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Patient-derived xenograft (PDX) models are an effective preclinical in vivo platform for testing the efficacy of novel drugs and drug combinations for cancer therapeutics. Here we describe a repository of 79 genomically and clinically annotated lung cancer PDXs available from The Jackson Laboratory that have been extensively characterized for histopathologic features, mutational profiles, gene expression, and copy-number aberrations. Most of the PDXs are models of non–small cell lung cancer (NSCLC), including 37 lung adenocarcinoma (LUAD) and 33 lung squamous cell carcinoma (LUSC) models. Other lung cancer models in the repository include four small cell carcinomas, two large cell neuroendocrine carcinomas, two adenosquamous carcinomas, and one pleomorphic carcinoma. Models with both de novo and acquired resistance to targeted therapies with tyrosine kinase inhibitors are available in the collection. The genomic profiles of the LUAD and LUSC PDX models are consistent with those observed in patient tumors from The Cancer Genome Atlas and previously characterized gene expression-based molecular subtypes. Clinically relevant mutations identified in the original patient tumors were confirmed in engrafted PDX tumors. Treatment studies performed in a subset of the models recapitulated the responses expected on the basis of the observed genomic profiles. These models therefore serve as a valuable preclinical platform for translational cancer research. Significance: Patient-derived xenografts of lung cancer retain key features observed in the originating patient tumors and show expected responses to treatment with standard-of-care agents, providing experimentally tractable and reproducible models for preclinical investigations.

Supplementary Figure from A Genomically and Clinically Annotated Patient-Derived Xenograft Resource for Preclinical Research in Non–Small Cell Lung Cancer

Patient-derived tumor xenograft (PDX) mouse models have emerged as an important oncology research platform to study tumor evolution, mechanisms of drug response and resistance, and tailoring chemotherapeutic approaches for individual patients. The lack of robust standards for reporting on PDX models has hampered the ability of researchers to find relevant PDX models and associated data. Here we present the PDX models minimal information standard (PDX-MI) for reporting on the generation, quality assurance, and use of PDX models. PDX-MI defines the minimal information for describing the clinical attributes of a patient's tumor, the processes of implantation and passaging of tumors in a host mouse strain, quality assurance methods, and the use of PDX models in cancer research. Adherence to PDX-MI standards will facilitate accurate search results for oncology models and their associated data across distributed repository databases and promote reproducibility in research studies using these models. Cancer Res; 77(21); e62–66. ©2017 AACR.

Patient-derived xenograft (PDX) models are an effective preclinical in vivo platform for testing the efficacy of novel drugs and drug combinations for cancer therapeutics. Here we describe a repository of 79 genomically and clinically annotated lung cancer PDXs available from The Jackson Laboratory that have been extensively characterized for histopathologic features, mutational profiles, gene expression, and copy-number aberrations. Most of the PDXs are models of non–small cell lung cancer (NSCLC), including 37 lung adenocarcinoma (LUAD) and 33 lung squamous cell carcinoma (LUSC) models. Other lung cancer models in the repository include four small cell carcinomas, two large cell neuroendocrine carcinomas, two adenosquamous carcinomas, and one pleomorphic carcinoma. Models with both de novo and acquired resistance to targeted therapies with tyrosine kinase inhibitors are available in the collection. The genomic profiles of the LUAD and LUSC PDX models are consistent with those observed in patient tumors from The Cancer Genome Atlas and previously characterized gene expression-based molecular subtypes. Clinically relevant mutations identified in the original patient tumors were confirmed in engrafted PDX tumors. Treatment studies performed in a subset of the models recapitulated the responses expected on the basis of the observed genomic profiles. These models therefore serve as a valuable preclinical platform for translational cancer research.Significance:Patient-derived xenografts of lung cancer retain key features observed in the originating patient tumors and show expected responses to treatment with standard-of-care agents, providing experimentally tractable and reproducible models for preclinical investigations.

Supplementary Table from A Genomically and Clinically Annotated Patient-Derived Xenograft Resource for Preclinical Research in Non–Small Cell Lung Cancer

A figure showing the process of generating and using PDX models.

Development of candidate cancer treatments is a resource-intensive process, with the research community continuing to investigate options beyond static genomic characterization. Toward this goal, we have established the genomic landscapes of 536 patient-derived xenograft (PDX) models across 25 cancer types, together with mutation, copy number, fusion, transcriptomic profiles, and NCI-MATCH arms. Compared with human tumors, PDXs typically have higher purity and fit to investigate dynamic driver events and molecular properties via multiple time points from same case PDXs. Here, we report on dynamic genomic landscapes and pharmacogenomic associations, including associations between activating oncogenic events and drugs, correlations between whole-genome duplications and subclone events, and the potential PDX models for NCI-MATCH trials. Lastly, we provide a web portal having comprehensive pan-cancer PDX genomic profiles and source code to facilitate identification of more druggable events and further insights into PDXs’ recapitulation of human tumors., Patient-derived xenograft models (PDX) have been extensively used to study the molecular and clinical features of cancers. Here the authors present a cohort of 536 PDX models from 25 cancers, as well as their genomic and evolutionary profiles and their suitability for clinical trials.

