Minimising Immunohistochemical False Negative ER Classification Using a Complementary 23 Gene Expression Signature of ER Status
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Mendeley | Further Information

{"title"=>"Minimising immunohistochemical false negative ER classification using a complementary 23 gene expression signature of ER status", "type"=>"journal", "authors"=>[{"first_name"=>"Qiyuan", "last_name"=>"Li", "scopus_author_id"=>"55578517900"}, {"first_name"=>"Aron C.", "last_name"=>"Eklund", "scopus_author_id"=>"8057455300"}, {"first_name"=>"Nicolai", "last_name"=>"Juul", "scopus_author_id"=>"35787424600"}, {"first_name"=>"Benjamin", "last_name"=>"Haibe-Kains", "scopus_author_id"=>"23667678400"}, {"first_name"=>"Christopher T.", "last_name"=>"Workman", "scopus_author_id"=>"7003703698"}, {"first_name"=>"Andrea L.", "last_name"=>"Richardson", "scopus_author_id"=>"7402533820"}, {"first_name"=>"Zoltan", "last_name"=>"Szallasi", "scopus_author_id"=>"7003492676"}, {"first_name"=>"Charles", "last_name"=>"Swanton", "scopus_author_id"=>"7005912775"}], "year"=>2010, "source"=>"PLoS ONE", "identifiers"=>{"scopus"=>"2-s2.0-78649923835", "sgr"=>"78649923835", "issn"=>"19326203", "doi"=>"10.1371/journal.pone.0015031", "pmid"=>"21152022", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "pui"=>"360115037"}, "id"=>"b62916a3-7214-32a7-b680-e4a0cf5e1114", "abstract"=>"BACKGROUND: Expression of the oestrogen receptor (ER) in breast cancer predicts benefit from endocrine therapy. Minimising the frequency of false negative ER status classification is essential to identify all patients with ER positive breast cancers who should be offered endocrine therapies in order to improve clinical outcome. In routine oncological practice ER status is determined by semi-quantitative methods such as immunohistochemistry (IHC) or other immunoassays in which the ER expression level is compared to an empirical threshold. The clinical relevance of gene expression-based ER subtypes as compared to IHC-based determination has not been systematically evaluated. Here we attempt to reduce the frequency of false negative ER status classification using two gene expression approaches and compare these methods to IHC based ER status in terms of predictive and prognostic concordance with clinical outcome.\\n\\nMETHODOLOGY/PRINCIPAL FINDINGS: Firstly, ER status was discriminated by fitting the bimodal expression of ESR1 to a mixed Gaussian model. The discriminative power of ESR1 suggested bimodal expression as an efficient way to stratify breast cancer; therefore we identified a set of genes whose expression was both strongly bimodal, mimicking ESR expression status, and highly expressed in breast epithelial cell lines, to derive a 23-gene ER expression signature-based classifier. We assessed our classifiers in seven published breast cancer cohorts by comparing the gene expression-based ER status to IHC-based ER status as a predictor of clinical outcome in both untreated and tamoxifen treated cohorts. In untreated breast cancer cohorts, the 23 gene signature-based ER status provided significantly improved prognostic power compared to IHC-based ER status (P = 0.006). In tamoxifen-treated cohorts, the 23 gene ER expression signature predicted clinical outcome (HR = 2.20, P = 0.00035). These complementary ER signature-based strategies estimated that between 15.1% and 21.8% patients of IHC-based negative ER status would be classified with ER positive breast cancer.\\n\\nCONCLUSION/SIGNIFICANCE: Expression-based ER status classification may complement IHC to minimise false negative ER status classification and optimise patient stratification for endocrine therapies.", "link"=>"http://www.mendeley.com/research/minimising-immunohistochemical-false-negative-er-classification-using-complementary-23-gene-expressi", "reader_count"=>27, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>4, "Librarian"=>1, "Researcher"=>11, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>2, "Other"=>2, "Student > Master"=>3, "Professor"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>4, "Librarian"=>1, "Researcher"=>11, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>2, "Other"=>2, "Student > Master"=>3, "Professor"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>1, "Biochemistry, Genetics and Molecular Biology"=>8, "Medicine and Dentistry"=>7, "Agricultural and Biological Sciences"=>6, "Psychology"=>3, "Computer Science"=>2}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>7}, "Psychology"=>{"Psychology"=>3}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>6}, "Computer Science"=>{"Computer Science"=>2}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>8}, "Unspecified"=>{"Unspecified"=>1}}, "reader_count_by_country"=>{"Netherlands"=>1, "Sweden"=>2, "Hungary"=>1}, "group_count"=>3}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/815586"], "description"=>"<p>Summary of breast cancer cohorts used in this study.</p>", "links"=>[], "tags"=>["cancer", "cohorts"], "article_id"=>485947, "categories"=>["Biological Sciences", "Cell Biology", "Mathematics", "Cancer", "Molecular Biology", "Genetics"], "users"=>["Qiyuan Li", "Aron C. Eklund", "Nicolai Juul", "Benjamin Haibe-Kains", "Christopher T. Workman", "Andrea L. Richardson", "Zoltan Szallasi", "Charles Swanton"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0015031.t001", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Summary_of_breast_cancer_cohorts_used_in_this_study_/485947", "title"=>"Summary of breast cancer cohorts used in this study.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-12-01 01:39:07"}
  • {"files"=>["https://ndownloader.figshare.com/files/815252"], "description"=>"<p>Meta-analysis in four cohorts suggests that ER-statuses based on expression profile indicates stronger prognostic power than IHC based ER-status. The hazard ratios were estimated based on ER status determined by IHC (a.), ESR1 expression (b.) and 23-gene signature (c.), separately.</p>", "links"=>[], "tags"=>["ihc-based", "expression-based", "er"], "article_id"=>485610, "categories"=>["Biological Sciences", "Cell Biology", "Mathematics", "Cancer", "Molecular Biology", "Genetics"], "users"=>["Qiyuan Li", "Aron C. Eklund", "Nicolai Juul", "Benjamin Haibe-Kains", "Christopher T. Workman", "Andrea L. Richardson", "Zoltan Szallasi", "Charles Swanton"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0015031.g002", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Prognostic_power_of_IHC_based_and_expression_based_ER_status_/485610", "title"=>"Prognostic power of IHC-based and expression-based ER status.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-12-01 01:33:30"}
  • {"files"=>["https://ndownloader.figshare.com/files/815542"], "description"=>"<p>LN: lymph node status.</p>", "links"=>[], "tags"=>["cox", "regression", "lymph", "node", "er", "proliferation", "cohorts", "esr1-expression", "23-gene"], "article_id"=>485900, "categories"=>["Biological Sciences", "Cell Biology", "Mathematics", "Cancer", "Molecular Biology", "Genetics"], "users"=>["Qiyuan Li", "Aron C. Eklund", "Nicolai Juul", "Benjamin Haibe-Kains", "Christopher T. Workman", "Andrea L. Richardson", "Zoltan Szallasi", "Charles Swanton"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0015031.t003", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Multivariate_Cox_regression_of_lymph_node_status_ER_status_proliferation_MKI67_and_their_interaction_in_four_reference_cohorts_with_regard_to_IHC_based_ESR1_expression_based_and_23_gene_signature_based_ER_status_/485900", "title"=>"Multivariate Cox regression of lymph node status, ER status, proliferation (MKI67) and their interaction in four reference cohorts with regard to IHC-based, ESR1-expression based and 23-gene signature based ER status.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-12-01 01:38:20"}
