Abstract
Oesophageal cancer is one of the most aggressive cancers and is the sixth leading cause of cancer death worldwide1. Approximately 70% of global oesophageal cancer cases occur in China, with oesophageal squamous cell carcinoma (ESCC) being the histopathological form in the vast majority of cases (>90%)2,3. Currently, there are limited clinical approaches for the early diagnosis and treatment of ESCC, resulting in a 10% five-year survival rate for patients. However, the full repertoire of genomic events leading to the pathogenesis of ESCC remains unclear. Here we describe a comprehensive genomic analysis of 158 ESCC cases, as part of the International Cancer Genome Consortium research project. We conducted whole-genome sequencing in 17 ESCC cases and whole-exome sequencing in 71 cases, of which 53 cases, plus an additional 70 ESCC cases not used in the whole-genome and whole-exome sequencing, were subjected to array comparative genomic hybridization analysis. We identified eight significantly mutated genes, of which six are well known tumour-associated genes (TP53, RB1, CDKN2A, PIK3CA, NOTCH1, NFE2L2), and two have not previously been described in ESCC (ADAM29 and FAM135B). Notably, FAM135B is identified as a novel cancer-implicated gene as assayed for its ability to promote malignancy of ESCC cells. Additionally, MIR548K, a microRNA encoded in the amplified 11q13.3-13.4 region, is characterized as a novel oncogene, and functional assays demonstrate that MIR548K enhances malignant phenotypes of ESCC cells. Moreover, we have found that several important histone regulator genes (MLL2 (also called KMT2D), ASH1L, MLL3 (KMT2C), SETD1B, CREBBP and EP300) are frequently altered in ESCC. Pathway assessment reveals that somatic aberrations are mainly involved in the Wnt, cell cycle and Notch pathways. Genomic analyses suggest that ESCC and head and neck squamous cell carcinoma share some common pathogenic mechanisms, and ESCC development is associated with alcohol drinking. This study has explored novel biological markers and tumorigenic pathways that would greatly improve therapeutic strategies for ESCC.
This is a preview of subscription content, access via your institution
Access options
Subscribe to this journal
Receive 51 print issues and online access
$199.00 per year
only $3.90 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Accession codes
References
Kamangar, F., Dores, G. M. & Anderson, W. F. Patterns of cancer incidence, mortality, and prevalence across five continents: defining priorities to reduce cancer disparities in different geographic regions of the world. J. Clin. Oncol. 24, 2137–2150 (2006)
Xu, Y., Yu, X., Chen, Q. & Mao, W. Neoadjuvant versus adjuvant treatment: which one is better for resectable esophageal squamous cell carcinoma? World J. Surg. Oncol. 10, 173 (2012)
Zhang, S. W. et al. An analysis of incidence and mortality of esophageal cancer in China, 2003–2007. China Cancer 21, 241–247 (2012)
The Caner Genome Atlas Research Network Comprehensive genomic characterization of squamous cell lung cancers. