Cancer biomarker discovery: the entropic hallmark

Emerging methods and techniques for cancer biomarker discovery

Modern cancer research depends heavily on the identification and validation of biomarkers because they provide important information about the diagnosis, prognosis, and response to treatment of the cancer. This review will provide a comprehensive overview of cancer biomarkers, including their development phases and recent breakthroughs in transcriptomics and computational techniques for detecting these biomarkers. Blood-based biomarkers have great potential for non-invasive tumor dynamics and treatment response monitoring. These include circulating tumor DNA, exosomes, and microRNAs. Comprehensive molecular profiles are provided by multi-omic technologies, which combine proteomics, metabolomics, and genomes to support the identification of biomarkers and the targeting of therapeutic interventions. Genetic changes are detected by next-generation sequencing, and patterns of protein expression are found by protein arrays and mass spectrometry. Tumor heterogeneity and clonal evolution can be understood using metabolic profiling and single-cell studies. It is projected that the use of several biomarkers-genetic, protein, mRNA, microRNA, and DNA profiles, among others-will rise, enabling multi-biomarker analysis and improving individualised treatment plans. Biomarker identification and patient outcome prediction are further improved by developments in AI algorithms and imaging techniques. Robust biomarker validation and reproducibility require cooperation between industry, academia, and doctors. Biomarkers can provide individualized care, meet unmet clinical needs, and enhance patient outcomes despite some obstacles. Precision medicine will continue to take shape as scientific research advances and the integration of biomarkers with cutting-edge technologies continues to offer a more promising future for personalized cancer care.

Characterizing heterogeneity along EMT and metabolic axes in colorectal cancer reveals underlying consensus molecular subtype-specific trends

Colorectal cancer (CRC) is highly heterogeneous with variable survival outcomes and therapeutic vulnerabilities. A commonly used classification system in CRC is the Consensus Molecular Subtypes (CMS) based on gene expression patterns. However, how these CMS categories connect to axes of phenotypic plasticity and heterogeneity remains unclear. Here, in our analysis of CMS-specific TCGA data and 101 bulk transcriptomic datasets, we found the epithelial phenotype score to be consistently positively correlated with scores of glycolysis, OXPHOS and FAO pathways, while PD-L1 activity scores positively correlated with mesenchymal phenotype scoring, revealing possible interconnections among plasticity axes. Single-cell RNA-sequencing analysis of patient samples revealed that that CMS2 and CMS3 subtype samples were relatively more epithelial as compared to CMS1 and CMS4. CMS1 revealed two subpopulations: one close to CMS4 (more mesenchymal) and the other closer to CMS2 or CMS3 (more epithelial), indicating a partial EMT-like behavior. Consistent observations were made in single-cell analysis of metabolic axes and PD-L1 activity scores. Together, our results quantify the patterns of two functional interconnected axes of phenotypic heterogeneity - EMT and metabolic reprogramming - in a CMS-specific manner in CRC.

Recent Advances in Plasma-Engineered Polymers for Biomarker-Based Viral Detection and Highly Multiplexed Analysis

Infectious diseases remain a pervasive threat to global and public health, especially in many countries and rural urban areas. The main causes of such severe diseases are the lack of appropriate analytical methods and subsequent treatment strategies due to limited access to centralized and equipped medical centers for detection. Rapid and accurate diagnosis in biomedicine and healthcare is essential for the effective treatment of pathogenic viruses as well as early detection. Plasma-engineered polymers are used worldwide for viral infections in conjunction with molecular detection of biomarkers. Plasma-engineered polymers for biomarker-based viral detection are generally inexpensive and offer great potential. For biomarker-based virus detection, plasma-based polymers appear to be potential biological probes and have been used directly with physiological components to perform highly multiplexed analyses simultaneously. The simultaneous measurement of multiple clinical parameters from the same sample volume is possible using highly multiplexed analysis to detect human viral infections, thereby reducing the time and cost required to collect each data point. This article reviews recent studies on the efficacy of plasma-engineered polymers as a detection method against human pandemic viruses. In this review study, we examine polymer biomarkers, plasma-engineered polymers, highly multiplexed analyses for viral infections, and recent applications of polymer-based biomarkers for virus detection. Finally, we provide an outlook on recent advances in the field of plasma-engineered polymers for biomarker-based virus detection and highly multiplexed analysis.

