Fast Image-Guided Stratification Using Anti-PDL1 Gold Nanoparticles for Cancer Immunotherapy

Cancer immunotherapy has made enormous progress in offering safer and more effective treatments for the disease. Specifically, programmed death-ligand 1 antibody (αPDL1), designed to perform immune checkpoint blockade (ICB), is now considered a pillar in cancer immunotherapy. However, due to the complexity and heterogeneity of tumors, as well as the diversity in patient response, ICB therapy has only a 30 percent success rate, at most; moreover, the efficacy of ICB can be evaluated only two months after start of treatment. Therefore, early identification of potential responders and non-responders to therapy, using non-invasive means, is crucial for improving treatment decisions. In this talk, I will show a straightforward approach for fast, image-guided prediction of therapeutic response to ICB. In a colon cancer mouse model, we demonstrate that the combination of computed tomography imaging and gold nanoparticles conjugated to αPDL1 allowed prediction of therapeutic response, as early as 48 hours after treatment. This was achieved by non-invasive measurement of nanoparticle accumulation levels within the tumors. Moreover, we show that the nanoparticles efficiently prevented tumor growth with only a fifth of the standard dosage of clinical care. This technology may be developed into a powerful tool for early and non-invasive patient stratification as responders or non-responders.