Sonopermeation combined with stroma normalization enables complete cure using nano-immunotherapy in murine breast tumors

基质 免疫疗法 规范化(社会学) 医学 癌症研究 病理 免疫学 免疫系统 免疫组织化学 社会学 人类学
作者
Constantina Neophytou,Antonia Charalambous,Chrysovalantis Voutouri,Simón I. Angeli,Myrofora Panagi,Triantafyllos Stylianopoulos,Fotios Mpekris
出处
期刊:Journal of Controlled Release [Elsevier BV]
卷期号:: 113722-113722
标识
DOI:10.1016/j.jconrel.2025.113722
摘要

Nano-immunotherapy shows great promise in improving patient outcomes, as seen in advanced triple-negative breast cancer, but it does not cure the disease, with median survival under two years. Therefore, understanding resistance mechanisms and developing strategies to enhance its effectiveness in breast cancer is crucial. A key resistance mechanism is the pronounced desmoplasia in the tumor microenvironment, which leads to dysfunction of tumor blood vessels and thus, to hypoperfusion, limited drug delivery and hypoxia. Ultrasound sonopermeation and agents that normalize the tumor stroma have been employed separately to restore vascular abnormalities in tumors with some success. Here, we performed in vivo studies in two murine, orthotopic breast tumor models to explore if combination of ultrasound sonopermeation with a stroma normalization drug can synergistically improve tumor perfusion and enhance the efficacy of nano-immunotherapy. We found that the proposed combinatorial treatment can drastically reduce primary tumor growth and in many cases tumors were no longer measurable. Overall survival studies showed that all mice that received the combination treatment survived and rechallenge experiments revealed that the survivors obtained immunological memory. Employing ultrasound elastography and contrast enhanced ultrasound along with proteomics analysis, flow cytometry and immunofluorescene staining, we found the combinatorial treatment reduced tumor stiffness to normal levels, restoring tumor perfusion and oxygenation. Furthermore, it increased infiltration and activity of immune cells and altered the levels of immunosupportive chemokines. Finally, using machine learning analysis, we identified that tumor stiffness, CD8+ T cells and M2-type macrophages were strong predictors of treatment response.

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