Polyhedral Oligomeric Silsesquioxane Based Material for Tumor Heterogeneity MR Visualization and Precise Treatment

Xiaochuan Geng , Zhiguo Zhuang , Jiahui Zhang , Xinxin Zhao , Haiying Lyu , Zi Fu , Xi Liu , Ting Qian , Zhihui Li , Tongtong Chen , Qing Zhang , Yan Zhou , Yong Lu , Gang Wei

SusMat ›› 2026, Vol. 6 ›› Issue (2) : e70061

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SusMat ›› 2026, Vol. 6 ›› Issue (2) :e70061 DOI: 10.1002/sus2.70061
RESEARCH ARTICLE
Polyhedral Oligomeric Silsesquioxane Based Material for Tumor Heterogeneity MR Visualization and Precise Treatment
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Abstract

Malignant tumors whose mortality rates up to 97% possess high heterogeneity that leads to rapid growth, strong invasive capability, and low drug sensitivity. Quantitative magnetic resonance imaging enables direct visualization of tumors. However, gadolinium-based contrast agents used in clinical practice have significant drawbacks of short half-life, non-specificity, and toxicity after gadolinium ions leakage. Herein, this study constructs a nanomaterial termed FFDP that utilizes octa (3-mercaptopropyl) silsesquioxane (POSS-SH) as functional bridge where the double-bonded folate, double-bonded doxorubicin, iron (II, III) oxide, and black phosphorus are grafted by chemical and metallic bonds through “click chemistry” approach. The FFDP being administered intravenously, enables real-time targeted enrichment of MR contrast that corresponding to the tumor heterogeneity and allow for precise tumor treatment at the site of in-situ triple-negative breast cancer (TNBC). Moreover, FFDP can deliver the anti-tumor drug into the deep tumor tissue. In an animal model of in-situ TNBC, efficient tumor ablation can be performed under FFDP enhanced quantitative magnetic resonance imaging. In conclusion, the FFDP nanomaterial designed in this study achieves real-time spatial tumor heterogeneity observation and precision tumor treatment. In clinical practice, POSS-based nanomaterials possess immense potential for MRI-guided individualized precision treatment for tumors.

Keywords

heterogeneity / magnetic resonance imaging / POSS / tumor

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Xiaochuan Geng, Zhiguo Zhuang, Jiahui Zhang, Xinxin Zhao, Haiying Lyu, Zi Fu, Xi Liu, Ting Qian, Zhihui Li, Tongtong Chen, Qing Zhang, Yan Zhou, Yong Lu, Gang Wei. Polyhedral Oligomeric Silsesquioxane Based Material for Tumor Heterogeneity MR Visualization and Precise Treatment. SusMat, 2026, 6 (2) : e70061 DOI:10.1002/sus2.70061

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References

[1]

Y. Chhabra and A. T. Weeraratna, “Fibroblasts in Cancer: Unity in Heterogeneity,” Cell 186 (2023): 1580-1609.

[2]

P. L. Bedard, A. R. Hansen, M. J. Ratain, and L. L. Siu, “Tumour Heterogeneity in the Clinic,” Nature 501 (2013): 355-364.

[3]

I. Dagogo-Jack and A. T. Shaw, “Tumour Heterogeneity and Resistance to Cancer Therapies,” Nature Reviews Clinical Oncology 15 (2018): 81-94.

[4]

J. Gao, X. Jiang, S. Lei, et al., “A Region-Confined PROTAC Nanoplatform for Spatiotemporally Tunable Protein Degradation and Enhanced Cancer Therapy,” Nature Communications 15 (2024): 6608.

[5]

C. M. Yamazaki, A. Yamaguchi, Y. Anami, et al., “Antibody-Drug Conjugates with Dual Payloads for Combating Breast Tumor Heterogeneity and Drug Resistance,” Nature Communications 12 (2021): 3528.

[6]

H. Zhou, Y. Hu, R. Luo, et al., “Multi-Region Exome Sequencing Reveals the Intratumoral Heterogeneity of Surgically Resected Small Cell Lung Cancer,” Nature Communications 12 (2021): 5431.

