Robust BRCA1-like classification of copy number profiles of samples repeated across different datasets and platforms

Breast cancers with BRCA1 germline mutation have a characteristic DNA copy number (CN) pattern. We developed a test that assigns CN profiles to be ‘BRCA1-like’ or ‘non-BRCA1-like’, which refers to resembling a BRCA1-mutated tumor or resembling a tumor without a BRCA1 mutation, respectively. Approxim...

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Vydané v:	Molecular oncology Ročník 9; číslo 7; s. 1274 - 1286
Hlavní autori:	Schouten, Philip C., Grigoriadis, Anita, Kuilman, Thomas, Mirza, Hasan, Watkins, Johnathan A., Cooke, Saskia A., van Dyk, Ewald, Severson, Tesa M., Rueda, Oscar M., Hoogstraat, Marlous, Verhagen, Caroline V.M., Natrajan, Rachael, Chin, Suet-Feung, Lips, Esther H., Kruizinga, Janneke, Velds, Arno, Nieuwland, Marja, Kerkhoven, Ron M., Krijgsman, Oscar, Vens, Conchita, Peeper, Daniel, Nederlof, Petra M., Caldas, Carlos, Tutt, Andrew N., Wessels, Lodewyk F., Linn, Sabine C.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States Elsevier B.V 01.08.2015 John Wiley & Sons, Inc John Wiley and Sons Inc Wiley
Predmet:	Artificial chromosomes Bacterial artificial chromosomes BRCA1 BRCA1 protein Breast cancer Breast Neoplasms - genetics Chemotherapy Classification Cohort Studies Comparative Genomic Hybridization Copy number Copy number aberration profiles Datasets Datasets as Topic Deoxyribonucleic acid DNA DNA damage DNA Methylation DNA repair Female Gene Dosage Genes, BRCA1 Genomes Humans Hybridization Medical research Mutation Next-generation sequencing Oligonucleotides Platinum Randomized Controlled Trials as Topic Research Paper Research Papers Tumors United Kingdom > UK Germany hg 18 BAC SNP6 Breast cancer Copy number aberration profiles BRCA1 CN aCGH NG720 FFPE MIP DNA NG135 SNR Classification hg19 VN BAC32K NGS BAC3K dsDNA
ISSN:	1574-7891, 1878-0261, 1878-0261
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Abstract	Breast cancers with BRCA1 germline mutation have a characteristic DNA copy number (CN) pattern. We developed a test that assigns CN profiles to be ‘BRCA1-like’ or ‘non-BRCA1-like’, which refers to resembling a BRCA1-mutated tumor or resembling a tumor without a BRCA1 mutation, respectively. Approximately one third of the BRCA1-like breast cancers have a BRCA1 mutation, one third has hypermethylation of the BRCA1 promoter and one third has an unknown reason for being BRCA1-like. This classification is indicative of patients' response to high dose alkylating and platinum containing chemotherapy regimens, which targets the inability of BRCA1 deficient cells to repair DNA double strand breaks. We investigated whether this classification can be reliably obtained with next generation sequencing and copy number platforms other than the bacterial artificial chromosome (BAC) array Comparative Genomic Hybridization (aCGH) on which it was originally developed. We investigated samples from 230 breast cancer patients for which a CN profile had been generated on two to five platforms, comprising low coverage CN sequencing, CN extraction from targeted sequencing panels (CopywriteR), Affymetrix SNP6.0, 135K/720K oligonucleotide aCGH, Affymetrix Oncoscan FFPE (MIP) technology, 3K BAC and 32K BAC aCGH. Pairwise comparison of genomic position-mapped profiles from the original aCGH platform and other platforms revealed concordance. For most cases, biological differences between samples exceeded the differences between platforms within one sample. We observed the same classification across different platforms in over 80% of the patients and kappa values of at least 0.36. Differential classification could be attributed to CN profiles that were not strongly associated to one class. In conclusion, we have shown that the genomic regions that define our BRCA1-like classifier are robustly measured by different CN profiling technologies, providing the possibility to retro- and prospectively investigate BRCA1-like classification across a wide range of CN platforms. •We investigated concordance of BRCA1-like classification of copy number profiles across technologies.•We included array-based and (targeted) next generation sequencing profiling techniques.•BRCA1-like classification was highly concordant across techniques and datasets.•10% misclassification can be attributed to poorer quality, weak class association and experimental variation.•We concluded that BRCA1-like classification of breast cancer is robust across techniques and datasets.
