Benjamin Haibe-Kains

University of Toronto, Toronto, Ontario, Canada

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Publications (136)848.38 Total impact

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    ABSTRACT: Genome-wide expression profiling is increasingly being used to identify transcriptional changes induced by drugs and environmental stressors. In this context the TG-GATEs project (Toxicogenomics Project-Genomics Assisted Toxicity Evaluation system) generated transcriptional profiles from rat liver samples and human/rat cultured primary hepatocytes exposed to more than 100 different chemicals. To assess the capacity of the cell culture models to recapitulate pathways induced by chemicals in vivo, we leveraged the TG-GATEs dataset to compare the early transcriptional responses observed in the liver of rats treated with a large set of chemicals to those of cultured rat and human primary hepatocytes challenged with the same compounds in vitro. We developed a new pathway-based computational pipeline that efficiently combines gene set enrichment analysis (GSEA) using Reactome pathways and biclustering to identify common modules of pathways that are modulated by several chemicals in vivo and in vitro across species. We found that chemicals induce conserved patterns of early transcriptional responses in in vitro and in vivo settings, and across human and rat. These responses involved pathways of cell survival, inflammation, xenobiotic metabolism, oxidative stress, and apoptosis. Moreover, our results support TGF-beta receptor signalling pathway as a candidate biomarker associated with exposure to environmental toxicants in primary human hepatocytes. Our integrative analysis of toxicogenomics data provides a comprehensive overview of biochemical perturbations affected by a large panel of chemicals. Furthermore, we show that the early toxicological response occurring in animals is recapitulated in human and rat primary hepatocyte cultures at the molecular level, indicating that these models reproduce key pathways in response to chemical stress. These findings expand our understanding and interpretation of toxicogenomics data from human hepatocytes exposed to environmental toxicants.
    Environmental Health Perspectives 07/2015; DOI:10.1289/ehp.1409157 · 7.98 Impact Factor
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    ABSTRACT: Epithelial-stromal crosstalk plays a critical role in invasive breast cancer pathogenesis; however, little is known on a systems level about how epithelial-stromal interactions evolve during carcinogenesis. We develop a framework for building genome-wide epithelial-stromal co-expression networks composed of pairwise co-expression relationships between mRNA levels of genes expressed in the epithelium and stroma across a population of patients. We apply this method to laser capture micro-dissection expression profiling datasets in the setting of breast carcinogenesis. Our analysis shows that epithelial-stromal co-expression networks undergo extensive rewiring during carcinogenesis, with the emergence of distinct network hubs in normal breast, and estrogen receptor-positive and estrogen receptor-negative invasive breast cancer, and the emergence of distinct patterns of functional network enrichment. In contrast to normal breast, the strongest epithelial-stromal co-expression relationships in invasive breast cancer mostly represent self-loops, in which the same gene is co-expressed in epithelial and stromal regions. We validate this observation using an independent laser capture micro-dissection dataset and confirm that self-loop interactions are significantly increased in cancer by performing computational image analysis of epithelial and stromal protein expression using images from the Human Protein Atlas. Epithelial-stromal co-expression network analysis represents a new approach for systems-level analyses of spatially localized transcriptomic data. The analysis provides new biological insights into the rewiring of epithelial-stromal co-expression networks and the emergence of epithelial-stromal co-expression self-loops in breast cancer. The approach may facilitate the development of new diagnostics and therapeutics targeting epithelial-stromal interactions in cancer.
