Identification of novel kinase targets for the treatment of estrogen receptor-negative breast cancer

PURPOSE

Previous gene expression profiling studies of breast cancer have focused on the entire genome to identify genes differentially expressed between estrogen receptor (ER) alpha-positive and ER-alpha-negative cancers.

EXPERIMENTAL DESIGN

Here, we used gene expression microarray profiling to identify a distinct kinase gene expression profile that identifies ER-negative breast tumors and subsets ER-negative breast tumors into four distinct subtypes.

RESULTS

Based on the types of kinases expressed in these clusters, we identify a cell cycle regulatory subset, a S6 kinase pathway cluster, an immunomodulatory kinase-expressing cluster, and a mitogen-activated protein kinase pathway cluster. Furthermore, we show that this specific kinase profile is validated using independent sets of human tumors and is also seen in a panel of breast cancer cell lines. Kinase expression knockdown studies show that many of these kinases are essential for the growth of ER-negative, but not ER-positive, breast cancer cell lines. Finally, survival analysis of patients with breast cancer shows that the S6 kinase pathway signature subtype of ER-negative cancers confers an extremely poor prognosis, whereas patients whose tumors express high levels of immunomodulatory kinases have a significantly better prognosis.

CONCLUSIONS

This study identifies a list of kinases that are prognostic and may serve as druggable targets for the treatment of ER-negative breast cancer.

Confounding effects in "A six-gene signature predicting breast cancer lung metastasis"

The majority of breast cancer deaths result from metastases rather than from direct effects of the primary tumor itself. Recently, Landemaine and colleagues described a six-gene signature purported to predict lung metastasis risk. They analyzed gene expression in 23 metastases from breast cancer patients (5 lung, 18 non-lung) identifying a 21-gene signature. Expression of 16 of these was analyzed in primary breast tumors from 72 patients with known outcome, and six were selected that were predictive of lung metastases: DSC2, TFCP2L1, UGT8, ITGB8, ANP32E, and FERMT1. Despite the value of such a signature, our analysis indicates that this analysis ignored potentially important confounding factors and that their signature is instead a surrogate for molecular subtype.

Left ventricular global transcriptional profiling in human end-stage dilated cardiomyopathy

We employed ABI high-density oligonucleotide microarrays containing 31,700 sixty-mer probes (representing 27,868 annotated human genes) to determine differential gene expression in idiopathic dilated cardiomyopathy (DCM). We identified 626 up-regulated and 636 down-regulated genes in DCM compared to controls. Most significant changes occurred in the tricarboxylic acid cycle, angiogenesis, and apoptotic signaling pathways, among which 32 apoptosis- and 13 MAPK activity-related genes were altered. Inorganic cation transporter, catalytic activities, energy metabolism and electron transport-related processes were among the most critically influenced pathways. Among the up-regulated genes were HTRA1 (6.9-fold), PDCD8(AIFM1) (5.2) and PRDX2 (4.4) and the down-regulated genes were NR4A2 (4.8), MX1 (4.3), LGALS9 (4), IFNA13 (4), UNC5D (3.6) and HDAC2 (3) (p<0.05), all of which have no clearly defined cardiac-related function yet. Gene ontology and enrichment analysis also revealed significant alterations in mitochondrial oxidative phosphorylation, metabolism and Alzheimer's disease pathways. Concordance was also confirmed for a significant number of genes and pathways in an independent validation microarray dataset. Furthermore, verification by real-time RT-PCR showed a high degree of consistency with the microarray results. Our data demonstrate an association of DCM with alterations in various cellular events and multiple yet undeciphered genes that may contribute to heart muscle disease pathways.

HPV16 E7 oncogene expression in normal human epithelial cells causes molecular changes indicative of an epithelial to mesenchymal transition

Cancer-associated epithelial to mesenchymal transition (EMT) is crucial for invasion and metastasis. Molecular hallmarks of EMT include down-regulation of the epithelial adhesion protein E-cadherin and de-novo expression of N-cadherin and the mesenchymal intermediate filament proteins vimentin and fibronectin. Expression of HPV16 E7 in normal human epithelial cells caused increased levels of vimentin and fibronectin, whereas the epithelial adhesion protein E-cadherin was expressed at decreased levels. Similar expression patterns of vimentin, fibronectin and E-cadherin were also detected in cells expressing HPV16 E6 and E7 or the entire HPV16 early transcriptional unit. HPV16 E6 and E7 were each able to induce N-cadherin expression. Interestingly, these changes in expression levels of EMT-associated proteins are not similarly reflected at the level of mRNA expression, suggesting that HPV16 oncoproteins also modulate EMT through non-transcriptional mechanisms. Hence, HPV16 oncoproteins may contribute to malignant progression through EMT induction.