AbstractList	Breast cancers with BRCA1 germline mutation have a characteristic DNA copy number (CN) pattern. We developed a test that assigns CN profiles to be ‘BRCA1‐like’ or ‘non‐BRCA1‐like’, which refers to resembling a BRCA1‐mutated tumor or resembling a tumor without a BRCA1 mutation, respectively. Approximately one third of the BRCA1‐like breast cancers have a BRCA1 mutation, one third has hypermethylation of the BRCA1 promoter and one third has an unknown reason for being BRCA1‐like. This classification is indicative of patients' response to high dose alkylating and platinum containing chemotherapy regimens, which targets the inability of BRCA1 deficient cells to repair DNA double strand breaks. We investigated whether this classification can be reliably obtained with next generation sequencing and copy number platforms other than the bacterial artificial chromosome (BAC) array Comparative Genomic Hybridization (aCGH) on which it was originally developed. We investigated samples from 230 breast cancer patients for which a CN profile had been generated on two to five platforms, comprising low coverage CN sequencing, CN extraction from targeted sequencing panels (CopywriteR), Affymetrix SNP6.0, 135K/720K oligonucleotide aCGH, Affymetrix Oncoscan FFPE (MIP) technology, 3K BAC and 32K BAC aCGH. Pairwise comparison of genomic position‐mapped profiles from the original aCGH platform and other platforms revealed concordance. For most cases, biological differences between samples exceeded the differences between platforms within one sample. We observed the same classification across different platforms in over 80% of the patients and kappa values of at least 0.36. Differential classification could be attributed to CN profiles that were not strongly associated to one class. In conclusion, we have shown that the genomic regions that define our BRCA1‐like classifier are robustly measured by different CN profiling technologies, providing the possibility to retro‐ and prospectively investigate BRCA1‐like classification across a wide range of CN platforms. Highlights We investigated concordance of BRCA1‐like classification of copy number profiles across technologies. We included array‐based and (targeted) next generation sequencing profiling techniques. BRCA1‐like classification was highly concordant across techniques and datasets. 10% misclassification can be attributed to poorer quality, weak class association and experimental variation. We concluded that BRCA1‐like classification of breast cancer is robust across techniques and datasets. Breast cancers with BRCA1 germline mutation have a characteristic DNA copy number (CN) pattern. We developed a test that assigns CN profiles to be ‘BRCA1-like’ or ‘non-BRCA1-like’, which refers to resembling a BRCA1-mutated tumor or resembling a tumor without a BRCA1 mutation, respectively. Approximately one third of the BRCA1-like breast cancers have a BRCA1 mutation, one third has hypermethylation of the BRCA1 promoter and one third has an unknown reason for being BRCA1-like. This classification is indicative of patients' response to high dose alkylating and platinum containing chemotherapy regimens, which targets the inability of BRCA1 deficient cells to repair DNA double strand breaks. We investigated whether this classification can be reliably obtained with next generation sequencing and copy number platforms other than the bacterial artificial chromosome (BAC) array Comparative Genomic Hybridization (aCGH) on which it was originally developed. We investigated samples from 230 breast cancer patients for which a CN profile had been generated on two to five platforms, comprising low coverage CN sequencing, CN extraction from targeted sequencing panels (CopywriteR), Affymetrix SNP6.0, 135K/720K oligonucleotide aCGH, Affymetrix Oncoscan FFPE (MIP) technology, 3K BAC and 32K BAC aCGH. Pairwise comparison of genomic position-mapped profiles from the original aCGH platform and other platforms revealed concordance. For most cases, biological differences between samples exceeded the differences between platforms within one sample. We observed the same classification across different platforms in over 80% of the patients and kappa values of at least 0.36. Differential classification could be attributed to CN profiles that were not strongly associated to one class. In conclusion, we have shown that the genomic regions that define our BRCA1-like classifier are robustly measured by different CN profiling technologies, providing the possibility to retro- and prospectively investigate BRCA1-like classification across a wide range of CN platforms. •We investigated concordance of BRCA1-like classification of copy number profiles across technologies.•We included array-based and (targeted) next generation sequencing profiling techniques.•BRCA1-like classification was highly concordant across techniques and datasets.•10% misclassification can be attributed to poorer quality, weak class association and experimental variation.