Summary: In this article, we introduce a robust and efficient strategy for deriving global and allele-specific copy number alternations (CNA) from cancer whole exome sequencing data based on Log R ratios and B-allele frequencies. Applying the approach to the analysis of over 200 skin cancer samples, we demonstrate its utility for discovering distinct CNA events and for deriving ancillary information such as tumor purity

Chen, Hao

Chen, Mengjie

Krauthammer, Michael

Pornputtapong, Natapol

Powers, R. Scott

Wang, Xuefeng

Yu, Xiaoqing

Zhang, Nancy R.

English

PubMed

Carolina Digital Repository

Genome analysisGlobal copy number profiling of cancergenomesXuefeng Wang1,2*, Mengjie Chen3, Xiaoqing Yu4,Natapol Pornputtapong5,2, Hao Chen6, Nancy R. Zhang7,R. Scott Powers8 and Michael Krauthammer5,2*1Department of Family, Population & Preventive Medicine, Stony Brook University, Stony Brook, NY 11794, USA,2Department of Pathology, Yale School of Medicine, New Haven, CT 06520, USA, 3Departments of Biostatistics andGenetics, University of North Carolina, Chapel Hill, NC 27599, USA, 4Department of Biostatistics, 5Program inComputational Biology and Bioinformatics, Yale University, New Haven, CT 06520, 6Department of Statistics,University of California, Davis, CA 9516, USA, 7Department of Statistics, The Wharton School, University ofPennsylvania, PA 19104, USA and 8Department of Pathology, Stony Brook University, Stony Brook, NY 11794, USA*To whom correspondence should be addressed.Associate Editor: John HancockReceived on July 3, 2015; revised on October 16, 2015; accepted on November 6, 2015AbstractSummary: In this article, we introduce a robust and efficient strategy for deriving global and allele-specific copy number alternations (CNA) from cancer whole exome sequencing data based on Log Rratios and B-allele frequencies. Applying the approach to the analysis of over 200 skin cancer samples,we demonstrate its utility for discovering distinct CNA events and for deriving ancillary informationsuch as tumor purity.Availability and implementation: https://github.com/xfwang/CLOSEContact: xuefeng.wang@stonybrook.edu or michael.krauthammer@yale.eduSupplementary information: Supplementary data are available at Bioinformatics online.1 IntroductionDNA copy number alternations (CNA) have been extensivelystudied in cancer. Copy number amplifications and homozygous de-letions from CNA play an important role in the development of can-cer. Genome-wide CNA detection of large number of tumor samplesis a vital step to identify cancer driver events and to gain insightsinto cancer progression and prevention. Next-generation sequencing(NGS) technologies have quickly become the CNA analysis platformof choice due to higher resolution and sensitivity compared to trad-itional array-based approaches such as comparative genomic hybrid-ization (CGH). However, NGS-specific CNA statistical andcomputational methods are currently not well established and aremostly based on the total copy number gains or losses as representedby the log ratio of sequence coverage in a tumor-normal pair. Theserelative measurements do not take into account tumor purity,clonality, as well as other valuable information accrued by NGSdata. In this correspondence, we highlight a set of concepts andanalytic tools that enables the fast and accurate dissection of whole-genome CNA profiles by integrating the totality of informationavailable from NGS data. Based on whole exome sequencing (WES)data of 253 melanoma tumor-normal pairs, we demonstratethat our approaches allow for a more accurate estimation of copynumber status and identification of otherwise undetectable CNApatterns.Well-developed array-based CNA analytical tools are primarilybased on the segmentation and smoothing of Log R Ratio (LRR) andB-allele frequency (BAF) (Wang et al., 2007), which provide orthog-onal evidence for copy number status. LRR corresponds to the differ-ence in the hybridization signal in a particular genome region betweennormal and tumor DNA, and BAF measures the relative contributionsof the maternal and paternal alleles to this signal. Most NGS-basedVC The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com 926Bioinformatics, 32(6), 2016, 926–928doi: 10.1093/bioinformatics/btv676Advance Access Publication Date: 16 November 2015Applications Notetools, however, rely solely on LRR due to the inherent challenges in as-sessing BAF. First, the density of observed single nucleotide variants(SNVs) is relatively sparse, especially in WES. Also, the accuracy of theBAF measurement is dependent on the sequence coverage, which canvary between gene regions and sequencing modalities. Finally, the BAFis sensitive to sequencing and mapping errors. Therefore, BAF informa-tion remains underutilized in NGS