Opinion | Published:

Interrogating open issues in cancer precision medicine with patient-derived xenografts

Nature Reviews Cancer volume 17, pages 254268 (2017) | Download Citation

  • An Erratum to this article was published on 15 September 2017

This article has been updated

Abstract

Patient-derived xenografts (PDXs) have emerged as an important platform to elucidate new treatments and biomarkers in oncology. PDX models are used to address clinically relevant questions, including the contribution of tumour heterogeneity to therapeutic responsiveness, the patterns of cancer evolutionary dynamics during tumour progression and under drug pressure, and the mechanisms of resistance to treatment. The ability of PDX models to predict clinical outcomes is being improved through mouse humanization strategies and the implementation of co-clinical trials, within which patients and PDXs reciprocally inform therapeutic decisions. This Opinion article discusses aspects of PDX modelling that are relevant to these questions and highlights the merits of shared PDX resources to advance cancer medicine from the perspective of EurOPDX, an international initiative devoted to PDX-based research.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Change history

  • 15 September 2017

    In the online html version of this article, Joan Seoane's affiliations were not correct. He is also a member of the EurOPDX Consortium and is at the Vall d'Hebron Institute of Oncology, 08035 Barcelona, the Universitat Autònoma de Barcelona, 08193 Bellaterra, and the Institució Catalana de Recerca i Estudis Avançats (ICREA), 08010 Barcelona, Spain. This is correct in the print and PDF versions of the article.

References

  1. 1.

    & Translating cancer research into targeted therapeutics. Nature 467, 543–549 (2010).

  2. 2.

    et al. A primary xenograft model of small-cell lung cancer reveals irreversible changes in gene expression imposed by culture in vitro. Cancer Res. 69, 3364–3373 (2009).

  3. 3.

    Trial watch: phase II failures: 2008–2010. Nat. Rev. Drug Discov. 10, 328–329 (2011).

  4. 4.

    & Trial watch: phase II and phase III attrition rates 2011–2012. Nat. Rev. Drug Discov. 12, 569 (2013).

  5. 5.

    et al. How to improve R&D productivity: the pharmaceutical industry's grand challenge. Nat. Rev. Drug Discov. 9, 203–214 (2010).

  6. 6.

    et al. A molecularly annotated platform of patient-derived xenografts (“xenopatients”) identifies HER2 as an effective therapeutic target in cetuximab-resistant colorectal cancer. Cancer Discov. 1, 508–523 (2011).

  7. 7.

    et al. The genomic landscape of response to EGFR blockade in colorectal cancer. Nature 526, 263–267 (2015).

  8. 8.

    et al. Tumor grafts derived from women with breast cancer authentically reflect tumor pathology, growth, metastasis and disease outcomes. Nat. Med. 17, 1514–1520 (2011).

  9. 9.

    et al. High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response. Nat. Med. 21, 1318–1325 (2015).

  10. 10.

    et al. Patient-derived xenograft models: an emerging platform for translational cancer research. Cancer Discov. 4, 998–1013 (2014).

  11. 11.

    & Patient-derived tumor xenografts: transforming clinical samples into mouse models. Cancer Res. 73, 5315–5319 (2013).

  12. 12.

    et al. Patient-derived tumour xenografts as models for oncology drug development. Nat. Rev. Clin. Oncol. 9, 338–350 (2012).

  13. 13.

    , & Preclinical mouse cancer models: a maze of opportunities and challenges. Cell 163, 39–53 (2015).

  14. 14.

    & Tumorigenesis: it takes a village. Nat. Rev. Cancer 15, 473–483 (2015).

  15. 15.

    & The implications of clonal genome evolution for cancer medicine. N. Engl. J. Med. 368, 842–851 (2013).

  16. 16.

    et al. Inference of tumor evolution during chemotherapy by computational modeling and in situ analysis of genetic and phenotypic cellular diversity. Cell Rep. 6, 514–527 (2014).

  17. 17.

    & Evolution of the cancer stem cell model. Cell Stem Cell 14, 275–291 (2014).

  18. 18.

    et al. Variable clonal repopulation dynamics influence chemotherapy response in colorectal cancer. Science 339, 543–548 (2013).

  19. 19.

    , & Intra-tumour heterogeneity: a looking glass for cancer? Nat. Rev. Cancer 12, 323–334 (2012).

  20. 20.

    et al. Genetic clonal diversity predicts progression to esophageal adenocarcinoma. Nat. Genet. 38, 468–473 (2006).

  21. 21.

    et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 486, 346–352 (2012).

  22. 22.

    , , & A new genome-driven integrated classification of breast cancer and its implications. EMBO J. 32, 617–628 (2013).

  23. 23.

    et al. The clonal and mutational evolution spectrum of primary triple-negative breast cancers. Nature 486, 395–399 (2012).

  24. 24.

    et al. Dynamics of genomic clones in breast cancer patient xenografts at single-cell resolution. Nature 518, 422–426 (2015).

  25. 25.

    et al. The life history of 21 breast cancers. Cell 149, 994–1007 (2012).

  26. 26.

    et al. Studying clonal dynamics in response to cancer therapy using high-complexity barcoding. Nat. Med. 21, 440–448 (2015).

  27. 27.

    et al. Emergence of constitutively active estrogen receptor-α mutations in pretreated advanced estrogen receptor-positive breast cancer. Clin. Cancer Res. 20, 1757–1767 (2014).

  28. 28.

    et al. Multifocal clonal evolution characterized using circulating tumour DNA in a case of metastatic breast cancer. Nat. Commun. 6, 8760 (2015).