•We concluded that BRCA1-like classification of breast cancer is robust across techniques and datasets. Breast cancers with BRCA1 germline mutation have a characteristic DNA copy number (CN) pattern. We developed a test that assigns CN profiles to be ‘BRCA1‐like’ or ‘non‐BRCA1‐like’, which refers to resembling a BRCA1‐mutated tumor or resembling a tumor without a BRCA1 mutation, respectively. Approximately one third of the BRCA1‐like breast cancers have a BRCA1 mutation, one third has hypermethylation of the BRCA1 promoter and one third has an unknown reason for being BRCA1‐like. This classification is indicative of patients' response to high dose alkylating and platinum containing chemotherapy regimens, which targets the inability of BRCA1 deficient cells to repair DNA double strand breaks. We investigated whether this classification can be reliably obtained with next generation sequencing and copy number platforms other than the bacterial artificial chromosome (BAC) array Comparative Genomic Hybridization (aCGH) on which it was originally developed. We investigated samples from 230 breast cancer patients for which a CN profile had been generated on two to five platforms, comprising low coverage CN sequencing, CN extraction from targeted sequencing panels (CopywriteR), Affymetrix SNP6.0, 135K/720K oligonucleotide aCGH, Affymetrix Oncoscan FFPE (MIP) technology, 3K BAC and 32K BAC aCGH. Pairwise comparison of genomic position‐mapped profiles from the original aCGH platform and other platforms revealed concordance. For most cases, biological differences between samples exceeded the differences between platforms within one sample. We observed the same classification across different platforms in over 80% of the patients and kappa values of at least 0.36. Differential classification could be attributed to CN profiles that were not strongly associated to one class. In conclusion, we have shown that the genomic regions that define our BRCA1‐like classifier are robustly measured by different CN profiling technologies, providing the possibility to retro‐ and prospectively investigate BRCA1‐like classification across a wide range of CN platforms. Breast cancers with BRCA1 germline mutation have a characteristic DNA copy number (CN) pattern. We developed a test that assigns CN profiles to be ‘BRCA1‐like’ or ‘non‐BRCA1‐like’, which refers to resembling a BRCA1‐mutated tumor or resembling a tumor without a BRCA1 mutation, respectively. Approximately one third of the BRCA1‐like breast cancers have a BRCA1 mutation, one third has hypermethylation of the BRCA1 promoter and one third has an unknown reason for being BRCA1‐like. This classification is indicative of patients' response to high dose alkylating and platinum containing chemotherapy regimens, which targets the inability of BRCA1 deficient cells to repair DNA double strand breaks. We investigated whether this classification can be reliably obtained with next generation sequencing and copy number platforms other than the bacterial artificial chromosome (BAC) array Comparative Genomic Hybridization (aCGH) on which it was originally developed. We investigated samples from 230 breast cancer patients for which a CN profile had been generated on two to five platforms, comprising low coverage CN sequencing, CN extraction from targeted sequencing panels (CopywriteR), Affymetrix SNP6.0, 135K/720K oligonucleotide aCGH, Affymetrix Oncoscan FFPE (MIP) technology, 3K BAC and 32K BAC aCGH. Pairwise comparison of genomic position‐mapped profiles from the original aCGH platform and other platforms revealed concordance. For most cases, biological differences between samples exceeded the differences between platforms within one sample. We observed the same classification across different platforms in over 80% of the patients and kappa values of at least 0.36. Differential classification could be attributed to CN profiles that were not strongly associated to one class. In conclusion, we have shown that the genomic regions that define our BRCA1‐like classifier are robustly measured by different CN profiling technologies, providing the possibility to retro‐ and prospectively investigate BRCA1‐like classification across a wide range of CN platforms. We investigated concordance of BRCA1‐like classification of copy number profiles across technologies. We included array‐based and (targeted) next generation sequencing profiling techniques. BRCA1‐like classification was highly concordant across techniques and datasets. 10% misclassification can be attributed to poorer quality, weak class association and experimental variation. We concluded that BRCA1‐like classification of breast cancer is robust across techniques and datasets. Breast cancers with BRCA1 germline mutation have a characteristic DNA copy number (CN) pattern. We developed a test that assigns CN profiles to be ‘BRCA1‐like’ or ‘non‐BRCA1‐like’, which refers to resembling a BRCA1‐mutated tumor or resembling a tumor without a BRCA1 mutation, respectively. Approximately one third of the BRCA1‐like breast cancers have a BRCA1 mutation, one third has hypermethylation of the BRCA1 promoter and one third has an unknown reason for being BRCA1‐like. This classification is indicative of patients' response to high dose alkylating and platinum containing chemotherapy regimens, which targets the inability of BRCA1 deficient cells to repair DNA double strand breaks. We investigated whether this classification can be reliably obtained with next generation sequencing and copy number platforms other than the bacterial artificial chromosome (BAC) array Comparative Genomic Hybridization (aCGH) on which it was originally developed. We investigated samples from 230 breast cancer patients for which a CN profile had been generated on two to five platforms, comprising low coverage CN sequencing, CN extraction from targeted sequencing panels (CopywriteR), Affymetrix SNP6.0, 