A prognostic model integrating PET-derived metrics and image texture analyses with clinical risk factors from GOYA

Image texture analysis (radiomics) uses radiographic images to quantify characteristics that may identify tumour heterogeneity and associated patient outcomes. Using fluoro‐deoxy‐glucose positron emission tomography/computed tomography (FDG‐PET/CT)‐derived data, including quantitative metrics, image texture analysis and other clinical risk factors, we aimed to develop a prognostic model that predicts survival in patients with previously untreated diffuse large B‐cell lymphoma (DLBCL) from GOYA (NCT01287741). Image texture features and clinical risk factors were combined into a random forest model and compared with the international prognostic index (IPI) for DLBCL based on progression‐free survival (PFS) and overall survival (OS) predictions. Baseline FDG‐PET scans were available for 1263 patients, 832 patients of these were cell‐of‐origin (COO)‐evaluable. Patients were stratified by IPI or radiomics features plus clinical risk factors into low‐, intermediate‐ and high‐risk groups. The random forest model with COO subgroups identified a clearer high‐risk population (45% 2‐year PFS [95% confidence interval (CI) 40%–52%]; 65% 2‐year OS [95% CI 59%–71%]) than the IPI (58% 2‐year PFS [95% CI 50%–67%]; 69% 2‐year OS [95% CI 62%–77%]). This study confirms that standard clinical risk factors can be combined with PET‐derived image texture features to provide an improved prognostic model predicting survival in untreated DLBCL

Expression of specific inflammasome gene modules stratifies older individuals into two extreme clinical and immunological states

Low-grade, chronic inflammation has been associated with many diseases of aging, but the mechanisms responsible for producing this inflammation remain unclear. Inflammasomes can drive chronic inflammation in the context of an infectious disease or cellular stress, and they trigger the maturation of interleukin-1β (IL-1β). Here we find that the expression of specific inflammasome gene modules stratifies older individuals into two extremes: those with constitutive expression of IL-1β, nucleotide metabolism dysfunction, elevated oxidative stress, high rates of hypertension and arterial stiffness; and those without constitutive expression of IL-1β, who lack these characteristics. Adenine and N4-acetylcytidine, nucleotide-derived metabolites that are detectable in the blood of the former group, prime and activate the NLRC4 inflammasome, induce the production of IL-1β, activate platelets and neutrophils and elevate blood pressure in mice. In individuals over 85 years of age, the elevated expression of inflammasome gene modules was associated with all-cause mortality. Thus, targeting inflammasome components may ameliorate chronic inflammation and various other age-associated conditions

Differential Effects of Comorbidity on Antihypertensive and Glucose-Regulating Treatment in Diabetes Mellitus – A Cohort Study

BACKGROUND: Comorbidity is often mentioned as interfering with "optimal" treatment decisions in diabetes care. It is suggested that diabetes- related comorbidity will increase adequate treatment, whereas diabetes- unrelated comorbidity may decrease this process of care. We hypothesized that these effects differ according to expected priority of the conditions. METHODS: We evaluated the relationship between comorbidity and treatment intensification in a study of 11,248 type 2 diabetes patients using the GIANTT (Groningen Initiative to Analyse type 2 diabetes Treatment) database. We formed a cohort of patients with a systolic blood pressure >/= 140 mmHg (6,820 hypertensive diabetics), and a cohort of patients with an HbA1c >/= 7% (3,589 hyperglycemic diabetics) in 2007. We differentiated comorbidity by diabetes-related or unrelated conditions and by priority. High priority conditions include conditions that are life- interfering, incident or requiring new medication treatment. We performed Cox regression analyses to assess association with treatment intensification, defined as dose increase, start, or addition of drugs. RESULTS: In both the hypertensive and hyperglycemic cohort, only patients with incident diabetes-related comorbidity had a higher chance of treatment intensification (HR 4.48, 2.33-8.62 (p<0.001) for hypertensives; HR 2.37, 1.09-5.17 (p = 0.030) for hyperglycemics). Intensification of hypertension treatment was less likely when a new glucose-regulating drug was prescribed (HR 0.24, 0.06-0.97 (p = 0.046)). None of the prevalent or unrelated comorbidity was significantly associated with treatment intensification. CONCLUSIONS: Diabetes-related comorbidity induced better risk factor treatment only for incident cases, implying that appropriate care is provided more often when complications occur. Diabetes- unrelated comorbidity did not affect hypertension or hyperglycemia management, even when it was incident or life-interfering. Thus, the observed "undertreatment" in diabetes care cannot be explained by constraints caused by such comorbidity

Non-equivalence of Wnt and R-spondin ligands during Lgr5+ intestinal stem-cell self-renewal

The canonical Wnt/β-catenin signaling pathway governs diverse developmental, homeostatic and pathologic processes. Palmitoylated Wnt ligands engage cell surface Frizzled (Fzd) receptors and Lrp5/6 co-receptors enabling β-catenin nuclear translocation and Tcf/Lef-dependent gene transactivation1–3. Mutations in Wnt downstream signaling components have revealed diverse functions presumptively attributed to Wnt ligands themselves, although direct attribution remains elusive, as complicated by redundancy between 19 mammalian Wnts and 10 Fzds1 and Wnt hydrophobicity2,3. For example, individual Wnt ligand mutations have not revealed homeostatic phenotypes in the intestinal epithelium4, an archetypal canonical Wnt pathway-dependent rapidly self-renewing tissue whose regeneration is fueled by proliferative crypt Lgr5+ intestinal stem cells (ISCs)5–9. R-spondin ligands (Rspo1–4) engage distinct Lgr4-6 and Rnf43/Znrf3 receptor classes10–13, markedly potentiate canonical Wnt/β-catenin signaling and induce intestinal organoid growth in vitro and Lgr5+ ISCs in vivo8,14–17. However, the interchangeability, functional cooperation and relative contributions of Wnt versus Rspo ligands to in vivo canonical Wnt signaling and ISC biology remain unknown. Here, we deconstructed functional roles of Wnt versus Rspo ligands in the intestinal crypt stem cell niche. We demonstrate that the default fate of Lgr5+ ISCs is lineage commitment, escape from which requires both Rspo and Wnt ligands. However, gain-of-function studies using Rspo versus a novel non-lipidated Wnt analog reveal qualitatively distinct, non-interchangeable roles for these ligands in ISCs. Wnts are insufficient to induce Lgr5+ ISC self-renewal, but rather confer a basal competency by maintaining Rspo receptor expression that enables Rspo to actively drive and specify the extent of stem cell expansion. This functionally non-equivalent yet cooperative interplay between Wnt and Rspo ligands establishes a molecular precedent for regulation of mammalian stem cells by distinct priming and self-renewal factors, with broad implications for precision control of tissue regeneration

Gene set meta-analysis with Quantitative Set Analysis for Gene Expression (QuSAGE).

Small sample sizes combined with high person-to-person variability can make it difficult to detect significant gene expression changes from transcriptional profiling studies. Subtle, but coordinated, gene expression changes may be detected using gene set analysis approaches. Meta-analysis is another approach to increase the power to detect biologically relevant changes by integrating information from multiple studies. Here, we present a framework that combines both approaches and allows for meta-analysis of gene sets. QuSAGE meta-analysis extends our previously published QuSAGE framework, which offers several advantages for gene set analysis, including fully accounting for gene-gene correlations and quantifying gene set activity as a full probability density function. Application of QuSAGE meta-analysis to influenza vaccination response shows it can detect significant activity that is not apparent in individual studies