Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

PDX models for functional precision oncology and discovery science

Nature Reviews Cancer volume 25, pages 153–166 (2025)Cite this article

Subjects

Abstract

Precision oncology relies on detailed molecular analysis of how diverse tumours respond to various therapies, with the aim to optimize treatment outcomes for individual patients. Patient-derived xenograft (PDX) models have been key to preclinical validation of precision oncology approaches, enabling the analysis of each tumour’s unique genomic landscape and testing therapies that are predicted to be effective based on specific mutations, gene expression patterns or signalling abnormalities. To extend these standard precision oncology approaches, the field has strived to complement the otherwise static and often descriptive measurements with functional assays, termed functional precision oncology (FPO). By utilizing diverse PDX and PDX-derived models, FPO has gained traction as an effective preclinical and clinical tool to more precisely recapitulate patient biology using in vivo and ex vivo functional assays. Here, we explore advances and limitations of PDX and PDX-derived models for precision oncology and FPO. We also examine the future of PDX models for precision oncology in the age of artificial intelligence. Integrating these two disciplines could be the key to fast, accurate and cost-effective treatment prediction, revolutionizing oncology and providing patients with cancer with the most effective, personalized treatments.

Access through your institution
Buy or subscribe

This is a preview of subscription content, access via your institution

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Patient-derived xenograft pipeline.
Fig. 2: The premise of functional precision oncology.
Fig. 3: Scalability of drug testing in various patient-derived models.

Similar content being viewed by others

References

  1. Schwartzberg, L., Kim, E. S., Liu, D. & Schrag, D. Precision oncology: who, how, what, when, and when not? Am. Soc. Clin. Oncol. Educ. Book. 37, 160–169 (2017).

    PubMed  Google Scholar 

  2. Flaherty, K. T. et al. The Molecular Analysis for Therapy Choice (NCI-MATCH) trial: lessons for genomic trial design. J. Natl Cancer Inst. 112, 1021–1029 (2020). This paper outlines a landmark precision oncology trial.

    PubMed  PubMed Central  Google Scholar 

  3. Letai, A., Bhola, P. & Welm, A. L. Functional precision oncology: testing tumors with drugs to identify vulnerabilities and novel combinations. Cancer Cell 40, 26–35 (2022).

    CAS  PubMed  Google Scholar 

  4. Letai, A. Functional precision cancer medicine—moving beyond pure genomics. Nat. Med. 23, 1028–1035 (2017).

    CAS  PubMed  Google Scholar 

  5. van Renterghem, A. W. J., van de Haar, J. & Voest, E. E. Functional precision oncology using patient-derived assays: bridging genotype and phenotype. Nat. Rev. Clin. Oncol. 20, 305–317 (2023).

    PubMed  Google Scholar 

  6. Bose, S. et al. A path to translation: how 3D patient tumor avatars enable next generation precision oncology. Cancer Cell 40, 1448–1453 (2022).

    PubMed  PubMed Central  Google Scholar 

  7. Woo, X. Y. et al. Conservation of copy number profiles during engraftment and passaging of patient-derived cancer xenografts. Nat. Genet. 53, 86–99 (2021). This paper demonstrates genomic fidelity between patient tumours and PDXs for a range of cancer types.

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Liu, Y. et al. Patient-derived xenograft models in cancer therapy: technologies and applications. Signal Transduct. Target. Ther. 8, 160 (2023).

    PubMed  PubMed Central  Google Scholar 

  9. Sun, H. et al. Comprehensive characterization of 536 patient-derived xenograft models prioritizes candidates for targeted treatment. Nat. Commun. 12, 5086 (2021). This paper investigates genomic and pharmacogenomic relationships between human tumours and pan-cancer PDX models.

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Byrne, A. T. et al. Interrogating open issues in cancer precision medicine with patient-derived xenografts. Nat. Rev. Cancer 17, 254–268 (2017).

    CAS  PubMed  Google Scholar 

  11. Zanella, E. R., Grassi, E. & Trusolino, L. Towards precision oncology with patient-derived xenografts. Nat. Rev. Clin. Oncol. 19, 719–732 (2022).

    PubMed  Google Scholar 

  12. Lynch, I. T. et al. Cancer “avatars”: patient-derived xenograft growth correlation with postoperative recurrence and survival in pancreaticobiliary cancer. J. Am. Coll. Surg. 237, 483–500 (2023).

    PubMed  PubMed Central  Google Scholar 

  13. Castillo-Ecija, H. et al. Prognostic value of patient-derived xenograft engraftment in pediatric sarcomas. J. Pathol. Clin. Res. 7, 338–349 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Chen, Q. et al. Patient-derived xenograft model engraftment predicts poor prognosis after surgery in patients with pancreatic cancer. Pancreatology 20, 485–492 (2020).

    CAS  PubMed  Google Scholar 

  15. Vaklavas, C. et al. TOWARDS study: PDX engraftment predicts poor survival in newly diagnosed triple negative breast cancer patients. JCO Precis. Oncon. 8, e2300724 (2024). This paper demonstrates in a prospective clinical trial that PDX models can be used to assess recurrence risk.

    Google Scholar 

  16. Hidalgo, M. et al. A pilot clinical study of treatment guided by personalized tumorgrafts in patients with advanced cancer. Mol. Cancer Ther. 10, 1311–1316 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Astone, M., Dankert, E. N., Alam, S. K. & Hoeppner, L. H. Fishing for cures: the alLURE of using zebrafish to develop precision oncology therapies. npj Precis. Oncol. 1, 39 (2017).

    PubMed  PubMed Central  Google Scholar 

  18. Moy, R. H. et al. Defining and targeting esophagogastric cancer genomic subsets with patient-derived xenografts. JCO Precis. Oncol. 6, e2100242 (2022).