  29. 29.

    et al. A biobank of breast cancer explants with preserved intra-tumor heterogeneity to screen anticancer compounds. Cell 167, 1–15 (2016).

  30. 30.

    et al. A new model of patient tumor-derived breast cancer xenografts for preclinical assays. Clin. Cancer Res. 13, 3989–3998 (2007).

  31. 31.

    et al. Endocrine-therapy-resistant ESR1 variants revealed by genomic characterization of breast-cancer-derived xenografts. Cell Rep. 4, 1116–1130 (2013).

  32. 32.

    , & Maintaining tumor heterogeneity in patient-derived tumor xenografts. Cancer Res. 75, 2963–2968 (2015).

  33. 33.

    et al. Acquired resistance to endocrine treatments is associated with tumor-specific molecular changes in patient-derived luminal breast cancer xenografts. Clin. Cancer Res. 20, 4314–4325 (2014).

  34. 34.

    et al. Mechanisms of therapy resistance in patient-derived xenograft models of BRCA1-deficient breast cancer. J. Natl Cancer Inst. 108, djw148 (2016).

  35. 35.

    et al. Intra- and inter-tumor heterogeneity in a vemurafenib-resistant melanoma patient and derived xenografts. EMBO Mol. Med. 7, 1104–1118 (2015).

  36. 36.

    et al. Acquired resistance and clonal evolution in melanoma during BRAF inhibitor therapy. Cancer Discov. 4, 80–93 (2014).

  37. 37.

    et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016).

  38. 38.

    et al. BRAFV600E kinase domain duplication identified in therapy-refractory melanoma patient-derived xenografts. Cell Rep. 16, 263–277 (2016).

  39. 39.

    et al. DNA barcoding reveals diverse growth kinetics of human breast tumour subclones in serially passaged xenografts. Nat. Commun. 5, 5871 (2014).

  40. 40.

    , & Biology, detection, and clinical implications of circulating tumor cells. EMBO Mol. Med. 7, 1–11 (2015).

  41. 41.

    & Metastatic colonization by circulating tumour cells. Nature 529, 298–306 (2016).

  42. 42.

    et al. A cell initiating human acute myeloid leukaemia after transplantation into SCID mice. Nature 367, 645–648 (1994).

  43. 43.

    et al. Biological and molecular heterogeneity of breast cancers correlates with their cancer stem cell content. Cell 140, 62–73 (2010).

  44. 44.

    , , & Stem cells, cancer, and cancer stem cells. Nature 414, 105–111 (2001).

  45. 45.

    , & Identification of human pancreatic cancer stem cells. Methods Mol. Biol. 568, 161–173 (2009).

  46. 46.

    et al. Single-cell analysis reveals a stem-cell program in human metastatic breast cancer cells. Nature 526, 131–135 (2015).

  47. 47.

    et al. Intrinsic resistance of tumorigenic breast cancer cells to chemotherapy. J. Natl Cancer Inst. 100, 672–679 (2008).

  48. 48.

    et al. Colon cancer stem cells dictate tumor growth and resist cell death by production of interleukin-4. Cell Stem Cell 1, 389–402 (2007).

  49. 49.

    , , , & Prospective identification of tumorigenic breast cancer cells. Proc. Natl Acad. Sci. USA 100, 3983–3988 (2003).

  50. 50.

    et al. The requirement for freshly isolated human colorectal cancer (CRC) cells in isolating CRC stem cells. Br. J. Cancer 112, 539–546 (2015).

  51. 51.

    , , & Cancer stem cell niche: the place to be. Cancer Res. 71, 634–639 (2011).

  52. 52.

    et al. ALDH1-positive cancer stem cells predict engraftment of primary breast tumors and are governed by a common stem cell program. Cancer Res. 73, 7290–7300 (2013).

  53. 53.

    et al. Intracellular autofluorescence: a biomarker for epithelial cancer stem cells. Nat. Methods 11, 1161–1169 (2014).

  54. 54.

    et al. Microenvironmental hCAP-18/LL-37 promotes pancreatic ductal adenocarcinoma by activating its cancer stem cell compartment. Gut 64, 1921–1935 (2015).

  55. 55.

    , & Cancer stem cells: impact, heterogeneity, and uncertainty. Cancer Cell 21, 283–296 (2012).

  56. 56.

    et al. Selective induction of chemotherapy resistance of mammary tumors in a conditional mouse model for hereditary breast cancer. Proc. Natl Acad. Sci. USA 104, 12117–12122 (2007).

  57. 57.

    et al. Sunitinib inhibits tumor growth and synergizes with cisplatin in orthotopic models of cisplatin-sensitive and cisplatin-resistant human testicular germ cell tumors. Clin. Cancer Res. 15, 3384–3395 (2009).

  58. 58.

    et al. The PDGFRβ–AKT pathway contributes to CDDP-acquired resistance in testicular germ cell tumors. Clin. Cancer Res. 20, 658–667 (2014).

  59. 59.

    et al. Anti-estrogen resistance in human breast tumors is driven by JAG1-NOTCH4-dependent cancer stem cell activity. Cell Rep. 12, 1968–1977 (2015).

  60. 60.

    et al. Early adaptation and acquired resistance to CDK4/6 inhibition in estrogen receptor-positive breast cancer. Cancer Res. 76, 2301–2313 (2016).

  61. 61.

    et al. Single-cell mRNA sequencing identifies subclonal heterogeneity in anti-cancer drug responses of lung adenocarcinoma cells. Genome Biol. 16, 127 (2015).

  62. 62.

    et al. Modeling of response to endocrine therapy in a panel of human luminal breast cancer xenografts. Breast Cancer Res. Treat. 133, 595–606 (2012).

