_publications.bib

@article{Lu2016,
  abstract = {Several types of pediatric cancers reportedly contain high-frequency missense mutations in histone H3, yet the underlying oncogenic mechanism remains poorly characterized. Here we report that the H3 lysine 36-to-methionine (H3K36M) mutation impairs the differentiation of mesenchymal progenitor cells and generates undifferentiated sarcoma in vivo. H3K36M mutant nucleosomes inhibit the enzymatic activities of several H3K36 methyltransferases. Depleting H3K36 methyltransferases, or expressing an H3K36I mutant that similarly inhibits H3K36 methylation, is sufficient to phenocopy the H3K36M mutation. After the loss of H3K36 methylation, a genome-wide gain in H3K27 methylation leads to a redistribution of polycomb repressive complex 1 and de-repression of its target genes known to block mesenchymal differentiation. Our findings are mirrored in human undifferentiated sarcomas in which novel K36M/I mutations in H3.1 are identified.},
  author = {Lu, Chao and Jain, Siddhant U and Hoelper, Dominik and Bechet, Denise and Molden, Rosalynn C and Ran, Leili and Murphy, Devan and Venneti, Sriram and Hameed, Meera and Pawel, Bruce R and Wunder, Jay S and Dickson, Brendan C and Lundgren, Stefan M and Jani, Krupa S and {De Jay}, Nicolas and Papillon-Cavanagh, Simon and Andrulis, Irene L and Sawyer, Sarah L and Grynspan, David and Turcotte, Robert E and Nadaf, Javad and Fahiminiyah, Somayyeh and Muir, Tom W and Majewski, Jacek and Thompson, Craig B and Chi, Ping and Garcia, Benjamin A and Allis, C David and Jabado, Nada and Lewis, Peter W},
  doi = {10.1126/science.aac7272},
  file = {:Users/ndejay/Papers/Lu et al. - 2016 - Histone H3K36 mutations promote sarcomagenesis through altered histone methylation landscape.pdf:pdf},
  issn = {1095-9203},
  journal = {Science},
  month = {may},
  number = {6287},
  pages = {844--9},
  pmid = {27174990},
  title = {{Histone H3K36 mutations promote sarcomagenesis through altered histone methylation landscape.}},
  url = {http://www.sciencemag.org/cgi/doi/10.1126/science.aac7272 http://www.ncbi.nlm.nih.gov/pubmed/27174990},
  volume = {352},
  year = {2016}
}
@article{Levesque2016,
  abstract = {Polymorphisms and decreased activity of methylenetetrahydrofolate reductase (MTHFR) are linked to disease, including cancer. However, epigenetic regulation has not been thoroughly studied. Our goal was to generate DNA methylation profiles of murine/human MTHFR gene regions and examine methylation in brain and liver tumors. Pyrosequencing in four murine tissues revealed minimal DNA methylation in the CpG island. Higher methylation was seen in liver or intestine in the CpG island shore 5' to the upstream translational start site or in another region 3' to the downstream start site. In the latter region, there was negative correlation between expression and methylation. Three orthologous regions were investigated in human MTHFR, as well as a fourth region between the two translation start sites. We found significantly increased methylation in three regions (not the CpG island) in pediatric astrocytomas compared with control brain, with decreased expression in tumors. Methylation in hepatic carcinomas was also increased in the three regions compared with normal liver, but the difference was significant for only one CpG. This work, the first overview of the Mthfr/MTHFR epigenetic landscape, suggests regulation through methylation in some regions, demonstrates increased methylation/decreased expression in pediatric astrocytomas, and should serve as a resource for future epigenetic studies.},
  author = {L{\'{e}}vesque, Nancy and Leclerc, Daniel and Gayden, Tenzin and Lazaris, Anthoula and {De Jay}, Nicolas and Petrillo, Stephanie and Metrakos, Peter and Jabado, Nada and Rozen, Rima},
  doi = {10.1007/s00335-016-9624-0},