    Genome biology 06/2015; 16(1):128. DOI:10.1186/s13059-015-0675-4 · 10.47 Impact Factor
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    ABSTRACT: Radiomics provides a comprehensive quantification of tumor phenotypes by extracting and mining large number of quantitative image features. To reduce the redundancy and compare the prognostic characteristics of radiomic features across cancer types, we investigated cancer-specific radiomic feature clusters in four independent Lung and Head &Neck (H) cancer cohorts (in total 878 patients). Radiomic features were extracted from the pre-treatment computed tomography (CT) images. Consensus clustering resulted in eleven and thirteen stable radiomic feature clusters for Lung and H cancer, respectively. These clusters were validated in independent external validation cohorts using rand statistic (Lung RS = 0.92, p < 0.001, H RS = 0.92, p < 0.001). Our analysis indicated both common as well as cancer-specific clustering and clinical associations of radiomic features. Strongest associations with clinical parameters: Prognosis Lung CI = 0.60 ± 0.01, Prognosis H CI = 0.68 ± 0.01; Lung histology AUC = 0.56 ± 0.03, Lung stage AUC = 0.61 ± 0.01, H HPV AUC = 0.58 ± 0.03, H stage AUC = 0.77 ± 0.02. Full utilization of these cancer-specific characteristics of image features may further improve radiomic biomarkers, providing a non-invasive way of quantifying and monitoring tumor phenotypic characteristics in clinical practice.
    Scientific Reports 06/2015; 5:11044. DOI:10.1038/srep11044 · 5.58 Impact Factor
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    ABSTRACT: To uncover the mechanistic connections between radiomic features, molecular pathways, and clinical outcomes, to develop radiomic based predictors of pathway activation states in individual patients, and to assess whether combining radiomic with clinical and genomic data improves prognostication. We analyzed two independent lung cancer cohorts totaling 351 patients, for whom diagnostic computed tomography (CT) scans, gene-expression profiles, and clinical outcomes were available. The tumor phenotype was characterized based on 636 radiomic features describing tumor intensity, texture, shape and size. We performed an integrative analysis by developing and independently validating association modules of coherently expressed radiomic features and molecular pathways. These modules were statistically tested for significant associations to overall survival (OS), TNM stage, and pathologic histology. We identified thirteen radiomic-pathway association modules (p < 0.05), the most prominent of which were associated with the immune system, p53 pathway, and other pathways involved in cell cycle regulation. Eleven modules were significantly associated with clinical outcomes (p < 0.05). Strong predictive power for pathway activation states in individual patients was observed using radiomics; the strongest per module predictions ranged from an intra-tumor heterogeneity feature predicting RNA III polymerase transcription (AUC 0.62, p = 0.03), to a tumor intensity dispersion feature predicting pyruvate metabolism and citric acid TCA cycle (AUC 0.72, p < 10-⁶). Stepwise combinations of radiomic data with clinical outcomes and gene expression profiles resulted in consistent increases of prognostic power to predict OS (concordance index max = 0.73, p < 10-(9)). This study demonstrates that radiomic approaches permit a non-invasive assessment of molecular and clinical characteristics of tumors, and therefore have the unprecedented potential to cost-effectively advance clinical decision-making using routinely acquired, standard-of-care imaging data. We show that prognostic value complementary to clinical and genomic information can be obtained by radiomic strategies.
    Medical Physics 06/2015; 42(6):3602. DOI:10.1118/1.4925587 · 3.01 Impact Factor
  • Swneke D Bailey · Carl Virtanen · Benjamin Haibe-Kains · Mathieu Lupien
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    ABSTRACT: Detection of allelic imbalances in ChIP-Seq reads is a powerful approach to identify functional non-coding single nucleotide variants (SNVs), either polymorphisms or mutations, which modulate the affinity of transcription factors for chromatin. We present ABC, a computational tool that identifies allele specific binding of transcription factors from aligned ChIP-Seq reads at heterozygous SNVs. ABC controls for potential false positives resulting from biases introduced by the use of short sequencing reads in ChIP-Seq and can efficiently process a large number of heterozygous SNVs. ABC successfully identifies previously characterized functional SNVs, such as the rs4784227 breast cancer risk associated SNP that modulates the affinity of FOXA1 for the chromatin. ABC is written in PERL and can be run on any platform with both PERL (≥5.18.1) and R (≥3.1.1) installed. The script requires the PERL Statistics::R module. The code is open-source under an Artistic-2.0 license and versioned on GitHub (https://github.com/mlupien/ABC/). Supplementary information can be found at Bioinformatics online CONTACT: mlupien@uhnres.utoronto.ca. © The Author (2015). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
    Bioinformatics 05/2015; DOI:10.1093/bioinformatics/btv321 · 4.62 Impact Factor
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    ABSTRACT: Molecular subtyping is instrumental towards selection of model systems for fundamental research in tumour pathogenesis, and clinical patient assessment. Medulloblastoma (MB) is a highly heterogeneous, malignant brain tumour that is the most common cause of cancer-related deaths in children. Current MB classification schemes require large sample sizes, and standard reference samples, for subtype predictions. Such approaches are impractical in clinical settings with limited tumour biopsies, and unsuitable for model system predictions where standard reference samples are unavailable. Our developed Medullo-Model To Subtypes (MM2S) classifier stratifies single MB gene expression profiles without reference samples or replicates. Our pathway-centric approach facilitates subtype predictions of patient samples, and model systems including cell lines and mouse models. MM2S demonstrates >96% accuracy for patients of well-characterized normal cerebellum, WNT, or SHH subtypes, and the less-characterized Group4 (86%) and Group3 (78.2%). MM2S also enables classification of MB cell lines and mouse models into their human counterparts. Copyright © 2015. Published by Elsevier Inc.
