Abstract
Esophageal squamous cell carcinoma (ESCC) shows remarkable variation in incidence that is not fully explained by known lifestyle and environmental risk factors. It has been speculated that an unknown exogenous exposure(s) could be responsible. Here we combine the fields of mutational signature analysis with cancer epidemiology to study 552 ESCC genomes from eight countries with varying incidence rates. Mutational profiles were similar across all countries studied. Associations between specific mutational signatures and ESCC risk factors were identified for tobacco, alcohol, opium and germline variants, with modest impacts on mutation burden. We find no evidence of a mutational signature indicative of an exogenous exposure capable of explaining differences in ESCC incidence. Apolipoprotein B mRNA-editing enzyme, catalytic polypeptide-like (APOBEC)-associated mutational signatures single-base substitution (SBS)2 and SBS13 were present in 88% and 91% of cases, respectively, and accounted for 25% of the mutation burden on average, indicating that APOBEC activation is a crucial step in ESCC tumor development.
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Data availability
Whole-genome sequencing data and patient metadata were deposited in the European Genome–Phenome Archive (EGA) associated with study EGAS00001002725. BAM files for all cases included in the final analysis were deposited in dataset EGAD00001006868, and patient metadata were deposited in dataset EGAD00001006732. All other data are provided in the accompanying Supplementary Tables.
Code availability
All algorithms used for data analysis are publicly available with repositories noted in the respective method sections and in the accompanying Nature Research Reporting Summary. Code used for regression analysis and figures is available at https://gitlab.com/Mutographs/Mutographs_ESCC.
References
Bray, F. et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 68, 394–424 (2018).
Arnold, M., Soerjomataram, I., Ferlay, J. & Forman, D. Global incidence of oesophageal cancer by histological subtype in 2012. Gut 64, 381–387 (2015).
Abnet, C. C., Arnold, M. & Wei, W. Q. Epidemiology of esophageal squamous cell carcinoma. Gastroenterology 154, 360–373 (2018).
Murphy, G. et al. International cancer seminars: a focus on esophageal squamous cell carcinoma. Ann. Oncol. 28, 2086–2093 (2017).
Sheikh, M. et al. Individual and combined effects of environmental risk factors for esophageal cancer based on results from the Golestan Cohort Study. Gastroenterology 156, 1416–1427 (2019).
McCormack, V. A. et al. Informing etiologic research priorities for squamous cell esophageal cancer in Africa: a review of setting-specific exposures to known and putative risk factors. Int. J. Cancer 140, 259–271 (2017).
Mello, F. W. et al. The synergistic effect of tobacco and alcohol consumption on oral squamous cell carcinoma: a systematic review and meta-analysis. Clin. Oral Investig. 23, 2849–2859 (2019).
Alexandrov, L. B. et al. The repertoire of mutational signatures in human cancer. Nature 578, 94–101 (2020).
Alexandrov, L. B. et al. Clock-like mutational processes in human somatic cells. Nat. Genet. 47, 1402–1407 (2015).
Alexandrov, L. B. et al. Mutational signatures associated with tobacco smoking in human cancer. Science 354, 618–622 (2016).
Riva, L. et al. The mutational signature profile of known and suspected human carcinogens in mice. Nat. Genet. 52, 1189–1197 (2020).
Scelo, G. et al. Variation in genomic landscape of clear cell renal cell carcinoma across Europe. Nat. Commun. 5, 5135 (2014).
Gao, Y. B. et al. Genetic landscape of esophageal squamous cell carcinoma. Nat. Genet. 46, 1097–1102 (2014).
Song, Y. et al. Identification of genomic alterations in oesophageal squamous cell cancer. Nature 508, 91–95 (2014).
Zhang, L. et al. Genomic analyses reveal mutational signatures and frequently altered genes in esophageal squamous cell carcinoma. Am. J. Hum. Genet. 96, 597–611 (2015).
Sawada, G. et al. Genomic landscape of esophageal squamous cell carcinoma in a Japanese population. Gastroenterology 150, 1171–1182 (2016).
