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BackgroundTherapies directed at augmenting regulatory T cell (Treg) activities in vivo as a systemic treatment for autoimmune disorders and transplantation may be associated with significant off-target effects, including a generalized immunosuppression that may compromise beneficial immune responses to infections and cancer cells. Adoptive cellular therapies using purified expanded Tregs represents an attractive alternative to systemic treatments, with results from animal studies noting increased therapeutic potency of antigen-specific Tregs over polyclonal populations. However, current methodologies are limited in terms of the capacity to isolate and expand a sufficient quantity of endogenous antigen-specific Tregs for therapeutic intervention. Moreover, FOXP3+ Tregs fall largely within the CD4+ T cell subset and are thus routinely MHC class II-specific, whereas class I-specific Tregs may function optimally in vivo by facilitating direct tissue recognition.Methodology/principal findingsTo overcome these limitations, we have developed a novel means for generating large numbers of antigen-specific Tregs involving lentiviral T cell receptor (TCR) gene transfer into in vitro expanded polyclonal natural Treg populations. Tregs redirected with a high-avidity class I-specific TCR were capable of recognizing the melanoma antigen tyrosinase in the context of HLA-A*0201 and could be further enriched during the expansion process by antigen-specific reactivation with peptide loaded artificial antigen presenting cells. These in vitro expanded Tregs continued to express FOXP3 and functional TCRs, and maintained the capacity to suppress conventional T cell responses directed against tyrosinase, as well as bystander T cell responses. Using this methodology in a model tumor system, murine Tregs designed to express the tyrosinase TCR effectively blocked antigen-specific effector T cell (Teff) activity as determined by tumor cell growth and luciferase reporter-based imaging.Conclusions/significanceThese results support the feasibility of class I-restricted TCR transfer as a promising strategy to redirect the functional properties of Tregs and provide for a more efficacious adoptive cell therapy.

Objectives: 1) To use data-driven method to examine clinical codes (risk factors) of a medical condition in primary care electronic health records (EHRs) that can accurately predict a diagnosis of the condition in secondary care EHRs. 2) To develop and validate a disease phenotyping algorithm for rheumatoid arthritis using primary care EHRs. Methods: This study linked routine primary and secondary care EHRs in Wales, UK. A machine learning based scheme was used to identify patients with rheumatoid arthritis from primary care EHRs via the following steps: i) selection of variables by comparing relative frequencies of Read codes in the primary care dataset associated with disease case compared to non-disease control (disease/non-disease based on the secondary care diagnosis); ii) reduction of predictors/associated variables using a Random Forest method, iii) induction of decision rules from decision tree model. The proposed method was then extensively validated on an independent dataset, and compared for performance with two existing deterministic algorithms for RA which had been developed using expert clinical knowledge. Results: Primary care EHRs were available for 2,238,360 patients over the age of 16 and of these 20,667 were also linked in the secondary care rheumatology clinical system. In the linked dataset, 900 predictors (out of a total of 43,100 variables) in the primary care record were discovered more frequently in those with versus those without RA. These variables were reduced to 37 groups of related clinical codes, which were used to develop a decision tree model. The final algorithm identified 8 predictors related to diagnostic codes for RA, medication codes, such as those for disease modifying anti-rheumatic drugs, and absence of alternative diagnoses such as psoriatic arthritis. The proposed data-driven method performed as well as the expert clinical knowledge based methods. Conclusion: Data-driven scheme, such as ensemble machine learning methods, has the potential of identifying the most informative predictors in a cost-effective and rapid way to accurately and reliably classify rheumatoid arthritis or other complex medical conditions in primary care EHRs. [ABSTRACT FROM AUTHOR]