Towards precision oncology with patient-derived xenografts

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.