scholarly journals The landscape of genomic alterations across childhood cancers

Nature ◽  
2018 ◽  
Vol 555 (7696) ◽  
pp. 321-327 ◽  
Author(s):  
Susanne N. Gröbner ◽  
◽  
Barbara C. Worst ◽  
Joachim Weischenfeldt ◽  
Ivo Buchhalter ◽  
...  
Keyword(s):  
Cancer Biology ◽  
Copy Number Variant ◽  
Genetic Alterations ◽  
Childhood Cancers ◽  
Mutational Signatures ◽  
Driver Genes ◽  
Mutation Status ◽  
Cancer Driver ◽  
Cancer Types ◽  
Pan Cancer

Abstract Pan-cancer analyses that examine commonalities and differences among various cancer types have emerged as a powerful way to obtain novel insights into cancer biology. Here we present a comprehensive analysis of genetic alterations in a pan-cancer cohort including 961 tumours from children, adolescents, and young adults, comprising 24 distinct molecular types of cancer. Using a standardized workflow, we identified marked differences in terms of mutation frequency and significantly mutated genes in comparison to previously analysed adult cancers. Genetic alterations in 149 putative cancer driver genes separate the tumours into two classes: small mutation and structural/copy-number variant (correlating with germline variants). Structural variants, hyperdiploidy, and chromothripsis are linked to TP53 mutation status and mutational signatures. Our data suggest that 7–8% of the children in this cohort carry an unambiguous predisposing germline variant and that nearly 50% of paediatric neoplasms harbour a potentially druggable event, which is highly relevant for the design of future clinical trials.

scholarly journals DriverDBv3: a multi-omics database for cancer driver gene research

2019 ◽  
Author(s):  
Shu-Hsuan Liu ◽  
Pei-Chun Shen ◽  
Chen-Yang Chen ◽  
An-Ni Hsu ◽  
Yi-Chun Cho ◽  
...  
Keyword(s):  
Cancer Biology ◽  
Synergistic Effects ◽  
Driver Gene ◽  
Driver Genes ◽  
Specific Patient ◽  
Mutation Status ◽  
Cancer Driver ◽  
Cancer Driver Gene ◽  
Significant Survival ◽  
Gene Database

Abstract An integrative multi-omics database is needed urgently, because focusing only on analysis of one-dimensional data falls far short of providing an understanding of cancer. Previously, we presented DriverDB, a cancer driver gene database that applies published bioinformatics algorithms to identify driver genes/mutations. The updated DriverDBv3 database (http://ngs.ym.edu.tw/driverdb) is designed to interpret cancer omics’ sophisticated information with concise data visualization. To offer diverse insights into molecular dysregulation/dysfunction events, we incorporated computational tools to define CNV and methylation drivers. Further, four new features, CNV, Methylation, Survival, and miRNA, allow users to explore the relations from two perspectives in the ‘Cancer’ and ‘Gene’ sections. The ‘Survival’ panel offers not only significant survival genes, but gene pairs synergistic effects determine. A fresh function, ‘Survival Analysis’ in ‘Customized-analysis,’ allows users to investigate the co-occurring events in user-defined gene(s) by mutation status or by expression in a specific patient group. Moreover, we redesigned the web interface and provided interactive figures to interpret cancer omics’ sophisticated information, and also constructed a Summary panel in the ‘Cancer’ and ‘Gene’ sections to visualize the features on multi-omics levels concisely. DriverDBv3 seeks to improve the study of integrative cancer omics data by identifying driver genes and contributes to cancer biology.

scholarly journals Pan-cancer detection of driver genes at the single-patient resolution

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Joel Nulsen ◽  
Hrvoje Misetic ◽  
Christopher Yau ◽  
Francesca D. Ciccarelli
Keyword(s):  
False Positive Rate ◽  
Genetic Alterations ◽  
Therapeutic Interventions ◽  
Precision Oncology ◽  
Cancer Type ◽  
Rare Cancer ◽  
Driver Genes ◽  
Cancer Data ◽  
Cancer Types ◽  
Pan Cancer

