Biology subjects

Lupien, M.

Publications and source records attributed to Lupien, M..

24 records · Page 2Linked to original sources

bioRxiv2018

SMuRF: a novel tool to identify genomic regions enriched for somatic point mutations

MotivationSingle Nucleotide Variants (SNVs), including somatic point mutations and Single Nucleotide Polymorphisms (SNPs), in noncoding cis-regulatory elements (CREs) can affect gene regulation and lead to disease development (Zhou et al., 2016; Zhang et al., 2014). Others have previously developed methods to identify important clusters of somatic point mutations based on proximity (Weinhold et al., 2014) or the enrichment of inherited risk-SNPs at CREs (Ahmed et al., 2017). Here, we present SMuRF (Significantly Mutated Region Finder), a user-friendly command-line tool to identify these significantly mutated regions from user-defined genomic intervals and SNVs.\n\nResultsSMuRF identified 72 significantly mutated CREs in liver cancer, including known mutated gene promoters as well as previously unreported regions.\n\nAvailabilityThe source code for SMuRF is open-source and freely available on GitHub (https://github.com/LupienLabOrganization/SMuRF) under the GNU GPLv3 license. SMuRF is implemented in Bash and R; it runs on any platform with Bash ([≥]4.1.2), R ([≥]3.3.0) and BEDTools ([≥]2.26.0). It requires the following R packages: GenomicRanges, gtools, gplots, ggplot2, data.table, psych, and dplyr.\n\nSupplementary InformationSupplementary information available at Bioinformatics online.\n\nContactpaul.guilhamon@uhnresearch.ca; mlupien@uhnres.utoronto.ca

bioinformatics↗

bioRxiv2017

Candidate cancer driver mutations in super-enhancers and long-range chromatin interaction networks

A comprehensive catalogue of the mutations that drive tumorigenesis and progression is essential to understanding tumor biology and developing therapies. Protein-coding driver mutations have been well-characterized by large exome-sequencing studies, however many tumors have no mutations in protein-coding driver genes. Non-coding mutations are thought to explain many of these cases, however few non-coding drivers besides TERT promoter are known. To fill this gap, we analyzed 150,000 cis-regulatory regions in 1,844 whole cancer genomes from the ICGC-TCGA PCAWG project. Using our new method, ActiveDriverWGS, we found 41 frequently mutated regulatory elements (FMREs) enriched in non-coding SNVs and indels (FDR<0.05) characterized by aging-associated mutation signatures and frequent structural variants. Most FMREs are distal from genes, reported here for the first time and also recovered by additional driver discovery methods. FMREs were enriched in super-enhancers, H3K27ac enhancer marks of primary tumors and long-range chromatin interactions, suggesting that the mutations drive cancer by distally controlling gene expression through threedimensional genome organization. In support of this hypothesis, the chromatin interaction network of FMREs and target genes revealed associations of mutations and differential gene expression of known and novel cancer genes (e.g., CNNB1IP1, RCC1), activation of immune response pathways and altered enhancer marks. Thus distal genomic regions may include additional, infrequently mutated drivers that act on target genes via chromatin loops. Our study is an important step towards finding such regulatory regions and deciphering the somatic mutation landscape of the non-coding genome.

cancer biology↗

bioRxiv2017

CREAM: Clustering of genomic REgions Analysis Method

Cellular identity relies on cell type-specific gene expression profiles controlled by cis-regulatory elements (CREs), such as promoters, enhancers and anchors of chromatin interactions. CREs are unevenly distributed across the genome, giving rise to distinct subsets such as individual CREs and Clusters Of cis-Regulatory Elements (COREs), also known as super-enhancers. Identifying COREs is a challenge due to technical and biological features that entail variability in the distribution of distances between CREs within a given dataset. To address this issue, we developed a new unsupervised machine learning approach termed Clustering of genomic REgions Analysis Method (CREAM) that outperforms the Ranking Of Super Enhancer (ROSE) approach. Specifically CREAM identified COREs are enriched in CREs strongly bound by master transcription factors according to ChIP-seq signal intensity, are proximal to highly expressed genes, are preferentially found near genes essential for cell growth and are more predictive of cell identity. Moreover, we show that CREAM enables subtyping primary prostate tumor samples according to their CORE distribution across the genome. We further show that COREs are enriched compared to individual CREs at TAD boundaries and these are preferentially bound by CTCF and factors of the cohesin complex (e.g.: RAD21 and SMC3). Finally, using CREAM against transcription factor ChIP-seq reveals CTCF and cohesin-specific COREs preferentially at TAD boundaries compared to intra-TADs. CREAM is available as an open source R package (https://CRAN.R-project.org/package=CREAM) to identify COREs from cis-regulatory annotation datasets from any biological samples.

