Abstract
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.
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