    PubMed  PubMed Central  Google Scholar 

  19. Guan, Z. et al. Individualized drug screening based on next generation sequencing and patient derived xenograft model for pancreatic cancer with bone metastasis. Mol. Med. Rep. 16, 4784–4790 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Nicolle, R. et al. Pancreatic adenocarcinoma therapeutic targets revealed by tumor–stroma cross-talk analyses in patient-derived xenografts. Cell Rep. 21, 2458–2470 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Conage-Pough, J. E. et al. WSD-0922, a novel brain-penetrant inhibitor of epidermal growth factor receptor, promotes survival in glioblastoma mouse models. Neurooncol. Adv. 5, vdad066 (2023).

    PubMed  PubMed Central  Google Scholar 

  22. Pandya, P. H. et al. Integrative multi-OMICs identifies therapeutic response biomarkers and confirms fidelity of clinically annotated, serially passaged patient-derived xenografts established from primary and metastatic pediatric and AYA solid tumors. Cancers (Basel) 15, 259 (2022).

    PubMed  Google Scholar 

  23. Hemming, M. L. et al. Preclinical modeling of leiomyosarcoma identifies susceptibility to transcriptional CDK inhibitors through antagonism of E2F-driven oncogenic gene expression. Clin. Cancer Res. 28, 2397–2408 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Hsu, C. L. et al. Integrated genomic analyses in PDX model reveal a cyclin-dependent kinase inhibitor palbociclib as a novel candidate drug for nasopharyngeal carcinoma. J. Exp. Clin. Cancer Res. 37, 233 (2018).

    PubMed  PubMed Central  Google Scholar 

  25. Karamboulas, C. et al. Patient-derived xenografts for prognostication and personalized treatment for head and neck squamous cell carcinoma. Cell Rep. 25, 1318–1331.e4 (2018).

    CAS  PubMed  Google Scholar 

  26. Punzi, S. et al. Development of personalized therapeutic strategies by targeting actionable vulnerabilities in metastatic and chemotherapy-resistant breast cancer PDXs. Cells 8, 605 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Kohale, I. N. et al. Identification of Src family kinases as potential therapeutic targets for chemotherapy-resistant triple negative breast cancer. Cancers (Basel) 14, 4220 (2022).

    CAS  PubMed  Google Scholar 

  28. Saridogan, T. et al. Efficacy of futibatinib, an irreversible fibroblast growth factor receptor inhibitor, in FGFR-altered breast cancer. Sci. Rep. 13, 20223 (2023).

    PubMed  PubMed Central  Google Scholar 

  29. Kim, M. et al. Efficacy of the MDM2 inhibitor SAR405838 in glioblastoma is limited by poor distribution across the blood–brain barrier. Mol. Cancer Ther. 17, 1893–1901 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Fiore, D. et al. A novel JAK1 mutant breast implant-associated anaplastic large cell lymphoma patient-derived xenograft fostering pre-clinical discoveries. Cancers (Basel) 12, 1603 (2020).

    CAS  PubMed  Google Scholar 

  31. De Coninck, S. et al. Targeting hyperactive platelet-derived growth factor receptor-β signaling in T-cell acute lymphoblastic leukemia and lymphoma. Haematologica 109, 1373–1384 (2024).

    PubMed  Google Scholar 

  32. Paolino, J. et al. Integration of genomic sequencing drives therapeutic targeting of PDGFRA in T-cell acute lymphoblastic leukemia/lymphoblastic lymphoma. Clin. Cancer Res. 29, 4613–4626 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Rivera, M. et al. Patient-derived xenograft (PDX) models of colorectal carcinoma (CRC) as a platform for chemosensitivity and biomarker analysis in personalized medicine. Neoplasia 23, 21–35 (2021).

    CAS  PubMed  Google Scholar 

  34. Krausert, S. et al. Predictive modeling of resistance to SMO inhibition in a patient-derived orthotopic xenograft model of SHH medulloblastoma. Neurooncol. Adv. 4, vdac026 (2022).

    PubMed  PubMed Central  Google Scholar 

  35. Potter, D. S. et al. Dynamic BH3 profiling identifies pro-apoptotic drug combinations for the treatment of malignant pleural mesothelioma. Nat. Commun. 14, 2897 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Henlius. FDA grants fast track designation to Henlius’ EGFR-targeting ADC HLX42 for NSCLC patients with disease progression on EGFR targeted therapies. https://www.henlius.com/en/NewsDetails-4409-26.html (2023).

  37. Petrosyan, V. et al. Identifying biomarkers of differential chemotherapy response in TNBC patient-derived xenografts with a CTD/WGCNA approach. iScience 26, 105799 (2023).

    CAS  PubMed  Google Scholar 

  38. Zoeller, J. J. et al. Navitoclax enhances the effectiveness of EGFR-targeted antibody–drug conjugates in PDX models of EGFR-expressing triple-negative breast cancer. Breast Cancer Res. 22, 132 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Yao, Y. M. et al. Mouse PDX trial suggests synergy of concurrent inhibition of RAF and EGFR in colorectal cancer with BRAF or KRAS mutations. Clin. Cancer Res. 23, 5547–5560 (2017).

    CAS  PubMed  Google Scholar 

  40. Napolitano, S. et al. Antitumor efficacy of dual blockade with encorafenib + cetuximab in combination with chemotherapy in human BRAFV600E-mutant colorectal cancer. CliaCancer Res. 29, 2299–2309 (2023).

    CAS  Google Scholar 

  41. Sorokin, A. V. et al. Targeting RAS mutant colorectal cancer with dual inhibition of MEK and CDK4/6. Cancer Res. 82, 3335–3344 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Harris, F. R. et al. Targeting HER2 in patient-derived xenograft ovarian cancer models sensitizes tumors to chemotherapy. Mol. Oncol. 13, 132–152 (2019).

    CAS  PubMed  Google Scholar 

  43. Liu, L. et al. Establishment of a high-fidelity patient-derived xenograft model for cervical cancer enables the evaluation of patient’s response to conventional and novel therapies. J. Transl. Med. 21, 611 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Ughetto, S. et al. Personalized therapeutic strategies in HER2-driven gastric cancer. Gastric Cancer 24, 897–912 (2021).

