58,501 research outputs found
Pancancer analysis of DNA methylation-driven genes using MethylMix.
Aberrant DNA methylation is an important mechanism that contributes to oncogenesis. Yet, few algorithms exist that exploit this vast dataset to identify hypo- and hypermethylated genes in cancer. We developed a novel computational algorithm called MethylMix to identify differentially methylated genes that are also predictive of transcription. We apply MethylMix to 12 individual cancer sites, and additionally combine all cancer sites in a pancancer analysis. We discover pancancer hypo- and hypermethylated genes and identify novel methylation-driven subgroups with clinical implications. MethylMix analysis on combined cancer sites reveals 10 pancancer clusters reflecting new similarities across malignantly transformed tissues
Correlated fragile site expression allows the identification of candidate fragile genes involved in immunity and associated with carcinogenesis
Common fragile sites (cfs) are specific regions in the human genome that are
particularly prone to genomic instability under conditions of replicative
stress. Several investigations support the view that common fragile sites play
a role in carcinogenesis. We discuss a genome-wide approach based on graph
theory and Gene Ontology vocabulary for the functional characterization of
common fragile sites and for the identification of genes that contribute to
tumour cell biology. CFS were assembled in a network based on a simple measure
of correlation among common fragile site patterns of expression. By applying
robust measurements to capture in quantitative terms the non triviality of the
network, we identified several topological features clearly indicating
departure from the Erdos-Renyi random graph model. The most important outcome
was the presence of an unexpected large connected component far below the
percolation threshold. Most of the best characterized common fragile sites
belonged to this connected component. By filtering this connected component
with Gene Ontology, statistically significant shared functional features were
detected. Common fragile sites were found to be enriched for genes associated
to the immune response and to mechanisms involved in tumour progression such as
extracellular space remodeling and angiogenesis. Our results support the
hypothesis that fragile sites serve a function; we propose that fragility is
linked to a coordinated regulation of fragile genes expression.Comment: 18 pages, accepted for publication in BMC Bioinformatic
Comprehensive analysis of normal adjacent to tumor transcriptomes.
Histologically normal tissue adjacent to the tumor (NAT) is commonly used as a control in cancer studies. However, little is known about the transcriptomic profile of NAT, how it is influenced by the tumor, and how the profile compares with non-tumor-bearing tissues. Here, we integrate data from the Genotype-Tissue Expression project and The Cancer Genome Atlas to comprehensively analyze the transcriptomes of healthy, NAT, and tumor tissues in 6506 samples across eight tissues and corresponding tumor types. Our analysis shows that NAT presents a unique intermediate state between healthy and tumor. Differential gene expression and protein-protein interaction analyses reveal altered pathways shared among NATs across tissue types. We characterize a set of 18 genes that are specifically activated in NATs. By applying pathway and tissue composition analyses, we suggest a pan-cancer mechanism of pro-inflammatory signals from the tumor stimulates an inflammatory response in the adjacent endothelium
Network-based stratification of tumor mutations.
Many forms of cancer have multiple subtypes with different causes and clinical outcomes. Somatic tumor genome sequences provide a rich new source of data for uncovering these subtypes but have proven difficult to compare, as two tumors rarely share the same mutations. Here we introduce network-based stratification (NBS), a method to integrate somatic tumor genomes with gene networks. This approach allows for stratification of cancer into informative subtypes by clustering together patients with mutations in similar network regions. We demonstrate NBS in ovarian, uterine and lung cancer cohorts from The Cancer Genome Atlas. For each tissue, NBS identifies subtypes that are predictive of clinical outcomes such as patient survival, response to therapy or tumor histology. We identify network regions characteristic of each subtype and show how mutation-derived subtypes can be used to train an mRNA expression signature, which provides similar information in the absence of DNA sequence
Sparse integrative clustering of multiple omics data sets
High resolution microarrays and second-generation sequencing platforms are
powerful tools to investigate genome-wide alterations in DNA copy number,
methylation and gene expression associated with a disease. An integrated
genomic profiling approach measures multiple omics data types simultaneously in
the same set of biological samples. Such approach renders an integrated data
resolution that would not be available with any single data type. In this
study, we use penalized latent variable regression methods for joint modeling
of multiple omics data types to identify common latent variables that can be
used to cluster patient samples into biologically and clinically relevant
disease subtypes. We consider lasso [J. Roy. Statist. Soc. Ser. B 58 (1996)
267-288], elastic net [J. R. Stat. Soc. Ser. B Stat. Methodol. 67 (2005)
301-320] and fused lasso [J. R. Stat. Soc. Ser. B Stat. Methodol. 67 (2005)
91-108] methods to induce sparsity in the coefficient vectors, revealing
important genomic features that have significant contributions to the latent
variables. An iterative ridge regression is used to compute the sparse
coefficient vectors. In model selection, a uniform design [Monographs on
Statistics and Applied Probability (1994) Chapman & Hall] is used to seek
"experimental" points that scattered uniformly across the search domain for
efficient sampling of tuning parameter combinations. We compared our method to
sparse singular value decomposition (SVD) and penalized Gaussian mixture model
(GMM) using both real and simulated data sets. The proposed method is applied
to integrate genomic, epigenomic and transcriptomic data for subtype analysis
in breast and lung cancer data sets.Comment: Published in at http://dx.doi.org/10.1214/12-AOAS578 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Genome-Wide Associations of Signaling Pathways in Glioblastoma Multiforme