Information entropy of quantitative chemometric endogenous fluorescence improves photonic lung cancer diagnosis

Quantitative chemometric widefield endogenous fluorescence microscopy (CFM) maps the endogenous absolute chromophore concentration and spatial distribution in cells and tissue sections label-free from fluorescence color images under broadband excitation and detection. By quantifying the endogenous chromophores, including tryptophan, elastin, reduced nicotinamide adenine dinucleotide [NAD(P)H], and flavin adenine dinucleotide (FAD), CFM reveals the biochemical environment and subcellular structure. Here we show that the chromophore information entropy, marking its spatial distribution pattern of quantitative chemometric endogenous fluorescence at the microscopic scale, improves photonic lung cancer diagnosis with independent diagnostic power to the cellular metabolism biomarker. NAD(P)H and FAD's information entropy is found to decrease from normal to perilesional to cancerous tissue, whereas the information entropy for the redox ratios [FAD/tryptophan and FAD/NAD(P)H] is smaller for the normal tissue than both perilesional and cancerous tissue. CFM imaging of the specimen's inherent biochemical and structural properties eliminates the dependence on measurement details and facilitates robust, accurate diagnosis. The synergy of quantifying absolute chromophore concentration and information entropy achieves high accuracies for a three-class classification of lung tissue into normal, perilesional, and cancerous ones and a three-class classification of lung cancers into grade 1, grade 2, and grade 3 using a support vector machine, outperforming the chromophore concentration biomarkers.

Single Cell Systems Analysis: Decision Geometry In Outliers

MOTIVATION

Anti-cancer therapeutics of the highest calibre currently focus on combinatorial targeting of specific oncoproteins and tumour suppressors. Clinical relapse depends upon intratumoral heterogeneity which serves as substrate variation during evolution of resistance to therapeutic regimens.

RESULTS

The present review advocates single cell systems biology as the optimal level of analysis for remediation of clinical relapse. Graph theory approaches to understanding decision-making in single cells may be abstracted one level further, to the geometry of decision-making in outlier cells, in order to define evolution-resistant cancer biomarkers. Systems biologists currently working with omics data are invited to consider phase portrait analysis as a mediator between graph theory and deep learning approaches. Perhaps counter-intuitively, the tangible clinical needs of cancer patients may depend upon the adoption of higher level mathematical abstractions of cancer biology.

SUPPLEMENTARY INFORMATION

supplementary data available at Bioinformatics online.

Biomaterial Scaffolds Recruit an Aggressive Population of Metastatic Tumor Cells In Vivo

For most cancers, metastasis is the point at which clinical treatment shifts from curative intent to extending survival. Biomaterial implants acting as a synthetic premetastatic niche recruit metastatic cancer cells and provide a survival advantage, and their use as a diagnostic platform requires assessing their relevance to disease progression. Here, we showed that scaffold-captured tumor cells (SCAF) were 30 times more metastatic to the lung than primary tumor (PT) cells, similar to cells derived from lung micrometastases (LUNG). SCAF cells were more aggressive in vitro, demonstrated higher levels of migration, invasion, and mammosphere formation, and had a greater proportion of cancer stem cells than PT. SCAF cells were highly enriched for gene expression signatures associated with metastasis and had associated genomic structural changes, including globally enhanced entropy. Collectively, our findings demonstrate that SCAF cells are distinct from PT and more closely resemble LUNG, indicating that tumor cells retrieved from scaffolds are reflective of cells at metastatic sites. SIGNIFICANCE: These findings suggest that metastatic tumor cells captured by a biomaterial scaffold may serve as a diagnostic for molecular staging of metastasis.Graphical Abstract: http://cancerres.aacrjournals.org/content/canres/79/8/2042/F1.large.jpg.

Aquaporins in cancer development: opportunities for bioinorganic chemistry to contribute novel chemical probes and therapeutic agents

Aquaporins (AQPs) are membrane proteins allowing permeation of water, glycerol & hydrogen peroxide across biomembranes, and playing an important role in water homeostasis in different organs, exocrine gland secretion, urine concentration, skin moisturization, fat metabolism and neural signal transduction. Notably, a large number of studies showed that AQPs are closely associated with cancer biological functions and expressed in more than 20 human cancer cell types. Furthermore, AQP expression is positively correlated with tumour types, grades, proliferation, migration, angiogenesis, as well as tumour-associated oedema, rendering these membrane channels attractive as both diagnostic and therapeutic targets in cancer. Recent developments in the field of AQPs modulation have identified coordination metal-based complexes as potent and selective inhibitors of aquaglyceroporins, opening new avenues in the application of inorganic compounds in medicine and chemical biology. The present review is aimed at providing an overview on AQP structure and function, mainly in relation to cancer. In this context, the exploration of coordination metal compounds as possible inhibitors of aquaporins may open the way to novel chemical approaches to study AQP roles in tumour growth and potentially to new drug families. Thus, we describe recent results in the field and reflect upon the potential of inorganic chemistry in providing compounds to modulate the activity of "elusive" membrane targets as the aquaporins.

Basal-like breast cancer: molecular profiles, clinical features and survival outcomes

BACKGROUND

Basal-like constitutes an important molecular subtype of breast cancer characterised by an aggressive behaviour and a limited therapy response. The outcome of patients within this subtype is, however, divergent. Some individuals show an increased risk of dying in the first five years, and others a long-term survival of over ten years after the diagnosis. In this study, we aim at identifying markers associated with basal-like patients' survival and characterising subgroups with distinct disease outcome.