Patient-derived xenograft models (PDXs) are an effective preclinical in vivo platform for testing the efficacy of novel drug and drug combinations for cancer therapeutics. Here we describe a repository of 79 genomically and clinically annotated lung cancer PDXs available from The Jackson Laboratory that have been extensively characterized for histopathological features, mutational profiles, gene expression, and copy number aberrations. Most of the PDXs are models of non-small cell lung cancer (NSCLC), including 37 lung adenocarcinoma (LUAD) and 33 lung squamous cell carcinoma (LUSC) models. Other lung cancer models in the repository include four small cell carcinomas, two large cell neuroendocrine carcinomas, two adenosquamous carcinomas, and one pleomorphic carcinoma. Models with both de novo and acquired resistance to targeted therapies with tyrosine kinase inhibitors are available in the collection. The genomic profiles of the LUAD and LUSC PDX models are consistent with those observed in patient tumors of the same tumor type from The Cancer Genome Atlas (TCGA) and to previously characterized gene expression-based molecular subtypes. Clinically relevant mutations identified in the original patient tumors were confirmed in engrafted tumors. Treatment studies performed for a subset of the models recapitulated the responses expected based on the observed genomic profiles.SignificanceThe collection of lung cancer Patient Derived Xenograft (PDX) models maintained at The Jackson Laboratory retain both the histologic features and treatment-relevant genomic alterations observed in the originating patient tumors and show expected responses to treatment with standard-of-care agents. The models serve as a valuable preclinical platform for translational cancer research. Information and data for the models are freely available from the Mouse Models of Human Cancer database (MMHCdb, http://tumor.informatics.jax.org/mtbwi/pdxSearch.do).

Patient-derived xenografts (PDXs) model human intra-tumoral heterogeneity in the context of the intact tissue of immunocompromised mice. Histological imaging via hematoxylin and eosin (H&E) staining is performed on PDX samples for routine assessment and, in principle, captures the complex interplay between tumor and stromal cells. Deep learning (DL)-based analysis of large human H&E image repositories has extracted inter-cellular and morphological signals correlated with disease phenotype and therapeutic response. Here, we present an extensive, pan-cancer repository of nearly 1,000 PDX and paired human progenitor H&E images. These images, curated from the PDXNet consortium, are associated with genomic and transcriptomic data, clinical metadata, pathological assessment of cell composition, and, in several cases, detailed pathological annotation of tumor, stroma, and necrotic regions. We demonstrate that DL can be applied to these images to classify tumor regions with an accuracy of 0.87. Further, we show that DL can predict xenograft-transplant lymphoproliferative disorder, the unintended outgrowth of human lymphocytes at the transplantation site, with an accuracy of 0.97. This repository enables PDX-specific investigations of cancer biology through histopathological analysis and contributes important model system data that expand on existing human histology repositories. We expect the PDXNet Image Repository to be valuable for controlled digital pathology analysis, both for the evaluation of technical issues such as stain normalization and for development of novel computational methods based on spatial behaviors within cancer tissues. Citation Format: Brian S. White, Xing Yi Woo, Soner Koc, Todd Sheridan, Steven B. Neuhauser, Shidan Wang, Yvonne A. Evrard, John David Landua, R Jay Mashl, Sherri R. Davies, Bingliang Fang, Maria Gabriela Raso, Kurt W. Evans, Matthew H. Bailey, Yeqing Chen, Min Xiao, Jill Rubinstein, Ali Foroughi pour, Lacey Elizabeth Dobrolecki, Maihi Fujita, Junya Fujimoto, Guanghua Xiao, Ryan C. Fields, Jacqueline L. Mudd, Xiaowei Xu, Melinda G. Hollingshead, Shahanawaz Jiwani, PDXNet consortium, Tiffany A. Wallace, Jeffrey A. Moscow, James H. Doroshow, Nicholas Mitsiades, Salma Kaochar, Chong-xian Pan, Moon S. Chen, Luis G. Carvajal-Carmona, Alana L. Welm, Bryan E. Welm, Ramaswamy Govindan, Shunqiang Li, Michael A. Davies, Jack A. Roth, Funda Meric-Bernstam, Yang Xie, Meenhard Herlyn, Li Ding, Michael T. Lewis, Carol J. Bolt, Dennis A. Dean, Jeffrey H. Chuang. A pan-cancer PDX histology image repository with genomic and pathological annotations for deep learning analysis. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5407.

The laboratory mouse is the premier animal model system for in vivo studies of the genetic and genomic basis of cancer in humans. Although thousands of mouse models have been generated, finding relevant data and knowledge about these models is complicated by a general lack of compliance in the published literature with nomenclature and annotation standards for genes, alleles, mouse strains, and cancer types. The Mouse Models of Human Cancer database (MMHCdb; tumor.informatics.jax.org) is an expertly curated knowledgebase of cancer phenotypes reported for diverse types of mouse models of human cancer such as inbred mouse strains, genetically engineered mouse models (GEMMs), Patient Derived Xenografts (PDXs), and mouse genetic diversity panels (e.g., the Collaborative Cross). MMHCdb includes data on more than 60,000 mouse models for over 1200 tumor classifications curated from more than 25,000 peer-reviewed publications. One of the primary goals of the MMHCdb is to highlight the impact of genetic background on the incidence and presentation of different tumor types in mice. The same allele on different backgrounds can result in very different cancer characteristics and, therefore, impact the appropriateness of a model for a specific research application. In MMHCdb, users can review the impact of genetic background on the frequency of spontaneous tumors for inbred mouse strains using an interactive table generated from different published and unpublished data sources. In addition, color-coded tabular summaries of individual papers are available that allow researchers to quickly assess how genetic background affects cancer phenotypes in the mouse models reported in a specific publication. We will highlight examples of how genetic background can profoundly change the types and frequencies of tumor types that can be expected in mouse models of human cancer. MMHCdb is supported by NCI R01 CA089713 Citation Format: Dale A. Begley, Debra M. Krupke, Steven B. Neuhauser, Emily L. Jocoy, John P. Sundberg, Carol J. Bult. The impact of genetic background on cancer phenotypes of mouse models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 14.

