CiteULike | Further Information

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Mendeley | Further Information

{"title"=>"Survival-related profile, pathways, and transcription factors in Ovarian cancer", "type"=>"journal", "authors"=>[{"first_name"=>"Anne P.G.", "last_name"=>"Crijns", "scopus_author_id"=>"6505850100"}, {"first_name"=>"Rudolf S.N.", "last_name"=>"Fehrmann", "scopus_author_id"=>"20733843600"}, {"first_name"=>"Steven", "last_name"=>"De Jong", "scopus_author_id"=>"26643528300"}, {"first_name"=>"Frans", "last_name"=>"Gerbens", "scopus_author_id"=>"6602152371"}, {"first_name"=>"Gert Jan", "last_name"=>"Meersma", "scopus_author_id"=>"57196476337"}, {"first_name"=>"Harry G.", "last_name"=>"Klip", "scopus_author_id"=>"6602794774"}, {"first_name"=>"Harry", "last_name"=>"Hollema", "scopus_author_id"=>"7005085030"}, {"first_name"=>"Robert M.W.", "last_name"=>"Hofstra", "scopus_author_id"=>"7006771436"}, {"first_name"=>"Gerard J.Te", "last_name"=>"Meerman", "scopus_author_id"=>"7005647887"}, {"first_name"=>"Elisabeth G.E.", "last_name"=>"De Vries", "scopus_author_id"=>"7201772732"}, {"first_name"=>"Ate G.J.", "last_name"=>"Van Der Zee", "scopus_author_id"=>"7005877880"}], "year"=>2009, "source"=>"PLoS Medicine", "identifiers"=>{"issn"=>"15491277", "scopus"=>"2-s2.0-61449106520", "sgr"=>"61449106520", "pui"=>"354254062", "isbn"=>"1549-1676 (Electronic)\\r1549-1277 (Linking)", "pmid"=>"19192944", "doi"=>"10.1371/journal.pmed.1000024"}, "id"=>"a6add251-e844-3d45-b56b-805ea3b1d501", "abstract"=>"BACKGROUND: Ovarian cancer has a poor prognosis due to advanced stage at presentation and either intrinsic or acquired resistance to classic cytotoxic drugs such as platinum and taxoids. Recent large clinical trials with different combinations and sequences of classic cytotoxic drugs indicate that further significant improvement in prognosis by this type of drugs is not to be expected. Currently a large number of drugs, targeting dysregulated molecular pathways in cancer cells have been developed and are introduced in the clinic. A major challenge is to identify those patients who will benefit from drugs targeting these specific dysregulated pathways.The aims of our study were (1) to develop a gene expression profile associated with overall survival in advanced stage serous ovarian cancer, (2) to assess the association of pathways and transcription factors with overall survival, and (3) to validate our identified profile and pathways/transcription factors in an independent set of ovarian cancers. METHODS AND FINDINGS: According to a randomized design, profiling of 157 advanced stage serous ovarian cancers was performed in duplicate using approximately 35,000 70-mer oligonucleotide microarrays. A continuous predictor of overall survival was built taking into account well-known issues in microarray analysis, such as multiple testing and overfitting. A functional class scoring analysis was utilized to assess pathways/transcription factors for their association with overall survival. The prognostic value of genes that constitute our overall survival profile was validated on a fully independent, publicly available dataset of 118 well-defined primary serous ovarian cancers. Furthermore, functional class scoring analysis was also performed on this independent dataset to assess the similarities with results from our own dataset. An 