Bayesian adaptive trial designs for neoantigen based immunotherapy and borrowing strength across subpopulations within the trial and from external controls
CERSI Collaborators: Joseph Ross, MD, MHS (Yale), Nilay Shah, PhD (formerly at Mayo Clinic, now at Delta Airlines), Molly Jeffery, PhD (Mayo Clinic), Jun Yin, PhD (Mayo Clinic) (PI)
FDA Collaborators: Adnan Jaigirdar, MD, Pourab Roy, PhD, Julie Schneider, PhD, Rajeshwari Sridhara, PhD, Zhenzhen Xu, PhD
Project Start Date: October 2020
Regulatory Science Challenge
Cancer immunotherapy has changed the landscape of modern oncology. Immune checkpoint inhibitors have emerged as an effective form of immunotherapy, and the development of novel therapies targeting tumor-specific antigens (or neo-antigens) has been an active area of investigation. Many cancer immunotherapy development programs use a basket trial/master protocol strategy where a single investigational agent (or drug combination) is tested on multiple cancer populations as defined by specific characteristics such as disease state, histology, or biomarkers (e.g., neoantigens). These basket trials typically use single arm designs and may be prone to selection bias and confounding due to the lack of randomization.
The focus of this research is to create a Bayesian statistical method to enable faster identification of promising immunotherapies to potentially accelerate the development of safe and effective treatments for patients with cancer. Bayesian approaches allow incorporation of accumulating evidence from the ongoing trial and sources external to the trial. PDUFA VI identified developing novel clinical trial designs (including Bayesian methodologies) as a priority area for regulatory science research. This study will develop a Bayesian model that borrows information from the different single arms /disease populations within a master protocol to efficiently identify promising drugs for further development by classifying active and inactive response clusters of patients.
Project Description and Goals
This study will develop a mathematical model that will make sequential adaptive subgroup-specific decisions while clustering subtypes that have similar response to treatment. The model will be evaluated through simulation and by analyzing data from real basket trials available at the Mayo Clinic. This research will also try to minimize the potential bias/confounding in a basket trial due to the lack of randomization by using external controls (generated using matching techniques such as propensity score analysis). The synthesized datasets will then be compared to determine the treatment effects both through simulations and by utilizing available clinical trial data.