1
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Mas-Ponte D, Supek F. Mutation rate heterogeneity at the sub-gene scale due to local DNA hypomethylation. Nucleic Acids Res 2024; 52:4393-4408. [PMID: 38587182 PMCID: PMC11077091 DOI: 10.1093/nar/gkae252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 03/21/2024] [Accepted: 03/26/2024] [Indexed: 04/09/2024] Open
Abstract
Local mutation rates in human are highly heterogeneous, with known variability at the scale of megabase-sized chromosomal domains, and, on the other extreme, at the scale of oligonucleotides. The intermediate, kilobase-scale heterogeneity in mutation risk is less well characterized. Here, by analyzing thousands of somatic genomes, we studied mutation risk gradients along gene bodies, representing a genomic scale spanning roughly 1-10 kb, hypothesizing that different mutational mechanisms are differently distributed across gene segments. The main heterogeneity concerns several kilobases at the transcription start site and further downstream into 5' ends of gene bodies; these are commonly hypomutated with several mutational signatures, most prominently the ubiquitous C > T changes at CpG dinucleotides. The width and shape of this mutational coldspot at 5' gene ends is variable across genes, and corresponds to variable interval of lowered DNA methylation depending on gene activity level and regulation. Such hypomutated loci, at 5' gene ends or elsewhere, correspond to DNA hypomethylation that can associate with various landmarks, including intragenic enhancers, Polycomb-marked regions, or chromatin loop anchor points. Tissue-specific DNA hypomethylation begets tissue-specific local hypomutation. Of note, direction of mutation risk is inverted for AID/APOBEC3 cytosine deaminase activity, whose signatures are enriched in hypomethylated regions.
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Affiliation(s)
- David Mas-Ponte
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology (BIST), 08028 Barcelona, Spain
| | - Fran Supek
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology (BIST), 08028 Barcelona, Spain
- Biotech Research and Innovation Centre (BRIC), Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
- Catalan Institution for Research and Advanced Studies (ICREA), 08010 Barcelona, Spain
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2
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Cheng C, Liu L, Bao Y, Yi J, Quan W, Xue Y, Sun L, Zhang Y. SUVA: splicing site usage variation analysis from RNA-seq data reveals highly conserved complex splicing biomarkers in liver cancer. RNA Biol 2021; 18:157-171. [PMID: 34152934 DOI: 10.1080/15476286.2021.1940037] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Most of the current alternative splicing (AS) analysis tools are powerless to analyse complex splicing. To address this, we developed SUVA (Splice sites Usage Variation Analysis) that decomposes complex splicing events into five types of splice junction pairs. By analysing real and simulated data, SUVA showed higher sensitivity and accuracy in detecting AS events than the compared methods. Notably, SUVA detected extensive complex AS events and screened out 69 highly conserved and dominant AS events associated with cancer. The cancer-associated complex AS events in FN1 and the co-regulated RNA-binding proteins were significantly correlated with patient survival.
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Affiliation(s)
- Chao Cheng
- ABLife BioBigData Institute, Wuhan, Hubei China.,Center for Genome Analysis, ABLife Inc., Wuhan, Hubei China
| | - Lei Liu
- National Engineering Laboratory for Druggable Gene and Protein Screening, Northeast Normal University, Changchun China
| | - Yongli Bao
- National Engineering Laboratory for Druggable Gene and Protein Screening, Northeast Normal University, Changchun China
| | - Jingwen Yi
- National Engineering Laboratory for Druggable Gene and Protein Screening, Northeast Normal University, Changchun China
| | - Weili Quan
- ABLife BioBigData Institute, Wuhan, Hubei China
| | - Yaqiang Xue
- ABLife BioBigData Institute, Wuhan, Hubei China
| | - Luguo Sun
- National Engineering Laboratory for Druggable Gene and Protein Screening, Northeast Normal University, Changchun China
| | - Yi Zhang
- ABLife BioBigData Institute, Wuhan, Hubei China.,Center for Genome Analysis, ABLife Inc., Wuhan, Hubei China
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3
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Bányai L, Trexler M, Kerekes K, Csuka O, Patthy L. Use of signals of positive and negative selection to distinguish cancer genes and passenger genes. eLife 2021; 10:e59629. [PMID: 33427197 PMCID: PMC7877913 DOI: 10.7554/elife.59629] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Accepted: 01/10/2021] [Indexed: 12/14/2022] Open
Abstract
A major goal of cancer genomics is to identify all genes that play critical roles in carcinogenesis. Most approaches focused on genes positively selected for mutations that drive carcinogenesis and neglected the role of negative selection. Some studies have actually concluded that negative selection has no role in cancer evolution. We have re-examined the role of negative selection in tumor evolution through the analysis of the patterns of somatic mutations affecting the coding sequences of human genes. Our analyses have confirmed that tumor suppressor genes are positively selected for inactivating mutations, oncogenes, however, were found to display signals of both negative selection for inactivating mutations and positive selection for activating mutations. Significantly, we have identified numerous human genes that show signs of strong negative selection during tumor evolution, suggesting that their functional integrity is essential for the growth and survival of tumor cells.
