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One of the most important articles of the year. I have been saying this for years but this is the first time I saw it in print. They propose a new method of clinical trials where many different treatments can be tested against each other in a rational way. As they evaluate results, the trial adapts. If they see that certain biomarkers influence outcome, they can change the randomization based on that biomarker to increase the chances of good results. For example, if a drug only seems to help people with a specific target, they stop using it on patients who do not have that target - they can still get randomized to the other treatments.
If a treatment doesn't perform well, they drop it and rotate in the next one. If a treatment looks good, they drop it out of this trial and launch a traditional trial of it.
This way they can test a large number of treatments, identify biomarkers that may be used to personalize treatments, and significantly reduce the amount of time (and money) needed to find the cure. It also improves the chances that the trial will help the participants - as once it is clear a treatment doesn't work - or if it doesn't work with that patient's markers, it is dropped.
Biomarker-based adaptive trials for patients with glioblastoma—lessons from I-SPY 2
Brian M. Alexander⇑, Patrick Y. Wen, Lorenzo Trippa, David A. Reardon, Wai-Kwan Alfred Yung, Giovanni Parmigiani and Donald A. Berry
+ Author Affiliations
Department of Radiation Oncology (B.M.A.) and the Center for Neuro-Oncology (B.M.A., P.Y.W., D.A.R.), Dana-Farber/Brigham and Women's Cancer Center, Harvard Medical School, Boston, Massachusetts; Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts (L.T., G.P.); Department of Neuro-Oncology (W.K.A.Y.) and Department of Biostatistics (D.A.B.), The University of Texas MD Anderson Cancer Center, Houston, Texas
Corresponding Author: Brian M. Alexander, MD, MPH, Dana-Farber/Brigham and Women's Cancer Center, Department of Radiation Oncology, 75 Francis Street, ASB1-L2 Boston, MA 02115 (bmalexander@lroc.harvard.edu).
Received March 11, 2013.
Accepted April 28, 2013.
Abstract
The traditional clinical trials infrastructure may not be ideally suited to evaluate the numerous therapeutic hypotheses that result from the increasing number of available targeted agents combined with the various methodologies to molecularly subclassify patients with glioblastoma. Additionally, results from smaller screening studies are rarely translated to successful larger confirmatory studies, potentially related to a lack of efficient control arms or the use of unvalidated surrogate endpoints. Streamlining clinical trials and providing a flexible infrastructure for biomarker development is clearly needed for patients with glioblastoma. The experience developing and implementing the I-SPY studies in breast cancer may serve as a guide to developing such trials in neuro-oncology.
Key words
adaptive Bayesian biomarker clinical trials glioblastoma
© The Author(s) 2013. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.