    Genomics 05/2015; DOI:10.1016/j.ygeno.2015.05.002 · 2.79 Impact Factor
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    ABSTRACT: Cervical cancer is the third most common cancer in women globally, and despite treatment, distant metastasis and nodal recurrence will still develop in approximately 30% of patients. The ability to predict which patients are likely to experience distant relapse would allow clinicians to better tailor treatment. Previous studies have investigated the role of chromosomal instability (CIN) in cancer, which can promote tumour initiation and growth; a hallmark of human malignancies. In this study, we sought to examine the published CIN70 gene signature in a cohort of cervical cancer patients treated at the Princess Margaret (PM) Cancer Centre and an independent cohort of The Cancer Genome Atlas (TCGA) cervical cancer patients, to determine if this CIN signature associated with patient outcome. Cervical cancer samples were collected from 79 patients, treated between 2000-2007 at the PM, prior to undergoing curative chemo-radiation. Total RNA was extracted from each patient sample and analyzed using the GeneChip Human Genome U133 Plus 2.0 array (Affymetrix). High CIN70 scores were significantly related to increased chromosomal alterations in TCGA cervical cancer patients, including a higher percentage of genome altered and a higher number of copy number alterations. In addition, this same CIN70 signature was shown to be predictive of para-aortic nodal relapse in the PM Cancer Centre cohort. These findings demonstrate that chromosomal instability plays an important role in cervical cancer, and is significantly associated with patient outcome. For the first time, this CIN70 gene signature provided prognostic value for patients with cervical cancer.
    BMC Cancer 05/2015; 15(1):361. DOI:10.1186/s12885-015-1372-0 · 3.32 Impact Factor
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    ABSTRACT: A common and aggressive subtype of soft-tissue sarcoma, undifferentiated pleomorphic sarcoma (UPS) was examined to determine the role of micro-RNAs (miRNAs) in modulating distant metastasis. Following histopathologic review, 110 fresh frozen clinically annotated UPS samples were divided into two independent cohorts for Training (42 patients), and Validation (68 patients) analyses. Global miRNA profiling on the Training Set and functional analysis in vitro suggested that miRNA-138 and its downstream RHO-ROCK cell adhesion pathway was a convergent target of miRNAs associated with the development of metastasis. A six-miRNA signature set prognostic of distant metastasis-free survival (DMFS) was developed from Training Set miRNA expression values. Using the six-miRNA signature, patients were successfully categorized into high- and low-risk groups for DMFS in an independent Validation Set, with a hazard ratio (HR) of 2.25 (p = 0.048). After adjusting for other known prognostic variables such as age, gender, tumor grade, size, depth, and treatment with radiotherapy, the six-miRNA signature retained prognostic value with a HR of 3.46 (p < 0.001). A prognostic miRNA biomarker for clinical validation was thus identified along with a functional pathway that modulates UPS metastatic phenotype.