Background: eQTL analysis is a powerful method that allows the identification of causal genomic alterations, providing an explanation of expression changes of single genes. However, genes mediate their biological roles in groups rather than in isolation, prompting us to extend the concept of eQTLs to whole gene pathways. Methods: We combined matched genomic alteration and gene expression data of glioblastoma patients and determined associations between the expression of signaling pathways and genomic copy number alterations with a non-linear machine learning approach. Results: Expectedly, over-expressed pathways were largely associated to tag-loci on chromosomes with signature alterations. Surprisingly, tag-loci that were associated to under-expressed pathways were largely placed on other chromosomes, an observation that held for composite effects between chromosomes as well. Indicating their biological relevance, identified genomic regions were highly enriched with genes having a reported driving role in gliomas. Furthermore, we found pathways that were significantly enriched with such driver genes. Conclusions: Driver genes and their associated pathways may represent a functional core that drive the tumor emergence and govern the signaling apparatus in GBMs. In addition, such associations may be indicative of drug combinations for the treatment of brain tumors that follow similar patterns of common and diverging alterations
Joint co-clustering: co-clustering of genomic and clinical bioimaging data
AbstractFor better understanding the genetic mechanisms underlying clinical observations, and better defining a group of potential candidates for protein-family-inhibiting therapy, it is interesting to determine the correlations between genomic, clinical data and data coming from high resolution and fluorescent microscopy. We introduce a computational method, called joint co-clustering, that can find co-clusters or groups of genes, bioimaging parameters and clinical traits that are believed to be closely related to each other based on the given empirical information. As bioimaging parameters, we quantify the expression of growth factor receptor EGFR/erb-B family in non-small cell lung carcinoma (NSCLC) through a fully-automated computer-aided analysis approach. This immunohistochemical analysis is usually performed by pathologists via visual inspection of tissue samples images. Our fully-automated techniques streamlines this error-prone and time-consuming process, thereby facilitating analysis and diagnosis. Experimental results for several real-life datasets demonstrate the high quantitative precision of our approach. The joint co-clustering method was tested with the receptor EGFR/erb-B family data on non-small cell lung carcinoma (NSCLC) tissue and identified statistically significant co-clusters of genes, receptor protein expression and clinical traits. The validation of our results with the literature suggest that the proposed method can provide biologically meaningful co-clusters of genes and traits and that it is a very promising approach to analyse large-scale biological data and to study multi-factorial genetic pathologies through their genetic alterations
The dynamics of gene expression changes in a mouse model of oral tumorigenesis may help refine prevention and treatment strategies in patients with oral cancer.
A better understanding of the dynamics of molecular changes occurring during the early stages of oral tumorigenesis may help refine prevention and treatment strategies. We generated genome-wide expression profiles of microdissected normal mucosa, hyperplasia, dysplasia and tumors derived from the 4-NQO mouse model of oral tumorigenesis. Genes differentially expressed between tumor and normal mucosa defined the "tumor gene set" (TGS), including 4 non-overlapping gene subsets that characterize the dynamics of gene expression changes through different stages of disease progression. The majority of gene expression changes occurred early or progressively. The relevance of these mouse gene sets to human disease was tested in multiple datasets including the TCGA and the Genomics of Drug Sensitivity in Cancer project. The TGS was able to discriminate oral squamous cell carcinoma (OSCC) from normal oral mucosa in 3 independent datasets. The OSCC samples enriched in the mouse TGS displayed high frequency of CASP8 mutations, 11q13.3 amplifications and low frequency of PIK3CA mutations. Early changes observed in the 4-NQO model were associated with a trend toward a shorter oral cancer-free survival in patients with oral preneoplasia that was not seen in multivariate analysis. Progressive changes observed in the 4-NQO model were associated with an increased sensitivity to 4 different MEK inhibitors in a panel of 51 squamous cell carcinoma cell lines of the areodigestive tract. In conclusion, the dynamics of molecular changes in the 4-NQO model reveal that MEK inhibition may be relevant to prevention and treatment of a specific molecularly-defined subgroup of OSCC
Characterization of the ZFX family of transcription factors that bind downstream of the start site of CpG island promoters
Our study focuses on a family of ubiquitously expressed human C₂H₂ zinc finger proteins comprised of ZFX, ZFY and ZNF711. Although their protein structure suggests that ZFX, ZFY and ZNF711 are transcriptional regulators, the mechanisms by which they influence transcription have not yet been elucidated. We used CRISPR-mediated deletion to create bi-allelic knockouts of ZFX and/or ZNF711 in female HEK293T cells (which naturally lack ZFY). We found that loss of either ZFX or ZNF711 reduced cell growth and that the double knockout cells have major defects in proliferation. RNA-seq analysis revealed that thousands of genes showed altered expression in the double knockout clones, suggesting that these TFs are critical regulators of the transcriptome. To gain insight into how these TFs regulate transcription, we created mutant ZFX proteins and analyzed them for DNA binding and transactivation capability. We found that zinc fingers 11–13 are necessary and sufficient for DNA binding and, in combination with the N terminal region, constitute a functional transactivator. Our functional analyses of the ZFX family provides important new insights into transcriptional regulation in human cells by members of the large, but under-studied family of C₂H₂ zinc finger proteins
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Clinical metagenomics.
Clinical metagenomic next-generation sequencing (mNGS), the comprehensive analysis of microbial and host genetic material (DNA and RNA) in samples from patients, is rapidly moving from research to clinical laboratories. This emerging approach is changing how physicians diagnose and treat infectious disease, with applications spanning a wide range of areas, including antimicrobial resistance, the microbiome, human host gene expression (transcriptomics) and oncology. Here, we focus on the challenges of implementing mNGS in the clinical laboratory and address potential solutions for maximizing its impact on patient care and public health
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