PDX Finder: A Portal for Patient-Derived tumor Xenograft Model Discovery

Conte, Nathalie; Mason, Jeremy; Halmagyi, Csaba; Neuhauser, Steven B.; Begley, Dale A.; Krupke, Debra M.; Parkinson, Helen; Meehan, Terrence F.; Bult, Carol J.

doi:10.1101/291443

Cited by 25 publications

(27 citation statements)

References 18 publications

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“…The volume of tumors and body weight of mice were checked once or twice a week. The volume was calculated as (tumor length x tumor width 2 ) / 2. When a tumor reached >700~1000 mm 3 , the mouse was sacrificed, and tumor tissues were stored.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Conservation of copy number profiles during engraftment and passaging of patient-derived cancer xenografts

Woo¹,

Giordano

Srivastava³

et al. 2019

Preprint

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41 a PDXNET consortium 42 b EurOPDX consortium 43 # These authors contributed equally to this work.44 § These authors jointly supervised this work. ABSTRACT 107Patient-derived xenografts (PDXs) are resected human tumors engrafted into mice for preclinical 108 studies and therapeutic testing. It has been proposed that the mouse host affects tumor evolution 109 during PDX engraftment and propagation, impacting the accuracy of PDX modeling of human 110 cancer. Here we exhaustively analyze copy number alterations (CNAs) in 1451 PDX and matched 111 patient tumor (PT) samples from 509 PDX models. CNA inferences based on DNA sequencing 112 and microarray data displayed substantially higher resolution and dynamic range than gene 113 expression-based inferences, and they also showed strong CNA conservation from PTs through 114 late-passage PDXs. CNA recurrence analysis of 130 colorectal and breast PT/PDX-early/PDX-115 late trios confirmed high-resolution CNA retention. We observed no significant enrichment of 116 cancer-related genes in PDX-specific CNAs across models. Moreover, CNA differences between 117 patient and PDX tumors were comparable to variations in multi-region samples within patients. 118Our study demonstrates the lack of systematic copy number evolution driven by the PDX mouse 119 host. 121 MAIN 122A variety of models of human cancer have been used to study basic biological processes and 123 predict responses to treatment. For example, mouse models with genetically engineered 124 mutations in oncogenes and tumor suppressor genes have clarified the genetic and molecular 125 basis of tumor initiation and progression 1,2 , though responses sometimes differ between human 126 and mouse 3 . Cell lines have also been widely used to study cancer cells, but they lack the 127 heterogeneity and microenvironment of in vivo tumors and have shown limitations for predicting 128 clinical response 4 . Human tumors engrafted into transplant-compliant recipient mice (patient-129 derived xenografts, PDX) have advantages over prior systems for preclinical drug efficacy studies 130 because they allow researchers to directly study human cells and tissues in vivo 5-8 . Comparisons131 of genome characteristics and histopathology of primary tumors and xenografts of human breast 132 cancer 9-13 , ovarian cancer 14 , colorectal cancer 15 and lung cancer 16-18 , have demonstrated that the 133 biological properties of patient-derived tumors are largely preserved in xenografts. A growing body 134 of literature supports their use in cancer drug discovery and development 19-21 . 135A caveat to PDX models is that intratumoral evolution can occur during engraftment and 136 passaging 11,22-25 . Such evolution could potentially modify treatment response of PDXs with 137 respect to the patient tumors 23,26,27 , particularly if the evolution were to systematically alter cancer-138 related genes. This issue is related to multi-region comparisons of patient tumors 28-31 , for which 139 local mutational and immune infiltration variations have sugg...

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Section: Methodsmentioning

confidence: 99%

“…Animals were monitored weekly for tumor growth. The initial passage of material was grown to approximately 1000-2000mm 3 calculated using the following formula: weight (mg) = (tumor length x [tumor width] 2 ) / 2. Tumor material was then harvested, a portion cryopreserved, and the remainder implanted into NSG host mice.…”

Section: Methodsmentioning

confidence: 99%

Conservation of copy number profiles during engraftment and passaging of patient-derived cancer xenografts

