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* Institute of Statistics and Decision Sciences, Duke University,
Durham, NC 27708; Departments of Edited by Peter J. Bickel, University of California, Berkeley,
CA, and approved August 3, 2001 (received for review April 3, 2001)
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.
Genetics
Predicting the clinical status of human breast cancer by using
gene expression profiles
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,
,
,
,
, and
,§,¶
Surgery and
Genetics, Duke University Medical Center, Durham, NC
27710; and § Howard Hughes Medical Institute, Durham, NC
27710
¶
To whom reprint requests should be addressed. E-mail:
j.nevins{at}duke.edu.
www.pnas.org/cgi/doi/10.1073/pnas.201162998
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