scholarly journals ATAC-seq identifies thousands of extrachromosomal circular DNA in cancer and cell lines

2020 ◽  
Vol 6 (20) ◽  
pp. eaba2489 ◽  
Author(s):  
Pankaj Kumar ◽  
Shashi Kiran ◽  
Shekhar Saha ◽  
Zhangli Su ◽  
Teressa Paulsen ◽  
...  
Keyword(s):  
Cell Lines ◽  
Inverse Pcr ◽  
Driver Genes ◽  
Circular Dna ◽  
Cancer Driver ◽  
Genome Wide ◽  
Number Variation ◽  
Circular Dnas ◽  
Tumor Types ◽  
Pan Cancer

Extrachromosomal circular DNAs (eccDNAs) are somatically mosaic and contribute to intercellular heterogeneity in normal and tumor cells. Because short eccDNAs are poorly chromatinized, we hypothesized that they are sequenced by tagmentation in ATAC-seq experiments without any enrichment of circular DNA. Indeed, ATAC-seq identified thousands of eccDNAs in cell lines that were validated by inverse PCR and by metaphase FISH. ATAC-seq in gliomas and glioblastomas identify hundreds of eccDNAs, including one containing the well-known EGFR gene amplicon from chr7. More than 18,000 eccDNAs, many carrying known cancer driver genes, are identified in a pan-cancer analysis of ATAC-seq libraries from 23 tumor types. Somatically mosaic eccDNAs are identified by ATAC-seq even before amplification is recognized by genome-wide copy number variation measurements. Thus, ATAC-seq is a sensitive method to detect eccDNA present in a tumor at the pre-amplification stage and can be used to predict resistance to therapy.

scholarly journals ATAC-seq identifies thousands of extrachromosomal circular DNA in cancers and cell lines

2019 ◽  
Author(s):  
Pankaj Kumar ◽  
Shashi Kiran ◽  
Shekhar Saha ◽  
Zhangli Su ◽  
Teressa Paulsen ◽  
...  
Keyword(s):  
Tumor Cells ◽  
Cell Lines ◽  
High Throughput Sequencing ◽  
Rolling Circle Amplification ◽  
Inverse Pcr ◽  
Open Chromatin ◽  
Lower Grade ◽  
Circular Dna ◽  
Rolling Circle ◽  
Circular Dnas

AbstractExtrachromosomal circular DNAs (eccDNAs) lack centromeres and are not passed equally into daughter cells and thus provide a source of cell-cell heterogeneity in normal and tumor cells. Previously we and other groups purified circular DNA and linearized them by rolling circle amplification for paired end high throughput sequencing to identify eccDNA in cells and tissues. We hypothesized that many eccDNA will have more open chromatin and so will be susceptible to linearization and sequencing by tagmentation. Indeed, we find that ATAC-seq on cell lines and cancers, without any enrichment of circular DNA, identifies thousands of eccDNAs. The identified eccDNAs in cell lines were validated by inverse PCR on DNA that survives exonuclease digestion of linear DNA and by metaphase FISH. We demonstrate that ATAC-seq data generated in Lower Grade Gliomas (LGG) and Glioblastomas (GBM) identify eccDNAs, including one containing the well-known EGFR gene amplicon from chr7. Many of the eccDNAs are identified even before amplification of the locus is detected by genotyping arrays. Thus, standard ATAC-seq is a sensitive method to detect segments of DNA that are present as eccDNA in a subset of the tumor cells, ready to be further amplified under appropriate selection, as during therapy.

scholarly journals Erratum: Comprehensive identification of mutational cancer driver genes across 12 tumor types

2013 ◽  
Vol 3 (1) ◽  
Author(s):  
David Tamborero ◽  
Abel Gonzalez-Perez ◽  
Christian Perez-Llamas ◽  
Jordi Deu-Pons ◽  
Cyriac Kandoth ◽  
...  
Keyword(s):  
Driver Genes ◽  
Cancer Driver ◽  
Cancer Driver Genes ◽  
Tumor Types

A pan-cancer analysis of progression mechanisms and drug sensitivity in cancer cell lines

2019 ◽  
Vol 15 (6) ◽  
pp. 399-405 ◽  
Author(s):  
Julia L. Fleck ◽  
Ana B. Pavel ◽  
Christos G. Cassandras
Keyword(s):  
Cell Lines ◽  
Cancer Cell ◽  
Late Stage ◽  
Drug Sensitivity ◽  
Cancer Cell Lines ◽  
Resistance Mechanisms ◽  
General Effect ◽  
Tumor Types ◽  
Pan Cancer ◽  
Genetic Events