Abstract Background Identifying the complete repertoire of genes that drive cancer in individual patients is crucial for precision oncology. Most established methods identify driver genes that are recurrently altered across patient cohorts. However, mapping these genes back to patients leaves a sizeable fraction with few or no drivers, hindering our understanding of cancer mechanisms and limiting the choice of therapeutic interventions. Results We present sysSVM2, a machine learning software that integrates cancer genetic alterations with gene systems-level properties to predict drivers in individual patients. Using simulated pan-cancer data, we optimise sysSVM2 for application to any cancer type. We benchmark its performance on real cancer data and validate its applicability to a rare cancer type with few known driver genes. We show that drivers predicted by sysSVM2 have a low false-positive rate, are stable and disrupt well-known cancer-related pathways. Conclusions sysSVM2 can be used to identify driver alterations in patients lacking sufficient canonical drivers or belonging to rare cancer types for which assembling a large enough cohort is challenging, furthering the goals of precision oncology. As resources for the community, we provide the code to implement sysSVM2 and the pre-trained models in all TCGA cancer types (https://github.com/ciccalab/sysSVM2).

scholarly journals MEXCOWalk: Mutual Exclusion and Coverage Based Random Walk to Identify Cancer Modules

2019 ◽  
Author(s):  
Rafsan Ahmed ◽  
Ilyes Baali ◽  
Cesim Erten ◽  
Evis Hoxha ◽  
Hilal Kazan
Keyword(s):  
Random Walk ◽  
Mutual Exclusion ◽  
Risk Scores ◽  
Cancer Genes ◽  
Multiple Cancer ◽  
Driver Genes ◽  
Cancer Driver ◽  
Cancer Data ◽  
Cancer Types ◽  
Pan Cancer

AbstractMotivationGenomic analyses from large cancer cohorts have revealed the mutational heterogeneity problem which hinders the identification of driver genes based only on mutation profiles. One way to tackle this problem is to incorporate the fact that genes act together in functional modules. The connectivity knowledge present in existing protein-protein interaction networks together with mutation frequencies of genes and the mutual exclusivity of cancer mutations can be utilized to increase the accuracy of identifying cancer driver modules.ResultsWe present a novel edge-weighted random walk-based approach that incorporates connectivity information in the form of protein-protein interactions, mutual exclusion, and coverage to identify cancer driver modules. MEXCOWalk outperforms several state-of-the-art computational methods on TCGA pan-cancer data in terms of recovering known cancer genes, providing modules that are capable of classifying normal and tumor samples, and that are enriched for mutations in specific cancer types. Furthermore, the risk scores determined with output modules can stratify patients into low-risk and high-risk groups in multiple cancer types. MEXCOwalk identifies modules containing both well-known cancer genes and putative cancer genes that are rarely mutated in the pan-cancer data. The data, the source code, and useful scripts are available at:https://github.com/abu-compbio/[email protected]

scholarly journals Novel ratio-metric features enable the identification of new driver genes across cancer types

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Malvika Sudhakar ◽  
Raghunathan Rengaswamy ◽  
Karthik Raman
Keyword(s):  
Cancer Genomics ◽  
Low Frequency ◽  
Tumour Suppressor Genes ◽  
Functional Categories ◽  
Driver Genes ◽  
Nonsense Mutations ◽  
Cancer Driver ◽  
Selective Growth Advantage ◽  
Cancer Types ◽  
Pan Cancer

AbstractAn emergent area of cancer genomics is the identification of driver genes. Driver genes confer a selective growth advantage to the cell. While several driver genes have been discovered, many remain undiscovered, especially those mutated at a low frequency across samples. This study defines new features and builds a pan-cancer model, cTaG, to identify new driver genes. The features capture the functional impact of the mutations as well as their recurrence across samples, which helps build a model unbiased to genes with low frequency. The model classifies genes into the functional categories of driver genes, tumour suppressor genes (TSGs) and oncogenes (OGs), having distinct mutation type profiles. We overcome overfitting and show that certain mutation types, such as nonsense mutations, are more important for classification. Further, cTaG was employed to identify tissue-specific driver genes. Some known cancer driver genes predicted by cTaG as TSGs with high probability are ARID1A, TP53, and RB1. In addition to these known genes, potential driver genes predicted are CD36, ZNF750 and ARHGAP35 as TSGs and TAB3 as an oncogene. Overall, our approach surmounts the issue of low recall and bias towards genes with high mutation rates and predicts potential new driver genes for further experimental screening. cTaG is available at https://github.com/RamanLab/cTaG.

