scholarly journals An integrative analysis of the age-associated multi-omic landscape across cancers

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Kasit Chatsirisupachai ◽  
Tom Lesluyes ◽  
Luminita Paraoan ◽  
Peter Van Loo ◽  
João Pedro de Magalhães
Keyword(s):  
The Cancer Genome Atlas ◽  
Driver Genes ◽  
Cancer Driver ◽  
Molecular Alterations ◽  
Age Related ◽  
Incidence And Mortality ◽  
Cancer Genome Atlas ◽  
Cancer Types ◽  
Using Data ◽  
Transcriptomic Changes

AbstractAge is the most important risk factor for cancer, as cancer incidence and mortality increase with age. However, how molecular alterations in tumours differ among patients of different age remains largely unexplored. Here, using data from The Cancer Genome Atlas, we comprehensively characterise genomic, transcriptomic and epigenetic alterations in relation to patients’ age across cancer types. We show that tumours from older patients present an overall increase in genomic instability, somatic copy-number alterations (SCNAs) and somatic mutations. Age-associated SCNAs and mutations are identified in several cancer-driver genes across different cancer types. The largest age-related genomic differences are found in gliomas and endometrial cancer. We identify age-related global transcriptomic changes and demonstrate that these genes are in part regulated by age-associated DNA methylation changes. This study provides a comprehensive, multi-omics view of age-associated alterations in cancer and underscores age as an important factor to consider in cancer research and clinical practice.

scholarly journals An Integrative Analysis of the Age-Associated Genomic, Transcriptomic and Epigenetic Landscape across Cancers

2020 ◽  
Author(s):  
Kasit Chatsirisupachai ◽  
Tom Lesluyes ◽  
Luminita Paraoan ◽  
Peter Van Loo ◽  
João Pedro de Magalhães
Keyword(s):  
The Cancer Genome Atlas ◽  
Driver Genes ◽  
Cancer Driver ◽  
Molecular Alterations ◽  
Age Related ◽  
Incidence And Mortality ◽  
Cancer Genome Atlas ◽  
Cancer Types ◽  
Using Data ◽  
Transcriptomic Changes

AbstractAge is the most important risk factor for cancer, as cancer incidence and mortality increase with age. However, how molecular alterations in tumours differ among patients of different age remains largely unexplored. Here, using data from The Cancer Genome Atlas, we comprehensively characterised genomic, transcriptomic and epigenetic alterations in relation to patients’ age across cancer types. We showed that tumours from older patients present an overall increase in genomic instability, somatic copy-number alterations (SCNAs) and somatic mutations. Age-associated SCNAs and mutations were identified in several cancer-driver genes across different cancer types. The largest age-related genomic differences were found in gliomas and endometrial cancer. We identified age-related global transcriptomic changes and demonstrated that these genes are controlled by age-associated DNA methylation changes. This study provides a comprehensive view of age-associated alterations in cancer and underscores age as an important factor to consider in cancer research and clinical practice.

scholarly journals Inferring perturbation profiles of cancer samples

2020 ◽  
Author(s):  
Martin Pirkl ◽  
Niko Beerenwinkel
Keyword(s):  
Indirect Evidence ◽  
R Package ◽  
The Cancer Genome Atlas ◽  
Patient Specific ◽  
Driver Genes ◽  
Cancer Driver ◽  
Molecular Alterations ◽  
Incomplete Coverage ◽  
Cancer Genome Atlas ◽  
Gene Perturbations

AbstractMotivationCancer is one of the most prevalent diseases in the world. Tumors arise due to important genes changing their activity, e.g., when inhibited or over-expressed. But these gene perturbations are difficult to observe directly. Molecular profiles of tumors can provide indirect evidence of gene perturbations. However, inferring perturbation profiles from molecular alterations is challenging due to error-prone molecular measurements and incomplete coverage of all possible molecular causes of gene perturbations.ResultsWe have developed a novel mathematical method to analyze cancer driver genes and their patient-specific perturbation profiles. We combine genetic aberrations with gene expression data in a causal network derived across patients to infer unobserved perturbations. We show that our method can predict perturbations in simulations, CRISPR perturbation screens, and breast cancer samples from The Cancer Genome Atlas.AvailabilityThe method is available as the R-package nempi at https://github.com/cbg-ethz/[email protected], [email protected]

scholarly journals Inferring perturbation profiles of cancer samples

2021 ◽  
Author(s):  
Martin Pirkl ◽  
Niko Beerenwinkel
Keyword(s):  
Indirect Evidence ◽  
R Package ◽  
The Cancer Genome Atlas ◽  
Supplementary Information ◽  
Patient Specific ◽  
Driver Genes ◽  
Cancer Driver ◽  
Molecular Alterations ◽  
Incomplete Coverage ◽  
Gene Perturbations

