Evaluating Retinal Function in Age-Related Maculopathy with the ERG Photostress Test

PURPOSE. To evaluate the diagnostic potential of the electroretinogram (ERG) photostress test and the focal cone ERG in age-related maculopathy (ARM). METHODS. The cohort comprised 31 patients with ARM and 27 age-matched control subjects. The ERG photostress test was used to monitor cone adaptation after intense light adaptation. Focal 41- and 5-Hz cone ERGs were recorded monocularly (central 20°) to assess steady state retinal function. Univariate analysis identified electrophysiological parameters that differed between groups, and receiver operating characteristic (ROC) curves were constructed to assess their diagnostic potential. Logistic regression analysis determined the diagnostic potential of a model incorporating several independent predictors of ARM. RESULTS. The rate of recovery of the ERG photostress test was reduced (recovery was slower) in subjects with ARM. The parameter exhibited good diagnostic potential (P = 0.002, area under ROC curve = 0.74). The implicit times of the 5-Hz (a-wave, P = 0.002; b-wave, P < 0.001) and the 41-Hz (P < 0.001) focal cone ERGs were increased, and the 41-Hz focal cone ERG amplitude (P = 0.003) and focal to full-field amplitude ratio (P = 0.001) were reduced in the ARM group. Logistic regression analysis identified three independent predictors of ARM, including the rate of recovery of the ERG photostress test. CONCLUSIONS. Early ARM has a marked effect on the kinetics of cone adaptation. The clinical application of the ERG photostress test increases the sensitivity and specificity of a model for the diagnosis of ARM. Improved assessment of the functional integrity of the central retina will facilitate early diagnosis and evaluation of therapeutic interventions

Sex differences in oncogenic mutational processes

Sex differences have been observed in multiple facets of cancer epidemiology, treatment and biology, and in most cancers outside the sex organs. Efforts to link these clinical differences to specific molecular features have focused on somatic mutations within the coding regions of the genome. Here we report a pan-cancer analysis of sex differences in whole genomes of 1983 tumours of 28 subtypes as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium. We both confirm the results of exome studies, and also uncover previously undescribed sex differences. These include sex-biases in coding and non-coding cancer drivers, mutation prevalence and strikingly, in mutational signatures related to underlying mutational processes. These results underline the pervasiveness of molecular sex differences and strengthen the call for increased consideration of sex in molecular cancer research

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe

Effects of protein interaction data integration, representation and reliability on the use of network properties for drug target prediction

<p>Abstract</p> <p>Background</p> <p>Previous studies have noted that drug targets appear to be associated with higher-degree or higher-centrality proteins in interaction networks. These studies explicitly or tacitly make choices of different source databases, data integration strategies, representation of proteins and complexes, and data reliability assumptions. Here we examined how the use of different data integration and representation techniques, or different notions of reliability, may affect the efficacy of degree and centrality as features in drug target prediction.</p> <p>Results</p> <p>Fifty percent of drug targets have a degree of less than nine, and ninety-five percent have a degree of less than ninety. We found that drug targets are over-represented in higher degree bins – this relationship is only seen for the consolidated interactome and it is not dependent on n-ary interaction data or its representation. Degree acts as a weak predictive feature for drug-target status and using more reliable subsets of the data does not increase this performance. However, performance does increase if only cancer-related drug targets are considered. We also note that a protein’s membership in pathway records can act as a predictive feature that is better than degree and that high-centrality may be an indicator of a drug that is more likely to be withdrawn.</p> <p>Conclusions</p> <p>These results show that protein interaction data integration and cleaning is an important consideration when incorporating network properties as predictive features for drug-target status. The provided scripts and data sets offer a starting point for further studies and cross-comparison of methods.</p