Woo¹,

Giordano

Srivastava³

et al. 2019

Preprint

Self Cite

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“…To the right of the arrow are added-value knowledgebases ordered according to biological scale, from genes to proteins, structures, chemistry and systems. Updates in this NAR Database issue include ArrayExpress (26), BioSamples (27), ChEMBL (28), Complex Portal (29), ENA (30), Ensembl (9), GenCode (31), GWAS Catalogue (32), HGNC (33), InterPro (8), Open Targets (34), PDXfinder (35), Pfam (7), PRIDE (36), RNACentral (37), SIFTS (6) and UniProt (38). Not all EMBL-EBI resources are shown on the figure.…”

Section: Embl-ebi Data Resources In 2018mentioning

confidence: 99%

The European Bioinformatics Institute in 2018: tools, infrastructure and training

Cook

López

Stroe

et al. 2018

Nucleic Acids Research

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The European Bioinformatics Institute ( https://www.ebi.ac.uk/ ) archives, curates and analyses life sciences data produced by researchers throughout the world, and makes these data available for re-use globally ( https://www.ebi.ac.uk/ ). Data volumes continue to grow exponentially: total raw storage capacity now exceeds 160 petabytes, and we manage these increasing data flows while maintaining the quality of our services. This year we have improved the efficiency of our computational infrastructure and doubled the bandwidth of our connection to the worldwide web. We report two new data resources, the Single Cell Expression Atlas ( https://www.ebi.ac.uk/gxa/sc/ ), which is a component of the Expression Atlas; and the PDBe-Knowledgebase ( https://www.ebi.ac.uk/pdbe/pdbe-kb ), which collates functional annotations and predictions for structure data in the Protein Data Bank. Additionally, Europe PMC ( http://europepmc.org/ ) has added preprint abstracts to its search results, supplementing results from peer-reviewed publications. EMBL-EBI maintains over 150 analytical bioinformatics tools that complement our data resources. We make these tools available for users through a web interface as well as programmatically using application programming interfaces, whilst ensuring the latest versions are available for our users. Our training team, with support from all of our staff, continued to provide on-site, off-site and web-based training opportunities for thousands of researchers worldwide this year.

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“…This makes it challenging to find PDX models with specific characteristics such as a model with a specific mutation. Therefore, catalogs of PDX models [e.g., PDXFinder (18), EurOPDX (19), and PRoXe (20)] are being developed, which contain relevant information and provide links to model acquisition. Furthermore, as PDXs are becoming the goldstandard model for preclinical studies, better data standardization and analysis platforms are required to ensure consistency and reproducibility in PDX-based analysis.…”

Section: Introductionmentioning

confidence: 99%

Integrative Pharmacogenomics Analysis of Patient-Derived Xenografts

et al. 2019

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Identifying robust biomarkers of drug response constitutes a key challenge in precision medicine. Patient-derived tumor xenografts (PDX) have emerged as reliable preclinical models that more accurately recapitulate tumor response to chemoand targeted therapies. However, the lack of computational tools makes it difficult to analyze high-throughput molecular and pharmacologic profiles of PDX. We have developed Xenograft Visualization & Analysis (Xeva), an open-source software package for in vivo pharmacogenomic datasets that allows for quantification of variability in gene expression and pathway activity across PDX passages. We found that only a few genes and pathways exhibited passage-specific alterations and were therefore not suitable for biomarker discovery. Using the largest PDX pharmacogenomic dataset to date, we identified 87 pathways that are significantly associated with response to 51 drugs (FDR < 0.05). We found novel biomarkers based on gene expressions, copy number aberrations, and mutations predictive of drug response (concordance index > 0.60; FDR < 0.05). Our study demonstrates that Xeva provides a flexible platform for integrative analysis of preclinical in vivo pharmacogenomics data to identify biomarkers predictive of drug response, representing a major step forward in precision oncology. Significance: A computational platform for PDX data analysis reveals consistent gene and pathway activity across passages and confirms drug response prediction biomarkers in PDX. See related commentary by Meehan, p. 4324

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PDX Finder: A Portal for Patient-Derived tumor Xenograft Model Discovery

Cited by 25 publications

References 18 publications

Conservation of copy number profiles during engraftment and passaging of patient-derived cancer xenografts

Conservation of copy number profiles during engraftment and passaging of patient-derived cancer xenografts

The European Bioinformatics Institute in 2018: tools, infrastructure and training

Integrative Pharmacogenomics Analysis of Patient-Derived Xenografts

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