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Review

Patient-Derived Xenograft Models: An Emerging Platform for Translational Cancer Research

Manuel Hidalgo, Frederic Amant, Andrew V. Biankin, Eva Budinská, Annette T. Byrne, Carlos Caldas, Robert B. Clarke, Steven de Jong, Jos Jonkers, Gunhild Mari Mælandsmo, Sergio Roman-Roman, Joan Seoane, Livio Trusolino and Alberto Villanueva
Manuel Hidalgo
1Centro Nacional de Investigaciones Oncológicas, Madrid;
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  • For correspondence: mhidalgo@cnio.es
Frederic Amant
4Katholieke Universiteit Leuven, Leuven, Belgium;
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Andrew V. Biankin
5Wolfson Wohl Cancer Research Centre, Institute of Cancer Sciences, University of Glasgow, Glasgow;
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Eva Budinská
8Masaryk University, Brno, Czech Republic;
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Annette T. Byrne
9Royal College of Surgeons in Ireland, Dublin, Ireland;
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Carlos Caldas
6Cambridge Cancer Centre, Cambridge;
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Robert B. Clarke
7Breakthrough Breast Cancer Unit, Institute of Cancer Sciences, University of Manchester, Manchester, United Kingdom;
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Steven de Jong
10Department of Medical Oncology, University of Groningen, University Medical Centre Groningen, Groningen;
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Jos Jonkers
11The Netherlands Cancer Institute, Amsterdam, the Netherlands;
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Gunhild Mari Mælandsmo
12Oslo University Hospital, Oslo, Norway;
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Sergio Roman-Roman
13Institut Curie, Paris, France;
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Joan Seoane
2Vall d'Hebron Institute of Oncology;
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Livio Trusolino
14Candiolo Cancer Institute – FPO IRCCS; and
15Department of Oncology, University of Torino, Candiolo, Torino, Italy
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Alberto Villanueva
3Catalan Institute of Oncology-Bellvitge Biomedical Research Institute, L'Hospitalet de Llobregat, Barcelona, Spain;
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DOI: 10.1158/2159-8290.CD-14-0001 Published September 2014
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  • Figure 1.
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    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 on the basis of tumor types or on predefined molecular subtypes if that information is known and is 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 on the basis of 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 the 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. PD, pharmacodynamic.

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    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 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. D1, day 1.

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    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 Cancer Cell Line Encyclopedia (CCLE) or the NCI-60 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.

Tables

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  • Table 1.

    Key methodologic aspects of selected PDX collections

    ReferenceTumor typeAvailable modelsOriginProcurementProcessingMice strainImplantation siteEngraftment rate
    (28)CRC130MetastasisSurgeryFresh tumor pieces in MatrigelNOD/SCIDs.c.87%
    (29)CRC54Primary (35)SurgeryFresh tumor piecesNudes.c.64%
    Metastasis (19)
    (76)CRC41PrimarySurgeryFresh tumor piecesNudeOrthotopic89.1%
    (34)HBC25PrimarySurgeryFresh tumor piecesNudes.c.13%
    (30)HBC12Primary (4)Surgery/fluid drainageFresh tumor pieces in MatrigelNOD/SCID with estrogen supplementation for ER+ tumorsMammary fat pad27%
    Metastasis (8)
    (16)HBC24PrimaryBiopsies/surgery/fluid drainageFresh tumor piecesSCID/Beige and NSG w/wo estrogen and immortalized human fibro-blastsMammary fat pad3%–21%
    (21)NSCLC25PrimarySurgeryFresh tumor piecesNOD/SCIDs.c.25%
    (20, 22)NSCLC32PrimarySurgeryFresh tumor piecesNOD/SCIDRenal capsule90%
    (33)PDAC42PrimarySurgeryFresh tumor pieces in MatrigelNudes.c.61%
    (26)PDAC14PrimarySurgeryFresh tumor pieces in MatrigelNudes.c.NR
    (77)PDAC16Primary (11)SurgeryFresh tumor piecesNudeOrthotopic62%
    Metastasis (5)
    (78)SCCHN22PrimaryBiopsy/surgeryFresh tumor pieces in MatrigelNSGs.c.85%
    (25)SCCHN/SCC21PrimarySurgeryFresh tumor pieces in MatrigelNudes.c.54%
    FOM/FOT
    (14)Uveal Melanoma25Primary (73)SurgeryFresh tumor piecesNOD/SCIDs.c.28%
    Metastasis (17)

    NOTE: This table provides a summary of the methodologic approaches used to generate the most comprehensive PDX collections currently available.

