Pre-diagnostic blood immune markers, incidence and progression of B-cell lymphoma and multiple myeloma: Univariate and functionally informed multivariate analyses.

Recent prospective studies have shown that dysregulation of the immune system may precede the development of B-cell lymphomas (BCL) in immunocompetent individuals. However, to date, the studies were restricted to a few immune markers, which were considered separately. Using a nested case-control study within two European prospective cohorts, we measured plasma levels of 28 immune markers in samples collected a median of 6 years before diagnosis (range 2.01-15.97) in 268 incident cases of BCL (including multiple myeloma [MM]) and matched controls. Linear mixed models and partial least square analyses were used to analyze the association between levels of immune marker and the incidence of BCL and its main histological subtypes and to investigate potential biomarkers predictive of the time to diagnosis. Linear mixed model analyses identified associations linking lower levels of fibroblast growth factor-2 (FGF-2 p = 7.2 × 10-4 ) and transforming growth factor alpha (TGF-α, p = 6.5 × 10-5 ) and BCL incidence. Analyses stratified by histological subtypes identified inverse associations for MM subtype including FGF-2 (p = 7.8 × 10-7 ), TGF-α (p = 4.08 × 10-5 ), fractalkine (p = 1.12 × 10-3 ), monocyte chemotactic protein-3 (p = 1.36 × 10-4 ), macrophage inflammatory protein 1-alpha (p = 4.6 × 10-4 ) and vascular endothelial growth factor (p = 4.23 × 10-5 ). Our results also provided marginal support for already reported associations between chemokines and diffuse large BCL (DLBCL) and cytokines and chronic lymphocytic leukemia (CLL). Case-only analyses showed that Granulocyte-macrophage colony stimulating factor levels were consistently higher closer to diagnosis, which provides further evidence of its role in tumor progression. In conclusion, our study suggests a role of growth-factors in the incidence of MM and of chemokine and cytokine regulation in DLBCL and CLL.

Evolving DNA methylation and gene expression markers of B-cell chronic lymphocytic leukemia are present in pre-diagnostic blood samples more than 10 years prior to diagnosis.

BACKGROUND: B-cell chronic lymphocytic leukemia (CLL) is a common type of adult leukemia. It often follows an indolent course and is preceded by monoclonal B-cell lymphocytosis, an asymptomatic condition, however it is not known what causes subjects with this condition to progress to CLL. Hence the discovery of prediagnostic markers has the potential to improve the identification of subjects likely to develop CLL and may also provide insights into the pathogenesis of the disease of potential clinical relevance. RESULTS: We employed peripheral blood buffy coats of 347 apparently healthy subjects, of whom 28 were diagnosed with CLL 2.0-15.7 years after enrollment, to derive for the first time genome-wide DNA methylation, as well as gene and miRNA expression, profiles associated with the risk of future disease. After adjustment for white blood cell composition, we identified 722 differentially methylated CpG sites and 15 differentially expressed genes (Bonferroni-corrected p < 0.05) as well as 2 miRNAs (FDR < 0.05) which were associated with the risk of future CLL. The majority of these signals have also been observed in clinical CLL, suggesting the presence in prediagnostic blood of CLL-like cells. Future CLL cases who, at enrollment, had a relatively low B-cell fraction (<10%), and were therefore less likely to have been suffering from undiagnosed CLL or a precursor condition, showed profiles involving smaller numbers of the same differential signals with intensities, after adjusting for B-cell content, generally smaller than those observed in the full set of cases. A similar picture was obtained when the differential profiles of cases with time-to-diagnosis above the overall median period of 7.4 years were compared with those with shorted time-to-disease. Differentially methylated genes of major functional significance include numerous genes that encode for transcription factors, especially members of the homeobox family, while differentially expressed genes include, among others, multiple genes related to WNT signaling as well as the miRNAs miR-150-5p and miR-155-5p. CONCLUSIONS: Our findings demonstrate the presence in prediagnostic blood of future CLL patients, more than 10 years before diagnosis, of CLL-like cells which evolve as preclinical disease progresses, and point to early molecular alterations with a pathogenetic potential.

Blood-based omic profiling supports female susceptibility to tobacco smoke-induced cardiovascular diseases.

