Integrative clustering reveals a novel split in the luminal A subtype of breast cancer with impact on outcome
Background Breast cancer is a heterogeneous disease at the clinical and molecular level. In this study we integrate classifications extracted from five different molecular levels in order to identify integrated subtypes. Methods Tumor tissue from 425 patients with primary breast cancer from the Oslo...
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Published in | Breast cancer research : BCR Vol. 19; no. 1; pp. 44 - 18 |
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Main Authors | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
London
BioMed Central
29.03.2017
BMC |
Subjects | |
Online Access | Get full text |
ISSN | 1465-542X 1465-5411 1465-542X |
DOI | 10.1186/s13058-017-0812-y |
Cover
Abstract | Background
Breast cancer is a heterogeneous disease at the clinical and molecular level. In this study we integrate classifications extracted from five different molecular levels in order to identify integrated subtypes.
Methods
Tumor tissue from 425 patients with primary breast cancer from the Oslo2 study was cut and blended, and divided into fractions for DNA, RNA and protein isolation and metabolomics, allowing the acquisition of representative and comparable molecular data. Patients were stratified into groups based on their tumor characteristics from five different molecular levels, using various clustering methods. Finally, all previously identified and newly determined subgroups were combined in a multilevel classification using a “cluster-of-clusters” approach with consensus clustering.
Results
Based on DNA copy number data, tumors were categorized into three groups according to the complex arm aberration index. mRNA expression profiles divided tumors into five molecular subgroups according to PAM50 subtyping, and clustering based on microRNA expression revealed four subgroups. Reverse-phase protein array data divided tumors into five subgroups. Hierarchical clustering of tumor metabolic profiles revealed three clusters. Combining DNA copy number and mRNA expression classified tumors into seven clusters based on pathway activity levels, and tumors were classified into ten subtypes using integrative clustering. The final consensus clustering that incorporated all aforementioned subtypes revealed six major groups. Five corresponded well with the mRNA subtypes, while a sixth group resulted from a split of the luminal A subtype; these tumors belonged to distinct microRNA clusters. Gain-of-function studies using MCF-7 cells showed that microRNAs differentially expressed between the luminal A clusters were important for cancer cell survival. These microRNAs were used to validate the split in luminal A tumors in four independent breast cancer cohorts. In two cohorts the microRNAs divided tumors into subgroups with significantly different outcomes, and in another a trend was observed.
Conclusions
The six integrated subtypes identified confirm the heterogeneity of breast cancer and show that finer subdivisions of subtypes are evident. Increasing knowledge of the heterogeneity of the luminal A subtype may add pivotal information to guide therapeutic choices, evidently bringing us closer to improved treatment for this largest subgroup of breast cancer. |
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AbstractList | Breast cancer is a heterogeneous disease at the clinical and molecular level. In this study we integrate classifications extracted from five different molecular levels in order to identify integrated subtypes.
Tumor tissue from 425 patients with primary breast cancer from the Oslo2 study was cut and blended, and divided into fractions for DNA, RNA and protein isolation and metabolomics, allowing the acquisition of representative and comparable molecular data. Patients were stratified into groups based on their tumor characteristics from five different molecular levels, using various clustering methods. Finally, all previously identified and newly determined subgroups were combined in a multilevel classification using a "cluster-of-clusters" approach with consensus clustering.
Based on DNA copy number data, tumors were categorized into three groups according to the complex arm aberration index. mRNA expression profiles divided tumors into five molecular subgroups according to PAM50 subtyping, and clustering based on microRNA expression revealed four subgroups. Reverse-phase protein array data divided tumors into five subgroups. Hierarchical clustering of tumor metabolic profiles revealed three clusters. Combining DNA copy number and mRNA expression classified tumors into seven clusters based on pathway activity levels, and tumors were classified into ten subtypes using integrative clustering. The final consensus clustering that incorporated all aforementioned subtypes revealed six major groups. Five corresponded well with the mRNA subtypes, while a sixth group resulted from a split of the luminal A subtype; these tumors belonged to distinct microRNA clusters. Gain-of-function studies using MCF-7 cells showed that microRNAs differentially expressed between the luminal A clusters were important for cancer cell survival. These microRNAs were used to validate the split in luminal A tumors in four independent breast cancer cohorts. In two cohorts the microRNAs divided tumors into subgroups with significantly different outcomes, and in another a trend was observed.