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Affiliation(s)
- László Bányai
- Institute of Enzymology, Research Centre for Natural SciencesBudapestHungary
| | - Maria Trexler
- Institute of Enzymology, Research Centre for Natural SciencesBudapestHungary
| | - Krisztina Kerekes
- Institute of Enzymology, Research Centre for Natural SciencesBudapestHungary
| | - Orsolya Csuka
- Department of Pathogenetics, National Institute of OncologyBudapestHungary
| | - László Patthy
- Institute of Enzymology, Research Centre for Natural SciencesBudapestHungary
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4
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López S, Lim EL, Horswell S, Haase K, Huebner A, Dietzen M, Mourikis TP, Watkins TBK, Rowan A, Dewhurst SM, Birkbak NJ, Wilson GA, Van Loo P, Jamal-Hanjani M, Swanton C, McGranahan N. Interplay between whole-genome doubling and the accumulation of deleterious alterations in cancer evolution. Nat Genet 2020; 52:283-293. [PMID: 32139907 PMCID: PMC7116784 DOI: 10.1038/s41588-020-0584-7] [Citation(s) in RCA: 142] [Impact Index Per Article: 35.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Accepted: 01/28/2020] [Indexed: 01/24/2023]
Abstract
Whole-genome doubling (WGD) is a prevalent event in cancer, involving a doubling of the entire chromosome complement. However, despite its prevalence and prognostic relevance, the evolutionary selection pressures for WGD in cancer have not been investigated. Here, we combine evolutionary simulations with an analysis of cancer sequencing data to explore WGD during cancer evolution. Simulations suggest that WGD can be selected to mitigate the irreversible, ratchet-like, accumulation of deleterious somatic alterations, provided that they occur at a sufficiently high rate. Consistent with this, we observe an enrichment for WGD in tumor types with extensive loss of heterozygosity, including lung squamous cell carcinoma and triple-negative breast cancers, and we find evidence for negative selection against homozygous loss of essential genes before, but not after, WGD. Finally, we demonstrate that loss of heterozygosity and temporal dissection of mutations can be exploited to identify novel tumor suppressor genes and to obtain a deeper characterization of known cancer genes.
