The effects of mutational processes and selection on driver mutations across cancer types

AbstractThe colorectal adenoma-carcinoma sequence has provided a paradigmatic framework for understanding the successive somatic genetic changes and consequent clonal expansions leading to cancer. As for most cancer types, however, understanding of the earliest phases of colorectal neoplastic change, which may occur in morphologically normal tissue, is comparatively limited because of the difficulty of detecting somatic mutations in normal cells. Each colorectal crypt is a small clone of cells derived from a single recently-existing stem cell. Here, we whole genome sequenced hundreds of normal crypts from 42 individuals. Signatures of multiple mutational processes were revealed, some ubiquitous and continuous, others only found in some individuals, in some crypts or during some phases of the cell lineage from zygote to adult cell. Likely driver mutations were present in ∼1% of normal colorectal crypts in middle-aged individuals, indicating that adenomas and carcinomas are rare outcomes of a pervasive process of neoplastic change across morphologically normal colorectal epithelium.

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The effects of mutational processes and selection on driver mutations across cancer types

10.1101/149096 ◽

2017 ◽

Cited By ~ 2

Author(s):

Daniel Temko ◽

Ian PM Tomlinson ◽

Simone Severini ◽

Benjamin Schuster-Böckler ◽

Trevor A Graham

Keyword(s):

Driver Mutations ◽

Sequencing Data ◽

Epidemiological Evidence ◽

Dna Mismatch ◽

Cancer Types ◽

Key Driver ◽

Independent Effects ◽

Mutational Processes ◽

Mutation And Selection

ABSTRACTEpidemiological evidence has long associated environmental mutagens with increased cancer risk. However, links between specific mutation-causing processes and the acquisition of individual driver mutations have remained obscure. Here we have used public cancer sequencing data to infer the independent effects of mutation and selection on driver mutation complement. First, we detect associations between a range of mutational processes, including those linked to smoking, ageing, APOBEC and DNA mismatch repair (MMR) and the presence of key driver mutations across cancer types. Second, we quantify differential selection between well-known alternative driver mutations, including differences in selection between distinct mutant residues in the same gene. These results show that while mutational processes play a large role in determining which driver mutations are present in a cancer, the role of selection frequently dominates.

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Author Correction: The effects of mutational processes and selection on driver mutations across cancer types

Nature Communications ◽

10.1038/s41467-020-15800-0 ◽

2020 ◽

Vol 11 (1) ◽

Author(s):

Daniel Temko ◽

Ian P. M. Tomlinson ◽

Simone Severini ◽

Benjamin Schuster-Böckler ◽

Trevor A. Graham

Keyword(s):

Driver Mutations ◽

Cancer Types ◽

Mutational Processes

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Divergent mutational processes distinguish hypoxic and normoxic tumours

10.1101/531996 ◽

2019 ◽

Author(s):

Vinayak Bhandari ◽

Constance H. Li ◽

Robert G. Bristow ◽

Paul C. Boutros ◽

Keyword(s):

Critical Role ◽

Driver Mutations ◽

Driver Genes ◽

Evolutionary Trajectory ◽

Genomic Landscape ◽

Evolutionary Features ◽

Gene Level ◽

Cancer Types ◽

Low Levels ◽

Mutational Processes

AbstractMany primary tumours have low levels of molecular oxygen (hypoxia). Hypoxic tumours are more likely to metastasize to distant sites and respond poorly to multiple therapies. Surprisingly, then, the pan-cancer molecular hallmarks of tumour hypoxia remain poorly understood, with limited understanding of its associations with specific mutational processes, non-coding driver genes and evolutionary features. To fill this gap, we quantified hypoxia in 1,188 tumours spanning 27 cancer types. We show that elevated hypoxia is associated with increased mutational load across cancers, irrespective of the underlying mutational class. The proportion of mutations attributed to several mutational signatures of unknown aetiology are directly associated with the level of hypoxia, suggesting underlying mutational processes for these signatures. At the gene level, driver mutations in TP53, MYC and PTEN are enriched in tumours with high hypoxia, and mutations in PTEN interact with hypoxia to direct the evolutionary trajectory of tumours. Overall, this work demonstrates that hypoxia plays a critical role in shaping the genomic landscape of cancer.

