A genetic variant located in miR-423 is associated with reduced breast cancer risk

Background/Aim: Since microRNAs (miRNAs) act as translational regulators of multiple genes, single nucleotide polymorphisms (SNP) in them can have potentially wide-ranging effects. Using an association approach, this research examined the effects of the rs6505162 SNP, an A>C polymorphism located in the premiRNA region of miR-423, on breast cancer development. Materials and Methods: Caucasian Australian women with breast cancer and controls matched for age and ethnicity were genotyped for rs6505162 and their genotypic and allelic frequencies analysed for significant differences. Results: Analysis indicated that there were significant differences between the case and control populations (χ 2=6.70, p=0.035), with the CC genotype conferring reduced risk of breast cancer development (odds ratio=0.50 95% confidence interval=0.27-0.92, p=0.03). Conclusion: Further functional research is required to determine the mechanism of action of this SNP on miRNA function.

Gene expression profiling in human breast cancer – toward personalised therapeutics?

The most integrated approach toward understanding the multiple molecular events and mechanisms by which cancer may develop is the application of gene expression profiling using microarray technologies. As molecular alterations in breast cancer are complex and involve cross-talk between multiple cellular signalling pathways, microarray technology provides a means of capturing and comparing the expression patterns of the entire genome across multiple samples in a high throughput manner. Since the development of microarray technologies, together with the advances in RNA extraction methodologies, gene expression studies have revolutionised the means by which genes suitable as targets for drug development and individualised cancer treatment can be identified. As of the mid-1990s, expression microarrays have been extensively applied to the study of cancer and no cancer type has seen as much genomic attention as breast cancer. The most abundant area of breast cancer genomics has been the clarification and interpretation of gene expression patterns that unite both biological and clinical aspects of tumours. It is hoped that one day molecular profiling will transform diagnosis and therapeutic selection in human breast cancer toward more individualised regimes. Here, we review a number of prominent microarray profiling studies focussed on human breast cancer and examine their strengths, their limitations, clinical implications including prognostic relevance and gene signature significance along with potential improvements for the next generation of microarray studies.

Harnessing the complexity of gene expression data from cancer: from single gene to structural pathway methods

High-dimensional gene expression data provide a rich source of information because they capture the expression level of genes in dynamic states that reflect the biological functioning of a cell. For this reason, such data are suitable to reveal systems related properties inside a cell, e.g., in order to elucidate molecular mechanisms of complex diseases like breast or prostate cancer. However, this is not only strongly dependent on the sample size and the correlation structure of a data set, but also on the statistical hypotheses tested. Many different approaches have been developed over the years to analyze gene expression data to (I) identify changes in single genes, (II) identify changes in gene sets or pathways, and (III) identify changes in the correlation structure in pathways. In this paper, we review statistical methods for all three types of approaches, including subtypes, in the context of cancer data and provide links to software implementations and tools and address also the general problem of multiple hypotheses testing. Further, we provide recommendations for the selection of such analysis methods.

Meta-analysis of prostate cancer gene expression data identifies a novel discriminatory signature enriched for glycosylating enzymes

BACKGROUND: Tumorigenesis is characterised by changes in transcriptional control. Extensive transcript expression data have been acquired over the last decade and used to classify prostate cancers. Prostate cancer is, however, a heterogeneous multifocal cancer and this poses challenges in identifying robust transcript biomarkers.

METHODS: In this study, we have undertaken a meta-analysis of publicly available transcriptomic data spanning datasets and technologies from the last decade and encompassing laser capture microdissected and macrodissected sample sets.

RESULTS: We identified a 33 gene signature that can discriminate between benign tissue controls and localised prostate cancers irrespective of detection platform or dissection status. These genes were significantly overexpressed in localised prostate cancer versus benign tissue in at least three datasets within the Oncomine Compendium of Expression Array Data. In addition, they were also overexpressed in a recent exon-array dataset as well a prostate cancer RNA-seq dataset generated as part of the The Cancer Genomics Atlas (TCGA) initiative. Biologically, glycosylation was the single enriched process associated with this 33 gene signature, encompassing four glycosylating enzymes. We went on to evaluate the performance of this signature against three individual markers of prostate cancer, v-ets avian erythroblastosis virus E26 oncogene homolog (ERG) expression, prostate specific antigen (PSA) expression and androgen receptor (AR) expression in an additional independent dataset. Our signature had greater discriminatory power than these markers both for localised cancer and metastatic disease relative to benign tissue, or in the case of metastasis, also localised prostate cancer.