METHODS

We explored the genomic and transcriptomic profiles of 351 basal-like samples from the METABRIC and ROCK data sets. Two selection methods, labelled Differential and Survival filters, were employed to determine genes/probes that are differentially expressed in tumour and control samples, and are associated with overall survival. These probes were further used to define molecular subgroups, which vary at the microRNA level and in DNA copy number.

RESULTS

We identified the expression signature of 80 probes that distinguishes between two basal-like subgroups with distinct clinical features and survival outcomes. Genes included in this list have been mainly linked to cancer immune response, epithelial-mesenchymal transition and cell cycle. In particular, high levels of CXCR6, HCST, C3AR1 and FPR3 were found in Basal I; whereas HJURP, RRP12 and DNMT3B appeared over-expressed in Basal II. These genes exhibited the highest betweenness centrality and node degree values and play a key role in the basal-like breast cancer differentiation. Further molecular analysis revealed 17 miRNAs correlated to the subgroups, including hsa-miR-342-5p, -150, -155, -200c and -17. Additionally, increased percentages of gains/amplifications were detected on chromosomes 1q, 3q, 8q, 10p and 17q, and losses/deletions on 4q, 5q, 8p and X, associated with reduced survival.

CONCLUSIONS

The proposed signature supports the existence of at least two subgroups of basal-like breast cancers with distinct disease outcome. The identification of patients at a low risk may impact the clinical decisions-making by reducing the prescription of high-dose chemotherapy and, consequently, avoiding adverse effects. The recognition of other aggressive features within this subtype may be also critical for improving individual care and for delineating more effective therapies for patients at high risk.

Big data for big questions: it is time for data analysts to act

Pablo Moscato speaks to Francesca Lake, Managing Editor Australian Research Council Future Fellow Prof. Pablo Moscato was born in 1964 in La Plata, Argentina. Obtaining his B.Sc. in Physics at University of La Plata, his PhD was defended at UNICAMP, Brazil. While at the California Institute of Technology Concurrent Computation Program he developed, in collaboration with Michael Norman, the first application of a methodology later called 'memetic algorithms', which is now widely used internationally. He is the founding co-director of the Priority Research Centre for Bioinformatics, Biomarker Discovery and Information-based Medicine (CIBM) (2006-present) and the funding director of the Newcastle Bioinformatics Initiative (2002-2006) of The University of Newcastle (Australia). He is also Chief Investigator of the Australian Research Council Centre in Bioinformatics. He is one of Australia's most cited computer scientists. Over the past 7 years, he has introduced a unifying hallmark of cancer progression based on the changes of information theory quantifiers, and developed a novel mathematical model and an associated solution procedure based on combinatorial optimization techniques to identify drug combinations for cancer therapeutics. In addition, he has identified proteomic signatures to predict the clinical symptoms of Alzheimer's disease, among other 'firsts'. He is a member of the Editorial Board of Future Science OA.

Measuring intratumor heterogeneity by network entropy using RNA-seq data

Intratumor heterogeneity (ITH) is observed at different stages of tumor progression, metastasis and reouccurence, which can be important for clinical applications. We used RNA-sequencing data from tumor samples, and measured the level of ITH in terms of biological network states. To model complex relationships among genes, we used a protein interaction network to consider gene-gene dependency. ITH was measured by using an entropy-based distance metric between two networks, nJSD, with Jensen-Shannon Divergence (JSD). With nJSD, we defined transcriptome-based ITH (tITH). The effectiveness of tITH was extensively tested for the issues related with ITH using real biological data sets. Human cancer cell line data and single-cell sequencing data were investigated to verify our approach. Then, we analyzed TCGA pan-cancer 6,320 patients. Our result was in agreement with widely used genome-based ITH inference methods, while showed better performance at survival analysis. Analysis of mouse clonal evolution data further confirmed that our transcriptome-based ITH was consistent with genetic heterogeneity at different clonal evolution stages. Additionally, we found that cell cycle related pathways have significant contribution to increasing heterogeneity on the network during clonal evolution. We believe that the proposed transcriptome-based ITH is useful to characterize heterogeneity of a tumor sample at RNA level.

A Novel Clustering Methodology Based on Modularity Optimisation for Detecting Authorship Affinities in Shakespearean Era Plays

In this study we propose a novel, unsupervised clustering methodology for analyzing large datasets. This new, efficient methodology converts the general clustering problem into the community detection problem in graph by using the Jensen-Shannon distance, a dissimilarity measure originating in Information Theory. Moreover, we use graph theoretic concepts for the generation and analysis of proximity graphs. Our methodology is based on a newly proposed memetic algorithm (iMA-Net) for discovering clusters of data elements by maximizing the modularity function in proximity graphs of literary works. To test the effectiveness of this general methodology, we apply it to a text corpus dataset, which contains frequencies of approximately 55,114 unique words across all 168 written in the Shakespearean era (16th and 17th centuries), to analyze and detect clusters of similar plays. Experimental results and comparison with state-of-the-art clustering methods demonstrate the remarkable performance of our new method for identifying high quality clusters which reflect the commonalities in the literary style of the plays.