We created the PDX Network (PDXNet) Portal (https://portal.pdxnetwork.org/) to centralize access to the National Cancer Institute-funded PDXNet consortium resources (i.e., PDX models, sequencing data, treatment response data, and bioinformatics workflows), to facilitate collaboration among researchers, and to make resources easily available for research. The portal includes sections for resources, analysis results, metrics for PDXNet activities, data processing protocols, and training materials for processing PDX data.The initial portal release highlights PDXNet model and data resources, including 334 new models across 33 cancer types. Tissue samples of these models were deposited in the NCI’s Patient-Derived Model Repository (PDMR) for public access. These models have 2,822 associated sequencing files from 873 samples across 307 patients, which are hosted on the Cancer Genomics Cloud powered by Seven Bridges and the NCI Cancer Data Service for long-term storage and access with dbGaP permissions. The portal also includes results from standardized analysis workflows on PDXNet sequencing files and PDMR data (2,594 samples from 463 patients across 78 disease types). These 15 analysis workflows for whole-exome and RNA-Seq data are freely available, robust, validated, and standardized.The model and data lists will grow substantially over the next two years and will be continuously updated as new data are available. PDXNet models support multi-agent treatment studies, determination of sensitivity and resistance mechanisms, and preclinical trials. The PDXNet portal is a centralized location for these data and resources, which we expect to be of significant utility for the cancer research community.

Patient-derived tumor xenograft (PDX) mouse models are a versatile oncology research platform for studying tumor biology and for testing chemotherapeutic approaches tailored to genomic characteristics of individual patients’ tumors. PDX models are generated and distributed by a diverse group of academic labs, multi-institution consortia and contract research organizations. The distributed nature of PDX repositories and the use of different metadata standards for describing model characteristics presents a significant challenge to identifying PDX models relevant to specific cancer research questions. The Jackson Laboratory and EMBL-EBI are addressing these challenges by co-developing PDX Finder, a comprehensive open global catalog of PDX models and their associated datasets. Within PDX Finder, model attributes are harmonized and integrated using a previously developed community minimal information standard to support consistent searching across the originating resources. Links to repositories are provided from the PDX Finder search results to facilitate model acquisition and/or collaboration. The PDX Finder resource currently contains information for 1985 PDX models of diverse cancers including those from large resources such as the Patient-Derived Models Repository, PDXNet and EurOPDX. Individuals or organizations that generate and distribute PDXs are invited to increase the ‘findability’ of their models by participating in the PDX Finder initiative at www.pdxfinder.org.

Patient-derived tumor xenograft (PDX) mouse models have emerged as an important oncology research platform to study tumor evolution, mechanisms of drug response and resistance, and tailoring chemotherapeutic approaches for individual patients. The lack of robust standards for reporting on PDX models has hampered the ability of researchers to find relevant PDX models and associated data. Here we present the PDX models minimal information standard (PDX-MI) for reporting on the generation, quality assurance, and use of PDX models. PDX-MI defines the minimal information for describing the clinical attributes of a patient's tumor, the processes of implantation and passaging of tumors in a host mouse strain, quality assurance methods, and the use of PDX models in cancer research. Adherence to PDX-MI standards will facilitate accurate search results for oncology models and their associated data across distributed repository databases and promote reproducibility in research studies using these models. Cancer Res; 77(21); e62–66. ©2017 AACR.

Research using laboratory mice has led to fundamental insights into the molecular genetic processes that govern cancer initiation, progression, and treatment response. Although thousands of scientific articles have been published about mouse models of human cancer, collating information and data for a specific model is hampered by the fact that many authors do not adhere to existing annotation standards when describing models. The interpretation of experimental results in mouse models can also be confounded when researchers do not factor in the effect of genetic background on tumor biology. The Mouse Tumor Biology (MTB) database is an expertly curated, comprehensive compendium of mouse models of human cancer. Through the enforcement of nomenclature and related annotation standards, MTB supports aggregation of data about a cancer model from diverse sources and assessment of how genetic background of a mouse strain influences the biological properties of a specific tumor type and model utility. Cancer Res; 77(21); e67–70. ©2017 AACR.

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.

Patient-derived tumor xenograft (PDX) models are a critical oncology platform for cancer research, drug development and personalized medicine. Because of the heterogeneous nature of PDXs repositories, finding models of interest is a challenge. The Jackson Laboratory and EMBL-EBI are developing PDX Finder, the world's largest open PDX database containing millions of phenomic information from over 4300 models (www.pdxfinder.org, PMID: 30535239). In support of this initiative, we developed the PDX Minimal Information standard (PDX-MI) which defines metadata necessary to describe models (PMID: 29092942). Within PDX Finder, critical attributes like diagnosis, drug names or genes are harmonized into a cohesive ontological data model based on PDX-MI. An intuitive search and faceted search interface allow users to select models based on clinical/PDX attributes, tumor markers, dataset availability and/or drug dosing results. We provide PDX, patient, drug and molecular data detail pages where all available information can be browsed and downloaded. To further facilitate user's model selection, we are linking key external resources like publication platforms and cancer-specific annotation tools enabling exploration and prioritization of PDX variation data (COSMIC, CIViC, OncoMx, OpenCRAVAT). Links to originating resource protocols and contact information are provided, facilitating data understanding and further collaboration. Alongside database development activities, PDX Finder has undertaken activities to tackle areas of standards and tool development, data integration and outreach. PDX Finder provides key expertise and software components to support several worldwide consortia including PDXNet, PDMR and EurOPDX. We are driving the development of, and promoting the use of descriptive standards to facilitate data interoperability and promote global sharing of models. Our standard has become established in the community for data exchange, adopted by PDX providers, consortia, and informatics tools integrating PDX data. It has been re-used by different initiatives in the context of data collection and data modeling allowing adherence to the FAIR data principles - Findability, Accessibility, Interoperability and Reusability. PDX Finder is increasing awareness of PDX models, facilitating data integration, and enabling international collaboration, maximizing the investment in, and translational capabilities of these important models of human cancer. PDX Finder is freely available under an Apache 2 license (github.com/pdxfinder). Work supported by NCI U24 CA204781 01 (ended 31Aug2020), U24 CA253539, and R01 CA089713. Citation Format: Zinaida Perova, Csaba Halmagyi, Abayomi Mosaku, Nathalie Conte, Jeremy Mason, Alex Follette, Ross Thorne, Mauricio Martinez, Steven Neuhauser, Dale Begley, Debra Krupke, Helen Parkinson, Terrence Meehan, Carol Bult. PDX Finder: An open and global catalogue of patient-derived xenograft models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr LB017.

