CTLA4-Linked Autoimmunity in the Pathogenesis of Endometriosis and Related Infertility: A Systematic Review.

Several studies, although with conflicting results, have sought to determine the concentration of soluble CTLA4 antigens in peripheral blood plasma and peritoneal fluid in patients with endometriosis-related infertility. A systematic review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) through a search of the following databases: MEDLINE, EMBASE, Global Health, The Cochrane Library, Health Technology Assessment Database and Web of Science, and Clinical Trials research register. We included observational or prospective human and animal studies with any features related to endometriosis and/or infertility studies involving CTLA4-related pathogenesis published in English. The results of studies in which the size and characteristics of the observed groups were not stated were excluded. From the initial pool of 73 publications identified and screened, we finally included 5 articles to summarize the most recent knowledge about CTLA4-linked autoimmunity in the pathogenesis of endometriosis and related infertility. Evidence from clinical studies shows that CTLA4-based autoimmunity is involved in the maintenance of chronic inflammation in the peritoneal environment, with pre-clinical evidence of anti-CTLA antibodies as a potential novel target therapy for endometriosis. However, CTLA4 gene analyses do not support findings of CTLA4-linked autoimmunity as a primary determinant of the pathogenesis of endometriosis. These findings underlie the role of complex interactions within the family of immune checkpoint molecules involved. Further studies are needed to investigate the clinical relevance of anti-CTLA target therapy, taking into account the potential adverse events and repercussions of novel immunologic therapy modalities. However, with the general scarcity of studies investigating this topic, the clinical importance of CTLA4 autoimmunity still remains unclear.

An electrochemical biosensor for the assessment of tumor immunotherapy based on the detection of immune checkpoint protein programmed death ligand-1.

Although immunotherapy is now well established in cancer management, not every patient responds. Existing methods for assessing tumor immunotherapy responses, such as immunohistochemistry of the immune checkpoint protein programmed death ligand-1 (PD-L1), require destructive tissue analysis; furthermore, real-time in vivo monitoring would be beneficial for assessing tumor responses. Here we establish an electrochemical biosensor which was developed based on molybdenum disulfide (MoS2) and multi-wall carbon nanotubes (MWCNTs) used to modify the electrode and PD-L1 antibody-quantum dot (QD) conjugate as a dual optical and electrochemical label. The compositions, electrochemical performance, specificity of nanocomposite and probe were characterized. Paving the way for clinical application, the prepared biosensor detects differences in PD-L1 levels in diverse tumor cell types, tumors derived from mice or cancer patients, and it is reproducible and selective in both phosphate-buffered saline and serum. This study demonstrates that electrochemical sensing is a desirable technology for the in-situ and dynamic determination of biomarkers on the cellular level of for the assessment of tumor immunotherapy.

QSP-IO: A Quantitative Systems Pharmacology Toolbox for Mechanistic Multiscale Modeling for Immuno-Oncology Applications.

Immunotherapy has shown great potential in the treatment of cancer; however, only a fraction of patients respond to treatment, and many experience autoimmune-related side effects. The pharmaceutical industry has relied on mathematical models to study the behavior of candidate drugs and more recently, complex, whole-body, quantitative systems pharmacology (QSP) models have become increasingly popular for discovery and development. QSP modeling has the potential to discover novel predictive biomarkers as well as test the efficacy of treatment plans and combination therapies through virtual clinical trials. In this work, we present a QSP modeling platform for immuno-oncology (IO) that incorporates detailed mechanisms for important immune interactions. This modular platform allows for the construction of QSP models of IO with varying degrees of complexity based on the research questions. Finally, we demonstrate the use of the platform through two example applications of immune checkpoint therapy.