Data-driven normalization strategies for high-throughput quantitative RT-PCR

BACKGROUND

High-throughput real-time quantitative reverse transcriptase polymerase chain reaction (qPCR) is a widely used technique in experiments where expression patterns of genes are to be profiled. Current stage technology allows the acquisition of profiles for a moderate number of genes (50 to a few thousand), and this number continues to grow. The use of appropriate normalization algorithms for qPCR-based data is therefore a highly important aspect of the data preprocessing pipeline.

RESULTS

We present and evaluate two data-driven normalization methods that directly correct for technical variation and represent robust alternatives to standard housekeeping gene-based approaches. We evaluated the performance of these methods against a single gene housekeeping gene method and our results suggest that quantile normalization performs best. These methods are implemented in freely-available software as an R package qpcrNorm distributed through the Bioconductor project.

CONCLUSION

The utility of the approaches that we describe can be demonstrated most clearly in situations where standard housekeeping genes are regulated by some experimental condition. For large qPCR-based data sets, our approaches represent robust, data-driven strategies for normalization.

Elevated expression of protein biosynthesis genes in liver and muscle of hibernating black bears (Ursus americanus)

We conducted a large-scale gene expression screen using the 3,200 cDNA probe microarray developed specifically for Ursus americanus to detect expression differences in liver and skeletal muscle that occur during winter hibernation compared with animals sampled during summer. The expression of 12 genes, including RNA binding protein motif 3 (Rbm3), that are mostly involved in protein biosynthesis, was induced during hibernation in both liver and muscle. The Gene Ontology and Gene Set Enrichment analysis consistently showed a highly significant enrichment of the protein biosynthesis category by overexpressed genes in both liver and skeletal muscle during hibernation. Coordinated induction in transcriptional level of genes involved in protein biosynthesis is a distinctive feature of the transcriptome in hibernating black bears. This finding implies induction of translation and suggests an adaptive mechanism that contributes to a unique ability to reduce muscle atrophy over prolonged periods of immobility during hibernation. Comparing expression profiles in bears to small mammalian hibernators shows a general trend during hibernation of transcriptional changes that include induction of genes involved in lipid metabolism and carbohydrate synthesis as well as depression of genes involved in the urea cycle and detoxification function in liver.

Time-series integrated "omic" analyses to elucidate short-term stress-induced responses in plant liquid cultures

The research that aims at furthering our understanding of plant primary metabolism has intensified during the last decade. The presented study validated a systems biology methodological framework for the analysis of stress-induced molecular interaction networks in the context of plant primary metabolism, as these are expressed during the first hours of the stress treatment. The framework involves the application of time-series integrated full-genome transcriptomic and polar metabolomic analyses on plant liquid cultures. The latter were selected as the model system for this type of analysis, because they provide a well-controlled growth environment, ensuring that the observed plant response is due only to the applied perturbation. An enhanced gas chromatography-mass spectrometry (GC-MS) metabolomic data correction strategy and a new algorithm for the significance analysis of time-series "omic" data are used to extract information about the plant's transcriptional and metabolic response to the applied stress from the acquired datasets; in this article, it is the first time that these are applied for the analysis of a large biological dataset from a complex eukaryotic system. The case-study involved Arabidopsis thaliana liquid cultures subjected for 30 h to elevated (1%) CO2 stress. The advantages and validity of the methodological framework are discussed in the context of the known A. thaliana or plant, in general, physiology under the particular stress. Of note, the ability of the methodology to capture dynamic aspects of the observed molecular response allowed for 9 and 24 h of treatment to be indicated as corresponding to shifts in both the transcriptional and metabolic activity; analysis of the pathways through which these activity changes are manifested provides insight to regulatory processes.

Extracting meaning from functional genomics experiments

The completion of draft genome sequences for human, mouse, rat, and an increasing number of other species, has provided us with preliminary gene catalogues for many organisms of medical and scientific interests. Interpreting these gene lists in the context of the organism's underlying biology, however, remains difficult. The development of DNA microarrays provided one potential source of data to help interpret gene function; by profiling global patterns of gene expression across diverse conditions, it was hoped that we might be able to develop insight into biological function. But the power of these functional genomics assays, as well as assays in proteomics and metabolomics, is that they primarily give us lists of differentially expressed genes that can be correlated with particular phenotypic states, but which remain difficult to link mechanistically to the biology driving the phenotype.

