Literature DB >> 23475192

Omics-based clinical trial designs.

Marc Buyse1, Stefan Michiels.   

Abstract

PURPOSE OF REVIEW: The derivation of signatures using -omics technologies is increasingly integrated in the design of clinical trials in oncology. In this review, we investigate the clinical trial designs for the validation of prognostic and predictive signatures. RECENT
FINDINGS: Using real-life breast cancer trial examples, we highlight the pros and cons of clinical utility designs for prognostic signatures. For predictive signatures, we first review alternative procedures to test the effect of treatment in the overall population as well as in the signature-positive or signature-negative subgroup. We proceed to show why the recent literature on signature-based strategy designs discourages the use of this design. We conclude by discussing adaptive signature designs to identify and validate a signature in a single trial using cross-validation techniques.
SUMMARY: Use of -omics technologies should not be an add-on to clinical trials, it must become an integral part of their design.

Entities:  

Mesh:

Year:  2013        PMID: 23475192     DOI: 10.1097/CCO.0b013e32835ff2fe

Source DB:  PubMed          Journal:  Curr Opin Oncol        ISSN: 1040-8746            Impact factor:   3.645


  9 in total

Review 1.  Precision medicine needs randomized clinical trials.

Authors:  Everardo D Saad; Xavier Paoletti; Tomasz Burzykowski; Marc Buyse
Journal:  Nat Rev Clin Oncol       Date:  2017-02-07       Impact factor: 66.675

Review 2.  Biomarker-Guided Non-Adaptive Trial Designs in Phase II and Phase III: A Methodological Review.

Authors:  Miranta Antoniou; Ruwanthi Kolamunnage-Dona; Andrea L Jorgensen
Journal:  J Pers Med       Date:  2017-01-25

3.  Integrative analysis of longitudinal metabolomics data from a personal multi-omics profile.

Authors:  Larissa Stanberry; George I Mias; Winston Haynes; Roger Higdon; Michael Snyder; Eugene Kolker
Journal:  Metabolites       Date:  2013-09-03

4.  Genomic, Epigenomic, and Transcriptomic Profiling towards Identifying Omics Features and Specific Biomarkers That Distinguish Uterine Leiomyosarcoma and Leiomyoma at Molecular Levels.

Authors:  Tomoko Miyata; Kenzo Sonoda; Junko Tomikawa; Chiharu Tayama; Kohji Okamura; Kayoko Maehara; Hiroaki Kobayashi; Norio Wake; Kiyoko Kato; Kenichiro Hata; Kazuhiko Nakabayashi
Journal:  Sarcoma       Date:  2015-12-28

Review 5.  Biomarker-Guided Adaptive Trial Designs in Phase II and Phase III: A Methodological Review.

Authors:  Miranta Antoniou; Andrea L Jorgensen; Ruwanthi Kolamunnage-Dona
Journal:  PLoS One       Date:  2016-02-24       Impact factor: 3.240

Review 6.  Statistical controversies in clinical research: prognostic gene signatures are not (yet) useful in clinical practice.

Authors:  S Michiels; N Ternès; F Rotolo
Journal:  Ann Oncol       Date:  2016-09-15       Impact factor: 32.976

7.  Identification of biomarker-by-treatment interactions in randomized clinical trials with survival outcomes and high-dimensional spaces.

Authors:  Nils Ternès; Federico Rotolo; Georg Heinze; Stefan Michiels
Journal:  Biom J       Date:  2016-11-15       Impact factor: 2.207

8.  Evaluation of a chemoresponse assay as a predictive marker in the treatment of recurrent ovarian cancer: further analysis of a prospective study.

Authors:  C Tian; D J Sargent; T C Krivak; M A Powell; M J Gabrin; S L Brower; R L Coleman
Journal:  Br J Cancer       Date:  2014-07-08       Impact factor: 7.640

9.  Pharmacogenetics driving personalized medicine: analysis of genetic polymorphisms related to breast cancer medications in Italian isolated populations.

Authors:  Massimiliano Cocca; Davide Bedognetti; Martina La Bianca; Paolo Gasparini; Giorgia Girotto
Journal:  J Transl Med       Date:  2016-01-22       Impact factor: 5.531

  9 in total

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