Gene Expression-Based Classification of Non-Small Cell Lung Carcinomas and Survival Prediction
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{"title"=>"Gene Expression-Based Classification of Non-Small Cell Lung Carcinomas and Survival Prediction", "type"=>"journal", "authors"=>[{"first_name"=>"Jun", "last_name"=>"Hou"}, {"first_name"=>"Joachim", "last_name"=>"Aerts"}, {"first_name"=>"Bianca", "last_name"=>"den Hamer"}, {"first_name"=>"Wilfred", "last_name"=>"van IJcken"}, {"first_name"=>"Michael", "last_name"=>"den Bakker"}, {"first_name"=>"Peter", "last_name"=>"Riegman"}, {"first_name"=>"Cor", "last_name"=>"van der Leest"}, {"first_name"=>"Peter", "last_name"=>"van der Spek"}, {"first_name"=>"John A.", "last_name"=>"Foekens"}, {"first_name"=>"Henk C.", "last_name"=>"Hoogsteden"}, {"first_name"=>"Frank", "last_name"=>"Grosveld"}, {"first_name"=>"Sjaak", "last_name"=>"Philipsen"}], "year"=>2010, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"1932-6203", "scopus"=>"2-s2.0-77956418156", "sgr"=>"77956418156", "pui"=>"359494236", "isbn"=>"1932-6203 (Electronic)1932-6203 (Linking)", "pmid"=>"20421987", "doi"=>"10.1371/journal.pone.0010312"}, "id"=>"ea6de172-c361-3d83-881a-784d6b1df8e3", "abstract"=>"BACKGROUND: Current clinical therapy of non-small cell lung cancer depends on histo-pathological classification. This approach poorly predicts clinical outcome for individual patients. Gene expression profiling holds promise to improve clinical stratification, thus paving the way for individualized therapy. METHODOLOGY AND PRINCIPAL FINDINGS: A genome-wide gene expression analysis was performed on a cohort of 91 patients. We used 91 tumor- and 65 adjacent normal lung tissue samples. We defined sets of predictor genes (probe sets) with the expression profiles. The power of predictor genes was evaluated using an independent cohort of 96 non-small cell lung cancer- and 6 normal lung samples. We identified a tumor signature of 5 genes that aggregates the 156 tumor and normal samples into the expected groups. We also identified a histology signature of 75 genes, which classifies the samples in the major histological subtypes of non-small cell lung cancer. Correlation analysis identified 17 genes which showed the best association with post-surgery survival time. This signature was used for stratification of all patients in two risk groups. Kaplan-Meier survival curves show that the two groups display a significant difference in post-surgery survival time (p = 5.6E-6). The performance of the signatures was validated using a patient cohort of similar size (Duke University, n = 96). Compared to previously published prognostic signatures for NSCLC, the 17 gene signature performed well on these two cohorts. CONCLUSIONS: The gene signatures identified are promising tools for histo-pathological classification of non-small cell lung cancer, and may improve the prediction of clinical outcome.", "link"=>"http://www.mendeley.com/research/gene-expressionbased-classification-nonsmall-cell-lung-carcinomas-survival-prediction", "reader_count"=>115, "reader_count_by_academic_status"=>{"Unspecified"=>4, "Professor > Associate Professor"=>10, "Researcher"=>34, "Student > Doctoral Student"=>4, "Student > Ph. D. Student"=>26, "Student > Postgraduate"=>8, "Student > Master"=>9, "Other"=>4, "Student > Bachelor"=>9, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>2, "Professor"=>4}, "reader_count_by_user_role"=>{"Unspecified"=>4, "Professor > Associate Professor"=>10, "Researcher"=>34, "Student > Doctoral Student"=>4, "Student > Ph. D. Student"=>26, "Student > Postgraduate"=>8, "Student > Master"=>9, "Other"=>4, "Student > Bachelor"=>9, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>2, "Professor"=>4}, "reader_count_by_subject_area"=>{"Unspecified"=>12, "Engineering"=>2, "Biochemistry, Genetics and Molecular Biology"=>16, "Mathematics"=>2, "Medicine and Dentistry"=>24, "Agricultural and Biological Sciences"=>43, "Pharmacology, Toxicology and Pharmaceutical Science"=>2, "Psychology"=>2, "Chemistry"=>2, "Social Sciences"=>1, "Computer Science"=>8, "Immunology and