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PDX-MI: Minimal Information for Patient-Derived Tumor Xenograft Models

Terrence F. Meehan, Nathalie Conte, Theodore Goldstein, Giorgio Inghirami, Mark A. Murakami, Sebastian Brabetz, Zhiping Gu, Jeffrey A. Wiser, Patrick Dunn, Dale A. Begley, Debra M. Krupke, Andrea Bertotti, Alejandra Bruna, Matthew H. Brush, Annette T. Byrne, Carlos Caldas, Amanda L. Christie, Dominic A. Clark, Heidi Dowst, Jonathan R. Dry, James H. Doroshow, Olivier Duchamp, Yvonne A. Evrard, Stephane Ferretti, Kristopher K. Frese, Neal C. Goodwin, Danielle Greenawalt, Melissa A. Haendel, Els Hermans, Peter J. Houghton, Jos Jonkers, Kristel Kemper, Tin O. Khor, Michael T. Lewis, K.C. Kent Lloyd, Jeremy Mason, Enzo Medico, Steven B. Neuhauser, James M. Olson, Daniel S. Peeper, Oscar M. Rueda, Je Kyung Seong, Livio Trusolino, Emilie Vinolo, Robert J. Wechsler-Reya, David M. Weinstock, Alana Welm, S. John Weroha, Frédéric Amant, Stefan M. Pfister, Marcel Kool, Helen Parkinson, Atul J. Butte and Carol J. Bult
Terrence F. Meehan
1European Molecular Biology Laboratory-European Bioinformatics Institute, Hinxton, United Kingdom.
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  • For correspondence: tmeehan@ebi.ac.uk
Nathalie Conte
1European Molecular Biology Laboratory-European Bioinformatics Institute, Hinxton, United Kingdom.
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Theodore Goldstein
2Institute for Computational Health Sciences, University of California, San Francisco, California.
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Giorgio Inghirami
3Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, New York.
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Mark A. Murakami
4Dana-Farber Cancer Institute and Harvard Medical School, Boston, Massachusetts.
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Sebastian Brabetz
5Hopp-Children's Cancer Center at the NCT Heidelberg (KiTZ), Heidelberg, Germany.
6Division of Pediatric Neuro-oncology, German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK), Heidelberg, Germany.
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Zhiping Gu
7Northrop Grumman Information Systems Health IT, Rockville, Maryland.
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Jeffrey A. Wiser
7Northrop Grumman Information Systems Health IT, Rockville, Maryland.
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Patrick Dunn
7Northrop Grumman Information Systems Health IT, Rockville, Maryland.
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Dale A. Begley
8The Jackson Laboratory, Bar Harbor, Maine.
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Debra M. Krupke
8The Jackson Laboratory, Bar Harbor, Maine.
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Andrea Bertotti
9Candiolo Cancer Institute, FPO-IRCC, Department of Oncology, University of Torino, Torino, Italy.
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Alejandra Bruna
10Cancer Research UK Cambridge Institute, Cambridge Cancer Centre, University of Cambridge, Cambridge, United Kingdom.
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Matthew H. Brush
11Department of Medical Informatics and Clinical Epidemiology and OHSU Library, Oregon Health and Science University, Portland, Oregon.
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Annette T. Byrne
12Royal College of Surgeons in Ireland, Ireland.
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Carlos Caldas
10Cancer Research UK Cambridge Institute, Cambridge Cancer Centre, University of Cambridge, Cambridge, United Kingdom.
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Amanda L. Christie
4Dana-Farber Cancer Institute and Harvard Medical School, Boston, Massachusetts.
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Dominic A. Clark
1European Molecular Biology Laboratory-European Bioinformatics Institute, Hinxton, United Kingdom.
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Heidi Dowst
13Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, Texas.
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Jonathan R. Dry
14Oncology Innovative Medicines and Early Development, AstraZeneca R&D Boston, Waltham, Massachusetts.
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James H. Doroshow
15Center for Cancer Research, National Cancer Institute, Bethesda, Maryland.
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Olivier Duchamp
16Oncodesign Biotechnology and IMODI Consortium, France.
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Yvonne A. Evrard
17Leidos Biomedical Research, Inc, Frederick National Laboratory for Cancer Research, Frederick, Maryland.
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Stephane Ferretti
18Oncology Disease Area, Novartis Institutes for Biomedical Research, Switzerland.
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Kristopher K. Frese
19Cancer Research UK Manchester Institute, The University of Manchester, Manchester, United Kingdom.
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Neal C. Goodwin
