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Open Access Highly Accessed Correspondence

Criteria for the use of omics-based predictors in clinical trials: explanation and elaboration

Lisa M McShane1*, Margaret M Cavenagh2, Tracy G Lively3, David A Eberhard4, William L Bigbee5, P Mickey Williams6, Jill P Mesirov7, Mei-Yin C Polley8, Kelly Y Kim9, James V Tricoli10, Jeremy MG Taylor11, Deborah J Shuman12, Richard M Simon13, James H Doroshow12 and Barbara A Conley14

Author affiliations

1 Biometric Research Branch, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, Room 5W130, MSC 9735, 9609 Medical Center Drive, Bethesda, MD 20892-9735, USA

2 Cancer Diagnosis Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, Room 4W432, MSC 9730, 9609 Medical Center Drive, Bethesda, MD 20892, USA

3 Cancer Diagnosis Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, Room 4W420, MSC 9730, 9609 Medical Center Drive, Bethesda, MD 20892, USA

4 Department of Pathology and Lineberger Comprehensive Cancer Center, Brinkhous-Bullitt Bldg., Campus Box 7525, University of North Carolina, Chapel Hill, NC 27599, USA

5 Department of Pathology and University of Pittsburgh Cancer Institute, Hillman Cancer Center, UPCI Research Pavilion, Suite 2.32b, 5117 Centre Avenue, Pittsburgh, PA 15213, USA

6 Frederick National Laboratory for Cancer Research, National Cancer Institute, National Institutes of Health, Bldg. 320, Room 2, 1050 Boyles Street, Frederick, MD 21702, USA

7 Computational Biology and Bioinformatics, Broad Institute of Massachusetts Institute of Technology and Harvard University, 7 Cambridge Center, Cambridge, MA 02142, USA

8 Biometric Research Branch, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, Room 5W638, 9609 Medical Center Drive, Bethesda, MD 20892, USA

9 Cancer Diagnosis Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, Room 4W430, 9609 Medical Center Drive, Bethesda, MD 20892, USA

10 Cancer Diagnosis Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, Room 3W526, 9609 Medical Center Drive, Bethesda, MD 20892, USA

11 Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 48109, USA

12 Office of the Director, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, Room 3A44, 31 Center Drive, Bethesda, MD 20892, USA

13 Biometric Research Branch, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, Room 5W110, 9609 Medical Center Drive, Bethesda, MD 20892, USA

14 Cancer Diagnosis Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, Room 4W426, 9609 Medical Center Drive, Bethesda, MD 20892, USA

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Citation and License

BMC Medicine 2013, 11:220  doi:10.1186/1741-7015-11-220

Published: 17 October 2013

Abstract

High-throughput ‘omics’ technologies that generate molecular profiles for biospecimens have been extensively used in preclinical studies to reveal molecular subtypes and elucidate the biological mechanisms of disease, and in retrospective studies on clinical specimens to develop mathematical models to predict clinical endpoints. Nevertheless, the translation of these technologies into clinical tests that are useful for guiding management decisions for patients has been relatively slow. It can be difficult to determine when the body of evidence for an omics-based test is sufficiently comprehensive and reliable to support claims that it is ready for clinical use, or even that it is ready for definitive evaluation in a clinical trial in which it may be used to direct patient therapy. Reasons for this difficulty include the exploratory and retrospective nature of many of these studies, the complexity of these assays and their application to clinical specimens, and the many potential pitfalls inherent in the development of mathematical predictor models from the very high-dimensional data generated by these omics technologies. Here we present a checklist of criteria to consider when evaluating the body of evidence supporting the clinical use of a predictor to guide patient therapy. Included are issues pertaining to specimen and assay requirements, the soundness of the process for developing predictor models, expectations regarding clinical study design and conduct, and attention to regulatory, ethical, and legal issues. The proposed checklist should serve as a useful guide to investigators preparing proposals for studies involving the use of omics-based tests. The US National Cancer Institute plans to refer to these guidelines for review of proposals for studies involving omics tests, and it is hoped that other sponsors will adopt the checklist as well.

Keywords:
Analytical validation; Biomarker; Diagnostic test; Genomic classifier; Model validation; Molecular profile; Omics; Personalized medicine; Precision Medicine; Treatment selection