Meta-analysis of microarray studies reveals a novel hematopoietic progenitor cell signature and demonstrates feasibility of inter-platform data integration

Validation of the 18-gene classifier as a prognostic biomarker of distant metastasis in breast cancer

We validated an 18-gene classifier (GC) initially developed to predict local/regional recurrence after mastectomy in estimating distant metastasis risk. The 18-gene scoring algorithm defines scores as: <21, low risk; ≥21, high risk. Six hundred eighty-three patients with primary operable breast cancer and fresh frozen tumor tissues available were included. The primary outcome was the 5-year probability of freedom from distant metastasis (DMFP). Two external datasets were used to test the predictive accuracy of 18-GC. The 5-year rates of DMFP for patients classified as low-risk (n = 146, 21.7%) and high-risk (n = 537, 78.6%) were 96.2% (95% CI, 91.1%-98.8%) and 80.9% (74.6%-81.9%), respectively (median follow-up interval, 71.8 months). The 5-year rates of DMFP of the low-risk group in stage I (n = 62, 35.6%), stage II (n = 66, 20.1%), and stage III (n = 18, 10.3%) were 100%, 94.2% (78.5%-98.5%), and 90.9% (50.8%-98.7%), respectively. Multivariate analysis revealed that 18-GC is an independent prognostic factor of distant metastasis (adjusted hazard ratio, 5.1; 95% CI, 1.8-14.1; p = 0.0017) for scores of ≥21. External validation showed that the 5-year rate of DMFP in the low- and high-risk patients was 94.1% (82.9%-100%) and 80.3% (70.7%-89.9%, p = 0.06) in a Singapore dataset, and 89.5% (81.9%-94.1%) and 73.6% (67.2%-79.0%, p = 0.0039) in the GEO-GSE20685 dataset, respectively. In conclusion, 18-GC is a viable prognostic biomarker for breast cancer to estimate distant metastasis risk.

An Eighteen-Gene Classifier Predicts Locoregional Recurrence in Post-Mastectomy Breast Cancer Patients

We previously identified 34 genes of interest (GOI) in 2006 to aid the oncologists to determine whether post-mastectomy radiotherapy (PMRT) is indicated for certain patients with breast cancer. At this time, an independent cohort of 135 patients having DNA microarray study available from the primary tumor tissue samples was chosen. Inclusion criteria were 1) mastectomy as the first treatment, 2) pathology stages I-III, 3) any locoregional recurrence (LRR) and 4) no PMRT. After inter-platform data integration of Affymetrix U95 and U133 Plus 2.0 arrays and quantile normalization, in this paper we used 18 of 34 GOI to divide the mastectomy patients into high and low risk groups. The 5-year rate of freedom from LRR in the high-risk group was 30%. In contrast, in the low-risk group it was 99% (p < 0.0001). Multivariate analysis revealed that the 18-gene classifier independently predicts rates of LRR regardless of nodal status or cancer subtype.

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No molecular markers are available to determine post-mastectomy radiotherapy for breast cancer patients.

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Statistically about 2-8% N0 and 20% of N1 mastectomy patients would develop local/regional recurrence (LRR).

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An 18-gene classifier is capable of identifying patients at risk for LRR after mastectomy.

There is no consensus whether to give post-mastectomy radiotherapy to all N1 patients according to the St. Gallen consensus meeting in 2015. Currently about one half of N1 mastectomy patients have been given adjuvant radiotherapy in the United States. From a separate patient population, we now have developed an 18-gene classifier included in our previously published 34-gene panel. This diminished multigene set of classifier is capable of differentiating post-mastectomy breast cancer patients into the low- and high-risk groups. This new classifier has the potential to spare low-risk N1 patients from radiotherapy and at the same time help identify high-risk N0 patients for radiotherapy.