86-gene overall survival profile discriminated between patients with unfavorable and favorable prognosis (median survival, 19 versus 41 mo, respectively; permutation p-value of log-rank statistic = 0.015) and maintained its independent prognostic value in multivariate analysis. Genes that composed the overall survival profile were also able to discriminate between the two risk groups in the independent dataset. In our dataset 17/167 pathways and 13/111 transcription factors were associated with overall survival, of which 16 and 12, respectively, were confirmed in the independent dataset. CONCLUSIONS: Our study provides new clues to genes, pathways, and transcription factors that contribute to the clinical outcome of serous ovarian cancer and might be exploited in designing new treatment strategies.", "link"=>"http://www.mendeley.com/research/survivalrelated-profile-pathways-transcription-factors-ovarian-cancer", "reader_count"=>103, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>13, "Researcher"=>42, "Student > Doctoral Student"=>4, "Student > Ph. D. Student"=>17, "Student > Postgraduate"=>3, "Student > Master"=>6, "Other"=>7, "Student > Bachelor"=>4, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>1, "Professor"=>4}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>13, "Researcher"=>42, "Student > Doctoral Student"=>4, "Student > Ph. D. 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• {"type"=>"COMMENT", "annotationUri"=>"info:doi/10.1371/annotation/c538075e-d71a-4f39-a525-7be89a1456ab", "title"=>"clinical utility? optimally cytoreduced?", "body"=>"Author: Arvind Bakhru\nPosition: Resident\nInstitution: Univ of Maryland\nE-mail: abakhru@gmail.com\nSubmitted Date: March 01, 2009\nPublished Date: March 2, 2009\nThis comment was originally posted as a “Reader Response” on the publication date indicated above. All Reader Responses are now available as comments.\n\nIn your aritcle, it is mentioned that in the multivariate analysis:\n\"Age, stage, grade, debulking status, and chemotherapy regimens showed no difference in distribution between the predicted low- and\nhigh-risk groups.\"\nIn the multivariate analysis, only the OSP and debulking status were significant.\n\nThis seems to indicate that all these genese together creating a genetic profile is equivalent to our standard age/stage/grade for predicting final survival outcome for patients. Is there, then, clinical utility for determining the genetic profile? Clearly there are therapeutic and research opportunities presented by this paper.\n\nMoreover, it would be nice to know if you have the data for debulking less than 1cm and less than 0.5cm. The authors used 2cm, but most U.S. centers use less than 1cm or less than 0.5cm or no visible disease as 'optimally cytoreduced' - see sloan kettering data. It may make that variable much stronger...", "isRemoved"=>false, "created"=>"2009-03-31T00:34:53Z", "lastModified"=>"2009-03-31T00:34:53Z", "creator"=>{"userId"=>"172479"}, "highlightedText"=>"", "competingInterestStatement"=>{"creatorWasPrompted"=>true, "hasCompetingInterests"=>false}, "parentArticle"=>{"doi"=>"info:doi/10.1371/journal.pmed.1000024", "state"=>"published", "journals"=>{"PLoSMedicine"=>{"journalKey"=>"PLoSMedicine", "eIssn"=>"1549-1676", "title"=>"PLOS Medicine"}}}, "replyTreeSize"=>0, "mostRecentActivity"=>"2009-03-31T00:34:53Z", "replies"=>[]}

