Genome‐wide investigation of intragenic DNA methylation identifies ZMIZ1 gene as a prognostic marker in glioblastoma and multiple cancer types

2019 ◽  
Vol 145 (12) ◽  
pp. 3425-3435 ◽  
Author(s):  
Dimitrios Mathios ◽  
Taeyoung Hwang ◽  
Yuanxuan Xia ◽  
Jillian Phallen ◽  
Yuan Rui ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Prognostic Marker ◽  
Multiple Cancer ◽  
Genome Wide ◽  
Cancer Types

scholarly journals Systematic identification of non-coding somatic single nucleotide variants associated with altered transcription and DNA methylation in adult and pediatric cancers

NAR Cancer ◽  
2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Fengju Chen ◽  
Yiqun Zhang ◽  
Chad J Creighton
Keyword(s):  
Dna Methylation ◽  
Brain Tumor ◽  
Low Grade ◽  
Single Nucleotide Variants ◽  
Multiple Cancer ◽  
Single Nucleotide ◽  
Altered Expression ◽  
Wide Range ◽  
Mutational Hotspots ◽  
Cancer Types

Abstract Whole-genome sequencing combined with transcriptomics can reveal impactful non-coding single nucleotide variants (SNVs) in cancer. Here, we developed an integrative analytical approach that, as a first step, identifies genes altered in expression or DNA methylation in association with nearby somatic SNVs, in contrast to alternative approaches that first identify mutational hotspots. Using genomic datasets from the Pan-Cancer Analysis of Whole Genomes (PCAWG) consortium and the Children's Brain Tumor Tissue Consortium (CBTTC), we identified hundreds of genes and associated CpG islands for which the nearby presence of a non-coding somatic SNV recurrently associated with altered expression or DNA methylation, respectively. Genomic regions upstream or downstream of genes, gene introns and gene untranslated regions were all involved. The PCAWG adult cancer cohort yielded different significant SNV-expression associations from the CBTTC pediatric brain tumor cohort. The SNV-expression associations involved a wide range of cancer types and histologies, as well as potential gain or loss of transcription factor binding sites. Notable genes with SNV-associated increased expression include TERT, COPS3, POLE2 and HDAC2—involving multiple cancer types—MYC, BCL2, PIM1 and IGLL5—involving lymphomas—and CYHR1—involving pediatric low-grade gliomas. Non-coding somatic SNVs show a major role in shaping the cancer transcriptome, not limited to mutational hotspots.

scholarly journals Pan-cancer subtyping in a 2D-map shows substructures that are driven by specific combinations of molecular characteristics

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Erdogan Taskesen ◽  
Sjoerd M. H. Huisman ◽  
Ahmed Mahfouz ◽  
Jesse H. Krijthe ◽  
Jeroen de Ridder ◽  
...  
Keyword(s):  
Therapy Response ◽  
Visual Exploration ◽  
Molecular Characteristics ◽  
Cancer Type ◽  
Breast Cancers ◽  
Data Types ◽  
Multiple Cancer ◽  
Genome Wide ◽  
Genome Wide Data ◽  
Cancer Types

Abstract The use of genome-wide data in cancer research, for the identification of groups of patients with similar molecular characteristics, has become a standard approach for applications in therapy-response, prognosis-prediction, and drug-development. To progress in these applications, the trend is to move from single genome-wide measurements in a single cancer-type towards measuring several different molecular characteristics across multiple cancer-types. Although current approaches shed light on molecular characteristics of various cancer-types, detailed relationships between patients within cancer clusters are unclear. We propose a novel multi-omic integration approach that exploits the joint behavior of the different molecular characteristics, supports visual exploration of the data by a two-dimensional landscape, and inspection of the contribution of the different genome-wide data-types. We integrated 4,434 samples across 19 cancer-types, derived from TCGA, containing gene expression, DNA-methylation, copy-number variation and microRNA expression data. Cluster analysis revealed 18 clusters, where three clusters showed a complex collection of cancer-types, squamous-cell-carcinoma, colorectal cancers, and a novel grouping of kidney-cancers. Sixty-four samples were identified outside their tissue-of-origin cluster. Known and novel patient subgroups were detected for Acute Myeloid Leukemia’s, and breast cancers. Quantification of the contributions of the different molecular types showed that substructures are driven by specific (combinations of) molecular characteristics.