CONCLUSION: In conclusion, robust transcript biomarkers are present within datasets assembled over many years and cohorts and our study provides both examples and a strategy for refining and comparing datasets to obtain additional markers as more data are generated.

Early detection of ovarian cancer in samples pre-diagnosis using CA125 and MALDI-MS peaks

Aim: A nested case-control discovery study was undertaken 10 test whether information within the serum peptidome can improve on the utility of CA125 for early ovarian cancer detection. Materials and Methods: High-throughput matrix-assisted laser desorption ionisation mass spectrometry (MALDI-MS) was used to profile 295 serum samples from women pre-dating their ovarian cancer diagnosis and from 585 matched control samples. Classification rules incorporating CA125 and MS peak intensities were tested for discriminating ability. Results: Two peaks were found which in combination with CA125 discriminated cases from controls up to 15 and 11 months before diagnosis, respectively, and earlier than using CA125 alone. One peak was identified as connective tissue-activating peptide III (CTAPIII), whilst the other was putatively identified as platelet factor 4 (PF4). ELISA data supported the down-regulation of PF4 in early cancer cases. Conclusion: Serum peptide information with CA125 improves lead time for early detection of ovarian cancer. The candidate markers are platelet-derived chemokines, suggesting a link between platelet function and tumour development.

Doxorubicin Affects Expression of Proteins of Neuronal Pathways in MCF-7 Breast Cancer Cells.

In the present article, we report on the semi-quantitative proteome analysis and related changes in protein expression of the MCF-7 breast cancer cell line following treatment with doxorubicin, using the precursor acquisition independent from ion count (PAcIFIC) mass spectrometry method. PAcIFIC represents a cost-effective and easy-to-use proteomics approach, enabling for deep proteome sequencing with minimal sample handling. The acquired proteomic data sets were searched for regulated Reactome pathways and Gene Ontology annotation terms using a new algorithm (SetRank). Using this approach, we identified pathways with significant changes (≤0.05), such as chromatin organization, DNA binding, embryo development, condensed chromosome, sequence-specific DNA binding, response to oxidative stress and response to toxin, as well as others. These sets of pathways are already well-described as being susceptible to chemotherapeutic drugs. Additionally, we found pathways related to neuron development, such as central nervous system neuron differentiation, neuron projection membrane and SNAP receptor activity. These later pathways might indicate biological mechanisms on the molecular level causing the known side-effect of doxorubicin chemotherapy, characterized as cognitive impairment, also called 'chemo brain'. Mass spectrometry data are available via ProteomeXchange with identifier PXD002998.

Mutational Heterogeneity in Cancer

Thesis (Ph.D.)--University of Washington, 2014-06

Markov Random Fields in Cancer Mutation Dependencies

Even though a large amount of evidence would suggest that PP2A serine/threonine protein phosphatase acts as a tumour suppressor the genomics data to support this claim is limited. We fit a sparse binary Markov random field with individual sample's total mutational frequency as an additional covariate to model the dependencies between the mutations occurring in the PP2A encoding genes. We utilize the data from recent large scale cancer genomics studies, where the whole genome from a human tumour biopsy has been analysed. Our results show a complex network of interactions between the occurrence of mutations in our twenty examined genes. According to our analysis the mutations occurring in the genes PPP2R1A, PPP2R3A, and PPP2R2B are identified as the key mutations. These genes form the core of the network of conditional dependency between the mutations in the investigated twenty genes. Additionally, we note that the mutations occurring in PPP2R4 seem to be more influential in samples with higher number of total mutations. The mutations occurring in the set of genes suggested by our results has been shown to contribute to the transformation of human cells. We conclude that our evidence further supports the claim that PP2A acts as a tumour suppressor and restoring PP2A activity is an appealing therapeutic strategy.

