Geographic variation of mutagenic exposures in kidney cancer genomes
Jazyk angličtina Země Velká Británie, Anglie Médium print-electronic
Typ dokumentu srovnávací studie, časopisecké články
Grantová podpora
Wellcome Trust - United Kingdom
001
World Health Organization - International
T32 CA067754
NCI NIH HHS - United States
PubMed
38693263
PubMed Central
PMC11111402
DOI
10.1038/s41586-024-07368-2
PII: 10.1038/s41586-024-07368-2
Knihovny.cz E-zdroje
- MeSH
- genom lidský genetika MeSH
- genomika MeSH
- hypertenze epidemiologie MeSH
- incidence MeSH
- karcinom z renálních buněk * genetika epidemiologie chemicky indukované MeSH
- kouření tabáku škodlivé účinky genetika MeSH
- kyseliny aristolochové škodlivé účinky MeSH
- lidé MeSH
- mutace * MeSH
- mutageny * škodlivé účinky MeSH
- nádory ledvin * genetika epidemiologie chemicky indukované MeSH
- obezita epidemiologie MeSH
- rizikové faktory MeSH
- vystavení vlivu životního prostředí * škodlivé účinky analýza MeSH
- zeměpis * MeSH
- Check Tag
- lidé MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- srovnávací studie MeSH
- Geografické názvy
- Japonsko epidemiologie MeSH
- Rumunsko epidemiologie MeSH
- Srbsko epidemiologie MeSH
- Thajsko epidemiologie MeSH
- Názvy látek
- kyseliny aristolochové MeSH
- mutageny * MeSH
International differences in the incidence of many cancer types indicate the existence of carcinogen exposures that have not yet been identified by conventional epidemiology make a substantial contribution to cancer burden1. In clear cell renal cell carcinoma, obesity, hypertension and tobacco smoking are risk factors, but they do not explain the geographical variation in its incidence2. Underlying causes can be inferred by sequencing the genomes of cancers from populations with different incidence rates and detecting differences in patterns of somatic mutations. Here we sequenced 962 clear cell renal cell carcinomas from 11 countries with varying incidence. The somatic mutation profiles differed between countries. In Romania, Serbia and Thailand, mutational signatures characteristic of aristolochic acid compounds were present in most cases, but these were rare elsewhere. In Japan, a mutational signature of unknown cause was found in more than 70% of cases but in less than 2% elsewhere. A further mutational signature of unknown cause was ubiquitous but exhibited higher mutation loads in countries with higher incidence rates of kidney cancer. Known signatures of tobacco smoking correlated with tobacco consumption, but no signature was associated with obesity or hypertension, suggesting that non-mutagenic mechanisms of action underlie these risk factors. The results of this study indicate the existence of multiple, geographically variable, mutagenic exposures that potentially affect tens of millions of people and illustrate the opportunities for new insights into cancer causation through large-scale global cancer genomics.
Biomedical Sciences Graduate Program University of California San Diego La Jolla CA USA
Cancer Ageing and Somatic Mutation Wellcome Sanger Institute Cambridge UK
Cancer Epidemiology Unit The Nuffield Department of Population Health University of Oxford Oxford UK
Centre for Biodiversity Genomics University of Guelph Guelph Ontario Canada
Clinic of Nephrology Faculty of Medicine Military Medical Academy Belgrade Serbia
Department of Anatomic Pathology A C Camargo Cancer Center São Paulo Brazil
Department of Bioengineering University of California San Diego La Jolla CA USA
Department of Botany and Genetics Institute of Biosciences Vilnius University Vilnius Lithuania
Department of Cancer Epidemiology and Genetics Masaryk Memorial Cancer Institute Brno Czech Republic
Department of Cellular and Molecular Medicine University of California San Diego La Jolla CA USA
Department of Environmental Epidemiology Nofer Institute of Occupational Medicine Łódź Poland
Department of Epidemiology A C Camargo Cancer Center São Paulo Brazil
Department of Pathology Barretos Cancer Hospital Barretos Brazil
Department of Pathology Graduate School of Medicine The University of Tokyo Bunkyo ku Japan
Department of Pathology Radboud University Medical Centre Nijmegen Netherlands
Department of Surgery Division of Urology Sao Paulo Federal University São Paulo Brazil
Department of Urology A C Camargo Cancer Center São Paulo Brazil
Department of Urology Barretos Cancer Hospital Barretos Brazil
Department of Urology N N Blokhin National Medical Research Centre of Oncology Moscow Russia
Department of Urology University Hospital Dr D Misovic Clinical Center Belgrade Serbia
Division of Cancer Epidemiology and Genetics National Cancer Institute Rockville MD USA
Division of Cancer Genomics National Cancer Center Research Institute Chuo ku Japan
Evidence Synthesis and Classification Branch International Agency for Research on Cancer Lyon France
Faculdade Ciências Médicas de Minas Gerais Belo Horizonte Brazil
Faculty of Health Sciences Palacky University Olomouc Czech Republic
Genomic Epidemiology Branch International Agency for Research on Cancer Lyon France
Hasheminejad Kidney Center Iran University of Medical Sciences Tehran Iran
International Organization for Cancer Prevention and Research Belgrade Serbia
Laboratory of Genetic Diagnostic National Cancer Institute Vilnius Lithuania
Latin American Renal Cancer Group São Paulo Brazil
Leeds Institute of Medical Research at St James's University of Leeds Leeds UK
Life and Health Sciences Research Institute School of Medicine Minho University Braga Portugal
Molecular Oncology Research Center Barretos Cancer Hospital Barretos Brazil
Moores Cancer Center University of California San Diego La Jolla CA USA
Nutrition and Metabolism Branch International Agency for Research on Cancer Lyon France
Observational and Pragmatic Research Institute Pte Ltd Singapore Singapore
Oncopathology Research Center Iran University of Medical Sciences Tehran Iran
Ontario Tumour Bank Ontario Institute for Cancer Research Toronto Ontario Canada
Service of Urology Hospital de Clínicas de Porto Alegre Porto Alegre Brazil
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Brennan P, Davey-Smith G. Identifying novel causes of cancers to enhance cancer prevention: new strategies are needed. J. Natl Cancer Inst. 2022;114:353–360. doi: 10.1093/jnci/djab204. PubMed DOI PMC
Hsieh JJ, et al. Renal cell carcinoma. Nat. Rev. Dis. Primers. 2017;3:17009. doi: 10.1038/nrdp.2017.9. PubMed DOI PMC
Koh G, Degasperi A, Zou X, Momen S, Nik-Zainal S. Mutational signatures: emerging concepts, caveats and clinical applications. Nat. Rev. Cancer. 2021;21:619–637. doi: 10.1038/s41568-021-00377-7. PubMed DOI
Alexandrov LB, et al. The repertoire of mutational signatures in human cancer. Nature. 2020;578:94–101. doi: 10.1038/s41586-020-1943-3. PubMed DOI PMC
Scelo G, et al. Variation in genomic landscape of clear cell renal cell carcinoma across Europe. Nat. Commun. 2014;5:5135. doi: 10.1038/ncomms6135. PubMed DOI
Mitchell TJ, et al. Timing the landmark events in the evolution of clear cell renal cell cancer: TRACERx renal. Cell. 2018;173:611–623.e17. doi: 10.1016/j.cell.2018.02.020. PubMed DOI PMC
Campbell PJ, et al. Pan-cancer analysis of whole genomes. Nature. 2020;578:82–93. doi: 10.1038/s41586-020-1969-6. PubMed DOI PMC
Degasperi A, et al. A practical framework and online tool for mutational signature analyses show intertissue variation and driver dependencies. Nat. Cancer. 2020;1:249–263. doi: 10.1038/s43018-020-0027-5. PubMed DOI PMC
The Cancer Genome Atlas Research Network. Comprehensive molecular characterization of clear cell renal cell carcinoma. Nature. 2013;499:43–49. doi: 10.1038/nature12222. PubMed DOI PMC
Mutographs. Cancer Grand Challengeshttps://cancergrandchallenges.org/teams (2023).
