miRNAs as biomarkers of therapeutic response to HER2-targeted treatment in breast cancer: A systematic review.

Breast cancer is the most common type of lethal cancer in women globally. Women have a 1 in 8 chance of developing breast cancer in their lifetime. Among the four primary molecular subtypes (luminal A, luminal B, HER2+, and triple-negative), HER2+ accounts for 20-25 % of all breast cancer and is rather aggressive. Although the treatment outcome of HER2+ breast cancer patients has been significantly improved with anti-HER2 agents, primary and acquired drug resistance present substantial clinical issues, limiting the benefits of HER2-targeted treatment. MicroRNAs (miRNAs) play a central role in regulating acquired drug resistance. miRNA are single-stranded, non-coding RNAs of around 20-25 nucleotides, known for essential roles in regulating gene expression at the post-transcriptional level. Increasing evidence has demonstrated that miRNA-mediated alteration of gene expression is associated with tumorigenesis, metastasis, and tumor response to treatment. Comprehensive knowledge of miRNAs as potential markers of drug response can help provide valuable guidance for treatment prognosis and personalized medicine for breast cancer patients.

Sustainable management of the potato cyst nematode, Globodera rostochiensis, with two microbial fermentation products.

Potato cyst nematodes (PCN) cause an overall 9% yield loss of total potato production worldwide. Research on sustainable management of PCN is still under progress. Two microbial fermentation products (MFPs) from Alltech, a proprietary blend formulated with a bacterial fermentation media and a Cu component (MFP5075), and a microbial based product (MFP3048), were evaluated against the PCN Globodera rostochiensis. In laboratory tests, effectiveness of the MFPs was recorded in terms of PCN juveniles (J2) hatching from cysts, J2 mortality and their attraction toward potato roots using pluronic gel. Greenhouse trials were conducted to study the effect of the products on PCN infestation in potato plants and a pilot scale experiment was conducted to study the impact of these MFPs on nematode biodiversity in garden soil. All treatments were performed within a concentration range of 0, 0.5, 1, and 2% (v/v) MFP5075 and 2, 6, 10, and 20 g/10 ml (w/v) MFP3048. The attraction assay, juvenile hatching and the PCN infestation in plants results were compared with those in an untreated control and a commercial nematicide (Nemguard™) treatment. After 24 h of treatment with 0.5 and 1% MFP5075, a 13-fold and 43-fold reduction, respectively, relative to J2 survival was recorded compared to that of untreated control. However, no J2 survived at 2% and above concentration of the MFP5075 treatment. Treatment with MFP3048 was effective in causing mortality of J2 only after 48-h. In the attraction assay, a 20-fold and 8-fold reduction in number of J2 attracted toward potato roots was observed, when treated with MFP5075, compared to the untreated and the Nemguard™ treatment, respectively. Subsequently, 30-35 PCN cysts were treated with both products dissolved in potato root diffusate and the results were recorded in terms of number of J2 hatched in each treatment after 10 days. No J2 hatched in the MFP5075 treatment, whereas mean numbers (±SE) of 243 ± 11.5, 30 ± 2.5, and 1.3 ± 0.6 J2 were noted in the untreated control, MFP3048, and the Nemguard™ treatment, respectively. The treatment with the MFPs compromised the integrity of the unhatched J2, which looked granular, whereas the internal organs of the unhatched J2 could be clearly identified in the untreated control. In plant infestation studies, treatment with MFP3048 and MFP5075 caused 90.6 and 84.9 percent reduction in PCN infestation, respectively, in terms of cysts developed on roots compared to untreated control. Overall, results indicate that the MFPs could potentially provide a promising alternative for sustainable PCN management.

Integrative omics reveals MYCN as a global suppressor of cellular signalling and enables network-based therapeutic target discovery in neuroblastoma.

Despite intensive study, many mysteries remain about the MYCN oncogene's functions. Here we focus on MYCN's role in neuroblastoma, the most common extracranial childhood cancer. MYCN gene amplification occurs in 20% of cases, but other recurrent somatic mutations are rare. This scarcity of tractable targets has hampered efforts to develop new therapeutic options. We employed a multi-level omics approach to examine MYCN functioning and identify novel therapeutic targets for this largely un-druggable oncogene. We used systems medicine based computational network reconstruction and analysis to integrate a range of omic techniques: sequencing-based transcriptomics, genome-wide chromatin immunoprecipitation, siRNA screening and interaction proteomics, revealing that MYCN controls highly connected networks, with MYCN primarily supressing the activity of network components. MYCN's oncogenic functions are likely independent of its classical heterodimerisation partner, MAX. In particular, MYCN controls its own protein interaction network by transcriptionally regulating its binding partners.Our network-based approach identified vulnerable therapeutically targetable nodes that function as critical regulators or effectors of MYCN in neuroblastoma. These were validated by siRNA knockdown screens, functional studies and patient data. We identified ß-estradiol and MAPK/ERK as having functional cross-talk with MYCN and being novel targetable vulnerabilities of MYCN-amplified neuroblastoma. These results reveal surprising differences between the functioning of endogenous, overexpressed and amplified MYCN, and rationalise how different MYCN dosages can orchestrate cell fate decisions and cancerous outcomes. Importantly, this work describes a systems-level approach to systematically uncovering network based vulnerabilities and therapeutic targets for multifactorial diseases by integrating disparate omic data types.