    Oncotarget 04/2015; · 6.63 Impact Factor
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    ABSTRACT: We recently identified two robust ovarian cancer subtypes, defined by the expression of genes involved in angiogenesis, with significant differences in clinical outcome. To identify potential regulatory mechanisms that distinguish the subtypes we applied PANDA, a method that uses an integrative approach to model information flow in gene regulatory networks. We find distinct differences between networks that are active in the angiogenic and non-angiogenic subtypes, largely defined by a set of key transcription factors that, although previously reported to play a role in angiogenesis, are not strongly differentially-expressed between the subtypes. Our network analysis indicates that these factors are involved in the activation (or repression) of different genes in the two subtypes, resulting in differential expression of their network targets. Mechanisms mediating differences between subtypes include a previously unrecognized pro-angiogenic role for increased genome-wide DNA methylation and complex patterns of combinatorial regulation. The models we develop require a shift in our interpretation of the driving factors in biological networks away from the genes themselves and toward their interactions. The observed regulatory changes between subtypes suggest therapeutic interventions that may help in the treatment of ovarian cancer.
    BMC Bioinformatics 04/2015; 16(1):115. DOI:10.1186/s12859-015-0551-y · 2.67 Impact Factor
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    ABSTRACT: Quantitative validation of gene regulatory networks (GRN) inferred from observational expression data is a difficult task usually involving time intensive and costly laboratory experiments. We were able to show that gene knock-down experiments can be used to quantitatively assess the quality of large-scale GRNs via a purely data-driven approach (Olsen et al. 2014). Our new validation framework also enables the statistical comparison of multiple network inference techniques, which was a long-standing challenge in the field.
    04/2015; 33. DOI:10.1016/j.gdata.2015.03.011
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    ABSTRACT: Radiomics provides opportunities to quantify the tumor phenotype non-invasively by applying a large number of quantitative imaging features. This study evaluates computed-tomography (CT) radiomic features for their capability to predict distant metastasis (DM) for lung adenocarcinoma patients. We included two datasets: 98 patients for discovery and 84 for validation. The phenotype of the primary tumor was quantified on pre-treatment CT-scans using 635 radiomic features. Univariate and multivariate analysis was performed to evaluate radiomics performance using the concordance index (CI). Thirty-five radiomic features were found to be prognostic (CI>0.60, FDR<5%) for DM and twelve for survival. It is noteworthy that tumor volume was only moderately prognostic for DM (CI=0.55, p-value=2.77×10(-5)) in the discovery cohort. A radiomic-signature had strong power for predicting DM in the independent validation dataset (CI=0.61, p-value=1.79×10(-17)). Adding this radiomic-signature to a clinical model resulted in a significant improvement of predicting DM in the validation dataset (p-value=1.56×10(-11)). Although only basic metrics are routinely quantified, this study shows that radiomic features capturing detailed information of the tumor phenotype can be used as a prognostic biomarker for clinically-relevant factors such as DM. Moreover, the radiomic-signature provided additional information to clinical data. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.
    Radiotherapy and Oncology 03/2015; 114(3). DOI:10.1016/j.radonc.2015.02.015 · 4.86 Impact Factor
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    ABSTRACT: Medulloblastoma comprises four distinct molecular variants with distinct genetics, transcriptomes, and outcomes. Subgroup affiliation has been previously shown to remain stable at the time of recurrence, which likely reflects their distinct cells of origin. However, a therapeutically relevant question that remains unanswered is subgroup stability in the metastatic compartment. We assembled a cohort of 12-paired primary-metastatic tumors collected in the MAGIC consortium, and established their molecular subgroup affiliation by performing integrative gene expression and DNA methylation analysis. Frozen tissues were collected and profiled using Affymetrix gene expression arrays and Illumina methylation arrays. Class prediction and hierarchical clustering were performed using existing published datasets. Our molecular analysis, using consensus integrative genomic data, establishes the unequivocal maintenance of molecular subgroup affiliation in metastatic medulloblastoma. We further validated these findings by interrogating a non-overlapping cohort of 19 pairs of primary-metastatic tumors from the Burdenko Neurosurgical Institute using an orthogonal technique of immunohistochemical staining. This investigation represents the largest reported primary-metastatic paired cohort profiled to date and provides a unique opportunity to evaluate subgroup-specific molecular aberrations within the metastatic compartment. Our findings further support the hypothesis that medulloblastoma subgroups arise from distinct cells of origin, which are carried forward from ontogeny to oncology.