    CAS  PubMed  Google Scholar 

  45. Teichman, J. et al. Hedgehog inhibition mediates radiation sensitivity in mouse xenograft models of human esophageal adenocarcinoma. PLoS One 13, e0194809 (2018).

    PubMed  PubMed Central  Google Scholar 

  46. Kawaguchi, K. et al. MEK inhibitor trametinib in combination with gemcitabine regresses a patient-derived orthotopic xenograft (PDOX) pancreatic cancer nude mouse model. Tissue Cell 52, 124–128 (2018).

    CAS  PubMed  Google Scholar 

  47. Kawaguchi, K. et al. Tumor targeting Salmonella typhimurium A1-R in combination with gemcitabine (GEM) regresses partially GEM-resistant pancreatic cancer patient-derived orthotopic xenograft (PDOX) nude mouse models. Cell Cycle 17, 2019–2026 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Kawaguchi, K. et al. Targeting altered cancer methionine metabolism with recombinant methioninase (rMETase) overcomes partial gemcitabine-resistance and regresses a patient-derived orthotopic xenograft (PDOX) nude mouse model of pancreatic cancer. Cell Cycle 17, 868–873 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Kawaguchi, K. et al. Tumor-targeting Salmonella typhimurium A1-R sensitizes melanoma with a BRAF-V600E mutation to vemurafenib in a patient-derived orthotopic xenograft (PDOX) nude mouse model. J. Cell. Biochem. 118, 2314–2319 (2017).

    CAS  PubMed  Google Scholar 

  50. Vidal, A. et al. Lurbinectedin (PM01183), a new DNA minor groove binder, inhibits growth of orthotopic primary graft of cisplatin-resistant epithelial ovarian cancer. Clin. Cancer Res. 18, 5399–5411 (2012).

    CAS  PubMed  Google Scholar 

  51. Parmar, K. et al. The CHK1 inhibitor prexasertib exhibits monotherapy activity in high-grade serous ovarian cancer models and sensitizes to PARP inhibition. Clin. Cancer Res. 25, 6127–6140 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Zala, M. et al. Functional precision oncology for follicular lymphoma with patient-derived xenograft in avian embryos. Leukemia 38, 430–434 (2024).

    PubMed  PubMed Central  Google Scholar 

  53. Simovic, M. et al. Carbon ion radiotherapy eradicates medulloblastomas with chromothripsis in an orthotopic Li–Fraumeni patient-derived mouse model. Neuro Oncol. 23, 2028–2041 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Wu, Y. et al. Combining the tyrosine kinase inhibitor cabozantinib and the mTORC1/2 inhibitor sapanisertib blocks ERK pathway activity and suppresses tumor growth in renal cell carcinoma. Cancer Res. 83, 4161–4178 (2023).

    PubMed  PubMed Central  Google Scholar 

  55. Lupo, B. et al. Colorectal cancer residual disease at maximal response to EGFR blockade displays a druggable Paneth cell-like phenotype. Sci. Transl. Med. 12, eaax8313 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Leto, S. M. et al. Synthetic lethal interaction with BCL-XL blockade deepens response to cetuximab in patient-derived models of metastatic colorectal cancer. Clin. Cancer Res. 29, 1102–1113 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Vangala, D. et al. Secondary resistance to anti-EGFR therapy by transcriptional reprogramming in patient-derived colorectal cancer models. Genome Med. 13, 116 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Beekhof, R. et al. Phosphoproteomics of patient-derived xenografts identifies targets and markers associated with sensitivity and resistance to EGFR blockade in colorectal cancer. Sci. Transl. Med. 15, eabm3687 (2023).

    CAS  PubMed  Google Scholar 

  59. Wang, Q. et al. Case report: Two patients with EGFR exon 20 insertion mutanted non-small cell lung cancer precision treatment using patient-derived xenografts in zebrafish embryos. Front. Oncol. 12, 884798 (2022).

    PubMed  PubMed Central  Google Scholar 

  60. Marin-Bejar, O. et al. Evolutionary predictability of genetic versus nongenetic resistance to anticancer drugs in melanoma. Cancer Cell 39, 1135–1149.e8 (2021).

    CAS  PubMed  Google Scholar 

  61. Rambow, F. et al. Toward minimal residual disease-directed therapy in melanoma. Cell 174, 843–855.e19 (2018).

    CAS  PubMed  Google Scholar 

  62. Li, F. et al. Regulation of TORC1 by MAPK signaling determines sensitivity and acquired resistance to trametinib in pediatric BRAFV600E brain tumor models. Clin. Cancer Res. 28, 3836–3849 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. Krepler, C. et al. Personalized preclinical trials in BRAF inhibitor-resistant patient-derived xenograft models identify second-line combination therapies. Clin. Cancer Res. 22, 1592–1602 (2016).

    CAS  PubMed  Google Scholar 

  64. Zhang, L. et al. B-cell lymphoma patient-derived xenograft models enable drug discovery and are a platform for personalized therapy. Clin. Cancer Res. 23, 4212–4223 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Chen, J. et al. Using patient-derived xenograft (PDX) models as a ‘black box’ to identify more applicable patients for ADP-ribose polymerase inhibitor (PARPi) treatment in ovarian cancer: searching for novel molecular and clinical biomarkers and performing a prospective preclinical trial. Cancers (Basel) 14, 4649 (2022).

    CAS  PubMed  Google Scholar 

  66. Hurley, R. M. et al. Characterization of a RAD51C-silenced high-grade serous ovarian cancer model during development of PARP inhibitor resistance. NAR Cancer 3, zcab028 (2021).

    PubMed  PubMed Central  Google Scholar 

  67. Einarsdottir, B. O. et al. A patient-derived xenograft pre-clinical trial reveals treatment responses and a resistance mechanism to karonudib in metastatic melanoma. Cell Death Dis. 9, 810 (2018).

    PubMed  PubMed Central  Google Scholar 

  68. Lazzari, L. et al. Patient-Derived xenografts and matched cell lines identify pharmacogenomic vulnerabilities in colorectal cancer. Clin. Cancer Res. 25, 6243–6259 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. Lin, S. et al. An in vivo CRISPR screening platform for prioritizing therapeutic targets in AML. Cancer Discov. 12, 432–449 (2022).