Gene-environment interactions reveal a homeostatic role for cholesterol metabolism during dietary folate perturbation in mice

Dietary folate supplementation can dramatically reduce the severity and incidence of several common birth defects and adult diseases that are associated with anomalies in homocysteine and folate metabolism. The common polymorphisms that adversely affect these metabolic pathways do not fully account for the particular birth defects and adult diseases that occur in at-risk individuals. To test involvement of folate, homocysteine, and other pathways in disease pathogenesis and treatment response, we analyzed global and pathway-specific changes in gene expression and levels of selected metabolites after depletion and repletion of dietary folate in two genetically distinct inbred strains of mice. Compared with the C57BL/6J strain, A/J showed greater homeostatic response to folate perturbation by retaining a higher serum folate level and minimizing global gene expression changes. Remarkably, folate perturbation led to systematic strain-specific differences only in the expression profile of the cholesterol biosynthesis pathway and to changes in levels of serum and liver total cholesterol. By genetically increasing serum and liver total cholesterol levels in APOE-deficient mice, we modestly but significantly improved folate retention during folate depletion, suggesting that homeostasis among the homocysteine, folate and cholesterol metabolic pathways contributes to the beneficial effects of dietary folate supplementation.

Promoting coherent minimum reporting guidelines for biological and biomedical investigations: the MIBBI project

The Minimum Information for Biological and Biomedical Investigations (MIBBI) project provides a resource for those exploring the range of extant minimum information checklists and fosters coordinated development of such checklists.

An optimized predictive strategy for interactome mapping

We present an optimized experimental strategy that can accelerate progress toward identifying the majority of pairwise protein interactions. Our method involves applying a predictive algorithm, based on the existing data, to identify protein pairs likely to interact and prioritizing these for screening. The approach is iterative as additional data allows one to refine predictions directing the next stage of experimentation.

Seeded Bayesian Networks: constructing genetic networks from microarray data

BACKGROUND

DNA microarrays and other genomics-inspired technologies provide large datasets that often include hidden patterns of correlation between genes reflecting the complex processes that underlie cellular metabolism and physiology. The challenge in analyzing large-scale expression data has been to extract biologically meaningful inferences regarding these processes - often represented as networks - in an environment where the datasets are often imperfect and biological noise can obscure the actual signal. Although many techniques have been developed in an attempt to address these issues, to date their ability to extract meaningful and predictive network relationships has been limited. Here we describe a method that draws on prior information about gene-gene interactions to infer biologically relevant pathways from microarray data. Our approach consists of using preliminary networks derived from the literature and/or protein-protein interaction data as seeds for a Bayesian network analysis of microarray results.

RESULTS

Through a bootstrap analysis of gene expression data derived from a number of leukemia studies, we demonstrate that seeded Bayesian Networks have the ability to identify high-confidence gene-gene interactions which can then be validated by comparison to other sources of pathway data.

CONCLUSION

The use of network seeds greatly improves the ability of Bayesian Network analysis to learn gene interaction networks from gene expression data. We demonstrate that the use of seeds derived from the biomedical literature or high-throughput protein-protein interaction data, or the combination, provides improvement over a standard Bayesian Network analysis, allowing networks involving dynamic processes to be deduced from the static snapshots of biological systems that represent the most common source of microarray data. Software implementing these methods has been included in the widely used TM4 microarray analysis package.

Sources of variation in baseline gene expression levels from toxicogenomics study control animals across multiple laboratories

Background

The use of gene expression profiling in both clinical and laboratory settings would be enhanced by better characterization of variance due to individual, environmental, and technical factors. Meta-analysis of microarray data from untreated or vehicle-treated animals within the control arm of toxicogenomics studies could yield useful information on baseline fluctuations in gene expression, although control animal data has not been available on a scale and in a form best served for data-mining.

Results

A dataset of control animal microarray expression data was assembled by a working group of the Health and Environmental Sciences Institute's Technical Committee on the Application of Genomics in Mechanism Based Risk Assessment in order to provide a public resource for assessments of variability in baseline gene expression. Data from over 500 Affymetrix microarrays from control rat liver and kidney were collected from 16 different institutions. Thirty-five biological and technical factors were obtained for each animal, describing a wide range of study characteristics, and a subset were evaluated in detail for their contribution to total variability using multivariate statistical and graphical techniques.

Conclusion

The study factors that emerged as key sources of variability included gender, organ section, strain, and fasting state. These and other study factors were identified as key descriptors that should be included in the minimal information about a toxicogenomics study needed for interpretation of results by an independent source. Genes that are the most and least variable, gender-selective, or altered by fasting were also identified and functionally categorized. Better characterization of gene expression variability in control animals will aid in the design of toxicogenomics studies and in the interpretation of their results.

