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Modeling risk stratification in human cancer.

Abstract : MOTIVATION: Despite huge prognostic promises, gene expression-based survival assessment is rarely used in clinical routine. Main reasons include difficulties in performing and reporting analyses and restriction in most methods to one high-risk group with the vast majority of patients being unassessed. The present study aims at limiting these difficulties by (i) mathematically defining the number of risk groups without any a priori assumption; (ii) computing the risk of an independent cohort by considering each patient as a new patient incorporated to the validation cohort and (iii) providing an open-access Web site to freely compute risk for every new patient. RESULTS: Using the gene expression profiles of 551 patients with multiple myeloma, 602 with breast-cancer and 460 with glioma, we developed a model combining running log-rank tests under controlled chi-square conditions and multiple testing corrections to build a risk score and a classification algorithm using simultaneous global and between-group log-rank chi-square maximization. For each cancer entity, we provide a statistically significant three-group risk prediction model, which is corroborated with publicly available validation cohorts. CONCLUSION: In constraining between-group significances, the risk score compares favorably with previous risk classifications. AVAILABILITY: Risk assessment is freely available on the Web at https://gliserv.montp.inserm.fr/PrognoWeb/ for personal or test data files. Web site implementation in Perl, R and Apache.
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https://www.hal.inserm.fr/inserm-00806666
Contributor : Monique Frei <>
Submitted on : Tuesday, April 2, 2013 - 10:42:08 AM
Last modification on : Wednesday, July 10, 2019 - 7:14:02 PM
Long-term archiving on: : Sunday, April 2, 2017 - 11:08:51 PM

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Thierry Rème, Dirk Hose, Charles Theillet, Bernard Klein. Modeling risk stratification in human cancer.. Bioinformatics, Oxford University Press (OUP), 2013, 29 (9), pp.1149-57. ⟨10.1093/bioinformatics/btt124⟩. ⟨inserm-00806666⟩

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