The six integrated subtypes identified confirm the heterogeneity of breast cancer and show that finer subdivisions of subtypes are evident. Increasing knowledge of the heterogeneity of the luminal A subtype may add pivotal information to guide therapeutic choices, evidently bringing us closer to improved treatment for this largest subgroup of breast cancer. Abstract Background Breast cancer is a heterogeneous disease at the clinical and molecular level. In this study we integrate classifications extracted from five different molecular levels in order to identify integrated subtypes. Methods Tumor tissue from 425 patients with primary breast cancer from the Oslo2 study was cut and blended, and divided into fractions for DNA, RNA and protein isolation and metabolomics, allowing the acquisition of representative and comparable molecular data. Patients were stratified into groups based on their tumor characteristics from five different molecular levels, using various clustering methods. Finally, all previously identified and newly determined subgroups were combined in a multilevel classification using a “cluster-of-clusters” approach with consensus clustering. Results Based on DNA copy number data, tumors were categorized into three groups according to the complex arm aberration index. mRNA expression profiles divided tumors into five molecular subgroups according to PAM50 subtyping, and clustering based on microRNA expression revealed four subgroups. Reverse-phase protein array data divided tumors into five subgroups. Hierarchical clustering of tumor metabolic profiles revealed three clusters. Combining DNA copy number and mRNA expression classified tumors into seven clusters based on pathway activity levels, and tumors were classified into ten subtypes using integrative clustering. The final consensus clustering that incorporated all aforementioned subtypes revealed six major groups. Five corresponded well with the mRNA subtypes, while a sixth group resulted from a split of the luminal A subtype; these tumors belonged to distinct microRNA clusters. Gain-of-function studies using MCF-7 cells showed that microRNAs differentially expressed between the luminal A clusters were important for cancer cell survival. These microRNAs were used to validate the split in luminal A tumors in four independent breast cancer cohorts. In two cohorts the microRNAs divided tumors into subgroups with significantly different outcomes, and in another a trend was observed. Conclusions The six integrated subtypes identified confirm the heterogeneity of breast cancer and show that finer subdivisions of subtypes are evident. Increasing knowledge of the heterogeneity of the luminal A subtype may add pivotal information to guide therapeutic choices, evidently bringing us closer to improved treatment for this largest subgroup of breast cancer. Background Breast cancer is a heterogeneous disease at the clinical and molecular level. In this study we integrate classifications extracted from five different molecular levels in order to identify integrated subtypes. Methods Tumor tissue from 425 patients with primary breast cancer from the Oslo2 study was cut and blended, and divided into fractions for DNA, RNA and protein isolation and metabolomics, allowing the acquisition of representative and comparable molecular data. Patients were stratified into groups based on their tumor characteristics from five different molecular levels, using various clustering methods. Finally, all previously identified and newly determined subgroups were combined in a multilevel classification using a “cluster-of-clusters” approach with consensus clustering. Results Based on DNA copy number data, tumors were categorized into three groups according to the complex arm aberration index. mRNA expression profiles divided tumors into five molecular subgroups according to PAM50 subtyping, and clustering based on microRNA expression revealed four subgroups. Reverse-phase protein array data divided tumors into five subgroups. Hierarchical clustering of tumor metabolic profiles revealed three clusters. Combining DNA copy number and mRNA expression classified tumors into seven clusters based on pathway activity levels, and tumors were classified into ten subtypes using integrative clustering. The final consensus clustering that incorporated all aforementioned subtypes revealed six major groups. Five corresponded well with the mRNA subtypes, while a sixth group resulted from a split of the luminal A subtype; these tumors belonged to distinct microRNA clusters. Gain-of-function studies using MCF-7 cells showed that