Fast Image-Guided Stratification Using Gold Nanoparticles for Cancer Immunotherapy

שלחו לחבר
Rinat Meir, Faculty of Engineering, Bar-Ilan University
BIU Engineering Building 1103, Room 329

Cancer immunotherapy has made enormous progress in offering safer and more effective treatments for the disease. However, due to the complexity and heterogeneity of tumors, as well as the diversity in patient response, immunotherapies have only a 30% success rate, at most; moreover, the efficacy of the therapy can be evaluated only two months after start of treatment. Therefore, early identification of potential responders and non-responders to therapy, using noninvasive means, is crucial for improving treatment decisions. In this talk, we report a straightforward approach for image-guided prediction of therapeutic response to cancer immunotherapy. This is achieved by the combination of computed tomography imaging and gold nanoparticles as contrast agents. We demonstrate the capabilities of this approach with an antibody named αPDL1, designed to perform immune check-point blockade, which is now considered a pillar in cancer immunotherapy. In addition, we apply the approach to immune cell-based therapy in which T-cells and Natural Killer cells are labeled with gold nanoparticles before transplantation.

* PhD research supervised by Prof. Rachela Popovtzer