20Champions Oncology, Baltimore, Maryland.
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Danielle Greenawalt
21Translational Bioinformatics Bristol-Myers Squibb, Pennington, New Jersey.
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Melissa A. Haendel
11Department of Medical Informatics and Clinical Epidemiology and OHSU Library, Oregon Health and Science University, Portland, Oregon.
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Els Hermans
22Katholieke Universiteit Leuven, Leuven, Belgium.
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Peter J. Houghton
23Greehey Children's Cancer Research Institute, University of Texas Health Science Center at San Antonio, San Antonio, Texas.
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Jos Jonkers
24The Netherlands Cancer Institute, Amsterdam, the Netherlands.
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Kristel Kemper
24The Netherlands Cancer Institute, Amsterdam, the Netherlands.
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Tin O. Khor
25Institute for Applied Cancer Science, Center for Co-Clinical Trial, The University of Texas MD Anderson Cancer Center, Houston, Texas.
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Michael T. Lewis
26The Lester and Sue Smith Breast Center, Departments of Molecular and Cellular Biology and Radiology, Baylor College of Medicine, Houston, Texas.
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K.C. Kent Lloyd
27Department of Surgery, School of Medicine, and Mouse Biology Program, University of California Davis, Davis, California.
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Jeremy Mason
1European Molecular Biology Laboratory-European Bioinformatics Institute, Hinxton, United Kingdom.
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Enzo Medico
9Candiolo Cancer Institute, FPO-IRCC, Department of Oncology, University of Torino, Torino, Italy.
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Steven B. Neuhauser
8The Jackson Laboratory, Bar Harbor, Maine.
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James M. Olson
28Fred Hutchinson Cancer Research Center, Seattle Children's Hospital, Seattle, Washington.
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Daniel S. Peeper
24The Netherlands Cancer Institute, Amsterdam, the Netherlands.
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Oscar M. Rueda
10Cancer Research UK Cambridge Institute, Cambridge Cancer Centre, University of Cambridge, Cambridge, United Kingdom.
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Je Kyung Seong
29Research Institute for Veterinary Science and Korea Mouse Phenotyping Center, Seoul, Republic of Korea.
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Livio Trusolino
9Candiolo Cancer Institute, FPO-IRCC, Department of Oncology, University of Torino, Torino, Italy.
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Emilie Vinolo
30Seeding Science SAS, Paris, France.
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Robert J. Wechsler-Reya
31Tumor Initiation and Maintenance Program, NCI-Designated Cancer Center, La Jolla, California.
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David M. Weinstock
4Dana-Farber Cancer Institute and Harvard Medical School, Boston, Massachusetts.
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Alana Welm
32Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah.
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S. John Weroha
33Department of Oncology, Mayo Clinic College of Medicine, Rochester, Minnesota.
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Frédéric Amant
24The Netherlands Cancer Institute, Amsterdam, the Netherlands.
34University of Leuven, Leuven, Belgium.
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Stefan M. Pfister
5Hopp-Children's Cancer Center at the NCT Heidelberg (KiTZ), Heidelberg, Germany.
6Division of Pediatric Neuro-oncology, German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK), Heidelberg, Germany.
35Department of Pediatric Hematology and Oncology, Heidelberg University Hospital, Heidelberg, Germany.
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Marcel Kool
5Hopp-Children's Cancer Center at the NCT Heidelberg (KiTZ), Heidelberg, Germany.
6Division of Pediatric Neuro-oncology, German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK), Heidelberg, Germany.
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Helen Parkinson
1European Molecular Biology Laboratory-European Bioinformatics Institute, Hinxton, United Kingdom.
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Atul J. Butte
2Institute for Computational Health Sciences, University of California, San Francisco, California.
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Carol J. Bult
8The Jackson Laboratory, Bar Harbor, Maine.
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DOI: 10.1158/0008-5472.CAN-17-0582 Published November 2017
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Abstract

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.

