Clinical Value of Prognosis Gene Expression Signatures in Colorectal Cancer: A Systematic Review
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{"title"=>"Clinical Value of Prognosis Gene Expression Signatures in Colorectal Cancer: A Systematic Review", "type"=>"journal", "authors"=>[{"first_name"=>"Rebeca", "last_name"=>"Sanz-Pamplona", "scopus_author_id"=>"49864477000"}, {"first_name"=>"Antoni", "last_name"=>"Berenguer", "scopus_author_id"=>"7004865608"}, {"first_name"=>"David", "last_name"=>"Cordero", "scopus_author_id"=>"36086972600"}, {"first_name"=>"Samantha", "last_name"=>"Riccadonna", "scopus_author_id"=>"23499476900"}, {"first_name"=>"Xavier", "last_name"=>"Solé", "scopus_author_id"=>"15926089000"}, {"first_name"=>"Marta", "last_name"=>"Crous-Bou", "scopus_author_id"=>"23968557100"}, {"first_name"=>"Elisabet", "last_name"=>"Guinó", "scopus_author_id"=>"6507677192"}, {"first_name"=>"Xavier", "last_name"=>"Sanjuan", "scopus_author_id"=>"6602888252"}, {"first_name"=>"Sebastiano", "last_name"=>"Biondo", "scopus_author_id"=>"7004671109"}, {"first_name"=>"Antonio", "last_name"=>"Soriano", "scopus_author_id"=>"16204040900"}, {"first_name"=>"Giuseppe", "last_name"=>"Jurman", "scopus_author_id"=>"6602367398"}, {"first_name"=>"Gabriel", "last_name"=>"Capella", "scopus_author_id"=>"24368272500"}, {"first_name"=>"Cesare", "last_name"=>"Furlanello", "scopus_author_id"=>"6701821823"}, {"first_name"=>"Victor", "last_name"=>"Moreno", "scopus_author_id"=>"7103038363"}], "year"=>2012, "source"=>"PLoS ONE", "identifiers"=>{"scopus"=>"2-s2.0-84868674633", "sgr"=>"84868674633", "issn"=>"19326203", "doi"=>"10.1371/journal.pone.0048877", "pmid"=>"23145004", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "pui"=>"366025093"}, "id"=>"a0874667-53af-3ec4-ba9f-ede1c257225f", "abstract"=>"INTRODUCTION: The traditional staging system is inadequate to identify those patients with stage II colorectal cancer (CRC) at high risk of recurrence or with stage III CRC at low risk. A number of gene expression signatures to predict CRC prognosis have been proposed, but none is routinely used in the clinic. The aim of this work was to assess the prediction ability and potential clinical usefulness of these signatures in a series of independent datasets.\\n\\nMETHODS: A literature review identified 31 gene expression signatures that used gene expression data to predict prognosis in CRC tissue. The search was based on the PubMed database and was restricted to papers published from January 2004 to December 2011. Eleven CRC gene expression datasets with outcome information were identified and downloaded from public repositories. Random Forest classifier was used to build predictors from the gene lists. Matthews correlation coefficient was chosen as a measure of classification accuracy and its associated p-value was used to assess association with prognosis. For clinical usefulness evaluation, positive and negative post-tests probabilities were computed in stage II and III samples.\\n\\nRESULTS: Five gene signatures showed significant association with prognosis and provided reasonable prediction accuracy in their own training datasets. Nevertheless, all signatures showed low reproducibility in independent data. Stratified analyses by stage or microsatellite instability status showed significant association but limited discrimination ability, especially in stage II tumors. From a clinical perspective, the most predictive signatures showed a minor but significant improvement over the classical staging system.\\n\\nCONCLUSIONS: The published signatures show low prediction accuracy but moderate clinical usefulness. Although gene expression data may inform prognosis, better strategies for signature validation are needed to encourage their widespread use in the clinic.", "link"=>"http://www.mendeley.com/research/clinical-value-prognosis-gene-expression-signatures-colorectal-cancer-systematic-review", "reader_count"=>55, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>4, "Student > Doctoral Student"=>4, "Researcher"=>12, "Student > Ph. D. Student"=>11, "Student > Postgraduate"=>6, "Student > Master"=>11, "Other"=>3, "Student > Bachelor"=>3, "Professor"=>1}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>4, "Student > Doctoral Student"=>4, "Researcher"=>12, "Student > Ph. D. Student"=>11, "Student > Postgraduate"=>6, "Student > Master"=>11, "Other"=>3, "Student > Bachelor"=>3, "Professor"=>1}, "reader_count_by_subject_area"=>{"Engineering"=>2, "Unspecified"=>2, "Environmental Science"=>1, "Biochemistry, Genetics and Molecular Biology"=>4, "Nursing and Health Professions"=>1, "Medicine and Dentistry"=>19, "Agricultural and Biological Sciences"=>17, "Sports and Recreations"=>1, "Computer Science"=>8}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>2}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>19}, "Sports and Recreations"=>{"Sports and Recreations"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>17}, "Computer Science"=>{"Computer Science"=>8}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>4}, "Unspecified"=>{"Unspecified"=>2}, "Environmental Science"=>{"Environmental Science"=>1}}, "reader_count_by_country"=>{"Belgium"=>1, "United States"=>1, "Taiwan"=>1, "Denmark"=>1, "Spain"=>1}, "group_count"=>2}