    CAS  PubMed  Google Scholar 

  70. Xie, J. et al. Optimization of a clofarabine-based drug combination regimen for the preclinical evaluation of pediatric acute lymphoblastic leukemia. Pediatr. Blood Cancer 67, e28133 (2020).

    PubMed  Google Scholar 

  71. Carlet, M. et al. In vivo inducible reverse genetics in patients’ tumors to identify individual therapeutic targets. Nat. Commun. 12, 5655 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. Zhang, F. et al. Characterization of drug responses of mini patient-derived xenografts in mice for predicting cancer patient clinical therapeutic response. Cancer Commun. 38, 60 (2018).

    CAS  Google Scholar 

  73. Pettersen, S. et al. Breast cancer patient-derived explant cultures recapitulate in vivo drug responses. Front. Oncol. 13, 1040665 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. Cotler, M. J. et al. Machine-learning aided in situ drug sensitivity screening predicts treatment outcomes in ovarian PDX tumors. Transl. Oncol. 21, 101427 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  75. Gao, H. et al. High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response. Nat. Med. 21, 1318–1325 (2015). This paper uses a large biobank of PDXs to screen drugs for many tumour types in a 1 × 1 × 1 design to approximate patient clinical trials.

    CAS  PubMed  Google Scholar 

  76. Lalazar, G. et al. Identification of novel therapeutic targets for fibrolamellar carcinoma using patient-derived xenografts and direct-from-patient screening. Cancer Discov. 11, 2544–2563 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  77. Zhuo, J. et al. The distinct responsiveness of cytokeratin 19-positive hepatocellular carcinoma to regorafenib. Cell Death Dis. 12, 1084 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. Kiyuna, T. et al. Eribulin suppressed cisplatinum- and doxorubicin-resistant recurrent lung metastatic osteosarcoma in a patient-derived orthotopic xenograft mouse model. Anticancer Res. 39, 4775–4779 (2019).

    CAS  PubMed  Google Scholar 

  79. Miyake, K. et al. Gemcitabine combined with docetaxel precisely regressed a recurrent leiomyosarcoma peritoneal metastasis in a patient-derived orthotopic xenograft (PDOX) model. Biochem. Biophys. Res. Commun. 509, 1041–1046 (2019).

    CAS  PubMed  Google Scholar 

  80. Zhang, Z. et al. A patient-derived orthotopic xenograft (PDOX) nude-mouse model precisely identifies effective and ineffective therapies for recurrent leiomyosarcoma. Pharmacol. Res. 142, 169–175 (2019).

    CAS  PubMed  Google Scholar 

  81. Kawaguchi, K. et al. Patterns of sensitivity to a panel of drugs are highly individualised for undifferentiated/unclassified soft tissue sarcoma (USTS) in patient-derived orthotopic xenograft (PDOX) nude-mouse models. J. Drug Target. 27, 211–216 (2019).

    CAS  PubMed  Google Scholar 

  82. Igarashi, K. et al. Pazopanib regresses a doxorubicin-resistant synovial sarcoma in a patient-derived orthotopic xenograft mouse model. Tissue Cell 58, 107–111 (2019).

    CAS  PubMed  Google Scholar 

  83. Igarashi, K. et al. Recombinant methioninase in combination with doxorubicin (DOX) overcomes first-line DOX resistance in a patient-derived orthotopic xenograft nude-mouse model of undifferentiated spindle-cell sarcoma. Cancer Lett. 417, 168–173 (2018).

    CAS  PubMed  Google Scholar 

  84. Igarashi, K. et al. Temozolomide regresses a doxorubicin-resistant undifferentiated spindle-cell sarcoma patient-derived orthotopic xenograft (PDOX): precision-oncology nude-mouse model matching the patient with effective therapy. J. Cell. Biochem. 119, 6598–6603 (2018).

    CAS  PubMed  Google Scholar 

  85. Kiyuna, T. et al. Trabectedin arrests a doxorubicin-resistant PDGFRA-activated liposarcoma patient-derived orthotopic xenograft (PDOX) nude mouse model. BMC Cancer 18, 840 (2018).

    PubMed  PubMed Central  Google Scholar 

  86. Kawaguchi, K. et al. Vemurafenib-resistant BRAF-V600E-mutated melanoma is regressed by MEK-targeting drug trametinib, but not cobimetinib in a patient-derived orthotopic xenograft (PDOX) mouse model. Oncotarget 7, 71737–71743 (2016).

    PubMed  PubMed Central  Google Scholar 

  87. Rusert, J. M. et al. Functional precision medicine identifies new therapeutic candidates for medulloblastoma. Cancer Res. 80, 5393–5407 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  88. Olesinski, E. A. et al. Acquired multidrug resistance in AML is caused by low apoptotic priming in relapsed myeloblasts. Blood Cancer Discov. 5, 180–201 (2024).

    CAS  PubMed  PubMed Central  Google Scholar 

  89. Golebiewska, A. et al. Patient-derived organoids and orthotopic xenografts of primary and recurrent gliomas represent relevant patient avatars for precision oncology. Acta Neuropathol. 140, 919–949 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  90. Cappelli, L. V. et al. Endothelial cell–leukemia interactions remodel drug responses, uncovering T-ALL vulnerabilities. Blood 141, 503–518 (2023).

    CAS  PubMed  Google Scholar 

  91. Lim, J. J. et al. Rational drug combination design in patient-derived avatars reveals effective inhibition of hepatocellular carcinoma with proteasome and CDK inhibitors. JaExp. Clin. Cancer Res. 41, 249 (2022).