Comparative molecular dynamics simulations of mitogen-activated protein kinase-activated protein kinase 5

The mitogen-activated protein kinase-activated protein kinase MK5 is a substrate of the mitogen-activated protein kinases p38, ERK3 and ERK4. Cell culture and animal studies have demonstrated that MK5 is involved in tumour suppression and promotion, embryogenesis, anxiety, cell motility and cell cycle regulation. In the present study, homology models of MK5 were used for molecular dynamics (MD) simulations of: (1) MK5 alone; (2) MK5 in complex with an inhibitor; and (3) MK5 in complex with the interaction partner p38α. The calculations showed that the inhibitor occupied the active site and disrupted the intramolecular network of amino acids. However, intramolecular interactions consistent with an inactive protein kinase fold were not formed. MD with p38α showed that not only the p38 docking region, but also amino acids in the activation segment, αH helix, P-loop, regulatory phosphorylation region and the C-terminal of MK5 may be involved in forming a very stable MK5-p38α complex, and that p38α binding decreases the residual fluctuation of the MK5 model. Electrostatic Potential Surface (EPS) calculations of MK5 and p38α showed that electrostatic interactions are important for recognition and binding.

Myeloma is characterized by stage-specific alterations in DNA methylation that occur early during myelomagenesis

Epigenetic changes play important roles in carcinogenesis and influence initial steps in neoplastic transformation by altering genome stability and regulating gene expression. To characterize epigenomic changes during the transformation of normal plasma cells to myeloma, we modified the HpaII tiny fragment enrichment by ligation-mediated PCR assay to work with small numbers of purified primary marrow plasma cells. The nano-HpaII tiny fragment enrichment by ligation-mediated PCR assay was used to analyze the methylome of CD138(+) cells from 56 subjects representing premalignant (monoclonal gammopathy of uncertain significance), early, and advanced stages of myeloma, as well as healthy controls. Plasma cells from premalignant and early stages of myeloma were characterized by striking, widespread hypomethylation. Gene-specific hypermethylation was seen to occur in the advanced stages, and cell lines representative of relapsed cases were found to be sensitive to decitabine. Aberrant demethylation in monoclonal gammopathy of uncertain significance occurred primarily in CpG islands, whereas differentially methylated loci in cases of myeloma occurred predominantly outside of CpG islands and affected distinct sets of gene pathways, demonstrating qualitative epigenetic differences between premalignant and malignant stages. Examination of the methylation machinery revealed that the methyltransferase, DNMT3A, was aberrantly hypermethylated and underexpressed, but not mutated in myeloma. DNMT3A underexpression was also associated with adverse overall survival in a large cohort of patients, providing insights into genesis of hypomethylation in myeloma. These results demonstrate widespread, stage-specific epigenetic changes during myelomagenesis and suggest that early demethylation can be a potential contributor to genome instability seen in myeloma. We also identify DNMT3A expression as a novel prognostic biomarker and suggest that relapsed cases can be therapeutically targeted by hypomethylating agents.

Impaired clearance of apoptotic cells leads to HMGB1 release in the bone marrow of patients with myelodysplastic syndromes and induces TLR4-mediated cytokine production