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Histone Modifying Enzymes in Gynaecological Cancers

Cancers ◽

10.3390/cancers13040816 ◽

2021 ◽

Vol 13 (4) ◽

pp. 816

Author(s):

Priya Ramarao-Milne ◽

Olga Kondrashova ◽

Sinead Barry ◽

John D. Hooper ◽

Jason S. Lee ◽

...

Keyword(s):

Genome Instability ◽

Cpg Islands ◽

Gene Promoter ◽

Driver Mutations ◽

Promoter Regions ◽

Post Translational Modifications ◽

Chromatin Dynamics ◽

Cancer Types ◽

Histone Modifying Enzymes

Genetic and epigenetic factors contribute to the development of cancer. Epigenetic dysregulation is common in gynaecological cancers and includes altered methylation at CpG islands in gene promoter regions, global demethylation that leads to genome instability and histone modifications. Histones are a major determinant of chromosomal conformation and stability, and unlike DNA methylation, which is generally associated with gene silencing, are amenable to post-translational modifications that induce facultative chromatin regions, or condensed transcriptionally silent regions that decondense resulting in global alteration of gene expression. In comparison, other components, crucial to the manipulation of chromatin dynamics, such as histone modifying enzymes, are not as well-studied. Inhibitors targeting DNA modifying enzymes, particularly histone modifying enzymes represent a potential cancer treatment. Due to the ability of epigenetic therapies to target multiple pathways simultaneously, tumours with complex mutational landscapes affected by multiple driver mutations may be most amenable to this type of inhibitor. Interrogation of the actionable landscape of different gynaecological cancer types has revealed that some patients have biomarkers which indicate potential sensitivity to epigenetic inhibitors. In this review we describe the role of epigenetics in gynaecological cancers and highlight how it may exploited for treatment.

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Multi-omic data helps improve prediction of personalised tumor suppressors and oncogenes

10.1101/2022.01.13.476163 ◽

2022 ◽

Author(s):

Malvika Sudhakar ◽

Raghunathan Rengaswamy ◽

Karthik Raman

Keyword(s):

Tumour Progression ◽

Suppressor Gene ◽

Tumour Suppressor Gene ◽

Driver Mutations ◽

Cancer Type ◽

Expression Data ◽

Multiple Cancer ◽

Driver Genes ◽

Cancer Types ◽

Omic Data

The progression of tumorigenesis starts with a few mutational and structural driver events in the cell. Various cohort-based computational tools exist to identify driver genes but require a large number of samples to produce reliable results. Many studies use different methods to identify driver mutations/genes from mutations that have no impact on tumour progression; however, a small fraction of patients show no mutational events in any known driver genes. Current unsupervised methods map somatic and expression data onto a network to identify the perturbation in the network. Our method is the first machine learning model to classify genes as tumour suppressor gene (TSG), oncogene (OG) or neutral, thus assigning the functional impact of the gene in the patient. In this study, we develop a multi-omic approach, PIVOT (Personalised Identification of driVer OGs and TSGs), to train on experimentally or computationally validated mutational and structural driver events. Given the lack of any gold standards for the identification of personalised driver genes, we label the data using four strategies and, based on classification metrics, show gene-based labelling strategies perform best. We build different models using SNV, RNA, and multi-omic features to be used based on the data available. Our models trained on multi-omic data improved predictions compared to mutation and expression data, achieving an accuracy >0.99 for BRCA, LUAD and COAD datasets. We show network and expression-based features contribute the most to PIVOT. Our predictions on BRCA, COAD and LUAD cancer types reveal commonly altered genes such as TP53, and PIK3CA, which are predicted drivers for multiple cancer types. Along with known driver genes, our models also identify new driver genes such as PRKCA, SOX9 and PSMD4. Our multi-omic model labels both CNV and mutations with a more considerable contribution by CNV alterations. While predicting labels for genes mutated in multiple samples, we also label rare driver events occurring in as few as one sample. We also identify genes with dual roles within the same cancer type. Overall, PIVOT labels personalised driver genes as TSGs and OGs and also identifies rare driver genes. PIVOT is available at https://github.com/RamanLab/PIVOT.

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A Pan-Cancer Census of Dominant Tumor Immune Archetypes

10.1101/2021.04.26.441344 ◽

2021 ◽

Author(s):

Alexis J. Combes ◽

Bushra Samad ◽

Jessica Tsui ◽

Nayvin W. Chew ◽

Peter Yan ◽

...

