Functional and genetic analysis of haplotypic sequence variation at the nicastrin genomic locus

Nicastrin (NCSTN) is a component of the ?-secretase complex and therefore potentially a candidate risk gene for Alzheimer's disease. Here, we have developed a novel functional genomics methodology to express common locus haplotypes to assess functional differences. DNA recombination was used to engineer 5 bacterial artificial chromosomes (BACs) to each express a different haplotype of the NCSTN locus. Each NCSTN-BAC was delivered to knockout nicastrin (Ncstn(-/-)) cells and clonal NCSTN-BAC(+)/Ncstn(-/-) cell lines were created for functional analyses. We showed that all NCSTN-BAC haplotypes expressed nicastrin protein and rescued ?-secretase activity and amyloid beta (Aß) production in NCSTN-BAC(+)/Ncstn(-/-) lines. We then showed that genetic variation at the NCSTN locus affected alternative splicing in human postmortem brain tissue. However, there was no robust functional difference between clonal cell lines rescued by each of the 5 different haplotypes. Finally, there was no statistically significant association of NCSTN with disease risk in the 4 cohorts. We therefore conclude that it is unlikely that common variation at the NCSTN locus is a risk factor for Alzheimer's disease.

Dose-response relationship in phase 1 clinical trials: a European Drug Development Network (EDDN) Collaboration Study

Introduction: Because a dose–response relationship is characteristic of conventional chemotherapy, this concept is widely used for the development of novel cytotoxic (CTX) drugs. However, the need to reach the MTD to obtain optimal benefit with molecularly targeted agents (MTA) is controversial. In this study, we evaluated the relationship between dose and efficacy in a large cohort of phase I patients with solid tumors.<br/><br/>Experimental Design: We collected data on 1,182 consecutive patients treated in phase I trials in 14 European institutions in 2005–2007. Inclusion criteria were: (i) patients treated within completed single-agent studies in which a maximum-administered dose was defined and (ii) RECIST/survival data available.<br/><br/>Results: Seventy-two percent of patients were included in trials with MTA (N = 854) and 28% in trials with CTX (N = 328). The objective response (OR) rate was 3% and disease control at 6 months was 11%. OR for CTX was associated with higher doses (median 92% of MTD); this was not the case for MTA, where patients achieving OR received a median of 50% of MTD. For trials with MTA, patients treated at intermediate doses (40%–80%) had better survival compared with those receiving low or high doses (P = 0.038). On the contrary, there was a direct association between higher dose and better OS for CTX agents (P = 0.003).<br/><br/>Conclusion: Although these results support the development of novel CTX based on MTD, we found no direct relationship between higher doses and response with MTA in unselected patients. However, the longest OS was seen in patients treated with MTA at intermediate doses (40%–80% of MTD)

Convergent genetic and expression data implicate immunity in Alzheimer's disease

<p>Background: Late-onset Alzheimer's disease (AD) is heritable with 20 genes showing genome-wide association in the International Genomics of Alzheimer's Project (IGAP). To identify the biology underlying the disease, we extended these genetic data in a pathway analysis.</p><p>Methods: The ALIGATOR and GSEA algorithms were used in the IGAP data to identify associated functional pathways and correlated gene expression networks in human brain.</p><p>Results: ALIGATOR identified an excess of curated biological pathways showing enrichment of association. Enriched areas of biology included the immune response (P = 3.27 X 10(-12) after multiple testing correction for pathways), regulation of endocytosis (P = 1.31 X 10(-11)), cholesterol transport (P = 2.96 X 10(-9)), and proteasome-ubiquitin activity (P = 1.34 X 10(-6)). Correlated gene expression analysis identified four significant network modules, all related to the immune response (corrected P = .002-.05).</p><p>Conclusions: The immime response, regulation of endocytosis, cholesterol transport, and protein ubiquitination represent prime targets for AD therapeutics. (C) 2015 Published by Elsevier Inc. on behalf of The Alzheimer's Association.</p>

Common polygenic variation enhances risk prediction for Alzheimer's disease.

The identification of subjects at high risk for Alzheimer’s disease is important for prognosis and early intervention. We investigated the polygenic architecture of Alzheimer’s disease and the accuracy of Alzheimer’s disease prediction models, including and excluding the polygenic component in the model. This study used genotype data from the powerful dataset comprising 17 008 cases and 37 154 controls obtained from the International Genomics of Alzheimer’s Project (IGAP). Polygenic score analysis tested whether the alleles identified to associate with disease in one sample set were significantly enriched in the cases relative to the controls in an independent sample. The disease prediction accuracy was investigated in a subset of the IGAP data, a sample of 3049 cases and 1554 controls (for whom APOE genotype data were available) by means of sensitivity, specificity, area under the receiver operating characteristic curve (AUC) and positive and negative predictive values. We observed significant evidence for a polygenic component enriched in Alzheimer’s disease (P = 4.9 × 10−26). This enrichment remained significant after APOE and other genome-wide associated regions were excluded (P = 3.4 × 10−19). The best prediction accuracy AUC = 78.2% (95% confidence interval 77–80%) was achieved by a logistic regression model with APOE, the polygenic score, sex and age as predictors. In conclusion, Alzheimer’s disease has a significant polygenic component, which has predictive utility for Alzheimer’s disease risk and could be a valuable research tool complementing experimental designs, including preventative clinical trials, stem cell selection and high/low risk clinical studies. In modelling a range of sample disease prevalences, we found that polygenic scores almost doubles case prediction from chance with increased prediction at polygenic extremes.