qpure: A Tool to Estimate Tumor Cellularity from Genome-Wide Single-Nucleotide Polymorphism Profiles
Tumour cellularity, the relative proportion of tumour and normal cells in a sample, affects the sensitivity of mutation detection, copy number analysis, cancer gene expression and methylation profiling. Tumour cellularity is traditionally estimated by pathological review of sectioned specimens; howe...
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| Vydáno v: | PloS one Ročník 7; číslo 9; s. e45835 |
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| Hlavní autoři: | , , , , , , , , , , , , , , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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United States
Public Library of Science
25.09.2012
Public Library of Science (PLoS) |
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| ISSN: | 1932-6203, 1932-6203 |
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| Abstract | Tumour cellularity, the relative proportion of tumour and normal cells in a sample, affects the sensitivity of mutation detection, copy number analysis, cancer gene expression and methylation profiling. Tumour cellularity is traditionally estimated by pathological review of sectioned specimens; however this method is both subjective and prone to error due to heterogeneity within lesions and cellularity differences between the sample viewed during pathological review and tissue used for research purposes. In this paper we describe a statistical model to estimate tumour cellularity from SNP array profiles of paired tumour and normal samples using shifts in SNP allele frequency at regions of loss of heterozygosity (LOH) in the tumour. We also provide qpure, a software implementation of the method. Our experiments showed that there is a medium correlation 0.42 ([Formula: see text]-value=0.0001) between tumor cellularity estimated by qpure and pathology review. Interestingly there is a high correlation 0.87 ([Formula: see text]-value [Formula: see text] 2.2e-16) between cellularity estimates by qpure and deep Ion Torrent sequencing of known somatic KRAS mutations; and a weaker correlation 0.32 ([Formula: see text]-value=0.004) between IonTorrent sequencing and pathology review. This suggests that qpure may be a more accurate predictor of tumour cellularity than pathology review. qpure can be downloaded from https://sourceforge.net/projects/qpure/. |
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| AbstractList | Tumour cellularity, the relative proportion of tumour and normal cells in a sample, affects the sensitivity of mutation detection, copy number analysis, cancer gene expression and methylation profiling. Tumour cellularity is traditionally estimated by pathological review of sectioned specimens; however this method is both subjective and prone to error due to heterogeneity within lesions and cellularity differences between the sample viewed during pathological review and tissue used for research purposes. In this paper we describe a statistical model to estimate tumour cellularity from SNP array profiles of paired tumour and normal samples using shifts in SNP allele frequency at regions of loss of heterozygosity (LOH) in the tumour. We also provide qpure, a software implementation of the method. Our experiments showed that there is a medium correlation 0.42 ([Formula: see text]-value=0.0001) between tumor cellularity estimated by qpure and pathology review. Interestingly there is a high correlation 0.87 ([Formula: see text]-value [Formula: see text] 2.2e-16) between cellularity estimates by qpure and deep Ion Torrent sequencing of known somatic KRAS mutations; and a weaker correlation 0.32 ([Formula: see text]-value=0.004) between IonTorrent sequencing and pathology review. This suggests that qpure may be a more accurate predictor of tumour cellularity than pathology review. qpure can be downloaded from https://sourceforge.net/projects/qpure/. Tumour cellularity, the relative proportion of tumour and normal cells in a sample, affects the sensitivity of mutation detection, copy number analysis, cancer gene expression and