    Abbreviations: CRC, colorectal cancer; FOM, floor of the mouth; FOT, floor of the tongue; HBC, human breast cancer; NR, not reported; PDAC, pancreatic ductal adenocarcinoma; RCC, renal cell cancer; s.c., subcutaneous implantation; SCC, squamous cell carcinoma; SCCHN, squamous cell carcinoma of the head and neck.

    • Table 2.

      Fidelity and stability of PDX models

      ReferenceTumor typeOriginal tumor-first passageSubsequent passages
      (28)CRCConserved histopathology characteristics between donor and PDX models.Stable CNA across passages.
      Similarities in CNA between donor and PDX models.
      Consistent KRAS, NRAS, BRAF, and PI3K mutation status.
      (29)CRCUnsupervised clustering analysis using aCGH and GE shows that the donor tumors and PDX clustered together.Stable aCGH and GE profile across passages.
      203 differentially expressed annotated genes correspond to stroma-related genes and pathways.
      (31, 34)HBCConserved IHC expression of ER, PR, and HER2.Stable CNA and GE profile across passages.
      Analysis of CNA showed 14/18 paired tumors–PDX shared more than 56% CNA.Variations in stromal related genes.
      16/18 paired tumors–PDX clustered together in unsupervised hierarchical analysis.
      PDX showed losses in 176 and gains in 202 chromosome regions compared with primary tumors.
      Stable GE profile with less than 5% variations.
      (30)HBCConserved histopathology characteristics between donor and PDX models.Stable IHC profile over time.
      Conserved IHC expression for CK, E-cadherin, β-catenin, vimentin, ER, PR, and HER2.
      Unsupervised clustering analysis using GE shows that donor tumors and PDX clustered together.
      Maintenance in the pattern of CNA.
      Intrinsic breast cancer subtypes concordant between the donor tumors and PDX.
      (16)HBCConserved histopathology characteristics between donor and PDX models.Stable histopathologic and IHC expression.
      Conserved IHC expression for CK, p53, Ki67, ER, PR, HER2, and EGFR.Stable GE, RPPA, and SNP across passages.
      Intrinsic breast cancer subtypes represented in PDX models.
      (21)NSCLCConserved histopathologic characteristics between donor and PDX models.
      Conserved IHC expression of Ki67 and EpCAM.
      Unsupervised clustering analysis using GE shows the donor tumors and PDX clustered together with correlation coefficient ranging from 0.78 to 0.94.
      134 differentially expressed genes correspond to cell adhesion and immune response genes and pathways.
      (26, 33)PDACConcordance in mutations in KRAS and DPC4.Concordance in gemcitabine response between F3 and F6.
      Conserved GE profile (R2 = 0.69).Enrichment in angiogenesis gene signature in F5.
      (25)SCC/SCCHNConserved histopathologic characteristics between donor and PDX models.High correlation (R2 ∼ 0.94) in GE from F2-F4.
      High correlation (R2 = 0.91) in EGFR expression.Concordance in cetuximab response between F2 and F4.
      High correlation (R2 ∼ 0.8) in GE.
      Variation in immune-related pathways.
      (15)RCCConserved histopathologic characteristics.Conserved histopathologic characteristics.
      Donor and PDX models cluster together in unsupervised hierarchical clustering analysis using GE.Serial passages clustered together in unsupervised hierarchical clustering analysis.
      PDX retained CNA from the donor tumor.Maintains CNA of the donor tumor.
      Similar mutation landscape in NGS studies.

      NOTE: This table summarizes the data from different studies in which PDX models have been compared with donor tumors using a variety of methods.

