Time-course analysis of genome-wide gene expression data from hormone-responsive human breast cancer cells

<p>Abstract</p> <p>Background</p> <p>Microarray experiments enable simultaneous measurement of the expression levels of virtually all transcripts present in cells, thereby providing a ‘molecular picture’ of the cell state. On the other hand, the genomic responses to a pharmacological or hormonal stimulus are dynamic molecular processes, where time influences gene activity and expression. The potential use of the statistical analysis of microarray data in time series has not been fully exploited so far, due to the fact that only few methods are available which take into proper account temporal relationships between samples.</p> <p>Results</p> <p>We compared here four different methods to analyze data derived from a time course mRNA expression profiling experiment which consisted in the study of the effects of estrogen on hormone-responsive human breast cancer cells. Gene expression was monitored with the innovative Illumina BeadArray platform, which includes an average of 30-40 replicates for each probe sequence randomly distributed on the chip surface. We present and discuss the results obtained by applying to these datasets different statistical methods for serial gene expression analysis. The influence of the normalization algorithm applied on data and of different parameter or threshold choices for the selection of differentially expressed transcripts has also been evaluated. In most cases, the selection was found fairly robust with respect to changes in parameters and type of normalization. We then identified which genes showed an expression profile significantly affected by the hormonal treatment over time. The final list of differentially expressed genes underwent cluster analysis of functional type, to identify groups of genes with similar regulation dynamics.</p> <p>Conclusions</p> <p>Several methods for processing time series gene expression data are presented, including evaluation of benefits and drawbacks of the different methods applied. The resulting protocol for data analysis was applied to characterization of the gene expression changes induced by estrogen in human breast cancer ZR-75.1 cells over an entire cell cycle.</p

Quantitative expression profiling of highly degraded RNA from formalin-fixed, paraffin-embedded breast tumor biopsies by oligonucleotide microarrays.

Microarray-based gene expression profiling is well suited for parallel quantitative analysis of large numbers of RNAs, but its application to cancer biopsies, particularly formalin-fixed, paraffin-embedded (FFPE) archived tissues, is limited by the poor quality of the RNA recovered. This represents a serious drawback, as FFPE tumor tissue banks are available with clinical and prognostic annotations, which could be exploited for molecular profiling studies, provided that reliable analytical technologies are found. We applied and evaluated here a microarray-based cDNA-mediated annealing, selection, extension and ligation (DASL) assay for analysis of 502 mRNAs in highly degraded total RNA extracted from cultured cells or FFPE breast cancer (MT) biopsies. The study included quantitative and qualitative comparison of data obtained by analysis of the same RNAs with genome-wide oligonucleotide microarrays vs DASL arrays and, by DASL, before and after extensive in vitro RNA fragmentation. The DASL-based expression profiling assay applied to RNA extracted from MCF-7 cells, before or after 24 h stimulation with a mitogenic dose of 17b-estradiol, consistently allowed to detect hormone-induced gene expression changes following extensive RNA degradation in vitro. Comparable results where obtained with tumor RNA extracted from FFPE MT biopsies (6 to 19 years old). The method proved itself sensitive, reproducible and accurate, when compared to results obtained by microarray analysis of RNA extracted from snap-frozen tissue of the same tumor

YAP contributes to DNA methylation remodeling upon mouse embryonic stem cell differentiation

The Yes-associated protein (YAP), one of the major effectors of the Hippo pathway together with its related protein WW-domain-containing transcription regulator 1 (WWTR1; also known as TAZ), mediates a range of cellular processes from proliferation and death to morphogenesis. YAP and WW-domain-containing transcription regulator 1 (WWTR1; also known as TAZ) regulate a large number of target genes, acting as coactivators of DNA-binding transcription factors or as negative regulators of transcription by interacting with the nucleosome remodeling and histone deacetylase complexes. YAP is expressed in self-renewing embryonic stem cells (ESCs), although it is still debated whether it plays any crucial roles in the control of either stemness or differentiation. Here we show that the transient downregulation of YAP in mouse ESCs perturbs cellular homeostasis, leading to the inability to differentiate properly. Bisulfite genomic sequencing revealed that this transient knockdown caused a genome-wide alteration of the DNA methylation remodeling that takes place during the early steps of differentiation, suggesting that the phenotype we observed might be due to the dysregulation of some of the mechanisms involved in regulation of ESC exit from pluripotency. By gene expression analysis, we identified two molecules that could have a role in the altered genome-wide methylation profile: the long noncoding RNA ephemeron, whose rapid upregulation is crucial for the transition of ESCs into epiblast, and the methyltransferase-like protein Dnmt3l, which, during the embryo development, cooperates with Dnmt3a and Dnmt3b to contribute to the de novo DNA methylation that governs early steps of ESC differentiation. These data suggest a new role for YAP in the governance of the epigenetic dynamics of exit from pluripotency

