Clinical Experience for Cancer Immunotherapy Trial Design

Organizers: Anastasia Ivanova (UNC), Freda Cooner (Amgen)
Chair: Freda Cooner (Amgen)
Vice Chair: Robert Barrier (PAREXEL)

Mustafa Khasraw (Duke)
Ming Zhou (BMS)
Meng Summer Xia (Lilly)


Title: Immunotherapy Trials with Biologic Primary Endpoints
Speaker: Mustafa Khasraw (Duke)

Immunotherapy with immune checkpoint inhibitors has dramatically altered the treatment landscape for many cancer types such as melanoma and non-small cell lung cancer. However, long-term disease control is only achieved in a minority of patients, even in highly responsive tumor subtypes. While many new classes of immunotherapies are in development, the FDA has only approved a few for solid tumors outside of the programmed death-1 and cytotoxic T-lymphocyte antigen 4 antibodies. Consequently, efficient clinical trials methods to evaluate the large number of new treatments are needed. Challenges facing developments of immunotherapeutic agents and their combinations will be outlined. Statistical design properties that can address the unique aspects of immunotherapy and how to integrate new immune monitoring and technologies like single cell sequencing and liquid biopsies will be discussed from the perspective of a clinician.

Title: Predicting Analysis Times in Clinical Trials with Non-Proportional Hazards
Speaker: Ming Zhou (BMS)

Real-time projections of analysis times based on pooled blinded time-to-event data of on-going trials have important operational and logistical implications, such as planning for the timing of data safety monitoring committee meetings, interim and final analyses. Data mining of clinical trial data provides the opportunity to develop and apply new predictive methods; here we do so for prediction of analysis times. A proportional hazards (PH) model often forms the basis for the predictions, but PH models are often ill-equipped to address non-PH conditions. The delayed separation of Kaplan-Meier curves and durable long-term responses are examples of non-PH, and have been observed in event-driven immuno-oncology studies. Prediction of analysis times using established methods for non-PH scenarios often suffer a lack of accuracy, precision, and applicability. We propose and investigate new approaches and compared them with an existing methodology across a variety of scenarios drawn from synthetic data, and from actual Immuno-Oncology trials data at BMS. The comparisons suggest improved accuracy, precision, and applicability over conventional methods in non-PH scenarios.

Title: A Bayesian three-tier quantitative decision-making framework for single arm studies in early phase oncology
Speaker: Meng Xia (Lilly)

In early phase oncology drug development, single arm expansion cohorts are increasingly being used to drive the decisions for future development of the drug. Decision makings based on such studies with small sample size and early surrogate efficacy endpoints are extremely challenging. In particular, given the tremendous competition in the development of immunotherapies, expedition of the most promising programs is desired. To this end, we have proposed a Bayesian adaptive three-tier (BAT) approach to facilitate the decision-making process. With pre-specified Bayesian decision criteria, three types of decisions regarding the future development of the drug can be made: (1) terminating the program, (2) considering totality of evidence or additional proof-of-concept (POC) studies, and (3) accelerating the program. The BAT approach inherits all the benefits of Bayesian decision-making approaches, and formally allows the option of acceleration. We illustrate the proposed Bayesian approach through examples and compare the properties of the proposed design with some other existing designs through simulations.