bioRxivBioinformaticsPreprint
KuLGaP: A Selective Measure for Assessing Therapy Response in Patient-Derived Xenografts
Janosch OrtmanniD, Ladislav RampášekiD, Elijah Tai, Arvind Singh MeriD, Ruoshi ShiiD, Erin Stewart, Céline MascauxiD, Aline Fusco FaresiD, Nhu‐An PhamiD, Gangesh Beri, Christopher B. EelesiD, Denis Tkachuk, Chantal Ho, Shingo SakashitaiD, Jessica WeissiD, Xiaoqian Jiang, Geoffrey LiuiD, David W. CesconiD, Catherine Adell O'BrieniD, Sheng Jie GuoiD, Ming‐Sound TsaoiD, Benjamin Haibe‐KainsiD, Anna GoldenbergiD
Abstract
Quantifying response to drug treatment in mouse models of human cancer is important for treatment development and assignment, and yet remains a challenging task. A preferred measure to quantify this response should take into account as much of the experimental data as possible, i.e. 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. KuLGaP is more selective than currently existing measures, reduces the risk of false positive calls and improves translation of the lab results to clinical practice.
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