Thermodynamic measures of cancer: Gibbs free energy and entropy of protein-protein interactions

Thermodynamics is an important driving factor for chemical processes and for life. Earlier work has shown that each cancer has its own molecular signaling network that supports its life cycle and that different cancers have different thermodynamic entropies characterizing their signaling networks. The respective thermodynamic entropies correlate with 5-year survival for each cancer. We now show that by overlaying mRNA transcription data from a specific tumor type onto a human protein-protein interaction network, we can derive the Gibbs free energy for the specific cancer. The Gibbs free energy correlates with 5-year survival (Pearson correlation of -0.7181, p value of 0.0294). Using an expression relating entropy and Gibbs free energy to enthalpy, we derive an empirical relation for cancer network enthalpy. Combining this with previously published results, we now show a complete set of extensive thermodynamic properties and cancer type with 5-year survival.

The application of information theory for the research of aging and aging-related diseases

This article reviews the application of information-theoretical analysis, employing measures of entropy and mutual information, for the study of aging and aging-related diseases. The research of aging and aging-related diseases is particularly suitable for the application of information theory methods, as aging processes and related diseases are multi-parametric, with continuous parameters coexisting alongside discrete parameters, and with the relations between the parameters being as a rule non-linear. Information theory provides unique analytical capabilities for the solution of such problems, with unique advantages over common linear biostatistics. Among the age-related diseases, information theory has been used in the study of neurodegenerative diseases (particularly using EEG time series for diagnosis and prediction), cancer (particularly for establishing individual and combined cancer biomarkers), diabetes (mainly utilizing mutual information to characterize the diseased and aging states), and heart disease (mainly for the analysis of heart rate variability). Few works have employed information theory for the analysis of general aging processes and frailty, as underlying determinants and possible early preclinical diagnostic measures for aging-related diseases. Generally, the use of information-theoretical analysis permits not only establishing the (non-linear) correlations between diagnostic or therapeutic parameters of interest, but may also provide a theoretical insight into the nature of aging and related diseases by establishing the measures of variability, adaptation, regulation or homeostasis, within a system of interest. It may be hoped that the increased use of such measures in research may considerably increase diagnostic and therapeutic capabilities and the fundamental theoretical mathematical understanding of aging and disease.

EntropyExplorer: an R package for computing and comparing differential Shannon entropy, differential coefficient of variation and differential expression

Background

Differential Shannon entropy (DSE) and differential coefficient of variation (DCV) are effective metrics for the study of gene expression data. They can serve to augment differential expression (DE), and be applied in numerous settings whenever one seeks to measure differences in variability rather than mere differences in magnitude. A general purpose, easily accessible tool for DSE and DCV would help make these two metrics available to data scientists. Automated p value computations would additionally be useful, and are often easier to interpret than raw test statistic values alone.

Results

EntropyExplorer is an R package for calculating DSE, DCV and DE. It also computes corresponding p values for each metric. All features are available through a single R function call. Based on extensive investigations in the literature, the Fligner-Killeen test was chosen to compute DCV p values. No standard method was found to be appropriate for DSE, and so permutation testing is used to calculate DSE p values.

Conclusions

EntropyExplorer provides a convenient resource for calculating DSE, DCV, DE and associated p values. The package, along with its source code and reference manual, are freely available from the CRAN public repository at http://cran.r-project.org/web/packages/EntropyExplorer/index.html.

Electronic supplementary material

The online version of this article (doi:10.1186/s13104-015-1786-4) contains supplementary material, which is available to authorized users.

Identification of important long non-coding RNAs and highly recurrent aberrant alternative splicing events in hepatocellular carcinoma through integrative analysis of multiple RNA-Seq datasets

Hepatocellular carcinoma (HCC) is an aggressive and deadly cancer. The molecular pathogenesis of the disease remains poorly understood. To better understand HCC biology and explore potential biomarkers and therapeutic targets, we investigated the whole transcriptome of HCC. Considering the genetic heterogeneity of HCC, four datasets from four studies consisting of 15 pairs of HCC and adjacent normal samples were analyzed. We observed that the number of lncRNAs expressed in each HCC sample was consistently greater than the adjacent normal sample. Moreover, 15 lncRNAs were identified expressed in five to seven HCC tissues but were not detected in any adjacent normal tissue. Differential expression analysis detected 35 up- and 80 down-regulated lncRNAs in HCC samples compared with adjacent normal samples. In addition, five differentially expressed lncRNAs were predicted to play a role in oxidation and reduction process. With regard to splicing alterations, we identified nine highly recurrent differential splicing events belonging to eight genes USO1, RPS24, CCDC50, THNSL2, NUMB, FN1 (two events), SLC39A14 and NR1I3. Of them, splicing alterations of SLC39A14 and NR1I3 were reported for the association with HCC for the first time. The splicing dysregulation in HCC may be influenced by three splicing factors ESRP2, CELF2 and SRSF5 which were significantly down-regulated in HCC samples. This study revealed uncharacterized aspects of HCC transcriptome and identified important lncRNAs and splicing isoforms with the potential to serve as biomarkers and therapeutic targets for the disease.