Sequencing small RNA: introduction and data analysis fundamentals.

Small RNAs are important transcriptional regulators within cells. With the advent of powerful Next Generation Sequencing platforms, sequencing small RNAs seems to be an obvious choice to understand their expression and its downstream effect. Additionally, sequencing provides an opportunity to identify novel and polymorphic miRNA. However, the biggest challenge is the appropriate data analysis pipeline, which is still in phase of active development by various academic groups. This chapter describes basic and advanced steps for small RNA sequencing analysis including quality control, small RNA alignment and quantification, differential expression analysis, novel small RNA identification, target prediction, and downstream analysis. We also provide a list of various resources for small RNA analysis.

Microarray analysis of mRNAs: experimental design and data analysis fundamentals.

Microarray technology has made it possible to quantify gene expression of thousands of genes in a single experiment. With the technological advancement, it is now possible to quantify expression of all known genes using a single microarray chip. With this volume of data and the possibility of improper quantification of expression beyond our control, the challenge lies in appropriate experimental design and the data analysis.This chapter describes the different types of experimental design for experiments involving microarray analysis (with their specific advantages and disadvantages). It considers the optimum number of replicates for a particular type of experiment. Additionally, this chapter describes the fundamentals of data analysis and the data analysis pipeline to be followed in most common types of microarray experiment.

Software and tools for microarray data analysis.

A typical microarray experiment results in series of images, depending on the experimental design and number of samples. Software analyses the images to obtain the intensity at each spot and quantify the expression for each transcript. This is followed by normalization, and then various data analysis techniques are applied on the data. The whole analysis pipeline requires a large number of software to accurately handle the massive amount of data. Fortunately, there are large number of freely available and commercial software to churn the massive amount of data to manageable sets of differentially expressed genes, functions, and pathways. This chapter describes the software and tools which can be used to analyze the gene expression data right from the image analysis to gene list, ontology, and pathways.

Decreasing Txnip mRNA and protein levels in pancreatic MIN6 cells reduces reactive oxygen species and restores glucose regulated insulin secretion.

BACKGROUND AND AIMS: Recently, thioredoxin-interacting protein (Txnip) expression has been implicated in a number of cellular events associated with diabetes, with increased Txnip levels associated with reduced glucose uptake into peripheral tissues, increased reactive oxygen species (ROS) in endothelial cells, beta cell glucotoxicity and apoptosis. The potential relevance of Txnip with regards to glucose-regulated insulin secretion (GSIS), a fundamentally important characteristic of beta cells and insulin-producing cells being considered as a possible cell therapy for diabetes, has not yet been investigated. METHODS: Here, studying glucose-responsive MIN6 B1(GSIS) and cells which had significantly reduced response to glucose after time in culture i.e. MIN6 B1(Non-GSIS), using ELISAs; qRT-PCR; immunoprecipitation and Western blotting; transient and stable (siRNA/shRNA and cDNA) approaches to achieve Txnip knock-down or over-expression, respectively,we established a direct association between Txnip expression and GSIS. RESULTS: Specifically, increasing Txnip levels correlate with increased intracellular ROS levels and with significant GSIS loss.Conversely, both transient and stable knock-down of Txnip expression was associated with GSIS recovery. CONCLUSION: This, we believe, is another reason in favour of targeting Txnip as a novel approach for diabetes-related therapy.

Feasibility and relevance of global expression profiling of gene transcripts in serum from breast cancer patients using whole genome microarrays and quantitative RT-PCR.

