@article{gao2015high,
  title={High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response},
  author={Gao, Hui and Korn, Joshua M and Ferretti, St{\'e}phane and Monahan, John E and Wang, Youzhen and Singh, Mallika and Zhang, Chao and Schnell, Christian and Yang, Guizhi and Zhang, Yun and others},
  journal={Nature medicine},
  volume={21},
  number={11},
  pages={1318},
  year={2015},
  publisher={Nature Publishing Group}
}

@article {MerXeva,
	author = {Mer, Arvind Singh and Ba-alawi, Wail and Smirnov, Petr and Wang, Yi Xiao and Brew, Ben and Ortmann, Janosch and Tsao, Ming-Sound and Cescon, David and Goldenberg, Anna and Haibe-Kains, Benjamin},
	title = {Integrative Pharmacogenomics Analysis of Patient Derived Xenografts},
	elocation-id = {471227},
	year = {2018},
	doi = {10.1101/471227},
	publisher = {Cold Spring Harbor Laboratory},
	abstract = {One of the key challenges in cancer precision medicine is finding robust biomarkers of drug response. Patient-derived tumor xenografts (PDXs) have emerged as reliable preclinical models since they better recapit
ulate tumor response to chemo- and targeted therapies. However, the lack of standard tools poses a challenge in the analysis of PDXs with molecular and pharmacological profiles. Efficient storage, access and analysis is key to the
realization of the full potential of PDX pharmacogenomic data. We have developed Xeva (XEnograft Visualization \&amp; Analysis), an open-source software package for processing, visualization and integrative analysis of a compendium
 of in vivo pharmacogenomic datasets. The Xeva package follows the PDX minimum information (PDX-MI) standards and can handle both replicate-based and 1x1x1 experimental designs. We used Xeva to characterize the variability of gene
expression and pathway activity across passages. We found that only a few genes and pathways have passage specific alterations (median intraclass correlation of 0.53 for genes and positive enrichment score for 92.5\% pathways). For
 example, activity of the mRNA 3{\textquoteright}-end processing and elongation arrest and recovery pathways were strongly affected by model passaging (gene set enrichment analysis false discovery rate [FDR] \&lt;5\%). We then leve
raged our platform to link the drug response and the pathways whose activity is consistent across passages by mining the Novartis PDX Encyclopedia (PDXE) data containing 1,075 PDXs spanning 5 tissue types and 62 anticancer drugs. W
e identified 87 pathways significantly associated with response to 51 drugs (FDR \&lt; 5\%), including associations such as erlotinib response and signaling by EGFR in cancer pathways and MAP kinase activation in TLR cascade and bi
nimetinib response. Among the significant pathway-drug associations, we found novel biomarkers based on gene expressions, Copy Number Aberrations (CNAs) and mutations predictive of drug response (concordance index \&gt; 0.60; FDR \
&lt; 0.05). Xeva provides a flexible platform for integrative analysis of preclinical in vivo pharmacogenomics data to identify biomarkers predictive of drug response, a major step toward precision oncology.},
	URL = {https://www.biorxiv.org/content/early/2018/11/16/471227},
	eprint = {https://www.biorxiv.org/content/early/2018/11/16/471227.full.pdf},
	journal = {bioRxiv}
}