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Affiliation(s)
- Saioa López
- Cancer Genome Evolution Research Group, University College London Cancer Institute, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Emilia L Lim
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, Francis Crick Institute and University College London Cancer Institute, London, UK
| | - Stuart Horswell
- Bioinformatics and Biostatistics Group, Francis Crick Institute, London, UK
| | - Kerstin Haase
- Cancer Genomics Laboratory, Francis Crick Institute, London, UK
| | - Ariana Huebner
- Cancer Genome Evolution Research Group, University College London Cancer Institute, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, Francis Crick Institute and University College London Cancer Institute, London, UK
| | - Michelle Dietzen
- Cancer Genome Evolution Research Group, University College London Cancer Institute, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, Francis Crick Institute and University College London Cancer Institute, London, UK
| | - Thanos P Mourikis
- Cancer Genome Evolution Research Group, University College London Cancer Institute, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Thomas B K Watkins
- Cancer Evolution and Genome Instability Laboratory, Francis Crick Institute and University College London Cancer Institute, London, UK
| | - Andrew Rowan
- Cancer Evolution and Genome Instability Laboratory, Francis Crick Institute and University College London Cancer Institute, London, UK
| | - Sally M Dewhurst
- Laboratory for Cell Biology and Genetics, The Rockefeller University, New York, NY, USA
| | - Nicolai J Birkbak
- Cancer Evolution and Genome Instability Laboratory, Francis Crick Institute and University College London Cancer Institute, London, UK
- Department of Molecular Medicine, Aarhus University, Aarhus, Denmark
| | - Gareth A Wilson
- Cancer Evolution and Genome Instability Laboratory, Francis Crick Institute and University College London Cancer Institute, London, UK
| | - Peter Van Loo
- Cancer Genomics Laboratory, Francis Crick Institute, London, UK
- Department of Human Genetics, University of Leuven, Leuven, Belgium
| | - Mariam Jamal-Hanjani
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Department of Medical Oncology, University College London Hospitals NHS Foundation Trust, London, UK
| | - Charles Swanton
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK.
- Cancer Evolution and Genome Instability Laboratory, Francis Crick Institute and University College London Cancer Institute, London, UK.
| | - Nicholas McGranahan
- Cancer Genome Evolution Research Group, University College London Cancer Institute, London, UK.
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK.
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5
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Salichos L, Meyerson W, Warrell J, Gerstein M. Estimating growth patterns and driver effects in tumor evolution from individual samples. Nat Commun 2020; 11:732. [PMID: 32024824 PMCID: PMC7002450 DOI: 10.1038/s41467-020-14407-9] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Accepted: 11/26/2019] [Indexed: 01/01/2023] Open
Abstract
Tumors accumulate thousands of mutations, and sequencing them has given rise to methods for finding cancer drivers via mutational recurrence. However, these methods require large cohorts and underperform for low recurrence. Recently, ultra-deep sequencing has enabled accurate measurement of VAFs (variant-allele frequencies) for mutations, allowing the determination of evolutionary trajectories. Here, based solely on the VAF spectrum for an individual sample, we report on a method that identifies drivers and quantifies tumor growth. Drivers introduce perturbations into the spectrum, and our method uses the frequency of hitchhiking mutations preceding a driver to measure this. As validation, we use simulation models and 993 tumors from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium with previously identified drivers. Then we apply our method to an ultra-deep sequenced acute myeloid leukemia (AML) tumor and identify known cancer genes and additional driver candidates. In summary, our framework presents opportunities for personalized driver diagnosis using sequencing data from a single individual.
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Affiliation(s)
- Leonidas Salichos
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - William Meyerson
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Jonathan Warrell
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Mark Gerstein
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA.
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA.
- Department of Computer Science, Yale University, New Haven, CT, 06520, USA.
- Center for Biomedical Data Science, Yale University, New Haven, CT, 06520, USA.
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6
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Supek F, Lehner B. Scales and mechanisms of somatic mutation rate variation across the human genome. DNA Repair (Amst) 2019; 81:102647. [PMID: 31307927 DOI: 10.1016/j.dnarep.2019.102647] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Cancer genome sequencing has revealed that somatic mutation rates vary substantially across the human genome and at scales from megabase-sized domains to individual nucleotides. Here we review recent work that has both revealed the major mutation biases that operate across the genome and the molecular mechanisms that cause them. The default mutation rate landscape in mammalian genomes results in active genes having low mutation rates because of a combination of factors that increase DNA repair: early DNA replication, transcription, active chromatin modifications and accessible chromatin. Therefore, either an increase in the global mutation rate or a redistribution of mutations from inactive to active DNA can increase the rate at which consequential mutations are acquired in active genes. Several environmental carcinogens and intrinsic mechanisms operating in tumor cells likely cause cancer by this second mechanism: by specifically increasing the mutation rate in active regions of the genome.
