Most Random Gene Expression Signatures Are Significantly Associated with Breast Cancer Outcome
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{"title"=>"Most random gene expression signatures are significantly associated with breast cancer outcome", "type"=>"journal", "authors"=>[{"first_name"=>"David", "last_name"=>"Venet", "scopus_author_id"=>"6505852978"}, {"first_name"=>"Jacques E.", "last_name"=>"Dumont", "scopus_author_id"=>"35400206500"}, {"first_name"=>"Vincent", "last_name"=>"Detours", "scopus_author_id"=>"6602341540"}], "year"=>2011, "source"=>"PLoS Computational Biology", "identifiers"=>{"issn"=>"1553734X", "scopus"=>"2-s2.0-80055090025", "pui"=>"362834443", "doi"=>"10.1371/journal.pcbi.1002240", "isbn"=>"1553-7358 (Electronic)\\r1553-734X (Linking)", "sgr"=>"80055090025", "pmid"=>"22028643"}, "id"=>"230202f9-67ce-3584-b3db-1b1d18a2668f", "abstract"=>"Bridging the gap between animal or in vitro models and human disease is essential in medical research. Researchers often suggest that a biological mechanism is relevant to human cancer from the statistical association of a gene expression marker (a signature) of this mechanism, that was discovered in an experimental system, with disease outcome in humans. We examined this argument for breast cancer. Surprisingly, we found that gene expression signatures-unrelated to cancer-of the effect of postprandial laughter, of mice social defeat and of skin fibroblast localization were all significantly associated with breast cancer outcome. We next compared 47 published breast cancer outcome signatures to signatures made of random genes. Twenty-eight of them (60%) were not significantly better outcome predictors than random signatures of identical size and 11 (23%) were worst predictors than the median random signature. More than 90% of random signatures >100 genes were significant outcome predictors. We next derived a metagene, called meta-PCNA, by selecting the 1% genes most positively correlated with proliferation marker PCNA in a compendium of normal tissues expression. Adjusting breast cancer expression data for meta-PCNA abrogated almost entirely the outcome association of published and random signatures. We also found that, in the absence of adjustment, the hazard ratio of outcome association of a signature strongly correlated with meta-PCNA (R(2) = 0.9). This relation also applied to single-gene expression markers. Moreover, >50% of the breast cancer transcriptome was correlated with meta-PCNA. A corollary was that purging cell cycle genes out of a signature failed to rule out the confounding effect of proliferation. Hence, it is questionable to suggest that a mechanism is relevant to human breast cancer from the finding that a gene expression marker for this mechanism predicts human breast cancer outcome, because most markers do. The methods we present help to overcome this problem.", "link"=>"http://www.mendeley.com/research/most-random-gene-expression-signatures-significantly-associated-breast-cancer-outcome-10", "reader_count"=>455, "reader_count_by_academic_status"=>{"Unspecified"=>9, "Professor > Associate Professor"=>42, "Librarian"=>2, "Researcher"=>132, "Student > Doctoral Student"=>18, "Student > Ph. D. Student"=>132, "Student > Postgraduate"=>12, "Student > Master"=>38, "Other"=>26, "Student > Bachelor"=>21, "Lecturer"=>4, "Lecturer > Senior Lecturer"=>3, "Professor"=>16}, "reader_count_by_user_role"=>{"Unspecified"=>9, "Professor > Associate Professor"=>42, "Librarian"=>2, "Researcher"=>132, "Student > Doctoral Student"=>18, "Student > Ph. D. Student"=>132, "Student > Postgraduate"=>12, "Student > Master"=>38, "Other"=>26, "Student > Bachelor"=>21, "Lecturer"=>4, "Lecturer > Senior Lecturer"=>3, "Professor"=>16}, "reader_count_by_subject_area"=>{"Unspecified"=>17, "Agricultural and Biological Sciences"=>243, "Business, Management and Accounting"=>1, "Chemistry"=>4, "Computer Science"=>48, "Engineering"=>5, "Environmental Science"=>1, "Biochemistry, Genetics and Molecular Biology"=>63, "Materials Science"=>1, "Mathematics"=>4, "Medicine and Dentistry"=>54, "Neuroscience"=>2, "Design"=>1, "Pharmacology, Toxicology and Pharmaceutical Science"=>1, "Physics and Astronomy"=>2, "Psychology"=>2, "Social Sciences"=>1, "Immunology and Microbiology"=>5}, "reader_count_by_subdiscipline"=>{"Materials Science"=>{"Materials Science"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>54}, "Social Sciences"=>{"Social Sciences"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>2}, "Psychology"=>{"Psychology"=>2}, "Mathematics"=>{"Mathematics"=>4}, "Unspecified"=>{"Unspecified"=>17}, "Environmental Science"=>{"Environmental Science"=>1}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>1}, "Design"=>{"Design"=>1}, "Engineering"=>{"Engineering"=>5}, "Chemistry"=>{"Chemistry"=>4}, "Neuroscience"=>{"Neuroscience"=>2}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>5}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>243}, "Computer Science"=>{"Computer Science"=>48}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>63}}, "reader_count_by_country"=>{"Republic of Singapore"=>2, "Hong Kong"=>1, "United States"=>33, "Portugal"=>2, "Russia"=>1, "Sweden"=>1, "South Korea"=>1, "Netherlands"=>1, "China"=>1, "Brazil"=>1, "Poland"=>1, "France"=>2, "Nigeria"=>2, "Hungary"=>1, "Japan"=>1, "Ukraine"=>2, "United