  63. 63.

    et al. A renewable tissue resource of phenotypically stable, biologically and ethnically diverse, patient-derived human breast cancer xenograft models. Cancer Res. 73, 4885–4897 (2013).

  64. 64.

    et al. Modelling vemurafenib resistance in melanoma reveals a strategy to forestall drug resistance. Nature 494, 251–255 (2013).

  65. 65.

    et al. Reversible and adaptive resistance to BRAFV600E inhibition in melanoma. Nature 508, 118–122 (2014).

  66. 66.

    et al. Clinical utility of patient-derived xenografts to determine biomarkers of prognosis and map resistance pathways in EGFR-mutant lung adenocarcinoma. J. Clin. Oncol. 33, 2472–2480 (2015).

  67. 67.

    et al. Patient-derived xenografts for individualized care in advanced sarcoma. Cancer 120, 2006–2015 (2014).

  68. 68.

    et al. Molecular profiling of the residual disease of triple-negative breast cancers after neoadjuvant chemotherapy identifies actionable therapeutic targets. Cancer Discov. 4, 232–245 (2014).

  69. 69.

    et al. Effect of cellular senescence on the growth of HER2-positive breast cancers. J. Natl Cancer Inst. 107, djv020 (2015).

  70. 70.

    , & Human-SCID mouse chimeric models for the evaluation of anti-cancer therapies. Trends Immunol. 22, 386–393 (2001).

  71. 71.

    et al. Origin of the vasculature supporting growth of primary patient tumor xenografts. J. Transl Med. 11, 110 (2013).

  72. 72.

    , & Cancer immunoediting: integrating immunity's roles in cancer suppression and promotion. Science 331, 1565–1570 (2011).

  73. 73.

    et al. Human regulatory T cells do not suppress the antitumor immunity in the bone marrow: a role for bone marrow stromal cells in neutralizing regulatory T cells. Clin. Cancer Res. 19, 1467–1475 (2013).

  74. 74.

    et al. Human peripheral blood leucocyte non-obese diabetic-severe combined immunodeficiency interleukin-2 receptor gamma chain gene mouse model of xenogeneic graft-versus-host-like disease and the role of host major histocompatibility complex. Clin. Exp. Immunol. 157, 104–118 (2009).

  75. 75.

    , , , & Concise review: humanized models of tumor immunology in the 21st century: convergence of cancer research and tissue engineering. Stem Cells 33, 1696–1704 (2015).

  76. 76.

    , & Engineering humanized mice for improved hematopoietic reconstitution. Cell. Mol. Immunol. 9, 215–224 (2012).

  77. 77.

    , , & Novel humanized bone marrow niche xenotransplantation model allows superior engraftment of human normal and malignant hematopoietic cells and reveals myelofibrosis-initiating cells in the HSC compartment. Blood 124, 349 (2014).

  78. 78.

    et al. Human hemato-lymphoid system mice: current use and future potential for medicine. Annu. Rev. Immunol. 31, 635–674 (2013).

  79. 79.

    et al. G-CSF supplementation with chemotherapy can promote revascularization and subsequent tumor regrowth: prevention by a CXCR4 antagonist. Blood 118, 3426–3435 (2011).

  80. 80.

    et al. XactMice: humanizing mouse bone marrow enables microenvironment reconstitution in a patient-derived xenograft model of head and neck cancer. Oncogene 35, 290–300 (2016).

  81. 81.

    et al. Polymorphism in Sirpa modulates engraftment of human hematopoietic stem cells. Nat. Immunol. 8, 1313–1323 (2007).

  82. 82.

    et al. Establishment of and comparison between orthotopic xenograft and subcutaneous xenograft models of gallbladder carcinoma. Asian Pac. J. Cancer Prev. 15, 3747–3752 (2014).

  83. 83.

    Patient-derived orthotopic xenografts: better mimic of metastasis than subcutaneous xenografts. Nat. Rev. Cancer 15, 451–452 (2015).

  84. 84.

    , , , & Construction of orthotopic xenograft mouse models for human pancreatic cancer. Exp. Ther. Med. 10, 1033–1038 (2015).

  85. 85.

    et al. Combined inhibition of DDR1 and Notch signaling is a therapeutic strategy for KRAS-driven lung adenocarcinoma. Nat. Med. 22, 270–277 (2016).

  86. 86.

    , & Imaging preclinical tumour models: improving translational power. Nat. Rev. Cancer 14, 481–493 (2014).

  87. 87.

    et al. A new mouse model for the study of human breast cancer metastasis. PLoS ONE 7, e47995 (2012).

  88. 88.

    , , & Metastasis of breast tumor cells to brain is suppressed by phenethyl isothiocyanate in a novel metastasis model. PLoS ONE 8, e67278 (2013).

  89. 89.

    et al. Patient-derived xenografts from non-small cell lung cancer brain metastases are valuable translational platforms for the development of personalized targeted therapy. Clin. Cancer Res. 21, 1172–1182 (2015).

  90. 90.

    et al. Comprehensive models of human primary and metastatic colorectal tumors in immunodeficient and immunocompetent mice by chemokine targeting. Nat. Biotechnol. 33, 656–660 (2015).

  91. 91.

    et al. Application of sequencing, liquid biopsies, and patient-derived xenografts for personalized medicine in melanoma. Cancer Discov. 6, 286–299 (2016).

  92. 92.

    et al. Evaluating patient-derived colorectal cancer xenografts as preclinical models by comparison with patient clinical data. Cancer Res. 75, 1560–1566 (2015).

