Criteria for the use of omics-based predictors in clinical trials

Lisa M McShane, Margaret M Cavenagh, Tracy G Lively, David A Eberhard, William L Bigbee, P Mickey Williams, Jill P Mesirov, Mei-Yin C Polley, Kelly Y Kim, James V Tricoli, Jeremy M G Taylor, Deborah J Shuman, Richard M Simon, James H Doroshow, Barbara A Conley, Lisa M McShane, Margaret M Cavenagh, Tracy G Lively, David A Eberhard, William L Bigbee, P Mickey Williams, Jill P Mesirov, Mei-Yin C Polley, Kelly Y Kim, James V Tricoli, Jeremy M G Taylor, Deborah J Shuman, Richard M Simon, James H Doroshow, Barbara A Conley

Abstract

The US National Cancer Institute (NCI), in collaboration with scientists representing multiple areas of expertise relevant to 'omics'-based test development, has developed a checklist of criteria that can be used to determine the readiness of omics-based tests for guiding patient care in clinical trials. The checklist criteria cover issues relating to specimens, assays, mathematical modelling, clinical trial design, and ethical, legal and regulatory aspects. Funding bodies and journals are encouraged to consider the checklist, which they may find useful for assessing study quality and evidence strength. The checklist will be used to evaluate proposals for NCI-sponsored clinical trials in which omics tests will be used to guide therapy.

Conflict of interest statement

The authors declare no competing financial interests.

References

    1. Institute of Medicine. Evolution of Translational Omics: Lessons Learned and the Path Forward (eds Micheel, C. M., Nass, S. & Omenn, G. S. ) (The National Academies Press, 2012)A report produced by a committee formed in response to an NCI request for recommendations to strengthen omics-based test development and evaluation; this identifies best practices to enhance the development, evaluation and translation of omics-based tests while reinforcing steps to ensure that these tests are appropriately assessed for scientific validity before they are used to guide patient treatment in clinical trials.
    1. McShane LM, et al. Criteria for the use of omics-based predictors in clinical trials: explanation & elaboration. BMC Med. 2013;11:220. doi: 10.1186/1741-7015-11-220.
    1. Moore HM, et al. Biospecimen Reporting for Improved Study Quality (BRISQ) Cancer Cytopath. 2011;119:92–101. doi: 10.1002/cncy.20147.
    1. Dobbin KK, et al. Interlaboratory comparability study of cancer gene expression analysis using oligonucleotide microarrays. Clin. Cancer Res. 2005;11:565–572.
    1. Shi L, et al. The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nature Biotechnol. 2006;24:1151–1161. doi: 10.1038/nbt1239.
    1. Leek JT, et al. Tackling the widespread and critical impact of batch effects in high-throughput data. Nature Rev. Genet. 2010;11:733–739. doi: 10.1038/nrg2825.
    1. Dupuy A, Simon RM. Critical review of published microarray studies for cancer outcome and guidelines on statistical analysis and reporting. J. Natl. Cancer Inst. 2007;99:147–157. doi: 10.1093/jnci/djk018.
    1. Simon R, Radmacher MD, Dobbin K, McShane LM. Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification. J. Natl. Cancer Inst. 2003;95:14–18. doi: 10.1093/jnci/95.1.14.
    1. Subramanian J, Simon R. Gene expression-based prognostic signatures in lung cancer: ready for clinical use? J. Natl. Cancer Inst. 2010;102:464–474. doi: 10.1093/jnci/djq025.
    1. Molinaro AM, Simon R, Pfeiffer RM. Prediction error estimation: a comparison of resampling methods. Bioinformatics. 2005;21:3301–3307. doi: 10.1093/bioinformatics/bti499.
    1. Simon RM, Paik S, Hayes DF. Use of archived specimens in evaluation of prognostic and predictive biomarkers. J. Natl. Cancer Inst. 2009;101:1446–1452. doi: 10.1093/jnci/djp335.
    1. ICH Expert Working Group. International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use; (accessed, 19 February 2013)
    1. US Food and Drug Administration. Guidance for Industry: Computerized Systems Used in Clinical Investigations (US Department of Health and Human Services, 2007)
    1. Freidlin B, McShane LM, Korn EL. Randomized clinical trials with biomarkers: design issues. J. Natl. Cancer Inst. 2010;102:152–160. doi: 10.1093/jnci/djp477.
    1. US Department of Health & Human Services. SACHRP Letter to the Secretary: FAQs, Terms and Recommendations on Informed Consent and Research Use of Biospecimens; (accessed, 19 February 2013)
    1. Center for Medicare and Medicaid Services. Clinical Laboratory Improvement Amendments (CLIA); ?redirect = /CLIA (accessed, 19 February 2013)
    1. US Food and Drug Administration. Device Advice: Comprehensive Regulatory Assistance; . (accessed, 19 February 2013)
    1. US Food and Drug Administration. Investigational New Drug (IND) Application; (accessed, 19 February 2013)
    1. US Food and Drug Administration. Draft Guidance for Industry and FDA Staff: Medical Devices: the Pre-submission Program and Meetings with FDA Staff (US Department of Health & Human Services); (accessed, 19 February 2013)

Source: PubMed

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