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Classification of Breast Cancer Using Genetic Algorithms and Tissue Microarrays

Authors' Affiliation: Department of Pathology, Yale University School of Medicine, New Haven, Connecticut

Requests for reprints: David L. Rimm, Department of Pathology, Yale University School of Medicine, 310 Cedar Street, P.O. Box 208023, New Haven, CT 06520-8023. Phone: 203-737-4204; Fax: 203-737-5089; E-mail: david.rimm{at}yale.edu.

Purpose: A multitude of breast cancer mRNA profiling studies has stratified breast cancer and defined gene sets that correlate with outcome. However, the number of genes used to predict patient outcome or define tumor subtypes by RNA expression studies is variable, nonoverlapping, and generally requires specialized technologies that are beyond those used in the routine pathology laboratory. It would be ideal if the familiarity and streamlined nature of immunohistochemistry could be combined with the rigorously quantitative and highly specific properties of nucleic acid–based analysis to predict patient outcome.

Experimental Design: We have used AQUA-based objective quantitative analysis of tissue microarrays toward the goal of discovery of a minimal number of markers with maximal prognostic or predictive value that can be applied to the conventional formalin-fixed, paraffin-embedded tissue section.

Results: The minimal discovered multiplexed set of tissue biomarkers was GATA3, NAT1, and estrogen receptor. Genetic algorithms were then applied after division of our cohort into a training set of 223 breast cancer patients to discover a prospectively applicable solution that can define a subset of patients with 5-year survival of 96%. This algorithm was then validated on an internal validation set (n = 223, 5-year survival = 95.8%) and further validated on an independent cohort from Sweden, which showed 5-year survival of 92.7% (n = 149).

Conclusions: With further validation, this test has both the familiarity and specificity for widespread use in management of breast cancer. More generally, this work illustrates the potential for multiplexed biomarker discovery on the tissue microarray platform.

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