Development of reverse phase protein microarrays for clinical applications and patient-tailored therapy

While genomics provide important information about the somatic genetic changes, and RNA transcript profiling can reveal important expression changes that correlate with outcome and response to therapy, it is the proteins that do the work in the cell. At a functional level, derangements within the proteome, driven by post-translational and epigenetic modifications, such as phosphorylation, is the cause of a vast majority of human diseases. Cancer, for instance, is a manifestation of deranged cellular protein molecular networks and cell signaling pathways that are based on genetic changes at the DNA level. Importantly, the protein pathways contain the drug targets in signaling networks that govern overall cellular survival, proliferation, invasion and cell death. Consequently, the promise of proteomics resides in the ability to extend analysis beyond correlation to causality. A critical gap in the information knowledge base of molecular profiling is an understanding of the ongoing activity of protein signaling in human tissue: what is activated and “in use” within the human body at any given point in time. To address this gap, we have invented a new technology, called reverse phase protein microarrays, that can generate a functional read-out of cell signaling networks or pathways for an individual patient obtained directly from a biopsy specimen. This “wiring diagram” can serve as the basis for both, selection of a therapy and patient stratification.

New scientific paradigms for probiotics and prebiotics

The inaugural meeting of the International Scientific Association for Probiotics and Prebiotics (ISAPP) was held May 3 to May 5 2002 in London, Ontario, Canada. A group of 63 academic and industrial scientists from around the world convened to discuss current issues in the science of probiotics and prebiotics. ISAPP is a non-profit organization comprised of international scientists whose intent is to strongly support and improve the levels of scientific integrity and due diligence associated with the study, use, and application of probiotics and prebiotics. In addition, ISAPP values its role in facilitating communication with the public and healthcare providers and among scientists in related fields on all topics pertinent to probiotics and prebiotics. It is anticipated that such efforts will lead to development of approaches and products that are optimally designed for the improvement of human and animal health and well being. This article is a summary of the discussions, conclusions, and recommendations made by 8 working groups convened during the first ISAPP workshop focusing on the topics of: definitions, intestinal flora, extra-intestinal sites, immune function, intestinal disease, cancer, genetics and genomics, and second generation prebiotics. Humans have evolved in symbiosis with an estimated 1014 resident microorganisms. However, as medicine has widely defined and explored the perpetrators of disease, including those of microbial origin, it has paid relatively little attention to the microbial cells that constitute the most abundant life forms associated with our body. Microbial metabolism in humans and animals constitutes an intense biochemical activity in the body, with profound repercussions for health and disease. As understanding of the human genome constantly expands, an important opportunity will arise to better determine the relationship between microbial populations within the body and host factors (including gender, genetic background, and nutrition) and the concomitant implications for health and improved quality of life. Combined human and microbial genetic studies will determine how such interactions can affect human health and longevity, which communication systems are used, and how they can be influenced to benefit the host. Probiotics are defined as live microorganisms which, when administered in adequate amounts confer a health benefit on the host.1 The probiotic concept dates back over 100 years, but only in recent times have the scientific knowledge and tools become available to properly evaluate their effects on normal health and well being, and their potential in preventing and treating disease. A similar situation exists for