Sung H, et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA. 2021;71:209–249. PubMed
Nik-Zainal S, et al. Mutational processes molding the genomes of 21 breast cancers. Cell. 2012;149:979–993. doi: 10.1016/j.cell.2012.04.024. PubMed DOI PMC
Alexandrov LB, et al. Signatures of mutational processes in human cancer. Nature. 2013;500:415–421. doi: 10.1038/nature12477. PubMed DOI PMC
Drost J, et al. Use of CRISPR-modified human stem cell organoids to study the origin of mutational signatures in cancer. Science. 2017;358:234–238. doi: 10.1126/science.aao3130. PubMed DOI PMC
Hoang ML, et al. Mutational signature of aristolochic acid exposure as revealed by whole-exome sequencing. Sci. Transl. Med. 2013;5:197ra102. doi: 10.1126/scitranslmed.3006200. PubMed DOI PMC
Poon SL, et al. Genome-wide mutational signatures of aristolochic acid and its application as a screening tool. Sci. Transl. Med. 2013;5:197ra101. doi: 10.1126/scitranslmed.3006086. PubMed DOI
Grollman AP. Aristolochic acid nephropathy: harbinger of a global iatrogenic disease. Environ. Mol. Mutagen. 2013;54:1–7. doi: 10.1002/em.21756. PubMed DOI
Turesky RJ, et al. Aristolochic acid exposure in Romania and implications for renal cell carcinoma. Br. J. Cancer. 2016;114:76–80. doi: 10.1038/bjc.2015.402. PubMed DOI PMC
Wang X-M, et al. Integrative genomic study of Chinese clear cell renal cell carcinoma reveals features associated with thrombus. Nat. Commun. 2020;11:739. doi: 10.1038/s41467-020-14601-9. PubMed DOI PMC
Stefanovic V, Radovanovic Z. Balkan endemic nephropathy and associated urothelial cancer. Nat. Clin. Pract. Urol. 2008;5:105–112. doi: 10.1038/ncpuro1019. PubMed DOI
Huang MN, et al. Genome-scale mutational signatures of aflatoxin in cells, mice, and human tumors. Genome Res. 2017;27:1475–1486. doi: 10.1101/gr.220038.116. PubMed DOI PMC
Haradhvala NJ, et al. Mutational strand asymmetries in cancer genomes reveal mechanisms of DNA damage and repair. Cell. 2016;164:538–549. doi: 10.1016/j.cell.2015.12.050. PubMed DOI PMC
Otlu B, et al. Topography of mutational signatures in human cancer. Cell Rep. 2023;42:112930. doi: 10.1016/j.celrep.2023.112930. PubMed DOI PMC
Nik-Zainal S. The genome as a record of environmental exposure. Mutagenesis. 2015;30:763–770. PubMed PMC
Sato Y, et al. Integrated molecular analysis of clear-cell renal cell carcinoma. Nat. Genet. 2013;45:860–867. doi: 10.1038/ng.2699. PubMed DOI
Dempsey D, et al. Nicotine metabolite ratio as an index of cytochrome P450 2A6 metabolic activity. Clin. Pharmacol. Ther. 2004;76:64–72. doi: 10.1016/j.clpt.2004.02.011. PubMed DOI
Velenosi TJ, et al. Untargeted metabolomics reveals N,N,N-trimethyl-l-alanyl-l-proline betaine (TMAP) as a novel biomarker of kidney function. Sci. Rep. 2019;9:6831. doi: 10.1038/s41598-019-42992-3. PubMed DOI PMC
Nik-Zainal S, et al. The life history of 21 breast cancers. Cell. 2012;149:994–1007. doi: 10.1016/j.cell.2012.04.023. PubMed DOI PMC
Dentro SC, Wedge DC, van Loo P. Principles of reconstructing the subclonal architecture of cancers. Cold Spring Harb. Perspect. Med. 2017;7:a026625. doi: 10.1101/cshperspect.a026625. PubMed DOI PMC
Shearer JJ, et al. Serum concentrations of per- and polyfluoroalkyl substances and risk of renal cell carcinoma. J. Natl Cancer Inst. 2021;113:580–587. doi: 10.1093/jnci/djaa143. PubMed DOI PMC
Kucab JE, et al. A compendium of mutational signatures of environmental agents. Cell. 2019;177:821–836.e16. doi: 10.1016/j.cell.2019.03.001. PubMed DOI PMC
Gabriel AAG, et al. Genetic analysis of lung cancer and the germline impact on somatic mutation burden. J. Natl Cancer Inst. 2022;114:1159–1166. doi: 10.1093/jnci/djac087. PubMed DOI PMC
Liu Y, Gusev A, Heng YJ, Alexandrov LB, Kraft P. Somatic mutational profiles and germline polygenic risk scores in human cancer. Genome Med. 2022;14:14. doi: 10.1186/s13073-022-01016-y. PubMed DOI PMC
