KuLGaP: A Selective Measure for Assessing Therapy Response in Patient-Derived Xenografts

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.

[1]  Sheng Guo,et al.  The design, analysis and application of mouse clinical trials in oncology drug development , 2018, BMC Cancer.

[2]  Yiling Lu,et al.  A Comprehensive Patient-Derived Xenograft Collection Representing the Heterogeneity of Melanoma. , 2017, Cell reports.

[3]  B. Clarke,et al.  Novel combinations of PI3K-mTOR inhibitors with dacomitinib or chemotherapy in PTEN-deficient patient-derived tumor xenografts , 2017, Oncotarget.

[4]  B. Mao,et al.  Abstract 4534: Theory and methodology for the design and analysis of PDX mouse clinical trials , 2017 .

[5]  P. Iversen,et al.  Mouse PDX Trial Suggests Synergy of Concurrent Inhibition of RAF and EGFR in Colorectal Cancer with BRAF or KRAS Mutations , 2017, Clinical Cancer Research.

[6]  Ning Zhang,et al.  Intra-tumour molecular heterogeneity of clear cell renal cell carcinoma reveals the diversity of the response to targeted therapies using patient-derived xenograft models , 2017, Oncotarget.

[7]  Tero Aittokallio,et al.  Optimized design and analysis of preclinical intervention studies in vivo , 2016, Scientific Reports.

[8]  J. Wery,et al.  Cetuximab response in CRC patient-derived xenografts seems predicted by an expression based RAS pathway signature , 2016, Oncotarget.

[9]  Joshua M. Korn,et al.  High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response , 2015, Nature Medicine.

[10]  R. Scharpf,et al.  The Genomic Landscape of Response to EGFR Blockade in Colorectal Cancer , 2015, Nature.

[11]  I. Jurisica,et al.  Clinical Utility of Patient-Derived Xenografts to Determine Biomarkers of Prognosis and Map Resistance Pathways in EGFR-Mutant Lung Adenocarcinoma. , 2015, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[12]  Anton Nekrutenko,et al.  Ten Simple Rules for Reproducible Computational Research , 2013, PLoS Comput. Biol..

[13]  I. Măndoiu,et al.  Area under the curve as a tool to measure kinetics of tumor growth in experimental animals. , 2012, Journal of immunological methods.

[14]  S. Morton,et al.  A Proposal for Reporting , 2010 .

[15]  Skipper Seabold,et al.  Statsmodels: Econometric and Statistical Modeling with Python , 2010, SciPy.

[16]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[17]  Yuesheng Xu,et al.  On translation invariant operators which preserve the B-spline recurrence , 2008, Adv. Comput. Math..

[18]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[19]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[20]  Robert Gentleman,et al.  Statistical Applications in Genetics and Molecular Biology , 2005 .

[21]  David R. Anderson,et al.  Model selection and multimodel inference : a practical information-theoretic approach , 2003 .

[22]  I. Olkin,et al.  Meta-analysis of observational studies in epidemiology - A proposal for reporting , 2000 .

[23]  M. van Glabbeke,et al.  New guidelines to evaluate the response to treatment in solid tumors , 2000, Journal of the National Cancer Institute.

[24]  M Van Glabbeke,et al.  New guidelines to evaluate the response to treatment in solid tumors. European Organization for Research and Treatment of Cancer, National Cancer Institute of the United States, National Cancer Institute of Canada. , 2000, Journal of the National Cancer Institute.

[25]  Igor Vajda,et al.  About distances of discrete distributions satisfying the data processing theorem of information theory , 1997, IEEE Trans. Inf. Theory.

[26]  R. A. Leibler,et al.  On Information and Sufficiency , 1951 .