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Assessing the Significance of Conserved Genomic Aberrations Using High Resolution Genomic Microarrays

Guttman, Mitchell and Mies, Carolyn and Dudycz-Sulicz, Katarzyna and Diskin, Sharon J. and Baldwin, Don A. and Stoeckert, Christian J. and Grant, Gregory R. (2007) Assessing the Significance of Conserved Genomic Aberrations Using High Resolution Genomic Microarrays. PLoS Genetics, 3 (8). Art. No. e143. ISSN 1553-7390. PMCID PMC1950957. https://resolver.caltech.edu/CaltechAUTHORS:20161121-151605200

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[img] PDF (Figure S1. DNAcopy (CBS) Results for Six DCIS Samples on Chromosome 17) - Supplemental Material
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[img] PDF (Figure S2. Frequency Plot of Significant Aberrations in DCIS Samples) - Supplemental Material
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[img] PDF (Figure S3. MSA Analysis of Chromosome 16 for LCIS) - Supplemental Material
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[img] PDF (Figure S4. Frequency Plot of Significant Aberrations in LCIS Samples) - Supplemental Material
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[img] PDF (Figure S5. Frequency Plot of Significant Aberrations in Neuroblastoma Samples) - Supplemental Material
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[img] PDF (Figure S6. Results of the MSA Algorithm Run on T-ALL SNP Data [30]) - Supplemental Material
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[img] PDF (Figure S7. Results of Single Sample Method Followed by MSA Analysis Applied to T-ALL Data Profiled on a 250K SNP Array) - Supplemental Material
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[img] MS Excel (Table S1. MSA Significant Regions Detected in Neuroblastoma Dataset Published by Mosse et al. [31]) - Supplemental Material
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[img] MS Excel (Table S2. MSA Significant Regions Not Present in a STAC Analysis of the Neuroblastoma Data) - Supplemental Material
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Abstract

Genomic aberrations recurrent in a particular cancer type can be important prognostic markers for tumor progression. Typically in early tumorigenesis, cells incur a breakdown of the DNA replication machinery that results in an accumulation of genomic aberrations in the form of duplications, deletions, translocations, and other genomic alterations. Microarray methods allow for finer mapping of these aberrations than has previously been possible; however, data processing and analysis methods have not taken full advantage of this higher resolution. Attention has primarily been given to analysis on the single sample level, where multiple adjacent probes are necessarily used as replicates for the local region containing their target sequences. However, regions of concordant aberration can be short enough to be detected by only one, or very few, array elements. We describe a method called Multiple Sample Analysis for assessing the significance of concordant genomic aberrations across multiple experiments that does not require a-priori definition of aberration calls for each sample. If there are multiple samples, representing a class, then by exploiting the replication across samples our method can detect concordant aberrations at much higher resolution than can be derived from current single sample approaches. Additionally, this method provides a meaningful approach to addressing population-based questions such as determining important regions for a cancer subtype of interest or determining regions of copy number variation in a population. Multiple Sample Analysis also provides single sample aberration calls in the locations of significant concordance, producing high resolution calls per sample, in concordant regions. The approach is demonstrated on a dataset representing a challenging but important resource: breast tumors that have been formalin-fixed, paraffin-embedded, archived, and subsequently UV-laser capture microdissected and hybridized to two-channel BAC arrays using an amplification protocol. We demonstrate the accurate detection on simulated data, and on real datasets involving known regions of aberration within subtypes of breast cancer at a resolution consistent with that of the array. Similarly, we apply our method to previously published datasets, including a 250K SNP array, and verify known results as well as detect novel regions of concordant aberration. The algorithm has been fully implemented and tested and is freely available as a Java application at http://www.cbil.upenn.edu/MSA.


Item Type:Article
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1371/journal.pgen.0030143DOIArticle
http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.0030143PublisherArticle
http://dx.doi.org/10.1371/journal.pgen.0030143.sg001DOIFigure S1
http://dx.doi.org/10.1371/journal.pgen.0030143.sg002DOIFigure S2
http://dx.doi.org/10.1371/journal.pgen.0030143.sg003DOIFigure S3
http://dx.doi.org/10.1371/journal.pgen.0030143.sg004DOIFigure S4
http://dx.doi.org/10.1371/journal.pgen.0030143.sg005DOIFigure S5
http://dx.doi.org/10.1371/journal.pgen.0030143.sg006DOIFigure S6
http://dx.doi.org/10.1371/journal.pgen.0030143.sg007DOIFigure S7
http://dx.doi.org/10.1371/journal.pgen.0030143.st001DOITable S1
http://dx.doi.org/10.1371/journal.pgen.0030143.st002DOITable S2
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1950957/PubMed CentralArticle
ORCID:
AuthorORCID
Guttman, Mitchell0000-0003-4748-9352
Additional Information:© 2007 Guttman et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Received March 22, 2007; Accepted July 9, 2007; Published August 24, 2007. We would like to thank our colleagues at the Penn Center for Bioinformatics, especially John Tobias and Shilpa Rao, for helpful discussions and important feedback, Tom Eck for access to the STAC source code, and Warren J. Ewens for helpful discussions and statistical insights. We would also like to thank our colleagues at the Broad Institute, especially Noa Novashtern and Jason Funt, for helpful comments on the manuscript, Gad Getz, Jill P. Mesirov, and Eric S. Lander for helpful discussions, and Jim Robinson, Ted Liefeld, and Marc-Danie Nazarie for software help. The authors would especially like to thank Nadav Kupiec for help with all the illustrations and figures presented in this manuscript. Funding: This work was supported in part by a seed grant provided by the Penn Genomics Institute of the University of Pennsylvania. Author Contributions: MG and GRG conceived of and designed the MSA algorithm and the CGH-MSA software package. MG implemented the MSA algorithm and coded the CGH-MSA software package MG, CM, KDS, DAB, and GRG contributed to the design of the experiments described in this study. KDS performed the Array CGH experiments and Laser Capture Microdissection. CM provided the samples and differentiated the cases into appropriate pathological classifications. SJD provided computational insights, helpful feedback, and access to the STAC source code. CJS provided project guidance and support. MG and GRG optimized the STAC algorithm for use in MSA. MG and GRG developed the simulation model. MG and GRG wrote the paper. The authors have declared that no competing interests exist.
Funders:
Funding AgencyGrant Number
University of PennsylvaniaUNSPECIFIED
Issue or Number:8
PubMed Central ID:PMC1950957
Record Number:CaltechAUTHORS:20161121-151605200
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20161121-151605200
Official Citation:Guttman M, Mies C, Dudycz-Sulicz K, Diskin SJ, Baldwin DA, et al. (2007) Assessing the significance of conserved genomic aberrations using high resolution genomic microarrays. PLoS Genet 3(8): e143. doi:10.1371/journal.pgen.0030143
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:72213
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:21 Nov 2016 23:32
Last Modified:03 Oct 2019 16:15

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