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  • {"files"=>["https://ndownloader.figshare.com/files/547824"], "description"=>"<p>Clinical characteristics of datasets.</p>", "links"=>[], "tags"=>["characteristics"], "article_id"=>218302, "categories"=>["Cancer", "Biological Sciences", "Chemistry", "Cell Biology"], "users"=>["Rebeca Sanz-Pamplona", "Antoni Berenguer", "David Cordero", "Samantha Riccadonna", "Xavier Solé", "Marta Crous-Bou", "Elisabet Guinó", "Xavier Sanjuan", "Sebastiano Biondo", "Antonio Soriano", "Giuseppe Jurman", "Gabriel Capellá", "Cesare Furlanello", "Víctor Moreno"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0048877.t003", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Clinical_characteristics_of_datasets_/218302", "title"=>"Clinical characteristics of datasets.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-11-07 02:18:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/547773"], "description"=>"<p><b>Dataset:</b> GEO or Array Express dataset identifier; <b>Trained signatures:</b> signatures which used that dataset as training sample, if any; <b>Validation signatures:</b> signatures which used that dataset as independent validation sample; <b>Outcome:</b> type of relapse used for that dataset; <b>Minimum follow up:</b> minimum follow up required for that dataset, when this info was available; <b>Number of samples:</b> number of samples contained in that dataset, showing good and bad prognosis’ separately between brackets; <b>Clinical info</b>: samples ranges of stage and microsatellite status when this information was available; <b>Platform:</b> datasets’ hybridization platform. <b>*</b> NA: the authors do not provide clinical information about MSI and/or stage. No info: Although authors provide clinical information in the paper, samples are not labelled with this information in GEO or ArrayExpress. <b>a.</b> Stage II and III samples from data sets GSE17536 and GSE17537 were jointly used to derive signature VL10, but the later did not include enough events at these stage subgroups. <b>b.</b> Signature JS09 was built with Duke’s A and D and validated with Duke’s B and C samples.</p>", "links"=>[], "tags"=>["Computational biology", "oncology", "pathology", "Gastroenterology and hepatology"], "article_id"=>218248, "categories"=>["Cancer", "Biological Sciences", "Chemistry", "Cell Biology"], "users"=>["Rebeca Sanz-Pamplona", "Antoni Berenguer", "David Cordero", "Samantha Riccadonna", "Xavier Solé", "Marta Crous-Bou", "Elisabet Guinó", "Xavier Sanjuan", "Sebastiano Biondo", "Antonio Soriano", "Giuseppe Jurman", "Gabriel Capellá", "Cesare Furlanello", "Víctor Moreno"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0048877.t002", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Datasets_description_/218248", "title"=>"Datasets description.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-11-07 02:17:28"}
  • {"files"=>["https://ndownloader.figshare.com/files/547338"], "description"=>"<p>PRISMA Diagram which depicts the flow of information through the different phases of the prognosis signatures studies systematic review.</p>", "links"=>[], "tags"=>["diagram", "depicts", "phases", "prognosis", "signatures", "studies", "systematic"], "article_id"=>217809, "categories"=>["Cancer", "Biological Sciences", "Chemistry", "Cell Biology"], "users"=>["Rebeca Sanz-Pamplona", "Antoni Berenguer", "David Cordero", "Samantha Riccadonna", "Xavier Solé", "Marta Crous-Bou", "Elisabet Guinó", "Xavier Sanjuan", "Sebastiano Biondo", "Antonio Soriano", "Giuseppe Jurman", "Gabriel Capellá", "Cesare Furlanello", "Víctor Moreno"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0048877.g001", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_PRISMA_Diagram_which_depicts_the_flow_of_information_through_the_different_phases_of_the_prognosis_signatures_studies_systematic_review_/217809", "title"=>"PRISMA Diagram which depicts the flow of information through the different phases of the prognosis signatures studies systematic review.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-11-07 02:10:09"}