Abstract Motivation Cancer is one of the most prevalent diseases in the world. Tumors arise due to important genes changing their activity, e.g. when inhibited or over-expressed. But these gene perturbations are difficult to observe directly. Molecular profiles of tumors can provide indirect evidence of gene perturbations. However, inferring perturbation profiles from molecular alterations is challenging due to error-prone molecular measurements and incomplete coverage of all possible molecular causes of gene perturbations. Results We have developed a novel mathematical method to analyze cancer driver genes and their patient-specific perturbation profiles. We combine genetic aberrations with gene expression data in a causal network derived across patients to infer unobserved perturbations. We show that our method can predict perturbations in simulations, CRISPR perturbation screens and breast cancer samples from The Cancer Genome Atlas. Availability and implementation The method is available as the R-package nempi at https://github.com/cbg-ethz/nempi and http://bioconductor.org/packages/nempi. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals OncoVar: an integrated database and analysis platform for oncogenic driver variants in cancers

2020 ◽  
Vol 49 (D1) ◽  
pp. D1289-D1301 ◽  
Author(s):  
Tao Wang ◽  
Shasha Ruan ◽  
Xiaolu Zhao ◽  
Xiaohui Shi ◽  
Huajing Teng ◽  
...  
Keyword(s):  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Driver Mutations ◽  
Cancer Genes ◽  
Driver Genes ◽  
Cancer Driver ◽  
Cancer Cell Population ◽  
Cancer Types ◽  
Neutral Mutations ◽  
Analysis Platform

Abstract The prevalence of neutral mutations in cancer cell population impedes the distinguishing of cancer-causing driver mutations from passenger mutations. To systematically prioritize the oncogenic ability of somatic mutations and cancer genes, we constructed a useful platform, OncoVar (https://oncovar.org/), which employed published bioinformatics algorithms and incorporated known driver events to identify driver mutations and driver genes. We identified 20 162 cancer driver mutations, 814 driver genes and 2360 pathogenic pathways with high-confidence by reanalyzing 10 769 exomes from 33 cancer types in The Cancer Genome Atlas (TCGA) and 1942 genomes from 18 cancer types in International Cancer Genome Consortium (ICGC). OncoVar provides four points of view, ‘Mutation’, ‘Gene’, ‘Pathway’ and ‘Cancer’, to help researchers to visualize the relationships between cancers and driver variants. Importantly, identification of actionable driver alterations provides promising druggable targets and repurposing opportunities of combinational therapies. OncoVar provides a user-friendly interface for browsing, searching and downloading somatic driver mutations, driver genes and pathogenic pathways in various cancer types. This platform will facilitate the identification of cancer drivers across individual cancer cohorts and helps to rank mutations or genes for better decision-making among clinical oncologists, cancer researchers and the broad scientific community interested in cancer precision medicine.

scholarly journals Pan-cancer driver copy number alterations identified by joint expression/CNA data analysis

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Gaojianyong Wang ◽  
Dimitris Anastassiou
Keyword(s):  
Gene Expression ◽  
Copy Number ◽  
The Cancer Genome Atlas ◽  
Copy Number Alterations ◽  
Multiple Cancer ◽  
Cancer Driver ◽  
Large Gene ◽  
Cancer Genome Atlas ◽  
Cancer Types ◽  
Pan Cancer

Abstract Analysis of large gene expression datasets from biopsies of cancer patients can identify co-expression signatures representing particular biomolecular events in cancer. Some of these signatures involve genomically co-localized genes resulting from the presence of copy number alterations (CNAs), for which analysis of the expression of the underlying genes provides valuable information about their combined role as oncogenes or tumor suppressor genes. Here we focus on the discovery and interpretation of such signatures that are present in multiple cancer types due to driver amplifications and deletions in particular regions of the genome after doing a comprehensive analysis combining both gene expression and CNA data from The Cancer Genome Atlas.