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Affiliation(s)
- Fran Supek
- Genome Data Science, Institut de Recerca Biomedica (IRB Barcelona), The Barcelona Institute of Science and Technology, Baldiri Reixac 10, 08028, Barcelona, Spain; Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, 08010 Barcelona, Spain.
| | - Ben Lehner
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, 08010 Barcelona, Spain; Systems Biology Program, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Doctor Aiguader 88, 08003 Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain
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7
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Abstract
Synonymous mutations have been viewed as silent mutations, since they only affect the DNA and mRNA, but not the amino acid sequence of the resulting protein. Nonetheless, recent studies suggest their significant impact on splicing, RNA stability, RNA folding, translation or co-translational protein folding. Hence, we compile 659194 synonymous mutations found in human cancer and characterize their properties. We provide the user-friendly, comprehensive resource for synonymous mutations in cancer, SynMICdb (http://SynMICdb.dkfz.de), which also contains orthogonal information about gene annotation, recurrence, mutation loads, cancer association, conservation, alternative events, impact on mRNA structure and a SynMICdb score. Notably, synonymous and missense mutations are depleted at the 5'-end of the coding sequence as well as at the ends of internal exons independent of mutational signatures. For patient-derived synonymous mutations in the oncogene KRAS, we indicate that single point mutations can have a relevant impact on expression as well as on mRNA secondary structure. Synonymous mutations do not alter amino acid sequence but may exert oncogenic effects in other ways. Here, the authors present a catalogue of synonymous mutations in cancer and characterise their properties.
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8
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Zapata L, Pich O, Serrano L, Kondrashov FA, Ossowski S, Schaefer MH. Negative selection in tumor genome evolution acts on essential cellular functions and the immunopeptidome. Genome Biol 2018; 19:67. [PMID: 29855388 PMCID: PMC5984361 DOI: 10.1186/s13059-018-1434-0] [Citation(s) in RCA: 55] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2017] [Accepted: 04/20/2018] [Indexed: 01/08/2023] Open
Abstract
Background Natural selection shapes cancer genomes. Previous studies used signatures of positive selection to identify genes driving malignant transformation. However, the contribution of negative selection against somatic mutations that affect essential tumor functions or specific domains remains a controversial topic. Results Here, we analyze 7546 individual exomes from 26 tumor types from TCGA data to explore the portion of the cancer exome under negative selection. Although we find most of the genes neutrally evolving in a pan-cancer framework, we identify essential cancer genes and immune-exposed protein regions under significant negative selection. Moreover, our simulations suggest that the amount of negative selection is underestimated. We therefore choose an empirical approach to identify genes, functions, and protein regions under negative selection. We find that expression and mutation status of negatively selected genes is indicative of patient survival. Processes that are most strongly conserved are those that play fundamental cellular roles such as protein synthesis, glucose metabolism, and molecular transport. Intriguingly, we observe strong signals of selection in the immunopeptidome and proteins controlling peptide exposition, highlighting the importance of immune surveillance evasion. Additionally, tumor type-specific immune activity correlates with the strength of negative selection on human epitopes. Conclusions In summary, our results show that negative selection is a hallmark of cell essentiality and immune response in cancer. The functional domains identified could be exploited therapeutically, ultimately allowing for the development of novel cancer treatments. Electronic supplementary material The online version of this article (10.1186/s13059-018-1434-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Luis Zapata
- Genomic and Epigenomic Variation in Disease Group, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, 08003, Barcelona, Spain.,Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Oriol Pich
- Evolutionary Genomics Group, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, 08003, Barcelona, Spain.,Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Baldiri Reixac, 10, 08028, Barcelona, Spain
| | - Luis Serrano
- Design of Biological Systems Group, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, 08003, Barcelona, Spain.,Universitat Pompeu Fabra (UPF), Barcelona, Spain.,Institució Catalana de Recerca i Estudis Avançats (ICREA), Pg. Lluis Companys 23, 08010, Barcelona, Spain
| | - Fyodor A Kondrashov
- IST Austria (Institute of Science and Technology Austria), Am Campus 1, 3400, Klosterneuburg, Austria
| | - Stephan Ossowski
- Genomic and Epigenomic Variation in Disease Group, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, 08003, Barcelona, Spain. .,Universitat Pompeu Fabra (UPF), Barcelona, Spain. .,Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany.
| | - Martin H Schaefer
- Design of Biological Systems Group, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, 08003, Barcelona, Spain.
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