  file = {:Users/ndejay/Papers/L{\'{e}}vesque et al. - 2016 - Murine diettissue and human brain tumorigenesis alter MthfrMTHFR 5'-end methylation.pdf:pdf},
  isbn = {0033501696},
  issn = {1432-1777},
  journal = {Mammalian genome},
  month = {apr},
  number = {3-4},
  pages = {122--34},
  pmid = {26951114},
  title = {{Murine diet/tissue and human brain tumorigenesis alter Mthfr/MTHFR 5'-end methylation.}},
  url = {http://link.springer.com/10.1007/s00335-016-9624-0 http://www.ncbi.nlm.nih.gov/pubmed/26951114},
  volume = {27},
  year = {2016}
}
@article{Fontebasso2015,
  abstract = {Pilocytic astrocytoma (PA) is the most common brain tumor in children but is rare in adults, and hence poorly studied in this age group. We investigated 222 PA and report increased aneuploidy in older patients. Aneuploid genomes were identified in 45{\%} of adult compared with 17{\%} of pediatric PA. Gains were non-random, favoring chromosomes 5, 7, 6 and 11 in order of frequency, and preferentially affecting non-cerebellar PA and tumors with BRAF V600E mutations and not with KIAA1549-BRAF fusions or FGFR1 mutations. Aneuploid PA differentially expressed genes involved in CNS development, the unfolded protein response, and regulators of genomic stability and the cell cycle (MDM2, PLK2),whose correlated programs were overexpressed specifically in aneuploid PA compared to other glial tumors. Thus, convergence of pathways affecting the cell cycle and genomic stability may favor aneuploidy in PA, possibly representing an additional molecular driver in older patients with this brain tumor.},
  author = {Fontebasso, Adam M and Shirinian, Margret and Khuong-Quang, Dong-Anh and Bechet, Denise and Gayden, Tenzin and Kool, Marcel and {De Jay}, Nicolas and Jacob, Karine and Gerges, Noha and Hutter, Barbara and Şeker-Cin, Huriye and Witt, Hendrik and Montpetit, Alexandre and Brunet, S{\'{e}}bastien and Lepage, Pierre and Bourret, Genevi{\`{e}}ve and Klekner, Almos and Bogn{\'{a}}r, L{\'{a}}szl{\'{o}} and Hauser, Peter and Garami, Mikl{\'{o}}s and Farmer, Jean-Pierre and Montes, Jose-Luis and Atkinson, Jeffrey and Lambert, Sally and Kwan, Tony and Korshunov, Andrey and Tabori, Uri and Collins, V Peter and Albrecht, Steffen and Faury, Damien and Pfister, Stefan M and Paulus, Werner and Hasselblatt, Martin and Jones, David T W and Jabado, Nada},
  doi = {10.18632/oncotarget.5571},
  file = {:Users/ndejay/Papers/Fontebasso et al. - 2015 - Non-random aneuploidy specifies subgroups of pilocytic astrocytoma and correlates with older age.pdf:pdf},
  issn = {1949-2553},
  journal = {Oncotarget},
  keywords = {BRAF,MDM2,PLK2,aneuploidy,pilocytic astrocytoma},
  month = {oct},
  number = {31},
  pages = {31844--56},
  pmid = {26378811},
  title = {{Non-random aneuploidy specifies subgroups of pilocytic astrocytoma and correlates with older age.}},
  url = {http://www.ncbi.nlm.nih.gov/pubmed/26378811},
  volume = {6},
  year = {2015}
}
@article{Kleinman2014,
  abstract = {HIV-1 preferentially infects CD4+ T cells, causing fundamental changes that eventually lead to the release of new viral particles and cell death. To investigate in detail alterations in the transcriptome of the CD4+ T cells upon viral infection, we sequenced polyadenylated RNA isolated from Jurkat cells infected or not with HIV-1. We found a marked global alteration of gene expression following infection, with an overall trend toward induction of genes, indicating widespread modification of the host biology. Annotation and pathway analysis of the most deregulated genes showed that viral infection produces a down-regulation of genes associated with the nucleolus, in particular those implicated in regulating the different steps of ribosome biogenesis, such as ribosomal RNA (rRNA) transcription, pre-rRNA processing, and ribosome maturation. The impact of HIV-1 infection on genes involved in ribosome biogenesis was further validated in primary CD4+ T cells. Moreover, we provided evidence by Northern Blot experiments, that host pre-rRNA processing in Jurkat cells might be perturbed during HIV-1 infection, thus strengthening the hypothesis of a crosstalk between nucleolar functions and viral pathogenesis.},