Sequences of genetic events were identified that may help explain common patterns of oncogenesis across 22 tumor types. The general effect of late-stage mutations on drug sensitivity and resistance mechanisms in cancer cell lines was evaluated.

scholarly journals A systems genomics approach to uncover the molecular properties of cancer genes

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Felix Grassmann ◽  
Yudi Pawitan ◽  
Kamila Czene
Keyword(s):  
Cancer Survival ◽  
Association Studies ◽  
Gc Content ◽  
Molecular Properties ◽  
Molecular Profile ◽  
Genome Wide Association Studies ◽  
Cancer Genes ◽  
Driver Genes ◽  
Cancer Driver ◽  
Genome Wide

Abstract Genes involved in cancer are under constant evolutionary pressure, potentially resulting in diverse molecular properties. In this study, we explore 23 omic features from publicly available databases to define the molecular profile of different classes of cancer genes. Cancer genes were grouped according to mutational landscape (germline and somatically mutated genes), role in cancer initiation (cancer driver genes) or cancer survival (survival genes), as well as being implicated by genome-wide association studies (GWAS genes). For each gene, we also computed feature scores based on all omic features, effectively summarizing how closely a gene resembles cancer genes of the respective class. In general, cancer genes are longer, have a lower GC content, have more isoforms with shorter exons, are expressed in more tissues and have more transcription factor binding sites than non-cancer genes. We found that germline genes more closely resemble single tissue GWAS genes while somatic genes are more similar to pleiotropic cancer GWAS genes. As a proof-of-principle, we utilized aggregated feature scores to prioritize genes in breast cancer GWAS loci and found that top ranking genes were enriched in cancer related pathways. In conclusion, we have identified multiple omic features associated with different classes of cancer genes, which can assist prioritization of genes in cancer gene discovery.

scholarly journals Comprehensive identification of mutational cancer driver genes across 12 tumor types

2013 ◽  
Vol 3 (1) ◽  
Author(s):  
David Tamborero ◽  
Abel Gonzalez-Perez ◽  
Christian Perez-Llamas ◽  
Jordi Deu-Pons ◽  
Cyriac Kandoth ◽  
...  
Keyword(s):  
Driver Genes ◽  
Cancer Driver ◽  
Cancer Driver Genes ◽  
Tumor Types

scholarly journals Inferring Homologous Recombination Deficiency of Ovarian Cancer From the Landscape of Copy Number Variation at Subchromosomal and Genetic Resolutions

2021 ◽  
Vol 11 ◽  
Author(s):  
Meng Zhang ◽  
Si-Cong Ma ◽  
Jia-Le Tan ◽  
Jian Wang ◽  
Xue Bai ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Homologous Recombination ◽  
Copy Number Variation ◽  
Copy Number ◽  
The Cancer Genome Atlas ◽  
Homologous Recombination Deficiency ◽  
Number Variation ◽  
Cancer Genome Atlas ◽  
Tumor Types ◽  
Pan Cancer

BackgroundHomologous recombination deficiency (HRD) is characterized by overall genomic instability and has emerged as an indispensable therapeutic target across various tumor types, particularly in ovarian cancer (OV). Unfortunately, current detection assays are far from perfect for identifying every HRD patient. The purpose of this study was to infer HRD from the landscape of copy number variation (CNV).MethodsGenome-wide CNV landscape was measured in OV patients from the Australian Ovarian Cancer Study (AOCS) clinical cohort and >10,000 patients across 33 tumor types from The Cancer Genome Atlas (TCGA). HRD-predictive CNVs at subchromosomal resolution were identified through exploratory analysis depicting the CNV landscape of HRD versus non-HRD OV patients and independently validated using TCGA and AOCS cohorts. Gene-level CNVs were further analyzed to explore their potential predictive significance for HRD across tumor types at genetic resolution.ResultsAt subchromosomal resolution, 8q24.2 amplification and 5q13.2 deletion were predominantly witnessed in HRD patients (both p < 0.0001), whereas 19q12 amplification occurred mainly in non-HRD patients (p < 0.0001), compared with their corresponding counterparts within TCGA-OV. The predictive significance of 8q24.2 amplification (p < 0.0001), 5q13.2 deletion (p = 0.0056), and 19q12 amplification (p = 0.0034) was externally validated within AOCS. Remarkably, pan-cancer analysis confirmed a cross-tumor predictive role of 8q24.2 amplification for HRD (p < 0.0001). Further analysis of CNV in 8q24.2 at genetic resolution revealed that amplifications of the oncogenes, MYC (p = 0.0001) and NDRG1 (p = 0.0004), located on this fragment were also associated with HRD in a pan-cancer manner.ConclusionsThe CNV landscape serves as a generalized predictor of HRD in cancer patients not limited to OV. The detection of CNV at subchromosomal or genetic resolution could aid in the personalized treatment of HRD patients.

