Periodic Reporting for period 3 - Meta-Targeting (Macro-Nanomedicine to Treat Metastatic Cancer)
Período documentado: 2023-04-01 hasta 2024-09-30
Triple-negative breast cancer affects millions of patients worldwide. It presents with aggressive growth and is very difficult to treat, because of the lack of targets for molecular interventions. The goal of the Meta-Targeting project is to develop smart concepts and combination therapies for better management of metastatic triple-negative breast cancer. In this context, “smart” refers to materials and methods that can be realistically developed and pragmatically implemented in day-to-day clinical practice. The project is constructed in such a way that advances in multiple scientific disciplines are aligned and combined, including in chemical and pharmaceutical engineering (WP1), tumor microenvironment modulation (WP2), biomarker identification (WP3), overcoming multidrug resistance (WP4), and immunotherapy potentiation (WP5). Alone and together, progress in each of these project lines will contribute to a better understanding of and improved treatment options for (metastatic) breast cancer.
Multiple noteworthy advances have been made in the first half of the project. These include: (1) the development of a novel prodrug platform for specific activation in tumors and encapsulation in polymeric micelles; (2) the generation of experimental evidence demonstrating that pharmacological and physical tumor priming can enhance tumor-targeted drug delivery; (3) the identification of both imaging and immunohistochemical biomarkers to assist in cancer nanomedicine patient stratification; (4) the observation that drug pretreatment and multidrug resistance promote fibrogenesis in triple-negative breast cancer lesions; and (5) the fabrication of polymer-prodrug nanovesicles which are able to potently activate innate immune responses, providing novel opportunities for combined modality anticancer immunotherapy.
The identification of tumor tissue (and eventually biopsy) biomarkers for patient stratification is considered as a potential major step forward in cancer nanomedicine clinical translation. We and others have previously explored theranostic platforms and imaging biomarkers for patient selection. After almost a decade of efforts and discussions, we have realized that imaging nanomedicines in clinical trials is not easy to implement. We therefore set out to study >20 different biomarkers in >10 different tumor models, and used machine learning to identify key features predicting which tumors accumulate nanomedicines well and which not. Especially the latter is important, as it promotes the identification of true negative tumors (and patients) that should be excluded from cancer nanomedicine clinical trials.