bioinformatics↗

bioRxiv2017

C3D: A tool to predict 3D genomic interactions between cis-regulatory elements

MotivationThe 3D genome architecture influences the regulation of genes by facilitating chromatin interactions between distal cis-regulatory elements and gene promoters. We implement Cross Cell-type Correlation based on DNA accessibility (C3D), a highly customizable computational tool that predicts chromatin interactions using an unsupervised algorithm that utilizes correlations in chromatin measurements, such as DNaseI hypersensitivity signals.\n\nResultsC3D accurately predicts 32.7%, 18.3% and 24.1% of interactions, validated by ChIA-PET assays, between promoters and distal regions that overlie DNaseI hypersensitive sites in K562, MCF-7 and GM12878 cells, respectively.\n\nAvailabilitySource code is open-source and freely available on GitHub (https://github.com/LupienLabOrganization/C3D) under the GNU GPLv3 license. C3D is implemented in Bash and R; it runs on any platform with Bash ([≥]4.0), R ([≥]3.1.1) and BEDTools ([≥]2.19.0). It requires the following R packages: GenomicRanges, Sushi, data.table, preprocessCore and dynamicTreeCut.

bioinformatics↗

bioRxiv2017

Gene isoforms as expression-based biomarkers predictive of drug response in vitro

BackgroundOne of the main challenges in precision medicine is the identification of molecular features associated to drug response to provide clinicians with tools to select the best therapy for each individual cancer patient. The recent adoption of next-generation sequencing technologies enables accurate profiling of not only gene expression but also alternatively-spliced transcripts in large-scale pharmacogenomic studies. Given that altered mRNA splicing has been shown to be prominent in cancers, linking this feature to drug response will open new avenues of research in biomarker discovery.\n\nMethodsTo address the lack of reproducibility of drug sensitivity measurements across studies, we developed a meta-analytical framework combining the pharmacological data generated within the Cancer Cell Line Encyclopedia (CCLE) and the Genomics of Drug Sensitivity in Cancer (GDSC). Predictive models are fitted with CCLE RNA-seq data as predictor variables, controlled for tissue type, and combined GDSC and CCLE drug sensitivity values as dependent variables.\n\nResultsWe first validated the biomarkers identified from GDSC and CCLE using an existing pharmacogenomic dataset of 70 breast cancer cell lines. We further selected four drugs with the most promising biomarkers to test whether their predictive value is robust to change in pharmacological assay. We successfully validated 10 isoform-based biomarkers predictive of drug response in breast cancer, including TGFA-001 for the MEK tyrosine kinase inhibitor (TKI) AZD6244, DUOX-001 for the EGFR inhibitor erlotinib, and CPEB4-001 transcript expression associated with lack of sensitivity to paclitaxel.\n\nConclusionThe results of our meta-analysis of pharmacogenomic data suggest that isoforms represent a rich resource for biomarkers predictive of response to chemo- and targeted therapies. Our study also showed that the validation rate for this type of biomarkers is low (<50%) for most drugs, supporting the requirements for independent datasets to identify reproducible predictors of response to anticancer drugs.

bioinformatics↗

bioRxiv2016

Convergence of dispersed regulatory mutations predicts driver genes in prostate cancer

Cancer sequencing predicts driver genes using recurrent protein-altering mutations, but detecting recurrence for non-coding mutations remains unsolved. Here, we present a convergence framework for recurrence analysis of non-coding mutations using three-dimensional co-localization of epigenomically-identified regions. We define the regulatory plexus of each gene as its cell-type-specific three-dimensional gene-regulatory neighborhood, inferred using Hi-C chromosomal interactions and chromatin state annotations. Using 16 matched tumor-normal prostate transcriptomes, we predict tumor-upregulated genes, and find enriched plexus mutations in distal regulatory regions normally repressed in prostate, suggesting out-of-context de-repression. Using 55 matched tumor-normal prostate genomes, we predict 15 driver genes by convergence of dispersed, low-frequency mutations into high-frequency dysregulation events along prostate-specific plexi, while controlling for mutational heterogeneity across regions, chromatin states, and patients. These putative drivers play roles in growth signaling, immune evasion, mitochondrial function, and vascularization, suggesting higher-order pathway-level convergence. We experimentally validate the PLCB4 plexus and its ability to affect the canonical PI3K cancer pathway.

genomics↗