B cell regulation of the anti-tumor response and role in carcinogenesis

The balance between immune effector cells such as T cells and natural killer cells, and immunosuppressive Treg cells, dendritic, myeloid and monocytic sub-populations in the tumor microenvironment acts to calibrate the immune response to malignant cells. Accumulating evidence is pointing to a role for B cells in modulating the immune response to both solid tumors and hematologic cancer. Evidence from murine autoimmune models has defined B regulatory cell (Breg) subsets that express cytokines such as IL-10, TGF-β, and/or express immune regulatory ligands such as PD-L1, which can suppress T cell and/or natural killer cell responses. Multiple murine tumor models exhibit decreased tumor growth in B cell deficient or B cell depleted mice. In several of these models, B cells inhibit T cell mediated tumor immunity and/or facilitate conversion of T cells to CD4(+)CD25(+)FoxP3(+) T regs, which act to attenuate the innate and/or adaptive antitumor immune response. Mechanisms of suppression include the acquisition of inhibitory ligand expression, and phosphorylation of Stat3, and induction of IL-10 and TGF-β, resulting in a Breg phenotype. Breg suppressive activity may affect diverse cell subtypes, including T effector cells, NK cells, myeloid derived suppressor cells (MDSC) and/or tumor associated macrophages. B cells may also directly promote tumorigenesis through recruitment of inflammatory cells, and upregulation of pro-angiogenic genes and pro-metastatic collagenases. Breg infiltration has now been identified in a variety of solid tumor malignancies including but not limited to ovarian, gastric, non-small cell lung cancer, pancreatic, esophageal, head and neck, and hepatocellular carcinomas. Increasing evidence suggests that recruitment of B cells and acquisition of suppressive activity within the tumor bed may be an important mechanism through which B cells may modulate innate and/or adaptive anti-tumor immunity. B cell depletion in the clinic using anti-CD20 antibodies and/or inhibitors of BTK and/or other signaling pathways, may be a useful strategy for augmenting the anti-tumor immune response

A global reference for caesarean section rates (C-Model): a multicountry cross-sectional study.

OBJECTIVE: To generate a global reference for caesarean section (CS) rates at health facilities. DESIGN: Cross-sectional study. SETTING: Health facilities from 43 countries. POPULATION/SAMPLE: Thirty eight thousand three hundred and twenty-four women giving birth from 22 countries for model building and 10,045,875 women giving birth from 43 countries for model testing. METHODS: We hypothesised that mathematical models could determine the relationship between clinical-obstetric characteristics and CS. These models generated probabilities of CS that could be compared with the observed CS rates. We devised a three-step approach to generate the global benchmark of CS rates at health facilities: creation of a multi-country reference population, building mathematical models, and testing these models. MAIN OUTCOME MEASURES: Area under the ROC curves, diagnostic odds ratio, expected CS rate, observed CS rate. RESULTS: According to the different versions of the model, areas under the ROC curves suggested a good discriminatory capacity of C-Model, with summary estimates ranging from 0.832 to 0.844. The C-Model was able to generate expected CS rates adjusted for the case-mix of the obstetric population. We have also prepared an e-calculator to facilitate use of C-Model (www.who.int/reproductivehealth/publications/maternal_perinatal_health/c-model/en/). CONCLUSIONS: This article describes the development of a global reference for CS rates. Based on maternal characteristics, this tool was able to generate an individualised expected CS rate for health facilities or groups of health facilities. With C-Model, obstetric teams, health system managers, health facilities, health insurance companies, and governments can produce a customised reference CS rate for assessing use (and overuse) of CS. TWEETABLE ABSTRACT: The C-Model provides a customized benchmark for caesarean section rates in health facilities and systems