Microbiology"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>2}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>24}, "Chemistry"=>{"Chemistry"=>2}, "Social Sciences"=>{"Social Sciences"=>1}, "Psychology"=>{"Psychology"=>2}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>43}, "Computer Science"=>{"Computer Science"=>8}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>16}, "Mathematics"=>{"Mathematics"=>2}, "Unspecified"=>{"Unspecified"=>12}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>2}}, "reader_count_by_country"=>{"Sweden"=>1, "Netherlands"=>2, "Hungary"=>1, "United States"=>4, "Italy"=>1, "Germany"=>1, "Spain"=>1}, "group_count"=>4}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/854446"], "description"=>"<p>Multivariable proportional hazard analysis of the risk of death.</p>", "links"=>[], "tags"=>["proportional"], "article_id"=>524896, "categories"=>["Cancer", "Molecular Biology", "Medicine"], "users"=>["Jun Hou", "Joachim Aerts", "Bianca den Hamer", "Wilfred van IJcken", "Michael den Bakker", "Peter Riegman", "Cor van der Leest", "Peter van der Spek", "John A. Foekens", "Henk C. Hoogsteden", "Frank Grosveld", "Sjaak Philipsen"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0010312.t002", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Multivariable_proportional_hazard_analysis_of_the_risk_of_death_/524896", "title"=>"Multivariable proportional hazard analysis of the risk of death.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-04-22 01:21:36"}
  • {"files"=>["https://ndownloader.figshare.com/files/854170"], "description"=>"<p>Kaplan-Meier curves for the best performing signatures (by P-value) are shown for 82 Erasmus MC patients (left) and 89 Duke University NSCLC patients (right), fitted by their risk assignments. Grey bars indicate patients at last follow-up, still alive. P-values are between brackets if overall survival of the low risk group is actually lower than that of the high risk group.</p>", "links"=>[], "tags"=>["published", "prognostic"], "article_id"=>524617, "categories"=>["Cancer", "Molecular Biology", "Medicine"], "users"=>["Jun Hou", "Joachim Aerts", "Bianca den Hamer", "Wilfred van IJcken", "Michael den Bakker", "Peter Riegman", "Cor van der Leest", "Peter van der Spek", "John A. Foekens", "Henk C. Hoogsteden", "Frank Grosveld", "Sjaak Philipsen"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0010312.g006", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Survival_prediction_by_published_prognostic_signatures_/524617", "title"=>"Survival prediction by published prognostic signatures.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-04-22 01:16:57"}
  • {"files"=>["https://ndownloader.figshare.com/files/853625"], "description"=>"<p>Pairwise correlations between any two samples are displayed, based on 4791 informative probe sets. The colors of the cells represent Pearson's correlation coefficient values, with deeper red indicating higher positive and deeper blue lower negative correlations. The red diagonal line displays the self-to-self comparison of each sample. Histological classification of the samples is depicted along the diagonal; the key to the color code is shown at the bottom. Histo-path_1 & Histo-path_2: initial and second histo-pathological review.</p>", "links"=>[], "tags"=>["156", "samples", "patients"], "article_id"=>524071, "categories"=>["Cancer", "Molecular Biology", "Medicine"], "users"=>["Jun Hou", "Joachim Aerts", "Bianca den Hamer", "Wilfred van IJcken", "Michael den Bakker", "Peter Riegman", "Cor van der Leest", "Peter van der Spek", "John A. Foekens", "Henk C. Hoogsteden", "Frank Grosveld", "Sjaak Philipsen"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0010312.g001", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Correlation_view_of_156_samples_from_patients_with_NSCLC_/524071", "title"=>"Correlation view of 156 samples from patients with NSCLC.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-04-22 01:07:51"}
