24 research outputs found

    Molecular Insights into the Pathogenesis of Alzheimer's Disease and Its Relationship to Normal Aging

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    Alzheimer's disease (AD) is a complex neurodegenerative disorder that diverges from the process of normal brain aging by unknown mechanisms. We analyzed the global structure of age- and disease-dependent gene expression patterns in three regions from more than 600 brains. Gene expression variation could be almost completely explained by four transcriptional biomarkers that we named BioAge (biological age), Alz (Alzheimer), Inflame (inflammation), and NdStress (neurodegenerative stress). BioAge captures the first principal component of variation and includes genes statistically associated with neuronal loss, glial activation, and lipid metabolism. Normally BioAge increases with chronological age, but in AD it is prematurely expressed as if some of the subjects were 140 years old. A component of BioAge, Lipa, contains the AD risk factor APOE and reflects an apparent early disturbance in lipid metabolism. The rate of biological aging in AD patients, which cannot be explained by BioAge, is associated instead with NdStress, which includes genes related to protein folding and metabolism. Inflame, comprised of inflammatory cytokines and microglial genes, is broadly activated and appears early in the disease process. In contrast, the disease-specific biomarker Alz was selectively present only in the affected areas of the AD brain, appears later in pathogenesis, and is enriched in genes associated with the signaling and cell adhesion changes during the epithelial to mesenchymal (EMT) transition. Together these biomarkers provide detailed description of the aging process and its contribution to Alzheimer's disease progression

    Predictive Genes in Adjacent Normal Tissue Are Preferentially Altered by sCNV during Tumorigenesis in Liver Cancer and May Rate Limiting

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    Background: In hepatocellular carcinoma (HCC) genes predictive of survival have been found in both adjacent normal (AN) and tumor (TU) tissues. The relationships between these two sets of predictive genes and the general process of tumorigenesis and disease progression remains unclear. Methodology/Principal Findings: Here we have investigated HCC tumorigenesis by comparing gene expression, DNA copy number variation and survival using ~250 AN and TU samples representing, respectively, the pre-cancer state, and the result of tumorigenesis. Genes that participate in tumorigenesis were defined using a gene-gene correlation meta-analysis procedure that compared AN versus TU tissues. Genes predictive of survival in AN (AN-survival genes) were found to be enriched in the differential gene-gene correlation gene set indicating that they directly participate in the process of tumorigenesis. Additionally the AN-survival genes were mostly not predictive after tumorigenesis in TU tissue and this transition was associated with and could largely be explained by the effect of somatic DNA copy number variation (sCNV) in cis and in trans. The data was consistent with the variance of AN-survival genes being rate-limiting steps in tumorigenesis and this was confirmed using a treatment that promotes HCC tumorigenesis that selectively altered AN-survival genes and genes differentially correlated between AN and TU. Conclusions/Significance: This suggests that the process of tumor evolution involves rate-limiting steps related to the background from which the tumor evolved where these were frequently predictive of clinical outcome. Additionally treatments that alter the likelihood of tumorigenesis occurring may act by altering AN-survival genes, suggesting that the process can be manipulated. Further sCNV explains a substantial fraction of tumor specific expression and may therefore be a causal driver of tumor evolution in HCC and perhaps many solid tumor types. © 2011 Lamb et al.published_or_final_versio

    Effect of small-scale wildfires on the air parameters near the burning centers

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    The results of seminatural experiments on the study of steppe and field wildfires characteristic of the steppe and forest-steppe zones of Western Siberia are presented. Using infrared (IR) thermography methods, the main thermal characteristics of the fire front are derived, the flame turbulence scale is estimated, and changes in the structure function of the air refractive index are analyzed in the vicinity of a fire. The effect of a model fire on the change of meteorological parameters (wind velocity components, relative air humidity, and temperature) is ascertained. Large-scale turbulence is observed in the front of a seminatural fire, which is absent in laboratory conditions. The predominance of large-scale turbulence in a flame results in turbulization of the atmosphere in the vicinity of a combustion center. Strong heat release in the combustion zone and flame turbulence increase the vertical component of the wind velocity and produce fluctuations in the air refractive index, which is an indicator of atmospheric turbulization. This creates prerequisites for the formation of a proper wind during large fires. Variations in the gas and aerosol compositions of the atmosphere are measured in the vicinity of the experimental site

    Putative Biomarkers of Clinical Benefit With Pembrolizumab in Advanced Urothelial Cancer: Results from the KEYNOTE-045 and KEYNOTE-052 Landmark Trials

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    Purpose: In an exploratory analysis, we investigated the association between programmed death ligand 1 (PD-L1), tumor mutational burden (TMB), T-cell–inflamed gene expression profile (TcellinfGEP), and stromal signature with outcomes of pembrolizumab in urothelial carcinoma (UC). Patients and Methods: Patients with advanced UC received first-line pembrolizumab 200 mg every 3 weeks in the single-arm phase II KEYNOTE-052 trial (NCT02335424) and salvage pembrolizumab 200 mg every 3 weeks or chemotherapy (paclitaxel/docetaxel/vinflunine) in the randomized phase III KEYNOTE-045 trial (NCT02256436). The association of each biomarker (continuous variable) with objective response rate (ORR), progression-free survival (PFS), and overall survival (OS) was evaluated using logistic regression (ORR) and Cox PH (PFS, OS), adjusted for ECOG PS; nominal P values were calculated without multiplicity adjustment (one-sided, pembrolizumab; two-sided, chemotherapy). Significance was prespecified at a ¼ 0.05. Results: In KEYNOTE-052, PD-L1, TMB, and TcellinfGEP were significantly associated with improved outcomes; stromal signature was significantly associated with worse outcomes. In KEYNOTE-045, although findings for TMB and TcellinfGEP with pembrolizumab were consistent with those of KEYNOTE-052, PD-L1 was not significantly associated with improved outcomes, nor was stromal signature associated with worse outcomes with pembrolizumab; chemotherapy was not associated with outcomes in a consistent manner for any of the biomarkers. Hazard ratio (HR) estimates at prespecified cutoffs showed an advantage for pembrolizumab versus chemotherapy regardless of PD-L1 or TMB, with a trend toward lower HRs in the combined positive score ≥10 and the TMB ≥175 mutation/exome subgroup. For TcellinfGEP, PFS and OS HRs were lower in the TcellinfGEP-nonlow subgroup regardless of treatment. Conclusions: Multiple biomarkers characterizing the tumor microenvironment may help predict response to pembrolizumab monotherapy in UC, and potential clinical utility of these biomarkers may be context-dependent
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