prebiotics, defined by this group as non-digestible substances that provide a beneficial physiological effect on the host by selectively stimulating the favorable growth or activity of a limited number of indigenous bacteria. Prebiotics function complementary to, and possibly synergistically with, probiotics. Numerous studies are providing insights into the growth and metabolic influence of these microbial nutrients on health. Today, the science behind the function of probiotics and prebiotics still requires more stringent deciphering both scientifically and mechanistically. The explosion of publications and interest in probiotics and prebiotics has resulted in a body of collective research that points toward great promise. However, this research is spread among such a diversity of organisms, delivery vehicles (foods, pills, and supplements), and potential health targets such that general conclusions cannot easily be made. Nevertheless, this situation is rapidly changing on a number of important fronts. With progress over the past decade on the genetics of lactic acid bacteria and the recent, 2,3 and pending, 4 release of complete genome sequences for major probiotic species, the field is now armed with detailed information and sophisticated microbiological and bioinformatic tools. Similarly, advances in biotechnology could yield new probiotics and prebiotics designed for enhanced or expanded functionality. The incorporation of genetic tools within a multidisciplinary scientific platform is expected to reveal the contributions of commensals, probiotics, and prebiotics to general health and well being and explicitly identify the mechanisms and corresponding host responses that provide the basis for their positive roles and associated claims. In terms of human suffering, the need for effective new approaches to prevent and treat disease is paramount. The need exists not only to alleviate the significant mortality and morbidity caused by intestinal diseases worldwide (especially diarrheal diseases in children), but also for infections at non-intestinal sites. This is especially worthy of pursuit in developing nations where mortality is too often the outcome of food and water borne infection. Inasmuch as probiotics and prebiotics are able to influence the populations or activities of commensal microflora, there is evidence that they can also play a role in mitigating some diseases. 5,6 Preliminary support that probiotics and prebiotics may be useful as intervention in conditions including inflammatory bowel disease, irritable bowel syndrome, allergy, cancer (especially colorectal cancer of which 75% are associated with diet), vaginal and urinary tract infections in women, kidney stone disease, mineral absorption, and infections caused by Helicobacter pylori is emerging. Some metabolites of microbes in the gut may also impact systemic conditions ranging from coronary heart disease to cognitive function, suggesting the possibility that exogenously applied microbes in the form of probiotics, or alteration of gut microecology with prebiotics, may be useful interventions even in these apparently disparate conditions. Beyond these direct intervention targets, probiotic cultures can also serve in expanded roles as live vehicles to deliver biologic agents (vaccines, enzymes, and proteins) to targeted locations within the body. The economic impact of these disease conditions in terms of diagnosis, treatment, doctor and hospital visits, and time off work exceeds several hundred billion dollars. The quality of life impact is also of major concern. Probiotics and prebiotics offer plausible opportunities to reduce the morbidity associated with these conditions. The following addresses issues that emerged from 8 workshops (Definitions, Intestinal Flora, Extra-Intestinal Sites, Immune Function, Intestinal Disease, Cancer, Genomics, and Second Generation Prebiotics), reflecting the current scientific state of probiotics and prebiotics. This is not a comprehensive review, however the study emphasizes pivotal knowledge gaps, and recommendations are made as to the underlying scientific and multidisciplinary studies that will be required to advance our understanding of the roles and impact of prebiotics, probiotics, and the commensal microflora upon health and disease management.