experiments, and BAF is often usedin an ad hoc manner—in an attempt to validate the results obtainedfrom LRR. In this paper, we propose a fast procedure that is based onboth LRR and BAF and that works seamlessly with existing NGS pipe-lines. We show that the derived multivariate CNA profiles allow for anin-depth characterization of the somatic structural variations in cancersamples. A set of analysis scripts from vcf file preparation to the deriv-ation of allele-specific copy numbers are provided in the pipeline pack-age CLOSE (a toolkit for CNA/LOH analysis with Sequencing data)and its companion R package CLOSE-R.2 Methods and resultsA conceptual overview of our strategy is illustrated in Figure 1. NGSsequencing data is first segmented into LRR regions using existingprograms, and we only use the BAF of identified variant positionsthat are heterozygous in the paired normal tissues in the subsequentjoint modelling steps. The average or median LRR and BAF valuesare calculated for each segment. For convenience and as a mean toreduce data sparsity, the segmental BAF values are further convertedinto the LAF (lesser allele frequency) measures according the for-mula as described in the Supplementary Methods. We can thenapply a joint likelihood model at the segmental level for integratinginformation from both LRR and LAF data for deriving allele-specific copy number (ASCN) estimates. ASCN is a 2 dimensionalrepresentation of copy number data that encodes the status of the Aand B alleles (i.e. the maternal and paternal alleles) (LaFramboiseet al., 2005; Van Loo et al., 2010). To ensure reliable estimation, itis important to exclude outlier and less informative (normal)regions. We assign more weight to regions with LAF smaller than0.25, because they are much less prone to sequencing related biasand represent important anchor points for recognition of copy num-ber alterations. We derive sample purity and ploidy based on thetraditional full likelihood model and through a position-based ap-proach that explores distances from canonical coordinates (Li andLi, 2014).Importantly, we derived a generalized LRR-LAF mapping func-tion (Equation 8 in the Supplementary Methods) that can be usedwhen the sample ploidy parameter is not given or cannot be esti-mated with enough certainty. In this case, the ASCN estimates be-come relative measures, instead of absolute ones. Relative ASCNestimates are still sufficient in determining copy number status(Chen et al., 2014). This function was used recursively in CLOSEfor calculating canonical coordinates or lines under different set-tings. It provides a unified systematic method for determining copynumber status, as well as a basis for further exploring clonal mix-tures with a tumor.Both LRR and LAF measurements exhibit strong variability (Baoet al., 2014; Carter et al., 2012) with baseline values that are oftenshifted from null (0.5 for LAF) depending on sample purity. As a ro-bust alternative to the model- or likelihood- based method for copynumber detection, we propose to call absolute or relative ASCN basedon nonparametric Bayesian clustering with the Chinese RestaurantProcess (CRP). The cluster that is closest to the baseline coordinatescorresponds to normal regions, and other copy number alternation re-gions can be inferred accordingly. The derived CNA profile (Fig. 1b)greatly facilitates genome-wide data exploration by grouping samplesinto different CNA-sets. Importantly, the use of ASCN allows the de-tection of copy number neutral LOH (loss of heterozygosity) regions.These regions do no change their total DNA copy numbers and havebeen a blind spot for LRR-based tools. Therefore, CLOSE-R currentlyimplements three methods for copy number inference: (i) the proposedCRP or clustering based method; (ii) the likelihood- or model-basedmethod; and, for comparison, (iii) the joint segmentation model ofLRR and LAF modified based on the R package falcon (Chen et al.,2014). Based on a group of randomly selected samples, we comparedthe total and allele specific copy number estimates obtained from thesethree methods (Supplementary Fig. S2) and found consistent resultsacross different samples. We also compared the global parameter esti-mation implemented in CLOSE (based on the modified likelihood ap-proach) with a similar package Sequenza (Favero et al., 2015); theyreturned very close estimates for purity even at different ploidy levels(Supplementary Table S1 and Supplementary Fig. S3).A competitive advantage of the CRP approach is the straightfor-ward ability to explore tumor cellularity and subclonal cancer architec-ture using the allele specific copy numbers. As illustrated in Figure 1d,the minor allele copy number (minCN) approximately follows a mix-ture of one, two or three Gaussian distributions. The spacing betweenthe central point of the first cluster of minCN and that of the third, ifexistent, corresponds to the concentration ratio of the tumor sample.The peak in the middle, if existent, is useful for estimating aneuploidyor subclonality. The deviation of the main mode (of the density plot inFig. 