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Clin Cancer Res. 2018 Feb 15;24(4):737-743. doi: 10.1158/1078-0432.CCR-17-0764. Epub 2017 Aug 16.

Adaptive Global Innovative Learning Environment for Glioblastoma: GBM AGILE.

Author information

1
Center for Neuro-Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts. brian_alexander@dfci.harvard.edu.
2
National Foundation for Cancer Research, Bethesda, Maryland.
3
Department for Neurological Surgery, University of California-San Francisco, San Francisco, California.
4
Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas.
5
Berry Consultants, Austin, Texas.
6
Ludwig Institute for Cancer Research, University of California-San Diego, La Jolla, California.
7
Neuro-Oncology Program, University of California-Los Angeles, Los Angeles, California.
8
Department of Clinical Oncology, Capital Medical University, Beijing, China.
9
NHMRC Clinical Trials Centre, The University of Sydney Medical School, Australia.
10
Glioma Department, Beijing Shijitan Hospital, Capital Medical University, Beijing, China.
11
School of Biological and Health Systems Engineering, School of Computing, Informatics, and Decision Systems Engineering, Ira A. Fulton Schools of Engineering, Arizona State University, Tempe, Arizona.
12
National Biomarker Development Alliance, Arizona State University, Tempe, Arizona.
13
Complex Adaptive Systems Initiative, Arizona State University, Tempe, Arizona.
14
Center for Neuro-Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts.
15
Department of Neuro-Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
16
School of Life Sciences, Arizona State University, Tempe, Arizona.

Abstract

Glioblastoma (GBM) is a deadly disease with few effective therapies. Although much has been learned about the molecular characteristics of the disease, this knowledge has not been translated into clinical improvements for patients. At the same time, many new therapies are being developed. Many of these therapies have potential biomarkers to identify responders. The result is an enormous amount of testable clinical questions that must be answered efficiently. The GBM Adaptive Global Innovative Learning Environment (GBM AGILE) is a novel, multi-arm, platform trial designed to address these challenges. It is the result of the collective work of over 130 oncologists, statisticians, pathologists, neurosurgeons, imagers, and translational and basic scientists from around the world. GBM AGILE is composed of two stages. The first stage is a Bayesian adaptively randomized screening stage to identify effective therapies based on impact on overall survival compared with a common control. This stage also finds the population in which the therapy shows the most promise based on clinical indication and biomarker status. Highly effective therapies transition in an inferentially seamless manner in the identified population to a second confirmatory stage. The second stage uses fixed randomization to confirm the findings from the first stage to support registration. Therapeutic arms with biomarkers may be added to the trial over time, while others complete testing. The design of GBM AGILE enables rapid clinical testing of new therapies and biomarkers to speed highly effective therapies to clinical practice. Clin Cancer Res; 24(4); 737-43. ©2017 AACR.

PMID:
28814435
DOI:
10.1158/1078-0432.CCR-17-0764
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