Mixture-Regression Cluster Model applied to Longitudinal Microarray Experiments

Abstract The aim of this work is to explore various statistical techniques to identify genes which contribute to some change in phenotype level. For example, the response of fish kept under stressful conditions for various lengths of time. We aim to assess the level of differential expression of each gene in the tissue samples and also attempt to model the expression patterns of genes over time, not only to classify genes by similarities in expression patterns, but also to model these patterns as specified functions. The proposed Mixture-Regression Cluster Model is developed to model and cluster the genes into groups according to their expressions measured over time. This model is similar to that of the multivariate normal mixture model in that clusters are identified by the EM algorithm but is adapted to incorporate the flexibility of regression curves to fit the trends. In this way, additional features such as covariates, random effects and correlation structures can be incorporated into the model while potentially offering a considerable saving on the number of parameters required to model the trends

Screening of Exosomal MicroRNAs From Colorectal Cancer Cells

BACKGROUND: Cells release extracellular membrane vesicles including microvesicles known as exosomes. Exosomes contain microRNAs (miRNAs) however the full range within colorectal cancer cell secreted exosomes is unknown. OBJECTIVE: To identify the full range of exosome encapsulated miRNAs secreted from 2 colorectal cancer cell lines and to investigate engineering of exosomes over-expressing miRNAs. METHODS: Exosomes were isolated from HCT-116 and HT-29 cell lines. RNA was extracted from exosomes and microRNA array performed. Cells were engineered to express miR-379 (HCT-116-379) or a non-targeting control (HCT-116-NTC) and functional effects were determined. Exosomes secreted by engineered cells were transferred to recipient cells and the impact examined. RESULTS: Microvesicles 40-100 nm in size secreted by cell lines were visualised and confirmed to express exosomal protein CD63. HT-29 exosomes contained 409 miRNAs, HCT-116 exosomes contained 393, and 338 were common to exosomes from both cell lines. Selected targets were validated. HCT-116-379 cells showed decreased proliferation (12-15% decrease, p \u3c 0.001) and decreased migration (32-86% decrease, p \u3c 0.001) compared to controls. HCT-116-379 exosomes were enriched for miR-379. Confocal microscopy visualised transfer of HCT-116-379 exosomes to recipient cells. CONCLUSIONS: Colorectal cancer cells secrete a large number of miRNAs within exosomes. miR-379 decreases cell proliferation and migration, and miR-379 enriched exosomes can be engineered

Investigating the Potential and Pitfalls of EV-Encapsulated MicroRNAs as Circulating Biomarkers of Breast Cancer

Extracellular vesicles (EVs) shuttle microRNA (miRNA) throughout the circulation and are believed to represent a fingerprint of the releasing cell. We isolated and characterized serum EVs of breast tumour-bearing animals, breast cancer (BC) patients, and healthy controls. EVs were characterized using transmission electron microscopy (TEM), protein quantification, western blotting, and nanoparticle tracking analysis (NTA). Absolute quantitative (AQ)-PCR was employed to analyse EV-miR-451a expression. Isolated EVs had the appropriate morphology and size. Patient sera contained significantly more EVs than did healthy controls. In tumour-bearing animals, a correlation between serum EV number and tumour burden was observed. There was no significant relationship between EV protein yield and EV quantity determined by NTA, highlighting the requirement for direct quantification. Using AQ-PCR to relate miRNA copy number to EV yield, a significant increase in miRNA-451a copies/EV was detected in BC patient sera, suggesting potential as a novel biomarker of breast cancer

