Imaging immune checkpoint networks in cancer tissues with supermultiplexed SERS nanoprobes

Abstract

Combined immune checkpoint (ICP) inhibitors maximize immune response rates of patients compared to the single-drug treatment strategy in cancer immunotherapy, and prediction of such optimal combinations requires high-throughput imaging techniques and suitable data analysis. In this work, we report a rational strategy for predicting combined drugs of ICP inhibitors based on supermultiplexed surface-enhanced Raman scattering (SERS) imaging and correlation network analysis. To this end, we first built an ultrasensitive and supermultiplexed volume-active SERS (VASERS) nanoprobe platform, where Raman molecules are randomly arranged in 3D volumetric electromagnetic hotspots. By examining various bio-orthogonal Raman molecules with different electronic properties, we developed frequency modulation guidelines and achieved 32 resolvable colors in the Raman-silent region, the largest number of resolvable SERS colors demonstrated to date. We then demonstrated one-shot ten-color imaging of ICPs with high spectral resolution in clinical biopsies of breast cancer tissues, suggesting highly heterogeneous expression patterns of ICPs across tumor subtypes. Through correlation network analysis of these high-throughput Raman data, we investigated co-expression relationships among these ten-panel ICPs in cancer tissues and finally identified a variety of possible ICP combinations for synergistic immunotherapy of breast cancers, which may lead to novel therapeutical insights.

Keywords: Combination immunotherapy; Frequency modulation guidelines; Immune checkpoint networks; SERS; Supermultiplexing.