Predicting the clinical status of human breast cancer by using gene expression profiles
| Autor: | Carrie Blanchette, John A. Olson, Holly K. Dressman, Seiichi Ishida, Harry Zuzan, Erich Huang, Rainer Spang, Joseph R. Nevins, Jeffrey R. Marks, Mike West |
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| Rok vydání: | 2001 |
| Předmět: |
Breast Neoplasms
Computational biology Biology Bioinformatics Predictive Value of Tests DNA Microarray Analysis medicine Humans Gene Lymph node Estrogen Receptor Status Oligonucleotide Array Sequence Analysis Probability Multidisciplinary Reproducibility of Results Cancer Biological Sciences medicine.disease Phenotype Enzymes medicine.anatomical_structure Receptors Estrogen Bacillus anthracis Multigene Family Predictive value of tests Lymph Node Excision Female Lymph Nodes Bayesian linear regression |
| Zdroj: | Proceedings of the National Academy of Sciences. 98:11462-11467 |
| ISSN: | 1091-6490 0027-8424 |
| Popis: | Prognostic and predictive factors are indispensable tools in the treatment of patients with neoplastic disease. For the most part, such factors rely on a few specific cell surface, histological, or gross pathologic features. Gene expression assays have the potential to supplement what were previously a few distinct features with many thousands of features. We have developed Bayesian regression models that provide predictive capability based on gene expression data derived from DNA microarray analysis of a series of primary breast cancer samples. These patterns have the capacity to discriminate breast tumors on the basis of estrogen receptor status and also on the categorized lymph node status. Importantly, we assess the utility and validity of such models in predicting the status of tumors in crossvalidation determinations. The practical value of such approaches relies on the ability not only to assess relative probabilities of clinical outcomes for future samples but also to provide an honest assessment of the uncertainties associated with such predictive classifications on the basis of the selection of gene subsets for each validation analysis. This latter point is of critical importance in the ability to apply these methodologies to clinical assessment of tumor phenotype. |
| Databáze: | OpenAIRE |
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