Genome-wide cell-free DNA (cfDNA) methylation signatures and effect on tissue of origin (TOO) performance.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 3049-3049 ◽  
Author(s):  
Minetta C. Liu ◽  
Arash Jamshidi ◽  
Oliver Venn ◽  
Alexander P. Fields ◽  
M. Cyrus Maher ◽  
...  
Keyword(s):  
Cancer Detection ◽  
Early Stage ◽  
Stage Iv ◽  
Stage I ◽  
Multiple Cancer ◽  
Isolated Cells ◽  
Genome Wide ◽  
Ffpe Tumor ◽  
Formalin Fixed Paraffin Embedded ◽  
Cancer Types

3049 Background: For multi-cancer detection using cfDNA, TOO determination is critical to enable safe and efficient diagnostic follow-up. Previous array-based studies captured < 2% of genomic CpGs. Here, we report genome-wide fragment-level methylation patterns across 811 cancer cell methylomes representing 21 tumor types (97% of SEER cancer incidence), and define effects of this methylation database on TOO prediction within a machine learning framework. Methods: Genomic DNA from 655 formalin-fixed paraffin-embedded (FFPE) tumor tissues and 156 isolated cells from tumors was subjected to a prototype 30x whole-genome bisulfite sequencing (WGBS) assay, as previously reported in the Circulating Cell-free Genome Atlas (CCGA) study (NCT02889978). Two independent TOO models, one with and one without the methylation database, were fitted on training samples; each was used to predict on the test set. A WGBS classifier was used to detect cancer at 98% specificity; reported TOO results reflect percent agreement between predicted and true TOO among those detected cancers (166 cases: 81 stage I-III, 69 stage IV, 16 non-informative). Results: Genome-wide methylation data generated from this database allowed fragment-level analysis and coverage of ~30 million CpGs across the genome (~60-fold greater than array-based approaches). Incorrect TOO assignments decreased by 35% (20% to 13%) after incorporating methylation database information into TOO classification. Improvement was observed across all cancer types and was consistent in early-stage cancers (stage I-III). Respective performances in breast cancer (n = 23) were 87% vs 96%; in lung cancer (n = 32) were 85% vs 88%; in hepatobiliary (n = 10) were 70% vs 90%; and in pancreatic cancer (n = 17) were 94% vs 100%. Results using an optimized approach informed by these results in a large cohort of CCGA participants will be reported. Conclusions: Incorporating data from a large methylation database improved TOO performance in multiple cancer types. This supports feasibility of this methylation-based approach as an early cancer detection test across cancer types. Clinical trial information: NCT02889978.

scholarly journals Diagnostic classification based on DNA methylation profiles using sequential machine learning approaches

2021 ◽  
Author(s):  
M. W. Wojewodzic ◽  
J. P. Lavender
Keyword(s):  
Machine Learning ◽  
Dna Methylation ◽  
Machine Learning Algorithms ◽  
Aberrant Methylation ◽  
Learning Approaches ◽  
Tissue Samples ◽  
Non Invasive ◽  
Genome Wide ◽  
Cancer Types ◽  
Methylation Patterns