135K/720K oligonucleotide aCGH, Affymetrix Oncoscan FFPE (MIP) technology, 3K BAC and 32K BAC aCGH. Pairwise comparison of genomic position‐mapped profiles from the original aCGH platform and other platforms revealed concordance. For most cases, biological differences between samples exceeded the differences between platforms within one sample. We observed the same classification across different platforms in over 80% of the patients and kappa values of at least 0.36. Differential classification could be attributed to CN profiles that were not strongly associated to one class. In conclusion, we have shown that the genomic regions that define our BRCA1‐like classifier are robustly measured by different CN profiling technologies, providing the possibility to retro‐ and prospectively investigate BRCA1‐like classification across a wide range of CN platforms. We investigated concordance of BRCA1‐like classification of copy number profiles across technologies.We included array‐based and (targeted) next generation sequencing profiling techniques.BRCA1‐like classification was highly concordant across techniques and datasets.10% misclassification can be attributed to poorer quality, weak class association and experimental variation.We concluded that BRCA1‐like classification of breast cancer is robust across techniques and datasets. Breast cancers with BRCA1 germline mutation have a characteristic DNA copy number (CN) pattern. We developed a test that assigns CN profiles to be 'BRCA1-like' or 'non-BRCA1-like', which refers to resembling a BRCA1-mutated tumor or resembling a tumor without a BRCA1 mutation, respectively. Approximately one third of the BRCA1-like breast cancers have a BRCA1 mutation, one third has hypermethylation of the BRCA1 promoter and one third has an unknown reason for being BRCA1-like. This classification is indicative of patients' response to high dose alkylating and platinum containing chemotherapy regimens, which targets the inability of BRCA1 deficient cells to repair DNA double strand breaks. We investigated whether this classification can be reliably obtained with next generation sequencing and copy number platforms other than the bacterial artificial chromosome (BAC) array Comparative Genomic Hybridization (aCGH) on which it was originally developed. We investigated samples from 230 breast cancer patients for which a CN profile had been generated on two to five platforms, comprising low coverage CN sequencing, CN extraction from targeted sequencing panels (CopywriteR), Affymetrix SNP6.0, 135K/720K oligonucleotide aCGH, Affymetrix Oncoscan FFPE (MIP) technology, 3K BAC and 32K BAC aCGH. Pairwise comparison of genomic position-mapped profiles from the original aCGH platform and other platforms revealed concordance. For most cases, biological differences between samples exceeded the differences between platforms within one sample. We observed the same classification across different platforms in over 80% of the patients and kappa values of at least 0.36. Differential classification could be attributed to CN profiles that were not strongly associated to one class. In conclusion, we have shown that the genomic regions that define our BRCA1-like classifier are robustly measured by different CN profiling technologies, providing the possibility to retro- and prospectively investigate BRCA1-like classification across a wide range of CN platforms.Breast cancers with BRCA1 germline mutation have a characteristic DNA copy number (CN) pattern. We developed a test that assigns CN profiles to be 'BRCA1-like' or 'non-BRCA1-like', which refers to resembling a BRCA1-mutated tumor or resembling a tumor without a BRCA1 mutation, respectively. Approximately one third of the BRCA1-like breast cancers have a BRCA1 mutation, one third has hypermethylation of the BRCA1 promoter and one third has an unknown reason for being BRCA1-like. This classification is indicative of patients' response to high dose alkylating and platinum containing chemotherapy regimens, which targets the inability of BRCA1 deficient cells to repair DNA double strand breaks. We investigated whether this classification can be reliably obtained with next generation sequencing and copy number platforms other than the bacterial artificial chromosome (BAC) array Comparative Genomic Hybridization (aCGH) on which it was originally developed. We investigated samples from 230 breast cancer patients for which a CN profile had been generated on two to five platforms, comprising low coverage CN sequencing, CN extraction from targeted sequencing panels (CopywriteR), Affymetrix SNP6.0, 135K/720K oligonucleotide aCGH, Affymetrix Oncoscan FFPE (MIP) technology, 3K BAC and 32K BAC aCGH. Pairwise comparison of genomic position-mapped profiles from the original aCGH platform and other platforms revealed concordance. For most cases, biological differences between samples exceeded the differences between platforms within one sample. We observed the same classification across different platforms in over 80% of the patients and kappa values of at least 0.36. Differential classification could be attributed to CN profiles that were not strongly associated to one class. In conclusion, we have shown that the genomic regions that define our BRCA1-like classifier are robustly measured by different CN profiling technologies, providing the possibility to retro- and prospectively investigate BRCA1-like classification across a wide range of CN platforms.