scholarly journals Pan-cancer detection of driver genes at the single-patient resolution

2020 ◽  
Author(s):  
Joel Nulsen ◽  
Hrvoje Misetic ◽  
Christopher Yau ◽  
Francesca D. Ciccarelli
Keyword(s):  
False Positive Rate ◽  
Genetic Alterations ◽  
Therapeutic Interventions ◽  
Precision Oncology ◽  
Cancer Type ◽  
Rare Cancer ◽  
Driver Genes ◽  
Cancer Data ◽  
Cancer Types ◽  
Pan Cancer

ABSTRACTBackgroundIdentifying the complete repertoire of genes that drive cancer in individual patients is crucial for precision oncology. Most established methods identify driver genes that are recurrently altered across patient cohorts. However, mapping these genes back to patients leaves a sizeable fraction with few or no drivers, hindering our understanding of cancer mechanisms and limiting the choice of therapeutic interventions.ResultsWe present sysSVM2, a machine learning software that integrates cancer genetic alterations with gene systems-level properties to predict drivers in individual patients. Using simulated pan-cancer data, we optimise sysSVM2 for application to any cancer type. We benchmark its performance on real cancer data and validate its applicability to a rare cancer type with few known driver genes. We show that drivers predicted by sysSVM2 have a low false-positive rate, are stable and disrupt well-known cancer-related pathways.ConclusionssysSVM2 can be used to identify driver alterations in patients lacking sufficient canonical drivers or belonging to rare cancer types for which assembling a large enough cohort is challenging, furthering the goals of precision oncology. As resources for the community, we provide the code to implement sysSVM2 and the pre-trained models in all TCGA cancer types (https://github.com/ciccalab/sysSVM2).

scholarly journals Age influences on the molecular presentation of tumours

2022 ◽  
Vol 13 (1) ◽  
Author(s):  
Constance H. Li ◽  
Syed Haider ◽  
Paul C. Boutros
Keyword(s):  
Older Patients ◽  
Cancer Epidemiology ◽  
Clinical Implications ◽  
Mutational Signatures ◽  
Driver Genes ◽  
Cancer Driver ◽  
Mutational Landscape ◽  
Damage Repair ◽  
Age Dependent ◽  
Molecular Bases

AbstractCancer is often called a disease of aging. There are numerous ways in which cancer epidemiology and behaviour change with the age of the patient. The molecular bases for these relationships remain largely underexplored. To characterise them, we analyse age-associations in the nuclear and mitochondrial somatic mutational landscape of 20,033 tumours across 35 tumour-types. Age influences both the number of mutations in a tumour (0.077 mutations per megabase per year) and their evolutionary timing. Specific mutational signatures are associated with age, reflecting differences in exogenous and endogenous oncogenic processes such as a greater influence of tobacco use in the tumours of younger patients, but higher activity of DNA damage repair signatures in those of older patients. We find that known cancer driver genes such as CDKN2A and CREBBP are mutated in age-associated frequencies, and these alter the transcriptome and predict for clinical outcomes. These effects are most striking in brain cancers where alterations like SUFU loss and ATRX mutation are age-dependent prognostic biomarkers. Using three cancer datasets, we show that age shapes the somatic mutational landscape of cancer, with clinical implications.

scholarly journals Integrated Dissection of lncRNA-Perturbated Triplets Reveals Novel Prognostic Signatures Across Cancer Types

2020 ◽  
Vol 21 (17) ◽  
pp. 6087
Author(s):  
Yunzhen Wei ◽  
Limeng Zhou ◽  
Yingzhang Huang ◽  
Dianjing Guo
Keyword(s):  
Cancer Biology ◽  
Noncoding Rna ◽  
The Cancer Genome Atlas ◽  
Dynamic Changes ◽  
Computational Framework ◽  
Potential Biomarker ◽  
Cancer Genome Atlas ◽  
Cancer Types ◽  
Tcga Dataset ◽  
Pan Cancer