  • {"files"=>["https://ndownloader.figshare.com/files/547614"], "description"=>"<p>Prevalence probability of recurrence for stage II and III were assumed to be 20 and 34% respectively. Signatures are listed in decreasing order of post-tests probability differences.</p>", "links"=>[], "tags"=>["post-test", "probabilities", "recurrence", "ii", "iii"], "article_id"=>218095, "categories"=>["Cancer", "Biological Sciences", "Chemistry", "Cell Biology"], "users"=>["Rebeca Sanz-Pamplona", "Antoni Berenguer", "David Cordero", "Samantha Riccadonna", "Xavier Solé", "Marta Crous-Bou", "Elisabet Guinó", "Xavier Sanjuan", "Sebastiano Biondo", "Antonio Soriano", "Giuseppe Jurman", "Gabriel Capellá", "Cesare Furlanello", "Víctor Moreno"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0048877.g003", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Differences_between_positive_and_negative_post_test_probabilities_of_recurrence_and_their_95_confidence_interval_for_stage_II_A_and_stage_III_B_/218095", "title"=>"Differences between positive and negative post-test probabilities of recurrence and their 95% confidence interval for stage II (A) and stage III (B).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-11-07 02:14:55"}
  • {"files"=>["https://ndownloader.figshare.com/files/547862"], "description"=>"<p><b>Signature</b>: signature name; <b>Training dataset</b>: public training data set if used in this work; <b>Validation dataset</b>: public test data set if used in this work<b>; Signature size</b>: reported signature size in the original paper (genes or features):; <b>Training sample size (good + poor)</b>: sample size of training data set, separating good and poor prognosis when reported; <b>Training outcome</b>: outcome used to derive the signature; <b>Training platform</b>: platform used for the training data set; <b>Signature validation</b>: type of validation for signature if performed; <b>Independent validation outcome</b>: outcome used for independent validation if performed; V<b>alidation results:</b> for each validation performed, accuracy classification measures or association assessing if provided; <b>Reference</b>: PMID and reference for publishing paper. <b>*</b> Frequencies of subgroups were not available. <b>Abbreviations</b>: <b>TMA</b>: tissue microarray; <b>cv</b>: cross-validation; <b>ns</b>: not specified.</p>", "links"=>[], "tags"=>["signatures"], "article_id"=>218344, "categories"=>["Cancer", "Biological Sciences", "Chemistry", "Cell Biology"], "users"=>["Rebeca Sanz-Pamplona", "Antoni Berenguer", "David Cordero", "Samantha Riccadonna", "Xavier Solé", "Marta Crous-Bou", "Elisabet Guinó", "Xavier Sanjuan", "Sebastiano Biondo", "Antonio Soriano", "Giuseppe Jurman", "Gabriel Capellá", "Cesare Furlanello", "Víctor Moreno"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0048877.t001", "stats"=>{"downloads"=>0, "page_views"=>38, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Description_of_signatures_used_in_this_work_/218344", "title"=>"Description of signatures used in this work.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-11-07 02:19:04"}
  • {"files"=>["https://ndownloader.figshare.com/files/547457"], "description"=>"<p>Rows correspond to signatures and columns to datasets. Last column shows a pooled MCC across datasets using sample size as weights. Black lines delimit the first five signatures for which training datasets were available (cells highlighted in black). Cells representing signatures and datasets used to validate them are highlighted in blue. Color scale represents the MCC values: the darker the color, the higher MCC (see the legend). Negative values were collapsed to zero.</p>", "links"=>[], "tags"=>["matthews", "coefficient", "dataset", "analyses"], "article_id"=>217935, "categories"=>["Cancer", "Biological Sciences", "Chemistry", "Cell Biology"], "users"=>["Rebeca Sanz-Pamplona", "Antoni Berenguer", "David Cordero", "Samantha Riccadonna", "Xavier Solé", "Marta Crous-Bou", "Elisabet Guinó", "Xavier Sanjuan", "Sebastiano Biondo", "Antonio Soriano", "Giuseppe Jurman", "Gabriel Capellá", "Cesare Furlanello", "Víctor Moreno"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0048877.g002", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Heatmap_showing_Matthews_Correlation_Coefficient_MCC_values_for_each_signature_in_each_dataset_as_result_of_analyses_with_Random_Forest_/217935", "title"=>"Heatmap showing Matthews Correlation Coefficient (MCC) values for each signature in each dataset as result of analyses with Random Forest.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-11-07 02:12:15"}
  • {"files"=>["https://ndownloader.figshare.com/files/547720"], "description"=>"<p><b>Abbreviations</b>: <b>MCC</b>: Matthews Correlation Coefficient.</p>", "links"=>[], "tags"=>["10", "signatures", "ii", "iii"], "article_id"=>218195, "categories"=>["Cancer", "Biological Sciences", "Chemistry", "Cell Biology"], "users"=>["Rebeca Sanz-Pamplona", "Antoni Berenguer", "David Cordero", "Samantha Riccadonna", "Xavier Solé", "Marta Crous-Bou", "Elisabet Guinó", "Xavier Sanjuan", "Sebastiano Biondo", "Antonio Soriano", "Giuseppe Jurman", "Gabriel Capellá", "Cesare Furlanello", "Víctor Moreno"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0048877.t004", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Global_performance_of_top_10_signatures_for_all_stage_II_and_stage_III_samples_/218195", "title"=>"Global performance of top 10 signatures for all, stage II and stage III samples.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-11-07 02:16:35"}
