Opportunities and challenges for patient-derived models of brain tumors in functional precision medicine.

IF 6.8 1区 医学 Q1 ONCOLOGY NPJ Precision Oncology Pub Date : 2025-02-14 DOI:10.1038/s41698-025-00832-w
Breanna Mann, Nichole Artz, Rami Darawsheh, David E Kram, Shawn Hingtgen, Andrew B Satterlee
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引用次数: 0

Abstract

Here, we review a growing paradigm shift from genomics-based precision medicine toward functional precision medicine, which evaluates therapeutic efficacy by directly treating living patient tumors ex vivo to better predict patient-specific responses to treatment. We discuss several classes of patient-derived models of central nervous system tumors, highlighting unique features of each. Each class of models holds promise to improve treatment selection, prolong survival, and enhance patient outcomes.

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来源期刊
CiteScore
9.90
自引率
1.30%
发文量
87
审稿时长
18 weeks
期刊介绍: Online-only and open access, npj Precision Oncology is an international, peer-reviewed journal dedicated to showcasing cutting-edge scientific research in all facets of precision oncology, spanning from fundamental science to translational applications and clinical medicine.
期刊最新文献
Towards an interpretable deep learning model of cancer. Opportunities and challenges for patient-derived models of brain tumors in functional precision medicine. Author Correction: An automated deep learning pipeline for EMVI classification and response prediction of rectal cancer using baseline MRI: a multi-centre study. An integrated perspective on single-cell and spatial transcriptomic signatures in high-grade gliomas. RevCAR-mediated T-cell response against PD-L1-expressing cells turns suppression into activation.
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