methylation profiling. Tumour cellularity is traditionally estimated by pathological review of sectioned specimens; however this method is both subjective and prone to error due to heterogeneity within lesions and cellularity differences between the sample viewed during pathological review and tissue used for research purposes. In this paper we describe a statistical model to estimate tumour cellularity from SNP array profiles of paired tumour and normal samples using shifts in SNP allele frequency at regions of loss of heterozygosity (LOH) in the tumour. We also provide qpure, a software implementation of the method. Our experiments showed that there is a medium correlation 0.42 (-value = 0.0001) between tumor cellularity estimated by qpure and pathology review. Interestingly there is a high correlation 0.87 (-value 2.2e-16) between cellularity estimates by qpure and deep Ion Torrent sequencing of known somatic KRAS mutations; and a weaker correlation 0.32 (-value = 0.004) between IonTorrent sequencing and pathology review. This suggests that qpure may be a more accurate predictor of tumour cellularity than pathology review. qpure can be downloaded from Tumour cellularity, the relative proportion of tumour and normal cells in a sample, affects the sensitivity of mutation detection, copy number analysis, cancer gene expression and methylation profiling. Tumour cellularity is traditionally estimated by pathological review of sectioned specimens; however this method is both subjective and prone to error due to heterogeneity within lesions and cellularity differences between the sample viewed during pathological review and tissue used for research purposes. In this paper we describe a statistical model to estimate tumour cellularity from SNP array profiles of paired tumour and normal samples using shifts in SNP allele frequency at regions of loss of heterozygosity (LOH) in the tumour. We also provide qpure, a software implementation of the method. Our experiments showed that there is a medium correlation 0.42 (-value = 0.0001) between tumor cellularity estimated by qpure and pathology review. Interestingly there is a high correlation 0.87 (-value 2.2e-16) between cellularity estimates by qpure and deep Ion Torrent sequencing of known somatic KRAS mutations; and a weaker correlation 0.32 (-value = 0.004) between IonTorrent sequencing and pathology review. This suggests that qpure may be a more accurate predictor of tumour cellularity than pathology review. qpure can be downloaded from https://sourceforge.net/projects/qpure/. Tumour cellularity, the relative proportion of tumour and normal cells in a sample, affects the sensitivity of mutation detection, copy number analysis, cancer gene expression and methylation profiling. Tumour cellularity is traditionally estimated by pathological review of sectioned specimens; however this method is both subjective and prone to error due to heterogeneity within lesions and cellularity differences between the sample viewed during pathological review and tissue used for research purposes. In this paper we describe a statistical model to estimate tumour cellularity from SNP array profiles of paired tumour and normal samples using shifts in SNP allele frequency at regions of loss of heterozygosity (LOH) in the tumour. We also provide qpure, a software implementation of the method. Our experiments showed that there is a medium correlation 0.42 ([Formula: see text]-value=0.0001) between tumor cellularity estimated by qpure and pathology review. Interestingly there is a high correlation 0.87 ([Formula: see text]-value [Formula: see text] 2.2e-16) between cellularity estimates by qpure and deep Ion Torrent sequencing of known somatic KRAS mutations; and a weaker correlation 0.32 ([Formula: see text]-value=0.004) between IonTorrent sequencing and pathology review. This suggests that qpure may be a more accurate predictor of tumour cellularity than pathology review. qpure can be downloaded from https://sourceforge.net/projects/qpure/.Tumour cellularity, the relative proportion of tumour and normal cells in a sample, affects the sensitivity