Regulating SOS1 functions may result in targeted pan-KRAS therapies. Small-molecule SOS1 inhibitors showed promising anticancer potential, and the most advanced inhibitor BI 1701963 is currently under phase I clinical studies. SOS1 agonists provide new opportunities to treat cancer; however, the underlying mechanisms still warrant investigation. We here report the discovery of the first SOS1 PROTACs designed uniquely by connecting a VHL ligand to the reported SOS1 agonist, ensuring that the observed inhibitory activity results from degraders. The best compound 9d induced SOS1 degradation in various KRAS-driven cancer cells and displayed superior antiproliferation activity compared to the agonist itself. Tumor xenograft study clearly showed the promising antitumor potency of 9d against human lung cancer. This study provides good evidence of using agonists to design SOS1 PROTACs and demonstrates that targeted SOS1 degradation represents an effective therapeutic strategy for overcoming KRAS-driven cancers.

A fundamental challenge that arises in biomedicine is the need to characterize compounds in a relevant cellular context in order to reveal potential on-target or off-target effects. Recently, the fast accumulation of gene transcriptional profiling data provides us an unprecedented opportunity to explore the protein targets of chemical compounds from the perspective of cell transcriptomics and RNA biology. Here, we propose a novel Siamese spectral-based graph convolutional network (SSGCN) model for inferring the protein targets of chemical compounds from gene transcriptional profiles. Although the gene signature of a compound perturbation only provides indirect clues of the interacting targets, and the biological networks under different experiment conditions further complicate the situation, the SSGCN model was successfully trained to learn from known compound-target pairs by uncovering the hidden correlations between compound perturbation profiles and gene knockdown profiles. On a benchmark set and a large time-split validation dataset, the model achieved higher target inference accuracy as compared to previous methods such as Connectivity Map. Further experimental validations of prediction results highlight the practical usefulness of SSGCN in either inferring the interacting targets of compound, or reversely, in finding novel inhibitors of a given target of interest.