scholarly journals Global identification and characterization of tRNA-derived RNA fragment landscapes across human cancers

NAR Cancer ◽  
2020 ◽  
Vol 2 (4) ◽  
Author(s):  
Xiwei Sun ◽  
Juze Yang ◽  
Mengqian Yu ◽  
Dongxia Yao ◽  
Liyuan Zhou ◽  
...  
Keyword(s):  
The Cancer Genome Atlas ◽  
Cancer Driver ◽  
Cancer Pathogenesis ◽  
Genomic Landscape ◽  
High Sequence Conservation ◽  
Rna Fragments ◽  
Cancer Genome Atlas ◽  
Cancer Types

Abstract Transfer RNA-derived RNA fragments (tRFs) are a class of small non-coding RNAs that are abundant in many organisms, but their role in cancer has not been fully explored. Here, we report a functional genomic landscape of tRFs in 8118 specimens across 15 cancer types from The Cancer Genome Atlas. These tRFs exhibited characteristics of widespread expression, high sequence conservation, cytoplasmic localization, specific patterns of tRNA cleavage and conserved cleavage in tissues. A cross-tumor analysis revealed significant commonality among tRF expression subtypes from distinct tissues of origins, characterized by upregulation of a group of tRFs with similar size and activation of cancer-associated signaling. One of the largest superclusters was composed of 22 nt 3′-tRFs upregulated in 13 cancer types, all of which share the activation of Ras/MAPK, RTK and TSC/mTOR signaling. tRF-based subgrouping provided clinically relevant stratifications and significantly improved outcome prediction by incorporating clinical variables. Additionally, we discovered 11 cancer driver tRFs using an effective approach for accurately exploring cross-tumor and platform trends. As a proof of concept, we performed comprehensive functional assays on a non-microRNA driver tRF, 5′-IleAAT-8-1-L20, and validated its oncogenic roles in lung cancer in vitro and in vivo. Our study also provides a valuable tRF resource for identifying diagnostic and prognostic biomarkers, developing cancer therapy and studying cancer pathogenesis.

scholarly journals Identification of Potential Driver Genes Based on Multi-Genomic Data in Cervical Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Yuexun Xu ◽  
Hui Luo ◽  
Qunchao Hu ◽  
Haiyan Zhu
Keyword(s):  
Cervical Cancer ◽  
Molecular Classification ◽  
The Cancer Genome Atlas ◽  
Consensus Clustering ◽  
Driver Genes ◽  
Molecular Alterations ◽  
Wide Range ◽  
Number Variation ◽  
Cancer Genome Atlas ◽  
Significant Expression

Background: Cervical cancer became the third most common cancer among women, and genome characterization of cervical cancer patients has revealed the extensive complexity of molecular alterations. However, identifying driver mutation and depicting molecular classification in cervical cancer remain a challenge.Methods: We performed an integrative multi-platform analysis of a cervical cancer cohort from The Cancer Genome Atlas (TCGA) based on 284 clinical cases and identified the driver genes and possible molecular classification of cervical cancer.Results: Multi-platform integration showed that cervical cancer exhibited a wide range of mutation. The top 10 mutated genes were TTN, PIK3CA, MUC4, KMT2C, MUC16, KMT2D, SYNE1, FLG, DST, and EP300, with a mutation rate from 12 to 33%. Applying GISTIC to detect copy number variation (CNV), the most frequent chromosome arm-level CNVs included losses in 4p, 11p, and 11q and gains in 20q, 3q, and 1q. Then, we performed unsupervised consensus clustering of tumor CNV profiles and methylation profiles and detected four statistically significant expression subtypes. Finally, by combining the multidimensional datasets, we identified 10 potential driver genes, including GPR107, CHRNA5, ZBTB20, Rb1, NCAPH2, SCA1, SLC25A5, RBPMS, DDX3X, and H2BFM.Conclusions: This comprehensive analysis described the genetic characteristic of cervical cancer and identified novel driver genes in cervical cancer. These results provide insight into developing precision treatment in cervical cancer.

scholarly journals A Pan-cancer catalogue of driver protein interaction interfaces

2015 ◽  
Author(s):  
Eduard Porta-Pardo ◽  
Thomas Hrabe ◽  
Adam Godzik
Keyword(s):  
Protein Interaction ◽  
Specific Protein ◽  
The Cancer Genome Atlas ◽  
Driver Genes ◽  
Cancer Driver ◽  
Protein Protein Interaction ◽  
Cancer Mutations ◽  
Cancer Genome Atlas ◽  
Mutation Pattern ◽  
Pan Cancer

Despite their critical importance in maintaining the integrity of all cellular pathways, the specific role of mutations on protein-protein interaction (PPI) interfaces as cancer drivers, though known for some specific examples, has not been systematically studied. We analyzed missense somatic mutations in a pan-cancer cohort of 5,989 tumors from 23 projects of The Cancer Genome Atlas (TCGA) for enrichment on PPI interfaces using e-Driver, an algorithm to analyze the mutation pattern of specific protein regions such as PPI interfaces. We identified 128 PPI interfaces enriched in somatic cancer mutations. Our results support the notion that many mutations in well-established cancer driver genes, particularly those in critical network positions, act by altering PPI interfaces. Finally, focusing on individual interfaces we are also able to show how tumors driven by the same gene can have different behaviors, including patient outcomes, depending on whether specific interfaces are mutated or not.

Utilizing patient information to identify subtype heterogeneity of cancer driver genes

2021 ◽  
pp. 096228022110558
Author(s):  
Ho-Hsiang Wu ◽  
Xing Hua ◽  
Jianxin Shi ◽  
Nilanjan Chatterjee ◽  
Bin Zhu
Keyword(s):  
Type I Error ◽  
Smoking Status ◽  
The Cancer Genome Atlas ◽  
Type I ◽  
Driver Genes ◽  
Cancer Subtypes ◽  
Cancer Driver ◽  
The Status ◽  
Cancer Genome Atlas ◽  
Cancer Driver Genes

Identifying cancer driver genes is essential for understanding the mechanisms of carcinogenesis and designing therapeutic strategies. Although driver genes have been identified for many cancer types, it is still not clear whether the selection pressure of driver genes is homogeneous across cancer subtypes. We propose a statistical framework MutScot to improve the identification of driver genes and to investigate the heterogeneity of driver genes across cancer subtypes. Through simulation studies, we show that MutScot properly controls the type I error in detecting driver genes. In addition, we demonstrate that MutScot can identify subtype heterogeneity of driver genes. Applications to three studies in The Cancer Genome Atlas (TCGA) project showcase that MutScot has a desirable sensitivity for detecting driver genes and that MutScot identifies subtype heterogeneity of driver genes in breast cancer and lung cancer with regards to the status of hormone receptor and to the smoking status, respectively.

scholarly journals TIMER2.0 for analysis of tumor-infiltrating immune cells

2020 ◽  
Vol 48 (W1) ◽  
pp. W509-W514 ◽  
Author(s):  
Taiwen Li ◽  
Jingxin Fu ◽  
Zexian Zeng ◽  
David Cohen ◽  
Jing Li ◽  
...  
Keyword(s):  
Immune Cells ◽  
Large Scale ◽  
Immune Cell ◽  
The Cancer Genome Atlas ◽  
Multiple Cancer ◽  
Public Data ◽  
Cancer Genome Atlas ◽  
Cancer Types ◽  
Transcriptomic Changes ◽  
Web Platform

Abstract Tumor progression and the efficacy of immunotherapy are strongly influenced by the composition and abundance of immune cells in the tumor microenvironment. Due to the limitations of direct measurement methods, computational algorithms are often used to infer immune cell composition from bulk tumor transcriptome profiles. These estimated tumor immune infiltrate populations have been associated with genomic and transcriptomic changes in the tumors, providing insight into tumor–immune interactions. However, such investigations on large-scale public data remain challenging. To lower the barriers for the analysis of complex tumor–immune interactions, we significantly improved our previous web platform TIMER. Instead of just using one algorithm, TIMER2.0 (http://timer.cistrome.org/) provides more robust estimation of immune infiltration levels for The Cancer Genome Atlas (TCGA) or user-provided tumor profiles using six state-of-the-art algorithms. TIMER2.0 provides four modules for investigating the associations between immune infiltrates and genetic or clinical features, and four modules for exploring cancer-related associations in the TCGA cohorts. Each module can generate a functional heatmap table, enabling the user to easily identify significant associations in multiple cancer types simultaneously. Overall, the TIMER2.0 web server provides comprehensive analysis and visualization functions of tumor infiltrating immune cells.

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