    CAS  Google Scholar 

  92. Morikawa, A. et al. Optimizing precision medicine for breast cancer brain metastases with functional drug response assessment. Cancer Res. Commun. 3, 1093–1103 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  93. Bruna, A. et al. A biobank of breast cancer explants with preserved intra-tumor heterogeneity to screen anticancer compounds. Cell 167, 260–274.e22 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  94. Guillen, K. P. et al. A human breast cancer-derived xenograft and organoid platform for drug discovery and precision oncology. Nat. Cancer 3, 232–250 (2022). This paper uses matched PDXs and organoid models to increase the throughput and translatability of drug screening in patient-derived models.

    PubMed  PubMed Central  Google Scholar 

  95. Altunel, E. et al. Development of a precision medicine pipeline to identify personalized treatments for colorectal cancer. BMC Cancer 20, 592 (2020).

    PubMed  PubMed Central  Google Scholar 

  96. Somarelli, J. A. et al. A precision medicine drug discovery pipeline identifies combined CDK2 and 9 inhibition as a novel therapeutic strategy in colorectal cancer. Mol. Cancer Ther. 19, 2516–2527 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  97. Palmer, A. C. et al. A proof of concept for biomarker-guided targeted therapy against ovarian cancer based on patient-derived tumor xenografts. Cancer Res. 80, 4278–4287 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  98. Fazio, M., Ablain, J., Chuan, Y., Langenau, D. M. & Zon, L. I. Zebrafish patient avatars in cancer biology and precision cancer therapy. Nat. Rev. Cancer 20, 263–273 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  99. Yan, C. et al. Visualizing engrafted human cancer and therapy responses in immunodeficient zebrafish. Cell 177, 1903–1914.e14 (2019). This paper incorporates imaging to assess drug responses in zPDXs.

    CAS  PubMed  PubMed Central  Google Scholar 

  100. Costa, B. et al. Zebrafish avatar-test forecasts clinical response to chemotherapy in patients with colorectal cancer. Nat. Commun. 15, 4771 (2024). This paper is, to date, the largest clinical study using zPDXs for FPO, and forecasts patient progression with high accuracy.

    CAS  PubMed  PubMed Central  Google Scholar 

  101. Chen, X., Li, Y., Yao, T. & Jia, R. Benefits of zebrafish xenograft models in cancer research. Front. Cell Dev. Biol. 9, 616551 (2021).

    PubMed  PubMed Central  Google Scholar 

  102. Fior, R. et al. Single-cell functional and chemosensitive profiling of combinatorial colorectal therapy in zebrafish xenografts. Proc. Natl Acad. Sci. USA 114, E8234–E8243 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  103. Gatzweiler, C. et al. Functional therapeutic target validation using pediatric zebrafish xenograft models. Cancers (Basel) 14, 849 (2022).

    CAS  PubMed  Google Scholar 

  104. Wang, W. et al. Progress in building clinically relevant patient-derived tumor xenograft models for cancer research. Anim. Model. Exp. Med. 6, 381–398 (2023).

    CAS  Google Scholar 

  105. Somasagara, R. R. et al. Targeted therapy of human leukemia xenografts in immunodeficient zebrafish. Sci. Rep. 11, 5715 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  106. Villanueva, H. et al. Characterizing treatment resistance in muscle invasive bladder cancer using the chicken egg chorioallantoic membrane patient-derived xenograft model. Heliyon 8, e12570 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  107. Charbonneau, M. et al. Establishment of a ccRCC patient-derived chick chorioallantoic membrane model for drug testing. Front. Med. 9, 1003914 (2022).

    Google Scholar 

  108. Pizon, M. et al. Chick chorioallantoic membrane (CAM) assays as a model of patient-derived xenografts from circulating cancer stem cells (cCSCs) in breast cancer patients. Cancers (Basel) 14, 1476 (2022).

    CAS  PubMed  Google Scholar 

  109. Souto, E. P., Dobrolecki, L. E., Villanueva, H., Sikora, A. G. & Lewis, M. T. In vivo modeling of human breast cancer using cell line and patient-derived xenografts. J. Mammary Gland Biol. Neoplasia 27, 211–230 (2022).

    PubMed  PubMed Central  Google Scholar 

  110. Noto, F. K. et al. The SRG rat, a Sprague-Dawley Rag2/Il2rg double-knockout validated for human tumor oncology studies. PLoS One 15, e0240169 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  111. Isaksson, I. M., Theodorsson, A., Theodorsson, E. & Strom, J. O. Methods for 17β-oestradiol administration to rats. Scand. J. Clin. Lab. Invest. 71, 583–592 (2011).

    CAS  PubMed  Google Scholar 

  112. Ingberg, E., Theodorsson, A., Theodorsson, E. & Strom, J. O. Methods for long-term 17β-estradiol administration to mice. Gen. Comp. Endocrinol. 175, 188–193 (2012).

    CAS  PubMed  Google Scholar 

  113. Harvell, D. M. et al. Rat strain-specific actions of 17β-estradiol in the mammary gland: correlation between estrogen-induced lobuloalveolar hyperplasia and susceptibility to estrogen-induced mammary cancers. Proc. Natl Acad. Sci. USA 97, 2779–2784 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  114. Hendricks-Wenger, A. et al. Establishing an immunocompromised porcine model of human cancer for novel therapy development with pancreatic adenocarcinoma and irreversible electroporation. Sci. Rep. 11, 7584 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  115. Hoopes, P. J. et al. Porcine–human glioma xenograft model. Immunosuppression and model reproducibility. Cancer Treat. Res. Commun. 38, 100789 (2024).

    PubMed  PubMed Central  Google Scholar 

  116. Zhao, P. et al. Personalized treatment based on mini patient-derived xenografts and WES/RNA sequencing in a patient with metastatic duodenal adenocarcinoma. Cancer Commun. 38, 54 (2018).

    Google Scholar 

  117. Zhai, J. et al. Prediction of sensitivity and efficacy of clinical chemotherapy using larval zebrafish patient-derived xenografts of gastric cancer. Front. Cell Dev. Biol. 9, 680491 (2021).

    PubMed  PubMed Central  Google Scholar 

  118. Wang, Y., Cui, J. & Wang, L. Patient-derived xenografts: a valuable platform for clinical and preclinical research in pancreatic cancer. Chin. Clin. Oncol. 8, 17 (2019).