  • {"files"=>["https://ndownloader.figshare.com/files/815163"], "description"=>"<p>Schematic of deriving the 23-gene ER signature from epithelial specifically expressed genes.</p>", "links"=>[], "tags"=>["deriving", "23-gene", "er", "epithelial"], "article_id"=>485518, "categories"=>["Biological Sciences", "Cell Biology", "Mathematics", "Cancer", "Molecular Biology", "Genetics"], "users"=>["Qiyuan Li", "Aron C. Eklund", "Nicolai Juul", "Benjamin Haibe-Kains", "Christopher T. Workman", "Andrea L. Richardson", "Zoltan Szallasi", "Charles Swanton"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0015031.g001", "stats"=>{"downloads"=>1, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Schematic_of_deriving_the_23_gene_ER_signature_from_epithelial_specifically_expressed_genes_/485518", "title"=>"Schematic of deriving the 23-gene ER signature from epithelial specifically expressed genes.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-12-01 01:31:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/815355"], "description"=>"<p>KM curves of disease-free survival showing IHC based ER- tumours separated by (a.) ESR1 expression-based and (b) 23-gene signature-based ER status; false ER- (23-gene signature based ER+, IHC based ER-) showed significantly better outcome which is close to true ER+ (23-gene signature based ER+, IHC based ER+, black dashed-line). And IHC based ER- tumours are separated by (c.) ESR1 expression and (d.) 23-gene signature based ER status; false ER+ (23-gene based ER-, IHC based ER+) showed significantly worse outcome which is close to true ER- (23-gene signature based ER-, IHC based ER-, red dashed-line).</p>", "links"=>[], "tags"=>["er", "classifiers", "misclassification", "ihc-based"], "article_id"=>485714, "categories"=>["Biological Sciences", "Cell Biology", "Mathematics", "Cancer", "Molecular Biology", "Genetics"], "users"=>["Qiyuan Li", "Aron C. Eklund", "Nicolai Juul", "Benjamin Haibe-Kains", "Christopher T. Workman", "Andrea L. Richardson", "Zoltan Szallasi", "Charles Swanton"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0015031.g003", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Expression_based_ER_classifiers_reveal_misclassification_of_IHC_based_ER_status_which_associated_with_clinical_outcome_/485714", "title"=>"Expression-based ER classifiers reveal misclassification of IHC-based ER status which associated with clinical outcome.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-12-01 01:35:14"}
  • {"files"=>["https://ndownloader.figshare.com/files/406493", "https://ndownloader.figshare.com/files/406515", "https://ndownloader.figshare.com/files/406530", "https://ndownloader.figshare.com/files/406545", "https://ndownloader.figshare.com/files/406560", "https://ndownloader.figshare.com/files/406577"], "description"=>"<div><h3>Background</h3><p>Expression of the oestrogen receptor (ER) in breast cancer predicts benefit from endocrine therapy. Minimising the frequency of false negative ER status classification is essential to identify all patients with ER positive breast cancers who should be offered endocrine therapies in order to improve clinical outcome. In routine oncological practice ER status is determined by semi-quantitative methods such as immunohistochemistry (IHC) or other immunoassays in which the ER expression level is compared to an empirical threshold<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0015031#pone.0015031-BarnesDM1\">[1]</a>, <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0015031#pone.0015031-Weigelt1\">[2]</a>. The clinical relevance of gene expression-based ER subtypes as compared to IHC-based determination has not been systematically evaluated. Here we attempt to reduce the frequency of false negative ER status classification using two gene expression approaches and compare these methods to IHC based ER status in terms of predictive and prognostic concordance with clinical outcome.</p> <h3>Methodology/Principal Findings</h3><p>Firstly, ER status was discriminated by fitting the bimodal expression of ESR1 to a mixed Gaussian model. The discriminative power of ESR1 suggested bimodal expression as an efficient way to stratify breast cancer; therefore we identified a set of genes whose expression was both strongly bimodal, mimicking ESR expression status, and highly expressed in breast epithelial cell lines, to derive a 23-gene ER expression signature-based classifier. We assessed our classifiers in seven published breast cancer cohorts by comparing the gene expression-based ER status to IHC-based ER status as a predictor of clinical outcome in both untreated and tamoxifen treated cohorts. In untreated breast cancer cohorts, the 23 gene signature-based ER status provided significantly improved prognostic power compared to IHC-based ER status (P = 0.006). In tamoxifen-treated cohorts, the 23 gene ER expression signature predicted clinical outcome (HR = 2.20, P = 0.00035). These complementary ER signature-based strategies estimated that between 15.1% and 21.8% patients of IHC-based negative ER status would be classified with ER positive breast cancer.