Moody S, et al. Mutational signatures in esophageal squamous cell carcinoma from eight countries with varying incidence. Nat. Genet. 2021;53:1553–1563. doi: 10.1038/s41588-021-00928-6. PubMed DOI
Abascal F, et al. Somatic mutation landscapes at single-molecule resolution. Nature. 2021;593:405–410. doi: 10.1038/s41586-021-03477-4. PubMed DOI
Robinson PS, et al. Increased somatic mutation burdens in normal human cells due to defective DNA polymerases. Nat. Genet. 2021;53:1434–1442. doi: 10.1038/s41588-021-00930-y. PubMed DOI PMC
Robinson PS, et al. Inherited MUTYH mutations cause elevated somatic mutation rates and distinctive mutational signatures in normal human cells. Nat. Commun. 2022;13:3949. doi: 10.1038/s41467-022-31341-0. PubMed DOI PMC
Fowler JC, Jones PH. Somatic mutation: what shapes the mutational landscape of normal epithelia? Cancer Discov. 2022;12:1642–1655. doi: 10.1158/2159-8290.CD-22-0145. PubMed DOI PMC
Whalley JP, et al. Framework for quality assessment of whole genome cancer sequences. Nat. Commun. 2020;11:5040. doi: 10.1038/s41467-020-18688-y. PubMed DOI PMC
Bergmann EA, Chen B-J, Arora K, Vacic V, Zody MC. Conpair: concordance and contamination estimator for matched tumor–normal pairs. Bioinformatics. 2016;32:3196–3198. doi: 10.1093/bioinformatics/btw389. PubMed DOI PMC
Van Loo P, et al. Allele-specific copy number analysis of tumors. Proc. Natl Acad. Sci. USA. 2010;107:16910–16915. doi: 10.1073/pnas.1009843107. PubMed DOI PMC
Jones D, et al. cgpCaVEManWrapper: simple execution of CaVEMan in order to detect somatic single nucleotide variants in NGS data. Curr. Protoc. Bioinform. 2016;56:15.10.1–15.10.18. doi: 10.1002/cpbi.20. PubMed DOI PMC
Raine KM, et al. cgpPindel: identifying somatically acquired insertion and deletion events from paired end sequencing. Curr. Protoc. Bioinform. 2015;52:15.7.1–15.7.12. doi: 10.1002/0471250953.bi1507s52. PubMed DOI PMC
Kim S, et al. Strelka2: fast and accurate calling of germline and somatic variants. Nat. Methods. 2018;15:591–594. doi: 10.1038/s41592-018-0051-x. PubMed DOI
Bergstrom EN, et al. SigProfilerMatrixGenerator: a tool for visualizing and exploring patterns of small mutational events. BMC Genom. 2019;20:685. doi: 10.1186/s12864-019-6041-2. PubMed DOI PMC
Liu M, Wu Y, Jiang N, Boot A, Rozen SG. mSigHdp: hierarchical Dirichlet process mixture modeling for mutational signature discovery. NAR Genom. Bioinform. 2023;5:lqad005. doi: 10.1093/nargab/lqad005. PubMed DOI PMC
Islam SMA, et al. Uncovering novel mutational signatures by de novo extraction with SigProfilerExtractor. Cell Genom. 2022;2:100179. doi: 10.1016/j.xgen.2022.100179. PubMed DOI PMC
Senkin S. MSA: reproducible mutational signature attribution with confidence based on simulations. BMC Bioinform. 2021;22:540. doi: 10.1186/s12859-021-04450-8. PubMed DOI PMC
Martincorena I, et al. Universal patterns of selection in cancer and somatic tissues. Cell. 2017;171:1029–1041.e21. doi: 10.1016/j.cell.2017.09.042. PubMed DOI PMC
Tamborero D, et al. Cancer Genome Interpreter annotates the biological and clinical relevance of tumor alterations. Genome Med. 2018;10:25. doi: 10.1186/s13073-018-0531-8. PubMed DOI PMC
Gao J, et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci. Signal. 2013;6:pl1. doi: 10.1126/scisignal.2004088. PubMed DOI PMC
Díaz-Gay M, et al. Assigning mutational signatures to individual samples and individual somatic mutations with SigProfilerAssignment. Bioinformatics. 2023;39:btad756. doi: 10.1093/bioinformatics/btad756. PubMed DOI PMC
Wilcoxon F. Individual comparisons by ranking methods. Biometrics Bull. 1945;1:80. doi: 10.2307/3001968. DOI
Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B. 1995;57:289–300. doi: 10.1111/j.2517-6161.1995.tb02031.x. DOI
Dušek, L. et al. Epidemiology of Malignant Tumours in the Czech Republic, Version 7.0. Masaryk Universityhttp://www.svod.cz (2007).