1d) of the minor allele copy number from one captures the system-atic bias caused by sequencing and mapping errors. The DirichletProcess fit can be successfully applied to identify these peaks given apurity measure is as low as 0.5. The abstracted features from the CNAprofile aid in conducting the integrative analysis of multiple cancersamples. We performed three sets of hierarchical clustering of allof 253 available melanoma samples, based on minCN, and bothminor and major allele copy numbers, respectively (Fig. 1e, andSupplementary Fig. S4). The two-channel clustering shows major copyFig. 1. Genome-wide CNA profile of melanoma samples. (a) LRR and BAFcalls from WES of matched normal and tumor samples. (b) Bivariate gen-ome-wide allele-specific copy number profile (detailed in SupplementaryMaterials) and nonparametric Bayesian clustering based on ChineseRestaurant Process (CRP). (c) Predicted copy number status. (d) Density esti-mation of minor allele copy number estimation by the Dirichlet Process mix-ture of normal distributions. (e) Hierarchical clustering of 253 melonomasamples based on the minor allele copy number estimatesGlobal copy number profiling 927number events across the genome, including losses in chromosomes 9and 10, and gains of 1q, 7p, 7q and 8q (left panel, Supplementary Fig.S4). A clustering plot based on majCN reveals a set of melanomas,including YUMOKI, YURDE and YUWALI, with a distinct chromo-some 7 gain (right panel, top, in Supplementary Fig. S4), and a tightgrouping of melanoma samples from the same patients. The combinedclustering does not reveal the chromosome 7 finding and fails to groupsamples from the same patient (see YUCLAT and YUZEST). Because afew samples in our dataset were also submitted to TCGA, we wereable to compare our results to the publicly available copy-number seg-mentation results based on array data (Affymetrix SNP6), showingconsiderable consistency between the results (Supplementary Fig. S5).Taken together, this study describes a timely and lightweight so-lution for cancer CNA detection that is scalable to large-scalesequencing studies. A similar approach can be applied for accurateCNA detection in high-depth single-cell sequencing, and the solutionis extensible to whole genome sequencing. A remaining challenge isthe joint identification of unknown parameters such as ploidy andcellularity—in which parameters may not be identifiable withoutimposing additional assumptions. Nevertheless, we hope that oursolution offers a path forward for deconvolving the complexities ofthe cancer genome, and stimulates further interest in allele-specificCNA profiling based on LRR and BAF.3 ConclusionsWe propose a lightweight approach for assessing WES-based globalDNA copy number abberations that (i) replicates copy number callsfrom exisiting analysis methodologies; (ii) derives tumor ploidy andcellularity; and (iii) provides information for cross-tumor integrativeanalysis.AcknowledgementsThe authors would like to thank Bo Li for helpful discussion.FundingM.K. was supported by the National Cancer Institute (Yale SPORE in skincancer, P50 CA121974). M.C. was supported in part by the NationalInstitutes of Health (NIH) grants R01 CA082659 and P01CA142538. X.W.was supported in part by the NIH grant P20 CA192994.Conflict of Interest: none declared.ReferencesBao,L. et al. (2014) AbsCN-seq: a statistical method to estimate tumor purity,ploidy and absolute copy numbers from next-generation sequencing data.Bioinformatics, 30, 1056–1063.Chen,H. et al. (2014) Allele-specific copy number profiling by next-generationDNA sequencing. Nucleic Acids Res., 43:e23.Carter,S.L. et al. (2014) Absolute quantification of somatic DNA alterationsin human cancer. Nat. Biotechnol., 30, 413–421.Favero,F. et al. (2015) Sequenza: allele-specific copy number and mutationprofiles from tumor sequencing data. Ann. Oncol., 26: 64–70.LaFramboise,T. et al. (2005) Allele-specific amplification in cancer revealed bySNP array analysis. PLoS Comput. Biol., 1, e65.Li,B. and Li,J. (2014) A general framework for analyzing tumor subclonalityuing SNP array and DNA sequencing data. Genome Biol., 15, 473.Van Loo,P. et al. (2010) Allele-specific copy number analysis of tumors. Proc.Natl. Acad. Sci. USA, 107, 16910–16915.Wang,K. et al. (2007) PennCNV: an integrated hidden Markov modeldesigned for high-resolution copy number variation detection in whole-genome SNP genotyping data. Genome Res., 17, 1665–1674.928 X.Wang et al.