Mouse models play a crucial role in understanding the genetic and genomic basis of cancer and for the development of novel cancer therapies. Because data and information about these models are distributed and heterogeneous, gaining an appreciation for which models have been developed and what their characteristics are presents a major challenge for cancer researchers. The Mouse Models of Human Cancer Database (MMHCdb; http://tumor.informatics.jax.org) solves this challenge by providing a comprehensive, expertly curated on-line information resource about genetically engineered (GEMMs) and inbred mouse models of human cancer. In partnership with the European Bioinformatics Institute (EMBL/EBI), MMHCdb has also developed and implemented PDX Finder, a global catalog of Patient Derived Xenograft models (https://www.pdxfinder.org). Both MMHCdb and PDX Finder employ metadata standards to simplify the process of searching for relevant mouse models by diverse criteria including cancer type, type of strain, and genetic alteration. MMHCdb contains more than100,000 records on tumor frequency data for over 7,600 different mouse strains. These data are linked to over 4,700 references, 2,900 pathology reports, and 6,900 images. PDX Finder provides researchers with one stop access to information about more than 2,800 PDX models in repositories from around the world. MMHCdb and PDX Finder are supported, in part, by NIH/NCI CA089713. MMHCdb was formerly called the Mouse Tumor Biology database (MTB). Citation Format: Carol J. Bult, Debra M. Krupke, Steven B. Neuhauser, Joel E. Richardson, John P. Sundberg. Mouse models of human cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 435.

Patient-derived tumor xenograft (PDX) models are a critical oncology platform for cancer research, drug development and personalized medicine. Because of the heterogeneous nature of PDX repositories, finding models of interest is a challenge. The Jackson Laboratory and EMBL-EBI are developing PDX Finder, the world's largest open PDX database containing phenomic information of over 2900 models (www.pdxfinder.org, †). In support of this initiative, we developed the PDX Minimal Information standard (PDX-MI) which defines the minimal necessary metadata required to describe models (††). Within PDX Finder, critical attributes like diagnosis, drug names or genes are harmonized into a cohesive ontological data model based on PDX-MI. An intuitive, faceted search interface allows users to select models based on clinical/PDX attributes, tumor markers, dataset availability and/or drug dosing results. We provide PDX, patient, drug and molecular data details pages where all available information can be browsed and downloaded. To further facilitate users' model selection, we link key external resources like publication platforms and cancer specific annotation tools, enabling exploration and prioritisation of PDX variation data (COSMIC, CivicDb, OpenCRAVAT). Links to originating resource protocols and contact information are provided, facilitating data understanding and further collaboration. Alongside database development activities, PDX Finder has undertaken activities to tackle areas of standards and tool development, data integration and outreach. PDX Finder provides key expertise and software components to support several worldwide consortia including PDXNet, PDMR and EurOPDX. We are driving the development of, and promoting the use of descriptive standards to facilitate data interoperability and promote global sharing of models. Our standard has become established in the community for data exchange, adopted by PDX providers, consortia, and informatic tools integrating PDX data. It has been re-used by different initiatives in the context of data collection and data modelling allowing adherence to the FAIR data principles - Findability, Accessibility, Interoperability and Reusability. PDX Finder is increasing awareness of PDX models, facilitating data integration, and enabling international collaboration, maximising the investment in, and translational capabilities of these important models of human cancer. PDX Finder is freely available under an Apache 2 license (github.com/pdxfinder). Work supported by NCI U24 CA204781 01, R01 CA089713 and the European Molecular Biology Laboratory. † Conte et al, 2019. PDX Finder: A Portal for Patient-Derived tumor Xenograft Model Discovery. NAR, 2019 Jan.†† Meehan, Conte et al, 2017. PDX-MI: Minimal Information for Patient-Derived Tumor Xenograft Models. Cancer Res. 2017 Nov.PDXNet: www.pdxnetwork.org, PDMR: pdmr.cancer.gov, EUROPDX: www.europdx.eu Citation Format: Nathalie Conte, Csaba Halmagyi, Abayomi Mosaku, Jeremy C. Mason, Alex W. Follette, Ross Thorne, Steven Neuhauser, Dale Begley, Debbie M. Krupke, Helen Parkinson, Terrence Meehan, Carol Bult. PDX Finder: Largest global catalog of patient tumor derived xenograft models [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3212.