  93. 93.

    et al. Dual-targeted therapy with trastuzumab and lapatinib in treatment-refractory, KRAS codon 12/13 wild-type, HER2-positive metastatic colorectal cancer (HERACLES): a proof-of-concept, multicentre, open-label, phase 2 trial. Lancet Oncol. 17, 738–746 (2016).

  94. 94.

    et al. Evaluation and prognostic significance of circulating tumor cells in patients with non-small-cell lung cancer. J. Clin. Oncol. 29, 1556–1563 (2011).

  95. 95.

    et al. Circulating tumour cells as prognostic markers in progressive, castration-resistant prostate cancer: a reanalysis of IMMC38 trial data. Lancet Oncol. 10, 233–239 (2009).

  96. 96.

    et al. Meta-analysis of the prognostic value of circulating tumor cells in breast cancer. Clin. Cancer Res. 18, 5701–5710 (2012).

  97. 97.

    et al. Identification of a population of blood circulating tumor cells from breast cancer patients that initiates metastasis in a xenograft assay. Nat. Biotechnol. 31, 539–544 (2013).

  98. 98.

    et al. Tumorigenicity and genetic profiling of circulating tumor cells in small-cell lung cancer. Nat. Med. 20, 897–903 (2014).

  99. 99.

    , , , & Circulating tumor cells: a multifunctional biomarker. Clin. Cancer Res. 20, 2553–2568 (2014).

  100. 100.

    & Challenges in circulating tumour cell research. Nat. Rev. Cancer 14, 623–631 (2014).

  101. 101.

    , & Circulating tumor cells and circulating tumor DNA: challenges and opportunities on the path to clinical utility. Clin. Cancer Res. 21, 4786–4800 (2015).

  102. 102.

    et al. Generation of prostate cancer patient derived xenograft models from circulating tumor cells. J. Vis. Exp. 104, e53182 (2015).

  103. 103.

    et al. Analysis of circulating tumor cells derived from advanced gastric cancer. Int. J. Cancer 137, 991–998 (2015).

  104. 104.

    et al. Cancer therapy. Ex vivo culture of circulating breast tumor cells for individualized testing of drug susceptibility. Science 345, 216–220 (2014).

  105. 105.

    et al. Establishment and characterization of a cell line from human circulating colon cancer cells. Cancer Res. 75, 892–901 (2015).

  106. 106.

    et al. Relationship among circulating tumor cells, CEA and overall survival in patients with metastatic colorectal cancer. Ann. Oncol. 24, 420–428 (2013).

  107. 107.

    et al. The isolation and characterization of CTC subsets related to breast cancer dormancy. Sci. Rep. 5, 17533 (2015).

  108. 108.

    et al. Molecular analysis of circulating tumour cells-biology and biomarkers. Nat. Rev. Clin. Oncol. 11, 129–144 (2014).

  109. 109.

    et al. PIK3CA mutational status in circulating tumor cells can change during disease recurrence or progression in patients with breast cancer. Clin. Cancer Res. 20, 5823–5834 (2014).

  110. 110.

    et al. Circulating and disseminated tumor cells from breast cancer patient-derived xenograft-bearing mice as a novel model to study metastasis. Breast Cancer Res. 17, 3 (2015).

  111. 111.

    et al. Circulating tumor cells as a biomarker of response to treatment in patient-derived xenograft mouse models of pancreatic adenocarcinoma. PLoS ONE 9, e89474 (2014).

  112. 112.

    et al. HER2 expression identifies dynamic functional states within circulating breast cancer cells. Nature 537, 102–106 (2016).

  113. 113.

    & Lessons from the cancer genome. Cell 153, 17–37 (2013).

  114. 114.

    & Compensatory pathways in oncogenic kinase signaling and resistance to targeted therapies: six degrees of separation. Cancer Discov. 2, 876–880 (2012).

  115. 115.

    et al. Amplification of the MET receptor drives resistance to anti-EGFR therapies in colorectal cancer. Cancer Discov. 3, 658–673 (2013).

  116. 116.

    et al. HER2 activating mutations are targets for colorectal cancer treatment. Cancer Discov. 5, 832–841 (2015).

  117. 117.

    et al. Sustained inhibition of HER3 and EGFR is necessary to induce regression of HER2-amplified gastrointestinal carcinomas. Clin. Cancer Res. 21, 5519–5531 (2015).

  118. 118.

    et al. IGF2 is an actionable target that identifies a distinct subpopulation of colorectal cancer patients with marginal response to anti-EGFR therapies. Sci. Transl Med. 7, 272ra12 (2015).

  119. 119.

    et al. Intrinsic resistance to MEK inhibition in KRAS mutant lung and colon cancer through transcriptional induction of ERBB3. Cell Rep. 7, 86–93 (2014).

  120. 120.

    et al. Dual targeting of the epidermal growth factor receptor using the combination of cetuximab and erlotinib: preclinical evaluation and results of the phase II DUX study in chemotherapy-refractory, advanced colorectal cancer. J. Clin. Oncol. 30, 1505–1512 (2012).

  121. 121.

    et al. Combined BRAF and MEK inhibition versus BRAF inhibition alone in melanoma. N. Engl. J. Med. 371, 1877–1888 (2014).

  122. 122.

    et al. In vivo genetic screens of patient-derived tumors revealed unexpected frailty of the transformed phenotype. Cancer Discov. 6, 650–663 (2016).

  123. 123.

    et al. In vivo functional platform targeting patient-derived xenografts identifies WDR5-Myc association as a critical determinant of pancreatic cancer. Cell Rep. 16, 133–147 (2016).

