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Review
. 2014 Sep;4(9):998-1013.
doi: 10.1158/2159-8290.CD-14-0001. Epub 2014 Jul 15.

Patient-derived xenograft models: an emerging platform for translational cancer research

Manuel Hidalgo  1 , Frederic Amant  2 , Andrew V Biankin  3 , Eva Budinská  4 , Annette T Byrne  5 , Carlos Caldas  6 , Robert B Clarke  7 , Steven de Jong  8 , Jos Jonkers  9 , Gunhild Mari Mælandsmo  10 , Sergio Roman-Roman  11 , Joan Seoane  12 , Livio Trusolino  13 , Alberto Villanueva  14
Affiliations
Review

"VSports" Patient-derived xenograft models: an emerging platform for translational cancer research

Manuel Hidalgo et al. Cancer Discov. 2014 Sep.

Abstract (V体育平台登录)

Recently, there has been an increasing interest in the development and characterization of patient-derived tumor xenograft (PDX) models for cancer research. PDX models mostly retain the principal histologic and genetic characteristics of their donor tumor and remain stable across passages. These models have been shown to be predictive of clinical outcomes and are being used for preclinical drug evaluation, biomarker identification, biologic studies, and personalized medicine strategies. This article summarizes the current state of the art in this field, including methodologic issues, available collections, practical applications, challenges and shortcomings, and future directions, and introduces a European consortium of PDX models. VSports手机版.

Significance: PDX models are increasingly used in translational cancer research. These models are useful for drug screening, biomarker development, and the preclinical evaluation of personalized medicine strategies. This review provides a timely overview of the key characteristics of PDX models and a detailed discussion of future directions in the field. V体育安卓版.

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Figures

Figure 1
Figure 1. Proposed Preclinical Screening and Biomarker Study in PDX models
This figure graphically illustrates some of the key elements of a preclinical study in PDX models. These studies are likely to be more informative late in preclinical development or in parallel to phase I safety and pharmacology testing. Models can be selected based on tumor types or on predefined molecular subtypes if that information is known and of interest, or both. We propose a two-step approach. In Step 1, a limited number of models can be tested with the agent at doses and schedules known to be effective and pharmacologically active in earlier preclinical studies. Study endpoints need to be carefully selected based on the agent’s mechanism of action. Data from Step 1 can be used to proceed to Step 2 and to redefine model selection based on molecular understanding of responsive models. In Step 2, a larger repertoire of models can be treated. At the conclusion of the study a decision needs to be made to proceed to clinical development and prioritize biomarkers to be explored in the clinical phase.
Figure 2
Figure 2. Co-clinical trial approach with PDX models
A new version of the co-clinical trial concept is presented in which a PDX model is developed from a patient enrolled and treated in a clinical trial with a novel agent. This approach permits to have models with validated clinical outcome data that can be used to interrogate mechanisms of response and resistance as well as strategies to increase response and overcome resistance, for example, combination strategies.
Figure 3
Figure 3. Personalized medicine strategy
Depicted in this figure is a strategy for individualizing medicine that integrates genomic analysis of a patient tumor with testing in Avatar mouse models. The genomic analysis of a patient tumor is likely to show tens of potential therapeutically targetable mutations. Mining of genomic-drug response databases such as the CCLE or the NCI60 as well as knowledge of these mutations is likely to result in several potential therapeutic regimens for a given patient. The Avatar model can be used to test and rank these potential treatments to be administered to the patient. A post hoc analysis of this information can be added to existing data to further feed into the existing databases.
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