This Article Full Text Full Text (PDF) eLetters: Submit a response to this article Purchase Article View Shopping Cart Alert me when this article is cited Alert me when eLetters are posted Alert me if a correction is posted Citation Map Services E-mail this article link to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Reprints/Permissions Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Ioannidis, J. P. A. Search for Related Content PubMed PubMed Citation Articles by Ioannidis, J. P. A.

The Community Oncologist

Is Molecular Profiling Ready for Use in Clinical Decision Making?

Clinical and Molecular Epidemiology Unit, Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, and Biomedical Research Institute, Foundation for Research and Technology-Hellas, Ioannina, Greece and Institute for Clinical Research and Health Policy Studies, Tufts University School of Medicine, Boston, Massachusetts, USA

Key Words. Molecular profiling • Microarrays • Clinical use • Clinical practice • Prediction Prognosis

Correspondence: John P.A. Ioannidis, M.D., Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina 45110, Greece. Telephone: 302651097807; Fax: 302651097867; e-mail: jioannid{at}cc.uoi.gr

Molecular profiling, the classification of tissue or other specimens for diagnostic, prognostic, and predictive purposes based on multiple gene expression, is a technology that holds major promise for optimizing the management of patients with cancer. However, the use of these tests for clinical decision making presents many challenges to overcome. Assay development and data analysis in this field have been largely exploratory, and leave numerous possibilities for the introduction of bias. Standardization of profiles remains the exception. Classifier performance is usually overinterpreted by presenting the results as p-values or multiplicative effects (e.g., relative risks), while the absolute sensitivity and specificity of classification remain modest at best, especially when tested in large validation samples. Validation has often been done with suboptimal attention to methodology and protection from bias. The postulated classifier performance may be inflated compared to what these profiles can achieve. With the exception of breast cancer, we have little evidence about the incremental discrimination that molecular profiles can provide versus classic risk factors alone. Clinical trials have started to evaluate the utility of using molecular profiles for breast cancer management. Until we obtain data from these trials, the impact of these tests and the net benefit under real-life settings remain unknown. Optimal incorporation into clinical practice is not straightforward. Finally, cost-effectiveness is difficult to appreciate until these other challenges are addressed. Overall, molecular profiling is a fascinating and promising technology, but its incorporation into clinical decision making requires careful planning and robust evidence.

Disclosure of potential conflicts of interest is found at the end of this article.

This article has been cited by other articles:

				P. J. Park, S. W. Kong, T. Tebaldi, W. R. Lai, S. Kasif, and I. S. Kohane Integration of heterogeneous expression data sets extends the role of the retinol pathway in diabetes and insulin resistance Bioinformatics, December 1, 2009; 25(23): 3121 - 3127. [Abstract] [Full Text] [PDF]

				S M Metias, E Lianidou, and G M Yousef MicroRNAs in clinical oncology: at the crossroads between promises and problems J. Clin. Pathol., September 1, 2009; 62(9): 771 - 776. [Abstract] [Full Text] [PDF]

				U. Bacher, A. Kohlmann, and T. Haferlach Perspectives of gene expression profiling for diagnosis and therapy in haematological malignancies Briefings in Functional Genomics, May 27, 2009; (2009) elp011v1. [Abstract] [Full Text] [PDF]

				B. Lumbreras, L. A. Parker, M. Porta, M. Pollan, J. P.A. Ioannidis, and I. Hernandez-Aguado Overinterpretation of Clinical Applicability in Molecular Diagnostic Research Clin. Chem., April 1, 2009; 55(4): 786 - 794. [Abstract] [Full Text] [PDF]

				M. J. Duffy and J. Crown A Personalized Approach to Cancer Treatment: How Biomarkers Can Help Clin. Chem., November 1, 2008; 54(11): 1770 - 1779. [Abstract] [Full Text] [PDF]

				A. Kohlmann, E. Haschke-Becher, B. Wimmer, A. Huber-Wechselberger, S. Meyer-Monard, H. Huxol, U. Siegler, M. Rossier, T. Matthes, M. Rebsamen, et al. Intraplatform Reproducibility and Technical Precision of Gene Expression Profiling in 4 Laboratories Investigating 160 Leukemia Samples: The DACH Study Clin. Chem., October 1, 2008; 54(10): 1705 - 1715. [Abstract] [Full Text] [PDF]

				J. S. Ross, C. Hatzis, W. F. Symmans, L. Pusztai, and G. N. Hortobagyi Commercialized Multigene Predictors of Clinical Outcome for Breast Cancer Oncologist, May 1, 2008; 13(5): 477 - 493. [Abstract] [Full Text] [PDF]

				K. M. Smits, L. J. Schouten, B. A.C. van Dijk, C. A. Hulsbergen-van de Kaa, K. A.D. Wouters, E. Oosterwijk, M. van Engeland, and P. A. van den Brandt Genetic and Epigenetic Alterations in the von Hippel-Lindau Gene: the Influence on Renal Cancer Prognosis Clin. Cancer Res., February 1, 2008; 14(3): 782 - 787. [Abstract] [Full Text] [PDF]