  • {"files"=>["https://ndownloader.figshare.com/files/293363", "https://ndownloader.figshare.com/files/293405", "https://ndownloader.figshare.com/files/293468", "https://ndownloader.figshare.com/files/293527", "https://ndownloader.figshare.com/files/293573", "https://ndownloader.figshare.com/files/293633", "https://ndownloader.figshare.com/files/293686", "https://ndownloader.figshare.com/files/293726", "https://ndownloader.figshare.com/files/293772", "https://ndownloader.figshare.com/files/293838", "https://ndownloader.figshare.com/files/293897", "https://ndownloader.figshare.com/files/293971", "https://ndownloader.figshare.com/files/293995", "https://ndownloader.figshare.com/files/294018", "https://ndownloader.figshare.com/files/294039", "https://ndownloader.figshare.com/files/294057"], "description"=>"<div><h3>Introduction</h3><p>The traditional staging system is inadequate to identify those patients with stage II colorectal cancer (CRC) at high risk of recurrence or with stage III CRC at low risk. A number of gene expression signatures to predict CRC prognosis have been proposed, but none is routinely used in the clinic. The aim of this work was to assess the prediction ability and potential clinical usefulness of these signatures in a series of independent datasets.</p> <h3>Methods</h3><p>A literature review identified 31 gene expression signatures that used gene expression data to predict prognosis in CRC tissue. The search was based on the PubMed database and was restricted to papers published from January 2004 to December 2011. Eleven CRC gene expression datasets with outcome information were identified and downloaded from public repositories. Random Forest classifier was used to build predictors from the gene lists. Matthews correlation coefficient was chosen as a measure of classification accuracy and its associated p-value was used to assess association with prognosis. For clinical usefulness evaluation, positive and negative post-tests probabilities were computed in stage II and III samples.</p> <h3>Results</h3><p>Five gene signatures showed significant association with prognosis and provided reasonable prediction accuracy in their own training datasets. Nevertheless, all signatures showed low reproducibility in independent data. Stratified analyses by stage or microsatellite instability status showed significant association but limited discrimination ability, especially in stage II tumors. From a clinical perspective, the most predictive signatures showed a minor but significant improvement over the classical staging system.</p> <h3>Conclusions</h3><p>The published signatures show low prediction accuracy but moderate clinical usefulness. Although gene expression data may inform prognosis, better strategies for signature validation are needed to encourage their widespread use in the clinic.</p> </div>", "links"=>[], "tags"=>["prognosis", "signatures", "colorectal", "systematic", "review"], "article_id"=>117746, "categories"=>["Cancer", "Biological Sciences", "Chemistry", "Cell Biology"], "users"=>["Rebeca Sanz-Pamplona", "Antoni Berenguer", "David Cordero", "Samantha Riccadonna", "Xavier Solé", "Marta Crous-Bou", "Elisabet Guinó", "Xavier Sanjuan", "Sebastiano Biondo", "Antonio Soriano", "Giuseppe Jurman", "Gabriel Capellá", "Cesare Furlanello", "Víctor Moreno"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0048877.s001", "https://dx.doi.org/10.1371/journal.pone.0048877.s002", "https://dx.doi.org/10.1371/journal.pone.0048877.s003", "https://dx.doi.org/10.1371/journal.pone.0048877.s004", "https://dx.doi.org/10.1371/journal.pone.0048877.s005", "https://dx.doi.org/10.1371/journal.pone.0048877.s006", "https://dx.doi.org/10.1371/journal.pone.0048877.s007", "https://dx.doi.org/10.1371/journal.pone.0048877.s008", "https://dx.doi.org/10.1371/journal.pone.0048877.s009", "https://dx.doi.org/10.1371/journal.pone.0048877.s010", "https://dx.doi.org/10.1371/journal.pone.0048877.s011", "https://dx.doi.org/10.1371/journal.pone.0048877.s012", "https://dx.doi.org/10.1371/journal.pone.0048877.s013", "https://dx.doi.org/10.1371/journal.pone.0048877.s014", "https://dx.doi.org/10.1371/journal.pone.0048877.s015", "https://dx.doi.org/10.1371/journal.pone.0048877.s016"], "stats"=>{"downloads"=>33, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Clinical_Value_of_Prognosis_Gene_Expression_Signatures_in_Colorectal_Cancer_A_Systematic_Review__/117746", "title"=>"Clinical Value of Prognosis Gene Expression Signatures in Colorectal Cancer: A Systematic Review", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2012-11-07 02:09:06"}

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Relative Metric

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