Transcriptomic Heterogeneity in Cancer as a Consequence of Dysregulation of the Gene-Gene Interaction Network

Many pathways are dysregulated in cancer. Dysregulation of the regulatory network results in less control of transcript levels in the cell. Hence, dysregulation is reflected in the heterogeneity of the transcriptome: the more dysregulated the pathway, the more the transcriptomic heterogeneity. We identify four scenarios for a transcriptomic heterogeneity increase (i.e., pathway dysregulation) in cancer: (1) activation of a molecular switch, (2) a structural change in a regulator, (3) a temporal change in a regulator, and (4) weakening of gene–gene interactions. These mechanisms are statistically motivated, explored in silico, and their plausibility to occur in vivo illustrated by means of oncogenomics data of breast cancer studies.

The Discovery of Novel Biomarkers Improves Breast Cancer Intrinsic Subtype Prediction and Reconciles the Labels in the METABRIC Data Set

BACKGROUND

The prediction of breast cancer intrinsic subtypes has been introduced as a valuable strategy to determine patient diagnosis and prognosis, and therapy response. The PAM50 method, based on the expression levels of 50 genes, uses a single sample predictor model to assign subtype labels to samples. Intrinsic errors reported within this assay demonstrate the challenge of identifying and understanding the breast cancer groups. In this study, we aim to: a) identify novel biomarkers for subtype individuation by exploring the competence of a newly proposed method named CM1 score, and b) apply an ensemble learning, as opposed to the use of a single classifier, for sample subtype assignment. The overarching objective is to improve class prediction.

METHODS AND FINDINGS

The microarray transcriptome data sets used in this study are: the METABRIC breast cancer data recorded for over 2000 patients, and the public integrated source from ROCK database with 1570 samples. We first computed the CM1 score to identify the probes with highly discriminative patterns of expression across samples of each intrinsic subtype. We further assessed the ability of 42 selected probes on assigning correct subtype labels using 24 different classifiers from the Weka software suite. For comparison, the same method was applied on the list of 50 genes from the PAM50 method.

CONCLUSIONS

The CM1 score portrayed 30 novel biomarkers for predicting breast cancer subtypes, with the confirmation of the role of 12 well-established genes. Intrinsic subtypes assigned using the CM1 list and the ensemble of classifiers are more consistent and homogeneous than the original PAM50 labels. The new subtypes show accurate distributions of current clinical markers ER, PR and HER2, and survival curves in the METABRIC and ROCK data sets. Remarkably, the paradoxical attribution of the original labels reinforces the limitations of employing a single sample classifiers to predict breast cancer intrinsic subtypes.

Systems biology of cancer: entropy, disorder, and selection-driven evolution to independence, invasion and "swarm intelligence"

Our knowledge of the biology of solid cancer has greatly progressed during the last few years, and many excellent reviews dealing with the various aspects of this biology have appeared. In the present review, we attempt to bring together these subjects in a general systems biology narrative. It starts from the roles of what we term entropy of signaling and noise in the initial oncogenic events, to the first major transition of tumorigenesis: the independence of the tumor cell and the switch in its physiology, i.e., from subservience to the organism to its own independent Darwinian evolution. The development after independence involves a constant dynamic reprogramming of the cells and the emergence of a sort of collective intelligence leading to invasion and metastasis and seldom to the ultimate acquisition of immortality through inter-individual infection. At each step, the probability of success is minimal to infinitesimal, but the number of cells possibly involved and the time scale account for the relatively high occurrence of tumorigenesis and metastasis in multicellular organisms.

Estimation of Heterogeneity in Diagnostic Parameters of Age-related Diseases

The heterogeneity of parameters is a ubiquitous biological phenomenon, with critical implications for biological systems functioning in normal and diseased states. We developed a method to estimate the level of objects set heterogeneity with reference to particular parameters and applied it to type II diabetes and heart disease, as examples of age-related systemic dysfunctions. The Friedman test was used to establish the existence of heterogeneity. The Newman-Keuls multiple comparison method was used to determine clusters. The normalized Shannon entropy was used to provide the quantitative evaluation of heterogeneity. There was obtained an estimate for the heterogeneity of the diagnostic parameters in healthy subjects, as well as in heart disease and type II diabetes patients, which was strongly related to their age. With aging, as with the diseases, the level of heterogeneity (entropy) was reduced, indicating a formal analogy between these phenomena. The similarity of the patterns in aging and disease suggested a kind of "early aging" of the diseased subjects, or alternatively a "disease-like" aging process, with reference to these particular parameters. The proposed method and its validation on the chronic age-related disease samples may support a way toward a formal mathematical relation between aging and chronic diseases and a formal definition of aging and disease, as determined by particular heterogeneity (entropy) changes.