Patient-derived tumor xenograft (PDX) mouse models are a versatile oncology research platform for studying tumor biology and for testing chemotherapeutic approaches tailored to genomic characteristics of individual patient’s tumors. PDX models are generated and distributed by a diverse group of academic labs, research organizations, multi-institution consortia, and contract research organizations. The distributed nature of PDX repositories and the use of different standards in the associated metadata presents a significant challenge to finding PDX models relevant to specific cancer research questions. The Jackson Laboratory and EMBL-EBI are addressing these challenges by co-developing PDX Finder, a comprehensive open global catalog of PDX models and their associated datasets. Within PDX Finder, model attributes are harmonized and integrated using a previously developed community minimal information standard to support consistent searching across the originating resources. Links to repositories are provided from the PDX Finder search results to facilitate model acquisition and/or collaboration. The PDX Finder resource currently contains information for more than 1900 PDX models of diverse cancers including those from large resources such as the Patient-Derived Models Repository, PDXNet, and EurOPDX. Individuals or organizations that generate and distribute PDXs are invited to increase the “findability” of their models by participating in the PDX Finder initiative at www.pdxfinder.org.

Summary InterMine is a data integration warehouse and analysis software system developed for large and complex biological data sets. Designed for integrative analysis, it can be accessed through a user-friendly web interface. For bioinformaticians, extensive web services as well as programming interfaces for most common scripting languages support access to all features. The web interface includes a useful identifier look-up system, and both simple and sophisticated search options. Interactive results tables enable exploration, and data can be filtered, summarized, and browsed. A set of graphical analysis tools provide a rich environment for data exploration including statistical enrichment of sets of genes or other entities. InterMine databases have been developed for the major model organisms, budding yeast, nematode worm, fruit fly, zebrafish, mouse, and rat together with a newly developed human database. Here, we describe how this has facilitated interoperation and development of cross-organism analysis tools and reports. InterMine as a data exploration and analysis tool is also described. All the InterMine-based systems described in this article are resources freely available to the scientific community. genesis 53:547–560, 2015. © 2015 Wiley Periodicals, Inc.

The Mouse Tumor Biology Database (MTB) is designed to provide an electronic data storage, search, and analysis system for information on mouse models of human cancer. The MTB includes data on tumor frequency and latency, strain, germ line, and somatic genetics, pathologic notations, and photomicrographs. The MTB collects data from the primary literature, other public databases, and direct submissions from the scientific community. The MTB is a community resource that provides integrated access to mouse tumor data from different scientific research areas and facilitates integration of molecular, genetic, and pathologic data. Current status of MTB, search capabilities, data types, and future enhancements are described in this article.

Mouse models of human cancer have provided important insights into the genetic and molecular basis of human cancer and have been used to identify promising new treatment options for human patients. Genetically engineered mouse models (GEMMS) have been used to identify and characterize the basis of cancer susceptibility, tumor suppressor and oncogene function, and increasingly co-clinical studies of proposed therapeutic treatments. In recent years patient derived xenograft (PDX) models created by implanting human tumor tissue into immune deficient mouse hosts have become a major in vivo pre-clinical research platform for evaluating novel cancer therapies tailored to genomic properties of a patient's tumor. The diversity and distributed nature of GEMM and PDX mouse models, and the data generated from these models, present a significant challenge to researchers who are searching for mouse models relevant to their research. The Mouse Tumor Biology database (http://tumor.informatics.jax.org) is a comprehensive resource of information on both GEMM and PDX models of human cancer that has been expertly curated from peer-reviewed scientific publications and direct data submissions from individual investigators. MTB provides an easy to use search interface and tools for visualizing associated data from mouse models of human cancer. Standardized annotations using controlled vocabularies and official gene and mouse strain nomenclature ensures that researchers get accurate and comprehensive results to their searches. For GEMMs, MTB contains data from over 24,000 different spontaneous or endogenously induced tumors from genetically defined mice obtained from over 4,400 published manuscripts. Annotations include 88,000 tumor frequency records, over 2,200 pathology reports, and over 6,100 images. MTB also provides access to detailed clinical, pathological, expression and genomics data from over 450 PDX models with over 990 histology images. Information in MTB is cross-reference to cancer models data from other bioinformatics resources including PathBase, the Mouse Phenome Database (MPD), the Gene Expression Omnibus and ArrayExpress. Recent enhancements to MTB include the interactive cancer model summary table linking the most common fatal human cancers to relevant mouse models and interactive plots for dosing studies performed using PDX models. MTB has co-developed the PDX Finder resource in collaboration with EMBL-EBI to provide a comprehensive global catalogue of PDX models available for researchers. MTB is supported by NCI grant CA089713. Citation Format: Dale A. Begley, Debra M. Krupke, Steven B. Neuhauser, Joel E. Richardson, John P. Sundberg, Carol J. Bult. Identifying relevant mouse models of human cancer using the mouse tumor biology database (MTB) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5102.

