Precise reconstruction of the TME using bulk RNA-seq and a machine learning algorithm trained on artificial transcriptomes.

Cellular deconvolution algorithms virtually reconstruct tissue composition by analyzing the gene expression of complex tissues. We present the decision tree machine learning algorithm, Kassandra, trained on a broad collection of >9,400 tissue and blood sorted cell RNA profiles incorporated into millions of artificial transcriptomes to accurately reconstruct the tumor microenvironment (TME). Bioinformatics correction for technical and biological variability, aberrant cancer cell expression inclusion, and accurate quantification and normalization of transcript expression increased Kassandra stability and robustness. Performance was validated on 4,000 H&E slides and 1,000 tissues by comparison with cytometric, immunohistochemical, or single-cell RNA-seq measurements. Kassandra accurately deconvolved TME elements, showing the role of these populations in tumor pathogenesis and other biological processes. Digital TME reconstruction revealed that the presence of PD-1-positive CD8+ T cells strongly correlated with immunotherapy response and increased the predictive potential of established biomarkers, indicating that Kassandra could potentially be utilized in future clinical applications.

Application and utility of mass cytometry in vaccine development.

Mass cytometry enables highly multiplexed profiling of cellular immune responses in limited-volume samples, advancing prospects of a new era of systems immunology. The capabilities of mass cytometry offer expanded potential for deciphering immune responses to infectious diseases and to vaccines. Several studies have used mass cytometry to profile protective immune responses, both postinfection and postvaccination, although no vaccine-development program has yet systematically employed the technology from the outset to inform both candidate design and clinical evaluation. In this article, we review published mass cytometry studies relevant to vaccine development, briefly compare immune profiling by mass cytometry to other systems-level technologies, and discuss some general considerations for deploying mass cytometry in the context of vaccine development.-Reeves, P. M., Sluder, A. E., Raju Paul, S., Scholzen, A., Kashiwagi, S., Poznansky, M. C. Application and utility of mass cytometry in vaccine development.

Q-vaxcelerate: A distributed development approach for a new Coxiella burnetii vaccine.

Development of vaccines that are both safe and effective remains a costly and time-consuming challenge. To accelerate the pace of development and improve the efficacy and safety of candidate vaccines for both existing and emerging infectious agents, we have used a distributed development approach. This features the managed integration of individual expert groups having the requisite vaccine platforms, pre-clinical models, assays, skills and knowledge pertinent to a specific pathogen into a single, end-to-end development team capable of producing a new vaccine tailored to that particular agent. Distributed development focuses on integrating existing effort across multiple institutions rather than developing new capabilities or consolidating resources within an individual organization. Previously we have used the distributed development strategy to generate vaccine candidates for emerging viral diseases. Coxiella burnetii is a highly infectious and resilient bacterium and the causative agent of Q fever. Treatment for Q fever can require months of antibiotics. The current vaccine for Q-fever is only approved in Australia and requires prescreening due to the potential for severe reactogenicity in previously exposed individuals. Here we discuss Q-VaxCelerate, a distributed development consortium for the development of a new vaccine to prevent Q fever.