Liu M, et al. Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. Nat. Genet. 2019;51:237–244. doi: 10.1038/s41588-018-0307-5. PubMed DOI PMC
Yengo L, et al. Meta-analysis of genome-wide association studies for height and body mass index in ∼700000 individuals of European ancestry. Hum. Mol. Genet. 2018;27:3641–3649. doi: 10.1093/hmg/ddy271. PubMed DOI PMC
Lagou V, et al. Sex-dimorphic genetic effects and novel loci for fasting glucose and insulin variability. Nat. Commun. 2021;12:24. doi: 10.1038/s41467-020-19366-9. PubMed DOI PMC
Bycroft C, et al. The UK Biobank resource with deep phenotyping and genomic data. Nature. 2018;562:203–209. doi: 10.1038/s41586-018-0579-z. PubMed DOI PMC
Alexander DH, Novembre J, Lange K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 2009;19:1655–1664. doi: 10.1101/gr.094052.109. PubMed DOI PMC
Chang CC, et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience. 2015;4:7. doi: 10.1186/s13742-015-0047-8. PubMed DOI PMC
Shim H, et al. A multivariate genome-wide association analysis of 10 LDL subfractions, and their response to statin treatment, in 1868 Caucasians. PLoS ONE. 2015;10:e0120758. doi: 10.1371/journal.pone.0120758. PubMed DOI PMC
Choi SW, O’Reilly PF. PRSice-2: polygenic risk score software for biobank-scale data. Gigascience. 2019;8:giz082. doi: 10.1093/gigascience/giz082. PubMed DOI PMC
Loftfield E, et al. Novel biomarkers of habitual alcohol intake and associations with risk of pancreatic and liver cancers and liver disease mortality. J. Natl Cancer Inst. 2021;113:1542–1550. doi: 10.1093/jnci/djab078. PubMed DOI PMC
Gao J, Meyer K, Borucki K, Ueland PM. Multiplex Immuno-MALDI-TOF MS for targeted quantification of protein biomarkers and their proteoforms related to inflammation and renal dysfunction. Anal. Chem. 2018;90:3366–3373. doi: 10.1021/acs.analchem.7b04975. PubMed DOI
R Core Team. R: A Language and Environment for Statistical Computing.https://www.r-project.org/ (R Foundation for Statistical Computing, 2022).
Van Rossum, G. & Drake, F. L. Python 3 Reference Manual (CreateSpace, 2009).
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw.67, 10.18637/jss.v067.i01 (2015).
Bengtsson H. matrixStats: Functions that apply to rows and columns of matrices (and to vectors). https://cran.r-project.org/web/packages/matrixStats/index.html (2023).
Bates, D. et al. Matrix: Sparse and dense matrix classes and methods. https://matrix.r-forge.r-project.org/ (2023).
Chamberlain, S., Teucher, A. & Mahoney, M. geojsonio. https://github.com/ropensci/geojsonio (2023).
Hijmans, R. J. raster. https://github.com/rspatial/raster (2024).
Bivand, R. & Rundel, C. rgeos. https://github.com/cran/rgeos/ (2023).
Pebesma, E. & Bivand, R. Spatial Data Science: With Applications in R (Chapman and Hall/CRC, 2023).
Tennekes, M. tmaptools: Thematic map tools. https://github.com/r-tmap/tmaptools (2021).
Lin Pedersen, T. patchwork: The composer of plots. https://github.com/thomasp85/patchwork (2024).
Cheng, J. Leaflet. https://github.com/rstudio/leaflet (2023).
Wickham, H., François, R., Henry, L., Müller, K. & Vaughan, D. dplyr: A grammar of data manipulation. https://dplyr.tidyverse.org (2023).