BATS: a Bayesian user-friendly software for Analyzing Time Series microarray experiments

BATS is a user-friendly software for Bayesian Analysis of Time Series microarray experiments based on the novel, truly functional and fully Bayesian approach proposed in Angelini et at. (2006). The software is specifically designed for time series data. It allows an user to automatically identify and rank differentially expressed genes and to estimate their expression profiles. BATS successfully manages various technical difficulties which arise in microarray time-course experiments, such as a small number of observations, non-uniform sampling intervals, and presence of missing or multiple data. BATS can carry out analysis with both simulated and real experimental data. It also handles data from different platforms. 1 Availability: BATS is written in Matlab and executable in Windows (Macintosh and Linux version are currently under development). It is freely available upon request from the authors.

Dynamic changes in gene expression in vivo predict prognosis of tamoxifen-treated patients with breast cancer

Introduction: Tamoxifen is the most widely prescribed anti-estrogen treatment for patients with estrogen receptor (ER)-positive breast cancer. However, there is still a need for biomarkers that reliably predict endocrine sensitivity in breast cancers and these may well be expressed in a dynamic manner. Methods: In this study we assessed gene expression changes at multiple time points (days 1, 2, 4, 7, 14) after tamoxifen treatment in the ER-positive ZR-75-1 xenograft model that displays significant changes in apoptosis, proliferation and angiogenesis within 2 days of therapy. Results: Hierarchical clustering identified six time-related gene expression patterns, which separated into three groups: two with early/transient responses, two with continuous/late responses and two with variable response patterns. The early/transient response represented reductions in many genes that are involved in cell cycle and proliferation (e.g. BUB1B, CCNA2, CDKN3, MKI67, UBE2C), whereas the continuous/late changed genes represented the more classical estrogen response genes (e.g. TFF1, TFF3, IGFBP5). Genes and the proteins they encode were confirmed to have similar temporal patterns of expression in vitro and in vivo and correlated with reduction in tumour volume in primary breast cancer. The profiles of genes that were most differentially expressed on days 2, 4 and 7 following treatment were able to predict prognosis, whereas those most changed on days 1 and 14 were not, in four tamoxifen treated datasets representing a total of 404 patients. Conclusions: Both early/transient/proliferation response genes and continuous/late/estrogen-response genes are able to predict prognosis of primary breast tumours in a dynamic manner. Temporal expression of therapy-response genes is clearly an important factor in characterising the response to endocrine therapy in breast tumours which has significant implications for the timing of biopsies in neoadjuvant biomarker studies.Publisher PDFPeer reviewe

Global analysis of estrogen receptor beta binding to breast cancer cell genome reveals an extensive interplay with estrogen receptor alpha for target gene regulation

Background: Estrogen receptors alpha (ERa) and beta (ERb) are transcription factors (TFs) that mediate estrogen signaling and define the hormone-responsive phenotype of breast cancer (BC). The two receptors can be found co-expressed and play specific, often opposite, roles, with ERb being able to modulate the effects of ERa on gene transcription and cell proliferation. ERb is frequently lost in BC, where its presence generally correlates with a better prognosis of the disease. The identification of the genomic targets of ERb in hormone-responsive BC cells is thus a critical step to elucidate the roles of this receptor in estrogen signaling and tumor cell biology. Results: Expression of full-length ERb in hormone-responsive, ERa-positive MCF-7 cells resulted in a marked reduction in cell proliferation in response to estrogen and marked effects on the cell transcriptome. By ChIP-Seq we identified 9702 ERb and 6024 ERa binding sites in estrogen-stimulated cells, comprising sites occupied by either ERb, ERa or both ER subtypes. A search for TF binding matrices revealed that the majority of the binding sites identified comprise one or more Estrogen Response Element and the remaining show binding matrixes for other TFs known to mediate ER interaction with chromatin by tethering, including AP2, E2F and SP1. Of 921 genes differentially regulated by estrogen in ERb+ vs ERb- cells, 424 showed one or more ERb site within 10 kb. These putative primary ERb target genes control cell proliferation, death, differentiation, motility and adhesion, signal transduction and transcription, key cellular processes that might explain the biological and clinical phenotype of tumors expressing this ER subtype. ERb binding in close proximity of several miRNA genes and in the mitochondrial genome, suggests the possible involvement of this receptor in small non-coding RNA biogenesis and mitochondrial genome functions. Conclusions: Results indicate that the vast majority of the genomic targets of ERb can bind also ERa, suggesting that the overall action of ERb on the genome of hormone-responsive BC cells depends mainly on the relative concentration of both ERs in the cell

Estrogen mediated-activation of miR-191/425 cluster modulates tumorigenicity of breast cancer cells depending on estrogen receptor status.