Kingdom"=>9, "Switzerland"=>2, "Spain"=>2, "India"=>1, "Canada"=>5, "Belgium"=>2, "Norway"=>1, "Finland"=>1, "Denmark"=>1, "Mexico"=>1, "Italy"=>3, "Australia"=>3, "Germany"=>9}, "group_count"=>25}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/721351"], "description"=>"<p>A) Each point denotes a signature. The x-axis depicts the absolute value of the correlation of the first principal component of the signatures with meta-PCNA, the y-axis depicts the hazard ratio for outcome association. Details of the analysis for each data point are available in the Supporting Information (<a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002240#pcbi.1002240.s003\" target=\"_blank\">Text S1</a>). B) Distribution of the correlations of individual genes with meta-PCNA, for genes significantly associated with overall survival (red) and for all the genes spotted on the microarrays (black).</p>", "links"=>[], "tags"=>["prognostic", "transcriptional", "signals", "correlated"], "article_id"=>391712, "categories"=>["Cancer", "Genetics"], "users"=>["David Venet", "Jacques E. Dumont", "Vincent Detours"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002240.g004", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Most_prognostic_transcriptional_signals_are_correlated_with_meta_PCNA_/391712", "title"=>"Most prognostic transcriptional signals are correlated with meta-PCNA.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-10-20 00:28:32"}
  • {"files"=>["https://ndownloader.figshare.com/files/721152"], "description"=>"<p>The x-axis denotes the p-value of association with overall survival. Red dots stand for published signatures, yellow shapes depict the distribution of p-values for 1000 random signatures of identical size, with the lower 5% quantiles shaded in green and the median shown as black line. Signatures are ordered by increasing sizes.</p>", "links"=>[], "tags"=>["published", "signatures", "predictors"], "article_id"=>391514, "categories"=>["Cancer", "Genetics"], "users"=>["David Venet", "Jacques E. Dumont", "Vincent Detours"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002240.g002", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Most_published_signatures_are_not_significantly_better_outcome_predictors_than_random_signatures_of_identical_size_/391514", "title"=>"Most published signatures are not significantly better outcome predictors than random signatures of identical size.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-10-20 00:25:14"}
  • {"files"=>["https://ndownloader.figshare.com/files/721072"], "description"=>"<p>In plots A–C the NKI cohort was split into two groups using a signature of post-prandial laughter (panel A), localization of skin fibroblasts (panel B), social defeat in mice (panel C). In panels A–C, the fraction of patients alive (overall survival, OS) is shown as a function of time for both groups. Hazard ratios (HR) between groups and their associated p-values are given in bottom-left corners. Panel D depicts p-values for association with outcome for all MSigDB c2 signatures and random signatures of identical size as MSigDB c2 signatures.</p>", "links"=>[], "tags"=>["signatures"], "article_id"=>391430, "categories"=>["Cancer", "Genetics"], "users"=>["David Venet", "Jacques E. Dumont", "Vincent Detours"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002240.g001", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Association_of_negative_control_signatures_with_overall_survival_/391430", "title"=>"Association of negative control signatures with overall survival.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-10-20 00:23:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/366451", "https://ndownloader.figshare.com/files/366550", "https://ndownloader.figshare.com/files/366607"], "description"=>"<div><p>Bridging the gap between animal or <em>in vitro</em> models and human disease is essential in medical research. Researchers often suggest that a biological mechanism is relevant to human cancer from the statistical association of a gene expression marker (a signature) of this mechanism, that was discovered in an experimental system, with disease outcome in humans. We examined this argument for breast cancer. Surprisingly, we found that gene expression signatures—unrelated to cancer—of the effect of postprandial laughter, of mice social defeat and of skin fibroblast localization were all significantly associated with breast cancer outcome. We next compared 47 published breast cancer outcome signatures to signatures made of random genes. Twenty-eight of them (60%) were not significantly better outcome predictors than random signatures of identical size and 11 (23%) were worst predictors than the median random signature. More than 90% of random signatures >100 genes were significant outcome predictors. We next derived a metagene, called meta-PCNA, by selecting the 1% genes most positively correlated with proliferation marker PCNA in a compendium of normal tissues expression. Adjusting breast cancer expression data for meta-PCNA abrogated almost entirely the outcome association of published and random signatures. We also found that, in the absence of adjustment, the hazard ratio of outcome association of a signature strongly correlated with meta-PCNA (R<sup>2</sup> = 0.9). This relation also applied to single-gene expression markers. Moreover, >50% of the breast cancer transcriptome was correlated with meta-PCNA. A corollary was that purging cell cycle genes out of a signature failed to rule out the confounding effect of proliferation. Hence, it is questionable to suggest that a mechanism is relevant to human breast cancer from the finding that a gene expression marker for this mechanism predicts human breast cancer outcome, because most markers do. The methods we present help to overcome this problem.