Introduction: Several cell populations from the peripheral immune system interact to create a complex immunologic status during glioblastoma growth and response to therapy. The aim of this study was to integrate the impact of different immune cell populations present in glioblastoma tumor microenvironment on overall survival. Methodology: Gene expression and clinical data were generated by The Cancer Genome Atlas and previously reported meta-signatures representing cells of the immune system were used. The relationship between meta-signatures was evaluated through Pearson's correlation analyses. Survival analyses were performed through Kaplan-Meier plots and Cox regression model. Results and discussion: Meta-signatures corresponding to infiltrating immune cells with immunosuppressive roles, such as macrophages, NK and NK T cells, MDSCs and Tregs, correlated with poorer patient prognosis. Meta-signatures related to CD8+ T cells predicted improved survival only in patients with low immunosuppressive meta-signatures. By clustering the meta-signatures we found that the cluster containing high meta-signatures of macrophages, MDSCs and Tregs demonstrated the worst prognosis. Conclusion: Integrating the information provided by transcriptional signatures of immunological aspects is fundamental in understanding the impact of the immune system on patient survival. We found a predictive impact on survival with positive role for CD8 and negative roles for macrophages, MDSC, Tregs, NK and NK-T in glioblastoma patients. Understanding these regulatory and stimulatory factors of patients' immune system is essential to delineate an effective strategy to increase the anti-tumor immune response and to generate potential clinical benefits.

Data on mortality burden and excess deaths attributable to diabetes are sparse and frequently unreliable, particularly in low and middle-income countries. Estimates in Brazil to date have relied on death certificate data, which do not consider the multicausal nature of deaths. Our aim was to combine cohort data with national prevalence and mortality statistics to estimate the absolute number of deaths that could have been prevented if the mortality rates of people with diabetes were the same as for those without. In addition, we aimed to estimate the increase in burden when considering undiagnosed diabetes. We estimated self-reported diabetes prevalence from the National Health Survey (PNS) and overall mortality from the national mortality information system (SIM). We estimated the diabetes mortality rate ratio (rates of those with vs without diabetes) from the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil), an ongoing cohort study. Joining estimates from these three sources, we calculated for the population the absolute number and the fraction of deaths attributable to diabetes. We repeated our analyses considering both self-reported and unknown diabetes, the latter estimated based on single point-in-time glycemic determinations in ELSA-Brasil. Finally, we compared results with diabetes-related mortality information from death certificates. In 2013, 65 581 deaths, 9.1% of all deaths between the ages of 35-80, were attributable to known diabetes. If cases of unknown diabetes were considered, this figure would rise to 14.3%. In contrast, based on death certificates only, 5.3% of all death had diabetes as the underlying cause and 10.4% as any mentioned cause. In this first report of diabetes mortality burden in Brazil using cohort data to estimate diabetes mortality rate ratios and the prevalence of unknown diabetes, we showed marked underestimation of the current burden, especially when unknown cases of diabetes are also considered.

Several phenotypes that impact the capacity of cancer cells to survive and proliferate are dynamic. Here we used the number of cells in colonies as an assessment of fitness and devised a novel method called Dynamic Fitness Analysis (DynaFit) to measure the dynamics in fitness over the course of colony formation. DynaFit is based on the variance in growth rate of a population of founder cells compared with the variance in growth rate of colonies with different sizes. DynaFit revealed that cell fitness in cancer cell lines, primary cancer cells, and fibroblasts under unhindered growth conditions is dynamic. Key cellular mechanisms such as ERK signaling and cell-cycle synchronization differed significantly among cells in colonies after 2 to 4 generations and became indistinguishable from randomly sampled cells regarding these features. In the presence of cytotoxic agents, colonies reduced their variance in growth rate when compared with their founder cell, indicating a dynamic nature in the capacity to survive and proliferate in the presence of a drug. This finding was supported by measurable differences in DNA damage and induction of senescence among cells of colonies. The presence of epigenetic modulators during the formation of colonies stabilized their fitness for at least four generations. Collectively, these results support the understanding that cancer cell fitness is dynamic and its modulation is a fundamental aspect to be considered in comprehending cancer cell biology and its response to therapeutic interventions. Significance: Cancer cell fitness is dynamic over the course of the formation of colonies. This dynamic behavior is mediated by asymmetric mitosis, ERK activity, cell-cycle duration, and DNA repair capacity in the absence or presence of a drug.

OBJECTIVE Glycemic regression is common in real-world settings, but the contribution of regression to the mean (RTM) has been little investigated. We aimed to estimate glycemic regression before and after adjusting for RTM in a free-living cohort of adults with newly ascertained diabetes and intermediate hyperglycemia (IH). RESEARCH DESIGN AND METHODS The Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) is a cohort study of 15,105 adults screened between 2008 and 2010 with standardized oral glucose tolerance test and HbA_1c, repeated after 3.84 ± 0.42 years. After excluding those receiving medical treatment for diabetes, we calculated partial or complete regression before and after adjusting baseline values for RTM. RESULTS Regarding newly ascertained diabetes, partial or complete regression was seen in 49.4% (95% CI 45.2–53.7); after adjustment for RTM, in 20.2% (95% CI 12.1–28.3). Regarding IH, regression to normal levels was seen in 39.5% (95% CI 37.9–41.3) or in 23.7% (95% CI 22.6–24.3), depending on use of the World Health Organization (WHO) or the American Diabetes Association (ADA) definition, respectively; after adjustment, corresponding frequencies were 26.1% (95% CI 22.4–28.1) and 19.4% (95% CI 18.4–20.5). Adjustment for RTM reduced the number of cases detected at screening: 526 to 94 cases of diabetes, 3,118 to 1,986 cases of WHO-defined IH, and 6,182 to 5,711 cases of ADA-defined IH. Weight loss ≥2.6% was associated with greater regression from diabetes (relative risk 1.52, 95% CI 1.26–1.84) and IH (relative risk 1.30, 95% CI 1.17–1.45). CONCLUSIONS In this quasi–real-world setting, regression from diabetes at ∼4 years was common, less so for IH. Regression was frequently explained by RTM but, in part, also related to improved weight loss and homeostasis over the follow-up.