Literature DB >> 26827695

Clinical trial designs incorporating predictive biomarkers.

Lindsay A Renfro1, Himel Mallick2, Ming-Wen An3, Daniel J Sargent4, Sumithra J Mandrekar4.   

Abstract

Development of oncologic therapies has traditionally been performed in a sequence of clinical trials intended to assess safety (phase I), preliminary efficacy (phase II), and improvement over the standard of care (phase III) in homogeneous (in terms of tumor type and disease stage) patient populations. As cancer has become increasingly understood on the molecular level, newer "targeted" drugs that inhibit specific cancer cell growth and survival mechanisms have increased the need for new clinical trial designs, wherein pertinent questions on the relationship between patient biomarkers and response to treatment can be answered. Herein, we review the clinical trial design literature from initial to more recently proposed designs for targeted agents or those treatments hypothesized to have enhanced effectiveness within patient subgroups (e.g., those with a certain biomarker value or who harbor a certain genetic tumor mutation). We also describe a number of real clinical trials where biomarker-based designs have been utilized, including a discussion of their respective advantages and challenges. As cancers become further categorized and/or reclassified according to individual patient and tumor features, we anticipate a continued need for novel trial designs to keep pace with the changing frontier of clinical cancer research.
Copyright © 2016 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Adaptive trial design; Bayesian adaptive design; Biomarker-based design; Clinical trial design; Enrichment designs; Targeted therapies

Mesh:

Substances:

Year:  2016        PMID: 26827695      PMCID: PMC4737867          DOI: 10.1016/j.ctrv.2015.12.008

Source DB:  PubMed          Journal:  Cancer Treat Rev        ISSN: 0305-7372            Impact factor:   12.111


  78 in total

1.  A method for testing a prespecified subgroup in clinical trials.

Authors:  Yang Song; George Y H Chi
Journal:  Stat Med       Date:  2007-08-30       Impact factor: 2.373

Review 2.  A regulatory perspective on essential considerations in design and analysis of subgroups when correctly classified.

Authors:  Sue-Jane Wang; H M James Hung
Journal:  J Biopharm Stat       Date:  2014       Impact factor: 1.051

3.  Gefitinib or chemotherapy for non-small-cell lung cancer with mutated EGFR.

Authors:  Makoto Maemondo; Akira Inoue; Kunihiko Kobayashi; Shunichi Sugawara; Satoshi Oizumi; Hiroshi Isobe; Akihiko Gemma; Masao Harada; Hirohisa Yoshizawa; Ichiro Kinoshita; Yuka Fujita; Shoji Okinaga; Haruto Hirano; Kozo Yoshimori; Toshiyuki Harada; Takashi Ogura; Masahiro Ando; Hitoshi Miyazawa; Tomoaki Tanaka; Yasuo Saijo; Koichi Hagiwara; Satoshi Morita; Toshihiro Nukiwa
Journal:  N Engl J Med       Date:  2010-06-24       Impact factor: 91.245

Review 4.  Clinical trial designs for predictive marker validation in cancer treatment trials.

Authors:  Daniel J Sargent; Barbara A Conley; Carmen Allegra; Laurence Collette
Journal:  J Clin Oncol       Date:  2005-03-20       Impact factor: 44.544

5.  Disease control rate at 8 weeks predicts clinical benefit in advanced non-small-cell lung cancer: results from Southwest Oncology Group randomized trials.

Authors:  Primo N Lara; Mary W Redman; Karen Kelly; Martin J Edelman; Stephen K Williamson; John J Crowley; David R Gandara
Journal:  J Clin Oncol       Date:  2008-01-20       Impact factor: 44.544

6.  An adaptive clinical trials procedure for a sensitive subgroup examined in the multiple sclerosis context.

Authors:  Corinne A Riddell; Yinshan Zhao; John Petkau
Journal:  Stat Methods Med Res       Date:  2013-04-16       Impact factor: 3.021

7.  The use of Bayesian hierarchical models for adaptive randomization in biomarker-driven phase II studies.

Authors:  William T Barry; Charles M Perou; P Kelly Marcom; Lisa A Carey; Joseph G Ibrahim
Journal:  J Biopharm Stat       Date:  2015       Impact factor: 1.051

8.  Issues in clinical trial design for tumor marker studies.

Authors:  Daniel Sargent; Carmen Allegra
Journal:  Semin Oncol       Date:  2002-06       Impact factor: 4.929

Review 9.  Adaptive designs for confirmatory clinical trials with subgroup selection.