MicroRNAs (miRNAs), single-stranded non-coding RNAs, influence myriad biological processes that can contribute to cancer. Although tumor-suppressive and oncogenic functions have been characterized for some miRNAs, the majority of microRNAs have not been investigated for their ability to promote and modulate tumorigenesis. Here, we established that the miR-191/425 cluster is transcriptionally dependent on the host gene, DALRD3, and that the hormone 17β-estradiol (estrogen or E2) controls expression of both miR-191/425 and DALRD3. MiR-191/425 locus characterization revealed that the recruitment of estrogen receptor α (ERα) to the regulatory region of the miR-191/425-DALRD3 unit resulted in the accumulation of miR-191 and miR-425 and subsequent decrease in DALRD3 expression levels. We demonstrated that miR-191 protects ERα positive breast cancer cells from hormone starvation-induced apoptosis through the suppression of tumor-suppressor EGR1. Furthermore, enforced expression of the miR-191/425 cluster in aggressive breast cancer cells altered global gene expression profiles and enabled us to identify important tumor promoting genes, including SATB1, CCND2, and FSCN1, as targets of miR-191 and miR-425. Finally, in vitro and in vivo experiments demonstrated that miR-191 and miR-425 reduced proliferation, impaired tumorigenesis and metastasis, and increased expression of epithelial markers in aggressive breast cancer cells. Our data provide compelling evidence for the transcriptional regulation of the miR-191/425 cluster and for its context-specific biological determinants in breast cancers. Importantly, we demonstrated that the miR-191/425 cluster, by reducing the expression of an extensive network of genes, has a fundamental impact on cancer initiation and progression of breast cancer cells

Twist exome capture allows for lower average sequence coverage in clinical exome sequencing

Background Exome and genome sequencing are the predominant techniques in the diagnosis and research of genetic disorders. Sufficient, uniform and reproducible/consistent sequence coverage is a main determinant for the sensitivity to detect single-nucleotide (SNVs) and copy number variants (CNVs). Here we compared the ability to obtain comprehensive exome coverage for recent exome capture kits and genome sequencing techniques. Results We compared three different widely used enrichment kits (Agilent SureSelect Human All Exon V5, Agilent SureSelect Human All Exon V7 and Twist Bioscience) as well as short-read and long-read WGS. We show that the Twist exome capture significantly improves complete coverage and coverage uniformity across coding regions compared to other exome capture kits. Twist performance is comparable to that of both short- and long-read whole genome sequencing. Additionally, we show that even at a reduced average coverage of 70× there is only minimal loss in sensitivity for SNV and CNV detection. Conclusion We conclude that exome sequencing with Twist represents a significant improvement and could be performed at lower sequence coverage compared to other exome capture techniques

A Solve-RD ClinVar-based reanalysis of 1522 index cases from ERN-ITHACA reveals common pitfalls and misinterpretations in exome sequencing

Purpose Within the Solve-RD project (https://solve-rd.eu/), the European Reference Network for Intellectual disability, TeleHealth, Autism and Congenital Anomalies aimed to investigate whether a reanalysis of exomes from unsolved cases based on ClinVar annotations could establish additional diagnoses. We present the results of the “ClinVar low-hanging fruit” reanalysis, reasons for the failure of previous analyses, and lessons learned. Methods Data from the first 3576 exomes (1522 probands and 2054 relatives) collected from European Reference Network for Intellectual disability, TeleHealth, Autism and Congenital Anomalies was reanalyzed by the Solve-RD consortium by evaluating for the presence of single-nucleotide variant, and small insertions and deletions already reported as (likely) pathogenic in ClinVar. Variants were filtered according to frequency, genotype, and mode of inheritance and reinterpreted. Results We identified causal variants in 59 cases (3.9%), 50 of them also raised by other approaches and 9 leading to new diagnoses, highlighting interpretation challenges: variants in genes not known to be involved in human disease at the time of the first analysis, misleading genotypes, or variants undetected by local pipelines (variants in off-target regions, low quality filters, low allelic balance, or high frequency). Conclusion The “ClinVar low-hanging fruit” analysis represents an effective, fast, and easy approach to recover causal variants from exome sequencing data, herewith contributing to the reduction of the diagnostic deadlock