Utilizing patient information to identify subtype heterogeneity of 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.

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Inferring perturbation profiles of cancer samples

10.1101/2020.12.10.419077 ◽

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]

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A Pan-cancer catalogue of driver protein interaction interfaces

10.1101/015883 ◽

2015 ◽

Cited By ~ 1

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.

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

Nature Communications ◽

10.1038/s41467-021-22560-y ◽

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.

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The consequences of variant calling decisions in secondary analyses of cancer sequencing data

10.1101/2020.01.29.924860 ◽

2020 ◽

Cited By ~ 1

Author(s):

Carlos Garcia-Prieto ◽

Francisco Martinez Jimenez ◽

Alfonso Valencia ◽

Eduard Porta-Pardo

Keyword(s):

Cancer Genomics ◽

Cell Transformation ◽

Variant Calling ◽

The Cancer Genome Atlas ◽

Sequencing Data ◽

Driver Genes ◽

Somatic Variant ◽

Cancer Driver ◽

Wide Range ◽

Cancer Driver Genes

The analysis of cancer genomes provides fundamental information about its aetiology, the processes driving cell transformation or potential treatments. The first crucial step in the analysis of any tumor genome is the identification of somatic genetic variants that cancer cells have acquired during their evolution. For that purpose, a wide range of somatic variant callers have been developed in recent years. While there have been some efforts to benchmark somatic variant calling tools and strategies, the extent to which variant calling decisions impact the results of downstream analyses of tumor genomes remains unknown. Here we present a study to elucidate whether different variant callers (MuSE, MuTect2, SomaticSniper, VarScan2) and strategies to combine them (Consensus and Union) lead to different results in these three important downstream analyses of cancer genomics data: identification of cancer driver genes, quantification of mutational signatures and detection of clinically actionable variants. To this end, we tested how the results of these three analyses varied depending on the somatic mutation caller in five different projects from The Cancer Genome Atlas (TCGA). Our results show that variant calling decisions have a significant impact on these downstream analyses, creating important differences in driver genes identification and mutational processes attribution among variant call sets, as well as in the detection of clinically actionable targets. More importantly, it seems that Consensus, a very widely used strategy by the research community, is not the optimal strategy, as it can lead to the loss of some cancer driver genes and actionable mutations. On the other hand, the Union seems to be a legit strategy for some downstream analyses with a robust performance overall.

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Large-Scale Transcriptome Analysis Identified a Novel Cancer Driver Genes Signature for Predicting the Prognostic of Patients With Hepatocellular Carcinoma

Frontiers in Pharmacology ◽

10.3389/fphar.2021.638622 ◽

2021 ◽

Vol 12 ◽

Author(s):

Gao Li ◽

Xiaowei Du ◽

Xiaoxiong Wu ◽

Shen Wu ◽

Yufei Zhang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Critical Role ◽

Risk Groups ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Risk Scores ◽

Driver Genes ◽

Cancer Driver ◽

Cancer Driver Genes

Background: Hepatocellular carcinoma (HCC) is a common malignant tumor with high mortality and heterogeneity. Genetic mutations caused by driver genes are important contributors to the formation of the tumor microenvironment. The purpose of this study is to discuss the expression of cancer driver genes in tumor tissues and their clinical value in predicting the prognosis of HCC.Methods: All data were sourced from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and Gene Expression Omnibus (GEO) public databases. Differentially expressed and prognostic genes were screened by the expression distribution of the cancer driver genes and their relationship with survival. Candidate genes were subjected to functional enrichment and transcription factor regulatory network. We further constructed a prognostic signature and analyzed the survival outcomes and immune status between different risk groups.Results: Most cancer driver genes are specifically expressed in cancer tissues. Driver genes may influence HCC progression through processes such as transcription, cell cycle, and T-cell receptor-related pathways. Patients in different risk groups had significant survival differences (p < 0.05), and risk scores showed high predictive efficacy (AUC>0.69). Besides, risk subgroups were also associated with multiple immune functions and immune cell content.Conclusion: We confirmed the critical role of cancer driver genes in mediating HCC progression and the immune microenvironment. Risk subgroups contribute to the assessment of prognostic value in different patients and explain the heterogeneity of HCC.

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

10.1101/2020.08.25.266403 ◽

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.

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Faculty Opinions recommendation of Evaluating the evaluation of cancer driver genes.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.727060594.793535346 ◽

2017 ◽

Author(s):

Ron Shamir

Keyword(s):

Driver Genes ◽

Cancer Driver ◽

Cancer Driver Genes

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driveR: a novel method for prioritizing cancer driver genes using somatic genomics data

BMC Bioinformatics ◽

10.1186/s12859-021-04203-7 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Ege Ülgen ◽

O. Uğur Sezerman

Keyword(s):

Biological Knowledge ◽

Driver Gene ◽

Driver Genes ◽

Cancer Driver ◽

Prior Biological Knowledge ◽

Wilcoxon Rank Sum Test ◽

Cancer Genomes ◽

Novel Method ◽

Cancer Driver Genes ◽

Batch Analysis

Abstract Background Cancer develops due to “driver” alterations. Numerous approaches exist for predicting cancer drivers from cohort-scale genomics data. However, methods for personalized analysis of driver genes are underdeveloped. In this study, we developed a novel personalized/batch analysis approach for driver gene prioritization utilizing somatic genomics data, called driveR. Results Combining genomics information and prior biological knowledge, driveR accurately prioritizes cancer driver genes via a multi-task learning model. Testing on 28 different datasets, this study demonstrates that driveR performs adequately, achieving a median AUC of 0.684 (range 0.651–0.861) on the 28 batch analysis test datasets, and a median AUC of 0.773 (range 0–1) on the 5157 personalized analysis test samples. Moreover, it outperforms existing approaches, achieving a significantly higher median AUC than all of MutSigCV (Wilcoxon rank-sum test p < 0.001), DriverNet (p < 0.001), OncodriveFML (p < 0.001) and MutPanning (p < 0.001) on batch analysis test datasets, and a significantly higher median AUC than DawnRank (p < 0.001) and PRODIGY (p < 0.001) on personalized analysis datasets. Conclusions This study demonstrates that the proposed method is an accurate and easy-to-utilize approach for prioritizing driver genes in cancer genomes in personalized or batch analyses. driveR is available on CRAN: https://cran.r-project.org/package=driveR.

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Inferring perturbation profiles of cancer samples

Bioinformatics ◽

10.1093/bioinformatics/btab113 ◽

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.

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