AbstractAberrant methylation patterns in human DNA have great potential for the discovery of novel diagnostic and disease progression biomarkers. In this paper, we used machine learning algorithms to identify promising methylation sites for diagnosing cancerous tissue and to classify patients based on methylation values at these sites.We used genome-wide DNA methylation patterns from both cancerous and normal tissue samples, obtained from the Genomic Data Commons consortium and trialled our methods on three types of urological cancer. A decision tree was used to identify the methylation sites most useful for diagnosis.The identified locations were then used to train a neural network to classify samples as either cancerous or non-cancerous. Using this two-step approach we found strong indicative biomarker panels for each of the three cancer types.These methods could likely be translated to other cancers and improved by using non-invasive liquid methods such as blood instead of biopsy tissue.

scholarly journals Significant associations between driver gene mutations and DNA methylation alterations across many cancer types

2017 ◽  
Author(s):  
Yun-Ching Chen ◽  
Valer Gotea ◽  
Gennady Margolin ◽  
Laura Elnitski
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Thyroid Cancer ◽  
Gene Mutations ◽  
Driver Gene ◽  
Driver Genes ◽  
Cancer Subtypes ◽  
Genome Wide ◽  
Cancer Types ◽  
Methylation Patterns

AbstractRecent evidence shows that mutations in several driver genes can cause aberrant methylation patterns, a hallmark of cancer. In light of these findings, we hypothesized that the landscapes of tumor genomes and epigenomes are tightly interconnected. We measured this relationship using principal component analyses and methylation-mutation associations applied at the nucleotide level and with respect to genome-wide trends. We found a few mutated driver genes were associated with genome-wide patterns of aberrant hypomethylation or CpG island hypermethylation in specific cancer types. We identified associations between 737 mutated driver genes and site-specific methylation changes. Moreover, using these mutation-methylation associations, we were able to distinguish between two uterine and two thyroid cancer subtypes. The driver gene mutation-associated methylation differences between the thyroid cancer subtypes were linked to differential gene expression in JAK-STAT signaling, NADPH oxidation, and other cancer-related pathways. These results establish that driver-gene mutations are associated with methylation alterations capable of shaping regulatory network functions. In addition, the methodology presented here can be used to subdivide tumors into more homogeneous subsets corresponding to their underlying molecular characteristics, which could improve treatment efficacy.Author summaryMutations that alter the function of driver genes by changing DNA nucleotides have been recognized as a key player in cancer progression. Recent evidence showed that DNA methylation, a molecular signature that is used for controlling gene expression and that consists of cytosine residues with attached methyl groups in the context of CG dinucleotides, is also highly dysregulated in cancer and contributes to carcinogenesis. However, whether those methylation alterations correspond to mutated driver genes in cancer remains unclear. In this study, we analyzed 4,302 tumors from 18 cancer types and demonstrated that driver gene mutations are inherently connected with the aberrant DNA methylation landscape in cancer. We showed that those driver gene-associated methylation patterns can classify heterogeneous tumors in a cancer type into homogeneous subtypes and have the potential to influence the genes that contribute to tumor growth. This finding could help us to better understand the fundamental connection between driver gene mutations and DNA methylation alterations in cancer and to further improve the cancer treatment.

Genome-wide DNA methylation profiling of gastrointestinal neuroendocrine tumors to identify hypermethylation of mTOR, Wnt, and Notch pathways in GI NET pathogenesis.

2014 ◽  
Vol 32 (3_suppl) ◽  
pp. 212-212
Author(s):  
Anna Karpathakis ◽  
Harpreet Dibra ◽  
Tiffany Morris ◽  
Dahmane Oukrife ◽  
Christodoulos P Pipinikas ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Normal Tissue ◽  
Hepatic Metastases ◽  
Promoter Hypermethylation ◽  
Integrated Analysis ◽  
Metastatic Progression ◽  
Multiple Cancer ◽  
Genome Wide ◽  
Validation Set ◽  
Notch Pathways