    Acta Neuropathologica 03/2015; 129(3):449-57. DOI:10.1007/s00401-015-1389-0 · 10.76 Impact Factor
  • David W Cescon · Benjamin Haibe-Kains · Tak W Mak
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    ABSTRACT: Genomic sequencing studies of breast and other cancers have identified patterns of mutations that have been attributed to the endogenous mutator activity of APOBEC3B (A3B), a member of the AID/APOBEC family of cytidine deaminases. A3B gene expression is increased in many cancers, but its upstream drivers remain undefined. Furthermore, there exists a common germ-line deletion polymorphism (A3B(del)), which has been associated with a paradoxical increase in breast cancer risk. To examine causes and consequences of A3B expression and its constitutive absence in breast cancer, we analyzed two large clinically annotated genomic datasets [The Cancer Genome Atlas (TCGA) and the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC)]. We confirmed that A3B expression is associated with aggressive clinicopathologic characteristics and adverse outcomes and show that A3B expression is highly correlated with proliferative features (mitosis and cell cycle-related gene expression) in breast and 15 of 16 other solid tumor types. However, breast cancers arising in homozygous A3B(del) individuals with A3B absent did not differ in these features, indicating that A3B expression is a reflection rather than a direct cause of increased proliferation. Using gene set enrichment analysis (GSEA), we detected a pattern of immune activation in A3B(del) breast cancers, which seems to be related to hypermutation arising in A3B(del) carriers. Together, these results provide an explanation for A3B overexpression and its prognostic effect, giving context to additional study of this mutator as a cancer biomarker or putative drug target. In addition, although immune features of A3B(del) require additional study, these findings nominate the A3B(del) polymorphism as a potential predictor for cancer immunotherapy.
    Proceedings of the National Academy of Sciences 02/2015; 112(9). DOI:10.1073/pnas.1424869112 · 9.81 Impact Factor
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    ABSTRACT: Chromatin interactions connect distal regulatory elements to target gene promoters guiding stimulus- and lineage-specific transcription. Few factors securing chromatin interactions have so far been identified. Here, by integrating chromatin interaction maps with the large collection of transcription factor-binding profiles provided by the ENCODE project, we demonstrate that the zinc-finger protein ZNF143 preferentially occupies anchors of chromatin interactions connecting promoters with distal regulatory elements. It binds directly to promoters and associates with lineage-specific chromatin interactions and gene expression. Silencing ZNF143 or modulating its DNA-binding affinity using single-nucleotide polymorphisms (SNPs) as a surrogate of site-directed mutagenesis reveals the sequence dependency of chromatin interactions at gene promoters. We also find that chromatin interactions alone do not regulate gene expression. Together, our results identify ZNF143 as a novel chromatin-looping factor that contributes to the architectural foundation of the genome by providing sequence specificity at promoters connected with distal regulatory elements.
    Nature Communications 02/2015; 2:6186. DOI:10.1038/ncomms7186 · 10.74 Impact Factor
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    ABSTRACT: Purpose Despite significant improvement in locoregional control in the contemporary era of nasopharyngeal carcinoma (NPC) treatment, patients still suffer from a significant risk of distant metastasis (DM). Identifying those patients at risk of DM would aid in personalized treatment in the future. MicroRNAs (miRNAs) play many important roles in human cancers; hence, we proceeded to address the primary hypothesis that there is a miRNA expression signature capable of predicting DM for NPC patients. Methods and results The expression of 734 miRNAs was measured in 125 (Training) and 121 (Validation) clinically annotated NPC diagnostic biopsy samples. A 4-miRNA expression signature associated with risk of developing DM was identified by fitting a penalized Cox Proportion Hazard regression model to the Training data set (HR 8.25; p < 0.001), and subsequently validated in an independent Validation set (HR 3.2; p = 0.01). Pathway enrichment analysis indicated that the targets of miRNAs associated with DM appear to be converging on cell-cycle pathways. Conclusions This 4-miRNA signature adds to the prognostic value of the current "gold standard" of TNM staging. In-depth interrogation of these 4-miRNAs will provide important biological insights that could facilitate the discovery and development of novel molecularly targeted therapies to improve outcome for future NPC patients.