BACKGROUND: Previous studies, by ourselves and others, have indicated that gene transcripts are detectable extracellularly. Advancing on this work, in order to investigate the feasibility of analysing global gene expression profiles and so the possibility in the future of identifying panels of circulating mRNA biomarkers that may be diagnostic, prognostic or predictive for cancer, here we performed the first whole genome microarray analysis of human serum. PATIENTS AND METHODS: RNA was isolated from pre-surgery serum and corresponding breast tumour and normal tissue biopsies, and from post-surgery and normal control serum. Specimens were examined using Affymetrix whole genome microarrays and quantitative reverse-transcriptase polymerase chain reaction (qRT-PCR). RESULTS: Of the 54,675 mRNAs/variants analysed, approximately 8% and 45% were called Present in serum and breast tissue specimens, respectively. Differentially expressed genes were identified for each group of specimens analysed. Analysis, by qRT-PCR, of 3 selected transcripts further indicated that the nucleic acids detected were mRNA, not DNA. mRNAs are apparently present in serum and their global detection and identification can be successfully achieved using microarray technologies. CONCLUSION: The potential implication of this novel finding is that using microarrays it may be possible to identify a panel of extracellular mRNAs that are diagnostic, prognostic and/or predictive of outcome for cancer patients.

Prevalence and prognostic and predictive relevance of PRAME in breast cancer.

Preferentially expressed antigen of melanoma (PRAME) has been described as a potential candidate for immunotherapeutic targeting. However, the prognostic and predictive relevance of PRAME in breast cancer has never been investigated. PRAME gene expression was evaluated in 103 breast tumour biopsies, using quantitative reverse-transcriptase polymerase chain reaction (qRT-PCR). Normal breast tissue was also analysed for comparative purposes. All qRT-PCRs were performed in triplicate. Kaplan-Meier survival curves, Chi-squared and Cox Regression analyses were used to identify associations between PRAME expression and patients' clinicopathological and survival data. PRAME mRNA was detected in approximately 53% of tumour specimens and 37% of normal breast specimens. Kaplan-Meier analysis showed expression of PRAME to correlate significantly with unfavourable disease outcome for patients, in terms of both their disease-free survival (p = 0.0004) and overall survival (OS) (p = 0.0052) times from diagnosis. Multivariate analysis indicated PRAME expression to be an independent prognostic factor for shortened disease-free survival (p = 0.026) and OS (p = 0.02). Furthermore, for patients who received adjuvant chemotherapy, significantly (p = 0.0291) shorter relapse-free survival was achieved for those whose tumour expressed PRAME, compared to those that did not express this transcript. Our results suggest that PRAME mRNA expression may be a useful prognostic and predictive marker for breast cancer.

A microarray approach to translational medicine in breast cancer: how representative are cell line models of clinical conditions?

UNLABELLED: The aim of this study was to examine whether the degree to which cell lines model corresponding cells in vivo is an important aspect of their value as models in studying disease processes. MATERIALS AND METHODS: The work presented here utilizes gene expression data from two published microarray datasets to compare the differences and similarities among the two systems in order to identify major transcriptional changes in the adaptation process from a tissue to a cell line. RESULTS: Gene ontology and pathway analyses of comparator gene lists showed that the cell cycle related genes were significantly up-regulated in cell lines and immune response related genes were significantly up-regulated in clinical specimens. Estrogen receptor analysis also indicated differences in the clustering patterns of cell lines relative to clinical specimens. CONCLUSION: These findings suggest that significant differences in gene expression exist between clinical conditions and their respective cell line models and that these differences should be taken into account when extrapolating cell line results to in vivo systems.

Investigation of the molecular profile of basal cell carcinoma using whole genome microarrays.

BACKGROUND: Skin cancer accounts for 1/3 of all newly diagnosed cancer. Although seldom fatal, basal cell carcinoma (BCC) is associated with severe disfigurement and morbidity. BCC has a unique interest for researchers, as although it is often locally invasive, it rarely metastasises. This paper, reporting the first whole genome expression microarray analysis of skin cancer, aimed to investigate the molecular profile of BCC in comparison to non-cancerous skin biopsies. RNA from BCC and normal skin specimens was analysed using Affymetrix whole genome microarrays. A Welch t-test was applied to data normalised using dCHIP to identify significant differentially-expressed genes between BCC and normal specimens. Principal component analysis and support vector machine analysis were performed on resulting genelists, Genmapp was used to identify pathways affected, and GOstat aided identification of areas of gene ontology more highly represented on these lists than would be expected by chance. RESULTS: Following normalisation, specimens clustered into groups of BCC specimens and of normal skin specimens. Of the 54,675 gene transcripts/variants analysed, 3,921 were differentially expressed between BCC and normal skin specimens. Of these, 2,108 were significantly up-regulated and 1,813 were statistically significantly down-regulated in BCCs. CONCLUSION: Functional gene sets differentially expressed include those involved in transcription, proliferation, cell motility, apoptosis and metabolism. As expected, members of the Wnt and hedgehog pathways were found to be significantly different between BCC and normal specimens, as were many previously undescribed changes in gene expression between normal and BCC specimens, including basonuclin2 and mrp9. Quantitative-PCR analysis confirmed our microarray results, identifying novel potential biomarkers for BCC.