Approximate entropy of network parameters

We study the notion of approximate entropy within the framework of network theory. Approximate entropy is an uncertainty measure originally proposed in the context of dynamical systems and time series. We first define a purely structural entropy obtained by computing the approximate entropy of the so-called slide sequence. This is a surrogate of the degree sequence and it is suggested by the frequency partition of a graph. We examine this quantity for standard scale-free and Erdös-Rényi networks. By using classical results of Pincus, we show that our entropy measure often converges with network size to a certain binary Shannon entropy. As a second step, with specific attention to networks generated by dynamical processes, we investigate approximate entropy of horizontal visibility graphs. Visibility graphs allow us to naturally associate with a network the notion of temporal correlations, therefore providing the measure a dynamical garment. We show that approximate entropy distinguishes visibility graphs generated by processes with different complexity. The result probes to a greater extent these networks for the study of dynamical systems. Applications to certain biological data arising in cancer genomics are finally considered in the light of both approaches.

Towards the prediction of mutations in genomic sequences

Bio-systems are inherently complex information processing systems. Furthermore, physiological complexities of biological systems limit the formation of a hypothesis in terms of behavior and the ability to test hypothesis. More importantly the identification and classification of mutation in patients are centric topics in today's cancer research. Next generation sequencing (NGS) technologies can provide genome-wide coverage at a single nucleotide resolution and at reasonable speed and cost. The unprecedented molecular characterization provided by NGS offers the potential for an individualized approach to treatment. These advances in cancer genomics have enabled scientists to interrogate cancer-specific genomic variants and compare them with the normal variants in the same patient. Analysis of this data provides a catalog of somatic variants, present in tumor genome but not in the normal tissue DNA. In this dissertation, we present a new computational framework to the problem of predicting the number of mutations on a chromosome for a certain patient, which is a fundamental problem in clinical and research fields. We begin this dissertation with the development of a framework system that is capable of utilizing published data from a longitudinal study of patients with acute myeloid leukemia (AML), who's DNA from both normal as well as malignant tissues was subjected to NGS analysis at various points in time. By processing the sequencing data at the time of cancer diagnosis using the components of our framework, we tested it by predicting the genomic regions to be mutated at the time of relapse and, later, by comparing our results with the actual regions that showed mutations (discovered at relapse time). We demonstrate that this coupling of the algorithm pipeline can drastically improve the predictive abilities of searching a reliable molecular signature. Arguably, the most important result of our research is its superior performance to other methods like Radial Basis Function Network, Sequential Minimal Optimization, and Gaussian Process. In the final part of this dissertation, we present a detailed significance, stability and statistical analysis of our model. A performance comparison of the results are presented. This work clearly lays a good foundation for future research for other types of cancer.^

Exploiting DNA damage repair defects for effective targeting of acute myeloid leukaemia by PARP inhibitors

Abstract for 24th Biennial Congress of the European Association for Cancer Research, 9–12 July 2016, Manchester, UK. Poster Session: Cancer Genomics, Epigenetics and Genome Instability II: Monday 11 July 2016

Integrative functional genomics analysis of sustained polyploidy phenotypes in breast cancer cells identifies an oncogenic profile for GINS2

Aneuploidy is among the most obvious differences between normal and cancer cells. However, mechanisms contributing to development and maintenance of aneuploid cell growth are diverse and incompletely understood. Functional genomics analyses have shown that aneuploidy in cancer cells is correlated with diffuse gene expression signatures and that aneuploidy can arise by a variety of mechanisms, including cytokinesis failures, DNA endoreplication and possibly through polyploid intermediate states. Here, we used a novel cell spot microarray technique to identify genes with a loss-of-function effect inducing polyploidy and/or allowing maintenance of polyploid cell growth of breast cancer cells. Integrative genomics profiling of candidate genes highlighted GINS2 as a potential oncogene frequently overexpressed in clinical breast cancers as well as in several other cancer types. Multivariate analysis indicated GINS2 to be an independent prognostic factor for breast cancer outcome (p = 0.001). Suppression of GINS2 expression effectively inhibited breast cancer cell growth and induced polyploidy. In addition, protein level detection of nuclear GINS2 accurately distinguished actively proliferating cancer cells suggesting potential use as an operational biomarker.

Prognostic and Predictive Biomarkers in Resected Colon Cancer: Current Status and Future Perspectives for Integrating Genomics into Biomarker Discovery

The number of agents that are potentially effective in the adjuvant treatment of locally advanced resectable colon cancer is increasing. Consequently, it is important to ascertain which subgroups of patients will benefit from a specific treatment. Despite more than two decades of research into the molecular genetics of colon cancer, there is a lack of prognostic and predictive molecular biomarkers with proven utility in this setting. A secondary objective of the Pan European Trials in Adjuvant Colon Cancer-3 trial, which compared irinotecan in combination with 5-fluorouracil and leucovorin in the postoperative treatment of stage III and stage II colon cancer patients, was to undertake a translational research study to assess a panel of putative prognostic and predictive markers in a large colon cancer patient cohort. The Cancer and Leukemia Group B 89803 trial, in a similar design, also investigated the use of prognostic and predictive biomarkers in this setting. In this article, the authors, who are coinvestigators from these trials and performed similar investigations of biomarker discovery in the adjuvant treatment of colon cancer, review the current status of biomarker research in this field, drawing on their experiences and considering future strategies for biomarker discovery in the postgenomic era. The Oncologist 2010; 15: 390-404