Introduction

Patient-derived tumor xenograft (PDX) models are created by implanting tumor cells or fragments from patients with cancer into a transplant-compliant mouse host (Supplementary Fig. S1; refs. 1, 2). Human tumors that engraft successfully in host mice are subsequently fragmented and passaged multiple times to generate large cohorts of tumor-bearing mice. PDX models accurately reflect the patient's tumor properties, creating a powerful platform to study the molecular mechanisms of tumor growth and drug resistance as well as serving as patient “avatars” for predicting response to anticancer therapeutic compounds (3–5). The host strains for PDX model development are typically severely immunodeficient; however, “humanized” immune system mice engrafted with human immune cells are increasingly being used in xenograft studies to explore in vivo interactions between the immune system and cancer (6, 7).

Although many academic and commercial sources of PDX models have emerged in recent years, the size of the resources and the processes for creating and characterizing PDX models is quite variable. Crucial information about tumors, host strains, transplant, and quality assurance processes are inconsistently presented in both the scientific literature and in database resources, limiting the ability of researchers to find relevant models and associated data. A standardized data exchange format is needed to foster the ability of researchers to identify appropriate PDX models and to share information about them. As developers of NCI-funded informatics resources, we obtained the internal standards developed by four independent PDX model resources [the EurOPDX consortium (5), the IMODI consortium (France), the Patient-Derived Models Repository at NCI-Frederick, and The Jackson Laboratory PDX Resource (8)]. After comparing standards in use across these resources, we generated a draft PDX-minimal information standard (PDX-MI) that was reviewed and modified by the authors of this report. We propose that the standards described here serve as the starting point for community-wide adoption.

The PDX-MI Standard

The PDX-MI consists of four modules that reflect the process of generating, validating, and using a PDX model: clinical, model creation, model quality assurance, model study, and an additional associated metadata category (Table 1). Within each module, we define “essential” attributes that are required for accurate description and reporting on PDX models and “desirable” attributes that are frequently recorded by PDX producers and should be available.

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Table 1.

The PDX-MI consists of four modules that reflect the process of generating and validating a PDX model: clinical, model creation, model quality assurance, and model study/associated metadata

The clinical module is divided into two submodules: “clinical/patient” and “clinical/tumor.” “Clinical/patient” requires information about the patient from which the engrafted tumor originates, including age, sex, ethnicity, and disease diagnosis. To reduce the possibility of patient identification, PDX-MI recommends grouping ages into 5-year groups, although more granular groupings may be used in cases such as pediatric tumors if approved by a contributor's Institutional Review Board. Reporting on patient consent is considered essential as well. Some attributes of patient treatment history are listed as “desirable” as they can impact the characteristics of resulting PDX models but may be challenging to provide due to patient privacy or data inaccessibility. The “clinical/tumor” submodule reports on information about the originating tumor from which the PDX model is derived and includes tumor classification, anatomic location, and tumor histopathology. The presence or absence of specific diagnostic markers is listed as “essential” for tumor types where testing for such marker(s) is considered the clinical standard of care (e.g., FLT3 genotype in acute myelogenous leukemia). In addition, patient viral infection status has implications for disease biology as well as occupational safety and is included as a desirable field.

The model creation module of PDX-MI captures critical attributes in the creation of a PDX model. Host strain is reported using official strain nomenclature (http://www.informatics.jax.org/mgihome/nomen/index.shtml) as well as strain source and any modifications that “humanize” the host strain through engraftment of human immune-progenitor cells (6). Initial engraftment of the tumor describing processing of the tumor (solid or cell suspension) and the anatomic site of implantation (subcutaneous or orthotopic) is represented. Other model generation characteristics such as engraftment rates and therapeutic response data are considered desirable. A “subline” field indicates when a PDX model is derived from an existing model that has changed characteristics (e.g., loss/gain of a biomarker, change in therapy response).