[7]

S. Shafighi, A. Geras, B. Jurzysta, et al., “Integrative Spatial and Genomic Analysis of Tumor Heterogeneity with Tumoroscope,” Nature Communications 15 (2024): 9343.

[8]

R. E. Hynds, A. Huebner, D. R. Pearce, et al., “Representation of Genomic Intratumor Heterogeneity in Multi-Region Non-Small Cell Lung Cancer Patient-Derived Xenograft Models,” Nature Communications 15 (2024): 4653.

[9]

J. Yin, F. Dong, J. An, et al., “Pattern Recognition of Microcirculation with Super-Resolution Ultrasound Imaging Provides Markers for Early Tumor Response to Anti-Angiogenic Therapy,” Theranostics 14 (2024): 1312-1324.

[10]

Z. Shi, X. Huang, Z. Cheng, et al., “MRI-Based Quantification of Intratumoral Heterogeneity for Predicting Treatment Response to Neoadjuvant Chemotherapy in Breast Cancer,” Radiology 308 (2023): e222830.

[11]

J. E. Iglesias, B. Billot, Y. Balbastre, et al., “SynthSR: A Public AI Tool to Turn Heterogeneous Clinical Brain Scans Into High-resolution T1-weighted Images for 3D Morphometry,” Science Advances 9 (2023): eadd3607.

[12]

B. Du, S. Wang, Y. Ma, X. Li, and Y. Li, “Imaging Intratumoral Heterogeneity of Diffuse Pancreatic Neuroendocrine Tumor with Multitracer PET/CT,” Clinical Nuclear Medicine 49 (2024): 549-550.

[13]

P. Katiyar, J. Schwenck, L. Frauenfeld, et al., “Quantification of Intratumoural Heterogeneity in Mice and Patients via Machine-Learning Models Trained on PET-MRI Data,” Nature Biomedical Engineering 7 (2023): 1014-1027.

[14]

S. Yu, Y. Yang, Z. Wang, et al., “CT-Based Conventional Radiomics and Quantification of Intratumoral Heterogeneity for Predicting Benign and Malignant Renal Lesions,” Cancer Imaging 24 (2024): 130.

[15]

G. Yang, J. Bai, M. Hao, L. Zhang, Z. Fan, and X. Wang, “Enhancing Recurrence Risk Prediction for Bladder Cancer Using Multi-Sequence MRI Radiomics,” Insights into Imaging 15 (2024): 88.

[16]

L. S. Hu, F. D'Angelo, T. M. Weiskittel, et al., “Integrated Molecular and Multiparametric MRI Mapping of High-Grade Glioma Identifies Regional Biologic Signatures,” Nature Communications 14 (2023): 6066.

[17]

P. Yue, Z. Xu, K. Wan, et al., “Multiparametric Mapping by Cardiovascular Magnetic Resonance Imaging in Cardiac Tumors,” Journal of Cardiovascular Magnetic Resonance 25 (2023): 37.

[18]

I. Horvat-Menih, H. Li, A. N. Priest, et al., “High-Resolution and Highly Accelerated MRI T2 Mapping as a Tool to Characterise Renal Tumour Subtypes and Grades,” European Radiology Experimental 8 (2024): 76.

[19]

M. Iima, M. Kataoka, M. Honda, and D. Le Bihan, “Diffusion-Weighted MRI for the Assessment of Molecular Prognostic Biomarkers in Breast Cancer,” Korean Journal of Radiology 25 (2024): 623.

[20]

Y.-J. Guo, R. Yin, Q. Zhang, et al., “MRI-Based Kinetic Heterogeneity Evaluation in the Accurate Access of Axillary Lymph Node Status in Breast Cancer Using a Hybrid CNN-RNN Model,” Journal of Magnetic Resonance Imaging 60 (2024): 1352-1364.

[21]

S. Li, Y. Dai, J. Chen, F. Yan, and Y. Yang, “MRI-Based Habitat Imaging in Cancer Treatment: Current Technology, Applications, and Challenges,” Cancer Imaging 24 (2024): 107.

[22]

F. Bray, M. Laversanne, H. Sung, et al., “Global Cancer Statistics 2022: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries,” CA: A Cancer Journal for Clinicians 74 (2024): 229-263.