microRNAs differentially expressed between the luminal A clusters were important for cancer cell survival. These microRNAs were used to validate the split in luminal A tumors in four independent breast cancer cohorts. In two cohorts the microRNAs divided tumors into subgroups with significantly different outcomes, and in another a trend was observed. Conclusions The six integrated subtypes identified confirm the heterogeneity of breast cancer and show that finer subdivisions of subtypes are evident. Increasing knowledge of the heterogeneity of the luminal A subtype may add pivotal information to guide therapeutic choices, evidently bringing us closer to improved treatment for this largest subgroup of breast cancer. Breast cancer is a heterogeneous disease at the clinical and molecular level. In this study we integrate classifications extracted from five different molecular levels in order to identify integrated subtypes.BACKGROUNDBreast cancer is a heterogeneous disease at the clinical and molecular level. In this study we integrate classifications extracted from five different molecular levels in order to identify integrated subtypes.Tumor tissue from 425 patients with primary breast cancer from the Oslo2 study was cut and blended, and divided into fractions for DNA, RNA and protein isolation and metabolomics, allowing the acquisition of representative and comparable molecular data. Patients were stratified into groups based on their tumor characteristics from five different molecular levels, using various clustering methods. Finally, all previously identified and newly determined subgroups were combined in a multilevel classification using a "cluster-of-clusters" approach with consensus clustering.METHODSTumor tissue from 425 patients with primary breast cancer from the Oslo2 study was cut and blended, and divided into fractions for DNA, RNA and protein isolation and metabolomics, allowing the acquisition of representative and comparable molecular data. Patients were stratified into groups based on their tumor characteristics from five different molecular levels, using various clustering methods. Finally, all previously identified and newly determined subgroups were combined in a multilevel classification using a "cluster-of-clusters" approach with consensus clustering.Based on DNA copy number data, tumors were categorized into three groups according to the complex arm aberration index. mRNA expression profiles divided tumors into five molecular subgroups according to PAM50 subtyping, and clustering based on microRNA expression revealed four subgroups. Reverse-phase protein array data divided tumors into five subgroups. Hierarchical clustering of tumor metabolic profiles revealed three clusters. Combining DNA copy number and mRNA expression classified tumors into seven clusters based on pathway activity levels, and tumors were classified into ten subtypes using integrative clustering. The final consensus clustering that incorporated all aforementioned subtypes revealed six major groups. Five corresponded well with the mRNA subtypes, while a sixth group resulted from a split of the luminal A subtype; these tumors belonged to distinct microRNA clusters. Gain-of-function studies using MCF-7 cells showed that microRNAs differentially expressed between the luminal A clusters were important for cancer cell survival. These microRNAs were used to validate the split in luminal A tumors in four independent breast cancer cohorts. In two cohorts the microRNAs divided tumors into subgroups with significantly different outcomes, and in another a trend was observed.RESULTSBased on DNA copy number data, tumors were categorized into three groups according to the complex arm aberration index. mRNA expression profiles divided tumors into five molecular subgroups according to PAM50 subtyping, and clustering based on microRNA expression revealed four subgroups. Reverse-phase protein array data divided tumors into five subgroups. Hierarchical clustering of tumor metabolic profiles revealed three clusters. Combining DNA copy number and mRNA expression classified tumors into seven clusters based on pathway activity levels, and tumors were classified into ten subtypes using integrative clustering. The final consensus clustering that incorporated all aforementioned subtypes revealed six major groups. Five corresponded well with the mRNA subtypes, while a sixth group resulted from a split of the luminal A subtype; these tumors belonged to distinct microRNA clusters. Gain-of-function studies using MCF-7 cells showed that microRNAs differentially expressed between the luminal A