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Background & Aims: Colorectal cancer is an epigenetically heterogeneous disease, however, the extent and spectrum of the CpG island methylator phenotype (CIMP) is not clear. Methods: Genome-scale methylation and transcript expression were measured by DNA Methylation and RNA expression microarray in 216 unselected colorectal cancers, and findings were validated using The Cancer Genome Atlas 450K and RNA sequencing data. Mutations in epigenetic regulators were assessed using CIMP-subtyped Cancer Genome Atlas exomes. Results: CIMP-high cancers dichotomized into CIMP-H1 and CIMP-H2 based on methylation profile. KRAS mutation was associated significantly with CIMP-H2 cancers, but not CIMP-H1 cancers. Congruent with increasing methylation, there was a stepwise increase in patient age from 62 years in the CIMP-negative subgroup to 75 years in the CIMP-H1 subgroup (P < .0001). CIMP-H1 predominantly comprised consensus molecular subtype 1 cancers (70%) whereas consensus molecular subtype 3 was over-represented in the CIMP-H2 subgroup (55%). Polycomb Repressive Complex-2 (PRC2)-marked loci were subjected to significant gene body methylation in CIMP cancers (P < 1.6 × 10-78). We identified oncogenes susceptible to gene body methylation and Wnt pathway antagonists resistant to gene body methylation. CIMP cluster–specific mutations were observed in chromatin remodeling genes, such as in the SWItch/Sucrose Non-Fermentable and Chromodomain Helicase DNA-Binding gene families. Conclusions: There are 5 clinically and molecularly distinct subgroups of colorectal cancer. We show a striking association between CIMP and age, sex, and tumor location, and identify a role for gene body methylation in the progression of serrated neoplasia. These data support our recent findings that CIMP is uncommon in young patients and that BRAF mutant polyps in young patients may have limited potential for malignant progression. Keywords: DNA Methylation, CIMP, Colorectal Cancer, Epigenetics, BRAF, KRAS

Abstract Background The efficacy of vaccines aimed at inhibiting the growth of malaria parasites in the blood can be assessed by comparing the growth rate of parasitaemia in the blood of subjects treated with a test vaccine compared to controls. In studies using induced blood stage malaria (IBSM), a type of controlled human malaria infection, parasite growth rate has been measured using models with the intercept on the y-axis fixed to the inoculum size. A set of statistical models was evaluated to determine an optimal methodology to estimate parasite growth rate in IBSM studies. Methods Parasite growth rates were estimated using data from 40 subjects published in three IBSM studies. Data was fitted using 12 statistical models: log-linear, sine-wave with the period either fixed to 48 h or not fixed; these models were fitted with the intercept either fixed to the inoculum size or not fixed. All models were fitted by individual, and overall by study using a mixed effects model with a random effect for the individual. Results Log-linear models and sine-wave models, with the period fixed or not fixed, resulted in similar parasite growth rate estimates (within 0.05 log10 parasites per mL/day). Average parasite growth rate estimates for models fitted by individual with the intercept fixed to the inoculum size were substantially lower by an average of 0.17 log10 parasites per mL/day (range 0.06–0.24) compared with non-fixed intercept models. Variability of parasite growth rate estimates across the three studies analysed was substantially higher (3.5 times) for fixed-intercept models compared with non-fixed intercept models. The same tendency was observed in models fitted overall by study. Modelling data by individual or overall by study had minimal effect on parasite growth estimates. Conclusions The analyses presented in this report confirm that fixing the intercept to the inoculum size influences parasite growth estimates. The most appropriate statistical model to estimate the growth rate of blood-stage parasites in IBSM studies appears to be a log-linear model fitted by individual and with the intercept estimated in the log-linear regression. Future studies should use this model to estimate parasite growth rates.

Children with chronic health conditions may perform poorer at school. Associations may be confounded by numerous social factors. We aimed to estimate the effects of a chronic health condition on overall school performance in first graders with an emphasis on rigorous adjustment for potential confounders.A population-based cohort study was performed in the area of Mainz-Bingen (Germany). In 2015 all preschoolers were approached and the presence of a chronic health condition was assessed by parental questionnaires and preschool health examination data. The identification of a chronic health condition was based on special health care needs and presence of a doctor's diagnosis out of 24 school-relevant diseases. At the end of the first school year, overall school performance was assessed by teachers and rated on a 5-item scale ranging from -10 to +10.Of 3683 children approached, 2003 were enrolled. Overall school performance was available for 1462 children (51% boys). Of these, 52% suffered from a chronic health condition. Compared to children without a chronic health condition, children with special health care needs (15%) performed worse at school (adjusted mean difference: -0.95, 95% CI: [-1.55; -0.35], P = 0.002). Children with a doctor's diagnosis but without special health care needs (37%) did not perform worse at school. The effect was further analysed considering the extent of special health care needed.Chronic health conditions affect overall school performance early in primary school. To identify academically at-risk children, a chronic health condition identification based on special health care needs may be used.

Non-invasive markers of liver fibrosis are urgently required, especially for use in non-specialist settings. The aim of this study was to identify novel serum biomarkers of advanced fibrosis.We performed an unbiased screen of 120 serum analytes including cytokines, chemokines and proteases in 70 patients (35 without fibrosis, 35 with cirrhosis on biopsy), and selected a panel of 44 candidate biomarkers, which were subsequently measured in a mixed-etiology cohort of 432 patients with known serum HA, PIIINP and TIMP1 (which comprise the validated Enhanced Liver Fibrosis (ELF) test). Multivariate logistic regression modelling was used to generate models for the prediction of advanced or significant fibrosis (METAVIR ≥F3 and ≥F2, respectively); in addition to identifying biomarkers of disease activity and steatohepatitis.Seventeen analytes were significantly differentially expressed between patients with no advanced fibrosis and patients with advanced fibrosis, the most significant being hyaluronic acid (HA) and matrix metalloproteinase (MMP) 7 (p = 2.9E-41 and p = 1.0E-26, respectively). The optimal model for the prediction of advanced fibrosis comprised HA, MMP7, MMP1, alphafetoprotein (AFP) and the AST to platelet ratio index (APRI). We demonstrate enhanced diagnostic accuracy (AUROC = 0.938) compared to a model comprising HA, PIIINP and TIMP1 alone (ELF) (AUROC = 0.898, p