  124. 124.

    et al. Evaluation of alternative in vivo drug screening methodology: a single mouse analysis. Cancer Res. 76, 5798–5809 (2016).

  125. 125.

    et al. Inhibition of MEK and PI3K/mTOR suppresses tumor growth but does not cause tumor regression in patient-derived xenografts of RAS-mutant colorectal carcinomas. Clin. Cancer Res. 18, 2515–2525 (2012).

  126. 126.

    et al. Organoid models of human and mouse ductal pancreatic cancer. Cell 160, 324–338 (2015).

  127. 127.

    et al. Organoid cultures derived from patients with advanced prostate cancer. Cell 159, 176–187 (2014).

  128. 128.

    et al. Ductal pancreatic cancer modeling and drug screening using human pluripotent stem cell- and patient-derived tumor organoids. Nat. Med. 21, 1364–1371 (2015).

  129. 129.

    et al. Long-term expansion of epithelial organoids from human colon, adenoma, adenocarcinoma, and Barrett's epithelium. Gastroenterology 141, 1762–1772 (2011).

  130. 130.

    et al. Prospective derivation of a living organoid biobank of colorectal cancer patients. Cell 161, 933–945 (2015).

  131. 131.

    et al. Preserved genetic diversity in organoids cultured from biopsies of human colorectal cancer metastases. Proc. Natl Acad. Sci. USA 112, 13308–13311 (2015).

  132. 132.

    et al. A three-dimensional organoid culture system derived from human glioblastomas recapitulates the hypoxic gradients and cancer stem cell heterogeneity of tumors found in vivo. Cancer Res. 76, 2465–2477 (2016).

  133. 133.

    et al. Patient-derived models of acquired resistance can identify effective drug combinations for cancer. Science 346, 1480–1486 (2014).

  134. 134.

    , , , & The APL paradigm and the “co-clinical trial” project. Cancer Discov. 1, 108–116 (2011).

  135. 135.

    US National Library of Medicine. ClinicalTrials.gov (2016).

  136. 136.

    US National Library of Medicine. ClinicalTrials.gov (2016).

  137. 137.

    US National Library of Medicine. ClinicalTrials.gov (2016).

  138. 138.

    US National Library of Medicine. ClinicalTrials.gov (2016).

  139. 139.

    et al. A first-in-human phase I trial of LY2780301, a dual p70 S6 kinase and Akt Inhibitor, in patients with advanced or metastatic cancer. Invest. New Drugs 33, 710–719 (2015).

  140. 140.

    et al. Convergent loss of PTEN leads to clinical resistance to a PI(3)Kα inhibitor. Nature 518, 240–244 (2015).

  141. 141.

    et al. Prioritizing phase I treatment options through preclinical testing on personalized tumorgraft. J. Clin. Oncol. 30, e45–e48 (2012).

  142. 142.

    et al. Genome-wide cDNA microarray screening to correlate gene expression profiles with sensitivity of 85 human cancer xenografts to anticancer drugs. Cancer Res. 62, 518–527 (2002).

  143. 143.

    et al. Delineation of MGMT Hypermethylation as a biomarker for veliparib-mediated temozolomide-sensitizing therapy of glioblastoma. J. Natl Cancer Inst. 108, djv369 (2016).

  144. 144.

    US National Library of Medicine. ClinicalTrials.gov (2016).

  145. 145.

    et al. Proteomic profiling of patient-derived glioblastoma xenografts identifies a subset with activated EGFR: implications for drug development. J. Neurochem. 133, 730–738 (2015).

  146. 146.

    et al. Interplay of choline metabolites and genes in patient-derived breast cancer xenografts. Breast Cancer Res. 16, R5 (2014).

  147. 147.

    et al. Distinct choline metabolic profiles are associated with differences in gene expression for basal-like and luminal-like breast cancer xenograft models. BMC Cancer 10, 433 (2010).

  148. 148.

    , & Molecular causes of the aberrant choline phospholipid metabolism in breast cancer. Cancer Res. 64, 4270–4276 (2004).

  149. 149.

    et al. Metabolic imaging of patients with prostate cancer using hyperpolarized [1-13C]pyruvate. Sci. Transl. Med. 5, 198ra108 (2013).

  150. 150.

    et al. 31P MRSI and 1H MRS at 7 T: initial results in human breast cancer. NMR Biomed. 24, 1337–1342 (2011).

  151. 151.

    et al. In vivo 31P magnetic resonance spectroscopic imaging (MRSI) for metabolic profiling of human breast cancer xenografts. J. Magn. Reson. Imaging 41, 601–609 (2015).

  152. 152.

    et al. Patient-derived mammosphere and xenograft tumour initiation correlates with progression to metastasis. J. Mammary Gland Biol. Neoplasia (2016).

  153. 153.

    et al. Prognostic and functional importance of the engraftment-associated genes in the patient-derived xenograft models of triple-negative breast cancers. Breast Cancer Res. Treat. 154, 13–22 (2015).

  154. 154.

    et al. Tumor engraftment in nude mice and enrichment in stroma- related gene pathways predict poor survival and resistance to gemcitabine in patients with pancreatic cancer. Clin. Cancer Res. 17, 5793–5800 (2011).

  155. 155.

    et al. Patient-derived xenograft models for pancreatic adenocarcinoma demonstrate retention of tumor morphology through incorporation of murine stromal elements. Am. J. Pathol. 185, 1297–1303 (2015).

  156. 156.

    et al. Stromal contribution to the colorectal cancer transcriptome. Nat. Genet. 47, 312–319 (2015).

  157. 157.

    et al. Stromal gene expression defines poor-prognosis subtypes in colorectal cancer. Nat. Genet. 47, 320–329 (2015).