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• {"id"=>"226265829996630016", "text"=>"Connotea: Survival-Related Profile, Pathways, and Transcription Factors in Ovarian Cancer: Posted by andym to \"o... http://t.co/kgnNqOQV", "created_at"=>"2012-07-20T10:42:32Z", "user"=>"ecrc_andy", "user_name"=>"Andy MacLeod", "user_profile_image"=>"http://a0.twimg.com/sticky/default_profile_images/default_profile_5_normal.png"}

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Figshare

• {"files"=>["https://ndownloader.figshare.com/files/909942"], "description"=>"<p>Pathways are c-Myc, H-Ras, c-Src, E2F3, and β-catenin in our 157 ovarian tumor samples. Red indicates a high probability of activation and green indicates a low probability of activation. The probabilities of pathway activation were clustered according to the uncentered correlation measure. On the right of the figure the follow-up in months is depicted followed by the censoring status for each of the 157 samples.</p>", "links"=>[], "tags"=>["probabilities", "pathway", "activation", "oncogenic"], "article_id"=>580389, "categories"=>["Cancer", "Genetics"], "users"=>["Anne P. G Crijns", "Rudolf S. N Fehrmann", "Steven de Jong", "Frans Gerbens", "Gert Jan Meersma", "Harry G Klip", "Harry Hollema", "Robert M. W Hofstra", "Gerard J. te Meerman", "Elisabeth G. E de Vries", "Ate G. J van der Zee"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000024.g003", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Heatmap_Showing_Predicted_Probabilities_of_Pathway_Activation_for_the_Five_Oncogenic_Pathways_/580389", "title"=>"Heatmap Showing Predicted Probabilities of Pathway Activation for the Five Oncogenic Pathways", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-02-03 00:06:29"}
• {"files"=>["https://ndownloader.figshare.com/files/909755"], "description"=>"<p>Genes are <i>FGFBP1</i>, <i>FKBP7</i>, <i>TMEM45A</i>, and <i>CCL28</i> from our overall survival profile. ΔCt of the gene is obtained by subtracting the mean Ct value of <i>GAPDH</i> from the mean Ct value of the gene. Both axes are on log₂ scale.</p>", "links"=>[], "tags"=>["plots", "microarray", "qrt-pcr"], "article_id"=>580199, "categories"=>["Cancer", "Genetics"], "users"=>["Anne P. G Crijns", "Rudolf S. N Fehrmann", "Steven de Jong", "Frans Gerbens", "Gert Jan Meersma", "Harry G Klip", "Harry Hollema", "Robert M. W Hofstra", "Gerard J. te Meerman", "Elisabeth G. E de Vries", "Ate G. J van der Zee"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000024.g002", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Scatter_Plots_Showing_the_Microarray_Expression_Signal_Versus_the_916_Ct_Obtained_by_qRT_PCR_for_Four_Individual_Genes_/580199", "title"=>"Scatter Plots Showing the Microarray Expression Signal Versus the ΔCt Obtained by qRT-PCR for Four Individual Genes", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-02-03 00:03:19"}
• {"files"=>["https://ndownloader.figshare.com/files/449615", "https://ndownloader.figshare.com/files/449641", "https://ndownloader.figshare.com/files/449698", "https://ndownloader.figshare.com/files/449735", "https://ndownloader.figshare.com/files/449789", "https://ndownloader.figshare.com/files/449835", "https://ndownloader.figshare.com/files/449852", "https://ndownloader.figshare.com/files/449867"], "description"=>"<div><h3>Background</h3><p>Ovarian cancer has a poor prognosis due to advanced stage at presentation and either intrinsic or acquired resistance to classic cytotoxic drugs such as platinum and taxoids. Recent large clinical trials with different combinations and sequences of classic cytotoxic drugs indicate that further significant improvement in prognosis by this type of drugs is not to be expected. Currently a large number of drugs, targeting dysregulated molecular pathways in cancer cells have been developed and are introduced in the clinic. A major challenge is to identify those patients who will benefit from drugs targeting these specific dysregulated pathways.The aims of our study were (1) to develop a gene expression profile associated with overall survival in advanced stage serous ovarian cancer, (2) to assess the association of pathways and transcription factors with overall survival, and (3) to validate our identified profile and pathways/transcription factors in an independent set of ovarian cancers.