Differential Shannon entropy and differential coefficient of variation: alternatives and augmentations to differential expression in the search for disease-related genes

Differential expression has been a standard tool for analysing case-control transcriptomic data since the advent of microarray technology. It has proved invaluable in characterising the molecular mechanisms of disease. Nevertheless, the expression profile of a gene across samples can be perturbed in ways that leave the expression level unaltered, while a biological effect is nonetheless present. This paper describes and analyses differential Shannon entropy and differential coefficient of variation, two alternate techniques for identifying genes of interest. Ontological analysis across 16 human disease datasets demonstrates that these alternatives are effective at identifying disease-related genes not found by mere differential expression alone. Because the two alternate techniques are based on somewhat different mathematical formulations, they tend to produce somewhat different gene lists. Moreover, each may pinpoint genes completely overlooked by the other. Thus, measures of entropy and variation can be used to replace or better yet augment standard differential expression computations.

The gene regulatory network for breast cancer: integrated regulatory landscape of cancer hallmarks

In this study, we infer the breast cancer gene regulatory network from gene expression data. This network is obtained from the application of the BC3Net inference algorithm to a large-scale gene expression data set consisting of 351 patient samples. In order to elucidate the functional relevance of the inferred network, we are performing a Gene Ontology (GO) analysis for its structural components. Our analysis reveals that most significant GO-terms we find for the breast cancer network represent functional modules of biological processes that are described by known cancer hallmarks, including translation, immune response, cell cycle, organelle fission, mitosis, cell adhesion, RNA processing, RNA splicing and response to wounding. Furthermore, by using a curated list of census cancer genes, we find an enrichment in these functional modules. Finally, we study cooperative effects of chromosomes based on information of interacting genes in the beast cancer network. We find that chromosome 21 is most coactive with other chromosomes. To our knowledge this is the first study investigating the genome-scale breast cancer network.

Post-genomics nanotechnology is gaining momentum: nanoproteomics and applications in life sciences

The post-genomics era has brought about new Omics biotechnologies, such as proteomics and metabolomics, as well as their novel applications to personal genomics and the quantified self. These advances are now also catalyzing other and newer post-genomics innovations, leading to convergences between Omics and nanotechnology. In this work, we systematically contextualize and exemplify an emerging strand of post-genomics life sciences, namely, nanoproteomics and its applications in health and integrative biological systems. Nanotechnology has been utilized as a complementary component to revolutionize proteomics through different kinds of nanotechnology applications, including nanoporous structures, functionalized nanoparticles, quantum dots, and polymeric nanostructures. Those applications, though still in their infancy, have led to several highly sensitive diagnostics and new methods of drug delivery and targeted therapy for clinical use. The present article differs from previous analyses of nanoproteomics in that it offers an in-depth and comparative evaluation of the attendant biotechnology portfolio and their applications as seen through the lens of post-genomics life sciences and biomedicine. These include: (1) immunosensors for inflammatory, pathogenic, and autoimmune markers for infectious and autoimmune diseases, (2) amplified immunoassays for detection of cancer biomarkers, and (3) methods for targeted therapy and automatically adjusted drug delivery such as in experimental stroke and brain injury studies. As nanoproteomics becomes available both to the clinician at the bedside and the citizens who are increasingly interested in access to novel post-genomics diagnostics through initiatives such as the quantified self, we anticipate further breakthroughs in personalized and targeted medicine.

Graphical neuroimaging informatics: application to Alzheimer's disease

The Informatics Visualization for Neuroimaging (INVIZIAN) framework allows one to graphically display image and meta-data information from sizeable collections of neuroimaging data as a whole using a dynamic and compelling user interface. Users can fluidly interact with an entire collection of cortical surfaces using only their mouse. In addition, users can cluster and group brains according in multiple ways for subsequent comparison using graphical data mining tools. In this article, we illustrate the utility of INVIZIAN for simultaneous exploration and mining a large collection of extracted cortical surface data arising in clinical neuroimaging studies of patients with Alzheimer's Disease, mild cognitive impairment, as well as healthy control subjects. Alzheimer's Disease is particularly interesting due to the wide-spread effects on cortical architecture and alterations of volume in specific brain areas associated with memory. We demonstrate INVIZIAN's ability to render multiple brain surfaces from multiple diagnostic groups of subjects, showcase the interactivity of the system, and showcase how INVIZIAN can be employed to generate hypotheses about the collection of data which would be suitable for direct access to the underlying raw data and subsequent formal statistical analysis. Specifically, we use INVIZIAN show how cortical thickness and hippocampal volume differences between group are evident even in the absence of more formal hypothesis testing. In the context of neurological diseases linked to brain aging such as AD, INVIZIAN provides a unique means for considering the entirety of whole brain datasets, look for interesting relationships among them, and thereby derive new ideas for further research and study.