  158. 158.

    et al. Challenging the cancer molecular stratification dogma: intratumoral heterogeneity undermines consensus molecular subtypes and potential diagnostic value in colorectal cancer. Clin. Cancer Res. 22, 4095–4104 (2016).

  159. 159.

    et al. Scatter factor and hepatocyte growth factor: activities, properties, and mechanism. Cell Growth Differ. 3, 11–20 (1992).

  160. 160.

    et al. Microenvironment-derived HGF overcomes genetically determined sensitivity to anti-MET drugs. Cancer Res. 74, 6598–6609 (2014).

  161. 161.

    & Of mice and not men: differences between mouse and human immunology. J. Immunol. 172, 2731–2738 (2004).

  162. 162.

    et al. Knock-in of human HGF into the mouse genome maintains endogenous HGF regulation and supports the growth of HGF-dependent human cancer cell lines. Cancer Res. 69, abstr. 305 (2009).

  163. 163.

    et al. Phase I expansion and pharmacodynamic study of the oral MEK inhibitor RO4987655 (CH4987655) in selected patients with advanced cancer with RAS–RAF mutations. Clin. Cancer Res. 20, 4251–4261 (2014).

  164. 164.

    et al. Challenges, opportunities, and lessons learned in the bench-to-bedside translation of xenopatient studies. Clin. Cancer Res. 22 (16 Suppl.), abstr. IA20 (2016).

  165. 165.

    et al. The public repository of xenografts enables discovery and randomized phase II-like trials in mice. Cancer Cell 29, 574–586 (2016).

  166. 166.

    , , & LAS: a software platform to support oncological data management. J. Med. Syst. 36 (Suppl. 1), S81–S90 (2012).

  167. 167.

    et al. Phenotypic and transcriptional fidelity of patient-derived colon cancer xenografts in immune-deficient mice. PLoS ONE 8, e79874 (2013).

  168. 168.

    , , & Current advances in humanized mouse models. Cell. Mol. Immunol. 9, 208–214 (2012).

  169. 169.

    et al. Xenome — a tool for classifying reads from xenograft samples. Bioinformatics 28, i172–i178 (2012).

  170. 170.

    et al. A novel carcinoembryonic antigen T-cell bispecific antibody (CEA TCB) for the treatment of solid tumors. Clin. Cancer Res. 22, 3286–3297 (2016).

  171. 171.

    et al. NOD/SCID/γcnull mouse: an excellent recipient mouse model for engraftment of human cells. Blood 100, 3175–3182 (2002).

  172. 172.

    et al. Human lymphoid and myeloid cell development in NOD/LtSz-scid IL2Rγnull mice engrafted with mobilized human hemopoietic stem cells. J. Immunol. 174, 6477–6489 (2005).

  173. 173.

    , , & Humanized mice for immune system investigation: progress, promise and challenges. Nat. Rev. Immunol. 12, 786–798 (2012).

  174. 174.

    et al. Development of a human adaptive immune system in cord blood cell-transplanted mice. Science 304, 104–107 (2004).

  175. 175.

    et al. Establishment of a human allergy model using human IL-3/GM-CSF-transgenic NOG mice. J. Immunol. 191, 2890–2899 (2013).

  176. 176.

    et al. Development of human CD4+FoxP3+ regulatory T cells in human stem cell factor-, granulocyte-macrophage colony-stimulating factor-, and interleukin-3-expressing NOD-SCID IL2Rγnull humanized mice. Blood 117, 3076–3086 (2011).

  177. 177.

    et al. Development and function of human innate immune cells in a humanized mouse model. Nat. Biotechnol. 32, 364–372 (2014).

  178. 178.

    et al. Cetuximab monotherapy and cetuximab plus irinotecan in irinotecan-refractory metastatic colorectal cancer. N. Engl. J. Med. 351, 337–345 (2004).

  179. 179.

    et al. A retrospective observational study of clinicopathological features of KRAS, NRAS, BRAF and PIK3CA mutations in Japanese patients with metastatic colorectal cancer. BMC Cancer 15, 258 (2015).