    PubMed  Google Scholar 

  119. Costa, B., Estrada, M. F., Barroso, M. T. & Fior, R. Zebrafish patient-derived avatars from digestive cancers for anti-cancer therapy screening. Curr. Protoc. 2, e415 (2022).

    CAS  PubMed  Google Scholar 

  120. US National Library of Medicine. ClinicalTrials.gov http://www.clinicaltrials.gov/show/NCT01858168 (2024).

  121. Lindahl, G. et al. Zebrafish tumour xenograft models: a prognostic approach to epithelial ovarian cancer. npj Precis. Oncol. 8, 53 (2024).

    CAS  PubMed  PubMed Central  Google Scholar 

  122. US National Library of Medicine. ClinicalTrials.gov http://www.clinicaltrials.gov/show/NCT05464082 (2024).

  123. US National Library of Medicine. ClinicalTrials.gov http://www.clinicaltrials.gov/show/NCT04450706 (2024).

  124. US National Library of Medicine. ClinicalTrials.gov http://www.clinicaltrials.gov/show/NCT05504772 (2024).

  125. US National Library of Medicine. ClinicalTrials.gov http://www.clinicaltrials.gov/show/NCT04373928 (2023).

  126. Xu, X. et al. A living biobank of matched pairs of patient-derived xenografts and organoids for cancer pharmacology. PLoS One 18, e0279821 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  127. Scherer, S. D. et al. Breast cancer PDxO cultures for drug discovery and functional precision oncology. STAR Protoc. 4, 102402 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  128. Hamilton, J. G. et al. Clinician perspectives on communication and implementation challenges in precision oncology. Per. Med. 18, 559–572 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  129. Grauman, A., Ancillotti, M., Veldwijk, J. & Mascalzoni, D. Precision cancer medicine and the doctor–patient relationship: a systematic review and narrative synthesis. BMC Med. Inf. Decis. Mak. 23, 286 (2023).

    CAS  Google Scholar 

  130. Cheung, A. T. M. et al. Racial and ethnic disparities in a real-world precision oncology data registry. npj Precis. Oncol. 7, 7 (2023). This paper highlights the challenges and opportunities in precision oncology for diverse populations.

    PubMed  PubMed Central  Google Scholar 

  131. Aldrighetti, C. M., Niemierko, A., Van Allen, E., Willers, H. & Kamran, S. C. Racial and ethnic disparities among participants in precision oncology clinical studies. JAMA Netw. Open 4, e2133205 (2021).

    PubMed  PubMed Central  Google Scholar 

  132. O’Dwyer, P. J. et al. The NCI-MATCH trial: lessons for precision oncology. Nat. Med. 29, 1349–1357 (2023).

    PubMed  PubMed Central  Google Scholar 

  133. Yamada, H. Y. et al. Molecular disparities in colorectal cancers of white Americans, Alabama African Americans, and Oklahoma American Indians. npj Precis. Oncol. 7, 79 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  134. Martini, R. et al. African ancestry-associated gene expression profiles in triple-negative breast cancer underlie altered tumor biology and clinical outcome in women of African descent. Cancer Discov. 12, 2530–2551 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  135. US National Library of Medicine. ClinicalTrials.gov http://www.clinicaltrials.gov/show/NCT04410302 (2023).

  136. Acuna-Villaorduna, A., Baranda, J. C., Boehmer, J., Fashoyin-Aje, L. & Gore, S. D. Equitable access to clinical trials: how do we achieve it? Am. Soc. Clin. Oncol. Educ. Book 43, e389838 (2023).

    PubMed  Google Scholar 

  137. Lee, H. et al. Analysis and optimization of equitable US cancer clinical trial center access by travel time. JAMA Oncol. 10, 652–657 (2024).

    PubMed  PubMed Central  Google Scholar 

  138. Cho, S. Y. Patient-derived xenografts as compatible models for precision oncology. Lab. Anim. Res. 36, 14 (2020).

    PubMed  PubMed Central  Google Scholar 

  139. Valta, M. et al. Critical evaluation of the subcutaneous engraftments of hormone naive primary prostate cancer. Transl. Androl. Urol. 9, 1120–1134 (2020).

    PubMed  PubMed Central  Google Scholar 

  140. Dobrolecki, L. E. et al. Patient-derived xenograft (PDX) models in basic and translational breast cancer research. Cancer Metastasis Rev. 35, 547–573 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  141. DeRose, Y. S. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  142. Meehan, T. F. et al. PDX-MI: minimal information for patient-derived tumor xenograft models. Cancer Res. 77, e62–e66 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  143. Meric-Bernstam, F. et al. Assessment of patient-derived xenograft growth and antitumor activity: the NCI PDXNet consensus recommendations. Mol. Cancer Ther. 23, 924–938 (2024).

    CAS  PubMed  PubMed Central  Google Scholar 

  144. Koc, S. et al. PDXNet portal: patient-derived xenograft model, data, workflow and tool discovery. NAR Cancer 4, zcac014 (2022).

    PubMed  PubMed Central  Google Scholar 

  145. Pezalla, E. J. Payer view of personalized medicine. Am. J. Health Syst. Pharm. 73, 2007–2012 (2016).

    PubMed  Google Scholar 

  146. Wensink, G. E. et al. Patient-derived organoids as a predictive biomarker for treatment response in cancer patients. npj Precis. Oncol. 5, 30 (2021).

    PubMed  PubMed Central  Google Scholar 

  147. Jian, M. et al. A novel patient-derived organoids-based xenografts model for preclinical drug response testing in patients with colorectal liver metastases. J. Transl. Med. 18, 234 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  148. Zhu, X. et al. Individualized therapy based on the combination of mini-PDX and NGS for a patient with metastatic AFP-producing and HER-2 amplified gastric cancer. Oncol. Lett. 24, 411 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  149. Hegde, P. S. & Chen, D. S. Top 10 challenges in cancer immunotherapy. Immunity 52, 17–35 (2020).