Analysis of long non-coding RNA expression profiles in gastric cancer

AIM: To investigate the expression patterns of long non-coding RNAs (lncRNAs) in gastric cancer.

METHODS: Two publicly available human exon arrays for gastric cancer and data for the corresponding normal tissue were downloaded from the Gene Expression Omnibus (GEO). We re-annotated the probes of the human exon arrays and retained the probes uniquely mapping to lncRNAs at the gene level. LncRNA expression profiles were generated by using robust multi-array average method in affymetrix power tools. The normalized data were then analyzed with a Bioconductor package linear models for microarray data and genes with adjusted P-values below 0.01 were considered differentially expressed. An independent data set was used to validate the results.

RESULTS: With the computational pipeline established to re-annotate over 6.5 million probes of the Affymetrix Human Exon 1.0 ST array, we identified 136053 probes uniquely mapping to lncRNAs at the gene level. These probes correspond to 9294 lncRNAs, covering nearly 76% of the GENCODE lncRNA data set. By analyzing GSE27342 consisting of 80 paired gastric cancer and normal adjacent tissue samples, we identified 88 lncRNAs that were differentially expressed in gastric cancer, some of which have been reported to play a role in cancer, such as LINC00152, taurine upregulated 1, urothelial cancer associated 1, Pvt1 oncogene, small nucleolar RNA host gene 1 and LINC00261. In the validation data set GSE33335, 59% of these differentially expressed lncRNAs showed significant expression changes (adjusted P-value < 0.01) with the same direction.

CONCLUSION: We identified a set of lncRNAs differentially expressed in gastric cancer, providing useful information for discovery of new biomarkers and therapeutic targets in gastric cancer.

Blood-informative transcripts define nine common axes of peripheral blood gene expression

We describe a novel approach to capturing the covariance structure of peripheral blood gene expression that relies on the identification of highly conserved Axes of variation. Starting with a comparison of microarray transcriptome profiles for a new dataset of 189 healthy adult participants in the Emory-Georgia Tech Center for Health Discovery and Well-Being (CHDWB) cohort, with a previously published study of 208 adult Moroccans, we identify nine Axes each with between 99 and 1,028 strongly co-regulated transcripts in common. Each axis is enriched for gene ontology categories related to sub-classes of blood and immune function, including T-cell and B-cell physiology and innate, adaptive, and anti-viral responses. Conservation of the Axes is demonstrated in each of five additional population-based gene expression profiling studies, one of which is robustly associated with Body Mass Index in the CHDWB as well as Finnish and Australian cohorts. Furthermore, ten tightly co-regulated genes can be used to define each Axis as "Blood Informative Transcripts" (BITs), generating scores that define an individual with respect to the represented immune activity and blood physiology. We show that environmental factors, including lifestyle differences in Morocco and infection leading to active or latent tuberculosis, significantly impact specific axes, but that there is also significant heritability for the Axis scores. In the context of personalized medicine, reanalysis of the longitudinal profile of one individual during and after infection with two respiratory viruses demonstrates that specific axes also characterize clinical incidents. This mode of analysis suggests the view that, rather than unique subsets of genes marking each class of disease, differential expression reflects movement along the major normal Axes in response to environmental and genetic stimuli.

Evolutionary dynamics of gene and isoform regulation in Mammalian tissues

Most mammalian genes produce multiple distinct messenger RNAs through alternative splicing, but the extent of splicing conservation is not clear. To assess tissue-specific transcriptome variation across mammals, we sequenced complementary DNA from nine tissues from four mammals and one bird in biological triplicate, at unprecedented depth. We find that while tissue-specific gene expression programs are largely conserved, alternative splicing is well conserved in only a subset of tissues and is frequently lineage-specific. Thousands of previously unknown, lineage-specific, and conserved alternative exons were identified; widely conserved alternative exons had signatures of binding by MBNL, PTB, RBFOX, STAR, and TIA family splicing factors, implicating them as ancestral mammalian splicing regulators. Our data also indicate that alternative splicing often alters protein phosphorylatability, delimiting the scope of kinase signaling.