Using the TIGR gene index databases for biological discovery

The TIGR Gene Index web pages provide access to analyses of ESTs and gene sequences for nearly 60 species, as well as a number of resources derived from these. Each species-specific database is presented using a common format with a homepage. A variety of methods exist that allow users to search each species-specific database. Methods implemented currently include nucleotide or protein sequence queries using WU-BLAST, text-based searches using various sequence identifiers, searches by gene, tissue and library name, and searches using functional classes through Gene Ontology assignments. This protocol provides guidance for using the Gene Index Databases to extract information.

Epithelial progeny of estrogen-exposed breast progenitor cells display a cancer-like methylome

Estrogen imprinting is used to describe a phenomenon in which early developmental exposure to endocrine disruptors increases breast cancer risk later in adult life. We propose that long-lived, self-regenerating stem and progenitor cells are more susceptible to the exposure injury than terminally differentiated epithelial cells in the breast duct. Mammospheres, containing enriched breast progenitors, were used as an exposure system to simulate this imprinting phenomenon in vitro. Using MeDIP-chip, a methylation microarray screening method, we found that 0.5% (120 loci) of human CpG islands were hypermethylated in epithelial cells derived from estrogen-exposed progenitors compared with the non-estrogen-exposed control cells. This epigenetic event may lead to progressive silencing of tumor suppressor genes, including RUNX3, in these epithelial cells, which also occurred in primary breast tumors. Furthermore, normal tissue in close proximity to the tumor site also displayed RUNX3 hypermethylation, suggesting that this aberrant event occurs in early breast carcinogenesis. The high prevalence of estrogen-induced epigenetic changes in primary tumors and the surrounding histologically normal tissues provides the first empirical link between estrogen injury of breast stem/progenitor cells and carcinogenesis. This finding also offers a mechanistic explanation as to why a tumor suppressor gene, such as RUNX3, can be heritably silenced by epigenetic mechanisms in breast cancer.

Extracting biology from high-dimensional biological data

The promise of the genome project was that a complete sequence would provide us with information that would transform biology and medicine. But the 'parts list' that has emerged from the genome project is far from the 'wiring diagram' and 'circuit logic' we need to understand the link between genotype, environment and phenotype. While genomic technologies such as DNA microarrays, proteomics and metabolomics have given us new tools and new sources of data to address these problems, a number of crucial elements remain to be addressed before we can begin to close the loop and develop a predictive quantitative biology that is the stated goal of so much of current biological research, including systems biology. Our approach to this problem has largely been one of integration, bringing together a vast wealth of information to better interpret the experimental data we are generating in genomic assays and creating publicly available databases and software tools to facilitate the work of others. Recently, we have used a similar approach to trying to understand the biological networks that underlie the phenotypic responses we observe and starting us on the road to developing a predictive biology.

Development of the Minimum Information Specification for In Situ Hybridization and Immunohistochemistry Experiments (MISFISHIE)

We describe the creation process of the Minimum Information Specification for In Situ Hybridization and Immunohistochemistry Experiments (MISFISHIE). Modeled after the existing minimum information specification for microarray data, we created a new specification for gene expression localization experiments, initially to facilitate data sharing within a consortium. After successful use within the consortium, the specification was circulated to members of the wider biomedical research community for comment and refinement. After a period of acquiring many new suggested requirements, it was necessary to enter a final phase of excluding those requirements that were deemed inappropriate as a minimum requirement for all experiments. The full specification will soon be published as a version 1.0 proposal to the community, upon which a more full discussion must take place so that the final specification may be achieved with the involvement of the whole community.