We recently reported that differential gene expression and DNA methylation profiles in blood leukocytes of apparently healthy smokers predicts with remarkable efficiency diseases and conditions known to be causally associated with smoking, suggesting that blood-based omic profiling of human populations may be useful for linking environmental exposures to potential health effects. Here we report on the sex-specific effects of tobacco smoking on transcriptomic and epigenetic features derived from genome-wide profiling in white blood cells, identifying 26 expression probes and 92 CpG sites, almost all of which are affected only in female smokers. Strikingly, these features relate to numerous genes with a key role in the pathogenesis of cardiovascular disease, especially thrombin signaling, including the thrombin receptors on platelets F2R (coagulation factor II (thrombin) receptor; PAR1) and GP5 (glycoprotein 5), as well as HMOX1 (haem oxygenase 1) and BCL2L1 (BCL2-like 1) which are involved in protection against oxidative stress and apoptosis, respectively. These results are in concordance with epidemiological evidence of higher female susceptibility to tobacco-induced cardiovascular disease and underline the potential of blood-based omic profiling in hazard and risk assessment.

Omics for prediction of environmental health effects: Blood leukocyte-based cross-omic profiling reliably predicts diseases associated with tobacco smoking.

The utility of blood-based omic profiles for linking environmental exposures to their potential health effects was evaluated in 649 individuals, drawn from the general population, in relation to tobacco smoking, an exposure with well-characterised health effects. Using disease connectivity analysis, we found that the combination of smoking-modified, genome-wide gene (including miRNA) expression and DNA methylation profiles predicts with remarkable reliability most diseases and conditions independently known to be causally associated with smoking (indicative estimates of sensitivity and positive predictive value 94% and 84%, respectively). Bioinformatics analysis reveals the importance of a small number of smoking-modified, master-regulatory genes and suggest a central role for altered ubiquitination. The smoking-induced gene expression profiles overlap significantly with profiles present in blood cells of patients with lung cancer or coronary heart disease, diseases strongly associated with tobacco smoking. These results provide proof-of-principle support to the suggestion that omic profiling in peripheral blood has the potential of identifying early, disease-related perturbations caused by toxic exposures and may be a useful tool in hazard and risk assessment.

Cancer Biomarkers from Genome-Scale DNA Methylation: Comparison of Evolutionary and Semantic Analysis Methods.

DNA methylation profiling exploits microarray technologies, thus yielding a wealth of high-volume data. Here, an intelligent framework is applied, encompassing epidemiological genome-scale DNA methylation data produced from the Illumina's Infinium Human Methylation 450K Bead Chip platform, in an effort to correlate interesting methylation patterns with cancer predisposition and, in particular, breast cancer and B-cell lymphoma. Feature selection and classification are employed in order to select, from an initial set of ~480,000 methylation measurements at CpG sites, predictive cancer epigenetic biomarkers and assess their classification power for discriminating healthy versus cancer related classes. Feature selection exploits evolutionary algorithms or a graph-theoretic methodology which makes use of the semantics information included in the Gene Ontology (GO) tree. The selected features, corresponding to methylation of CpG sites, attained moderate-to-high classification accuracies when imported to a series of classifiers evaluated by resampling or blindfold validation. The semantics-driven selection revealed sets of CpG sites performing similarly with evolutionary selection in the classification tasks. However, gene enrichment and pathway analysis showed that it additionally provides more descriptive sets of GO terms and KEGG pathways regarding the cancer phenotypes studied here. Results support the expediency of this methodology regarding its application in epidemiological studies.

Exploring robust diagnostic signatures for cutaneous melanoma utilizing genetic and imaging data.

Multimodal data combined in an integrated dataset can be used to aim the identification of instrumental biological actions that trigger the development of a disease. In this paper, we use an integrated dataset related to cutaneous melanoma that fuses two separate sets providing complementary information (gene expression profiling and imaging). Our first goal is to select a subset of genes that comprise candidate genetic biomarkers. The derived gene signature is then utilized in order to select imaging features, which characterize disease at a macroscopic level, presenting the highest, mutual information content to the selected genes. Using information gain ratio measurements and exploration of the gene ontology tree, we identified a set of 32 uncorrelated genes with a pivotal role as regards molecular regulation of melanoma, which expression across samples correlates highly with the different pathological states. These genes steered the selection of a subset of uncorrelated imaging features based on their ranking according to mutual information measurements to the selected gene expression values. Selected genes and imaging features were used to train various classifiers that could generalize well when discriminating malignant from benign melanoma samples. Results on the selection on imaging features and classification were compared to feature selection based on a straight forward statistical selection and a stochastic-based methodology. Genes in the backstage of low-level biological processes showed to carry higher information content than the macroscopic imaging features.

