DNA methylation changes measured in pre‐diagnostic peripheral blood samples are associated with smoking and lung cancer risk

DNA methylation changes are associated with cigarette smoking. We used the Illumina Infinium HumanMethylation450 array to determine whether methylation in DNA from pre‐diagnostic, peripheral blood samples is associated with lung cancer risk. We used a case‐control study nested within the EPIC‐Italy cohort and a study within the MCCS cohort as discovery sets (a total of 552 case‐control pairs). We validated the top signals in 429 case‐control pairs from another 3 studies. We identified six CpGs for which hypomethylation was associated with lung cancer risk: cg05575921 in the AHRR gene (p‐value_pooled = 4 × 10^?17), cg03636183 in the F2RL3 gene (p‐value_pooled = 2 × 10 ^{? 13}), cg21566642 and cg05951221 in 2q37.1 (p‐value_pooled = 7 × 10^?16 and 1 × 10^?11 respectively), cg06126421 in 6p21.33 (p‐value_pooled = 2 × 10^?15) and cg23387569 in 12q14.1 (p‐value_pooled = 5 × 10^?7). For cg05951221 and cg23387569 the strength of association was virtually identical in never and current smokers. For all these CpGs except for cg23387569, the methylation levels were different across smoking categories in controls (p‐values_{heterogeneity} ≤ 1.8 x10 ^{? 7}), were lowest for current smokers and increased with time since quitting for former smokers. We observed a gain in discrimination between cases and controls measured by the area under the ROC curve of at least 8% (p‐values ≥ 0.003) in former smokers by adding methylation at the 6 CpGs into risk prediction models including smoking status and number of pack‐years. Our findings provide convincing evidence that smoking and possibly other factors lead to DNA methylation changes measurable in peripheral blood that may improve prediction of lung cancer risk.     

Combining randomized and non‐randomized evidence in network meta‐analysis

Non‐randomized studies aim to reveal whether or not interventions are effective in real‐life clinical practice, and there is a growing interest in including such evidence in the decision‐making process. We evaluate existing methodologies and present new approaches to using non‐randomized evidence in a network meta‐analysis of randomized controlled trials (RCTs) when the aim is to assess relative treatment effects. We first discuss how to assess compatibility between the two types of evidence. We then present and compare an array of alternative methods that allow the inclusion of non‐randomized studies in a network meta‐analysis of RCTs: the naïve data synthesis, the design‐adjusted synthesis, the use of non‐randomized evidence as prior information and the use of three‐level hierarchical models. We apply some of the methods in two previously published clinical examples comparing percutaneous interventions for the treatment of coronary in‐stent restenosis and antipsychotics in patients with schizophrenia. We discuss in depth the advantages and limitations of each method, and we conclude that the inclusion of real‐world evidence from non‐randomized studies has the potential to corroborate findings from RCTs, increase precision and enhance the decision‐making process. Copyright © 2017 John Wiley & Sons, Ltd.     

Epigenomic Alterations in Localized and Advanced Prostate Cancer

Although prostate cancer (PCa) is the second leading cause of cancer death among men worldwide, not all men diagnosed with PCa will die from the disease. A critical challenge, therefore, is to distinguish indolent PCa from more advanced forms to guide appropriate treatment decisions. We used Enhanced Reduced Representation Bisulfite Sequencing, a genome-wide high-coverage single-base resolution DNA methylation method to profile seven localized PCa samples, seven matched benign prostate tissues, and six aggressive castration-resistant prostate cancer (CRPC) samples. We integrated these data with RNA-seq and whole-genome DNA-seq data to comprehensively characterize the PCa methylome, detect changes associated with disease progression, and identify novel candidate prognostic biomarkers. Our analyses revealed the correlation of cytosine guanine dinucleotide island (CGI)-specific hypermethylation with disease severity and association of certain breakpoints (deletion, tandem duplications, and interchromosomal translocations) with DNA methylation. Furthermore, integrative analysis of methylation and single-nucleotide polymorphisms (SNPs) uncovered widespread allele-specific methylation (ASM) for the first time in PCa. We found that most DNA methylation changes occurred in the context of ASM, suggesting that variations in tumor epigenetic landscape of individuals are partly mediated by genetic differences, which may affect PCa disease progression. We further selected a panel of 13 CGIs demonstrating increased DNA methylation with disease progression and validated this panel in an independent cohort of 20 benign prostate tissues, 16 PCa, and 8 aggressive CRPCs. These results warrant clinical evaluation in larger cohorts to help distinguish indolent PCa from advanced disease.     

Proteomic Feature Maps: A new visualization approach in proteomics analysis

The different steps of a proteomics analysis workflow generate a plethora of features for each extracted proteomic object (a protein spot in 2D gel electrophoresis (2-DE), or a peptide peak in liquid chromatography–mass spectrometry (LC–MS) analysis). Yet, the joint visualization of multiple object features on 2D gel-like maps is rather limited in currently available proteomics software packages. We introduce a new, simple, and intuitive visualization method that utilizes spheres to represent proteomic objects on proteomic feature maps, and exploits the spheres size and color to provide simultaneous visualization of user-selected feature pairs. Our contribution, a unified and flexible visualization mechanism that can be easily applied at any stage of a 2-DE or a LC–MS based differential proteomics study, is demonstrated and discussed using five representative scenarios. The joint visualization of proteomic object features and their spatial distribution is a powerful tool for inspecting and comparing the proteomics analysis results, attracting the users attention to useful information, such as differential expression trends and patterns, and even assisting in the evaluation and refinement of a proteomics experiment.     

Antiproliferative effect of exemestane in lung cancer cells

Background  

Recent evidence suggests that estrogen signaling may be involved in the pathogenesis of non-small cell lung cancer (NSCLC). Aromatase is an enzyme complex that catalyses the final step in estrogen synthesis and is present in several tissues, including the lung. In the current study we investigated the activity of the aromatase inhibitor exemestane in human NSCLC cell lines H23 and A549.