This Article Full Text Full Text (PDF) Supplementary Data Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Anderson, K. Articles by Pusztai, L. Search for Related Content PubMed PubMed Citation Articles by Anderson, K. Articles by Pusztai, L.

Reproducibility of Gene Expression Signature–Based Predictions in Replicate Experiments

Authors' Affiliations: Departments of ¹ Breast Medical Oncology, ² Biostatistics and Applied Mathematics, ³ Cancer Genetics, ⁴ Breast Cancer Translational Research Laboratory, and ⁵ Pathology, University of Texas M.D. Anderson Cancer Center, Houston, Texas; and ⁶ Transcriptional Profiling Core Facility of Millennium Pharmaceuticals, Inc., Cambridge, Massachusetts

Requests for reprints: Lajos Pusztai, Department of Breast Medical Oncology, University of Texas M.D. Anderson Cancer Center, Unit 1354, P.O. Box 301439, Houston, TX 77230-1439. Phone: 713-792-2817; Fax: 713-794-4385; E-mail: lpusztai{at}mdanderson.org.

Purpose: The goals of this analysis were to (a) determine concordance of gene expression results from replicate experiments, (b) examine prediction agreement of multigene predictors on replicate data, and (c) assess the robustness of prediction results in the face of noise.

Patients and Methods: Affymetrix U133A gene chips were used for gene expression profiling of 97 fine-needle aspiration biopsies from breast cancer. Thirty-five cases were profiled in replicates: 17 within the same laboratory, 11 in two different laboratories, and 15 to assess manual and robotic labeling. We used data from 62 cases to develop 111 distinct pharmacogenomic predictors of response to therapy. These were tested on cases profiled in duplicates to determine prediction agreement and accuracy. To evaluate the robustness of the pharmacogenomic predictors, we also introduced random noise into the informative genes in one half of the replicates.

Results: The average concordance correlation coefficient was 0.978 (range, 0.96-0.99) for intralaboratory replicates, 0.962 (range, 0.94-0.98) for between-laboratory replicates, and 0.971 (range, 0.93-0.99) for manual versus robotic labeling. The mean % prediction agreement on replicate data was 97% (95% CI, 0.96-0.98; SD, 0.006), 92% (95% CI, 0.90-0.93; SD, 0.009), and 94% (95% CI, 0.92-0.95; SD, 0.008) for support vector machines, diagonal linear discriminant analysis, and k-nearest neighbor prediction methods, respectively. Mean accuracy in the test set was 77% (95% CI, 0.74-0.79; SD, 0.014), 66% (95% CI, 0.63-0.73; SD, 0.015), and 64% (95% CI, 0.60-0.67; SD, 0.016), respectively.

Conclusion: Gene expression results obtained with Affymetrix U133A chips are highly reproducible within and across two high-volume laboratories. Pharmacogenomic predictions yielded >90% agreement in replicate data.

This article has been cited by other articles:

				B. K.T. Tan, L. K. Tan, K. Yu, P. H. Tan, M. Lee, L. H. Sii, C. Y. Wong, G. H. Ho, A. W.Y. Yeo, P. K.H. Chow, et al. Clinical Validation of a Customized Multiple Signature Microarray for Breast Cancer Clin. Cancer Res., January 15, 2008; 14(2): 461 - 469. [Abstract] [Full Text] [PDF]

				L. Pusztai, K. Anderson, and K. R. Hess Pharmacogenomic Predictor Discovery in Phase II Clinical Trials for Breast Cancer Clin. Cancer Res., October 15, 2007; 13(20): 6080 - 6086. [Abstract] [Full Text] [PDF]

				F. Peintinger, K. Anderson, C. Mazouni, H. M. Kuerer, C. Hatzis, F. Lin, G. N. Hortobagyi, W. F. Symmans, and L. Pusztai Thirty-Gene Pharmacogenomic Test Correlates with Residual Cancer Burden after Preoperative Chemotherapy for Breast Cancer Clin. Cancer Res., July 15, 2007; 13(14): 4078 - 4082. [Abstract] [Full Text] [PDF]

				L. Pusztai Chips to Bedside: Incorporation of Microarray Data into Clinical Practice Clin. Cancer Res., December 15, 2006; 12(24): 7209 - 7214. [Full Text] [PDF]

				L. Pusztai, C. Mazouni, K. Anderson, Y. Wu, and W. F. Symmans Molecular Classification of Breast Cancer: Limitations and Potential Oncologist, September 1, 2006; 11(8): 868 - 877. [Abstract] [Full Text] [PDF]