  • {"files"=>["https://ndownloader.figshare.com/files/853880"], "description"=>"<p><b>A</b>: agglomerative hierarchical clustering of 23 NSCLC samples using the 518 probe set histology signature. The relative expression to the overall mean for each probe set (rows) in each sample (columns) is indicated by a color code. Correlation between the samples is depicted by the dendrogram. Histo-pathological diagnosis and predictions of histology subtype by Prediction Analysis of Microarrays, using the 518 and 75 probe set signatures, are shown by colored blocks. <b>B</b>: correlation dendrogram generated by agglomerative hierarchical clustering of all 91 Erasmus MC NSCLC samples using the 518 probe set signature. Histo-pathological diagnosis of the initial and second review, and prediction of histology subtype by Prediction Analysis of Microarrays using the 518 probe set signature, are shown by colored blocks.</p>", "links"=>[], "tags"=>["nsclc", "tumors", "518", "probe", "histology"], "article_id"=>524326, "categories"=>["Cancer", "Molecular Biology", "Medicine"], "users"=>["Jun Hou", "Joachim Aerts", "Bianca den Hamer", "Wilfred van IJcken", "Michael den Bakker", "Peter Riegman", "Cor van der Leest", "Peter van der Spek", "John A. Foekens", "Henk C. Hoogsteden", "Frank Grosveld", "Sjaak Philipsen"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0010312.g003", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Clustering_analysis_of_NSCLC_tumors_with_the_518_probe_set_histology_signature_/524326", "title"=>"Clustering analysis of NSCLC tumors with the 518 probe set histology signature.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-04-22 01:12:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/854078"], "description"=>"<p>Kaplan-Meier curves for <b>A</b>: 82 Erasmus MC NSCLC patients and <b>B</b>: 89 Duke University NSCLC patients fitted by their risk assignments based on the 17 probe set survival signature. The high- and low-risk groups differ significantly, indicated by the p-values. Grey bars indicate patients at last follow-up, still alive.</p>", "links"=>[], "tags"=>["17", "probe"], "article_id"=>524528, "categories"=>["Cancer", "Molecular Biology", "Medicine"], "users"=>["Jun Hou", "Joachim Aerts", "Bianca den Hamer", "Wilfred van IJcken", "Michael den Bakker", "Peter Riegman", "Cor van der Leest", "Peter van der Spek", "John A. Foekens", "Henk C. Hoogsteden", "Frank Grosveld", "Sjaak Philipsen"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0010312.g005", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_17_probe_set_signature_predicts_patient_survival_time_/524528", "title"=>"A 17 probe set signature predicts patient survival time.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-04-22 01:15:28"}
  • {"files"=>["https://ndownloader.figshare.com/files/854409"], "description"=>"<p>Characteristics of patients and samples.</p>", "links"=>[], "tags"=>["patients"], "article_id"=>524861, "categories"=>["Cancer", "Molecular Biology", "Medicine"], "users"=>["Jun Hou", "Joachim Aerts", "Bianca den Hamer", "Wilfred van IJcken", "Michael den Bakker", "Peter Riegman", "Cor van der Leest", "Peter van der Spek", "John A. Foekens", "Henk C. Hoogsteden", "Frank Grosveld", "Sjaak Philipsen"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0010312.t001", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Characteristics_of_patients_and_samples_/524861", "title"=>"Characteristics of patients and samples.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-04-22 01:21:01"}
  • {"files"=>["https://ndownloader.figshare.com/files/853985"], "description"=>"<p><b>A</b>: correlation dendrogram generated by agglomerative hierarchical clustering of all 91 Erasmus MC NSCLC samples using the 75 probe set histology signature. Histo-pathological diagnosis of the initial and second review, and prediction of histology subtype by Prediction Analysis of Microarrays using the 75- and 518 probe set histology signatures, are shown by colored blocks. <b>B</b>: correlation dendrogram generated by agglomerative hierarchical clustering of all 96 Duke University NSCLC samples using the 75 probe set histology signature. The reported histo-pathological diagnosis, and prediction of histology subtype by Prediction Analysis of Microarrays using the 75- and 518 probe set histology signatures, are shown by colored blocks. 