A composite framework for the statistical analysis of epidemiological DNA methylation data with the Infinium Human Methylation 450K BeadChip.

High-throughput DNA methylation profiling exploits microarray technologies thus providing a wealth of data, which however solicits rigorous, generic, and analytical pipelines for an efficient systems level analysis and interpretation. In this study, we utilize the Illumina's Infinium Human Methylation 450K BeadChip platform in an epidemiological cohort, targeting to associate interesting methylation patterns with breast cancer predisposition. The computational framework proposed here extends the--established in transcriptomic microarrays--logarithmic ratio of the methylated versus the unmethylated signal intensities, quoted as M-value. Moreover, intensity-based correction of the M-signal distribution is introduced in order to correct for batch effects and probe-specific errors in intensity measurements. This is accomplished through the estimation of intensity-related error measures from quality control samples included in each chip. Moreover, robust statistical measures exploiting the coefficient variation of DNA methylation measurements between control and case samples alleviate the impact of technical variation. The results presented here are juxtaposed to those derived by applying classical preprocessing and statistical selection methodologies. Overall, in comparison to traditional approaches, the superior performance of the proposed framework in terms of technical bias correction, along with its generic character, support its suitability for various microarray technologies.

Evaluation of texture features in hepatic tissue characterization from non-enhanced CT images.

Aim of this paper is to evaluate the diagnostic contribution of various types of texture features in discrimination of hepatic tissue in abdominal non-enhanced Computed Tomography (CT) images. Regions of Interest (ROIs) corresponding to the classes: normal liver, cyst, hemangioma, and hepatocellular carcinoma were drawn by an experienced radiologist. For each ROI, five distinct sets of texture features are extracted using First Order Statistics (FOS), Spatial Gray Level Dependence Matrix (SGLDM), Gray Level Difference Method (GLDM), Laws' Texture Energy Measures (TEM), and Fractal Dimension Measurements (FDM). In order to evaluate the ability of the texture features to discriminate the various types of hepatic tissue, each set of texture features, or its reduced version after genetic algorithm based feature selection, was fed to a feed-forward Neural Network (NN) classifier. For each NN, the area under Receiver Operating Characteristic (ROC) curves (Az) was calculated for all one-vs-all discriminations of hepatic tissue. Additionally, the total Az for the multi-class discrimination task was estimated. The results show that features derived from FOS perform better than other texture features (total Az: 0.802+/-0.083) in the discrimination of hepatic tissue.

Differential diagnosis of CT focal liver lesions using texture features, feature selection and ensemble driven classifiers.

OBJECTIVES: The aim of the present study is to define an optimally performing computer-aided diagnosis (CAD) architecture for the classification of liver tissue from non-enhanced computed tomography (CT) images into normal liver (C1), hepatic cyst (C2), hemangioma (C3), and hepatocellular carcinoma (C4). To this end, various CAD architectures, based on texture features and ensembles of classifiers (ECs), are comparatively assessed. MATERIALS AND METHODS: Number of regions of interests (ROIs) corresponding to C1-C4 have been defined by experienced radiologists in non-enhanced liver CT images. For each ROI, five distinct sets of texture features were extracted using first order statistics, spatial gray level dependence matrix, gray level difference method, Laws' texture energy measures, and fractal dimension measurements. Two different ECs were constructed and compared. The first one consists of five multilayer perceptron neural networks (NNs), each using as input one of the computed texture feature sets or its reduced version after genetic algorithm-based feature selection. The second EC comprised five different primary classifiers, namely one multilayer perceptron NN, one probabilistic NN, and three k-nearest neighbor classifiers, each fed with the combination of the five texture feature sets or their reduced versions. The final decision of each EC was extracted by using appropriate voting schemes, while bootstrap re-sampling was utilized in order to estimate the generalization ability of the CAD architectures based on the available relatively small-sized data set. RESULTS: The best mean classification accuracy (84.96%) is achieved by the second EC using a fused feature set, and the weighted voting scheme. The fused feature set was obtained after appropriate feature selection applied to specific subsets of the original feature set. CONCLUSIONS: The comparative assessment of the various CAD architectures shows that combining three types of classifiers with a voting scheme, fed with identical feature sets obtained after appropriate feature selection and fusion, may result in an accurate system able to assist differential diagnosis of focal liver lesions from non-enhanced CT images.