Excessive pro-inflammatory cytokine production in the bone marrow has been associated with the pathogenesis of myelodysplastic syndromes. We herein investigated the involvement of toll-like receptors and their endogenous ligands in the induction/maintenance of the inflammatory process in the marrow of patients with myelodysplastic syndromes. We evaluated the expression of toll-like receptors in marrow monocytes of patients (n=27) and healthy controls (n=25) by flow-cytometry and also assessed the activation of the respective signaling using a real-time polymerase chain reaction-based array. We measured the high mobility group box-1 protein, a toll-like receptor-4 ligand, in marrow plasma and long-term bone marrow culture supernatants by an enzyme-linked immunosorbent assay and we performed cross-over experiments using marrow plasma from patients and controls in the presence/absence of a toll-like receptor-4 inhibitor to evaluate the pro-inflammatory cytokine production by chemiluminescence. We assessed the apoptotic cell clearance capacity of patients' macrophages using a fluorescence microscopy-based assay. We found over-expression of toll-like receptor-4 in patients' marrow monocytes compared to that in controls; this over-expression was associated with up-modulation of 53 genes related to the respective signaling. Incubation of patients' monocytes with autologous, but not with normal, marrow plasma resulted in over-production of pro-inflammatory cytokines, an effect that was abrogated by the toll-like receptor-4 inhibitor suggesting that the pro-inflammatory cytokine production in myelodysplastic syndromes is largely mediated through toll-like receptor-4. The levels of high mobility group box-1 protein were increased in patients' marrow plasma and culture supernatants compared to the levels in controls. Patients' macrophages displayed an impaired capacity to engulf apoptotic cells and this defect was associated with excessive release of high mobility group box-1 protein by dying cells. A primary apoptotic cell clearance defect of marrow macrophages in myelodysplastic syndromes may contribute to the induction/maintenance of the inflammatory process through aberrant release of molecules inducing toll-like receptor-4 such as high mobility group box-1 protein.

Strengthening insights into host responses to mastitis infection in ruminants by combining heterogeneous microarray data sources

Background

Gene expression profiling studies of mastitis in ruminants have provided key but fragmented knowledge for the understanding of the disease. A systematic combination of different expression profiling studies via meta-analysis techniques has the potential to test the extensibility of conclusions based on single studies. Using the program Pointillist, we performed meta-analysis of transcription-profiling data from six independent studies of infections with mammary gland pathogens, including samples from cattle challenged in vivo with S. aureus, E. coli, and S. uberis, samples from goats challenged in vivo with S. aureus, as well as cattle macrophages and ovine dendritic cells infected in vitro with S. aureus. We combined different time points from those studies, testing different responses to mastitis infection: overall (common signature), early stage, late stage, and cattle-specific.

Results

Ingenuity Pathway Analysis of affected genes showed that the four meta-analysis combinations share biological functions and pathways (e.g. protein ubiquitination and polyamine regulation) which are intrinsic to the general disease response. In the overall response, pathways related to immune response and inflammation, as well as biological functions related to lipid metabolism were altered. This latter observation is consistent with the milk fat content depression commonly observed during mastitis infection. Complementarities between early and late stage responses were found, with a prominence of metabolic and stress signals in the early stage and of the immune response related to the lipid metabolism in the late stage; both mechanisms apparently modulated by few genes, including XBP1 and SREBF1.

The cattle-specific response was characterized by alteration of the immune response and by modification of lipid metabolism. Comparison of E. coli and S. aureus infections in cattle in vivo revealed that affected genes showing opposite regulation had the same altered biological functions and provided evidence that E. coli caused a stronger host response.

Conclusions

This meta-analysis approach reinforces previous findings but also reveals several novel themes, including the involvement of genes, biological functions, and pathways that were not identified in individual studies. As such, it provides an interesting proof of principle for future studies combining information from diverse heterogeneous sources.

Aberrant epigenetic and genetic marks are seen in myelodysplastic leukocytes and reveal Dock4 as a candidate pathogenic gene on chromosome 7q

Myelodysplastic syndromes (MDS) are characterized by abnormal and dysplastic maturation of all blood lineages. Even though epigenetic alterations have been seen in MDS marrow progenitors, very little is known about the molecular alterations in dysplastic peripheral blood cells. We analyzed the methylome of MDS leukocytes by the HELP assay and determined that it was globally distinct from age-matched controls and was characterized by numerous novel, aberrant hypermethylated marks that were located mainly outside of CpG islands and preferentially affected GTPase regulators and other cancer-related pathways. Additionally, array comparative genomic hybridization revealed that novel as well as previously characterized deletions and amplifications could also be visualized in peripheral blood leukocytes, thus potentially reducing the need for bone marrow samples for future studies. Using integrative analysis, potentially pathogenic genes silenced by genetic deletions and aberrant hypermethylation in different patients were identified. DOCK4, a GTPase regulator located in the commonly deleted 7q31 region, was identified by this unbiased approach. Significant hypermethylation and reduced expression of DOCK4 in MDS bone marrow stem cells was observed in two large independent datasets, providing further validation of our findings. Finally, DOCK4 knockdown in primary marrow CD34(+) stem cells led to decreased erythroid colony formation and increased apoptosis, thus recapitulating the bone marrow failure seen in MDS. These findings reveal widespread novel epigenetic alterations in myelodysplastic leukocytes and implicate DOCK4 as a pathogenic gene located on the 7q chromosomal region.