  author = {Kleinman, Claudia L and Doria, Margherita and Orecchini, Elisa and Giuliani, Erica and Galardi, Silvia and {De Jay}, Nicolas and Michienzi, Alessandro},
  doi = {10.1371/journal.pone.0113908},
  file = {:Users/ndejay/Papers/Kleinman et al. - 2014 - HIV-1 Infection Causes a Down-Regulation of Genes Involved in Ribosome Biogenesis.pdf:pdf},
  issn = {1932-6203},
  journal = {PloS one},
  month = {jan},
  number = {12},
  pages = {e113908},
  pmid = {25462981},
  title = {{HIV-1 Infection Causes a Down-Regulation of Genes Involved in Ribosome Biogenesis.}},
  url = {http://www.ncbi.nlm.nih.gov/pubmed/25462981},
  volume = {9},
  year = {2014}
}
@article{Fontebasso2014,
  abstract = {Pediatric midline high-grade astrocytomas (mHGAs) are incurable with few treatment targets identified. Most tumors harbor mutations encoding p.Lys27Met in histone H3 variants. In 40 treatment-naive mHGAs, 39 analyzed by whole-exome sequencing, we find additional somatic mutations specific to tumor location. Gain-of-function mutations in ACVR1 occur in tumors of the pons in conjunction with histone H3.1 p.Lys27Met substitution, whereas FGFR1 mutations or fusions occur in thalamic tumors associated with histone H3.3 p.Lys27Met substitution. Hyperactivation of the bone morphogenetic protein (BMP)-ACVR1 developmental pathway in mHGAs harboring ACVR1 mutations led to increased levels of phosphorylated SMAD1, SMAD5 and SMAD8 and upregulation of BMP downstream early-response genes in tumor cells. Global DNA methylation profiles were significantly associated with the p.Lys27Met alteration, regardless of the mutant histone H3 variant and irrespective of tumor location, supporting the role of this substitution in driving the epigenetic phenotype. This work considerably expands the number of potential treatment targets and further justifies pretreatment biopsy in pediatric mHGA as a means to orient therapeutic efforts in this disease.},
  author = {Fontebasso, Adam M and Papillon-Cavanagh, Simon and Schwartzentruber, Jeremy and Nikbakht, Hamid and Gerges, Noha and Fiset, Pierre-Olivier and Bechet, Denise and Faury, Damien and {De Jay}, Nicolas and Ramkissoon, Lori a and Corcoran, Aoife and Jones, David T W and Sturm, Dominik and Johann, Pascal and Tomita, Tadanori and Goldman, Stewart and Nagib, Mahmoud and Bendel, Anne and Goumnerova, Liliana and Bowers, Daniel C and Leonard, Jeffrey R and Rubin, Joshua B and Alden, Tord and Browd, Samuel and Geyer, J Russell and Leary, Sarah and Jallo, George and Cohen, Kenneth and Gupta, Nalin and Prados, Michael D and Carret, Anne-Sophie and Ellezam, Benjamin and Crevier, Louis and Klekner, Almos and Bognar, Laszlo and Hauser, Peter and Garami, Miklos and Myseros, John and Dong, Zhifeng and Siegel, Peter M and Malkin, Hayley and Ligon, Azra H and Albrecht, Steffen and Pfister, Stefan M and Ligon, Keith L and Majewski, Jacek and Jabado, Nada and Kieran, Mark W},
  doi = {10.1038/ng.2950},
  file = {:Users/ndejay/Papers/Fontebasso et al. - 2014 - Recurrent somatic mutations in ACVR1 in pediatric midline high-grade astrocytoma.pdf:pdf},
  issn = {1546-1718},
  journal = {Nature genetics},
  month = {may},
  number = {5},
  pages = {462--6},
  pmid = {24705250},
  publisher = {Nature Publishing Group},
  title = {{Recurrent somatic mutations in ACVR1 in pediatric midline high-grade astrocytoma.}},
  url = {http://www.ncbi.nlm.nih.gov/pubmed/24705250},
  volume = {46},
  year = {2014}
}
@article{Tarca2013,
