Magnetic Resonance Imaging-Based Radiogenomic Analysis Reveals Genomic Determinants for Nanoparticle Delivery into Tumors
Even though the enhanced permeability and retention (EPR) effect is applicable for the passive targeting of solid tumors, many nanodrugs have failed to achieve meaningful clinical outcomes due to the heterogeneity of EPR effect. Therefore, understanding the mechanism of the EPR effect is crucial to overcome the obstacles nanomedicines face in clinical translation. The aim of this study was to establish a reliable method to increase awareness of the critical influencing factors of nanoparticle (NP) transport into tumors based on the EPR effect using a combined radiogenomics and clinical magnetic resonance imaging (MRI) technique and gene set pathway enrichment analysis. Employing poly(lactic-<i>co</i>-glycolic acid) (PLGA)-coated Fe<sub>3</sub>O<sub>4</sub> NPs as the contrast agent, the monolayer and multilayer distribution of the NPs were observed and quantitatively analyzed by MRI, improving the accuracy of evaluating vascular permeability by MRI. By performing Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of genes and pathways, we identified a variety of genes affecting vascular permeability, such as Cldn1, Dlg2, Bves, Prkag3, Cldn10, and Cldn8, which are related to tight junctions and control the permeability of blood vessels in tumors. The method presented here provides an MRI-supported approach to increase the breadth of data collected from genetic screens, reveals genetic evidence of the presence of NPs in tumors and lays a foundation for clinical patient stratification and personalized treatment.
Authors
- Na Lü (ORCID: https://orcid.org/0000-0001-5771-247X)
- Ming Ma (ORCID: https://orcid.org/0000-0001-5324-4082)
- Hao Wan (ORCID: https://orcid.org/0000-0002-9990-2359)
- Mengjun Wang (ORCID: https://orcid.org/0000-0001-8416-048X)
- Haoan Wu (ORCID: https://orcid.org/0000-0002-9586-5060)
- Jing Tu (ORCID: https://orcid.org/0000-0002-1310-5915)
- Ying Zhao (ORCID: https://orcid.org/0000-0001-7776-5114)
- Shouhua Luo (ORCID: https://orcid.org/0000-0002-3307-1652)
- Jingyue Zhang (ORCID: https://orcid.org/0000-0002-7137-9148)
- Fengchao Zang
- Qianqian Li (ORCID: https://orcid.org/0000-0002-5340-5218)
- Yu Zhang (ORCID: https://orcid.org/0000-0002-0228-7979)
- Fangfang Shi (ORCID: https://orcid.org/0000-0002-0453-833X)
- Di Liu (ORCID: https://orcid.org/0009-0001-1171-6659)
- Mingze Lu
- Fei Wang (ORCID: https://orcid.org/0000-0001-6344-1872)
Institutions
- Zhongda Hospital Southeast University (CN)
- State Key Laboratory of Digital Medical Engineering (CN)
- Southeast University (CN)
Publication Details
- Journal
- ACS Nano
- Published
- 2024-12-12
- DOI
- https://doi.org/10.1021/acsnano.4c09387
- Citations
- 9
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 3.64
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Jiangsu Province
- Jiangsu Planned Projects for Postdoctoral Research Funds
- National Key Research and Development Program of China
- Fundamental Research Funds for the Central Universities