Patient-derived tumor xenograft (PDX) mouse models have emerged as an important oncology research platform to study tumor evolution, drug response and for tailoring chemotherapeutic approaches to individual patients. PDX models are produced and made available in repositories managed by small academic labs, large research consortia and contract research organizations. Because of the distributed and heterogeneous nature of PDX repositories, finding relevant models of interest to investigators is a challenge. To address this issue, The Jackson Laboratory and EMBL-EBI have co-developed the PDX Finder, a comprehensive open global catalogue of PDX models and their associated data across resources. In support this initiative, we coordinated the community initiative to develop the PDX models Minimal Information standard (PDX-MI) that defines the minimal information necessary for describing key elements of a PDX model including the clinical attributes of a patient's tumor, methods of implantation, host strain, and quality assurance methods used for model validation†. PDX-MI serves as the basis for PDX Finder's comprehensive search and attribute filtering options (e.g., tumor histology, molecular variant, drug response). Within PDX Finder, model attributes are harmonized and integrated into a cohesive ontological data model that supports consistent searching across the originating resources. From PDX Finder, direct links to these resources are provided to allow users to contact the relevant institution for model acquisition and further collaboration. PDX Finder is formally collaborating with several worldwide consortia including PDXnet and EurOPDX to increase “findability” of PDX models and to advance cancer research and drug discovery. PDX Finder is currently displaying over 1200 PDX models for a wide variety of cancers and is actively recruiting more models. The community is invited to explore and provide feedback on our portal as we build this rich resource at : www.pdxfinder.org. † Meehan et al, 2017. PDX-MI: Minimal Information for Patient-Derived Tumor Xenograft Models. Cancer Res. 2017 Nov 1;77(21):e62-e66. Citation Format: Nathalie A. Conte, Terrence F. Meehan, Dale A. Begley, Debbie M. Krupke, Csaba Halmagyi, Jeremy C. Mason, Abayomi Mosaku, Steven B. Neuhauser, Helen Parkinson, Carol J. Bult. PDX Finder: An open and global catalogue of patient tumor-derived xenograft models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3281.

The number and types of mouse models of human cancer and the volume and heterogeneity of information related to the characterization of these models is diverse and large. The distributed nature of the information and lack of conformance to terminology standards complicates integrated searches of these data and the identification of relevant mouse models for a particular study or application. The Mouse Tumor Biology database (MTB) (http://tumor.informatics.jax.org) provides online query tools to facilitate cohesive searches and visualization of these varied data, thus enabling the identification of appropriate mouse models of human cancer and potential therapeutic treatments. MTB is an expertly curated resource for information and data about genetically engineered mouse (GEM) strains, inbred strains, and patient-derived xenograft (PDX) models of human cancer. Enforcement of standard gene and strain nomenclature and use of controlled vocabularies within MTB enables complete and accurate searching of the published literature for relevant mouse models. Information in MTB is obtained from curation of peer-reviewed scientific publications and from direct data submissions from individual investigators and large-scale programs. MTB has a primary focus on the cancer predisposition of inbred strains of mice and the spectrum of cancers observed in GEM models. Additionally, the breadth of MTB's data coverage has expanded to encompass PDX models. Recent enhancements to MTB include an interactive mouse model chart that summarizes the number of traditional mouse models and PDXs organized by the top 20 cancer types for human mortality as reported by the American Cancer Society. The traditional mouse models listed are restricted to those in which the frequency of the tumor type is very high (reported colony size greater than or equal to twenty; reported tumor frequency greater than or equal to 80%). For PDX models, all the publicly available models from The Jackson Laboratory PDX repository are listed. MTB currently contains more than 87,000 tumor frequencies, 7,000+ mouse strain cohorts, and over 6,700 images from over 4,300 references. MTB also provides access to detailed clinical, pathologic, expression, and genomics data from over 400 PDX models. Information in MTB is integrated with cancer models data from other bioinformatics resources including PathBase, the Gene Expression Omnibus (GEO), and ArrayExpress. MTB is supported by NCI grant CA089713. Citation Format: Debra M. Krupke, Dale A. Begley, Steven B. Neuhauser, Joel E. Richardson, John P. Sundberg, Carol J. Bult. Mouse Tumor Biology (MTB) database–An integrated data resource for GEM, inbred strains, and PDX models of human cancer [abstract]. In: Proceedings of the AACR Special Conference: Advances in Modeling Cancer in Mice: Technology, Biology, and Beyond; 2017 Sep 24-27; Orlando, Florida. Philadelphia (PA): AACR; Cancer Res 2018;78(10 Suppl):Abstract nr B50.

Many mouse models have been created to study hematopoietic cancer types. There are over thirty hematopoietic tumor types and subtypes, both human and mouse, with various origins, characteristics and clinical prognoses. Determining the specific type of hematopoietic lesion produced in a mouse model and identifying mouse models that correspond to the human subtypes of these lesions has been a continuing challenge for the scientific community. The Mouse Tumor Biology Database (MTB; http://tumor.informatics.jax.org) is designed to facilitate use of mouse models of human cancer by providing detailed histopathologic and molecular information on lymphoma subtypes, including expertly annotated, on line, whole slide scans, and providing a repository for storing information on and querying these data for specific lymphoma models.