      Abbreviations: aCGH, comparative genomic hybridization array; CRC, colorectal cancer; GE, gene expression; HBC, human breast cancer; IHC, immunohistochemistry; NGS, next-generation sequencing, PR, progesterone receptor; RCC, renal cell cancer; RPPA, reverse phase protein array; SNP, single-nucleotide polymorphism; SCC, squamous cell carcinoma.

      • Table 3.

        Studies correlating PDX treatment results with clinical data

        Tumor type (reference)Definition activityStandard agentnRR (%)Clinical correlates
        CRCTR > 50%Cetuximab4710N/A
        CRC KRAS WT (28)Cetuximab3817
        CRC (29)T/C < 10%5-Fluorouracil5213N/A
        Oxaliplatin520
        Irinotecan4938
        Cetuximab5226
        HBC (34)Complete responseAC1776Response to treatment in the PDX model was concordant with clinical data in 5/7 patients.
        TGI > 50% or T/C GD > 2-foldDocetaxel1747
        Trastuzumab250
        GnRH antagonist1100
        HBC (16)RR > 30%Docetaxel71492% correlation between clinical responses and responses in PDX
        Doxorubicin40
        Trastuzumab–lapatinib1100
        NSCLC (20)Statistically significant decrement in tumor area in treated vs. control tumorsCisplatin–vinorelbine3228PDX models from 6/7 patients with early recurrent disease were resistant to the clinically used regimen.
        Cisplatin–docetaxel1942
        Cisplatin–gemcitabine1644
        NSCLC (21)T/C < 5%Etoposide254N/A
        Carboplatin2512
        Gemcitabine2512
        Paclitaxel2516
        Vinorelbine110
        Cetuximab2512
        Erlotinib251
        SCCHN (25)T/C < 20%Cetuximab119%N/A
        RCC (15)Statistically significant differences in TGISunitinib8ActiveN/A
        SirolimusActive
        ErlotinibInactive
        PDAC (26)T/C < 20%Gemcitabine1436N/A
        Erlotinib0
        Temsirolimus0
        PDAC (33)TGI > 85%Gemcitabine2317%Response to gemcitabine in the PDX model predicted longer time to progression in patients.

        NOTE: This table provides a summary of studies in which PDX models from different cancer types have been treated with agents used in the clinical care of these patients.

        Abbreviations: AC, adriamycin–cyclophosphamide; CRC, colorectal cancer; GD, growth delay; GnRH, gonadotrophin-releasing hormone; HBC, human breast cancer; N/A, not available; RR, response rate; TGI, tumor growth inhibition; TR, tumor regression; T/C, treated divided by control; WT, wild-type.

        Additional Files

        • Figures
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        • Supplementary Figure 1

          Files in this Data Supplement:

          • Supplementary Figure 1 - Genomics Mimicry for Personalized Cancer Treatment.
          • Supplementary Tables 1 - 2 - Supplementary Table 1: Mouse Strains Used to Develop PDX Models. Supplementary Table 2: Collection of PDX Models Available by the EuroPDX Consortium.
          • Supplementary Figure and Table Legends - Supplementary Figure and Tables Legends.
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        Cancer Discovery: 4 (9)
        September 2014
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        Patient-Derived Xenograft Models: An Emerging Platform for Translational Cancer Research
        Manuel Hidalgo, Frederic Amant, Andrew V. Biankin, Eva Budinská, Annette T. Byrne, Carlos Caldas, Robert B. Clarke, Steven de Jong, Jos Jonkers, Gunhild Mari Mælandsmo, Sergio Roman-Roman, Joan Seoane, Livio Trusolino and Alberto Villanueva
        Cancer Discov September 1 2014 (4) (9) 998-1013; DOI: 10.1158/2159-8290.CD-14-0001

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        Patient-Derived Xenograft Models: An Emerging Platform for Translational Cancer Research
        Manuel Hidalgo, Frederic Amant, Andrew V. Biankin, Eva Budinská, Annette T. Byrne, Carlos Caldas, Robert B. Clarke, Steven de Jong, Jos Jonkers, Gunhild Mari Mælandsmo, Sergio Roman-Roman, Joan Seoane, Livio Trusolino and Alberto Villanueva
        Cancer Discov September 1 2014 (4) (9) 998-1013; DOI: 10.1158/2159-8290.CD-14-0001
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