Download references

Acknowledgements

The authors would like to thank all members of the EurOPDX Consortium who also contributed to this article, and in particular S. Corso, S. Giordano, P. P. López-Casas, K. Moran-Jones and F. Nemati. The Caldas laboratory would like to thank the PGE team for their support, especially Lisa, Steve and Yi. A.T.B. is supported by Science Foundation Ireland under grants 13/CDA/2183 and 15/TIDA/2963 and further receives funding from the Irish Cancer Society Collaborative Cancer Research Centre BREAST-PREDICT Grant CCRC13GAL. D.G.A. and R.B.C. are supported by Breast Cancer Now. F.A., E.H. and J.C.M. received KULeuven GOA funding (GOA/14/012) and a research grant from Stichting tegen Kanker. J.A. is funded by the Breast Cancer Research Foundation, the Spanish Association Against Cancer (AECC) and the Instituto de Salud Carlos III (PI16/00253 and CIBER-ONC). A.V.B. and D.K.C. are supported by Cancer Research UK (C29717/A17263), the Wellcome Trust (10372/Z/14/Z), the Scottish Genomes Partnership — SEHHD-CSO 1175759/2158447, the Howat Foundation and Pancreatic Cancer UK. A.B., C.C. and O.M.R. have been supported by funding from Cancer Research UK and by the European Union to the EUROCAN Network of Excellence (FP7; grant number 260791). E.B. is supported by the CETOCOEN PLUS project (CZ.02.1.01/0.0/0.0/15_003/0000469) and the RECETOX Research Infrastructure (LM2015051). G.C. and D.V. were funded by NIH transformative R01CA156695 and European Research Council (ERC) Advanced grant 1400206AdG-322875. S.G.E. receives support from NCI grant 1UM1CA186688 for early-phase trials through the ET-CTN. E.G.S. is supported by the Spanish Ministry of Economy and Competitivity MINECO and from the ISCIII (SAF2014-55997; PIE13/00022, co-funded by FEDER funds/ European Regional Development Fund (ERDF) — a way to build Europe), by a Career Catalyst Grant from the Susan Komen Foundation (CCR13262449) and by a European Research Council Consolidator grant (CoG682935). M.A.J. is supported by an Irish Health Research Board Health Research Award (#HRA-POR-2014-547). S.D.J. is supported by the Dutch Cancer Society (grants RUG 2010-4833, RUG 2011-5231, RUG 2012-5477 and RUG 2014-6691). J.J. is funded by the Dutch Cancer Society (NKI 2011-5197 and EMCR 2014-7048), the Netherlands Organisation for Scientific Research (Zenith 93512009, Vici 91814643, CancerGenomiCs.nl) and the European Research Council (ERC-SyG CombatCancer). K.K. and D.S.P. are supported by the Dutch Cancer Society (NKI-2013-5799). L.L. and P.G.P. are funded by ERC Advanced Grant 341131 and Italian Association for Cancer Research (AIRC) Investigator Investigator Grant 14216. G.M.M. receives funds from the Norwegian Cancer Society (421851) and the Research Council of Norway (222262/F20). J.H.N. is funded by the Research Council of Norway under grant 250459/F20. H.G.P. is supported by the Instituto de Salud Carlos III and the Miguel Servet Program (MSII14/00037). V.S. is supported by the Instituto de Salud Carlos III (PI13/01714 and the Miguel Servet Program CP14/00028), by a Career Catalyst Grant from the Susan Komen Foundation CCR15330331 and the FERO Foundation. L.S. was funded by Worldwide Cancer Research (WCR/AICR Grant #13-1182), the European Research Council (CoG Grant #617473), the Instituto de Salud Carlos III (FIS Grant #PI13/01705) and the FERO Foundation. A.V. is supported by the Instituto de Salud Carlos III (PI13/0133 and PIE13/00022 (Oncoprofile)), Fundación Mutua Madrileña AP150932014 and a grant from the Spanish Association Against Cancer from Barcelona, AECC. A.B. is supported by AIRC (Investigator Grant project 15571). L.T. and E.M. are supported by the AIRC (Special Programme Molecular Clinical Oncology 5 × 1000, project 9970, and Investigator Grant projects, 14205 to L.T. and 12944 to E.M.) and also receive funding from the Fondazione Piemontese per la Ricerca sul Cancro-ONLUS (5 × 1000 Italian Ministry of Health 2011).

Author information

Affiliations

  1. EurOPDX Consortium and are at the Royal College of Surgeons in Ireland, Dublin 2, Ireland.

    • Annette T. Byrne
    •  & Monika A. Jarzabek
  2. EurOPDX Consortium and are at the Breast Cancer Now Research Unit, Division of Molecular and Clinical Cancer Sciences, Manchester Cancer Research Centre, University of Manchester, Manchester M20 4QL, UK.

    • Denis G. Alférez
    •  & Robert B. Clarke
  3. EurOPDX Consortium and are at the Katholieke Universiteit Leuven, 3000 Leuven, Belgium.

    • Frédéric Amant
    • , Daniela Annibali
    •  & Els Hermans
  4. The Netherlands Cancer Institute, Plesmanlaan 121, 1066CX Amsterdam, The Netherlands.

    • Frédéric Amant
  5. EurOPDX Consortium and are at the Vall d'Hebron Institute of Oncology, 08035 Barcelona, the Universitat Autònoma de Barcelona, 08193 Bellaterra, and the Institució Catalana de Recerca i Estudis Avançats (ICREA), 08010 Barcelona, Spain.

    • Joaquín Arribas
    • , Joan Seoane
    •  & Laura Soucek
  6. CIBERONC, 08035 Barcelona, Spain.

    • Joaquín Arribas
    •  & Joan Seoane
  7. EurOPDX Consortium and are at the Wolfson Wohl Cancer Research Centre, Institute of Cancer Sciences, University of Glasgow, Glasgow G61 1QH, UK.

    • Andrew V. Biankin
    •  & David K. Chang
  8. EurOPDX Consortium and are at Cancer Research UK Cambridge Institute, Cambridge Cancer Centre, University of Cambridge, Cambridge CB2 0RE, UK.

    • Alejandra Bruna
    • , Carlos Caldas
    •  & Oscar M. Rueda
  9. EurOPDX Consortium and is at the Institute of Biostatistics and Analyses, Faculty of Medicine, and Research Centre for Toxic Compounds in the Environment, Faculty of Science, Masarykova Univerzita, 625 00 Brno, Czech Republic.

    • Eva Budinská
  10. Hubrecht Institute, University Medical Centre Utrecht, and Princess Maxima Center for Pediatric Oncology, 3584CT Utrecht, The Netherlands.

    • Hans Clevers
  11. EurOPDX Consortium and are at Lausanne Branch, Ludwig Institute for Cancer Research at the University of Lausanne, 1066 Lausanne, Switzerland.

    • George Coukos
    •  & Dominique Vanhecke
  12. EurOPDX Consortium and is at the Institut Curie, PSL Research University, Translational Research Department, 75005 Paris, and Université Paris Descartes, Sorbonne Paris Cité, Faculté de Pharmacie de Paris, 75006 Paris, France.

