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.

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 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 Genomic profiling of microRNA target genes in colorectal cancer

Tumor Biology ◽  
2020 ◽  
Vol 42 (6) ◽  
pp. 101042832093351
Author(s):  
Adewale Oluwaseun Fadaka ◽  
Olalekan Olanrewaju Bakare ◽  
Ashley Pretorius ◽  
Ashwil Klein
Keyword(s):  
Colorectal Cancer ◽  
Protein Interaction ◽  
Tyrosine Kinases ◽  
Target Genes ◽  
Genetic Mutation ◽  
Tumor Formation ◽  
The Cancer Genome Atlas ◽  
Microrna Target ◽  
Driver Genes ◽  
Protein Protein Interaction

Colorectal cancer is the second and third most common cancer in men and women, respectively, worldwide. Alterations such as genetic and epigenetic are common in colorectal cancer and are the basis of tumor formation. The exploration of the molecular basis of colorectal cancer can drive a better understanding of the disease as well as guide the prognosis, therapeutics, and disease management. This study is aimed at investigating the genetic mutation profile of five candidate microRNAs (hsa-miR-513b-3p, hsa-miR-500b-3p, hsa-miR-500a-3p, hsa-miR-450b-3p, hsa-miR-193a-5p) targeted by seven genes (APC, KRAS, TCF7L2, EGFR, IGF1R, CASP8, and GNAS)) using in silico approaches. Two datasets (dataset 1 from our previous study and dataset two (The Cancer Genome Atlas, Nature 2012) were considered for this study. Protein–protein interaction, expression analysis, and genetic profiling were carried out using STRING, FireBrowse, and cBioPortal, respectively. Protein–protein interaction network showed that epidermal growth factor receptor has the highest connection among the target genes and this can be considered as the hub gene. Relative to other solid tumors, in colorectal cancer, six of the target genes were downregulated and only CASP8 was upregulated. Genes with protein tyrosine kinases domain were frequently altered in colorectal cancer and the most common alteration in these genes/domain are missense mutation. These results could serve as a lead in the identification of driver genes responsible for colorectal cancer initiation and progression. However, the intense mechanism of these results remains unclear and further experimental validation and molecular approaches are the focal points in the nearest future.

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.

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 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 Ranking cancer drivers via betweenness-based outlier detection and random walks

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Cesim Erten ◽  
Aissa Houdjedj ◽  
Hilal Kazan
Keyword(s):  
Cancer Genomics ◽  
Interaction Network ◽  
Molecular Data ◽  
Alternative Methods ◽  
Patient Specific ◽  
Cancer Genes ◽  
Driver Genes ◽  
Cancer Driver ◽  
Protein Protein Interaction ◽  
Genomic Studies

Abstract Background Recent cancer genomic studies have generated detailed molecular data on a large number of cancer patients. A key remaining problem in cancer genomics is the identification of driver genes. Results We propose BetweenNet, a computational approach that integrates genomic data with a protein-protein interaction network to identify cancer driver genes. BetweenNet utilizes a measure based on betweenness centrality on patient specific networks to identify the so-called outlier genes that correspond to dysregulated genes for each patient. Setting up the relationship between the mutated genes and the outliers through a bipartite graph, it employs a random-walk process on the graph, which provides the final prioritization of the mutated genes. We compare BetweenNet against state-of-the art cancer gene prioritization methods on lung, breast, and pan-cancer datasets. Conclusions Our evaluations show that BetweenNet is better at recovering known cancer genes based on multiple reference databases. Additionally, we show that the GO terms and the reference pathways enriched in BetweenNet ranked genes and those that are enriched in known cancer genes overlap significantly when compared to the overlaps achieved by the rankings of the alternative methods.

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 Molecular Correlates of Metastasis by Systematic Pan-Cancer Analysis Across The Cancer Genome Atlas

2018 ◽  
Vol 17 (2) ◽  
pp. 476-487 ◽  
Author(s):  
Fengju Chen ◽  
Yiqun Zhang ◽  
Sooryanarayana Varambally ◽  
Chad J. Creighton
Keyword(s):  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Cancer Genome Atlas ◽  
Pan Cancer ◽  
Genome Atlas

scholarly journals PR/SET Domain Family and Cancer: Novel Insights from the Cancer Genome Atlas

2018 ◽  
Vol 19 (10) ◽  
pp. 3250 ◽  
Author(s):  
Anna Sorrentino ◽  
Antonio Federico ◽  
Monica Rienzo ◽  
Patrizia Gazzerro ◽  
Maurizio Bifulco ◽  
...  
Keyword(s):  
Family Members ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Driver Gene ◽  
Rna Seq ◽  
Set Domain ◽  
Primary Tumors ◽  
Cancer Genome Atlas ◽  
Pan Cancer ◽  
Genome Atlas

The PR/SET domain gene family (PRDM) encodes 19 different transcription factors that share a subtype of the SET domain [Su(var)3-9, enhancer-of-zeste and trithorax] known as the PRDF1-RIZ (PR) homology domain. This domain, with its potential methyltransferase activity, is followed by a variable number of zinc-finger motifs, which likely mediate protein–protein, protein–RNA, or protein–DNA interactions. Intriguingly, almost all PRDM family members express different isoforms, which likely play opposite roles in oncogenesis. Remarkably, several studies have described alterations in most of the family members in malignancies. Here, to obtain a pan-cancer overview of the genomic and transcriptomic alterations of PRDM genes, we reanalyzed the Exome- and RNA-Seq public datasets available at The Cancer Genome Atlas portal. Overall, PRDM2, PRDM3/MECOM, PRDM9, PRDM16 and ZFPM2/FOG2 were the most mutated genes with pan-cancer frequencies of protein-affecting mutations higher than 1%. Moreover, we observed heterogeneity in the mutation frequencies of these genes across tumors, with cancer types also reaching a value of about 20% of mutated samples for a specific PRDM gene. Of note, ZFPM1/FOG1 mutations occurred in 50% of adrenocortical carcinoma patients and were localized in a hotspot region. These findings, together with OncodriveCLUST results, suggest it could be putatively considered a cancer driver gene in this malignancy. Finally, transcriptome analysis from RNA-Seq data of paired samples revealed that transcription of PRDMs was significantly altered in several tumors. Specifically, PRDM12 and PRDM13 were largely overexpressed in many cancers whereas PRDM16 and ZFPM2/FOG2 were often downregulated. Some of these findings were also confirmed by real-time-PCR on primary tumors.

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