The model quality assurance module captures information about tissue provenance and fidelity of the passaged tumor with respect to key characteristics of the patient tumor. Validation is required to confirm the PDX tumor is of the appropriate patient and not of murine origin nor consisting primarily of Epstein–Barr virus human B lymphocytic cells as both are frequently observed in PDX model creation (9). Other “desirable” quality assurance methods vary with tumor types and can include histopathology, assessment of human cancer biomarkers by IHC, in situ hybridization, and assessment of gene mutations and rearrangements, DNA methylation, or gene expression profiling. Some producers evaluate how well a PDX model recapitulates the originating tumor's response by measuring PDX tumor growth response to standard-of-care treatment and this is included as a desirable attribute. Additional desirable information includes DNA profiling of serial passages to corroborate lineage fidelity and animal health status from standard health surveillance programs. The current PDX-MI requires evidence of quality assurance but does not require every possible technique be performed as methods vary across resources.

Model study and other associated data

Tumors from PDX often undergo comprehensive genomic characterization and/or treatment in controlled dosing studies to define therapeutic response and resistance. PDX-MI includes “desirable” fields in the reporting of these studies that supplement existing guidelines for reporting on in vivo biomedical research (10). Additional optional metadata are accession IDs from data archives and citation IDs (including digital object identifiers) for publications describing the PDX model(s).

Challenges of Representing Data from PDX Models

Diversity of cancer subtypes

PDX models present unique challenges due to the specific approaches needed for the diversity of cancer subtypes. One challenge is that a subset of PDX models will require reporting on diagnostic biomarkers. For example, in breast cancer, testing for certain pathologic markers (estrogen receptor, progesterone receptor, and human epidermal growth factor 2) is considered the clinical standard of care for prognostic and predictive purposes and should therefore be considered essential for PDX-MI. Another challenge is that tumor grades and disease stages captured in the “clinical module,” which drive patient diagnosis and treatment, may be derived from scoring systems with diagnostic and geographic variation (11). PDX-MI will be flexible and allow users to report the system used clinically rather than enforce a particular one.

Terminology and vocabularies

PDX resources employ a combination of custom and community-developed vocabularies. This presents challenges in data integration, as it takes expert knowledge to map the divergent systems. For example, cancer diagnoses are represented within different PDX resources by terms from the NCI-Thesaurus (12), SNO-MED CT (13), MeSH (14), and the Disease Ontology (15). Free text descriptions are used for many PDX model attributes and a mix of generic, commercial, and chemical labels are used for drugs. Ontology resources and community model organism databases have been developing tools to semiautomate mapping of standards that produce unified indices to facilitate data query and discovery. Rather than impose a limited set of terms to describe a given minimal information attribute, PDX-MI will allow the reporting of a resource's internal standards. We will ensure the quality of standard mappings by facilitating feedback between the PDX producers and the developers of ontology tools.

Implementation and Future Directions

The current version of PDX-MI describes the minimal information needed to report on a PDX models to facilitate data integration and resource sharing. The authors of this report hope PDX-MI will serve as a guide for authors and journal editors in promoting rigorous yet attainable publication standards and as a template for managers of public molecular archives in the capturing of critical metadata required for submission of PDX model data. PDX-MI standards will also be implemented in an online resource being jointly developed by EMBL-EBI and the Jackson Laboratory called PDX Finder, www.pdxfinder.org (see Supplementary Video S1). This resource currently in the prototype phase will provide a comprehensive global catalog of PDX models available for researchers and their associated data across distributed repositories when formally launched at the end of 2017. PDX-MI will be used to validate data submissions from producers of PDX models and from data curated from the literature. PDX-MI will also inform scoring algorithms being developed in the NCI Oncology Models Forum to assess how well PDX models recapitulate hallmarks of human cancers.

Supplementary Video S1

A video describing PDX mice, minimal information, and why minimal information is needed in the PDX research field. Supplementary Video S1

Future versions of PDX-MI will capture additional details as procedures become more standardized. Input from clinical and translational professional societies will inform evolving requirements for diagnostic markers on a disease-specific basis. Given the recent success of immune checkpoint inhibitors in the treatment of cancer, improving “humanized immune system” PDX models is an area of intense research and PDX-MI will evolve to represent this. Other aspects of PDX models that are rapidly changing include improved surgical techniques and quality assurance methods. As we develop resources to capture and disseminate data related to PDX models, we will continue to improve and version PDX-MI to reflect the state of the art in the field. A web-based form to allow feedback from the community about the standard described here can be accessed at the Mouse Tumor Biology database website (http://tumor.informatics.jax.org). As has been demonstrated across multiple disciplines, a minimal standard adopted by a research community accelerates the rate of scientific discovery while reducing unnecessary duplication.

