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Case Study

Ozette’s AI in the
battle against Covid.

In-depth immune profiling reveals a T regulatory signature that correlates with COVID-19 disease severity, an important finding as Treg cells are potential targets for treatment strategies and fighting severe disease.

Summary

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic began in 2019 and rapidly scaled to a global presence of disease known as COVID-19. Despite the relatively fast implementation of testing and vaccination worldwide, the rise of emerging viral variants and breakthrough infections raises concerns that SARS-CoV-2 will continue to pose a global health threat. Similar concerns extend to other viral pathogens such as influenza virus, respiratory syncytial virus (RSV), and the next novel human respiratory virus we will encounter.

COVID-19 shares similarities with other respiratory infections, yet we still have a limited understanding of the immune response to SARS-CoV-2. To better understand the immune landscape of patients with severe COVID-19, a study led by Drs. Vick, Frutoso, Prlic, and Lund characterized the immune profile of hospitalized COVID-19 patients compared to those infected with influenza virus, RSV, and healthy controls. To aid these immune characterizations co-founders Evan Green and Raphael Gottardo applied computational methodology they developed, the precursor of Ozette’s technology, to conduct unbiased discovery and annotation of phenotypes in these single-cell immune data.

In this study, deep immune profiling provided by full annotation using shape-constrained trees (FAUST, a precursor to Ozette’s technology) revealed two key immune insights that inform how we think about respiratory viruses and treatment. First, they observed similar circulating immune cells and similar phenotypic changes between respiratory infections, indicating that similar treatments might be effective across these viral infections. Interestingly, there were key immune differentiators, including a SARS-CoV-2-specific signature composed of T regulatory cells, previously uncharacterized using standard methodology. While additional studies are needed, these initial insights inform potential therapeutic strategies to limit severe COVID-19 diseases.

Case Study

Going beneath the
surface of skin cancer.

We investigated our AI’s ability to predict treatment response to PD1 inhibiting immunotherapy in Merkel cell carcinoma and melanoma. We benchmarked our algorithmic approach against conventional analysis.

Case Study

Ozette’s AI aids advancement of cancer drug development.

Ozette’s technology, in conjunction with other new methods, reveal a distinct T cell population that suppresses immune effectiveness in the tumor microenvironment and helps inform drug development in immuno-oncology.

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