The laboratory mouse is the foremost model organism for interrogating the genetic and molecular basis of human cancer and is a powerful platform for identifying therapeutically effective targets for prevention and treatment of cancer. Research using genetically engineered mouse models (GEMMs) have led to important advances in our understanding of the genetic basis of cancer susceptibility, the function of tumor suppressors and oncogenes, and therapy responses in preclinical and co-clinical studies. Patient Derived Xenograft (PDX) models are an increasingly important model system for in vivo studies of human cancer. These models are created by implanting patient tumors into immunodeficient or humanized mouse hosts and are a powerful translational research platform for preclinical and co-clinical studies. The number of GEMM and PDX mouse models increases significantly every year and the diverse cancer-related data about human cancer models tend to be distributed in ways that makes it difficult for researchers to integrate and interpret the information to find the most relevant model for their research. The Mouse Tumor Biology database (http://tumor.informatics.jax.org) is an expertly curated resource for information and data about genetically defined mouse strains and PDX models of human cancer. MTB provides query tools to enable integrated searches and visualization of these varied data, thus facilitating the assessment of novel mouse models of human cancer and potential preventative and therapeutic treatments. Enforcement of controlled vocabularies and standard gene, allele and strain nomenclature within MTB facilitates precise and comprehensive queries of MTB for pertinent mouse models. MTB contains data from spontaneous or endogenously induced tumors from genetically defined mice including tumor classification, incidence, Quantitative Trait Loci, pathology reports, images and genetic changes in the tumor (somatic) and background strain (germline) genomes. The PDX resource enables queries based on tumor type, cancer diagnosis and genomic properties of the engrafted tumors. Information in MTB is obtained from curation of peer-reviewed scientific publications and direct data submissions from individual investigators and large-scale programs. New features in MTB include the Faceted Tumor Search Form and a Reported Mouse Models table linking the most common fatal human cancers to reported equivalent mouse models. MTB contains over 77,000 Tumor Frequencies and over 2,200 Pathology Reports with over 6,600 images from over 4,200 references. MTB provides access to detailed clinical, pathological, expression and genomics data from over 400 PDX models. Information in MTB is integrated with cancer models data from other bioinformatics resources including PathBase, the Gene Expression Omnibus and ArrayExpress. MTB is supported by NCI grant CA089713. Citation Format: Dale A. Begley, Debra M. Krupke, Steven B. Neuhauser, Joel E. Richardson, John P. Sundberg, Janan T. Eppig, Carol J. Bult. Identifying therapeutically relevant mouse and patient-derived xenograft (PDX) models of human cancer using the mouse tumor biology database (MTB) data resource [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2804. doi:10.1158/1538-7445.AM2017-2804

The Mouse Tumor Biology (MTB; http://tumor.informatics.jax.org) database is a unique online compendium of mouse models for human cancer. MTB provides online access to expertly curated information on diverse mouse models for human cancer and interfaces for searching and visualizing data associated with these models. The information in MTB is designed to facilitate the selection of strains for cancer research and is a platform for mining data on tumor development and patterns of metastases. MTB curators acquire data through manual curation of peer-reviewed scientific literature and from direct submissions by researchers. Data in MTB are also obtained from other bioinformatics resources including PathBase, the Gene Expression Omnibus and ArrayExpress. Recent enhancements to MTB improve the association between mouse models and human genes commonly mutated in a variety of cancers as identified in large-scale cancer genomics studies, provide new interfaces for exploring regions of the mouse genome associated with cancer phenotypes and incorporate data and information related to Patient-Derived Xenograft models of human cancers.

In recent years, the scientific community has generated an ever-increasing amount of data from a growing number of animal models of human cancers. Much of these data come from genetically engineered mouse models. Identifying appropriate models for skin cancer and related relevant genetic data sets from an expanding pool of widely disseminated data can be a daunting task. The Mouse Tumor Biology Database (MTB) provides an electronic archive, search and analysis system that can be used to identify dermatological mouse models of cancer, retrieve model-specific data and analyse these data. In this report, we detail MTB's contents and capabilities, together with instructions on how to use MTB to search for skin-related tumor models and associated data.

CORRIGENDUM: InterMOD: integrated data and tools for the unification of model organism research

Model organisms are widely used for understanding basic biology and have significantly contributed to the study of human disease. In recent years, genomic analysis has provided extensive evidence of widespread conservation of gene sequence and function amongst eukaryotes, allowing insights from model organisms to help decipher gene function in a wider range of species. The InterMOD consortium is developing an infrastructure based around the InterMine data warehouse system to integrate genomic and functional data from a number of key model organisms, leading the way to improved cross-species research. So far including budding yeast, nematode worm, fruit fly, zebrafish, rat and mouse, the project has set up data warehouses, synchronized data models and created analysis tools and links between data from different species. The project unites a number of major model organism databases, improving both the consistency and accessibility of comparative research, to the benefit of the wider scientific community.

The laboratory mouse is the premier model organism for understanding the genetic basis of human cancer and is a powerful platform for investigating novel targets for therapeutic intervention. Research using genetically engineered mouse models has led to key insights into the genetics of cancer susceptibility, the function of tumor suppressors and oncogenes, and therapy responses in pre-clinical and co-clinical studies. Patient Derived Xenografts (PDX) models are another model system for in vivo cancer studies. PDX models are created by implanting patient tumors into immunodeficient or humanized mouse hosts. PDX models are a powerful translational research platform for pre-clinical and co-clinical studies. The number of mouse models and the volume and heterogeneity of data related to the characterization of these models has increased dramatically in recent years, making integrated searches of these data and identifying relevant models a significant barrier to their effective use. The Mouse Tumor Biology database (MTB) (http://tumor.informatics.jax.org) provides on-line query tools to facilitate cohesive searches and visualization of these varied data, thus enabling the identification of novel mouse models of human cancer and potential therapeutic treatments. The Mouse Tumor Biology database is an expertly curated resource for information and data about genetically modified mouse strains and PDX models of human cancer. Enforcement of standard gene and strain nomenclature and use of controlled vocabularies within MTB enables complete and accurate searching of the published literature for relevant mouse models. MTB contains data from spontaneous or endogenously induced tumors from genetically defined mice including tumor classification, incidence and latency, tumor associated QTLs, pathology reports, images and genetic changes in the tumor (somatic) and background strain (germline) genomes. The PDX resource enables searches based on tumor type, cancer diagnosis, and genomic properties of the engrafted tumors. Information in MTB is obtained from curation of peer-reviewed scientific publications and from direct data submissions from individual investigators and large-scale programs. MTB contains over 71,000 Tumor Frequencies, and over 2,080 Pathology Reports with over 5,800 images from over 3,600 references. MTB also provides access to detailed clinical, pathological, expression and genomics data from over 450 PDX models. Information in MTB is integrated with cancer models data from other bioinformatics resources including PathBase, the Gene Expression Omnibus (GEO), and ArrayExpress. MTB is supported by NCI grant CA089713. Citation Format: Dale A. Begley, Debbie M. Krupke, Steven B. Neuhauser, Joel E. Richardson, John P. Sundberg, Janan T. Eppig, Carol J. Bult. The mouse tumor biology database (MTB): An integrated data resource for mouse and patient derived xenograft (PDX) models of human cancer. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 631.