75AS and 518AS: prediction without the LCC probe sets in the histology signatures, using 68 and 329 probe sets respectively (see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0010312#pone.0010312.s005\" target=\"_blank\">Tables S4</a> and <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0010312#pone.0010312.s006\" target=\"_blank\">S5</a>).</p>", "links"=>[], "tags"=>["histology", "subtype", "erasmus", "mc", "duke", "nsclc"], "article_id"=>524430, "categories"=>["Cancer", "Molecular Biology", "Medicine"], "users"=>["Jun Hou", "Joachim Aerts", "Bianca den Hamer", "Wilfred van IJcken", "Michael den Bakker", "Peter Riegman", "Cor van der Leest", "Peter van der Spek", "John A. Foekens", "Henk C. Hoogsteden", "Frank Grosveld", "Sjaak Philipsen"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0010312.g004", "stats"=>{"downloads"=>0, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Prediction_of_histology_subtype_of_Erasmus_MC_and_Duke_University_NSCLC_samples_/524430", "title"=>"Prediction of histology subtype of Erasmus MC and Duke University NSCLC samples.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-04-22 01:13:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/854296"], "description"=>"<p>In total 187 tumor samples from the Erasmus MC (n = 91) and Duke University (n = 96) cohorts are shown. Pairwise correlations between any two samples are displayed, based on 3495 informative probe sets. Histological classification of the samples, and the collection source, are depicted along the diagonal. The key to the color code is shown at the bottom. Histo-path_E1 & Histo-path_E2: initial and second histo-pathological review of Erasmus MC samples. Histo-path_D: histo-pathological review of Duke University samples; Histo-path_P-518 and Histo-path_P-75: predictions by PAM of histological subtypes using the 518 and 75 probe set signatures, respectively (see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0010312#pone.0010312.s005\" target=\"_blank\">Tables S4</a> and <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0010312#pone.0010312.s006\" target=\"_blank\">S5</a>).</p>", "links"=>[], "tags"=>["erasmus", "mc", "duke", "nsclc"], "article_id"=>524746, "categories"=>["Cancer", "Molecular Biology", "Medicine"], "users"=>["Jun Hou", "Joachim Aerts", "Bianca den Hamer", "Wilfred van IJcken", "Michael den Bakker", "Peter Riegman", "Cor van der Leest", "Peter van der Spek", "John A. Foekens", "Henk C. Hoogsteden", "Frank Grosveld", "Sjaak Philipsen"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0010312.g007", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Correlation_view_of_Erasmus_MC_and_Duke_University_NSCLC_samples_/524746", "title"=>"Correlation view of Erasmus MC and Duke University NSCLC samples.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-04-22 01:19:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/424075", "https://ndownloader.figshare.com/files/424257", "https://ndownloader.figshare.com/files/424285", "https://ndownloader.figshare.com/files/424299", "https://ndownloader.figshare.com/files/424325", "https://ndownloader.figshare.com/files/424343", "https://ndownloader.figshare.com/files/424360", "https://ndownloader.figshare.com/files/424372", "https://ndownloader.figshare.com/files/424387", "https://ndownloader.figshare.com/files/424404", "https://ndownloader.figshare.com/files/424417"], "description"=>"<div><h3>Background</h3><p>Current clinical therapy of non-small cell lung cancer depends on histo-pathological classification. This approach poorly predicts clinical outcome for individual patients. Gene expression profiling holds promise to improve clinical stratification, thus paving the way for individualized therapy.