Wickham, H., Miller, E. & Smith, D. haven: Import and export ‘SPSS’, ‘Stata’ and ‘SAS’ files. https://haven.tidyverse.org (2023).
Harrell Jr F. Hmisc: Harrell Miscellaneous. R package version 5.1-1. https://hbiostat.org/r/hmisc/ (2023).
Schauberger, P. & Walker, A. openxlsx: Read, write and edit xlsx files. https://github.com/ycphs/openxlsx (2022).
Bivand, R., Keitt, T. & Rowlingson, B. rgdal: Bindings for the ‘Geospatial’ data abstraction library. https://rgdal.r-forge.r-project.org (2023).
Wickham, H., Pedersen, T. L. & Seidel, D. scales: Scale functions for visualization. https://scales.r-lib.org (2023).
Wickham, H. stringr: Simple, consistent wrappers for common string operations. https://stringr.tidyverse.org (2023).
Wickham, H., Vaughan, D. & Girlich, M. tidyr: Tidy messy data. https://tidyr.tidyverse.org (2024).
Müller, K. & Wickham, H. tibble: Simple data frames. https://tibble.tidyverse.org/ (2023).
Dragulescu, A. & Arendt, C. xlsx: Read, write, format Excel 2007 and Excel 97/2000/XP/2003 files. https://github.com/colearendt/xlsx (2022).
Archer, E. rfPermute. https://github.com/EricArcher/rfPermute (2023).
Breiman L. Random forests. Mach. Learn. 2001;45:5–32. doi: 10.1023/A:1010933404324. DOI
Wickham, H. forcats: Tools for working with categorical variables (factors). https://forcats.tidyverse.org/ (2023).
The pandas development team. pandas-dev/pandas: Pandas. 10.5281/zenodo.7093122 (2022).
Harris CR, et al. Array programming with NumPy. Nature. 2020;585:357–362. doi: 10.1038/s41586-020-2649-2. PubMed DOI PMC
Virtanen P, et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods. 2020;17:261–272. doi: 10.1038/s41592-019-0686-2. PubMed DOI PMC
Seabold, S. & Perktold, J. statsmodels: Econometric and statistical modeling with Python. In 9th Python in Science Conference10.25080/Majora-92bf1922-011 (2010).
Luo, J. firthlogist. https://github.com/jzluo/firthlogist (2022).
Smith, N. J. et al. pydata/patsy. 10.5281/zenodo.5529350 (2021).
Kluyver, T. et al. in Positioning and Power in Academic Publishing: Players, Agents and Agendas (eds Loizides, F. & Schmidt, B.) 87–90 (2016).
Wickham, H. ggplot2: Elegant graphics for data analysis. https://ggplot2.tidyverse.org (2016).
Campitelli, E. ggnewscale: Multiple fill and color scales in ggplot2. 10.5281/zenodo.7971612 (2023).
Mike, F. C., Davis, T. L. & ggplot2 authors. ggpattern: ‘ggplot2’ pattern geoms. https://github.com/trevorld/ggpattern (2022).
Slowikowski, K. ggrepel: Automatically position non-overlapping text labels with ‘ggplot2’. https://github.com/slowkow/ggrepel (2024).
Yutani, H. ggsflabel. https://yutannihilation.github.io/ggsflabel/ (2023).
Dunnington, D. ggspatial: Spatial data framework for ggplot2. https://paleolimbot.github.io/ggspatial/ (2023).
Kassambara, A. ggpubr: ‘ggplot2’ based publication ready plots. https://rpkgs.datanovia.com/ggpubr/ (2023).
Wilke, C. O. cowplot: Streamlined plot theme and plot annotations for ‘ggplot2’. https://wilkelab.org/cowplot/ (2024).
Hunter JD. Matplotlib: A 2D graphics environment. Comput. Sci. Eng. 2007;9:90–95. doi: 10.1109/MCSE.2007.55. DOI
Waskom M. L. seaborn: statistical data visualization. J. Open Source Softw. 2021;6:3021. doi: 10.21105/joss.03021. DOI
He, Y. TMB_plotter. https://github.com/AlexandrovLab/TMB_plotter (2020).
Global Administrative Areas. GADM v4.1. https://gadm.org (2022).
Patterson, T. & Nathaniel, V. World Countries, 1:10 million. Natural Earth v5.1.1. https://www.naturalearthdata.com (2022).
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