Benjamin Haibe-Kains

University of Toronto, Toronto, Ontario, Canada

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Publications (149)881.86 Total impact

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    ABSTRACT: Pharmacogenomics holds great promise for the development of biomarkers of drug response and the design of new therapeutic options, which are key challenges in precision medicine. However, such data are scattered and lack standards for efficient access and analysis, consequently preventing the realization of the full potential of pharmacogenomics. To address these issues, we implemented PharmacoGx, an easy-to-use, open source package for integrative analysis of multiple pharmacogenomic datasets. We demonstrate the utility of our package in comparing large drug sensitivity datasets, such as the Genomics of Drug Sensitivity in Cancer and the Cancer Cell Line Encyclopedia. Moreover, we show how to use our package to easily perform Connectivity Map analysis. With increasing availability of drug-related data, our package will open new avenues of research for meta-analysis of pharmacogenomic data. Availability and implementation: PharmacoGx is implemented in R and can be easily installed on any system. The package is available from CRAN and its source code is available from GitHub. Contact: bhaibeka{at}uhnresearch.ca or benjamin.haibe.kains{at}utoronto.ca Supplementary information: Supplementary data are available at Bioinformatics online.
    No preview · Article · Dec 2015 · Bioinformatics
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    ABSTRACT: Breast cancer is one of the most frequent cancers among women. Extensive studies into the molecular heterogeneity of breast cancer have produced a plethora of molecular subtype classification and prognosis prediction algorithms, as well as numerous gene expression signatures. However, reimplementation of these algorithms is a tedious but important task to enable comparison of existing signatures and classification models between each other and with new models. Here, we present the genefu R/Bioconductor package, a multi-tiered compendium of bioinformatics algorithms and gene signatures for molecular subtyping and prognostication in breast cancer. Availability and implementation: The genefu package is available from Bioconductor. http://www.bioconductor.org/packages/devel/bioc/html/genefu.html. Source code is also available on Github https://github.com/bhklab/genefu. Contact: bhaibeka{at}uhnresearch.ca Supplementary information: Supplementary data are available at Bioinformatics online.
    No preview · Article · Nov 2015 · Bioinformatics
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    ABSTRACT: Molecular interrogation of a biological sample through DNA sequencing, RNA and microRNA profiling, proteomics and other assays, has the potential to provide a systems level approach to predicting treatment response and disease progression, and to developing precision therapies. Large publicly funded projects have generated extensive and freely available multi-assay data resources; however, bioinformatic and statistical methods for the analysis of such experiments are still nascent. We review multi-assay genomic data resources in the areas of clinical oncology, pharmacogenomics and other perturbation experiments, population genomics and regulatory genomics and other areas, and tools for data acquisition. Finally, we review bioinformatic tools that are explicitly geared toward integrative genomic data visualization and analysis. This review provides starting points for accessing publicly available data and tools to support development of needed integrative methods.
    Full-text · Article · Oct 2015 · Briefings in Bioinformatics
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    ABSTRACT: The cell surface nucleotidase CD73 is an immunosuppressive enzyme involved in tumor progression and metastasis. While preclinical studies suggest that CD73 can be targeted for cancer treatment, the clinical impact of CD73 in ovarian cancer remains unclear. In this study, we investigated the prognostic value of CD73 in high-grade serous (HGS) ovarian cancer using gene and protein expression analyses. Our results demonstrate that high levels of CD73 are significantly associated with shorter disease-free survival and overall survival in HGS ovarian cancer patients. Furthermore, high levels of CD73 expression in ovarian tumor cells abolished the good prognosis associated with intraepithelial CD8+ cells. Notably, CD73 gene expression was highest in the C1/stromal molecular subtype of HGS ovarian cancer and positively correlated with an epithelial-to-mesenchymal transition (EMT) gene signature. Moreover, in vitro studies revealed that CD73 and extracellular adenosine enhance ovarian tumor cell growth as well as expression of anti-apoptotic BCL-2 family members. Finally, in vivo co-injection of ID8 mouse ovarian tumor cells with mouse embryonic fibroblasts showed that CD73 expression in fibroblasts promotes tumor immune escape and thereby tumor growth. In conclusion, our study highlights a role for CD73 as a prognostic marker of patient survival and also as a candidate therapeutic target in HGS ovarian cancers.
    Full-text · Article · Sep 2015 · Cancer Research
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    ABSTRACT: Background: In 2012, two large pharmacogenomic studies, the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE), were published, each reported gene expression data and measures of drug response for a large number of drugs and hundreds of cell lines. In 2013, we published a comparative analysis that reported gene expression profiles for the 471 cell lines profiled in both studies and dose response measurements for the 15 drugs characterized in the common cell lines by both studies. While we found good concordance in gene expression profiles, there was substantial inconsistency in the drug responses reported by the GDSC and CCLE projects. Our paper was widely discussed and we received extensive feedback on the comparisons that we performed. This feedback, along with the release of new data, prompted us to revisit our initial analysis. Here we present a new analysis using these expanded data in which we address the most significant suggestions for improvements on our published analysis: that drugs with different response characteristics should have been treated differently, that targeted therapies and broad cytotoxic drugs should have been treated differently in assessing consistency, that