What happens if you cross 35 types of nanoparticles with nearly 500 types of cancer cells from more than 20 different tissues of origin? A collaborative screening project led by Hammond Lab researchers used machine learning to uncover thousands of gene-based biomarkers associated with nanoparticle trafficking and binding, including one protein that could be used to determine whether lipid nanoparticles will be taken up by a tumor. Their approach, described in Science, builds a strong foundation for understanding cell-nanoparticle interactions, and could help physicians figure out which patients’ tumors are most likely to respond to nanoparticle-based treatments.
The work was funded in part by the Marble Center for Cancer Nanomedicine.