  abstract = {MOTIVATION: After more than a decade since microarrays were used to predict phenotype of biological samples, real-life applications for disease screening and identification of patients who would best benefit from treatment are still emerging. The interest of the scientific community in identifying best approaches to develop such prediction models was reaffirmed in a competition style international collaboration called IMPROVER Diagnostic Signature Challenge whose results we describe herein. RESULTS: Fifty-four teams used public data to develop prediction models in four disease areas including multiple sclerosis, lung cancer, psoriasis and chronic obstructive pulmonary disease, and made predictions on blinded new data that we generated. Teams were scored using three metrics that captured various aspects of the quality of predictions, and best performers were awarded. This article presents the challenge results and introduces to the community the approaches of the best overall three performers, as well as an R package that implements the approach of the best overall team. The analyses of model performance data submitted in the challenge as well as additional simulations that we have performed revealed that (i) the quality of predictions depends more on the disease endpoint than on the particular approaches used in the challenge; (ii) the most important modeling factor (e.g. data preprocessing, feature selection and classifier type) is problem dependent; and (iii) for optimal results datasets and methods have to be carefully matched. Biomedical factors such as the disease severity and confidence in diagnostic were found to be associated with the misclassification rates across the different teams. AVAILABILITY: The lung cancer dataset is available from Gene Expression Omnibus (accession, GSE43580). The maPredictDSC R package implementing the approach of the best overall team is available at www.bioconductor.org or http://bioinformaticsprb.med.wayne.edu/.},
  author = {Tarca, Adi L and Lauria, Mario and Unger, Michael and Bilal, Erhan and Boue, Stephanie and {Kumar Dey}, Kushal and Hoeng, Julia and Koeppl, Heinz and Martin, Florian and Meyer, Pablo and Nandy, Preetam and Norel, Raquel and Peitsch, Manuel and Rice, Jeremy J and Romero, Roberto and Stolovitzky, Gustavo and Talikka, Marja and Xiang, Yang and Zechner, Christoph},
  doi = {10.1093/bioinformatics/btt492},
  file = {:Users/ndejay/Papers/Tarca et al. - 2013 - Strengths and limitations of microarray-based phenotype prediction lessons learned from the IMPROVER Diagnostic Si.pdf:pdf},
  issn = {1367-4811},
  journal = {Bioinformatics},
  month = {nov},
  number = {22},
  pages = {2892--9},
  pmid = {23966112},
  title = {{Strengths and limitations of microarray-based phenotype prediction: lessons learned from the IMPROVER Diagnostic Signature Challenge.}},
  url = {http://www.ncbi.nlm.nih.gov/pubmed/23966112},
  volume = {29},
  year = {2013}
}
@article{DeJay2013,
  abstract = {MOTIVATION: Feature selection is one of the main challenges in analyzing high-throughput genomic data. Minimum redundancy maximum relevance (mRMR) is a particularly fast feature selection method for finding a set of both relevant and complementary features. Here we describe the mRMRe R package, in which the mRMR technique is extended by using an ensemble approach to better explore the feature space and build more robust predictors. To deal with the computational complexity of the ensemble approach, the main functions of the package are implemented and parallelized in C using the openMP Application Programming Interface. RESULTS: Our ensemble mRMR implementations outperform the classical mRMR approach in terms of prediction accuracy. They identify genes more relevant to the biological context and may lead to richer biological interpretations. The parallelized functions included in the package show significant gains in terms of run-time speed when compared with previously released packages. AVAILABILITY: The R package mRMRe is available on Comprehensive R Archive Network and is provided open source under the Artistic-2.0 License. The code used to generate all the results reported in this application note is available from Supplementary File 1. CONTACT: bhaibeka@ircm.qc.ca SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.},