The Dynamics of Packets of Surface Gravity Waves in Smooth Inhomogeneous Media

Microarray technology measures genetic expression in the cells of a tissue sample and is often implemented to identify the functions of genes in an organism. The analysis of data produced from biological experiments such as microarrays leads to unique computational and statistical problems. The aim of this research is to first outline the statistical challenges that are faced at each stage of microarray data analysis, to briefly capture and summarise the vast spectrum of current methodology and research that has been initiated in order to embrace these problems, and to develop and implement new and unique techniques to address some issues that have not yet been dealt with. In particular gene expression profiles obtained from time-course microarray experiments exhibit a unique opportunity to model the trends and correlations between longitudinal genetic expressions. The proposed Mixture-Regression Cluster Model is developed t

An alternative estimation approach to fit a heterogeneity linear mixed model

International audienceAn alternative estimation approach is proposed to fit a linear mixed effects model where the random effects follow a finite mixture of normal distributions. This model, called a heterogeneity linear mixed model, is an interesting tool since it relaxes the classical normality assumption and is also perfectly suitable for classification purposes, based on longitudinal profiles. Instead of fitting directly the heterogeneity linear mixed model, we propose to fit an equivalent mixture of linear mixed models under some restrictions which is computationally simpler. Indeed, unlike the former model, the latter can be maximized analytically using an EM-algorithm and the obtained parameter estimates can be easily used to compute the parameter estimates of interest. We study and compare the behaviour of our approach on simulations. Finally, the use of our approach is illustrated on a real data set

An alternative estimation approach to fit a heterogeneity linear mixed model

International audienceAn alternative estimation approach is proposed to fit a linear mixed effects model where the random effects follow a finite mixture of normal distributions. This model, called a heterogeneity linear mixed model, is an interesting tool since it relaxes the classical normality assumption and is also perfectly suitable for classification purposes, based on longitudinal profiles. Instead of fitting directly the heterogeneity linear mixed model, we propose to fit an equivalent mixture of linear mixed models under some restrictions which is computationally simpler. Indeed, unlike the former model, the latter can be maximized analytically using an EM-algorithm and the obtained parameter estimates can be easily used to compute the parameter estimates of interest. We study and compare the behaviour of our approach on simulations. Finally, the use of our approach is illustrated on a real data set

An alternative estimation approach for the heterogeneity linear mixed model

International audienceIn this paper, an alternative estimation approach is proposed to fit linear mixed effects models where the random effects follow a finite mixture of normal distributions. This heterogeneity linear mixed model is an interesting tool since it relaxes the classical normality assumption and is also perfectly suitable for classification purposes, based on longitudinal profiles. Instead of fitting directly the heterogeneity linear mixed model, we propose to fit an equivalent mixture of linear mixed models under some restrictions which is computationally simpler. Unlike the former model, the latter can be maximized analytically using an EM-algorithm and the obtained parameter estimates can be easily used to compute the parameter estimates of interest

Survival outcomes are associated with genomic instability in luminal breast cancers.

Breast cancer is the leading cause of cancer related death among women. Breast cancers are generally diagnosed and treated based on clinical and histopathological features, along with subtype classification determined by the Prosigna Breast Cancer Prognostic Gene Signature Assay (also known as PAM50). Currently the copy number alteration (CNA) landscape of the tumour is not considered. We set out to examine the role of genomic instability (GI) in breast cancer survival since CNAs reflect GI and correlate with survival in other cancers. We focused on the 70% of breast cancers classified as luminal and carried out a comprehensive survival and association analysis using Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) data to determine whether CNA Score Quartiles derived from absolute CNA counts are associated with survival. Analysis revealed that patients diagnosed with luminal A breast cancer have a CNA landscape associated with disease specific survival, suggesting that CNA Score can provide a statistically robust prognostic factor. Furthermore, stratification of patients into subtypes based on gene expression has shown that luminal A and B cases overlap, and it is in this region we largely observe luminal A cases with reduced survival outlook. Therefore, luminal A breast cancer patients with quantitatively elevated CNA counts may benefit from more aggressive therapy. This demonstrates how individual genomic landscapes can facilitate personalisation of therapeutic interventions to optimise survival outcomes

