%0 Journal Article %T Modeling risk stratification in human cancer. %+ Cellules souches normales et cancéreuses %+ Department of Internal Medicine V %+ Institut de recherche en cancérologie de Montpellier (IRCM - U896 Inserm - UM1) %A Rème, Thierry %A Hose, Dirk %A Theillet, Charles %A Klein, Bernard %Z The Ligue Nationale Contre le Cancer, Paris, France (équipe labellisée 2099-2011); Institut National du Cancer, Paris, France (2008-047 to B Klein); The Hopp-foundation, Germany, The University of Heidelberg, The National Center for Tumor Diseases, Heidelberg, Germany (TRR79 to D Hose); the Deutsche Krebshilfe, Bonn, Germany. %< avec comité de lecture %@ 1367-4803 %J Bioinformatics %I Oxford University Press (OUP) %V 29 %N 9 %P 1149-57 %8 2013-05-01 %D 2013 %R 10.1093/bioinformatics/btt124 %M 23493321 %K Human Cancer %K Multiple myeloma %K prognostic %Z Life Sciences [q-bio]/ImmunologyJournal articles %X 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. %G English %2 https://inserm.hal.science/inserm-00806666/document %2 https://inserm.hal.science/inserm-00806666/file/Manuscript_template_Reme.pdf %L inserm-00806666 %U https://inserm.hal.science/inserm-00806666 %~ INSERM %~ UNIV-MONTP1 %~ FNCLCC %~ VALDAURELLE %~ BS %~ UNIV-MONTPELLIER %~ IRCM %~ UM1-UM2 %~ UM-2015-2021