    CAS  PubMed  Google Scholar 

  150. Chuprin, J. et al. Humanized mouse models for immuno-oncology research. Nat. Rev. Clin. Oncol. 20, 192–206 (2023).

    PubMed  PubMed Central  Google Scholar 

  151. Scherer, S. D. et al. An immune-humanized patient-derived xenograft model of estrogen-independent, hormone receptor positive metastatic breast cancer. Breast Cancer Res. 23, 100 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  152. Stossel, C. et al. Spectrum of response to platinum and PARP inhibitors in germline BRCA-associated pancreatic cancer in the clinical and preclinical setting. Cancer Discov. 13, 1826–1843 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  153. Yan, C. et al. Generation of orthotopic patient-derived xenografts in humanized mice for evaluation of emerging targeted therapies and immunotherapy combinations for melanoma. Cancers (Basel) 15, 3695 (2023).

    CAS  PubMed  Google Scholar 

  154. Zeleniak, A. et al. De novo construction of T cell compartment in humanized mice engrafted with iPSC-derived thymus organoids. Nat. Methods 19, 1306–1319 (2022). This paper describes an innovative way to overcome some aspects of immune deficiency in PDX models.

    CAS  PubMed  Google Scholar 

  155. Hamilton, N., Sabroe, I. & Renshaw, S. A. A method for transplantation of human HSCs into zebrafish, to replace humanised murine transplantation models. F1000Res 7, 594 (2018).

    PubMed  PubMed Central  Google Scholar 

  156. Agarwal, Y. et al. Development of humanized mouse and rat models with full-thickness human skin and autologous immune cells. Sci. Rep. 10, 14598 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  157. Boettcher, A. N. et al. Novel engraftment and T cell differentiation of human hematopoietic cells in ART–/–IL2RG–/Y SCID pigs. Front. Immunol. 11, 100 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  158. Bhinder, B., Gilvary, C., Madhukar, N. S. & Elemento, O. Artificial intelligence in cancer research and precision medicine. Cancer Discov. 11, 900–915 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  159. Perez-Lopez, R., Ghaffari Laleh, N., Mahmood, F. & Kather, J. N. A guide to artificial intelligence for cancer researchers. Nat. Rev. Cancer 24, 427–441 (2024).

    CAS  PubMed  Google Scholar 

  160. Mundi, P. S. et al. A transcriptome-based precision oncology platform for patient-therapy alignment in a diverse set of treatment-resistant malignancies. Cancer Discov. 13, 1386–1407 (2023). This paper describes the use of computational algorithms to predict drug response in PDXs.

    CAS  PubMed  PubMed Central  Google Scholar 

  161. Alvarez, M. J. et al. Functional characterization of somatic mutations in cancer using network-based inference of protein activity. Nat. Genet. 48, 838–847 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  162. Zushin, P. H., Mukherjee, S. & Wu, J. C. FDA Modernization Act 2.0: transitioning beyond animal models with human cells, organoids, and AI/ML-based approaches. J. Clin. Invest. 133, e175824 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  163. Shimizu, H. & Nakayama, K. I. A 23 gene-based molecular prognostic score precisely predicts overall survival of breast cancer patients. EBioMedicine 46, 150–159 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  164. Li, X., Hu, B., Li, H. & You, B. Application of artificial intelligence in the diagnosis of multiple primary lung cancer. Thorac. Cancer 10, 2168–2174 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  165. Luo, S. et al. Artificial intelligence-based collaborative filtering method with ensemble learning for personalized lung cancer medicine without genetic sequencing. Pharmacol. Res. 160, 105037 (2020).

    CAS  PubMed  Google Scholar 

  166. Liu, Q., Muglia, L. J. & Huang, L. F. Network as a biomarker: a novel network-based sparse bayesian machine for pathway-driven drug response prediction. Genes (Basel) 10, 602 (2019).

    CAS  PubMed  Google Scholar 

  167. Yamashita, R., Nishio, M., Do, R. K. G. & Togashi, K. Convolutional neural networks: an overview and application in radiology. Insights Imaging 9, 611–629 (2018).

    PubMed  PubMed Central  Google Scholar 

  168. Armstrong, P. B., Quigley, J. P. & Sidebottom, E. Transepithelial invasion and intramesenchymal infiltration of the chick embryo chorioallantois by tumor cell lines. Cancer Res. 42, 1826–1837 (1982).

    CAS  PubMed  Google Scholar 

  169. Tsimpaki, T. et al. Chick chorioallantoic membrane as a patient-derived xenograft model for uveal melanoma: imaging modalities for growth and vascular evaluation. Cancers (Basel) 15, 1436 (2023).

    PubMed  Google Scholar 

  170. Ribatti, D. The chick embryo chorioallantoic membrane (CAM). A multifaceted experimental model. Mech. Dev. 141, 70–77 (2016).

    CAS  PubMed  Google Scholar 

  171. DeBord, L. C. et al. The chick chorioallantoic membrane (CAM) as a versatile patient-derived xenograft (PDX) platform for precision medicine and preclinical research. Am. J. Cancer Res. 8, 1642–1660 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  172. Haldi, M., Ton, C., Seng, W. L. & McGrath, P. Human melanoma cells transplanted into zebrafish proliferate, migrate, produce melanin, form masses and stimulate angiogenesis in zebrafish. Angiogenesis 9, 139–151 (2006).

    PubMed  Google Scholar 

  173. Marques, I. J. et al. Metastatic behaviour of primary human tumours in a zebrafish xenotransplantation model. BMC Cancer 9, 128 (2009).

    PubMed  PubMed Central  Google Scholar 

  174. Fiebig, H. H. et al. Development of three human small cell lung cancer models in nude mice. Recent Results Cancer Res. 97, 77–86 (1985).

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  176. Ozaki, M. et al. A rat-based preclinical platform facilitating transcatheter hepatic arterial infusion in immunodeficient rats with liver xenografts of patient-derived pancreatic ductal adenocarcinoma. Sci. Rep. 14, 10529 (2024).

