Molecular classification of cancer: class discovery and class prediction by gene expression monitoring
- PMID: 10521349
- DOI: 10.1126/science.286.5439.531
Molecular classification of cancer: class discovery and class prediction by gene expression monitoring
Abstract
Although cancer classification has improved over the past 30 years, there has been no general approach for identifying new cancer classes (class discovery) or for assigning tumors to known classes (class prediction). Here, a generic approach to cancer classification based on gene expression monitoring by DNA microarrays is described and applied to human acute leukemias as a test case. A class discovery procedure automatically discovered the distinction between acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) without previous knowledge of these classes. An automatically derived class predictor was able to determine the class of new leukemia cases. The results demonstrate the feasibility of cancer classification based solely on gene expression monitoring and suggest a general strategy for discovering and predicting cancer classes for other types of cancer, independent of previous biological knowledge.
Similar articles
-
Acute leukemia: subtype discovery and prediction of outcome by gene expression profiling.Verh Dtsch Ges Pathol. 2003;87:66-71. Verh Dtsch Ges Pathol. 2003. PMID: 16888896
-
Classification of multiple cancer types by multicategory support vector machines using gene expression data.Bioinformatics. 2003 Jun 12;19(9):1132-9. doi: 10.1093/bioinformatics/btg102. Bioinformatics. 2003. PMID: 12801874
-
A gene selection algorithm based on the gene regulation probability using maximal likelihood estimation.Biotechnol Lett. 2005 Apr;27(8):597-603. doi: 10.1007/s10529-005-3253-0. Biotechnol Lett. 2005. PMID: 15973495
-
Multifaceted approach to the diagnosis and classification of acute leukemias.Clin Chem. 2000 Aug;46(8 Pt 2):1252-9. Clin Chem. 2000. PMID: 10926919 Review.
-
Acute leukemias in children with Down syndrome.Pediatr Clin North Am. 2008 Feb;55(1):53-70, x. doi: 10.1016/j.pcl.2007.11.001. Pediatr Clin North Am. 2008. PMID: 18242315 Review.
Cited by
-
Deep learning assisted cancer disease prediction from gene expression data using WT-GAN.BMC Med Inform Decis Mak. 2024 Oct 24;24(1):311. doi: 10.1186/s12911-024-02712-y. BMC Med Inform Decis Mak. 2024. PMID: 39449042 Free PMC article.
-
Robust modeling of differential gene expression data using normal/independent distributions: a Bayesian approach.PLoS One. 2015 Apr 24;10(4):e0123791. doi: 10.1371/journal.pone.0123791. eCollection 2015. PLoS One. 2015. PMID: 25910040 Free PMC article.
-
Hierarchical gene selection and genetic fuzzy system for cancer microarray data classification.PLoS One. 2015 Mar 30;10(3):e0120364. doi: 10.1371/journal.pone.0120364. eCollection 2015. PLoS One. 2015. PMID: 25823003 Free PMC article.
-
LINC00460 Is a Dual Biomarker That Acts as a Predictor for Increased Prognosis in Basal-Like Breast Cancer and Potentially Regulates Immunogenic and Differentiation-Related Genes.Front Oncol. 2021 Apr 12;11:628027. doi: 10.3389/fonc.2021.628027. eCollection 2021. Front Oncol. 2021. PMID: 33912452 Free PMC article.
-
Ranking Series of Cancer-Related Gene Expression Data by Means of the Superposing Significant Interaction Rules Method.Biomolecules. 2020 Sep 8;10(9):1293. doi: 10.3390/biom10091293. Biomolecules. 2020. PMID: 32911598 Free PMC article.
Publication types
MeSH terms
Substances
LinkOut - more resources
Full Text Sources
Other Literature Sources
Molecular Biology Databases
Miscellaneous