TRAM (Transcriptome Mapper): database-driven creation and analysis of transcriptome maps from multiple sources

Background

Several tools have been developed to perform global gene expression profile data analysis, to search for specific chromosomal regions whose features meet defined criteria as well as to study neighbouring gene expression. However, most of these tools are tailored for a specific use in a particular context (e.g. they are species-specific, or limited to a particular data format) and they typically accept only gene lists as input.

Results

TRAM (Transcriptome Mapper) is a new general tool that allows the simple generation and analysis of quantitative transcriptome maps, starting from any source listing gene expression values for a given gene set (e.g. expression microarrays), implemented as a relational database. It includes a parser able to assign univocal and updated gene symbols to gene identifiers from different data sources. Moreover, TRAM is able to perform intra-sample and inter-sample data normalization, including an original variant of quantile normalization (scaled quantile), useful to normalize data from platforms with highly different numbers of investigated genes. When in 'Map' mode, the software generates a quantitative representation of the transcriptome of a sample (or of a pool of samples) and identifies if segments of defined lengths are over/under-expressed compared to the desired threshold. When in 'Cluster' mode, the software searches for a set of over/under-expressed consecutive genes. Statistical significance for all results is calculated with respect to genes localized on the same chromosome or to all genome genes. Transcriptome maps, showing differential expression between two sample groups, relative to two different biological conditions, may be easily generated. We present the results of a biological model test, based on a meta-analysis comparison between a sample pool of human CD34+ hematopoietic progenitor cells and a sample pool of megakaryocytic cells. Biologically relevant chromosomal segments and gene clusters with differential expression during the differentiation toward megakaryocyte were identified.

Conclusions

TRAM is designed to create, and statistically analyze, quantitative transcriptome maps, based on gene expression data from multiple sources. The release includes FileMaker Pro database management runtime application and it is freely available at http://apollo11.isto.unibo.it/software/, along with preconfigured implementations for mapping of human, mouse and zebrafish transcriptomes.

Reduced SMAD7 leads to overactivation of TGF-beta signaling in MDS that can be reversed by a specific inhibitor of TGF-beta receptor I kinase

Even though myelodysplastic syndromes (MDS) are characterized by ineffective hematopoiesis, the molecular alterations that lead to marrow failure have not been well elucidated. We have previously shown that the myelosuppressive TGF-β pathway is constitutively activated in MDS progenitors. Because there is conflicting data about upregulation of extracellular TGF-β levels in MDS, we wanted to determine the molecular basis of TGF-β pathway overactivation and consequent hematopoietic suppression in this disease. We observed that SMAD7, a negative regulator of TGF-β receptor I (TBRI) kinase, is markedly decreased in a large meta-analysis of gene expression studies from MDS marrow-derived CD34(+) cells. SMAD7 protein was also found to be significantly decreased in MDS marrow progenitors when examined immunohistochemically in a bone marrow tissue microarray. Reduced expression of SMAD7 in hematopoietic cells led to increased TGF-β-mediated gene transcription and enhanced sensitivity to TGF-β-mediated suppressive effects. The increased TGF-β signaling due to SMAD7 reduction could be effectively inhibited by a novel clinically relevant TBRI (ALK5 kinase) inhibitor, LY-2157299. LY-2157299 could inhibit TGF-β-mediated SMAD2 activation and hematopoietic suppression in primary hematopoietic stem cells. Furthermore, in vivo administration of LY-2157299 ameliorated anemia in a TGF-β overexpressing transgenic mouse model of bone marrow failure. Most importantly, treatment with LY-2157199 stimulated hematopoiesis from primary MDS bone marrow specimens. These studies demonstrate that reduction in SMAD7 is a novel molecular alteration in MDS that leads to ineffective hematopoiesis by activating of TGF-β signaling in hematopoietic cells. These studies also illustrate the therapeutic potential of TBRI inhibitors in MDS.

