© 2005 Oxford University Press
BRIEF COMMUNICATION |
Limits of Predictive Models Using Microarray Data for Breast Cancer Clinical Treatment Outcome
Affiliations of authors: Department of Experimental Oncology, Istituto Nazionale per lo Studio e la Cura dei Tumori, Milano, Italy (JFR, LL, LDC, DC, SV, MGD, MG, MAP); Molecular Cancer Genetics Group, Fondazione Istituto FIRC di Oncologia Molecolare (IFOM), Milano, Italy (JFR, LL, LDC, MG, MAP)
Correspondence to: James F. Reid, Fondazione Istituto FIRC di Oncologia Molecolare (IFOM), Milano, Italy (e-mail: james.reid{at}ifom-ieo-campus.it); Manuela Gariboldi, Istituto Nazionale per lo Studio e la Cura dei Tumori, Milano, Italy (e-mail: manuela.gariboldi{at}istitutotumori.mi.it); Marco A. Pierotti, Istituto Nazionale per lo Studio e la Cura dei Tumori, Milano, Italy (e-mail: marco.pierotti{at}istitutotumori.mi.it).
Data from microarray studies have been used to develop predictive models for treatment outcome in breast cancer, such as a recently proposed predictive model for antiestrogen response after tamoxifen treatment that was based on the expression ratio of two genes. We attempted to validate this model on an independent cohort of 58 patients with resectable estrogen receptor–positive breast cancer. We measured expression of the genes HOXB13 and IL17BR with real time–quantitative polymerase chain reaction and assessed the association between their expression and outcome by use of univariate logistic regression, area under the receiver-operating-characteristic curve (AUC), a two-sample t test, and a Mann–Whitney test. We also applied standard supervised methods to the original microarray dataset and to another independent dataset from similar patients to estimate the classification accuracy obtainable by using more than two genes in a microarray-based predictive model. We could not validate the performance of the two-gene predictor on our cohort of samples (relation between outcome and the following genes estimated by logistic regression: for HOXB13, odds ratio [OR] = 1.04, 95% confidence interval [CI] = 0.92 to 1.16, P = .54; for IL17BR, OR = 0.69, 95% CI = 0.40 to 1.20, P = .18; and for HOXB13/IL17BR, OR = 1.30, 95% CI = 0.88 to 1.93, P = .18). Similar results were obtained with the AUC, a two-sample two-sided t test, and a Mann–Whitney test. In addition, estimates of classification accuracies applied to two independent microarray datasets highlighted the poor performance of treatment-response predictive models that can be achieved with the sample sizes of patients and informative genes to date.
CiteULike 
Connotea 
Del.icio.us What's this?
Correspondence about this Article
- Re: Limits of Predictive Models Using Microarray Data for Breast Cancer Clinical Treatment Outcome
- Maurice P. H. M. Jansen, John A. Foekens, Jan G. M. Klijn, and Els M. J. J. Berns
J Natl Cancer Inst 2005 97: 1851-1852.[Extract] [Full Text] [PDF]
Editorial about this Article
- Development and Validation of Therapeutically Relevant Multi-Gene Biomarker Classifiers
- Richard Simon
J Natl Cancer Inst 2005 97: 866-867.[Extract] [Full Text] [PDF]
This article has been cited by other articles:
|
|
 |
|
  F. Correa Geyer and J. S. Reis-Filho Microarray-based Gene Expression Profiling as a Clinical Tool for Breast Cancer Management: Are We There Yet? International Journal of Surgical Pathology, August 1, 2009; 17(4): 285 - 302. [Abstract] [PDF]
|
|
|
|
 |
|
 S. P. Pitroda, N. N. Khodarev, M. A. Beckett, D. W. Kufe, and R. R. Weichselbaum From the Cover: MUC1-induced alterations in a lipid metabolic gene network predict response of human breast cancers to tamoxifen treatment PNAS, April 7, 2009; 106(14): 5837 - 5841. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 C. A. Beam, W. Gao, V. Macias, W. Liang, and A. Kajdacsy Balla Sequential Testing Approach As an Efficient and Easier Alternative for the Validation of New Predictive Technologies in the Clinic J. Clin. Oncol., March 1, 2009; 27(7): 1087 - 1090. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 S. Lee Mistakes in validating the accuracy of a prediction classifier in high-dimensional but small-sample microarray data Statistical Methods in Medical Research, December 1, 2008; 17(6): 635 - 642. [Abstract] [PDF]
|
|
|
|
 |
|
 M. Chanrion, V. Negre, H. Fontaine, N. Salvetat, F. Bibeau, G. M. Grogan, L. Mauriac, D. Katsaros, F. Molina, C. Theillet, et al. A Gene Expression Signature that Can Predict the Recurrence of Tamoxifen-Treated Primary Breast Cancer Clin. Cancer Res., March 15, 2008; 14(6): 1744 - 1752. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 L. Marchionni, R. F. Wilson, A. C. Wolff, S. Marinopoulos, G. Parmigiani, E. B. Bass, and S. N. Goodman Systematic Review: Gene Expression Profiling Assays in Early-Stage Breast Cancer Ann Intern Med, March 4, 2008; 148(5): 358 - 369. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