Authors:  Nigel Stallard; Thomas Hamborg; Nicholas Parsons; Tim Friede
Journal:  J Biopharm Stat       Date:  2014       Impact factor: 1.051

10.  Randomised proof-of-concept phase II trial comparing targeted therapy based on tumour molecular profiling vs conventional therapy in patients with refractory cancer: results of the feasibility part of the SHIVA trial.

Authors:  C Le Tourneau; X Paoletti; N Servant; I Bièche; D Gentien; T Rio Frio; A Vincent-Salomon; V Servois; J Romejon; O Mariani; V Bernard; P Huppe; G Pierron; F Mulot; C Callens; J Wong; C Mauborgne; E Rouleau; C Reyes; E Henry; Q Leroy; P Gestraud; P La Rosa; L Escalup; E Mitry; O Trédan; J-P Delord; M Campone; A Goncalves; N Isambert; C Gavoille; M Kamal
Journal:  Br J Cancer       Date:  2014-04-24       Impact factor: 7.640

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  22 in total

1.  The Adaptive designs CONSORT Extension (ACE) statement: a checklist with explanation and elaboration guideline for reporting randomised trials that use an adaptive design.

Authors:  Munyaradzi Dimairo; Philip Pallmann; James Wason; Susan Todd; Thomas Jaki; Steven A Julious; Adrian P Mander; Christopher J Weir; Franz Koenig; Marc K Walton; Jon P Nicholl; Elizabeth Coates; Katie Biggs; Toshimitsu Hamasaki; Michael A Proschan; John A Scott; Yuki Ando; Daniel Hind; Douglas G Altman
Journal:  BMJ       Date:  2020-06-17

2.  Use of big data in drug development for precision medicine.

Authors:  Rosa S Kim; Nicolas Goossens; Yujin Hoshida
Journal:  Expert Rev Precis Med Drug Dev       Date:  2016-04-28

3.  Relative efficiency of precision medicine designs for clinical trials with predictive biomarkers.

Authors:  Weichung Joe Shih; Yong Lin
Journal:  Stat Med       Date:  2017-12-04       Impact factor: 2.373

Review 4.  Biomarker-Driven Oncology Clinical Trials: Key Design Elements, Types, Features, and Practical Considerations.

Authors:  Chen Hu; James J Dignam
Journal:  JCO Precis Oncol       Date:  2019-10-24

Review 5.  Guidelines for Statistical Reporting in Medical Journals.

Authors:  Fang-Shu Ou; Jennifer G Le-Rademacher; Karla V Ballman; Alex A Adjei; Sumithra J Mandrekar
Journal:  J Thorac Oncol       Date:  2020-08-25       Impact factor: 15.609

6.  Testing many treatments within a single protocol over 10 years at MRC Clinical Trials Unit at UCL: Multi-arm, multi-stage platform, umbrella and basket protocols.

Authors:  Mahesh Kb Parmar; Matthew R Sydes; Fay H Cafferty; Babak Choodari-Oskooei; Ruth E Langley; Louise Brown; Patrick Pj Phillips; Melissa R Spears; Sam Rowley; Richard Kaplan; Nicholas D James; Timothy Maughan; Nicholas Paton; Patrick J Royston
Journal:  Clin Trials       Date:  2017-08-22       Impact factor: 2.486

7.  Biomarker threshold adaptive designs for survival endpoints.

Authors:  Guoqing Diao; Jun Dong; Donglin Zeng; Chunlei Ke; Alan Rong; Joseph G Ibrahim
Journal:  J Biopharm Stat       Date:  2018-02-13       Impact factor: 1.051

8.  Bayesian population finding with biomarkers in a randomized clinical trial.

Authors:  Satoshi Morita; Peter Müller
Journal:  Biometrics       Date:  2017-03-03       Impact factor: 2.571

Review 9.  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

Review 10.  Accelerating Therapeutic Development through Innovative Trial Design in Colorectal Cancer.

Authors:  Michael Lam; Jonathan M Loree; Allan Anderson Lima Pereira; Yun Shin Chun; Scott Kopetz
Journal:  Curr Treat Options Oncol       Date:  2018-02-27
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