Author	Nieuwland, Marja Verhagen, Caroline V.M. Wessels, Lodewyk F. Krijgsman, Oscar Mirza, Hasan Peeper, Daniel Linn, Sabine C. Tutt, Andrew N. Grigoriadis, Anita Rueda, Oscar M. Velds, Arno Kerkhoven, Ron M. Kuilman, Thomas Caldas, Carlos Nederlof, Petra M. Natrajan, Rachael van Dyk, Ewald Schouten, Philip C. Kruizinga, Janneke Cooke, Saskia A. Chin, Suet-Feung Hoogstraat, Marlous Vens, Conchita Watkins, Johnathan A. Lips, Esther H. Severson, Tesa M.
AuthorAffiliation	16 Division of Medical Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands 11 Department of Pathology, Netherlands Cancer Institute, Amsterdam, The Netherlands 6 Department of Medical Oncology, University Medical Center Utrecht, Utrecht, The Netherlands 2 Breakthrough Breast Cancer Research Unit, Department of Research Oncology, Guy's Hospital, King's College London School of Medicine, London, United Kingdom 5 Cancer Research UK Cambridge Research Institute, Li Ka Shing Centre, Cambridge, UK 1 Department of Molecular Pathology, Netherlands Cancer Institute, Amsterdam, The Netherlands 13 Cambridge Experimental Cancer Medicine Centre and NIHR Cambridge Biomedical, Research Centre, Cambridge University Hospitals NHS, Cambridge, UK 14 Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, The Netherlands 8 Division of Biological Stress Response, Netherlands Cancer Institute, Amsterdam, The Netherlands 4 Department of Molecular Carc
AuthorAffiliation_xml	– name: 8 Division of Biological Stress Response, Netherlands Cancer Institute, Amsterdam, The Netherlands – name: 14 Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, The Netherlands – name: 16 Division of Medical Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands – name: 7 Netherlands Center for Personalized Cancer Treatment, Utrecht, The Netherlands – name: 1 Department of Molecular Pathology, Netherlands Cancer Institute, Amsterdam, The Netherlands – name: 12 Department of Oncology, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK – name: 9 The Breakthrough Breast Cancer Research Centre, The Institute of Cancer Research, London, UK – name: 6 Department of Medical Oncology, University Medical Center Utrecht, Utrecht, The Netherlands – name: 10 Genomics Core Facility, Netherlands Cancer Institute, Amsterdam, The Netherlands – name: 15 Department of Pathology, University Medical Center Utrecht, Utrecht, The Netherlands – name: 2 Breakthrough Breast Cancer Research Unit, Department of Research Oncology, Guy's Hospital, King's College London School of Medicine, London, United Kingdom – name: 11 Department of Pathology, Netherlands Cancer Institute, Amsterdam, The Netherlands – name: 4 Department of Molecular Carcinogenesis, Netherlands Cancer Institute, Amsterdam, The Netherlands – name: 5 Cancer Research UK Cambridge Research Institute, Li Ka Shing Centre, Cambridge, UK – name: 13 Cambridge Experimental Cancer Medicine Centre and NIHR Cambridge Biomedical, Research Centre, Cambridge University Hospitals NHS, Cambridge, UK – name: 3 Division of Molecular Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands
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Marlous surname: Hoogstraat fullname: Hoogstraat, Marlous organization: Department of Molecular Pathology, Netherlands Cancer Institute, Amsterdam, The Netherlands – sequence: 11 givenname: Caroline V.M. surname: Verhagen fullname: Verhagen, Caroline V.M. organization: Division of Biological Stress Response, Netherlands Cancer Institute, Amsterdam, The Netherlands – sequence: 12 givenname: Rachael surname: Natrajan fullname: Natrajan, Rachael organization: The Breakthrough Breast Cancer Research Centre, The Institute of Cancer Research, London, UK – sequence: 13 givenname: Suet-Feung surname: Chin fullname: Chin, Suet-Feung organization: Cancer Research UK Cambridge Research Institute, Li Ka Shing Centre, Cambridge, UK – sequence: 14 givenname: Esther H. surname: Lips fullname: Lips, Esther H. organization: Department of Molecular Pathology, Netherlands Cancer Institute, Amsterdam, The Netherlands – sequence: 15 givenname: Janneke surname: Kruizinga fullname: Kruizinga, Janneke organization: Genomics Core Facility, Netherlands