    • Virginie Dangles-Marie
  13. University of Colorado Cancer Center, Aurora, Colorado 80045, USA.

    • S. Gail Eckhardt
  14. EurOPDX Consortium and is at the Cancer Epigenetics and Biology Program, Bellvitge Biomedical Research Institute IDIBELL, 08908 L'Hospitalet de Llobregat, Barcelona, Spain.

    • Eva Gonzalez-Suarez
  15. EurOPDX Consortium and is at Beth Israel Deaconess Medical Center, Boston, Harvard Medical School, Boston, Massachusetts 02215, USA.

    • Manuel Hidalgo
  16. EurOPDX Consortium and is at the University Medical Centre Groningen, University of Groningen, 9713GZ Groningen, The Netherlands.

    • Steven de Jong
  17. EurOPDX Consortium and are at The Netherlands Cancer Institute, Plesmanlaan 121, 1066CX Amsterdam, The Netherlands.

    • Jos Jonkers
    • , Kristel Kemper
    •  & Daniel S. Peeper
  18. EurOPDX Consortium and are at the Department of Experimental Oncology, European Institiute of Oncology, 20139 Milan, Italy.

    • Luisa Lanfrancone
    •  & Pier Giuseppe Pelicci
  19. EurOPDX Consortium and are at Oslo University Hospital, Institute for Cancer Research, 0424 Oslo, Norway.

    • Gunhild Mari Mælandsmo
    •  & Jens Henrik Norum
  20. EurOPDX Consortium and are at Institut Curie, PSL Research University, Translational Research Department, 75005 Paris, France.

    • Elisabetta Marangoni
    •  & Sergio Roman-Roman
  21. EurOPDX Consortium and is at the Laboratory for Molecular Cancer Biology, Department of Oncology, Katholieke Universiteit Leuven, and the Center for Cancer Biology, VIB, 3000 Leuven, Belgium.

    • Jean-Christophe Marine
  22. EurOPDX Consortium and are at the Candiolo Cancer Institute IRCCS and Department of Oncology, University of Torino, 10060 Candiolo, Torino, Italy.

    • Enzo Medico
    • , Andrea Bertotti
    •  & Livio Trusolino
  23. EurOPDX Consortium and are at the Vall d'Hebron Institute of Oncology and CIBERONC, 08035 Barcelona, Spain.

    • Héctor G. Palmer
    • , Alejandro Piris-Gimenez
    •  & Violeta Serra
  24. EurOPDX Consortium and is at the Program Against Cancer Therapeutic Resistance (ProCURE), Catalan Institute of Oncology ICO, Bellvitge Biomedical Research Institute IDIBELL, 08098 L'Hospitalet de Llobregat, Barcelona, and Xenopat S.L., Business Bioincubator, Bellvitge Health Science Campus, 08907 L'Hospitalet de Llobregat, Barcelona, Spain.

    • Alberto Villanueva
  25. Seeding Science SAS, 75020 Paris, France.

    • Emilie Vinolo

Authors

  1. Search for Annette T. Byrne in:

  2. Search for Denis G. Alférez in:

  3. Search for Frédéric Amant in:

  4. Search for Daniela Annibali in:

  5. Search for Joaquín Arribas in:

  6. Search for Andrew V. Biankin in:

  7. Search for Alejandra Bruna in:

  8. Search for Eva Budinská in:

  9. Search for Carlos Caldas in:

  10. Search for David K. Chang in:

  11. Search for Robert B. Clarke in:

  12. Search for Hans Clevers in:

  13. Search for George Coukos in:

  14. Search for Virginie Dangles-Marie in:

  15. Search for S. Gail Eckhardt in:

  16. Search for Eva Gonzalez-Suarez in:

  17. Search for Els Hermans in:

  18. Search for Manuel Hidalgo in:

  19. Search for Monika A. Jarzabek in:

  20. Search for Steven de Jong in:

  21. Search for Jos Jonkers in:

  22. Search for Kristel Kemper in:

  23. Search for Luisa Lanfrancone in:

  24. Search for Gunhild Mari Mælandsmo in:

  25. Search for Elisabetta Marangoni in:

  26. Search for Jean-Christophe Marine in:

  27. Search for Enzo Medico in:

  28. Search for Jens Henrik Norum in:

  29. Search for Héctor G. Palmer in:

  30. Search for Daniel S. Peeper in:

  31. Search for Pier Giuseppe Pelicci in:

  32. Search for Alejandro Piris-Gimenez in:

  33. Search for Sergio Roman-Roman in:

  34. Search for Oscar M. Rueda in:

  35. Search for Joan Seoane in:

  36. Search for Violeta Serra in:

  37. Search for Laura Soucek in:

  38. Search for Dominique Vanhecke in:

  39. Search for Alberto Villanueva in:

  40. Search for Emilie Vinolo in:

  41. Search for Andrea Bertotti in:

  42. Search for Livio Trusolino in:

Competing interests

Grants from Celgene and Boehringer-Ingelheim, honoraria from Roche and Genentech, consultancy for Roche, Genentech, Novartis and Sanofi-Aventis (G.C.), consultancy for Oncodesign and funding by Novartis (S.R.R.), founder of the spin-off Xenopat S.L. (A.V.). The other authors declare no competing interests.

Corresponding authors

Correspondence to Annette T. Byrne or Livio Trusolino.

Supplementary information

PDF files

  1. 1.

    Supplementary information S1 (table)

    Examples of improvements in humanised mouse models for PDX studies

About this article

Publication history

Published

DOI

https://doi.org/10.1038/nrc.2016.140

Further reading