clusters were important for cancer cell survival. These microRNAs were used to validate the split in luminal A tumors in four independent breast cancer cohorts. In two cohorts the microRNAs divided tumors into subgroups with significantly different outcomes, and in another a trend was observed.The six integrated subtypes identified confirm the heterogeneity of breast cancer and show that finer subdivisions of subtypes are evident. Increasing knowledge of the heterogeneity of the luminal A subtype may add pivotal information to guide therapeutic choices, evidently bringing us closer to improved treatment for this largest subgroup of breast cancer.CONCLUSIONSThe six integrated subtypes identified confirm the heterogeneity of breast cancer and show that finer subdivisions of subtypes are evident. Increasing knowledge of the heterogeneity of the luminal A subtype may add pivotal information to guide therapeutic choices, evidently bringing us closer to improved treatment for this largest subgroup of breast cancer. Background Breast cancer is a heterogeneous disease at the clinical and molecular level. In this study we integrate classifications extracted from five different molecular levels in order to identify integrated subtypes. Methods Tumor tissue from 425 patients with primary breast cancer from the Oslo2 study was cut and blended, and divided into fractions for DNA, RNA and protein isolation and metabolomics, allowing the acquisition of representative and comparable molecular data. Patients were stratified into groups based on their tumor characteristics from five different molecular levels, using various clustering methods. Finally, all previously identified and newly determined subgroups were combined in a multilevel classification using a “cluster-of-clusters” approach with consensus clustering. Results Based on DNA copy number data, tumors were categorized into three groups according to the complex arm aberration index. mRNA expression profiles divided tumors into five molecular subgroups according to PAM50 subtyping, and clustering based on microRNA expression revealed four subgroups. Reverse-phase protein array data divided tumors into five subgroups. Hierarchical clustering of tumor metabolic profiles revealed three clusters. Combining DNA copy number and mRNA expression classified tumors into seven clusters based on pathway activity levels, and tumors were classified into ten subtypes using integrative clustering. The final consensus clustering that incorporated all aforementioned subtypes revealed six major groups. Five corresponded well with the mRNA subtypes, while a sixth group resulted from a split of the luminal A subtype; these tumors belonged to distinct microRNA clusters. Gain-of-function studies using MCF-7 cells showed that microRNAs differentially expressed between the luminal A clusters were important for cancer cell survival. These microRNAs were used to validate the split in luminal A tumors in four independent breast cancer cohorts. In two cohorts the microRNAs divided tumors into subgroups with significantly different outcomes, and in another a trend was observed. Conclusions The six integrated subtypes identified confirm the heterogeneity of breast cancer and show that finer subdivisions of subtypes are evident. Increasing knowledge of the heterogeneity of the luminal A subtype may add pivotal information to guide therapeutic choices, evidently bringing us closer to improved treatment for this largest subgroup of breast cancer. |
ArticleNumber | 44 |
Author | Jernström, Sandra Møller, Elen K. Kåresen, Rolf Krohn, Marit Frigessi, Arnoldo Bukholm, Ida R. K. Lingjærde, Ole Christian Lüders, Torben Sauer, Torill Sahlberg, Kristine K. Leivonen, Suvi-Katri Caldas, Carlos Geisler, Jürgen Mills, Gordon B. Zhao, Wei Vaske, Charles J. Vitelli, Valeria Rødland, Einar Kumar, Surendra Naume, Bjørn Børresen-Dale, Anne-Lise Overgaard, Jens Giskeødegård, Guro F. Bathen, Tone Frost Nord, Silje Kristensen, Vessela N. Tramm, Trine Aure, Miriam Ragle Alsner, Jan Vollan, Hans Kristian Moen Schlichting, Ellen Haukaas, Tonje Husby Mælandsmo, Gunhild M. Due, Eldri U. |
