Concordance among Gene-Expression–Based Predictors for Breast Cancer

• Published in: The New England journal of medicine Vol. 355; no. 6; pp. 560 - 569
• Main Authors: Fan, Cheng, Oh, Daniel S, Wessels, Lodewyk, Weigelt, Britta, Nuyten, Dimitry S.A, Nobel, Andrew B, van't Veer, Laura J, Perou, Charles M
• Format: Journal Article
• Language: English
• Published: Boston, MA Massachusetts Medical Society 10.08.2006
• Subjects: Analysis of Variance; Biological and medical sciences; Breast cancer; Breast Neoplasms - genetics; Breast Neoplasms - mortality; Cancer therapies; Female; Gene Expression; Gene Expression Profiling; General aspects; Gynecology. Andrology. Obstetrics; Humans; Mammary gland diseases; Medical sciences; Models, Genetic; Multivariate analysis; Patients; Phenotype; Prognosis; Proportional Hazards Models; Receptor, ErbB-2; Receptors, Estrogen; Survival Analysis; Tumors; Medicine; Mammary gland diseases; Concordance; Prediction; Breast cancer; Mammary gland; Malignant tumor; Gene expression; Predictive factor
• ISSN: 0028-4793; 1533-4406
• DOI: 10.1056/NEJMoa052933

There are now many published examples of gene-expression–based methods to aid in the determination of prognosis, and some of these methods are available to the clinician. This study of five models shows that four of the five classified cancers concordantly, despite minimal overlap in the gene sets used. There are now many published examples of gene-expression–based methods to aid in the determination of prognosis. This study of five models shows that four of the five classified cancers concordantly, despite minimal overlap in the gene sets used. Many studies of gene expression have identified expression profiles and gene sets that are prognostic, predictive, or both for patients with breast cancer. 1 – 12 Comparisons of the lists of genes derived from some of these apparently similar studies show that they overlap only slightly, if at all. The reasons for this lower-than-expected overlap are not completely known, but they probably include differences in the patient cohorts, microarray platforms, and mathematical methods of analysis. An important and unanswered question, however, is whether these predictors are actually concordant with respect to their predictions for individual patients. Here, we describe our analysis of . . .