DNA methylation alterations in multiple myeloma as a model for epigenetic changes in cancer

Epigenetics refers to heritable modifications of the genome that are not a result of changes in the DNA sequence and result in phenotypic changes. These changes can be stably transmitted through cell division and are potentially reversible. Epigenetic events are very important during normal development wherein a single progenitor cell proliferates and differentiates into various somatic cell types. This process occurs through modification of the genome without changing the genetic code. Because epigenetic control of gene expression is so important, aberrant epigenetic regulation can lead to disease and cancer. This article reviews epigenetic changes seen in cancer by examining epigenetic changes commonly found in multiple myeloma, a common hematologic malignancy of plasma cells. Epigenetic control of gene expression can be exerted by changes in DNA methylation, histone modifications, and expression of noncoding RNAs. Each of these regulatory mechanisms interacts with the others at different genomic locations and can be measured quantitatively within the cell, requiring that we consider these mechanisms not individually but as a biological system. DNA methylation was the earliest discovered epigenetic regulator and has been the focus of most investigations in cancer. We have thus focused on DNA methylation changes in the pathogenesis of multiple myeloma, which promises to become an excellent model for systems biological studies of epigenomic dysregulation in human disease.

Functional clustering and lineage markers: insights into cellular differentiation and gene function from large-scale microarray studies of purified primary cell populations

Very large microarray datasets showing gene expression across multiple tissues and cell populations provide a window on the transcriptional networks that underpin the differences in functional activity between biological systems. Clusters of co-expressed genes provide lineage markers, candidate regulators of cell function and, by applying the principle of guilt by association, candidate functions for genes of currently unknown function. We have analysed a dataset comprising pure cell populations from hemopoietic and non-hemopoietic cell types (http://biogps.gnf.org). Using a novel network visualisation and clustering approach, we demonstrate that it is possible to identify very tight expression signatures associated specifically with embryonic stem cells, mesenchymal cells and hematopoietic lineages. Selected examples validate the prediction that gene function can be inferred by co-expression. One expression cluster was enriched in phagocytes, which, alongside endosome-lysosome constituents, contains genes that may make up a 'pathway' for phagocyte differentiation. Promoters of these genes are enriched for binding sites for the ETS/PU.1 and MITF families. Another cluster was associated with the production of a specific extracellular matrix, with high levels of gene expression shared by cells of mesenchymal origin (fibroblasts, adipocytes, osteoblasts and myoblasts). We discuss the limitations placed upon such data by the presence of alternative promoters with distinct tissue specificity within many protein-coding genes.

DNA methylation profiling using HpaII tiny fragment enrichment by ligation-mediated PCR (HELP)

The HELP assay is a technique that allows genome-wide analysis of cytosine methylation. Here we describe the assay, its relative strengths and weaknesses, and the transition of the assay from a microarray to massively-parallel sequencing-based foundation.

Can survival prediction be improved by merging gene expression data sets?