Screening of exosomal microRNAs from colorectal cancer cells

BACKGROUND: Cells release extracellular membrane vesicles including microvesicles known as exosomes. Exosomes contain microRNAs (miRNAs) however the full range within colorectal cancer cell secreted exosomes is unknown.OBJECTIVE: To identify the full range of exosome encapsulated miRNAs secreted from 2 colorectal cancer cell lines and to investigate engineering of exosomes over-expressing miRNAs.METHODS: Exosomes were isolated from HCT-116 and HT-29 cell lines. RNA was extracted from exosomes and microRNA array performed. Cells were engineered to express miR-379 (HCT-116-379) or a non-targeting control (HCT-116-NTC) and functional effects were determined. Exosomes secreted by engineered cells were transferred to recipient cells and the impact examined.RESULTS: Microvesicles 40-100 nm in size secreted by cell lines were visualised and confirmed to express exosomal protein CD63. HT-29 exosomes contained 409 miRNAs, HCT-116 exosomes contained 393, and 338 were common to exosomes from both cell lines. Selected targets were validated. HCT-116-379 cells showed decreased proliferation (12-15% decrease, pFunding support was received from Breast Cancer Research and the Irish Cancer Society Collaborative Cancer Research Centre BREAST-PREDICT Grant CCRC13GAL.peer-reviewe

Arsenic in groundwater in south west Ireland: Occurrence, controls, and hydrochemistry

Globally numerous regions have been identified with elevated arsenic within groundwater which can result in potential adverse health risks. In Ireland, a previous national-scale research assessment of groundwater identified isolated clusters of elevated arsenic and indicated that lithology was a major controlling factor on arsenic in groundwater. Complementary comparisons of national-scale and regional-scale groundwater assessments of arsenic are lacking in Europe when compared to other global regions. The aims of this study were to demonstrate the value of a regional-scale groundwater hydrochemistry dataset with an existing national-scale approach, describe anomalies that can become the focus of attention for public health and economic reasons, and to provide a wider context for arsenic in groundwater within Ireland and Europe. Regional-scale data using 470 locations comprising 1,493 analyses using several hydrochemical parameters (arsenic, pH, conductivity, iron, manganese, sodium, potassium, calcium, magnesium, and total hardness) in south west Ireland were integrated with geological, hydrogeological, and land use datasets. Statistical analysis was performed using a combination of methods including score tests of geological groups using an empirical cumulative distribution function plot in addition to spatial analysis. Results revealed that hydrochemical parameters exhibited different spatial clusters, which was generally associated with lithology. Arsenic was elevated in sandstone derived bedrock. Weak correlation of arsenic with other hydrochemical parameters were observed and redox-sensitive elements like manganese and iron showed a greater diversity in spatial occurrence. This study has shown that the variation of hydrochemical parameters are controlled by regional geology. Finally, the paper focuses on anomalies identified by concentrations of individual ions or statistical associations in the context of, for example, historical mineral exploration and mining in the area and also discusses whether groundwater chemistry sampling on this scale can assist in future mineral exploration, as well as guiding the future development of high quality public and private water supplies.Funding based on research grant-aided by the Department of Communications, Energy and Natural Resources under the National Geoscience Programme 2007-2013. The views expressed in this study are the author's own and do not necessarily reflect the views and opinions of the Minister for Communications, Energy and Natural Resources. The authors acknowledge support of the HEA under PRTLI4 for licensing OSI Digital Imagery through the Ryan Institute. This work includes Ordnance Survey Ireland data reproduced under OSi License number NUIG220212. Unauthorized reproduction infringes Ordnance Survey Ireland and Government of Ireland copyright. © Ordnance Survey Ireland, 2012. The authors would like to thank the members of local government for data collection (particularly David Lenihan) and David Ball and Taly Hunter Williams for providing invaluable discussions on the manuscript. The authors would also like to thank David Ball for preparation of Figure 2 for this paper.peer-reviewe