</p> </div>", "links"=>[], "tags"=>["signatures", "are", "cancer"], "article_id"=>132419, "categories"=>["Cancer", "Genetics"], "users"=>["David Venet", "Jacques E. Dumont", "Vincent Detours"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1002240.s001", "https://dx.doi.org/10.1371/journal.pcbi.1002240.s002", "https://dx.doi.org/10.1371/journal.pcbi.1002240.s003"], "stats"=>{"downloads"=>9, "page_views"=>25, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Most_Random_Gene_Expression_Signatures_Are_Significantly_Associated_with_Breast_Cancer_Outcome/132419", "title"=>"Most Random Gene Expression Signatures Are Significantly Associated with Breast Cancer Outcome", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2011-10-20 00:40:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/721258"], "description"=>"<p>Hazard ratios for overall survival association of 48 signatures in the original dataset (blue) and the meta-PCNA-adjusted dataset (red). Box sizes are inversely related to the size of the confidence intervals. Related Kaplan-Meier plots are available in the Supporting Information (<a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002240#pcbi.1002240.s003\" target=\"_blank\">Text S1</a>).</p>", "links"=>[], "tags"=>["decreases", "prognostic", "abilities", "published"], "article_id"=>391617, "categories"=>["Cancer", "Genetics"], "users"=>["David Venet", "Jacques E. Dumont", "Vincent Detours"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002240.g003", "stats"=>{"downloads"=>1, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Meta_PCNA_adjustment_decreases_the_prognostic_abilities_of_published_signatures_/391617", "title"=>"Meta-PCNA adjustment decreases the prognostic abilities of published signatures.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-10-20 00:26:57"}
  • {"files"=>["https://ndownloader.figshare.com/files/721467"], "description"=>"<p>Each dot represents a published signature. A) Hazard ratios. B) Log rank p-values. Lower panels give correlation coefficients for corresponding scatter plots in the symmetric upper panels. OS, overall survival; RFS, recurrence-free survival. NKI, data from ref. <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002240#pcbi.1002240-vandeVijver1\" target=\"_blank\">[2]</a>; LOI, data from ref. <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002240#pcbi.1002240-Loi1\" target=\"_blank\">[56]</a>.</p>", "links"=>[], "tags"=>["predictions", "end-points"], "article_id"=>391831, "categories"=>["Cancer", "Genetics"], "users"=>["David Venet", "Jacques E. Dumont", "Vincent Detours"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002240.g006", "stats"=>{"downloads"=>2, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Reproducible_outcome_predictions_across_end_points_and_cohorts_/391831", "title"=>"Reproducible outcome predictions across end-points and cohorts.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-10-20 00:30:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/721544"], "description"=>"<p>Summary of analysis with different cohorts and end points.</p>", "links"=>[], "tags"=>["cohorts"], "article_id"=>391909, "categories"=>["Cancer", "Genetics"], "users"=>["David Venet", "Jacques E. Dumont", "Vincent Detours"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002240.t001", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Summary_of_analysis_with_different_cohorts_and_end_points_/391909", "title"=>"Summary of analysis with different cohorts and end points.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-10-20 00:31:49"}
  • {"files"=>["https://ndownloader.figshare.com/files/721418"], "description"=>"<p>Distribution of the correlations with meta-PCNA of genes in the Embryonic Stem Cell Module (blue, ref. <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002240#pcbi.1002240-Wong1\" target=\"_blank\">[15]</a>), of the correlations of the same module with its cell cycle genes removed (red) and of all of the genes spotted on the microarray (black).</p>", "links"=>[], "tags"=>["genes", "proliferation"], "article_id"=>391782, "categories"=>["Cancer", "Genetics"], "users"=>["David Venet", "Jacques E. Dumont", "Vincent Detours"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002240.g005", "stats"=>{"downloads"=>0, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Purging_cell_cycle_genes_from_a_signature_does_not_rule_out_proliferation_signals_/391782", "title"=>"Purging cell cycle genes from a signature does not rule out proliferation signals.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-10-20 00:29:42"}

PMC Usage Stats | Further Information

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Relative Metric

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