Kim, J. et al. Integrated genomic characterization of oesophageal carcinoma. Nature 541, 169–174 (2017).
Chang, J. et al. Genomic analysis of oesophageal squamous-cell carcinoma identifies alcohol drinking-related mutation signature and genomic alterations. Nat. Commun. 8, 15290 (2017).
Li, X. C. et al. A mutational signature associated with alcohol consumption and prognostically significantly mutated driver genes in esophageal squamous cell carcinoma. Ann. Oncol. 29, 938–944 (2018).
Mangalaparthi, K. K. et al. Mutational landscape of esophageal squamous cell carcinoma in an Indian cohort. Front. Oncol. 10, 1457 (2020).
Islam, S. M. A. et al. Uncovering novel mutational signatures by de novo extraction with SigProfilerExtractor. Preprint at bioRxiv https://doi.org/10.1101/2020.12.13.422570 (2020).
Martincorena, I. et al. Universal patterns of selection in cancer and somatic tissues. Cell 171, 1029–1041 (2017).
Middlebrooks, C. D. et al. Association of germline variants in the APOBEC3 region with cancer risk and enrichment with APOBEC-signature mutations in tumors. Nat. Genet. 48, 1330–1338 (2016).
Nik-Zainal, S. et al. Association of a germline copy number polymorphism of APOBEC3A and APOBEC3B with burden of putative APOBEC-dependent mutations in breast cancer. Nat. Genet. 46, 487–491 (2014).
Kidd, J. M., Newman, T. L., Tuzun, E., Kaul, R. & Eichler, E. E. Population stratification of a common APOBEC gene deletion polymorphism. PLoS Genet. 3, e63 (2007).
Campbell, P. J. et al. Pan-cancer analysis of whole genomes. Nature 578, 82–93 (2020).
Auton, A. et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).
MacGregor, S. et al. Associations of ADH and ALDH2 gene variation with self report alcohol reactions, consumption and dependence: an integrated analysis. Hum. Mol. Genet. 18, 580–593 (2009).
Cui, R. et al. Functional variants in ADH1B and ALDH2 coupled with alcohol and smoking synergistically enhance esophageal cancer risk. Gastroenterology 137, 1768–1775 (2009).
Wu, C. et al. Genome-wide association study identifies three new susceptibility loci for esophageal squamous-cell carcinoma in Chinese populations. Nat. Genet. 43, 679–684 (2011).
Gao, Y. et al. Risk factors for esophageal and gastric cancers in Shanxi Province, China: a case–control study. Cancer Epidemiol. 35, e91–e99 (2011).
Akbari, M. R. et al. Germline BRCA2 mutations and the risk of esophageal squamous cell carcinoma. Oncogene 27, 1290–1296 (2008).
Ko, J. M. Y. et al. BRCA2 loss-of-function germline mutations are associated with esophageal squamous cell carcinoma risk in Chinese. Int. J. Cancer 146, 1042–1051 (2020).
Deng, J. et al. Comparative genomic analysis of esophageal squamous cell carcinoma between Asian and Caucasian patient populations. Nat. Commun. 8, 1533 (2017).
Martincorena, I. et al. Somatic mutant clones colonize the human esophagus with age. Science 362, 911–917 (2018).
Yokoyama, A. et al. Age-related remodelling of oesophageal epithelia by mutated cancer drivers. Nature 565, 312–317 (2019).
Liu, X. et al. Genetic alterations in esophageal tissues from squamous dysplasia to carcinoma. Gastroenterology 153, 166–177 (2017).
Warnakulasuriya, S. et al. Carcinogenicity of opium consumption. Lancet Oncol. 21, 1407–1408 (2020).
Balmain, A. The critical roles of somatic mutations and environmental tumor-promoting agents in cancer risk. Nat. Genet. 52, 1139–1143 (2020).
Bergmann, E. A., Chen, B. J., Arora, K., Vacic, V. & Zody, M. C. Conpair: concordance and contamination estimator for matched tumor–normal pairs. Bioinformatics 32, 3196–3198 (2016).