Disclosure of Potential Conflicts of Interest

S. Ferretti is a laboratory head at Novartis. M.T. Lewis is a manager and limited partner at StemMed Holdings. E. Vinolo is a consultant/advisory board member for EurOPDX Consortium and XenTech. D.M. Weinstock reports receiving a commercial research grant from Novartis, AstraZeneca, Abbvie, Aileron, Roche, and Novartis, has ownership interest (including patents) in Travera, and has provided expert testimony for Monsanto. S.J. Weroha reports receiving a commercial research grant from Novartis, Genentech, and Tesaro and has ownership interest (including patents) in Mayo Clinic Ventures. A.J. Butte is a cofounder and scientific advisor at Personalis, Inc. and NuMedii, Inc., reports receiving a commercial research grant from Progenity, Inc., and has ownership interest (including patents) in NuMedii, Inc. No potential conflicts of interest were disclosed by the other authors.

Authors' Contributions

Conception and design: T.F. Meehan, T. Goldstein, G. Inghirami, S. Brabetz, Z. Gu, D.A. Begley, D.M. Krupke, C. Caldas, D.A. Clark, J.R. Dry, Y.A. Evrard, N.C. Goodwin, D. Greenawalt, E. Hermans, P.J. Houghton, T.O. Khor, J.M. Olson, J.K. Seong, R.J. Wechsler-Reya, S.J. Weroha, A.J. Butte, H. Parkinson, C.J. Bult

Development of methodology: T.F. Meehan, T. Goldstein, J.A. Wiser, P. Dunn, D.A. Begley, D.M. Krupke, J.H. Doroshow, N.C. Goodwin, D. Greenawalt, M.A. Haendel, E. Hermans, P.J. Houghton, T.O. Khor, M.T. Lewis, J.M. Olson, L. Trusolino, C.J. Bult

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): T.F. Meehan, N. Conte, D.A. Begley, Y.A. Evrard, E. Hermans, J. Jonkers, J.M. Olson, L. Trusolino, E. Vinolo, F. Amant, C.J. Bult

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): N. Conte, T. Goldstein, G. Inghirami, D.A. Begley, D.M. Krupke, H. Dowst, J.H. Doroshow, J.M. Olson, O.M. Rueda, J.K. Seong

Writing, review, and/or revision of the manuscript: T.F. Meehan, N. Conte, T. Goldstein, G. Inghirami, M.A. Murakami, S. Brabetz, Z. Gu, P. Dunn, D.A. Begley, D.M. Krupke, A. Bertotti, A. Bruna, M.H. Brush, A.T. Byrne, C. Caldas, A.L. Christie, D.A. Clark, J.R. Dry, O. Duchamp, Y.A. Evrard, S. Ferretti, K.K. Frese, N.C. Goodwin, D. Greenawalt, M.A. Haendel, E. Hermans, P.J. Houghton, K. Kemper, M.T. Lewis, K.C.K. Lloyd, E. Medico, S.B. Neuhauser, D.S. Peeper, J.K. Seong, E. Vinolo, R.J. Wechsler-Reya, D.M. Weinstock, A. Welm, S.J. Weroha, F. Amant, S.M. Pfister, M. Kool, A.J. Butte, C.J. Bult

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): T.F. Meehan, N. Conte, M.A. Murakami, P. Dunn, D.A. Begley, D.M. Krupke, C. Caldas, O. Duchamp, K.C.K. Lloyd, J. Mason, S.B. Neuhauser, S.M. Pfister, C.J. Bult

