Systematic Establishment of Robustness and Standards in Patient-Derived Xenograft Experiments and Analysis
Open Access
- 2 October 2019
- preprint content
- Published by Cold Spring Harbor Laboratory
- p. 790246
- https://doi.org/10.1101/790246
Abstract
Patient-Derived Xenografts (PDXs) are tumor-in-mouse models for cancer. PDX collections, such as those supported by the NCI PDXNet program, are powerful resources for preclinical therapeutic testing. However, variations in experimental design and analysis procedures have limited interpretability. To determine the robustness of PDX studies, the PDXNet tested temozolomide drug response for three pre-validated PDX models (sensitive, resistant, and intermediate) across four blinded PDX Development and Trial Centers (PDTCs) using independently selected SOPs. Each PDTC was able to correctly identify the sensitive, resistant, and intermediate models, and statistical evaluations were concordant across all groups. We also developed and benchmarked optimized PDX informatics pipelines, and these yielded robust assessments across xenograft biological replicates. These studies show that PDX drug responses and sequence results are reproducible across diverse experimental protocols. Here we share the range of experimental procedures that maintained robustness, as well as standardized cloud-based workflows for PDX exome-seq and RNA-Seq analysis and for evaluating growth.Keywords
This publication has 42 references indexed in Scilit:
- STAR: ultrafast universal RNA-seq alignerBioinformatics, 2012
- Xenome—a tool for classifying reads from xenograft samplesBioinformatics, 2012
- Integrative Genomics Viewer (IGV): high-performance genomics data visualization and explorationBriefings in Bioinformatics, 2012
- VarScan 2: Somatic mutation and copy number alteration discovery in cancer by exome sequencingGenome Research, 2012
- Molecular profiling of patient-derived breast cancer xenograftsBreast Cancer Research, 2012
- A framework for variation discovery and genotyping using next-generation DNA sequencing dataNature Genetics, 2011
- The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing dataGenome Research, 2010
- edgeR: a Bioconductor package for differential expression analysis of digital gene expression dataBioinformatics, 2009
- Pindel: a pattern growth approach to detect break points of large deletions and medium sized insertions from paired-end short readsBioinformatics, 2009
- New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1)European Journal Of Cancer, 2009