Global copy number profiling of cancer genomes

UnlabelledIn this article, we introduce a robust and efficient strategy for deriving global and allele-specific copy number alternations (CNA) from cancer whole exome sequencing data based on Log R ratios and B-allele frequencies. Applying the approach to the analysis of over 200 skin cancer samples, we demonstrate its utility for discovering distinct CNA events and for deriving ancillary information such as tumor purity.Availability and implementationhttps://github.com/xfwang/CLOSE CONTACT: xuefeng.wang@stonybrook.edu or michael.krauthammer@yale.eduSupplementary informationSupplementary data are available at Bioinformatics online

Zhang, Nancy R

Powers, R Scott

eScholarship - University of California

UC DavisUC Davis Previously Published WorksTitleGlobal copy number profiling of cancer genomesPermalinkhttps://escholarship.org/uc/item/51s5x058JournalBioinformatics, 32(6)ISSN1367-4803AuthorsWang, XuefengChen, MengjieYu, Xiaoqinget al.Publication Date2016-03-15DOI10.1093/bioinformatics/btv676 Peer reviewedeScholarship.org Powered by the California Digital LibraryUniversity of CaliforniaGenome analysisGlobal copy number profiling of cancergenomesXuefeng Wang1,2*, Mengjie Chen3, Xiaoqing Yu4,Natapol Pornputtapong5,2, Hao Chen6, Nancy R. Zhang7,R. Scott Powers8 and Michael Krauthammer5,2*1Department of Family, Population & Preventive Medicine, Stony Brook University, Stony Brook, NY 11794, USA,2Department of Pathology, Yale School of Medicine, New Haven, CT 06520, USA, 3Departments of Biostatistics andGenetics, University of North Carolina, Chapel Hill, NC 27599, USA, 4Department of Biostatistics, 5Program inComputational Biology and Bioinformatics, Yale University, New Haven, CT 06520, 6Department of Statistics,University of California, Davis, CA 9516, USA, 7Department of Statistics, The Wharton School, University ofPennsylvania, PA 19104, USA and 8Department of Pathology, Stony Brook University, Stony Brook, NY 11794, USA*To whom correspondence should be addressed.Associate Editor: John HancockReceived on July 3, 2015; revised on October 16, 2015; accepted on November 6, 2015AbstractSummary: In this article, we introduce a robust and efficient strategy for deriving global and allele-specific copy number alternations (CNA) from cancer whole exome sequencing data based on Log Rratios and B-allele frequencies. Applying the approach to the analysis of over 200 skin cancer samples,we demonstrate its utility for discovering distinct CNA events and for deriving ancillary informationsuch as tumor purity.Availability and implementation: https://github.com/xfwang/CLOSEContact: xuefeng.wang@stonybrook.edu or michael.krauthammer@yale.eduSupplementary information: Supplementary data are available at Bioinformatics online.1 IntroductionDNA copy number alternations (CNA) have been extensivelystudied in cancer. Copy number amplifications and homozygous de-letions from CNA play an important role in the development of can-cer. Genome-wide CNA detection of large number of tumor samplesis a vital step to identify cancer driver events and to gain insightsinto cancer progression and prevention. Next-generation sequencing(NGS) technologies have quickly become the CNA analysis platformof choice due to higher resolution and sensitivity compared to trad-itional array-based approaches such as comparative genomic hybrid-ization (CGH). However, NGS-specific CNA statistical andcomputational methods are currently not well established and aremostly based on the total copy number gains or losses as representedby the log ratio of sequence coverage in a tumor-normal pair. Theserelative measurements do not take into account tumor purity,clonality, as well as other