</p> <h3>Methods and Findings</h3><p>According to a randomized design, profiling of 157 advanced stage serous ovarian cancers was performed in duplicate using ∼35,000 70-mer oligonucleotide microarrays. A continuous predictor of overall survival was built taking into account well-known issues in microarray analysis, such as multiple testing and overfitting. A functional class scoring analysis was utilized to assess pathways/transcription factors for their association with overall survival. The prognostic value of genes that constitute our overall survival profile was validated on a fully independent, publicly available dataset of 118 well-defined primary serous ovarian cancers. Furthermore, functional class scoring analysis was also performed on this independent dataset to assess the similarities with results from our own dataset. An 86-gene overall survival profile discriminated between patients with unfavorable and favorable prognosis (median survival, 19 versus 41 mo, respectively; permutation <em>p</em>-value of log-rank statistic = 0.015) and maintained its independent prognostic value in multivariate analysis. Genes that composed the overall survival profile were also able to discriminate between the two risk groups in the independent dataset. In our dataset 17/167 pathways and 13/111 transcription factors were associated with overall survival, of which 16 and 12, respectively, were confirmed in the independent dataset.</p> <h3>Conclusions</h3><p>Our study provides new clues to genes, pathways, and transcription factors that contribute to the clinical outcome of serous ovarian cancer and might be exploited in designing new treatment strategies.</p> </div>", "links"=>[], "tags"=>["survival-related", "transcription", "factors", "ovarian", "cancer"], "article_id"=>148617, "categories"=>["Cancer", "Genetics"], "users"=>["Anne P. G Crijns", "Rudolf S. N Fehrmann", "Steven de Jong", "Frans Gerbens", "Gert Jan Meersma", "Harry G Klip", "Harry Hollema", "Robert M. W Hofstra", "Gerard J. te Meerman", "Elisabeth G. E de Vries", "Ate G. J van der Zee"], "doi"=>["https://dx.doi.org/10.1371/journal.pmed.1000024.sg001", "https://dx.doi.org/10.1371/journal.pmed.1000024.sg002", "https://dx.doi.org/10.1371/journal.pmed.1000024.sg003", "https://dx.doi.org/10.1371/journal.pmed.1000024.sg004", "https://dx.doi.org/10.1371/journal.pmed.1000024.st001", "https://dx.doi.org/10.1371/journal.pmed.1000024.st002", "https://dx.doi.org/10.1371/journal.pmed.1000024.st003", "https://dx.doi.org/10.1371/journal.pmed.1000024.st004"], "stats"=>{"downloads"=>20, "page_views"=>23, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Survival_Related_Profile_Pathways_and_Transcription_Factors_in_Ovarian_Cancer/148617", "title"=>"Survival-Related Profile, Pathways, and Transcription Factors in Ovarian Cancer", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2009-02-03 02:23:37"}
• {"files"=>["https://ndownloader.figshare.com/files/909611"], "description"=>"<div><p>(A) Present study, median survival of 19 mo versus 41 mo (<i>p</i> = 0.0014, log-rank), permutation <i>p</i>-value = 0.015.</p>\n <p>(B) Dressman et al. [<a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000024#pmed-1000024-b009\" target=\"_blank\">9</a>], median survival of 33 mo versus 108 mo (<i>p</i> < 0.0001, log-rank), permutation <i>p</i>-value = 0.007.</p></div>", "links"=>[], "tags"=>["curves", "patients", "median", "ovarian"], "article_id"=>580055, "categories"=>["Cancer", "Genetics"], "users"=>["Anne P. G Crijns", "Rudolf S. N Fehrmann", "Steven de Jong", "Frans Gerbens", "Gert Jan Meersma", "Harry G Klip", "Harry Hollema", "Robert M. W Hofstra", "Gerard J. te Meerman", "Elisabeth G. E de Vries", "Ate G. J van der Zee"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000024.g001", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Kaplan_Meier_Survival_Curves_for_the_Patients_Predicted_to_Have_Above_or_Below_Median_Risk_of_Death_Due_to_Ovarian_Cancer_/580055", "title"=>"Kaplan-Meier Survival Curves for the Patients Predicted to Have Above or Below Median Risk of Death Due to Ovarian Cancer", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-02-03 00:00:55"}

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• {"unique-ip"=>"17", "full-text"=>"16", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"5", "cited-by"=>"0", "year"=>"2019", "month"=>"9"}
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• {"unique-ip"=>"13", "full-text"=>"12", "pdf"=>"6", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"5", "cited-by"=>"0", "year"=>"2020", "month"=>"3"}
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• {"unique-ip"=>"16", "full-text"=>"15", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"6", "cited-by"=>"0", "year"=>"2020", "month"=>"7"}
• {"unique-ip"=>"11", "full-text"=>"6", "pdf"=>"10", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"8", "cited-by"=>"0", "year"=>"2020", "month"=>"8"}
• {"unique-ip"=>"14", "full-text"=>"14", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"4", "cited-by"=>"0", "year"=>"2020", "month"=>"9"}
• {"unique-ip"=>"30", "full-text"=>"56", "pdf"=>"8", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"14", "cited-by"=>"0", "year"=>"2020", "month"=>"10"}
• {"unique-ip"=>"7", "full-text"=>"5", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"11"}
• {"unique-ip"=>"17", "full-text"=>"20", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"5", "cited-by"=>"0", "year"=>"2020", "month"=>"12"}
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Relative Metric

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F1000Prime