A pan-cancer transcriptome analysis of exitron splicing identifies novel cancer driver genes and neoepitopes

2021 ◽  
Author(s):  
Ting-You Wang ◽  
Qi Liu ◽  
Yanan Ren ◽  
Sk. Kayum Alam ◽  
Li Wang ◽  
...  
Keyword(s):  
Transcriptome Analysis ◽  
Driver Genes ◽  
Cancer Driver ◽  
Cancer Transcriptome ◽  
Cancer Driver Genes ◽  
Pan Cancer

scholarly journals Pan-cancer analysis of non-oncogene addiction to DNA repair

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Luis Bermúdez-Guzmán
Keyword(s):  
Dna Repair ◽  
Cancer Cells ◽  
Cell Lines ◽  
The Cancer Genome Atlas ◽  
Molecular Signature ◽  
Systematic Evaluation ◽  
Oncogene Addiction ◽  
Driver Genes ◽  
Using Data ◽  
Pan Cancer

AbstractCancer cells usually depend on the aberrant function of one or few driver genes to initiate and promote their malignancy, an attribute known as oncogene addiction. However, cancer cells might become dependent on the normal cellular functions of certain genes that are not oncogenes but ensure cell survival (non-oncogene addiction). The downregulation or silencing of DNA repair genes and the consequent genetic and epigenetic instability is key to promote malignancy, but the activation of the DNA-damage response (DDR) has been shown to become a type of non-oncogene addiction that critically supports tumour survival. In the present study, a systematic evaluation of DNA repair addiction at the pan-cancer level was performed using data derived from The Cancer Dependency Map and The Cancer Genome Atlas (TCGA). From 241 DDR genes, 59 were identified as commonly essential in cancer cell lines. However, large differences were observed in terms of dependency scores in 423 cell lines and transcriptomic alterations across 18 cancer types. Among these 59 commonly essential genes, 14 genes were exclusively associated with better overall patient survival and 19 with worse overall survival. Notably, a specific molecular signature among the latter, characterized by DDR genes like UBE2T, RFC4, POLQ, BRIP1, and H2AFX showing the weakest dependency scores, but significant upregulation was strongly associated with worse survival. The present study supports the existence and importance of non-oncogenic addiction to DNA repair in cancer and may facilitate the identification of prognostic biomarkers and therapeutic opportunities.

scholarly journals Integration of multiple networks and pathways identifies cancer driver genes in pan-cancer analysis

BMC Genomics ◽  
2018 ◽  
Vol 19 (1) ◽  
Author(s):  
Claudia Cava ◽  
Gloria Bertoli ◽  
Antonio Colaprico ◽  
Catharina Olsen ◽  
Gianluca Bontempi ◽  
...  
Keyword(s):  
Driver Genes ◽  
Cancer Driver ◽  
Multiple Networks ◽  
Cancer Driver Genes ◽  
Pan Cancer

scholarly journals In silico saturation mutagenesis of cancer genes

2020 ◽  
Author(s):  
Ferran Muiños ◽  
Francisco Martinez-Jimenez ◽  
Oriol Pich ◽  
Abel Gonzalez-Perez ◽  
Nuria Lopez-Bigas
Keyword(s):  
In Silico ◽  
Tumor Development ◽  
Saturation Mutagenesis ◽  
Driver Mutations ◽  
Cancer Genes ◽  
Driver Genes ◽  
Cancer Driver ◽  
Mutation Probability ◽  
Tumor Types ◽  
Tumor Somatic Mutations

SummaryExtensive bioinformatics analysis of datasets of tumor somatic mutations data have revealed the presence of some 500-600 cancer driver genes. The identification of all potential driver mutations affecting cancer genes is essential to implement precision cancer medicine and to understand the interplay of mutation probability and selection in tumor development. Here, we present an in silico saturation mutagenesis approach to identify all driver mutations in 568 cancer genes across 66 tumor types. For most cancer genes the mutation probability across tissues --underpinned by active mutational processes-- influences which driver variants have been observed, although this differs significantly between tumor suppressor and oncogenes. The role of selection is apparent in some of the latter, the observed and unobserved driver mutations of which are equally likely to occur. The number of potential driver mutations in a cancer gene roughly determines how many mutations are available for detection across newly sequenced tumors.

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