Operational Ontology for Oncology (O3): A Professional Society-Based, Multistakeholder, Consensus-Driven Informatics Standard Supporting Clinical and Research Use of Real-World Data From Patients Treated for Cancer.

PURPOSE: The ongoing lack of data standardization severely undermines the potential for automated learning from the vast amount of information routinely archived in electronic health records (EHRs), radiation oncology information systems, treatment planning systems, and other cancer care and outcomes databases. We sought to create a standardized ontology for clinical data, social determinants of health, and other radiation oncology concepts and interrelationships. METHODS AND MATERIALS: The American Association of Physicists in Medicine's Big Data Science Committee was initiated in July 2019 to explore common ground from the stakeholders' collective experience of issues that typically compromise the formation of large inter- and intra-institutional databases from EHRs. The Big Data Science Committee adopted an iterative, cyclical approach to engaging stakeholders beyond its membership to optimize the integration of diverse perspectives from the community. RESULTS: We developed the Operational Ontology for Oncology (O3), which identified 42 key elements, 359 attributes, 144 value sets, and 155 relationships ranked in relative importance of clinical significance, likelihood of availability in EHRs, and the ability to modify routine clinical processes to permit aggregation. Recommendations are provided for best use and development of the O3 to 4 constituencies: device manufacturers, centers of clinical care, researchers, and professional societies. CONCLUSIONS: O3 is designed to extend and interoperate with existing global infrastructure and data science standards. The implementation of these recommendations will lower the barriers for aggregation of information that could be used to create large, representative, findable, accessible, interoperable, and reusable data sets to support the scientific objectives of grant programs. The construction of comprehensive "real-world" data sets and application of advanced analytical techniques, including artificial intelligence, holds the potential to revolutionize patient management and improve outcomes by leveraging increased access to information derived from larger, more representative data sets.

Characterisation of the Immunophenotype of Dogs with Primary Immune-Mediated Haemolytic Anaemia.

BACKGROUND: Immune-mediated haemolytic anaemia (IMHA) is reported to be the most common autoimmune disease of dogs, resulting in significant morbidity and mortality in affected animals. Haemolysis is caused by the action of autoantibodies, but the immunological changes that result in their production have not been elucidated. AIMS: To investigate the frequency of regulatory T cells (Tregs) and other lymphocyte subsets and to measure serum concentrations of cytokines and peripheral blood mononuclear cell expression of cytokine genes in dogs with IMHA, healthy dogs and dogs with inflammatory diseases. ANIMALS: 19 dogs with primary IMHA, 22 dogs with inflammatory diseases and 32 healthy control dogs. METHODS: Residual EDTA-anti-coagulated blood samples were stained with fluorophore-conjugated monoclonal antibodies and analysed by flow cytometry to identify Tregs and other lymphocyte subsets. Total RNA was also extracted from peripheral blood mononuclear cells to investigate cytokine gene expression, and concentrations of serum cytokines (interleukins 2, 6 10, CXCL-8 and tumour necrosis factor α) were measured using enhanced chemiluminescent assays. Principal component analysis was used to investigate latent variables that might explain variability in the entire dataset. RESULTS: There was no difference in the frequency or absolute numbers of Tregs among groups, nor in the proportions of other lymphocyte subsets. The concentrations of pro-inflammatory cytokines were greater in dogs with IMHA compared to healthy controls, but the concentration of IL-10 and the expression of cytokine genes did not differ between groups. Principal component analysis identified four components that explained the majority of the variability in the dataset, which seemed to correspond to different aspects of the immune response. CONCLUSIONS: The immunophenotype of dogs with IMHA differed from that of dogs with inflammatory diseases and from healthy control dogs; some of these changes could suggest abnormalities in peripheral tolerance that permit development of autoimmune disease. The frequency of Tregs did not differ between groups, suggesting that deficiency in the number of these cells is not responsible for development of IMHA.

The zinc cluster proteins Upc2 and Ecm22 promote filamentation in Saccharomyces cerevisiae by sterol biosynthesis-dependent and -independent pathways.

The transition between a unicellular yeast form to multicellular filaments is crucial for budding yeast foraging and the pathogenesis of many fungal pathogens such as Candida albicans. Here, we examine the role of the related transcription factors Ecm22 and Upc2 in Saccharomyces cerevisiae filamentation. Overexpression of either ECM22 or UPC2 leads to increased filamentation, whereas cells lacking both ECM22 and UPC2 do not exhibit filamentous growth. Ecm22 and Upc2 positively control the expression of FHN1, NPR1, PRR2 and sterol biosynthesis genes. These genes all play a positive role in filamentous growth, and their expression is upregulated during filamentation in an Ecm22/Upc2-dependent manner. Furthermore, ergosterol content increases during filamentous growth. UPC2 expression also increases during filamentation and is inhibited by the transcription factors Sut1 and Sut2. The expression of SUT1 and SUT2 in turn is under negative control of the transcription factor Ste12. We suggest that during filamentation Ste12 becomes activated and reduces SUT1/SUT2 expression levels. This would result in increased UPC2 levels and as a consequence to transcriptional activation of FHN1, NPR1, PRR2 and sterol biosynthesis genes. Higher ergosterol levels in combination with the proteins Fhn1, Npr1 and Prr2 would then mediate the transition to filamentous growth.