BACKGROUND

High-throughput gene expression profiling technologies generating a wealth of data, are increasingly used for characterization of tumor biopsies for clinical trials. By applying machine learning algorithms to such clinically documented data sets, one hopes to improve tumor diagnosis, prognosis, as well as prediction of treatment response. However, the limited number of patients enrolled in a single trial study limits the power of machine learning approaches due to over-fitting. One could partially overcome this limitation by merging data from different studies. Nevertheless, such data sets differ from each other with regard to technical biases, patient selection criteria and follow-up treatment. It is therefore not clear at all whether the advantage of increased sample size outweighs the disadvantage of higher heterogeneity of merged data sets. Here, we present a systematic study to answer this question specifically for breast cancer data sets. We use survival prediction based on Cox regression as an assay to measure the added value of merged data sets.

RESULTS

Using time-dependent Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) and hazard ratio as performance measures, we see in overall no significant improvement or deterioration of survival prediction with merged data sets as compared to individual data sets. This apparently was due to the fact that a few genes with strong prognostic power were not available on all microarray platforms and thus were not retained in the merged data sets. Surprisingly, we found that the overall best performance was achieved with a single-gene predictor consisting of CYB5D1.

CONCLUSIONS

Merging did not deteriorate performance on average despite (a) The diversity of microarray platforms used. (b) The heterogeneity of patients cohorts. (c) The heterogeneity of breast cancer disease. (d) Substantial variation of time to death or relapse. (e) The reduced number of genes in the merged data sets. Predictors derived from the merged data sets were more robust, consistent and reproducible across microarray platforms. Moreover, merging data sets from different studies helps to better understand the biases of individual studies and can lead to the identification of strong survival factors like CYB5D1 expression.

A new procedure for determining the genetic basis of a physiological process in a non-model species, illustrated by cold induced angiogenesis in the carp

BACKGROUND

Physiological processes occur in many species for which there is yet no sequenced genome and for which we would like to identify the genetic basis. For example, some species increase their vascular network to minimise the effects of reduced oxygen diffusion and increased blood viscosity associated with low temperatures. Since many angiogenic and endothelial genes have been discovered in man, functional homolog relationships between carp, zebrafish and human were used to predict the genetic basis of cold-induced angiogenesis in Cyprinus Carpio (carp). In this work, carp sequences were collected and built into contigs. Human-carp functional homolog relationships were derived via zebrafish using a new Conditional Stepped Reciprocal Best Hit (CSRBH) protocol. Data sources including publications, Gene Ontology and cDNA libraries were then used to predict the identity of known or potential angiogenic genes. Finally, re-analyses of cold carp microarray data identified carp genes up-regulated in response to low temperatures in heart and muscle.

RESULTS

The CSRBH approach outperformed all other methods and attained 8,726 carp to human functional homolog relationships for 16,650 contiguous sequences. This represented 3,762 non-redundant genes and 908 of them were predicted to have a role in angiogenesis. The total number of up-regulated differentially expressed genes was 698 and 171 of them were putatively angiogenic. Of these, 5 genes representing the functional homologs NCL, RHOA, MMP9, GRN and MAPK1 are angiogenesis-related genes expressed in response to low temperature.

CONCLUSION

We show that CSRBH functional homologs relationships and re-analyses of gene expression data can be combined in a non-model species to predict genes of biological interest before a genome sequence is fully available. Programs to run these analyses locally are available from http://www.cbrg.ox.ac.uk/~jherbert/.

The Einstein Center for Epigenomics: studying the role of epigenomic dysregulation in human disease

There is increasing interest in the role of epigenetic and transcriptional dysregulation in the pathogenesis of a range of human diseases, not just in the best-studied example of cancer. It is, however, quite difficult for an individual investigator to perform these studies, as they involve genome-wide molecular assays combined with sophisticated computational analytical approaches of very large datasets that may be generated from various resources and technologies. In 2008, the Albert Einstein College of Medicine in New York, USA established a Center for Epigenomics to facilitate the research programs of its investigators, providing shared resources for genome-wide assays and for data analysis. As a result, several avenues of research are now expanding, with cancer epigenomics being complemented by studies of the epigenomics of infectious disease and a neuroepigenomics program.