  author = {{De Jay}, Nicolas and Papillon-Cavanagh, Simon and Olsen, Catharina and El-Hachem, Nehme and Bontempi, Gianluca and Haibe-Kains, Benjamin},
  doi = {10.1093/bioinformatics/btt383},
  file = {:Users/ndejay/Papers/De Jay et al. - 2013 - mRMRe an R package for parallelized mRMR ensemble feature selection.pdf:pdf},
  issn = {1367-4811},
  journal = {Bioinformatics},
  month = {sep},
  number = {18},
  pages = {2365--8},
  pmid = {23825369},
  title = {{mRMRe: an R package for parallelized mRMR ensemble feature selection.}},
  url = {http://www.ncbi.nlm.nih.gov/pubmed/23825369},
  volume = {29},
  year = {2013}
}
@article{Papillon-Cavanagh2013,
  abstract = {BACKGROUND: An enduring challenge in personalized medicine lies in selecting the right drug for each individual patient. While testing of drugs on patients in large trials is the only way to assess their clinical efficacy and toxicity, we dramatically lack resources to test the hundreds of drugs currently under development. Therefore the use of preclinical model systems has been intensively investigated as this approach enables response to hundreds of drugs to be tested in multiple cell lines in parallel. METHODS: Two large-scale pharmacogenomic studies recently screened multiple anticancer drugs on over 1000 cell lines. We propose to combine these datasets to build and robustly validate genomic predictors of drug response. We compared five different approaches for building predictors of increasing complexity. We assessed their performance in cross-validation and in two large validation sets, one containing the same cell lines present in the training set and another dataset composed of cell lines that have never been used during the training phase. RESULTS: Sixteen drugs were found in common between the datasets. We were able to validate multivariate predictors for three out of the 16 tested drugs, namely irinotecan, PD-0325901, and PLX4720. Moreover, we observed that response to 17-AAG, an inhibitor of Hsp90, could be efficiently predicted by the expression level of a single gene, NQO1. CONCLUSION: These results suggest that genomic predictors could be robustly validated for specific drugs. If successfully validated in patients' tumor cells, and subsequently in clinical trials, they could act as companion tests for the corresponding drugs and play an important role in personalized medicine.},
  author = {Papillon-Cavanagh, Simon and {De Jay}, Nicolas and Hachem, Nehme and Olsen, Catharina and Bontempi, Gianluca and Aerts, Hugo J W L and Quackenbush, John and Haibe-Kains, Benjamin},
  doi = {10.1136/amiajnl-2012-001442},
  file = {:Users/ndejay/Papers/Papillon-Cavanagh et al. - 2013 - Comparison and validation of genomic predictors for anticancer drug sensitivity.pdf:pdf},
  issn = {1527-974X},
  journal = {Journal of the American Medical Informatics Association : JAMIA},
  keywords = {Antineoplastic Agents,Antineoplastic Agents: therapeutic use,Cell Line,Computational Biology,Databases,Genetic,Humans,Individualized Medicine,Models,Neoplasms,Neoplasms: drug therapy,Neoplasms: genetics,Pharmacogenetics,Transcriptome,Tumor},
  month = {jan},
  number = {4},
  pages = {597--602},
  pmid = {23355484},
  title = {{Comparison and validation of genomic predictors for anticancer drug sensitivity.}},
  url = {http://www.ncbi.nlm.nih.gov/pubmed/23355484 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3721163{\&}tool=pmcentrez{\&}rendertype=abstract},
  volume = {20},
  year = {2013}
}