. Khulan, M. Gissot, T. A. Richmond, Q. Chen, X. Zheng, K. Kim, and J. M. Greally An analytical pipeline for genomic representations used for cytosine methylation studies Bioinformatics, May 1, 2008; 24(9): 1161 - 1167. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
A. Tyybakinoja, E. Elonen, H. Vauhkonen, J. Saarela, and S. Knuutila Single nucleotide polymorphism microarray analysis of karyotypically normal acute myeloid leukemia reveals frequent copy number neutral loss of heterozygosity Haematologica, April 1, 2008; 93(4): 631 - 632. [Full Text] [PDF]
|
|
|
|
|
|
H. Bengtsson, R. Irizarry, B. Carvalho, and T. P. Speed Estimation and assessment of raw copy numbers at the single locus level Bioinformatics, March 15, 2008; 24(6): 759 - 767. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
G. Rigaill, P. Hupe, A. Almeida, P. La Rosa, J.-P. Meyniel, C. Decraene, and E. Barillot ITALICS: an algorithm for normalization and DNA copy number calling for Affymetrix SNP arrays Bioinformatics, March 15, 2008; 24(6): 768 - 774. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
E. H. Lips, R. van Eijk, E. J.R. de Graaf, P. G. Doornebosch, N. F.C.C. de Miranda, J. Oosting, T. Karsten, P. H.C. Eilers, R. A.E.M. Tollenaar, T. van Wezel, et al. Progression and Tumor Heterogeneity Analysis in Early Rectal Cancer Clin. Cancer Res., February 1, 2008; 14(3): 772 - 781. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
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]
|
|
|
|
|
|
L. P. Gondek, R. Tiu, C. L. O'Keefe, M. A. Sekeres, K. S. Theil, and J. P. Maciejewski Chromosomal lesions and uniparental disomy detected by SNP arrays in MDS, MDS/MPD, and MDS-derived AML Blood, February 1, 2008; 111(3): 1534 - 1542. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
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]
|
|
|
|
|
|
N. Kawamata, S. Ogawa, M. Zimmermann, M. Kato, M. Sanada, K. Hemminki, G. Yamatomo, Y. Nannya, R. Koehler, T. Flohr, et al. Molecular allelokaryotyping of pediatric acute lymphoblastic leukemias by high-resolution single nucleotide polymorphism oligonucleotide genomic microarray Blood, January 15, 2008; 111(2): 776 - 784. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
A. I. den Hollander, I. Lopez, S. Yzer, M. N. Zonneveld, I. M. Janssen, T. M. Strom, J. Y. Hehir-Kwa, J. A. Veltman, M. L. Arends, T. Meitinger, et al. Identification of Novel Mutations in Patients with Leber Congenital Amaurosis and Juvenile RP by Genome-wide Homozygosity Mapping with SNP Microarrays Invest. Ophthalmol. Vis. Sci., December 1, 2007; 48(12): 5690 - 5698. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
M. W. Jenner, P. E. Leone, B. A. Walker, F. M. Ross, D. C. Johnson, D. Gonzalez, L. Chiecchio, E. Dachs Cabanas, G. Paolo Dagrada, M. Nightingale, et al. Gene mapping and expression analysis of 16q loss of heterozygosity identifies WWOX and CYLD as being important in determining clinical outcome in multiple myeloma Blood, November 1, 2007; 110(9): 3291 - 3300. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
A. Mohamedali, J. Gaken, N. A. Twine, W. Ingram, N. Westwood, N. C. Lea, J. Hayden, N. Donaldson, C. Aul, N. Gattermann, et al. Prevalence and prognostic significance of allelic imbalance by single-nucleotide polymorphism analysis in low-risk myelodysplastic syndromes Blood, November 1, 2007; 110(9): 3365 - 3373. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
D. Pinto, C. Marshall, L. Feuk, and S. W. Scherer Copy-number variation in control population cohorts Hum. Mol. Genet., October 15, 2007; 16(R2): R168 - R173. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. Hoyer, A. Dreweke, C. Becker, I. Gohring, C. T Thiel, M. M Peippo, R. Rauch, M. Hofbeck, U. Trautmann, C. Zweier, et al. Molecular karyotyping in patients with mental retardation using 100K single-nucleotide polymorphism arrays J. Med. Genet., October 1, 2007; 44(10): 629 - 636. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
W. Liu, B.-L. Chang, S. Cramer, P. P. Koty, T. Li, J. Sun, A. R. Turner, C. Von Kap-Herr, P. Bobby, J. Rao, et al. Deletion of a Small Consensus Region at 6q15, Including the MAP3K7 Gene, Is Significantly Associated with High-Grade Prostate Cancers Clin. Cancer Res., September 1, 2007; 13(17): 5028 - 5033. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 W. White, S. Hills, R Gaddam, B. Holland, and D. Penny Treeness Triangles: Visualizing the Loss of Phylogenetic Signal Mol. Biol. Evol., September 1, 2007; 24(9): 2029 - 2039. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