212 Background: Aberrant DNA methylation is known to play an important role in the pathogenesis of many human cancers, however little is known about its role in gastrointestinal neuroendocrine tumours (GI NET) development. We report the first unbiased genome-wide DNA methylation analysis of a large cohort of GI NETs, aiming to identify key methylation variable positions (MVPs) specific to GI NETs which may contribute to tumorigenesis and metastatic progression. Methods: Illumina Infinium Human Methylation 450 Array analysis was performed on 56 cases of GI NET DNA extracted from macrodissected tumour (n=67) and normal (n=29) specimens. Tumours were gastrointestinal primaries (n=39) or metastases (liver, mesenteric, omental or lymph node, n=28) of low (n=35), intermediate (n=17) or high grade (n=3)(unknown grade n=12). Data analysis was performed using the "ChAMP" custom pipeline and pathway analyses were performed using "GREAT," "WebGestalt," and "GSEA" web tools. A Bonferroni adjusted significance threshold value of p<0.05 was used throughout. Results: In order to identify and validate a GI NET specific methylation signature our cohort was divided into a discovery set (31 cases) and validation set (25 cases). Comparison of primary GI NET tumours with normal small bowel identified a total of 77,916 MVPs, including 1,666 sites hypermethylated by over 30% in tumour compared to normal tissue. Application of the profile to the validation set correctly identified 85% of samples. Tumours demonstrated global hypomethylation relative to normal tissue. Gene ontology analysis identified methylation of multiple cancer related pathways (including the Wnt, mTOR and Notch pathways) as a feature of hepatic metastases of GI NET primaries. Increasing RASSF1 promoter hypermethylation was associated with higher tumour grade. Conclusions: This study is the first comprehensive analysis of the epigenetic profile of GI NETs and identifies potential novel biomarkers and therapeutic targets. We are currently performing integrated analysis of epigenomic, genomic and transcriptomic data to further define the pathways involved in GI NET pathogenesis.

scholarly journals ncRNA-eQTL: a database to systematically evaluate the effects of SNPs on non-coding RNA expression across cancer types

2019 ◽  
Vol 48 (D1) ◽  
pp. D956-D963 ◽  
Author(s):  
Jiang Li ◽  
Yawen Xue ◽  
Muhammad Talal Amin ◽  
Yanbo Yang ◽  
Jiajun Yang ◽  
...  
Keyword(s):  
Genome Wide Association Study ◽  
Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Eqtl Analysis ◽  
Nucleotide Polymorphisms ◽  
Survival Times ◽  
Multiple Cancer ◽  
Risk Alleles ◽  
Genome Wide ◽  
Cancer Types

Abstract Numerous studies indicate that non-coding RNAs (ncRNAs) have critical functions across biological processes, and single-nucleotide polymorphisms (SNPs) could contribute to diseases or traits through influencing ncRNA expression. However, the associations between SNPs and ncRNA expression are largely unknown. Therefore, genome-wide expression quantitative trait loci (eQTL) analysis to assess the effects of SNPs on ncRNA expression, especially in multiple cancer types, will help to understand how risk alleles contribute toward tumorigenesis and cancer development. Using genotype data and expression profiles of ncRNAs of &gt;8700 samples from The Cancer Genome Atlas (TCGA), we developed a computational pipeline to systematically identify ncRNA-related eQTLs (ncRNA-eQTLs) across 33 cancer types. We identified a total of 6 133 278 and 721 122 eQTL-ncRNA pairs in cis-eQTL and trans-eQTL analyses, respectively. Further survival analyses identified 8312 eQTLs associated with patient survival times. Furthermore, we linked ncRNA-eQTLs to genome-wide association study (GWAS) data and found 262 332 ncRNA-eQTLs overlapping with known disease- and trait-associated loci. Finally, a user-friendly database, ncRNA-eQTL (http://ibi.hzau.edu.cn/ncRNA-eQTL), was developed for free searching, browsing and downloading of all ncRNA-eQTLs. We anticipate that such an integrative and comprehensive resource will improve our understanding of the mechanistic basis of human complex phenotypic variation, especially for ncRNA- and cancer-related studies.

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