[23]

S. Y. Hwang, S. Park, and Y. Kwon, “Recent Therapeutic Trends and Promising Targets in Triple Negative Breast Cancer,” Pharmacology & Therapeutics 199 (2019): 30-57.

[24]

R. Bassiouni, M. O. Idowu, L. D. Gibbs, et al., “Spatial Transcriptomic Analysis of a Diverse Patient Cohort Reveals a Conserved Architecture in Triple-Negative Breast Cancer,” Cancer Research 83 (2023): 34-48.

[25]

C. Valenza, L. Guidi, E. Battaiotto, et al., “Targeting HER2 Heterogeneity in Breast and Gastrointestinal Cancers,” Trends in Cancer 10 (2024): 113-123.

[26]

S. D. Chang, S. Ghai, C. K. Kim, A. Oto, F. Giganti, and C. M. Moore, “MRI Targeted Prostate Biopsy Techniques: AJR Expert Panel Narrative Review,” American Journal of Roentgenology 217 (2021): 1263-1281.

[27]

J. Schwenck, D. Sonanini, J. M. Cotton, et al., “Advances in PET Imaging of Cancer,” Nature Reviews Cancer 23 (2023): 474-490.

[28]

A. Karthik, S. Kumar Sahoo, A. Kumar, et al., “Unified Approach for Accurate Brain Tumor Multi-Classification and Segmentation Through Fusion of Advanced Methodologies,” Biomedical Signal Processing and Control 100 (2025): 106872.

[29]

G. Wei, Y. Gu, N. Lin, et al., “Autonomous Bionanorobots via a Cage-Shaped Silsesquioxane Vehicle for in Vivo Heavy Metal Detoxification,” ACS Applied Materials & Interfaces 14 (2022): 29238-29249.

[30]

G. Wei, Y. Yao, H. Zhang, W. Cui, and Y. Lu, “Cage-Shaped Silsesquioxane with Multiactive Sites for Impeding Aggregation of Small Insoluble Organic Molecule,” Chemical Engineering Journal 470 (2023): 144330.

[31]

G. Wei, K. Zhang, Y. Gu, S. Guang, J. Feng, and H. Xu, “Novel Multifunctional Nano-Hybrid Polyhedral Oligomeric Silsesquioxane-Based Molecules with High Cell Permeability: Molecular Design and Application for Diagnosis and Treatment of Tumors,” Nanoscale 13 (2021): 2982-2994.

[32]

T. Sai, S. Ran, Z. Guo, P. Song, and Z. Fang, “Recent Advances in Fire-Retardant Carbon-Based Polymeric Nanocomposites Through Fighting Free Radicals,” SusMat 2 (2022): 411-434.

[33]

W. Yang, H. Ding, D. Puglia, et al., “Bio-Renewable Polymers Based on Lignin-Derived Phenol Monomers: Synthesis, Applications, and Perspectives,” SusMat 2 (2022): 535-568.

[34]

T. Chen, Z. Cai, X. Zhao, et al., “Dynamic Monitoring Soft Tissue Healing via Visualized Gd-Crosslinked Double Network MRI Microspheres,” Journal of Nanobiotechnology 22 (2024): 289.

[35]

Z. Gu, S. Guang, G. Wei, and H. Xu, “A Targeting Multiple-Stimuli Responsive POSS-Based Smart Nanomaterial for Enhanced Chemo-Photothermal Synergistic Therapy,” European Polymer Journal 213 (2024): 113131.

[36]

G. Stoll and M. Bendszus, “Imaging of Inflammation in the Peripheral and Central Nervous System by Magnetic Resonance Imaging,” Neuroscience 158 (2009): 1151-1160.

[37]

Y. Okuhata, “Delivery of Diagnostic Agents for Magnetic Resonance Imaging,” Advanced Drug Delivery Reviews 37 (1999): 121-137.

[38]

G. H. Simon, J. Bauer, O. Saborovski, et al., “T1 and T2 Relaxivity of Intracellular and Extracellular USPIO at 1.5T and 3T Clinical MR Scanning,” European Radiology 16 (2006): 738-745.

[39]

L. Fan, X. Wang, Q. Cao, Y. Yang, and D. Wu, “POSS-Based Supramolecular Amphiphilic Zwitterionic Complexes for Drug Delivery,” Biomaterials Science 7 (2019): 1984-1994.