Study supervision: N. Conte, T.O. Khor, H. Parkinson

Other (management of industry stakeholders): D.A. Clark

Grant Support

This project has been funded in whole or in part with federal funds from the National Cancer Institute, NIH: R01CA089713D (to C.J. Bult), P30CA034196 (to C.J. Bult), U24CA204781 (to H. Parkinson), U24CA195858 (to A.J. Butte), contract no. HHSN261200800001E (to J.H. Doroshow, Y.A. Evrard), R24OD011883 (M.H. Brush, M.A. Haendel), the Leukemia & Lymphoma Society SCOR grant (2015, 2016; to G. Inghirami), and the Science Foundation Ireland grants 13/CDA/2183 and 15/TIDA/2963 and the Irish Cancer Society Collaborative Cancer Research Centre BREAST-PREDICT Grant CCRC13GAL (A.T. Byrne). G. Inghirami, A. Bertotti, A. Bruna, A.T. Byrne, C. Caldas, E. Hermans, J. Jonkers, K. Kemper, E. Medico, D.S. Peeper, O.M. Rueda, L. Trusolino, and F. Amant are members of the EurOPDX Consortium.

Footnotes

  • Note: Supplementary data for this article are available at Cancer Research Online (http://cancerres.aacrjournals.org/).

  • Received February 27, 2017.
  • Revision received April 20, 2017.
  • Accepted August 25, 2017.
  • ©2017 American Association for Cancer Research.

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Cancer Research: 77 (21)
November 2017
Volume 77, Issue 21
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PDX-MI: Minimal Information for Patient-Derived Tumor Xenograft Models
Terrence F. Meehan, Nathalie Conte, Theodore Goldstein, Giorgio Inghirami, Mark A. Murakami, Sebastian Brabetz, Zhiping Gu, Jeffrey A. Wiser, Patrick Dunn, Dale A. Begley, Debra M. Krupke, Andrea Bertotti, Alejandra Bruna, Matthew H. Brush, Annette T. Byrne, Carlos Caldas, Amanda L. Christie, Dominic A. Clark, Heidi Dowst, Jonathan R. Dry, James H. Doroshow, Olivier Duchamp, Yvonne A. Evrard, Stephane Ferretti, Kristopher K. Frese, Neal C. Goodwin, Danielle Greenawalt, Melissa A. Haendel, Els Hermans, Peter J. Houghton, Jos Jonkers, Kristel Kemper, Tin O. Khor, Michael T. Lewis, K.C. Kent Lloyd, Jeremy Mason, Enzo Medico, Steven B. Neuhauser, James M. Olson, Daniel S. Peeper, Oscar M. Rueda, Je Kyung Seong, Livio Trusolino, Emilie Vinolo, Robert J. Wechsler-Reya, David M. Weinstock, Alana Welm, S. John Weroha, Frédéric Amant, Stefan M. Pfister, Marcel Kool, Helen Parkinson, Atul J. Butte and Carol J. Bult
Cancer Res November 1 2017 (77) (21) e62-e66; DOI: 10.1158/0008-5472.CAN-17-0582

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PDX-MI: Minimal Information for Patient-Derived Tumor Xenograft Models
Terrence F. Meehan, Nathalie Conte, Theodore Goldstein, Giorgio Inghirami, Mark A. Murakami, Sebastian Brabetz, Zhiping Gu, Jeffrey A. Wiser, Patrick Dunn, Dale A. Begley, Debra M. Krupke, Andrea Bertotti, Alejandra Bruna, Matthew H. Brush, Annette T. Byrne, Carlos Caldas, Amanda L. Christie, Dominic A. Clark, Heidi Dowst, Jonathan R. Dry, James H. Doroshow, Olivier Duchamp, Yvonne A. Evrard, Stephane Ferretti, Kristopher K. Frese, Neal C. Goodwin, Danielle Greenawalt, Melissa A. Haendel, Els Hermans, Peter J. Houghton, Jos Jonkers, Kristel Kemper, Tin O. Khor, Michael T. Lewis, K.C. Kent Lloyd, Jeremy Mason, Enzo Medico, Steven B. Neuhauser, James M. Olson, Daniel S. Peeper, Oscar M. Rueda, Je Kyung Seong, Livio Trusolino, Emilie Vinolo, Robert J. Wechsler-Reya, David M. Weinstock, Alana Welm, S. John Weroha, Frédéric Amant, Stefan M. Pfister, Marcel Kool, Helen Parkinson, Atul J. Butte and Carol J. Bult
Cancer Res November 1 2017 (77) (21) e62-e66; DOI: 10.1158/0008-5472.CAN-17-0582
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