</p> <h3>Conclusion/Significance</h3><p>Expression-based ER status classification may complement IHC to minimise false negative ER status classification and optimise patient stratification for endocrine therapies.</p> </div>", "links"=>[], "tags"=>["minimising", "immunohistochemical", "er", "classification", "complementary", "23"], "article_id"=>140312, "categories"=>["Biological Sciences", "Cell Biology", "Mathematics", "Cancer", "Molecular Biology", "Genetics"], "users"=>["Qiyuan Li", "Aron C. Eklund", "Nicolai Juul", "Benjamin Haibe-Kains", "Christopher T. Workman", "Andrea L. Richardson", "Zoltan Szallasi", "Charles Swanton"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0015031.s001", "https://dx.doi.org/10.1371/journal.pone.0015031.s002", "https://dx.doi.org/10.1371/journal.pone.0015031.s003", "https://dx.doi.org/10.1371/journal.pone.0015031.s004", "https://dx.doi.org/10.1371/journal.pone.0015031.s005", "https://dx.doi.org/10.1371/journal.pone.0015031.s006"], "stats"=>{"downloads"=>8, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Minimising_Immunohistochemical_False_Negative_ER_Classification_Using_a_Complementary_23_Gene_Expression_Signature_of_ER_Status/140312", "title"=>"Minimising Immunohistochemical False Negative ER Classification Using a Complementary 23 Gene Expression Signature of ER Status", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2010-12-01 00:05:12"}
  • {"files"=>["https://ndownloader.figshare.com/files/815432"], "description"=>"<p>(a.) ESR1 expression based ER status; (b.) 23-gene signature based ER status.</p>", "links"=>[], "tags"=>["er", "classifiers", "distant-metastases-free", "ihc", "patients", "treated"], "article_id"=>485786, "categories"=>["Biological Sciences", "Cell Biology", "Mathematics", "Cancer", "Molecular Biology", "Genetics"], "users"=>["Qiyuan Li", "Aron C. Eklund", "Nicolai Juul", "Benjamin Haibe-Kains", "Christopher T. Workman", "Andrea L. Richardson", "Zoltan Szallasi", "Charles Swanton"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0015031.g004", "stats"=>{"downloads"=>1, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Expression_based_ER_classifiers_predict_distant_metastases_free_survival_of_IHC_ER_positive_patients_treated_with_tamoxifen_/485786", "title"=>"Expression-based ER classifiers predict distant-metastases-free survival of IHC ER positive patients treated with tamoxifen.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-12-01 01:36:26"}
  • {"files"=>["https://ndownloader.figshare.com/files/815496"], "description"=>"<p>Comparison of ER status determined by microarray-based classifiers to the ER status based on conventional IHC method in 1846 samples from 10 published data sets.</p>", "links"=>[], "tags"=>["er", "microarray-based", "classifiers", "ihc", "1846", "samples", "10", "published"], "article_id"=>485851, "categories"=>["Biological Sciences", "Cell Biology", "Mathematics", "Cancer", "Molecular Biology", "Genetics"], "users"=>["Qiyuan Li", "Aron C. Eklund", "Nicolai Juul", "Benjamin Haibe-Kains", "Christopher T. Workman", "Andrea L. Richardson", "Zoltan Szallasi", "Charles Swanton"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0015031.t002", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_ER_status_determined_by_microarray_based_classifiers_to_the_ER_status_based_on_conventional_IHC_method_in_1846_samples_from_10_published_data_sets_/485851", "title"=>"Comparison of ER status determined by microarray-based classifiers to the ER status based on conventional IHC method in 1846 samples from 10 published data sets.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-12-01 01:37:31"}

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