Cancer Institute, Amsterdam, The Netherlands – sequence: 16 givenname: Arno surname: Velds fullname: Velds, Arno organization: Genomics Core Facility, Netherlands Cancer Institute, Amsterdam, The Netherlands – sequence: 17 givenname: Marja surname: Nieuwland fullname: Nieuwland, Marja organization: Genomics Core Facility, Netherlands Cancer Institute, Amsterdam, The Netherlands – sequence: 18 givenname: Ron M. surname: Kerkhoven fullname: Kerkhoven, Ron M. organization: Genomics Core Facility, Netherlands Cancer Institute, Amsterdam, The Netherlands – sequence: 19 givenname: Oscar surname: Krijgsman fullname: Krijgsman, Oscar organization: Division of Molecular Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands – sequence: 20 givenname: Conchita surname: Vens fullname: Vens, Conchita organization: Division of Biological Stress Response, Netherlands Cancer Institute, Amsterdam, The Netherlands – sequence: 21 givenname: Daniel surname: Peeper fullname: Peeper, Daniel organization: Division of Molecular Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands – sequence: 22 givenname: Petra M. orcidid: 0000-0002-2358-9765 surname: Nederlof fullname: Nederlof, Petra M. organization: Department of Pathology, Netherlands Cancer Institute, Amsterdam, The Netherlands – sequence: 23 givenname: Carlos surname: Caldas fullname: Caldas, Carlos organization: Cancer Research UK Cambridge Research Institute, Li Ka Shing Centre, Cambridge, UK – sequence: 24 givenname: Andrew N. surname: Tutt fullname: Tutt, Andrew N. organization: Breakthrough Breast Cancer Research Unit, Department of Research Oncology, Guy's Hospital, King's College London School of Medicine, London, United Kingdom – sequence: 25 givenname: Lodewyk F. surname: Wessels fullname: Wessels, Lodewyk F. organization: Department of Molecular Carcinogenesis, Netherlands Cancer Institute, Amsterdam, The Netherlands – sequence: 26 givenname: Sabine C. surname: Linn fullname: Linn, Sabine C. email: s.linn@nki.nl organization: Department of Molecular Pathology, Netherlands Cancer Institute, Amsterdam, The Netherlands
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Copyright	2015 The Authors 2015 The Authors. Published by FEBS Press and John Wiley & Sons Ltd. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved. 2015. This work is published under https://creativecommons.org/licenses/by-nc-nd/4.0/ (the "License"). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml	– notice: 2015 The Authors – notice: 2015 The Authors. Published by FEBS Press and John Wiley & Sons Ltd. – notice: Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved. – notice: 2015. This work is published under https://creativecommons.org/licenses/by-nc-nd/4.0/ (the "License"). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
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Snippet	Breast cancers with BRCA1 germline mutation have a characteristic DNA copy number (CN) pattern. We developed a test that assigns CN profiles to be ‘BRCA1-like’... Breast cancers with BRCA1 germline mutation have a characteristic DNA copy number (CN) pattern. We developed a test that assigns CN profiles to be ‘BRCA1‐like’... Breast cancers with BRCA1 germline mutation have a characteristic DNA copy number (CN) pattern. We developed a test that assigns CN profiles to be 'BRCA1-like'...
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SubjectTerms	Artificial chromosomes Bacterial artificial chromosomes BRCA1 BRCA1 protein Breast cancer Breast Neoplasms - genetics Chemotherapy Classification Cohort Studies Comparative Genomic Hybridization Copy number Copy number aberration profiles Datasets Datasets as Topic Deoxyribonucleic acid DNA DNA damage DNA Methylation DNA repair Female Gene Dosage Genes, BRCA1 Genomes Humans Hybridization Medical research Mutation Next-generation sequencing Oligonucleotides Platinum Randomized Controlled Trials as Topic Research Paper Research Papers Tumors
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Title	Robust BRCA1-like classification of copy number profiles of samples repeated across different datasets and platforms
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