Unveiling clusters of RNA transcript pairs associated with markers of Alzheimer's disease progression

Background

One primary goal of transcriptomic studies is identifying gene expression patterns correlating with disease progression. This is usually achieved by considering transcripts that independently pass an arbitrary threshold (e.g. p<0.05). In diseases involving severe perturbations of multiple molecular systems, such as Alzheimer’s disease (AD), this univariate approach often results in a large list of seemingly unrelated transcripts. We utilised a powerful multivariate clustering approach to identify clusters of RNA biomarkers strongly associated with markers of AD progression. We discuss the value of considering pairs of transcripts which, in contrast to individual transcripts, helps avoid natural human transcriptome variation that can overshadow disease-related changes.

Methodology/Principal Findings

We re-analysed a dataset of hippocampal transcript levels in nine controls and 22 patients with varying degrees of AD. A large-scale clustering approach determined groups of transcript probe sets that correlate strongly with measures of AD progression, including both clinical and neuropathological measures and quantifiers of the characteristic transcriptome shift from control to severe AD. This enabled identification of restricted groups of highly correlated probe sets from an initial list of 1,372 previously published by our group. We repeated this analysis on an expanded dataset that included all pair-wise combinations of the 1,372 probe sets. As clustering of this massive dataset is unfeasible using standard computational tools, we adapted and re-implemented a clustering algorithm that uses external memory algorithmic approach. This identified various pairs that strongly correlated with markers of AD progression and highlighted important biological pathways potentially involved in AD pathogenesis.

Conclusions/Significance

Our analyses demonstrate that, although there exists a relatively large molecular signature of AD progression, only a small number of transcripts recurrently cluster with different markers of AD progression. Furthermore, considering the relationship between two transcripts can highlight important biological relationships that are missed when considering either transcript in isolation.

Transcriptome-wide detection of differentially expressed coding and non-coding transcripts and their clinical significance in prostate cancer

Prostate cancer is a clinically and biologically heterogeneous disease. Deregulation of splice variants has been shown to contribute significantly to this complexity. High-throughput technologies such as oligonucleotide microarrays allow for the detection of transcripts that play a role in disease progression in a transcriptome-wide level. In this study, we use a publicly available dataset of normal adjacent, primary tumor, and metastatic prostate cancer samples (GSE21034) to detect differentially expressed coding and non-coding transcripts between these disease states. To achieve this, we focus on transcript-specific probe selection regions, that is, those probe sets that correspond unambiguously to a single transcript. Based on this, we are able to pinpoint at the transcript-specific level transcripts that are differentially expressed throughout prostate cancer progression. We confirm previously reported cases and find novel transcripts for which no prior implication in prostate cancer progression has been made. Furthermore, we show that transcript-specific differential expression has unique prognostic potential and provides a clinically significant source of biomarker signatures for prostate cancer risk stratification. The results presented here serve as a catalog of differentially expressed transcript-specific markers throughout prostate cancer progression that can be used as basis for further development and translation into the clinic.

Brain transcriptome perturbations in the Hfe(-/-) mouse model of genetic iron loading

Severe disruption of brain iron homeostasis can cause fatal neurodegenerative disease, however debate surrounds the neurologic effects of milder, more common iron loading disorders such as hereditary hemochromatosis, which is usually caused by loss-of-function polymorphisms in the HFE gene. There is evidence from both human and animal studies that HFE gene variants may affect brain function and modify risks of brain disease. To investigate how disruption of HFE influences brain transcript levels, we used microarray and real-time reverse transcription polymerase chain reaction to assess the brain transcriptome in Hfe(-/-) mice relative to wildtype AKR controls (age 10 weeks, n≥4/group). The Hfe(-/-) mouse brain showed numerous significant changes in transcript levels (p<0.05) although few of these related to proteins directly involved in iron homeostasis. There were robust changes of at least 2-fold in levels of transcripts for prominent genes relating to transcriptional regulation (FBJ osteosarcoma oncogene Fos, early growth response genes), neurotransmission (glutamate NMDA receptor Grin1, GABA receptor Gabbr1) and synaptic plasticity and memory (calcium/calmodulin-dependent protein kinase IIα Camk2a). As previously reported for dietary iron-supplemented mice, there were altered levels of transcripts for genes linked to neuronal ceroid lipofuscinosis, a disease characterized by excessive lipofuscin deposition. Labile iron is known to enhance lipofuscin generation which may accelerate brain aging. The findings provide evidence that iron loading disorders can considerably perturb levels of transcripts for genes essential for normal brain function and may help explain some of the neurologic signs and symptoms reported in hemochromatosis patients.

Urine markers and prostate cancer

Prostate cancer (PCa) is globally the most common cancer in men, with an estimated prevalence of more than two million cases. Given the poor success rate in treating advanced PCa, intervention in early stages may reduce the progression of a small, localized carcinoma to a large metastatic lesion, thereby reducing disease-related deaths. Urine is readily available and can be used to detect either exfoliated cancer cells or secreted products. The major advantages of urine-based assays are their noninvasive character and ability to monitor PCa with heterogeneous foci. The aim of this review was to summarize the current evidence regarding performance characteristics of tests proposed for urine-based prostate cancer detection.