Jones, D. et al. cgpCaVEManWrapper: simple execution of CaVEMan in order to detect somatic single nucleotide variants in NGS data. Curr. Protoc. Bioinformatics 2016, 15.10.1–15.10.18 (2016).
Raine, K. M. et al. cgpPindel: identifying somatically acquired insertion and deletion events from paired end sequencing. Curr. Protoc. Bioinformatics 52, 15.7.1–15.7.12 (2015).
Kim, S. et al. Strelka2: fast and accurate calling of germline and somatic variants. Nat. Methods 15, 591–594 (2018).
Bergstrom, E. N. et al. SigProfilerMatrixGenerator: a tool for visualizing and exploring patterns of small mutational events. BMC Genomics 20, 685 (2019).
Degasperi, A. et al. A practical framework and online tool for mutational signature analyses show intertissue variation and driver dependencies. Nat. Cancer 1, 249–263 (2020).
Nik-Zainal, S. et al. Landscape of somatic mutations in 560 breast cancer whole-genome sequences. Nature 534, 47–54 (2016).
Senkin, S. MSA: reproducible mutational signature attribution with confidence based on simulations. Preprint at bioRxiv https://doi.org/10.1101/2020.12.14.422764 (2020).
Buisson, R. et al. Passenger hotspot mutations in cancer driven by APOBEC3A and mesoscale genomic features. Science 364, eaaw2872 (2019).
Bergstrom, E. N., Barnes, M., Martincorena, I. & Alexandrov, L. B. Generating realistic null hypothesis of cancer mutational landscapes using SigProfilerSimulator. BMC Bioinformatics 21, 438 (2020).
Mas-Ponte, D. & Supek, F. DNA mismatch repair promotes APOBEC3-mediated diffuse hypermutation in human cancers. Nat. Genet. 52, 958–968 (2020).
Nik-Zainal, S. et al. Mutational processes molding the genomes of 21 breast cancers. Cell 149, 979–993 (2012).
Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).
Nasrollahzadeh, D. et al. Opium, tobacco, and alcohol use in relation to oesophageal squamous cell carcinoma in a high-risk area of Iran. Br. J. Cancer 98, 1857–1863 (2008).
Acknowledgements
This article is dedicated to the memory of Eleanor Wilde. We thank L. O’Neill, J. Hewinson, K. Roberts, N. Smerdon, J. Mack, E. Gray and the staff of DNA Pipelines at the Wellcome Sanger Institute for their contribution. We are grateful for the support provided by the IARC ESCCAPE team (M. Odour, N. Kigen, F. Some (Eldoret), G. Mushi (Moshi), M. Suwedi, T. Soloman, R. Malamba (Blantyre) and D. Middleton), S. Magat, P. Boutarin, D. Middleton and A. Hajimohammadsadegh, as well as IARC General Services, including the Laboratory Services and Biobank team led by Z. Kozlakidis, the Section of Support to Research overseen by T. Landesz and the Evidence Synthesis and Classification Section led by I. Cree, under IARC regular budget funding. We thank C. Giffen, P. Taylor and A. Hutchinson for help with data, sample preparation and processing; and I. Soerjomataram for assistance with Fig. 1a. We also thank P. Campbell, I. Martincorena, T. Butler, L. Moore, D. Leongamornlet, P. Robinson, T. Coorens, A. Steif, S. Cheema, B. Otlu, C. Lallemand, H. Renard, T. Cholin, P. Chopard, M. Vallee, M. Milojevic, M. Blanks and M. McCord for useful discussions. We also thank all patients involved in this study. This work was supported by a Cancer Grand Challenges Mutographs team award funded by Cancer Research UK (C98/A24032) awarded to M.R.S., P.B. and L.B.A. Work at the Wellcome Sanger Institute was also supported by the Wellcome Trust (grant number 206194), and work at the IARC/WHO was supported by regular budget funding. L.B.A. is an Abeloff V Scholar and is supported by an Alfred P. Sloan Research Fellowship. Research at UC San Diego was also supported by a Packard Fellowship for Science and Engineering to L.B.A. The work was also partly funded by R21CA191965 (D.M. and V.M.), Wereld Kanker Onderzoek Fonds (World Cancer Research Fund) (2018/1795) and the IARC Branch of Environment and Lifestyle Epidemiology (V.M.). The work of R.C.C.P. reported in this paper was undertaken during the tenure of an IARC Postdoctoral Fellowship at the International Agency for Research on Cancer. The laboratory of R.C.F. is funded by a Core Programme Grant from the Medical Research Council (RG84369); this research was supported by the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014); and OCCAMS2 was funded by a Programme Grant from Cancer Research UK (RG81771/84119). The work was also partly funded by the Practical Research Project for Innovative Cancer Control from the Japan Agency for Medical Research and Development (JP20ck0106547h0001 to T.S.). A.M.G. and N.H. were supported by the Intramural Research Program of the Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health. For the purpose of open access, the authors have applied a CC BY public copyright license to any author accepted manuscript version arising from this submission. Disclaimer: when authors are identified as personnel of the International Agency for Research on Cancer or the World Health Organization, the authors alone are responsible for the views expressed in this article, and they do not necessarily represent the decisions, policy or views of the International Agency for Research on Cancer or the World Health Organization.