valuable information accrued by NGSdata. In this correspondence, we highlight a set of concepts andanalytic tools that enables the fast and accurate dissection of whole-genome CNA profiles by integrating the totality of informationavailable from NGS data. Based on whole exome sequencing (WES)data of 253 melanoma tumor-normal pairs, we demonstratethat our approaches allow for a more accurate estimation of copynumber status and identification of otherwise undetectable CNApatterns.Well-developed array-based CNA analytical tools are primarilybased on the segmentation and smoothing of Log R Ratio (LRR) andB-allele frequency (BAF) (Wang et al., 2007), which provide orthog-onal evidence for copy number status. LRR corresponds to the differ-ence in the hybridization signal in a particular genome region betweennormal and tumor DNA, and BAF measures the relative contributionsof the maternal and paternal alleles to this signal. Most NGS-basedVC The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com 926Bioinformatics, 32(6), 2016, 926–928doi: 10.1093/bioinformatics/btv676Advance Access Publication Date: 16 November 2015Applications Notetools, however, rely solely on LRR due to the inherent challenges in as-sessing BAF. First, the density of observed single nucleotide variants(SNVs) is relatively sparse, especially in WES. Also, the accuracy of theBAF measurement is dependent on the sequence coverage, which canvary between gene regions and sequencing modalities. Finally, the BAFis sensitive to sequencing and mapping errors. Therefore, BAF informa-tion remains underutilized in NGS experiments, and BAF is often usedin an ad hoc manner—in an attempt to validate the results obtainedfrom LRR. In this paper, we propose a fast procedure that is based onboth LRR and BAF and that works seamlessly with existing NGS pipe-lines. We show that the derived multivariate CNA profiles allow for anin-depth characterization of the somatic structural variations in cancersamples. A set of analysis scripts from vcf file preparation to the deriv-ation of allele-specific copy numbers are provided in the pipeline pack-age CLOSE (a toolkit for CNA/LOH analysis with Sequencing data)and its companion R package CLOSE-R.2 Methods and resultsA conceptual overview of our strategy is illustrated in Figure 1. NGSsequencing data is first segmented into LRR regions using existingprograms, and we only use the BAF of identified variant positionsthat are heterozygous in the paired normal tissues in the subsequentjoint modelling steps. The average or median LRR and BAF valuesare calculated for each segment. For convenience and as a mean toreduce data sparsity, the segmental BAF values are further convertedinto the LAF (lesser allele frequency) measures according the for-mula as described in the Supplementary Methods. We can thenapply a joint likelihood model at the segmental level for integratinginformation from both LRR and LAF data for deriving allele-specific copy number (ASCN) estimates. ASCN is a 2 dimensionalrepresentation of copy number data that encodes the status of the Aand B alleles (i.e. the maternal and paternal alleles) (LaFramboiseet al., 2005; Van Loo et al., 2010). To ensure reliable estimation, itis important to exclude outlier and less informative (normal)regions. We assign more weight to regions with LAF smaller than0.25, because they are much less prone to sequencing related biasand represent important anchor points for recognition of copy num-ber alterations. We derive sample purity and ploidy based on thetraditional full likelihood model and through a position-based ap-proach that explores distances from canonical coordinates (Li andLi, 2014).Importantly, we derived a generalized LRR-LAF mapping func-tion (Equation 8 in the Supplementary Methods) that can be usedwhen the sample ploidy parameter is not given or cannot be esti-mated with enough certainty. In this case, the ASCN estimates be-come relative measures, instead of absolute ones. Relative ASCNestimates are still sufficient in determining copy number status(Chen et al., 2014). This function was used recursively in CLOSEfor calculating canonical coordinates or lines under different set-tings. It provides a unified systematic method for determining copynumber status, as well as a basis for further exploring clonal mix-tures with a tumor.Both LRR and LAF measurements exhibit strong variability (Baoet al., 2014; Carter et al., 2012) with baseline values that are oftenshifted from null (0.5 for LAF) depending on sample purity. As a ro-bust alternative to the model- or likelihood- based method for copynumber detection, we propose to call absolute or relative ASCN basedon nonparametric Bayesian clustering with the Chinese RestaurantProcess (CRP). The cluster that is closest to the baseline coordinatescorresponds to normal regions, and other copy number alternation re-gions can be inferred accordingly. The derived CNA profile (Fig. 1b)greatly facilitates genome-wide data exploration by grouping samplesinto different CNA-sets. Importantly, the use of ASCN allows the de-tection of copy number neutral LOH (loss of heterozygosity) regions.These regions do no change their total DNA copy numbers and havebeen a blind spot for LRR-based tools. Therefore, CLOSE-R currentlyimplements three methods for copy number inference: (i) the proposedCRP or clustering based method; (ii) the likelihood- or model-basedmethod; and, for comparison, (iii) the joint segmentation model ofLRR and LAF modified based on the R package falcon (Chen et al.,2014). Based on a group of randomly selected samples, we comparedthe total and allele specific copy number estimates obtained from thesethree methods (Supplementary Fig. S2) and found consistent resultsacross different samples. We also compared the global parameter esti-mation implemented in CLOSE (based on the modified likelihood ap-proach) with a similar package Sequenza (Favero et al., 2015); theyreturned very close estimates for purity even at different ploidy levels(Supplementary Table S1 and Supplementary Fig. S3).A competitive advantage of the CRP approach is the straightfor-ward ability to explore tumor cellularity and subclonal cancer architec-ture using the allele specific copy numbers. As illustrated in Figure 1d,the minor allele copy number (minCN) approximately follows a mix-ture of one, two or three Gaussian distributions. The spacing betweenthe central point of the first cluster of minCN and that of the third, ifexistent, corresponds to the concentration ratio of the tumor sample.The peak in the middle, if existent, is useful for estimating aneuploidyor subclonality. The deviation of the main mode (of the density plot inFig. 1d) of the minor allele copy number from one captures the system-atic bias caused by sequencing and mapping errors. The DirichletProcess fit can be successfully applied to identify these peaks given apurity measure is as low as 0.5. The abstracted features from the CNAprofile aid in conducting the integrative analysis of multiple cancersamples. We performed three sets of hierarchical clustering of allof 253 available melanoma samples, based on minCN, and bothminor and major allele copy numbers, respectively (Fig. 1e, andSupplementary Fig. S4). The two-channel clustering shows major copyFig. 