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  • Review Article
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Towards precision oncology with patient-derived xenografts

Nature Reviews Clinical Oncology volume 19, pages 719–732 (2022)Cite this article

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Abstract

Under the selective pressure of therapy, tumours dynamically evolve multiple adaptive mechanisms that make static interrogation of genomic alterations insufficient to guide treatment decisions. Clinical research does not enable the assessment of how various regulatory circuits in tumours are affected by therapeutic insults over time and space. Likewise, testing different precision oncology approaches informed by composite and ever-changing molecular information is hard to achieve in patients. Therefore, preclinical models that incorporate the biology and genetics of human cancers, facilitate analyses of complex variables and enable adequate population throughput are needed to pinpoint randomly distributed response predictors. Patient-derived xenograft (PDX) models are dynamic entities in which cancer evolution can be monitored through serial propagation in mice. PDX models can also recapitulate interpatient diversity, thus enabling the identification of response biomarkers and therapeutic targets for molecularly defined tumour subgroups. In this Review, we discuss examples from the past decade of the use of PDX models for precision oncology, from translational research to drug discovery. We elaborate on how and to what extent preclinical observations in PDX models have confirmed and/or anticipated findings in patients. Finally, we illustrate emerging methodological efforts that could broaden the application of PDX models by honing their predictive accuracy or improving their versatility.

Key points

  • The generation of patient-derived xenograft (PDX) models involves a selection bottleneck imposed by tumour engraftment, and subsequent propagation influences the evolutionary trajectories of cancer cells; therefore, these models can be used to investigate tumour clonal composition and competition during spontaneous tumour progression and under the selective pressure of treatment.

  • Work in which PDX models were studied at the moment of maximal tumour shrinkage during exposure to a given therapy has provided insights into lineage-specific phenotypic adaptations, which underlie the acquisition of drug tolerance and are responsible for sustaining residual disease.

  • The substitution of human stromal cells by mouse stromal cells that occurs after tumour implantation has enabled the identification of transcriptional signatures related to either cancer or stromal cells with predictive and prognostic value.

  • Large collections of PDX models have contributed to the discovery and validation of novel biomarkers of response to treatment and have aided the design of new therapeutic options, some of which have entered the clinic.

  • Next-generation models with higher tissue complexity (humanized mice) or easier manageability (non-mammal organisms, ex vivo cultures) are being developed that complement conventional PDX models.

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Fig. 1: PDX models as dynamic tools to trace cancer clonal evolution.
Fig. 2: Studying phenotypic rewiring in drug-tolerant ‘persister’ cells using PDX models.
Fig. 3: Discriminating the contributions of cancer cells and stromal cells in PDX models.

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Acknowledgements

The authors thank their friends and colleagues at the Laboratory of Translational Cancer Medicine at the Candiolo Cancer Institute for their comments and suggestions. The authors’ research is supported by Associazione Italiana per la Ricerca sul Cancro (Investigator Grant 22802 and AIRC 5×1000 grant 21091), AIRC–CRUK–FC AECC Accelerator Award 22795, European Union H2020 grant agreement no. 754923 COLOSSUS, and Fondazione Piemontese per la Ricerca sul Cancro–ONLUS 5×1000 Ministero della Salute 2016. L.T. is a member of the EurOPDX consortium. All figures were initially created with Biorender.com.

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  1. These authors contributed equally: Eugenia R. Zanella, Elena Grassi.

Authors and Affiliations

  1. Candiolo Cancer Institute – FPO IRCCS, Candiolo, Italy

    Eugenia R. Zanella, Elena Grassi & Livio Trusolino

  2. Department of Oncology, University of Torino, Candiolo, Italy

    Elena Grassi & Livio Trusolino

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  1. Eugenia R. Zanella
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L.T. conceived and wrote the manuscript. E.R.Z. and E.G. researched data for the article. All authors contributed to discussions of content, and reviewed and edited the manuscript before submission.

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Correspondence to Livio Trusolino.

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Competing interests

L.T. has received research grants from Menarini, Merck KGaA, Merus, Pfizer, Servier and Symphogen. E.R.Z. and E.G. declare no competing interests.

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Nature Reviews Clinical Oncology thanks E. Marangoni and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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ClinicalTrials.gov: https://clinicaltrials.gov/

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PDX Finder: https://www.pdxfinder.org/

PDXNet: https://www.pdxnetwork.org/

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Zanella, E.R., Grassi, E. & Trusolino, L. Towards precision oncology with patient-derived xenografts. Nat Rev Clin Oncol 19, 719–732 (2022). https://doi.org/10.1038/s41571-022-00682-6

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