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The study was conceived, designed and supervised by M.R.S., P.B. and L.B.A. Analysis of data was performed by S.M., S.Senkin., S.M.A.I., J.W., D.N., R.C.C.P., S.F., E.N.B., J.A., Y.H., A.K., K.S.-B., V.G., C.L., E.T., I.A., P.E.B., D.J., J.W.T. and J.M. Pathology review was carried out by B.A.-A., S.Serra., J.-Y.S., H.S., F.A.-A., M.S., A.Nikmanesh., M.E., P.R. and L.S.C. DNA extraction was carried out by C.C. Patient and sample recruitment was led by H.P., A.Niavarani., S.G., R.C.F., L.F.R., S.C.S.-L., C.D., B.T.M., D.M., A.M.G., N.H., R.M., A.F. and V.M. Patient and sample recruitment and sequencing for Japanese cases was led by T.S. Scientific project management was carried out by L.H., E.C., G.S. and S.P. S.M. and S.Senkin jointly contributed and were responsible for overall scientific coordination. The manuscript was written by S.M. and M.R.S. with contributions from all other authors.
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Extended data
Extended Data Fig. 1 Mutation burdens in ESCC.
Mutation burdens for SBS, DBS and ID variants show modest differences between countries using the Kruskal-Wallis (two-sided) test (n=552 biologically independent samples). Hypermutators in each group were defined using the interquartile range method, with cases with mutation burdens more than 1.5 IQR above Q3 removed from the analysis. Box and whiskers plots are in the style of Tukey. The line within the box is plotted at the median while the upper and lower ends are indicated 25th and 75th percentiles. Whiskers show 1.5*IQR (interquartile range) and values outside it are shown as individual data points. Countries are ordered by approximate incidence rate in ascending order.
Extended Data Fig. 2 HDP mutational signature analysis of ESCC.
(a) 33 components extracted by HDP. (b) Average relative attributions of SBS COSMIC signatures and SV de novo signatures are broadly similar between all countries. Signatures accounting for less than 5% on average (with the exception of SBS3 and SBS16) are grouped together into the ‘Other’ category. (c) Comparison of average relative attribution of signatures between HDP and Sigprofiler.
Extended Data Fig. 3 DBS78 mutational signature analysis of ESCC.
(a) TMB plot showing the frequency and mutations/mb for each of the extracted DBS78 de novo signatures. (b) TMB showing the frequency and mutations/mb for each DBS78 COSMIC reference signature identified in the ESCC cohort. (c) Four de novo DBS78 signatures extracted from 552 ESCC cases. (d) Mapping of de novo signatures extracted from ESCC decomposed into COSMIC reference signatures. (e) COSMIC reference signatures identified in the ESCC cohort with known etiologies.
Extended Data Fig. 4 ID83 mutational signature analysis of ESCC.
