Hypothetical generalized framework for a new imaging endpoint of therapeutic activity in early phase clinical trials in brain tumors
Benjamin M. Ellingson, Elizabeth R. Gerstner, Andrew B. Lassman, Caroline Chung, Howard Colman, Patricia E. Cole, David Leung, Joshua E. Allen, Manmeet S. Ahluwalia, Jerrold L. Boxerman, Matthew S. Brown, Jonathan Goldin, Edjah K. Nduom, Islam Hassan, Mark R. Gilbert, Ingo K. Mellinghoff, Michael Weller, Susan M. Chang, David Arons, Clair Meehan, Wendy Selig, Kirk Tanner, W.K. Alfred Yung, Martin J. van den Bent, Patrick Y. Wen, Timothy F. Cloughesy
Abstract
Imaging response assessment is a cornerstone of patient care and drug development in oncology. Clinicians/clinical researchers rely on tumor imaging to estimate the impact of new treatments and guide decision making for patients and candidate therapies. This is important in brain cancer, where associations between tumor size/growth and emerging neurological deficits are strong. Accurately measuring the impact of a new therapy on tumor growth early in clinical development, where patient numbers are small, would be valuable for decision making regarding late-stage development activation. Current attempts to measure the impact of a new therapy have limited influence on clinical development, as determination of progression, stability or response does not currently account for individual tumor growth kinetics prior to the initiation of experimental therapies. Therefore, we posit that imaging-based response assessment, often used as a tool for estimating clinical effect, is incomplete as it does not adequately account for growth trajectories or biological characteristics of tumors prior to the introduction of an investigational agent. Here, we propose modifications to the existing framework for evaluating imaging assessment in primary brain tumors that will provide a more reliable understanding of treatment effects. Measuring tumor growth trajectories prior to a given intervention may allow us to more confidently conclude whether there is an anti-tumor effect. This updated approach to imaging-based tumor response assessment is intended to improve our ability to select candidate therapies for later-stage development, including those that may not meet currently sought thresholds for "response" and ultimately lead to identification of effective treatments.