A. C. Wolff, D. Berry, L. A. Carey, M. Colleoni, M. Dowsett, M. Ellis, J. E. Garber, D. Mankoff, S. Paik, L. Pusztai, et al. Research Issues Affecting Preoperative Systemic Therapy for Operable Breast Cancer J. Clin. Oncol., February 10, 2008; 26(5): 806 - 813. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 S Tozlu-Kara, V Roux, C Andrieu, J Vendrell, S Vacher, V Lazar, F Spyratos, M Tubiana-Hulin, P Cohen, P Dessen, et al. Oligonucleotide microarray analysis of estrogen receptor {alpha}-positive postmenopausal breast carcinomas: identification of HRPAP20 and TIMELESS as outstanding candidate markers to predict the response to tamoxifen J. Mol. Endocrinol., October 1, 2007; 39(4): 305 - 318. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 I. Zlobec, R. Steele, L. Terracciano, J. R Jass, and A. Lugli Selecting immunohistochemical cut-off scores for novel biomarkers of progression and survival in colorectal cancer J. Clin. Pathol., October 1, 2007; 60(10): 1112 - 1116. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
M. P.H.M. Jansen, A. M. Sieuwerts, M. P. Look, K. Ritstier, M. E. Meijer-van Gelder, I. L. van Staveren, J. G.M. Klijn, J. A. Foekens, and E. M.J.J. Berns HOXB13-to-IL17BR Expression Ratio Is Related With Tumor Aggressiveness and Response to Tamoxifen of Recurrent Breast Cancer: A Retrospective Study J. Clin. Oncol., February 20, 2007; 25(6): 662 - 668. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 C. Shimizu, M. Ando, T. Kouno, N. Katsumata, and Y. Fujiwara Current Trends and Controversies over Pre-operative Chemotherapy for Women with Operable Breast Cancer Jpn. J. Clin. Oncol., January 3, 2007; (2007) hyl122v1. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
X.-J. Ma, S. G. Hilsenbeck, W. Wang, L. Ding, D. C. Sgroi, R. A. Bender, C. K. Osborne, D. C. Allred, and M. G. Erlander The HOXB13:IL17BR Expression Index Is a Prognostic Factor in Early-Stage Breast Cancer J. Clin. Oncol., October 1, 2006; 24(28): 4611 - 4619. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 C. Fan, D. S. Oh, L. Wessels, B. Weigelt, D. S.A. Nuyten, A. B. Nobel, L. J. van't Veer, and C. M. Perou Concordance among Gene-Expression-Based Predictors for Breast Cancer N. Engl. J. Med., August 10, 2006; 355(6): 560 - 569. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 L. Kaderali, T. Zander, U. Faigle, J. Wolf, J. L. Schultze, and R. Schrader CASPAR: a hierarchical bayesian approach to predict survival times in cancer from gene expression data Bioinformatics, June 15, 2006; 22(12): 1495 - 1502. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 Y. Cui, I. Parra, M. Zhang, S. G. Hilsenbeck, A. Tsimelzon, T. Furukawa, A. Horii, Z.-Y. Zhang, R. I. Nicholson, and S. A.W. Fuqua Elevated Expression of Mitogen-Activated Protein Kinase Phosphatase 3 in Breast Tumors: A Mechanism of Tamoxifen Resistance Cancer Res., June 1, 2006; 66(11): 5950 - 5959. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
W. F. Symmans Genomic testing for sensitivity of breast cancer to hormonal therapy. Clin. Cancer Res., April 1, 2006; 12(7): 1954 - 1955. [Full Text] [PDF]
|
|
|
|
|
|
M. P. Goetz, V. J. Suman, J. N. Ingle, A. M. Nibbe, D. W. Visscher, C. A. Reynolds, W. L. Lingle, M. Erlander, X.-J. Ma, D. C. Sgroi, et al. A two-gene expression ratio of homeobox 13 and interleukin-17B receptor for prediction of recurrence and survival in women receiving adjuvant tamoxifen. Clin. Cancer Res., April 1, 2006; 12(7 Pt 1): 2080 - 2087. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
P. Roepman, P. Kemmeren, L. F.A. Wessels, P. J. Slootweg, and F. C.P. Holstege Multiple Robust Signatures for Detecting Lymph Node Metastasis in Head and Neck Cancer Cancer Res., February 15, 2006; 66(4): 2361 - 2366. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
S. P. Linke, T. M. Bremer, C. D. Herold, G. Sauter, and C. Diamond A Multimarker Model to Predict Outcome in Tamoxifen-Treated Breast Cancer Patients Clin. Cancer Res., February 15, 2006; 12(4): 1175 - 1183. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 M. P. H. M. Jansen, J. A. Foekens, J. G. M. Klijn, and E. M. J. J. Berns Re: Limits of Predictive Models Using Microarray Data for Breast Cancer Clinical Treatment Outcome J Natl Cancer Inst, December 21, 2005; 97(24): 1851 - 1852. [Full Text] [PDF]
|
|
|
|
|
|
J. F. Reid, L. Lusa, L. De Cecco, D. Coradini, S. Veneroni, M. G. Daidone, M. Gariboldi, and M. A. Pierotti RESPONSE: Re: Limits of Predictive Models Using Microarray Data for Breast Cancer Clinical Treatment Outcome J Natl Cancer Inst, December 21, 2005; 97(24): 1852 - 1853. [Full Text] [PDF]
|
|
|
|
|
|
J. D. Brenton, L. A. Carey, A. A. Ahmed, and C. Caldas Molecular Classification and Molecular Forecasting of Breast Cancer: Ready for Clinical Application? J. Clin. Oncol., October 10, 2005; 23(29): 7350 - 7360. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
R. Simon Development and Validation of Therapeutically Relevant Multi-Gene Biomarker Classifiers J Natl Cancer Inst, June 15, 2005; 97(12): 866 - 867. [Full Text] [PDF]
|
|