Assessing therapy response in patient-derived xenografts
Janosch Ortmann, Ladislav Rampášek, Elijah Tai, Arvind Singh Mer, Ruoshi Shi, Erin Stewart, Céline Mascaux, Aline Fusco Fares, Nhu‐An Pham, Gangesh Beri, Christopher Eeles, Denis Tkachuk, Chantal Ho, Shingo Sakashita, Jessica Weiss, Xiaoqian Jiang, Geoffrey Liu, David W. Cescon, Catherine O′Brien, Sheng Guo, Ming‐Sound Tsao, Benjamin Haibe‐Kains, Anna Goldenberg
Abstract
Quantifying response to drug treatment in mouse models of human cancer is important for treatment development and assignment, yet remains a challenging task. To be able to translate the results of the experiments more readily, a preferred measure to quantify this response should take into account more of the available experimental data, including both tumor size over time and the variation among replicates. We propose a theoretically grounded measure, KuLGaP, to compute the difference between the treatment and control arms. We test and compare KuLGaP to four widely used response measures using 329 patient-derived xenograft (PDX) models. Our results show that KuLGaP is more selective than currently existing measures, reduces the risk of false-positive calls, and improves translation of the laboratory results to clinical practice. We also show that outcomes of human treatment better align with the results of the KuLGaP measure than other response measures. KuLGaP has the potential to become a measure of choice for quantifying drug treatment in mouse models as it can be easily used via the kulgap.ca website.