Keyword(s):

Immune System ◽

Expression Patterns ◽

Tumor Biology ◽

Surrounding Tissue ◽

Driver Mutations ◽

Significant Heterogeneity ◽

Tumor Immune Microenvironment ◽

Cancer Types ◽

System Archetypes ◽

Tumor Gene Expression

SUMMARYCancers display significant heterogeneity with respect to tissue of origin, driver mutations and other features of the surrounding tissue. It is likely that persistent tumors differentially engage inherent patterns–here ‘Archetypes’–of the immune system, to both benefit from a tumor immune microenvironment (TIME) and to disengage tumor-targeting. To discover dominant immune system archetypes, the Immunoprofiler Initiative (IPI) processed 364 individual tumors across 12 cancer types using standardized protocols. Computational clustering of flow cytometry and transcriptomic data obtained from cell sub compartments uncovered archetypes that exist across indications. These Immune composition-based archetypes differentiate tumors based upon unique immune and tumor gene-expression patterns. Archetypes discovered this way also tie closely to well-established classifications of tumor biology. The IPI resource provides a template for understanding cancer immunity as a collection of dominant patterns of immune infiltration and provides a rational path forward to learn how to modulate these patterns to improve therapy.

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When, why and how clonal diversity predicts future tumour growth

10.1101/2019.12.17.879270 ◽

2019 ◽

Author(s):

Robert Noble ◽

John T Burley ◽

Cécile Le Sueur ◽

Michael E Hochberg

Keyword(s):

Growth Rate ◽

Tumour Growth ◽

Evolutionary Process ◽

Clonal Diversity ◽

Driver Mutations ◽

Multiple Cancer ◽

Intratumour Heterogeneity ◽

Cancer Types ◽

The Relationship ◽

Tumour Growth Rate

AbstractIntratumour heterogeneity holds promise as a prognostic biomarker in multiple cancer types. However, the relationship between this marker and its clinical impact is mediated by an evolutionary process that is not well understood. Here we employ a spatial computational model of tumour evolution to assess when, why and how intratumour heterogeneity can be used to forecast tumour growth rate, an important predictor of clinical progression. We identify three conditions that can lead to a positive correlation between clonal diversity and subsequent growth rate: diversity is measured early in tumour development; selective sweeps are rare; and/or tumours vary in the rate at which they acquire driver mutations. Opposite conditions typically lead to negative correlation. Our results further suggest that prognosis can be better predicted on the basis of both clonal diversity and genomic instability than either factor alone. Nevertheless, we find that, for predicting tumour growth, clonal diversity is likely to perform worse than conventional measures of tumour stage and grade. We thus offer explanations – grounded in evolutionary theory – for empirical findings in various cancers. Our work informs the search for new prognostic biomarkers and contributes to the development of predictive oncology.

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

Nucleic Acids Research ◽

10.1093/nar/gkaa1033 ◽

2020 ◽

Vol 49 (D1) ◽

pp. D1289-D1301 ◽

Cited By ~ 2

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.

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The cancer-mutation network and the number and specificity of driver mutations

10.1101/237016 ◽

2017 ◽

Author(s):

Jaime Iranzo ◽

Iñigo Martincorena ◽

Eugene V. Koonin

Keyword(s):

Linear Regression ◽

Cancer Genomics ◽

Driver Mutations ◽

Bipartite Network ◽

Cancer Driver ◽

Cancer Mutation ◽

Cancer Types ◽

Extensive Information ◽

The Mean ◽

Network Component

AbstractCancer genomics has produced extensive information on cancer-associated genes but the number and specificity of cancer driver mutations remains a matter of debate. We constructed a bipartite network in which 7665 tumors from 30 cancer types are connected via shared mutations in 198 previously identified cancer-associated genes. We show that 27% of the tumors can be assigned to statistically supported modules, most of which encompass 1-2 cancer types. The rest of the tumors belong to a diffuse network component suggesting lower gene-specificity of driver mutations. Linear regression of the mutational loads in cancer-associated genes was used to estimate the number of drivers required for the onset of different cancers. The mean number of drivers is ~2, with a range of 1 to 5. Cancers that are associated to modules had more drivers than those from the diffuse network component, suggesting that unidentified and/or interchangeable drivers exist in the latter.

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