TumorBoost: normalization of allele-specific tumor copy numbers from a single pair of tumor-normal genotyping microarrays.
BACKGROUND
High-throughput genotyping microarrays assess both total DNA copy number and allelic composition, which makes them a tool of choice for copy number studies in cancer, including total copy number and loss of heterozygosity (LOH) analyses. Even after state of the art preprocessing methods, allelic signal estimates from genotyping arrays still suffer from systematic effects that make them difficult to use effectively for such downstream analyses.


RESULTS
We propose a method, TumorBoost, for normalizing allelic estimates of one tumor sample based on estimates from a single matched normal. The method applies to any paired tumor-normal estimates from any microarray-based technology, combined with any preprocessing method. We demonstrate that it increases the signal-to-noise ratio of allelic signals, making it significantly easier to detect allelic imbalances.


CONCLUSIONS
TumorBoost increases the power to detect somatic copy-number events (including copy-neutral LOH) in the tumor from allelic signals of Affymetrix or Illumina origin. We also conclude that high-precision allelic estimates can be obtained from a single pair of tumor-normal hybridizations, if TumorBoost is combined with single-array preprocessing methods such as (allele-specific) CRMA v2 for Affymetrix or BeadStudio's (proprietary) XY-normalization method for Illumina. A bounded-memory implementation is available in the open-source and cross-platform R package aroma.cn, which is part of the Aroma Project (http://www.aroma-project.org/).
Bengtsson
Henrik
H
Department of Statistics, University of California, Berkeley, USA. hb@stat.berkeley.edu
Neuvial
Pierre
P
Speed
Terence P
TP
D000483:Alleles; D018628:Gene Dosage / Q000235:genetics*; D020869:Gene Expression Profiling / Q000379:methods; D023281:Genomics / Q000379:methods*; D005838:Genotype; D009369:Neoplasms / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D012984:Software
J Comput Biol. 2008 Sep;15(7):857-66
18707534
Nucleic Acids Res. 2007;35(6):2013-25
17341461
Genome Res. 2009 Feb;19(2):276-83
19141597
Biostatistics. 2007 Apr;8(2):323-36
16787995
Am J Hum Genet. 2007 Jul;81(1):114-26
17564968
Nucleic Acids Res. 2009 Sep;37(16):5365-77
19581427
Bioinformatics. 2008 Mar 15;24(6):759-67
18204055
Biometrika. 2010 Sep;97(3):631-645
22822250
Bioinformatics. 2008 Oct 1;24(19):2209-14
18653518
PLoS One. 2009 Jun 26;4(6):e6057
19557126
Sci Am. 2007 Mar;296(3):50-7
17348159
Bioinformatics. 2010 Jan 15;26(2):153-60
19933593
Bioinformatics. 2004 Dec 12;20(18):3413-22
15381628
Genome Res. 2006 Sep;16(9):1136-48
16899659
Nat Genet. 2005 May;37(5):549-54
15838508
PLoS Comput Biol. 2005 Nov;1(6):e65
16322765
Biostatistics. 2007 Apr;8(2):485-99
17189563
Am J Hum Genet. 2008 Apr;82(4):903-15
18355774
Genome Biol. 2008;9(9):R136
18796136
PLoS Comput Biol. 2011 Jan 27;7(1):e1001060
21298078
Nat Genet. 2003 Aug;34(4):369-76
12923544
Biostatistics. 2010 Jan;11(1):164-75
19837654
Bioinformatics. 2009 Sep 1;25(17):2149-56
19535535
Bioinformatics. 2010 Jan 15;26(2):242-9
19906825
Bioinformatics. 2009 Oct 1;25(19):2621-3
19661241
BMC Bioinformatics. 2006 Mar 01;7:100
16509971
BMC Genomics. 2008 Oct 17;9:489
18928532
Biostatistics. 2004 Oct;5(4):557-72
15475419
Cell. 2000 Jan 7;100(1):57-70
10647931
BMC Bioinformatics. 2008 Apr 21;9:204
18426588
Bioinformatics. 2006 Jan 1;22(1):7-12
16267090
Nature. 2008 Oct 23;455(7216):1061-8
18772890
Proc Natl Acad Sci U S A. 2001 Jan 2;98(1):31-6
11134512
Bioinformatics. 2007 Mar 15;23(6):657-63
17234643
Nat Genet. 2008 Oct;40(10):1253-60
18776909
Bioinformatics. 2003 Dec 12;19(18):2397-403
14668223
Bioinformatics. 2009 Apr 1;25(7):861-7
19193730