Genetic basis of murine antibacterial defense to streptococcal lung infection

To evaluate the effect of genetic background on antibacterial defense to streptococcal infection, eight genetically diverse strains of mice (A/J, DBA/2J, CAST/Ei, FVB/NJ, BALB/cJ, C57BL/6J, 129/SvImJ, and C3H/HeJ) and tlr2-deficient mice (C57BL/6(tlr2-/-)) were infected with three doses of Streptococcus zooepidemicus (500, 5,000, or 50,000 colony-forming units) by alveolar challenge. There was a range of susceptibility between the strains at each dose and time point (6, 24, and 96 h). At the lowest dose, the 129/SvImJ and C3H/HeJ strains had significantly higher bacterial counts at all time points after infection, when compared to A/J, DBA/2J, CAST/Ei, FVB/NJ, which were resistant to infection at the low dose of innoculum. At the medium dose, 129/SvImJ and C3H/HeJ had higher bacterial counts, while A/J, DBA/2J, and BALB/cJ showed reduced streptococcal growth. After the highest dose of Streptococcus, there were minimal differences between strains, suggesting the protective impact of modifier genes can be overcome. TLR2-deficient animals contained increased bacterial load with reduced cytokines after 96 h when compared to C57BL/6J controls suggesting a role of innate immunity in late antibacterial defense. Overall, we identify vulnerable (129/SvlmJ and C3H/HeJ) and resistant (A/J, FVB, and DBA) mouse strains to streptococcal lung infection, which demonstrate divergent genetic expression profiles. These results demonstrate that innate differences in pulmonary host defense to S. zooepidemicus are dependent on host genetic factors.

Inferring steady state single-cell gene expression distributions from analysis of mesoscopic samples

BACKGROUND

A great deal of interest has been generated by systems biology approaches that attempt to develop quantitative, predictive models of cellular processes. However, the starting point for all cellular gene expression, the transcription of RNA, has not been described and measured in a population of living cells.

RESULTS

Here we present a simple model for transcript levels based on Poisson statistics and provide supporting experimental evidence for genes known to be expressed at high, moderate, and low levels.

CONCLUSION

Although the model describes a microscopic process occurring at the level of an individual cell, the supporting data we provide uses a small number of cells where the echoes of the underlying stochastic processes can be seen. Not only do these data confirm our model, but this general strategy opens up a potential new approach, Mesoscopic Biology, that can be used to assess the natural variability of processes occurring at the cellular level in biological systems.

A cricket Gene Index: a genomic resource for studying neurobiology, speciation, and molecular evolution

BACKGROUND

As the developmental costs of genomic tools decline, genomic approaches to non-model systems are becoming more feasible. Many of these systems may lack advanced genetic tools but are extremely valuable models in other biological fields. Here we report the development of expressed sequence tags (EST's) in an orthopteroid insect, a model for the study of neurobiology, speciation, and evolution.

RESULTS

We report the sequencing of 14,502 EST's from clones derived from a nerve cord cDNA library, and the subsequent construction of a Gene Index from these sequences, from the Hawaiian trigonidiine cricket Laupala kohalensis. The Gene Index contains 8607 unique sequences comprised of 2575 tentative consensus (TC) sequences and 6032 singletons. For each of the unique sequences, an attempt was made to assign a provisional annotation and to categorize its function using a Gene Ontology-based classification through a sequence-based comparison to known proteins. In addition, a set of unique 70 base pair oligomers that can be used for DNA microarrays was developed. All Gene Index information is posted at the DFCI Gene Indices web page

CONCLUSION

Orthopterans are models used to understand the neurophysiological basis of complex motor patterns such as flight and stridulation. The sequences presented in the cricket Gene Index will provide neurophysiologists with many genetic tools that have been largely absent in this field. The cricket Gene Index is one of only two gene indices to be developed in an evolutionary model system. Species within the genus Laupala have speciated recently, rapidly, and extensively. Therefore, the genes identified in the cricket Gene Index can be used to study the genomics of speciation. Furthermore, this gene index represents a significant EST resources for basal insects. As such, this resource is a valuable comparative tool for the understanding of invertebrate molecular evolution. The sequences presented here will provide much needed genomic resources for three distinct but overlapping fields of inquiry: neurobiology, speciation, and molecular evolution.

Microarray blob-defect removal improves array analysis

MOTIVATION

New generation Affymetrix oligonucleotide microarrays often have blob-like image defects that will require investigators to either repeat their hybridization assays or analyze their data with the defects left in place. We investigated the effect of analyzing a spike-in experiment on Affymetrix ENCODE tiling arrays in the presence of simulated blobs covering between 1 and 9% of the array area. Using two different ChIP-chip tiling array analysis programs (Affymetrix tiling array software, TAS, and model-based analysis of tiling arrays, MAT), we found that even the smallest blob defects significantly decreased the sensitivity and increased the false discovery rate (FDR) of the spike-in target prediction.

RESULTS

We introduced a new software tool, the microarray blob remover (MBR), which allows rapid visualization, detection and removal of various blob defects from the .CEL files of different types of Affymetrix microarrays. It is shown that using MBR significantly improves the sensitivity and FDR of a tiling array analysis compared to leaving the affected probes in the analysis.

AVAILABILITY

The MBR software and the sample array .CEL files used in this article are available at: http://liulab.dfci.harvard.edu/Software/MBR/MBR.htm