Many advances in the field of cancer biology have been made using mouse models of human cancer. The Mouse Tumor Biology (MTB, "http://tumor.informatics.jax.org":http://tumor.informatics.jax.org) database provides web-based access to data on spontaneous and induced tumors from genetically defined mice (inbred, hybrid, mutant, and genetically engineered strains of mice). These data include standardized tumor names and classifications, pathology reports and images, mouse genetics, genomic and cytogenetic changes occurring in the tumor, strain names, tumor frequency and latency, and literature citations.Although primary source for the data represented in MTB is peer-reviewed scientific literature an increasing amount of data is derived from disparate sources. MTB includes annotated histopathology images and cytogenetic assay images for mouse tumors where these data are available from The Jackson Laboratory’s mouse colonies and from outside contributors. MTB encourages direct submission of mouse tumor data and images from the cancer research community and provides investigators with a web-accessible tool for image submission and annotation. Integrated searches of the data in MTB are facilitated by the use of several controlled vocabularies and by adherence to standard nomenclature. MTB also provides links to other related online resources such as the Mouse Genome Database, Mouse Phenome Database, the Biology of the Mammary Gland Web Site, Festing's Listing of Inbred Strains of Mice, the JAX® Mice Web Site, and the Mouse Models of Human Cancers Consortium's Mouse Repository. MTB provides access to data on mouse models of cancer via the internet and has been designed to facilitate the selection of experimental models for cancer research, the evaluation of mouse genetic models of human cancer, the review of patterns of mutations in specific cancers, and the identification of genes that are commonly mutated across a spectrum of cancers.MTB is supported by NCI grant CA089713.

The increasing number and diversity of available mouse models of human cancer and their growing importance in scientific research have resulted in an enormous increase in the amount and types of data generated from these models. These models represent powerful tools for studying biological and genetic mechanisms of cancer and for translation into potential clinical therapeutics. However, the amount of available data makes it challenging to identify and evaluate specific models and data important for an individual laboratory's research. Placing these data in their proper genetic context is crucial to understanding the biochemical and molecular mechanisms of initiation, progression, and metastasis of different cancers. In addition, the ability of the immunodeficient NOD.Cg-Prkdcscid Il2rgtm1Wjl/SzJ (NSG) mouse strain to host patient derived xenograft (PDX) models only increases the number of available models and the data produced from them. The Mouse Tumor Biology (MTB) Database provides access to data from mouse and PDX models of human tumors and the tools to analyze these data, facilitating the discovery and evaluation of novel mouse and PDX models of human cancers (http://tumor.informatics.jax.org). MTB includes data on endogenously arising tumors (both spontaneous and induced) in genetically defined mice (inbred, hybrid, mutant, and genetically engineered mice) and information from PDX models of human tumors and provides freely available web access to these data. MTB integrates data from peer-reviewed literature, laboratories studying mouse models of human cancer, production mouse colonies at The Jackson Laboratory (JAX), colonies of aging mice from the Jackson Aging Center, and PDX data from the Jackson Laboratory Patient-derived xenograft resource. MTB also incorporates data from PathBase, and mouse gene expression data sets from NCBI's Gene Expression Omnibus (GEO) and the Array Express Database. Data include tumor classification, incidence and latency, tumor associated quantitative trait loci (QTL), pathology reports, images and genetic changes in tumors (somatic) and background strain (germline). Data type specific query forms (tumor, genetic etc.) allow detailed searches. MTB also can be searched using human gene symbols for orthologous mouse genes and associated data. Pathology images are submitted by the scientific community, from primary literature (with publisher permission), and from JAX colonies. MTB also includes immunohistochemistry data on over 500 antibodies with accompanying images of positive control samples and links to the respective vendors. MTB encourages direct submission of mouse tumor data and images from the cancer research community and has developed a web-based system to facilitate submission of data. Standard nomenclature, controlled vocabularies and literature citations facilitate data integration and robust searches. MTB is integrated with the Mouse Genome Informatics resource (MGI, http://www.informatics.jax.org) and provides links to other related online resources such as the Mouse Phenome Database (MPD), the Biology of the Mammary Gland Web Site, and the NCI Mouse Repository. MTB is supported by NCI grant CA089713. Citation Format: Dale A. Begley, Debra M. Krupke, Steven B. Neuhauser, Joel E. Richardson, John P. Sundberg, Carol J. Bult, Janan T. Eppig. The Mouse Tumor Biology (MTB) Database: An electronic tool for identifying and evaluating mouse and PDX models of human cancer. [abstract]. In: Proceedings of the AACR Special Conference: The Translational Impact of Model Organisms in Cancer; Nov 5-8, 2013; San Diego, CA. Philadelphia (PA): AACR; Mol Cancer Res 2014;12(11 Suppl):Abstract nr A05.