[40]

X. Ge, L. Dong, L. Sun, et al., “New Nanoplatforms Based on UCNPs Linking with Polyhedral Oligomeric Silsesquioxane (POSS) for Multimodal Bioimaging,” Nanoscale 7 (2015): 7206-7215.

[41]

Q. Yang, L. Li, W. Sun, Z. Zhou, and Y. Huang, “Dual Stimuli-Responsive Hybrid Polymeric Nanoparticles Self-Assembled From POSS-Based Starlike Copolymer-Drug Conjugates for Efficient Intracellular Delivery of Hydrophobic Drugs,” ACS Applied Materials & Interfaces 8 (2016): 13251-13261.

[42]

P. S. Rawat, A. Jaiswal, A. Khurana, J. S. Bhatti, and U. Navik, “Doxorubicin-Induced Cardiotoxicity: An Update on the Molecular Mechanism and Novel Therapeutic Strategies for Effective Management,” Biomedicine & Pharmacotherapy 139 (2021): 111708.

[43]

Y. Zhang, X. Zhan, J. Xiong, et al., “Temperature-Dependent Cell Death Patterns Induced by Functionalized Gold Nanoparticle Photothermal Therapy in Melanoma Cells,” Scientific Reports 8 (2018): 8720.

[44]

Y. Dönmez and U. Gündüz, “Reversal of Multidrug Resistance by Small Interfering RNA (siRNA) in Doxorubicin-Resistant MCF-7 Breast Cancer Cells,” Biomedicine & Pharmacotherapy 65 (2011): 85-89.

[45]

X. Xu, H. Lu, and R. Lee, “Near Infrared Light Triggered Photo/Immuno-Therapy Toward Cancers,” Frontiers in Bioengineering and Biotechnology 8 (2020): 488.

[46]

S. Abudukelimu, G. Wei, J. Huang, et al., “Polyhedral Oligomeric Silsesquioxane (POSS)-Based Hybrid Nanocomposite for Synergistic Chemo-Photothermal Therapy Against Pancreatic Cancer,” Chemical Engineering Journal 442 (2022): 136124.

[47]

E. Hoffmann, M. Masthoff, W. G. Kunz, et al., “Multiparametric MRI for Characterization of the Tumour Microenvironment,” Nature Reviews Clinical Oncology 21 (2024): 428-448.

[48]

W. Xia, A. R. Hilgenbrink, E. L. Matteson, M. B. Lockwood, J.-X. Cheng, and P. S. Low, “A Functional Folate Receptor Is Induced During Macrophage Activation and Can be Used to Target Drugs to Activated Macrophages,” Blood 113 (2009): 438-446.

[49]

X. Zhong, G. Wei, B. Liu, et al., “Polyhedral Oligomeric Silsesquioxane-Based Nanoparticles for Efficient Chemotherapy of Glioblastoma,” Small 19 (2023): e2207248.

[50]

H. A. Lucero, S. Patterson, S. Matsuura, and K. Ravid, “Quantitative Histological Image Analyses of Reticulin Fibers in a Myelofibrotic Mouse,” Journal of Biological Methods 3 (2016): 1.

[51]

J. F. A. Jansen, H. Schöder, N. Y. Lee, et al., “Tumor Metabolism and Perfusion in Head and Neck Squamous Cell Carcinoma: Pretreatment Multimodality Imaging with 1H Magnetic Resonance Spectroscopy, Dynamic Contrast-Enhanced MRI, and [18F]FDG-PET,” International Journal of Radiation Oncology*Biology*Physics 82 (2012): 299-307.

[52]

X. Li, T. Yong, Z. Wei, et al., “Reversing Insufficient Photothermal Therapy-Induced Tumor Relapse and Metastasis by Regulating Cancer-Associated Fibroblasts,” Nature Communications 13 (2022): 2794.

[53]

X. Chen, K. Oshima, D. Schott, et al., “Assessment of Treatment Response During Chemoradiation Therapy for Pancreatic Cancer Based on Quantitative Radiomic Analysis of Daily CTs: An Exploratory Study,” PLoS ONE 12 (2017): e0178961.

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