(a) TMB plot showing the frequency and mutations/mb for each of the extracted ID83 de novo signatures. (b) TMB showing the frequency and mutations/mb for each ID83 COSMIC reference signature identified in the ESCC cohort. (c) Eight de novo ID83 signatures extracted from 552 ESCC cases. (d) Mapping of de novo signatures extracted from ESCC decomposed into COSMIC reference signatures. For clarity, components amounting to less than 10% of the signature have been omitted. (e) COSMIC reference signatures identified in the ESCC cohort with known etiologies.
Extended Data Fig. 5 Average DBS spectra of ESCC from eight countries.
Average DBS78 spectra in 552 ESCC from eight countries. Countries are ordered by approximate incidence rate in ascending order.
Extended Data Fig. 6 Average indel spectra of ESCC from eight countries.
Average ID83 spectra in 552 ESCC from eight countries. Countries are ordered by approximate incidence rate in ascending order.
Extended Data Fig. 7 Differences in mutation signature mutation burden in ESCC.
Countries showing significant differences in attributed mutation burden of mutational signatures when grouped against all other countries (n=552 biologically independent samples). The Wilcoxon signed-rank test (two-sided) was used to test for significant differences, for signatures where a significant global difference was found using the Kruskal-Wallis (two-sided) test with multiple hypothesis corrections (Fig2). For clarity, cases with >1000 mutations attributed to ID2 are not displayed. Box and whiskers plots are in the style of Tukey. The line within the box is plotted at the median while the upper and lower ends are indicated 25th and 75th percentiles. Whiskers show 1.5*IQR (interquartile range) and values outside it are shown as individual data points.
Extended Data Fig. 8 Differences in extracted T>C rich signatures.
Comparison between T>C rich de novo signature extracted from all cases, the T>C rich de novo signature extracted from Iranian cases and COSMIC signature 12, showing that the T>C component of the Iranian signature is distinct from both the corresponding signature extracted from all cases and COSMIC signature 12.
Extended Data Fig. 9 Comparison of VAF at TCN and other contexts.
(a) C>G and C>T variants at TCN contexts have a similar median VAF to those at other contexts. Cases with tumor purity <50% and those with no attribution of APOBEC mutational signatures (COSMIC signatures SBS2 and SBS13) were excluded from the analysis (n=172 biologically independent samples). Box and whiskers plots are in the style of Tukey. The line within the box is plotted at the median while the upper and lower ends are indicated 25th and 75th percentiles. Whiskers show 1.5*IQR (interquartile range) and values outside it are shown as individual data points. (b) There is no shift in the median VAF (Median VAF(TCN) – Median VAF (other contexts)) of C>G and C>T variants at TCN contexts compared to variants at other contexts. Cases with tumor purity <50% and those with no attribution of APOBEC mutational signatures (COSMIC signatures SBS2 and SBS13) were excluded from the analysis. (c) No significant difference was found in the median VAF of C>G and C>T variants at TCN contexts compared to all other contexts in any individual country (n=170 biologically independent samples). UK cases were not included as only two cases were of sufficient purity. Countries are ordered by approximate incidence rate in ascending order. Box and whiskers plots are in the style of Tukey. The line within the box is plotted at the median while the upper and lower ends are indicated 25th and 75th percentiles. Whiskers show 1.5*IQR (interquartile range) and values outside it are shown as individual data points.
Extended Data Fig. 10 Genetic population structure based on principle component analysis.
(a) Scatter plots of principal components PC1 and PC2 based on genotype data showing the genetic structure of the ESCC cohort across different countries of origin. (b) Bar plot showing the percentage of explained variance by each principal component (eigenvalue in percent) across all countries of origin (c) Scatter plots of principal components PC1 and PC2 based on genotype data showing the genetic structure of the ESCC cases from China and Japan. (d) Bar plot showing the percentage of explained variance by each principal component in ESCC cases from China and Japan (eigenvalue in percent).
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Supplementary Tables 1–21.
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Moody, S., Senkin, S., Islam, S.M.A. et al. Mutational signatures in esophageal squamous cell carcinoma from eight countries with varying incidence. Nat Genet 53, 1553–1563 (2021). https://doi.org/10.1038/s41588-021-00928-6
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DOI: https://doi.org/10.1038/s41588-021-00928-6
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