K. L. Gorringe, S. Jacobs, E. R. Thompson, A. Sridhar, W. Qiu, D. Y.H. Choong, and I. G. Campbell High-Resolution Single Nucleotide Polymorphism Array Analysis of Epithelial Ovarian Cancer Reveals Numerous Microdeletions and Amplifications Clin. Cancer Res., August 15, 2007; 13(16): 4731 - 4739. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
Y. Xiao, M. R. Segal, Y.H. Yang, and R.-F. Yeh A multi-array multi-SNP genotyping algorithm for Affymetrix SNP microarrays Bioinformatics, June 15, 2007; 23(12): 1459 - 1467. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 Y. Nagano, D. H. Kim, L. Zhang, J. A White, J. C Yao, S. R Hamilton, and A. Rashid Allelic alterations in pancreatic endocrine tumors identified by genome-wide single nucleotide polymorphism analysis Endocr. Relat. Cancer, June 1, 2007; 14(2): 483 - 492. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
B.-l. Chang, W. Liu, J. Sun, L. Dimitrov, T. Li, A. R. Turner, S. L. Zheng, W. B. Isaacs, and J. Xu Integration of Somatic Deletion Analysis of Prostate Cancers and Germline Linkage Analysis of Prostate Cancer Families Reveals Two Small Consensus Regions for Prostate Cancer Genes at 8p Cancer Res., May 1, 2007; 67(9): 4098 - 4103. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 H. Aburatani Copy Number Analysis in Cancer Research Am. Assoc. Cancer Res. Educ. Book, April 14, 2007; 2007(1): 213 - 218. [Full Text] [PDF]
|
|
|
|
 |
|
 T. Laframboise, D. Harrington, and B. A. Weir PLASQ: a generalized linear model-based procedure to determine allelic dosage in cancer cells from SNP array data Biostat., April 1, 2007; 8(2): 323 - 336. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
B. Carvalho, H. Bengtsson, T. P. Speed, and R. A. Irizarry Exploration, normalization, and genotype calls of high-density oligonucleotide SNP array data Biostat., April 1, 2007; 8(2): 485 - 499. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
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]
|
|
|
|
|
|
S. Jacobs, E. R. Thompson, Y. Nannya, G. Yamamoto, R. Pillai, S. Ogawa, D. K. Bailey, and I. G. Campbell Genome-Wide, High-Resolution Detection of Copy Number, Loss of Heterozygosity, and Genotypes from Formalin-Fixed, Paraffin-Embedded Tumor Tissue Using Microarrays Cancer Res., March 15, 2007; 67(6): 2544 - 2551. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 J. Y. Hehir-Kwa, M. Egmont-Petersen, I. M. Janssen, D. Smeets, A. G. van Kessel, and J. A. Veltman Genome-wide Copy Number Profiling on High-density Bacterial Artificial Chromosomes, Single-nucleotide Polymorphisms, and Oligonucleotide Microarrays: A Platform Comparison based on Statistical Power Analysis DNA Res, March 15, 2007; (2007) dsm002v1. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
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]
|
|
|
|
|
|
L. Zhang, C. Wu, R. Carta, and H. Zhao Free energy of DNA duplex formation on short oligonucleotide microarrays Nucleic Acids Res., February 16, 2007; 35(3): e18 - e18. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
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]
|
|
|
|
|
|
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]
|
|
|
|
|
|
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]
|
|
|
|
|
|
B. A. Walker, P. E. Leone, M. W. Jenner, C. Li, D. Gonzalez, D. C. Johnson, F. M. Ross, F. E. Davies, and G. J. Morgan Integration of global SNP-based mapping and expression arrays reveals key regions, mechanisms, and genes important in the pathogenesis of multiple myeloma Blood, September 1, 2006; 108(5): 1733 - 1743. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 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]
|
|
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
| Cancer Research | Clinical Cancer Research |
| Cancer Epidemiology Biomarkers & Prevention | Molecular Cancer Therapeutics |
| Molecular Cancer Research | Cancer Prevention Research |
| Cancer Prevention Journals Portal | Cancer Reviews Online |
| Annual Meeting Education Book | Meeting Abstracts Online |