    Oncotarget 01/2015; · 6.63 Impact Factor
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    ABSTRACT: There is an urgent need for better therapeutics in head and neck squamous cell cancer (HNSCC) to improve survival and decrease treatment morbidity. Recent advances in high-throughput drug screening techniques and next-generation sequencing have identified new therapeutic targets in other cancer types, but an HNSCC-specific study has not yet been carried out. We have exploited data from two large-scale cell line projects to clearly describe the mutational and copy number status of HNSCC cell lines and identify candidate drugs with elevated efficacy in HNSCC. The genetic landscape of 42 HNSCC cell lines including mutational and copy number data from studies by Garnett et al., and Barretina et al., were analyzed. Data from Garnett et al. was interrogated for relationships between HNSCC cells versus the entire cell line pool using one- and two-way analyses of variance (ANOVAs). As only seven HNSCC cell lines were tested with drugs by Barretina et al., a similar analysis was not carried out. Recurrent mutations in human papillomavirus (HPV)-negative patient tumors were confirmed in HNSCC cell lines, however additional, recurrent, cell line-specific mutations were identified. Four drugs, Bosutinib, Docetaxel, BIBW2992, and Gefitinib, were found via multiple-test corrected ANOVA to have lower IC50 values, suggesting higher drug sensitivity, in HNSCC lines versus non-HNSCC lines. Furthermore, the PI3K inhibitor AZD6482 demonstrated significantly higher activity (as measured by the IC50) in HNSCC cell lines harbouring PIK3CA mutations versus those that did not. HNSCC-specific reanalysis of large-scale drug screening studies has identified candidate drugs that may be of therapeutic benefit and provided insights into strategies to target PIK3CA mutant tumors. PIK3CA mutations may represent a predictive biomarker for response to PI3K inhibitors. A large-scale study focused on HNSCC cell lines and including HPV-positive lines is necessary and has the potential to accelerate the development of improved therapeutics for patients suffering with head and neck cancer. This strategy can potentially be used as a template for drug discovery in any cancer type.
    11/2014; 15(1):66. DOI:10.1186/2050-6511-15-66
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    ABSTRACT: Microarrays have revolutionized breast cancer (BC) research by enabling studies of gene expression on a transcriptome-wide scale. Recently, RNA-Sequencing (RNA-Seq) has emerged as an alternative for precise readouts of the transcriptome. To date, no study has compared the ability of the two technologies to quantify clinically relevant individual genes and microarray-derived gene expression signatures (GES) in a set of BC samples encompassing the known molecular BC's subtypes. To accomplish this, the RNA from 57 BCs representing the four main molecular subtypes (triple negative, HER2 positive, luminal A, luminal B), was profiled with Affymetrix HG-U133 Plus 2.0 chips and sequenced using the Illumina HiSeq 2000 platform. The correlations of three clinically relevant BC genes, six molecular subtype classifiers, and a selection of 21 GES were evaluated. 16,097 genes common to the two platforms were retained for downstream analysis. Gene-wise comparison of microarray and RNA-Seq data revealed that 52% had a Spearman's correlation coefficient greater than 0.7 with highly correlated genes displaying significantly higher expression levels. We found excellent correlation between microarray and RNA-Seq for the estrogen receptor (ER; rs =0.973; 95%CI: 0.971-0.975), progesterone receptor (PgR; rs =0.95; 0.947-0.954), and human epidermal growth factor receptor 2 (HER2; rs =0.918; 0.912-0.923), while a few discordances between ER and PgR quantified by immunohistochemistry and RNA-Seq/microarray were observed. All the subtype classifiers evaluated agreed well (Cohen's kappa coefficients >0.8) and all the proliferation-based GES showed excellent Spearman correlations between microarray and RNA-Seq (all rs >0.965). Immune-, stroma- and pathway-based GES showed a lower correlation relative to prognostic signatures (all rs >0.6). To our knowledge, this is the first study to report a systematic comparison of RNA-Seq to microarray for the evaluation of single genes and GES clinically relevant to BC. According to our results, the vast majority of single gene biomarkers and well-established GES can be reliably evaluated using the RNA-Seq technology.
