Abstract

To accelerate the translation of cancer nanomedicine, we hypothesize that integrated genomic screens will improve understanding of the cellular processes governing nanoparticle trafficking. We developed a massively parallel high-throughput screening method leveraging barcoded, pooled cancer cell lines annotated with multi-omic data to investigate cell association patterns across a nanoparticle library spanning a range of formulations with clinical potential. This approach identified both the materials properties and cell-intrinsic features mediating nanoparticle-cell association. Coupling the data with machine learning algorithms, we constructed genomic nanoparticle trafficking networks and identified nanoparticle-specific biomarkers, including gene expression of SLC46A3. We engineered cell lines to validate SLC46A3 as a biomarker whose expression inversely predicts liposomal nanoparticle uptake both in vitro and in vivo. We further demonstrated the predictive capabilities extend beyond liposomal nanoparticles, regulating both uptake and transfection efficacy of solid lipid nanoparticles. Our work establishes the power of massively parallel pooled cell screens for nanoparticle delivery and enables the identification and utilization of biomarkers to rationally design nanoformulations for specific patient populations.