    CAS  PubMed  PubMed Central  Google Scholar 

  177. Lander, E. S. et al. Initial sequencing and analysis of the human genome. Nature 409, 860–921 (2001).

    CAS  PubMed  Google Scholar 

  178. Venter, J. C. et al. The sequence of the human genome. Science 291, 1304–1351 (2001).

    CAS  PubMed  Google Scholar 

  179. Collins, F. S. & Fink, L. The Human Genome Project. Alcohol Health Res. World 19, 190–195 (1995).

    PubMed  PubMed Central  Google Scholar 

  180. Baselga, J. et al. Phase II study of weekly intravenous recombinant humanized anti-p185HER2 monoclonal antibody in patients with HER2/neu-overexpressing metastatic breast cancer. J. Clin. Oncol. 14, 737–744 (1996).

    CAS  PubMed  Google Scholar 

  181. Hudis, C. A. Trastuzumab—mechanism of action and use in clinical practice. N. Engl. J. Med. 357, 39–51 (2007).

    CAS  PubMed  Google Scholar 

  182. Slamon, D. J. et al. Use of chemotherapy plus a monoclonal antibody against HER2 for metastatic breast cancer that overexpresses HER2. N. Engl. J. Med. 344, 783–792 (2001).

    CAS  PubMed  Google Scholar 

  183. Druker, B. J. et al. Efficacy and safety of a specific inhibitor of the BCR-ABL tyrosine kinase in chronic myeloid leukemia. N. Engl. J. Med. 344, 1031–1037 (2001).

    CAS  PubMed  Google Scholar 

  184. Cancer Genome Atlas Research Network et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat. Genet. 45, 1113–1120 (2013). This paper describes the seminal TCGA study, which was the primary driver for the precision medicine concept.

    PubMed Central  Google Scholar 

  185. Personalized Medicine Coalition. Personalized Medicine at FDA: The Scope and Significance of Progress in 2023 (PMC, 2024).

  186. Flaherty, K. T. et al. Molecular landscape and actionable alterations in a genomically guided cancer clinical trial: National Cancer Institute Molecular Analysis for Therapy Choice (NCI-MATCH). J. Clin. Oncol. 38, 3883–3894 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank members of the PDXNet for fruitful discussions that led to some of the concepts in this manuscript.

Author information

Author notes

  1. These authors contributed equally: Zannel Blanchard, Elisabeth A. Brown, Arevik Ghazaryan.

Authors and Affiliations

  1. Department of Oncological Sciences, University of Utah, Huntsman Cancer Institute, Salt Lake City, UT, USA

    Zannel Blanchard, Elisabeth A. Brown, Arevik Ghazaryan & Alana L. Welm

Authors
  1. Zannel Blanchard
    View author publications

    Search author on:PubMed Google Scholar

  2. Elisabeth A. Brown
    View author publications

    Search author on:PubMed Google Scholar

  3. Arevik Ghazaryan
    View author publications

    Search author on:PubMed Google Scholar

  4. Alana L. Welm
    View author publications

    Search author on:PubMed Google Scholar

Contributions

E.A.B., Z.B. and A.G. researched data for the article. All authors contributed substantially to discussion of the content, wrote the article and reviewed and/or edited the manuscript before submission.

Corresponding author

Correspondence to Alana L. Welm.

Ethics declarations

Competing interests

The University of Utah may license patient-derived xenograft (PDX) or PDX-derived models made by the Welm laboratory to for-profit companies, which may result in tangible property royalties to the University and members of the Welm laboratory who developed them. A.L.W. has research funding from AbbVie. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature Reviews Cancer thanks the anonymous reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Related links

CancerModels.org: https://www.cancermodels.org/

EurOPDX: https://europdx.eu/

NCI Patient-Derived Models Repository: https://pdmr.cancer.gov/

Paediatric Preclinical In Vivo Testing Consortium: https://preclinicalpivot.org/

PDX Network (PDXNet): https://www.pdxnetwork.org/

Precision Medicine Initiative: https://obamawhitehouse.archives.gov/precision-medicine

Singapore Translational Cancer Consortium: https://www.stcc.sg/

The Cancer Genome Atlas (TCGA): https://www.cancer.gov/ccg/research/genome-sequencing/tcga

The Human Genome Project: https://www.genome.gov/human-genome-project

Glossary

BH3 profiling

A functional assay in which cells are perturbed with BH3-domain peptides, and cytochrome c release from the mitochondria is measured to determine cellular dependence on individual anti-apoptotic or pro-apoptotic BH3 proteins.

Chorioallantoic membrane

(CAM). A vascularized membrane found in chicken eggs that can serve as the site of engraftment for patient-derived xenograft (PDX).

Deep learning

A subset of machine learning that applies artificial neural networks with multiple layers to recognize and learn patterns, simulating the structure and function of the human brain.

Genomic drivers

Mutations or other genetic alterations that contribute to the initiation or progression of disease.

Induced pluripotent stem cells

Stem cells generated from somatic cells through re-expression of key pluripotency transcription factors.

Institutional review boards

Groups of reviewers who monitor ethics of biomedical research involving human subjects.

Machine learning

A branch of artificial intelligence (AI) in which computers analyse and learn from vast datasets to uncover complex patterns at levels beyond human capability.

Master regulator

A gene product that regulates a large number of other genes within a biological system.

Neural networks

Networks of interconnected nodes, referred to as neurons, that function within machine learning systems to process data in a manner similar to the human brain.

Salmonella typhimurium A1-R

An adaptive anaerobic bacterium that cannot synthesize leucine and arginine and can be engineered to deliver cargo to tumours.

Take rates

A commonly used term referring to the success rate of engraftment when establishing patient-derived xenograft (PDX) models.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Blanchard, Z., Brown, E.A., Ghazaryan, A. et al. PDX models for functional precision oncology and discovery science. Nat Rev Cancer 25, 153–166 (2025). https://doi.org/10.1038/s41568-024-00779-3

Download citation

  • Accepted:

  • Published:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41568-024-00779-3

Search

Advanced search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer