Gene expression profiling: does it add predictive accuracy to clinical characteristics in cancer prognosis?
It is widely accepted that gene expression classifiers need to be externally validated by showing that they predict the outcome well enough on other patients than those from whose data the classifier was derived. Unfortunately, the gain in predictive accuracy by the classifier as compared to established clinical prognostic factors often is not quantified. Our objective is to illustrate the application of appropriate statistical measures for this purpose. In order to compare the predictive accuracies of a model based on the clinical factors only and of a model based on the clinical factors plus the gene classifier, we compute the decrease in predictive inaccuracy and the proportion of explained variation. These measures have been obtained for three studies of published gene classifiers: for survival of lymphoma patients, for survival of breast cancer patients and for the diagnosis of lymph node metastases in head and neck cancer. For the three studies our results indicate varying and possibly small added explained variation and predictive accuracy due to gene classifiers. Therefore, the gain of future gene classifiers should routinely be demonstrated by appropriate statistical measures, such as the ones we recommend.
Dunkler
Daniela
D
Core Unit for Medical Statistics and Informatics, Medical University of Vienna, Spitalgasse 23, A-1090 Vienna, Austria.
Michiels
Stefan
S
Schemper
Michael
M
D001943:Breast Neoplasms / Q000235:genetics*; D005260:Female; D020869:Gene Expression Profiling / Q000592:standards*; D006258:Head and Neck Neoplasms / Q000235:genetics*; D006801:Humans; D016393:Lymphoma, B-Cell / Q000235:genetics*; D011237:Predictive Value of Tests; D011379:Prognosis; D012680:Sensitivity and Specificity; D016019:Survival Analysis