Author_xml | – sequence: 1 givenname: Miriam Ragle surname: Aure fullname: Aure, Miriam Ragle organization: Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, The Norwegian Radium Hospital, K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo – sequence: 2 givenname: Valeria surname: Vitelli fullname: Vitelli, Valeria organization: Oslo Center for Biostatistics and Epidemiology, Institute of Basic Medical Science, University of Oslo – sequence: 3 givenname: Sandra surname: Jernström fullname: Jernström, Sandra organization: Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, The Norwegian Radium Hospital, K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo – sequence: 4 givenname: Surendra surname: Kumar fullname: Kumar, Surendra organization: Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, The Norwegian Radium Hospital, K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Department of Clinical Molecular Biology (EpiGen), Division of Medicine, Akershus University Hospital – sequence: 5 givenname: Marit surname: Krohn fullname: Krohn, Marit organization: Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, The Norwegian Radium Hospital, K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo – sequence: 6 givenname: Eldri U. surname: Due fullname: Due, Eldri U. organization: Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, The Norwegian Radium Hospital, K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo – sequence: 7 givenname: Tonje Husby surname: Haukaas fullname: Haukaas, Tonje Husby organization: K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU) – sequence: 8 givenname: Suvi-Katri surname: Leivonen fullname: Leivonen, Suvi-Katri organization: Genome-Scale Biology Research Program, University of Helsinki – sequence: 9 givenname: Hans Kristian Moen surname: Vollan fullname: Vollan, Hans Kristian Moen organization: Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, The Norwegian Radium Hospital, K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo – sequence: 10 givenname: Torben surname: Lüders fullname: Lüders, Torben organization: K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Department of Clinical Molecular Biology (EpiGen), Division of Medicine, Akershus University Hospital – sequence: 11 givenname: Einar surname: Rødland fullname: Rødland, Einar organization: Department of Tumor Biology, Institute for Cancer Research, Oslo University Hospital, The Norwegian Radium Hospital – sequence: 12 givenname: Charles J. surname: Vaske fullname: Vaske, Charles J. organization: Five3 Genomics, LLC – sequence: 13 givenname: Wei surname: Zhao fullname: Zhao, Wei organization: Department of Systems Biology, University of Texas M.D. Anderson Cancer Center – sequence: 14 givenname: Elen K. surname: Møller fullname: Møller, Elen K. organization: Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, The Norwegian Radium Hospital, K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo – sequence: 15 givenname: Silje surname: Nord fullname: Nord, Silje organization: Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, The Norwegian Radium Hospital, K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo – sequence: 16 givenname: Guro F. surname: Giskeødegård fullname: Giskeødegård, Guro F. organization: Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU) – sequence: 17 givenname: Tone Frost surname: Bathen fullname: Bathen, Tone Frost organization: K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU) – sequence: 18 givenname: Carlos surname: Caldas fullname: Caldas, Carlos organization: Cambridge University Hospitals Trust, Addenbrookes Hospital, Cancer Research UK Cambridge Institute, University of Cambridge – sequence: 19 givenname: Trine surname: Tramm fullname: Tramm, Trine organization: Department of Experimental Clinical Oncology, Aarhus University Hospital – sequence: 20 givenname: Jan surname: Alsner fullname: Alsner, Jan organization: Department of Experimental Clinical Oncology, Aarhus University Hospital – sequence: 21 givenname: Jens surname: Overgaard fullname: Overgaard, Jens organization: Department of Experimental Clinical Oncology, Aarhus University Hospital – sequence: 22 givenname: Jürgen surname: Geisler fullname: Geisler, Jürgen organization: Department of Oncology, Akershus University Hospital, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo – sequence: 23 givenname: Ida R. K. surname: Bukholm fullname: Bukholm, Ida R. K. organization: Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Department of Surgery, Akershus University Hospital – sequence: 24 givenname: Bjørn surname: Naume fullname: Naume, Bjørn organization: K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Department of Oncology, Division of Cancer Medicine, Oslo University Hospital – sequence: 25 givenname: Ellen surname: Schlichting fullname: Schlichting, Ellen organization: K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Department of Breast and Endocrine Surgery, Oslo University Hospital – sequence: 26 givenname: Torill surname: Sauer fullname: Sauer, Torill organization: K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Department of Pathology, Akershus University Hospital – sequence: 27 givenname: Gordon B. surname: Mills fullname: Mills, Gordon B. organization: Department of Systems Biology, University of Texas M.D. Anderson Cancer Center – sequence: 28 givenname: Rolf surname: Kåresen fullname: Kåresen, Rolf organization: K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Department of Breast and Endocrine Surgery, Oslo University Hospital – sequence: 29 givenname: Gunhild M. surname: Mælandsmo fullname: Mælandsmo, Gunhild M. organization: K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Department of Tumor Biology, Institute for Cancer Research, Oslo University Hospital, The Norwegian Radium Hospital – sequence: 30 givenname: Ole Christian surname: Lingjærde fullname: Lingjærde, Ole Christian organization: K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Centre for Cancer Biomedicine, University of Oslo, Department of Computer Science, University of Oslo – sequence: 31 givenname: Arnoldo surname: Frigessi fullname: Frigessi, Arnoldo organization: Oslo Center for Biostatistics and Epidemiology, Institute of Basic Medical Science, University of Oslo, Oslo Center for Biostatistics and Epidemiology, Oslo University Hospital – sequence: 32 givenname: Vessela N. surname: Kristensen fullname: Kristensen, Vessela N. organization: Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, The Norwegian Radium Hospital, K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Department of Clinical Molecular Biology (EpiGen), Division of Medicine, Akershus University Hospital – sequence: 33 givenname: Anne-Lise surname: Børresen-Dale fullname: Børresen-Dale, Anne-Lise organization: Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, The Norwegian Radium Hospital, K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo – sequence: 34 givenname: Kristine K. surname: Sahlberg fullname: Sahlberg, Kristine K. email: kristine.sahlberg@vestreviken.no organization: Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, The Norwegian Radium Hospital, K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Department of Research, Vestre Viken Hospital Trust |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/28356166$$D View this record in MEDLINE/PubMed |
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ContentType | Journal Article |
Contributor | Engebråten, Olav Sørlie, Therese Garred, Øystein Borgen, Elin Fritzman, Britt Geitvik, Gry A Russnes, Hege G Fodstad, Øystein Hofvind, Solveig Skjerven, Helle Kristine |
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Copyright | The Author(s). 2017 Copyright BioMed Central 2017 |
Copyright_xml | – notice: The Author(s). 2017 – notice: Copyright BioMed Central 2017 |
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Snippet | Background
Breast cancer is a heterogeneous disease at the clinical and molecular level. In this study we integrate classifications extracted from five... Breast cancer is a heterogeneous disease at the clinical and molecular level. In this study we integrate classifications extracted from five different... Background Breast cancer is a heterogeneous disease at the clinical and molecular level. In this study we integrate classifications extracted from five... Abstract Background Breast cancer is a heterogeneous disease at the clinical and molecular level. In this study we integrate classifications extracted from... |
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SubjectTerms | Bioinformatics Biomarkers, Tumor Biomedical and Life Sciences Biomedicine Breast cancer Breast Neoplasms - epidemiology Breast Neoplasms - genetics Breast Neoplasms - metabolism Breast Neoplasms - mortality Cancer Research Cell survival Cluster Analysis Consensus clustering Copy number Deoxyribonucleic acid DNA DNA Copy Number Variations DNA methylation Female Gene expression Gene Expression Profiling Gene Expression Regulation, Neoplastic Gene Regulatory Networks Genomes Hospitals Humans Integration Luminal A Medical prognosis Metabolic Networks and Pathways Metabolism Metabolomics Metabolomics - methods Metastasis MicroRNA MicroRNAs - genetics miRNA Mutation NMR Norway - epidemiology Nuclear magnetic resonance Oncology Patients Prognosis Protein arrays Protein expression Research Article RNA, Messenger - genetics Surgery Surgical Oncology Tumors |
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Title | Integrative clustering reveals a novel split in the luminal A subtype of breast cancer with impact on outcome |
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