HAVOC

While advances in breast cancer treatment have improved patient outcomes, some tumors remain highly resistant due to their ability to evade detection by the immune system. A key reason for this is the tumor microenvironment surrounding cancer cells, which includes immune cells, blood vessel cells (endothelial cells), fibroblasts, and structural components. In particular, tumor-associated blood vessel cells often take on abnormal functions that support tumor growth, prevent immune cell access, and contribute to treatment resistance. This project aims to develop a human-relevant model that replicates these tumor-microenvironment interactions in the laboratory. Using a microfluidic "tumor-on-chip" platform, we will build a miniaturized system that mimics key features of the tumor microenvironment. This platform will allow us to investigate how tumor cells interact with their surroundings, and to evaluate how different therapies affect these dynamics. We will start by optimizing the platform using commercially available and in-house derived cell lines. This will be followed by integration of primary cells directly derived from breast cancer patients, to recreate the complexity and patient-specific variability of real tumors as closely as possible. A central goal of the project is to test the effects of immunotherapies, including novel bispecific antibodies, alone or in combination with standard chemotherapies. By simulating the patients tumor environment on a chip, this approach offers a powerful tool to assess which treatment strategies are most likely to be effective for individual patients. Using modern techniques such as multiplexed fluorescence imaging, flow cytometry, cytokine profiling, and single-cell RNA sequencing, we will analyze cellular behavior and treatment responses at high resolution. Ultimately, we expect our platform to improve our understanding of how tumors resist therapy, and which cell types are responsible for this resistance. This will help identify more effective, personalized therapeutic strategies. In the long run, the platform may also be adapted to other tumor types, further increasing its impact in guiding personalized therapeutic recommendations.