of mutation detection, copy number analysis, cancer gene expression and methylation profiling. Tumour cellularity is traditionally estimated by pathological review of sectioned specimens; however this method is both subjective and prone to error due to heterogeneity within lesions and cellularity differences between the sample viewed during pathological review and tissue used for research purposes. In this paper we describe a statistical model to estimate tumour cellularity from SNP array profiles of paired tumour and normal samples using shifts in SNP allele frequency at regions of loss of heterozygosity (LOH) in the tumour. We also provide qpure, a software implementation of the method. Our experiments showed that there is a medium correlation 0.42 ([Formula: see text]-value=0.0001) between tumor cellularity estimated by qpure and pathology review. Interestingly there is a high correlation 0.87 ([Formula: see text]-value [Formula: see text] 2.2e-16) between cellularity estimates by qpure and deep Ion Torrent sequencing of known somatic KRAS mutations; and a weaker correlation 0.32 ([Formula: see text]-value=0.004) between IonTorrent sequencing and pathology review. This suggests that qpure may be a more accurate predictor of tumour cellularity than pathology review. qpure can be downloaded from https://sourceforge.net/projects/qpure/. |
| Audience | Academic |
| Author | Harliwong, Ivon Leonard, Conrad Wu, Jianmin Holmes, Oliver Cowley, Mark J. Waddell, Nic Song, Sarah Kassahn, Karin S. Miller, David Xu, Qinying Biankin, Andrew V. Anderson, Matthew Wilson, Peter Newell, Felicity Grimmond, Sean M. Pajic, Marina Pearson, John V. Gill, Anthony J. Johns, Amber L. Taylor, Darrin Pinese, Mark Nones, Katia Wood, Scott Fink, Lynn |
| AuthorAffiliation | 6 University of Sydney, Sydney, New South Wales, Australia 2 The Kinghorn Cancer Centre, Cancer Research Program, Garvan Institute of Medical Research, Darlinghurst, Sydney, New South Wales, Australia 3 Department of Surgery, Bankstown Hospital, Bankstown, Sydney, New South Wales, Australia 5 The University of Queensland, UQ Centre for Clinical Research, Brisbane, Queensland, Australia 1 Queensland Centre for Medical Genomics, Institute for Molecular Bioscience, University of Queensland, St Lucia, Brisbane, Queensland, Australia 7 Cancer Diagnosis Group, Northern Cancer Translational Research Unit, Royal North Shore Hospital, St Leonards, New South Wales, Australia 4 South Western Sydney Clinical School, Faculty of Medicine, University of New South Wales, Liverpool, New South Wales, Australia Cleveland Clinic Foundation, United States of America |
| AuthorAffiliation_xml | – name: 4 South Western Sydney Clinical School, Faculty of Medicine, University of New South Wales, Liverpool, New South Wales, Australia – name: 1 Queensland Centre for Medical Genomics, Institute for Molecular Bioscience, University of Queensland, St Lucia, Brisbane, Queensland, Australia – name: 7 Cancer Diagnosis Group, Northern Cancer Translational Research Unit, Royal North Shore Hospital, St Leonards, New South Wales, Australia – name: 3 Department of Surgery, Bankstown Hospital, Bankstown, Sydney, New South Wales, Australia – name: 2 The Kinghorn Cancer Centre, Cancer Research Program, Garvan Institute of Medical Research, Darlinghurst, Sydney, New South Wales, Australia – name: 6 University of Sydney, Sydney, New South Wales, Australia – name: 5 The University of Queensland, UQ Centre for Clinical Research, Brisbane, Queensland, Australia – name: Cleveland Clinic Foundation, United States of America |
