Open AccessIndividualized Screening Trial of Innovative Glioblastoma Therapy (INSIGhT): A Bayesian Adaptive Platform Trial to Develop Precision Medicines for Patients With Glioblastoma
Author(s) -
Brian M. Alexander,
Lorenzo Trippa,
Sarah Gaffey,
Isabel ArrillagaRomany,
Eudocia Q. Lee,
Mikael L. Rinne,
Manmeet Ahluwalia,
Howard Colman,
Geoffrey Fell,
Evanthia Galanis,
John de Groot,
Jan Drappatz,
Andrew B. Lassman,
David M. Meredith,
Louis B. Nabors,
Sandro Santagata,
David Schiff,
Mary Welch,
Keith L. Ligon,
Patrick Y. Wen
Publication year - 2019
Publication title -
jco precision oncology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.405
H-Index - 22
ISSN - 2473-4284
DOI - 10.1200/po.18.00071
Subject(s) - temozolomide , biomarker , medicine , randomization , precision medicine , clinical trial , clinical endpoint , oncology , computer science , radiation therapy , pathology , biology , biochemistry
PURPOSE Adequately prioritizing the numerous therapies and biomarkers available in late-stage testing for patients with glioblastoma (GBM) requires an efficient clinical testing platform. We developed and implemented INSIGhT (Individualized Screening Trial of Innovative Glioblastoma Therapy) as a novel adaptive platform trial (APT) to develop precision medicine approaches in GBM.METHODS INSIGhT compares experimental arms with a common control of standard concurrent temozolomide and radiation therapy followed by adjuvant temozolomide. The primary end point is overall survival. Patients with newly diagnosed unmethylated GBM who are IDH R132H mutation negative and with genomic data available for biomarker grouping are eligible. At the initiation of INSIGhT, three experimental arms (neratinib, abemaciclib, and CC-115), each with a proposed genomic biomarker, are tested simultaneously. Initial randomization is equal across arms. As the trial progresses, randomization probabilities adapt on the basis of accumulating results using Bayesian estimation of the biomarker-specific probability of treatment impact on progression-free survival. Treatment arms may drop because of low probability of treatment impact on overall survival, and new arms may be added. Detailed information on the statistical model and randomization algorithm is provided to stimulate discussion on trial design choices more generally and provide an example for other investigators developing APTs.CONCLUSION INSIGhT (NCT02977780) is an ongoing novel biomarker-based, Bayesian APT for patients with newly diagnosed unmethylated GBM. Our goal is to dramatically shorten trial execution timelines while increasing scientific power of results and biomarker discovery using adaptive randomization. We anticipate that trial execution efficiency will also be improved by using the APT format, which allows for the collaborative addition of new experimental arms while retaining the overall trial structure.