</p><h3>Methodology and Principal Findings</h3><p>A genome-wide gene expression analysis was performed on a cohort of 91 patients. We used 91 tumor- and 65 adjacent normal lung tissue samples. We defined sets of predictor genes (probe sets) with the expression profiles. The power of predictor genes was evaluated using an independent cohort of 96 non-small cell lung cancer- and 6 normal lung samples. We identified a tumor signature of 5 genes that aggregates the 156 tumor and normal samples into the expected groups. We also identified a histology signature of 75 genes, which classifies the samples in the major histological subtypes of non-small cell lung cancer. Correlation analysis identified 17 genes which showed the best association with post-surgery survival time. This signature was used for stratification of all patients in two risk groups. Kaplan-Meier survival curves show that the two groups display a significant difference in post-surgery survival time (p = 5.6E-6). The performance of the signatures was validated using a patient cohort of similar size (Duke University, n = 96). Compared to previously published prognostic signatures for NSCLC, the 17 gene signature performed well on these two cohorts.</p><h3>Conclusions</h3><p>The gene signatures identified are promising tools for histo-pathological classification of non-small cell lung cancer, and may improve the prediction of clinical outcome.</p></div>", "links"=>[], "tags"=>["expression-based", "classification", "non-small", "carcinomas"], "article_id"=>143731, "categories"=>["Cancer", "Molecular Biology", "Medicine"], "users"=>["Jun Hou", "Joachim Aerts", "Bianca den Hamer", "Wilfred van IJcken", "Michael den Bakker", "Peter Riegman", "Cor van der Leest", "Peter van der Spek", "John A. Foekens", "Henk C. Hoogsteden", "Frank Grosveld", "Sjaak Philipsen"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0010312.s001", "https://dx.doi.org/10.1371/journal.pone.0010312.s002", "https://dx.doi.org/10.1371/journal.pone.0010312.s003", "https://dx.doi.org/10.1371/journal.pone.0010312.s004", "https://dx.doi.org/10.1371/journal.pone.0010312.s005", "https://dx.doi.org/10.1371/journal.pone.0010312.s006", "https://dx.doi.org/10.1371/journal.pone.0010312.s007", "https://dx.doi.org/10.1371/journal.pone.0010312.s008", "https://dx.doi.org/10.1371/journal.pone.0010312.s009", "https://dx.doi.org/10.1371/journal.pone.0010312.s010", "https://dx.doi.org/10.1371/journal.pone.0010312.s011"], "stats"=>{"downloads"=>31, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Gene_Expression_Based_Classification_of_Non_Small_Cell_Lung_Carcinomas_and_Survival_Prediction/143731", "title"=>"Gene Expression-Based Classification of Non-Small Cell Lung Carcinomas and Survival Prediction", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2010-04-22 01:02:11"}
  • {"files"=>["https://ndownloader.figshare.com/files/853750"], "description"=>"<p><b>A</b>: Two-dimensional hierarchical clustering of 80 training samples, including tumors and healthy lung samples, was performed with 187 probe sets. The relative expression to the overall mean for each probe set (rows) in each sample (columns) is indicated by a color code. <b>B</b>: Hierarchical clustering of 156 tissue samples with 5 probe sets yields 2 groups, tumor and normal lung.</p>", "links"=>[], "tags"=>["clustering", "distinguishes", "tumors"], "article_id"=>524198, "categories"=>["Cancer", "Molecular Biology", "Medicine"], "users"=>["Jun Hou", "Joachim Aerts", "Bianca den Hamer", "Wilfred van IJcken", "Michael den Bakker", "Peter Riegman", "Cor van der Leest", "Peter van der Spek", "John A. Foekens", "Henk C. Hoogsteden", "Frank Grosveld", "Sjaak Philipsen"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0010312.g002", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Hierarchical_clustering_distinguishes_tumors_from_healthy_lung_tissue_/524198", "title"=>"Hierarchical clustering distinguishes tumors from healthy lung tissue.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-04-22 01:09:58"}

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  • {"unique-ip"=>"24", "full-text"=>"25", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"5", "supp-data"=>"8", "cited-by"=>"0", "year"=>"2018", "month"=>"11"}
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Relative Metric

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