Regulatory T Cells: Potential Target in Anticancer Immunotherapy

SummaryThe concept of regulatory T cells was first described in the early 1970s, and regulatory T cells were called suppressive T cells at that time. Studies that followed have demonstrated that these suppressive T cells negatively regulated tumor immunity and contributed to tumor growth in mice. Despite the importance of these studies, there was extensive skepticism about the existence of these cells, and the concept of suppressive T cells left the center stage of immunologic research for decades. Interleukin-2 receptor α-chain, CD25, was first demonstrated in 1995 to serve as a phenotypic marker for CD4+ regulatory cells. Henceforth, research of regulatory T cells boomed. Regulatory T cells are involved in the pathogenesis of cancer, autoimmune disease, transplantation immunology, and immune tolerance in pregnancy. Recent evidence has demonstrated that regulatory T cellmediated immunosuppression is one of the crucial tumor immune evasion mechanisms and the main obstacle of successful cancer immunotherapy. The mechanism and the potential clinical application of regulatory T cells in cancer immunotherapy are discussed

Developing a research diagnostic criteria for burning mouth syndrome: Results from an international Delphi process

© 2020 John Wiley & Sons LtdObjective: To develop a beta version of a preliminary set of empirically derived research diagnostic criteria (RDC) for burning mouth syndrome (BMS) through expert consensus, which can then be taken into a test period before publication of a final RDC/BMS. Design: A 6 round Delphi process with twelve experts in the field of BMS was used. The first round formed a focus group during which the purpose of the RDC and the definition of BMS was agreed upon, as well as the structure and contents. The remaining rounds were carried out virtually via email to achieve a consensus of the beta version of the RDC/BMS. Results: The definition of BMS was agreed to be ‘an intraoral burning or dysaesthetic sensation, recurring daily for more than 2 hours per day over more than 3 months, without evident causative lesions on clinical examination and investigation’. The RDC was based upon the already developed and validated RDC/TMD and formed three main parts: patient self-report; examination; and psychosocial self-report. A fourth additional part was also developed listing aspirational biomarkers which could be used as part of the BMS diagnosis where available, or to inform future research. Conclusion: This Delphi process has created a beta version of an RDC for use with BMS. This will allow future clinical research within BMS to be carried out to a higher standard, ensuring only patients with true BMS are included. Further validation studies will be required alongside refinement of the RDC as trialling progresses

Bottom-up GGM algorithm for constructing multilayered hierarchical gene regulatory networks that govern biological pathways or processes

BACKGROUND: Multilayered hierarchical gene regulatory networks (ML-hGRNs) are very important for understanding genetics regulation of biological pathways. However, there are currently no computational algorithms available for directly building ML-hGRNs that regulate biological pathways. RESULTS: A bottom-up graphic Gaussian model (GGM) algorithm was developed for constructing ML-hGRN operating above a biological pathway using small- to medium-sized microarray or RNA-seq data sets. The algorithm first placed genes of a pathway at the bottom layer and began to construct a ML-hGRN by evaluating all combined triple genes: two pathway genes and one regulatory gene. The algorithm retained all triple genes where a regulatory gene significantly interfered two paired pathway genes. The regulatory genes with highest interference frequency were kept as the second layer and the number kept is based on an optimization function. Thereafter, the algorithm was used recursively to build a ML-hGRN in layer-by-layer fashion until the defined number of layers was obtained or terminated automatically. CONCLUSIONS: We validated the algorithm and demonstrated its high efficiency in constructing ML-hGRNs governing biological pathways. The algorithm is instrumental for biologists to learn the hierarchical regulators associated with a given biological pathway from even small-sized microarray or RNA-seq data sets. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-016-0981-1) contains supplementary material, which is available to authorized users