The GPCR-Gαs-PKA signaling axis promotes T cell dysfunction and cancer immunotherapy failure.

Immune checkpoint blockade (ICB) targeting PD-1 and CTLA-4 has revolutionized cancer treatment. However, many cancers do not respond to ICB, prompting the search for additional strategies to achieve durable responses. G-protein-coupled receptors (GPCRs) are the most intensively studied drug targets but are underexplored in immuno-oncology. Here, we cross-integrated large singe-cell RNA-sequencing datasets from CD8+ T cells covering 19 distinct cancer types and identified an enrichment of Gαs-coupled GPCRs on exhausted CD8+ T cells. These include EP2, EP4, A2AR, ß1AR and ß2AR, all of which promote T cell dysfunction. We also developed transgenic mice expressing a chemogenetic CD8-restricted Gαs-DREADD to activate CD8-restricted Gαs signaling and show that a Gαs-PKA signaling axis promotes CD8+ T cell dysfunction and immunotherapy failure. These data indicate that Gαs-GPCRs are druggable immune checkpoints that might be targeted to enhance the response to ICB immunotherapies.

Digital Omicron detection using unscripted voice samples from social media.

The success of artificial intelligence in clinical environments relies upon the diversity and availability of training data. In some cases, social media data may be used to counterbalance the limited amount of accessible, well-curated clinical data, but this possibility remains largely unexplored. In this study, we mined YouTube to collect voice data from individuals with self-declared positive COVID-19 tests during time periods in which Omicron was the predominant variant1,2,3, while also sampling non-Omicron COVID-19 variants, other upper respiratory infections (URI), and healthy subjects. The resulting dataset was used to train a DenseNet model to detect the Omicron variant from voice changes. Our model achieved 0.85/0.80 specificity/sensitivity in separating Omicron samples from healthy samples and 0.76/0.70 specificity/sensitivity in separating Omicron samples from symptomatic non-COVID samples. In comparison with past studies, which used scripted voice samples, we showed that leveraging the intra-sample variance inherent to unscripted speech enhanced generalization. Our work introduced novel design paradigms for audio-based diagnostic tools and established the potential of social media data to train digital diagnostic models suitable for real-world deployment.