1. Genome-wide CNA profile of melanoma samples. (a) LRR and BAFcalls from WES of matched normal and tumor samples. (b) Bivariate gen-ome-wide allele-specific copy number profile (detailed in SupplementaryMaterials) and nonparametric Bayesian clustering based on ChineseRestaurant Process (CRP). (c) Predicted copy number status. (d) Density esti-mation of minor allele copy number estimation by the Dirichlet Process mix-ture of normal distributions. (e) Hierarchical clustering of 253 melonomasamples based on the minor allele copy number estimatesGlobal copy number profiling 927number events across the genome, including losses in chromosomes 9and 10, and gains of 1q, 7p, 7q and 8q (left panel, Supplementary Fig.S4). A clustering plot based on majCN reveals a set of melanomas,including YUMOKI, YURDE and YUWALI, with a distinct chromo-some 7 gain (right panel, top, in Supplementary Fig. S4), and a tightgrouping of melanoma samples from the same patients. The combinedclustering does not reveal the chromosome 7 finding and fails to groupsamples from the same patient (see YUCLAT and YUZEST). Because afew samples in our dataset were also submitted to TCGA, we wereable to compare our results to the publicly available copy-number seg-mentation results based on array data (Affymetrix SNP6), showingconsiderable consistency between the results (Supplementary Fig. S5).Taken together, this study describes a timely and lightweight so-lution for cancer CNA detection that is scalable to large-scalesequencing studies. A similar approach can be applied for accurateCNA detection in high-depth single-cell sequencing, and the solutionis extensible to whole genome sequencing. A remaining challenge isthe joint identification of unknown parameters such as ploidy andcellularity—in which parameters may not be identifiable withoutimposing additional assumptions. Nevertheless, we hope that oursolution offers a path forward for deconvolving the complexities ofthe cancer genome, and stimulates further interest in allele-specificCNA profiling based on LRR and BAF.3 ConclusionsWe propose a lightweight approach for assessing WES-based globalDNA copy number abberations that (i) replicates copy number callsfrom exisiting analysis methodologies; (ii) derives tumor ploidy andcellularity; and (iii) provides information for cross-tumor integrativeanalysis.AcknowledgementsThe authors would like to thank Bo Li for helpful discussion.FundingM.K. was supported by the National Cancer Institute (Yale SPORE in skincancer, P50 CA121974). M.C. was supported in part by the NationalInstitutes of Health (NIH) grants R01 CA082659 and P01CA142538. X.W.was supported in part by the NIH grant P20 CA192994.Conflict of Interest: none declared.ReferencesBao,L. et al. (2014) AbsCN-seq: a statistical method to estimate tumor purity,ploidy and absolute copy numbers from next-generation sequencing data.Bioinformatics, 30, 1056–1063.Chen,H. et al. (2014) Allele-specific copy number profiling by next-generationDNA sequencing. Nucleic Acids Res., 43:e23.Carter,S.L. et al. (2014) Absolute quantification of somatic DNA alterationsin human cancer. Nat. Biotechnol., 30, 413–421.Favero,F. et al. (2015) Sequenza: allele-specific copy number and mutationprofiles from tumor sequencing data. Ann. Oncol., 26: 64–70.LaFramboise,T. et al. (2005) Allele-specific amplification in cancer revealed bySNP array analysis. PLoS Comput. Biol., 1, e65.Li,B. and Li,J. (2014) A general framework for analyzing tumor subclonalityuing SNP array and DNA sequencing data. Genome Biol., 15, 473.Van Loo,P. et al. (2010) Allele-specific copy number analysis of tumors. Proc.Natl. Acad. Sci. USA, 107, 16910–16915.Wang,K. et al. (2007) PennCNV: an integrated hidden Markov modeldesigned for high-resolution copy number variation detection in whole-genome SNP genotyping data. Genome Res., 17, 1665–1674.928 X.Wang et al.

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