PDX models for functional precision oncology and discovery science.

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.

COI Statement

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.

References:

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

Article info

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Doi:

10.1038/s41568-024-00779-3

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PDX models for functional precision oncology and discovery science.

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.

COI Statement

References:

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