Long noncoding RNA (lncRNA)/microRNA(miRNA)/mRNA triplets contribute to cancer biology. However, identifying significative triplets remains a major challenge for cancer research. The dynamic changes among factors of the triplets have been less understood. Here, by integrating target information and expression datasets, we proposed a novel computational framework to identify the triplets termed as “lncRNA-perturbated triplets”. We applied the framework to five cancer datasets in The Cancer Genome Atlas (TCGA) project and identified 109 triplets. We showed that the paired miRNAs and mRNAs were widely perturbated by lncRNAs in different cancer types. LncRNA perturbators and lncRNA-perturbated mRNAs showed significantly higher evolutionary conservation than other lncRNAs and mRNAs. Importantly, the lncRNA-perturbated triplets exhibited high cancer specificity. The pan-cancer perturbator OIP5-AS1 had higher expression level than that of the cancer-specific perturbators. These lncRNA perturbators were significantly enriched in known cancer-related pathways. Furthermore, among the 25 lncRNA in the 109 triplets, lncRNA SNHG7 was identified as a stable potential biomarker in lung adenocarcinoma (LUAD) by combining the TCGA dataset and two independent GEO datasets. Results from cell transfection also indicated that overexpression of lncRNA SNHG7 and TUG1 enhanced the expression of the corresponding mRNA PNMA2 and CDC7 in LUAD. Our study provides a systematic dissection of lncRNA-perturbated triplets and facilitates our understanding of the molecular roles of lncRNAs in cancers.

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 S5-KL-1 CANCER GENOMIC MEDICINE: FROM BENCH TO CLINIC

2019 ◽  
Vol 1 (Supplement_2) ◽  
pp. ii4-ii4
Author(s):  
Hiroyuki Aburatani
Keyword(s):  
Cancer Biology ◽  
Alkylating Agents ◽  
Genomic Medicine ◽  
Cellular Transformation ◽  
Cancer Genome ◽  
Driver Genes ◽  
Cancer Driver ◽  
Personalized Oncology ◽  
The Individual ◽  
Next Generation Sequencing Ngs

Abstract Over the last two decades, genomic technology such as microarray and next generation sequencing (NGS) enabled comprehensive analysis of cancer genome. International cancer genome consortium, established in 2007, completed the analysis of 25,000 cases and has brought discovery of novel cancer driver genes and improved our understanding cancer biology. For example, discovery of IDH1/2 mutation in various cancers created a new concept, 2-hydroxyglutarate as Oncometabolite. The mutational signature patterns allow us to predict how the individual cancer was developed. Anti-cancer drugs, such as alkylating agents, occasionally modify the bases and introduce mutations through mispairing in replication. Currently we are aware that cancer is a genetic disease, where accumulation of genetic and epigenetic alterations in the genome leads to cellular transformation, and that mutation in each patient is unique. To realize the personalized oncology, clinical sequencing test was developed. This year a couple of NGS-based cancer panel tests have been approved for reimbursement by nation-wide healthcare system in Japan. In this seminar I will discuss the future improvement in genomic oncology.

scholarly journals Bayesian inference of cancer driver genes using signatures of positive selection

2017 ◽  
Author(s):  
Luis Zapata ◽  
Hana Susak ◽  
Oliver Drechsel ◽  
Marc R. Friedländer ◽  
Xavier Estivill ◽  
...  
Keyword(s):  
Bayesian Inference ◽  
Large Fraction ◽  
Driver Gene ◽  
Tumor Type ◽  
Sequencing Data ◽  
Cancer Etiology ◽  
Driver Genes ◽  
Cancer Driver ◽  
Cancer Types ◽  
Cancer Cell Fraction

AbstractTumors are composed of an evolving population of cells subjected to tissue-specific selection, which fuels tumor heterogeneity and ultimately complicates cancer driver gene identification. Here, we integrate cancer cell fraction, population recurrence, and functional impact of somatic mutations as signatures of selection into a Bayesian inference model for driver prediction. In an in-depth benchmark, we demonstrate that our model, cDriver, outperforms competing methods when analyzing solid tumors, hematological malignancies, and pan-cancer datasets. Applying cDriver to exome sequencing data of 21 cancer types from 6,870 individuals revealed 98 unreported tumor type-driver gene connections. These novel connections are highly enriched for chromatin-modifying proteins, hinting at a universal role of chromatin regulation in cancer etiology. Although infrequently mutated as single genes, we show that chromatin modifiers are altered in a large fraction of cancer patients. In summary, we demonstrate that integration of evolutionary signatures is key for identifying mutational driver genes, thereby facilitating the discovery of novel therapeutic targets for cancer treatment.

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