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

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

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 forguiding 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.

Related collections

Most cited references 18

Record: found
Abstract: not found
Article: not found

Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification.

Richard S. Simon, Michael Radmacher, Kevin K. Dobbin … (2003)

0 comments Cited 206 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Critical review of published microarray studies for cancer outcome and guidelines on statistical analysis and reporting.

Alain Dupuy, Richard M Simon (2007)

Both the validity and the reproducibility of microarray-based clinical research have been challenged. There is a need for critical review of the statistical analysis and reporting in published microarray studies that focus on cancer-related clinical outcomes. Studies published through 2004 in which microarray-based gene expression profiles were analyzed for their relation to a clinical cancer outcome were identified through a Medline search followed by hand screening of abstracts and full text articles. Studies that were eligible for our analysis addressed one or more outcomes that were either an event occurring during follow-up, such as death or relapse, or a therapeutic response. We recorded descriptive characteristics for all the selected studies. A critical review of outcome-related statistical analyses was undertaken for the articles published in 2004. Ninety studies were identified, and their descriptive characteristics are presented. Sixty-eight (76%) were published in journals of impact factor greater than 6. A detailed account of the 42 studies (47%) published in 2004 is reported. Twenty-one (50%) of them contained at least one of the following three basic flaws: 1) in outcome-related gene finding, an unstated, unclear, or inadequate control for multiple testing; 2) in class discovery, a spurious claim of correlation between clusters and clinical outcome, made after clustering samples using a selection of outcome-related differentially expressed genes; or 3) in supervised prediction, a biased estimation of the prediction accuracy through an incorrect cross-validation procedure. The most common and serious mistakes and misunderstandings recorded in published studies are described and illustrated. Based on this analysis, a proposal of guidelines for statistical analysis and reporting for clinical microarray studies, presented as a checklist of "Do's and Don'ts," is provided.

0 comments Cited 178 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Gene expression-based prognostic signatures in lung cancer: ready for clinical use?

Jyothi Subramanian, Richard T. Simon (2010)

A substantial number of studies have reported the development of gene expression-based prognostic signatures for lung cancer. The ultimate aim of such studies should be the development of well-validated clinically useful prognostic signatures that improve therapeutic decision making beyond current practice standards. We critically reviewed published studies reporting the development of gene expression-based prognostic signatures for non-small cell lung cancer to assess the progress made toward this objective. Studies published between January 1, 2002, and February 28, 2009, were identified through a PubMed search. Following hand-screening of abstracts of the identified articles, 16 were selected as relevant. Those publications were evaluated in detail for appropriateness of the study design, statistical validation of the prognostic signature on independent datasets, presentation of results in an unbiased manner, and demonstration of medical utility for the new signature beyond that obtained using existing treatment guidelines. Based on this review, we found little evidence that any of the reported gene expression signatures are ready for clinical application. We also found serious problems in the design and analysis of many of the studies. We suggest a set of guidelines to aid the design, analysis, and evaluation of prognostic signature studies. These guidelines emphasize the importance of focused study planning to address specific medically important questions and the use of unbiased analysis methods to evaluate whether the resulting signatures provide evidence of medical utility beyond standard of care-based prognostic factors.

0 comments Cited 117 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Journal

Journal ID (nlm-journal-id): 0410462

Journal ID (pubmed-jr-id): 6011

Journal ID (nlm-ta): Nature

Journal ID (iso-abbrev): Nature

Title: Nature

ISSN (Print): 0028-0836

ISSN (Electronic): 1476-4687

Publication date Nihms-submitted: 25 September 2014

Publication date (Print): 17 October 2013

Publication date PMC-release: 01 October 2014

Volume: 502

Issue: 7471

Pages: 317-320

Affiliations

[1 ]Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA.

[2 ]Department of Pathology and Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, North Carolina 27599, USA.

[3 ]Department of Pathology and University of Pittsburgh Cancer Institute, Hillman Cancer Center, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania 15213, USA.

[4 ]Frederick National Laboratory for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, Maryland 21702, USA.

[5 ]Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, Massachusetts 02142, USA.

[6 ]Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109, USA.

Author notes

Correspondence and requests for materials should be addressed to L.M.M. ( mcshanel@ 123456ctep.nci.nih.gov ).

Article

Manuscript ID: NIHMS630834

DOI: 10.1038/nature12564

PMC ID: 4180668

PubMed ID: 24132288

SO-VID: 2988dcbc-b5ff-493e-8ab0-72fd82931e89

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 Unported licence. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-sa/3.0

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

Read this article at

Abstract

Related collections

Recursive Rule based Visual Categorization

Most cited references 18

Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification.

Critical review of published microarray studies for cancer outcome and guidelines on statistical analysis and reporting.

Gene expression-based prognostic signatures in lung cancer: ready for clinical use?

Author and article information

Journal

Affiliations

Author notes

Article

History

Categories

Comments

Comment on this article

Similar content 763

Cited by 75

Most referenced authors 459