consistency of both molecular profiles and drug sensitivity measurements should both be compared across cell lines to accurately assess differences in the studies, that we missed some biomarkers that are consistent between studies, and that the software analysis tools we provided with our analysis should have been easier to run, particularly as the GDSC and CCLE released additional data. Methods: For each drug, we used published sensitivity data from the GDSC and CCLE to separately estimate drug dose-response curves. We then used two statistics, the area between drug dose-response curves (ABC) and the Matthews correlation coefficient (MCC), to robustly estimate the consistency of continuous and discrete drug sensitivity measures, respectively. We also used recently released RNA-seq data together with previously published gene expression microarray data to assess inter-platform reproducibility of cell line gene expression profiles. Results: This re-analysis supports our previous finding that gene expression data are significantly more consistent than drug sensitivity measurements. The use of new statistics to assess data consistency allowed us to identify two broad effect drugs -- 17-AAG and PD-0332901 -- and three targeted drugs -- PLX4720, nilotinib and crizotinib -- with moderate to good consistency in drug sensitivity data between GDSC and CCLE. Not enough sensitive cell lines were screened in both studies to robustly assess consistency for three other targeted drugs, PHA-665752, erlotinib, and sorafenib. Concurring with our published results, we found evidence of inconsistencies in pharmacological phenotypes for the remaining eight drugs. Further, to discover "consistency" between studies required the use of multiple statistics and the selection of specific measures on a case-by-case basis. Conclusion: Our results reaffirm our initial findings of an inconsistency in drug sensitivity measures for eight of fifteen drugs screened both in GDSC and CCLE, irrespective of which statistical metric was used to assess correlation. Taken together, our findings suggest that the phenotypic data on drug response in the GDSC and CCLE continue to present challenges for robust biomarker discovery. This re-analysis provides additional support for the argument that experimental standardization and validation of pharmacogenomic response will be necessary to advance the broad use of large pharmacogenomic screens.
    Full-text · Article · Sep 2015
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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.
    No preview · Article · Jul 2015 · Environmental Health Perspectives
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    Full-text · Dataset · Jul 2015
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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.
    Full-text · Article · Jun 2015 · Genome biology
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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.
    Full-text · Article · Jun 2015 · Scientific Reports
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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.
    No preview · Article · Jun 2015 · Medical Physics
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    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.
    Preview · Article · May 2015 · Bioinformatics
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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.
    No preview · Article · May 2015 · Genomics
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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.
    Full-text · Article · May 2015 · BMC Cancer
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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.
    Preview · Article · Apr 2015 · Oncotarget
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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.
    Full-text · Article · Apr 2015 · BMC Bioinformatics
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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.
    Full-text · Article · Apr 2015 · Genomics Data
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    ABSTRACT: Prior to applying genomic predictors to clinical samples, the genomic data must be properly normalized to ensure that the test set data are comparable to the data upon which the predictor was trained. The most effective normalization methods depend on data from multiple patients. From a biomedical perspective, this implies that predictions for a single patient may change depending on which other patient samples they are normalized with. This test set bias will occur when any cross-sample normalization is used before clinical prediction. We demonstrate that results from existing gene signatures which rely on normalizing test data may be irreproducible when the patient population changes composition or size using a set of curated, publicly-available breast cancer microarray experiments. As an alternative, we examine the use of gene signatures that rely on ranks from the data and show why signatures using rank-based features can avoid test set bias while maintaining highly accurate classification, even across platforms. The code, data, and instructions necessary to reproduce our entire analysis is available at https://github.com/prpatil/testsetbias. jtleek@gmail.com. © The Author (2015). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
    No preview · Article · Mar 2015 · Bioinformatics
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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.
    Full-text · Article · Mar 2015 · Radiotherapy and Oncology
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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.
    Full-text · Article · Mar 2015 · Acta Neuropathologica
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    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.
    Preview · Article · Feb 2015 · Proceedings of the National Academy of Sciences

Publication Stats

6k Citations
881.86 Total Impact Points

Institutions

  • 2014-2015
    • University of Toronto
      • Department of Medical Biophysics
      Toronto, Ontario, Canada
    • University Health Network
      Toronto, Ontario, Canada
  • 2013-2014
    • Université de Montréal
      Montréal, Quebec, Canada
    • 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
      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
      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