Modeling risk stratification in human cancer
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 unassesse...
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| Main Authors: | , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
March 14, 2013
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| In: |
Bioinformatics
Year: 2013, Volume: 29, Issue: 9, Pages: 1149-1157 |
| ISSN: | 1367-4811 |
| DOI: | 10.1093/bioinformatics/btt124 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1093/bioinformatics/btt124 Verlag, lizenzpflichtig, Volltext: https://academic.oup.com/bioinformatics/article/29/9/1149/222176 
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| Author Notes: | Thierry Rème, Dirk Hose, Charles Theillet and Bernard Klein |
| Summary: | 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.Contact:thierry.remeinserm.frSupplementary information:Supplementary data are available at Bioinformatics online. |
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| Item Description: | Gesehen am 22.12.2021 |
| Physical Description: | Online Resource |
| ISSN: | 1367-4811 |
| DOI: | 10.1093/bioinformatics/btt124 |