| Author_xml | – sequence: 1 givenname: Sarah surname: Song fullname: Song, Sarah – sequence: 2 givenname: Katia surname: Nones fullname: Nones, Katia – sequence: 3 givenname: David surname: Miller fullname: Miller, David – sequence: 4 givenname: Ivon surname: Harliwong fullname: Harliwong, Ivon – sequence: 5 givenname: Karin S. surname: Kassahn fullname: Kassahn, Karin S. – sequence: 6 givenname: Mark surname: Pinese fullname: Pinese, Mark – sequence: 7 givenname: Marina surname: Pajic fullname: Pajic, Marina – sequence: 8 givenname: Anthony J. surname: Gill fullname: Gill, Anthony J. – sequence: 9 givenname: Amber L. surname: Johns fullname: Johns, Amber L. – sequence: 10 givenname: Matthew surname: Anderson fullname: Anderson, Matthew – sequence: 11 givenname: Oliver surname: Holmes fullname: Holmes, Oliver – sequence: 12 givenname: Conrad surname: Leonard fullname: Leonard, Conrad – sequence: 13 givenname: Darrin surname: Taylor fullname: Taylor, Darrin – sequence: 14 givenname: Scott surname: Wood fullname: Wood, Scott – sequence: 15 givenname: Qinying surname: Xu fullname: Xu, Qinying – sequence: 16 givenname: Felicity surname: Newell fullname: Newell, Felicity – sequence: 17 givenname: Mark J. surname: Cowley fullname: Cowley, Mark J. – sequence: 18 givenname: Jianmin surname: Wu fullname: Wu, Jianmin – sequence: 19 givenname: Peter surname: Wilson fullname: Wilson, Peter – sequence: 20 givenname: Lynn surname: Fink fullname: Fink, Lynn – sequence: 21 givenname: Andrew V. surname: Biankin fullname: Biankin, Andrew V. – sequence: 22 givenname: Nic surname: Waddell fullname: Waddell, Nic – sequence: 23 givenname: Sean M. surname: Grimmond fullname: Grimmond, Sean M. – sequence: 24 givenname: John V. surname: Pearson fullname: Pearson, John V. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/23049875$$D View this record in MEDLINE/PubMed |
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| ContentType | Journal Article |
| Copyright | COPYRIGHT 2012 Public Library of Science Song et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License: https://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2012 Song et al 2012 Song et al |
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| DOI | 10.1371/journal.pone.0045835 |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Competing Interests: The authors have declared that no competing interests exist. Conceived and designed the experiments: SS NW JVP KSK. Performed the experiments: KN DM IH AJG M. Pinese M. Pajic. Analyzed the data: SS NW JVP. Contributed reagents/materials/analysis tools: ALJ AVB MA OH CL DT SW QX FN MJC JW LF PW. Wrote the paper: SS KN KSK LF NW SMG JVP. |
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| References_xml | – reference: 17971856 - PLoS One. 2007;2(10):e1093 – reference: 20858232 - Genome Biol. 2010;11(9):R92 – reference: 18355774 - Am J Hum Genet. 2008 Apr;82(4):903-15 – reference: 15199222 - Proc Natl Acad Sci U S A. 2004 Jun 15;101(24):9067-72 – reference: 20837533 - Proc Natl Acad Sci U S A. 2010 Sep 28;107(39):16910-5 – reference: 2453289 - Cell. 1988 May 20;53(4):549-54 – reference: 21716283 - Nat Methods. 2011 Jul;8(7):548-9 – reference: 16049314 - J Mol Diagn. 2005 Aug;7(3):413-21 – reference: 21776081 - Nature. 2011 Jul 21;475(7356):348-52 – reference: 20847746 - Nat Rev Genet. 2010 Oct;11(10):685-96 – reference: 16799556 - Nat Med. 2006 Jul;12(7):852-5 – reference: 20125086 - Nat Rev Genet. 2010 Mar;11(3):191-203 |
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| SubjectTerms | Algorithms Biology Cancer Cell Line, Tumor Computational Biology - methods Copy number Correlation Deoxyribonucleic acid Discriminant analysis DNA DNA methylation Estimates Exons Experiments Gene expression Gene Expression Regulation Gene Frequency Genetic aspects Genetic testing Genome-Wide Association Study Genomes Genomics Heterogeneity Heterozygosity Humans K-Ras protein Lesions Loss of Heterozygosity Mathematical models Mathematics Medical research Medicine Models, Genetic Models, Statistical Mutation Pancreatic cancer Pancreatic Neoplasms - genetics Pancreatic Neoplasms - metabolism Parameter estimation Pathology Physiological aspects Polymorphism Polymorphism, Single Nucleotide Regression Analysis Reviews Sensitivity analysis Sequence Analysis, DNA Single nucleotide polymorphisms Single-nucleotide polymorphism Software Statistical analysis Statistical methods Statistical models Tumors |
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| Title | qpure: A Tool to Estimate Tumor Cellularity from Genome-Wide Single-Nucleotide Polymorphism Profiles |
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