    BMC Genomics 11/2014; 15(1):1008. DOI:10.1186/1471-2164-15-1008 · 4.04 Impact Factor
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    Frank Emmert-Streib · Matthias Dehmer · Benjamin Haibe-Kains
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    ABSTRACT: In this paper, we shed light on approaches that are currently used to infer networks from gene expression data with respect to their biological meaning. As we will show, the biological interpretation of these networks depends on the chosen theoretical perspective. For this reason, we distinguish a statistical perspective from a mathematical modeling perspective and elaborate their differences and implications. Our results indicate the imperative need for a genomic network ontology in order to avoid increasing confusion about the biological interpretation of inferred networks, which can be even enhanced by approaches that integrate multiple data sets, respectively, data types.
    Frontiers in Genetics 08/2014; 5:299. DOI:10.3389/fgene.2014.00299
  • Frank Emmert-Streib · Matthias Dehmer · Benjamin Haibe-Kains
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    ABSTRACT: In recent years gene regulatory networks (GRNs) have attracted a lot of interest and many methods have been introduced for their statistical inference from gene expression data. However, despite their popularity, GRNs are widely misunderstood. For this reason, we provide in this paper a general discussion and perspective of gene regulatory networks. Specifically, we discuss their meaning, the consistency among different network inference methods, ensemble methods, the assessment of GRNs, the estimated number of existing GRNs and their usage in different application domains. Furthermore, we discuss open questions and necessary steps in order to utilize gene regulatory networks in a clinical context and for personalized medicine.
    Frontiers in Cell and Developmental Biology 08/2014; 2. DOI:10.3389/fcell.2014.00038

Publication Stats

5k Citations
848.38 Total Impact Points

Institutions

  • 2014–2015
    • University of Toronto
      • Department of Medical Biophysics
      Toronto, Ontario, Canada
    • University Health Network
      Toronto, Ontario, Canada
    • Université de Montréal
      Montréal, Quebec, Canada
  • 2013
    • Institut de recherches cliniques de Montréal
      Montréal, Quebec, Canada
  • 2010–2013
    • Dana-Farber Cancer Institute
      • • Center for Cancer Computational Biology
      • • Department of Biostatistics and Computational Biology
      Boston, Massachusetts, United States
    • Harvard Medical School
      • Department of Surgery
      Boston, Massachusetts, United States
  • 2006–2013
    • Institut Jules Bordet
      • Department of Nuclear Medicine
      Bruxelles, Brussels Capital Region, Belgium
  • 2005–2013
    • Université Libre de Bruxelles
      • • J.-C. Heuson Breast Cancer Translational Research Laboratory
      • • Laboratory of Cancer Epigenetics
      • • Faculty of Sciences
      • • Laboratory of Experimental Hematology
      Bruxelles, Brussels Capital, Belgium
  • 2011–2012
    • Harvard University
      Cambridge, Massachusetts, United States
    • University of Texas MD Anderson Cancer Center
      Houston, Texas, United States
  • 2008
    • University Hospital Brussels
      Bruxelles, Brussels Capital, Belgium
    • Microarrays
      Huntsville, Alabama, United States
    • Vrije Universiteit Brussel
      • Department of Computer Science
      Bruxelles, Brussels Capital, Belgium
  • 2007
    • Peter MacCallum Cancer Centre
      Melbourne, Victoria, Australia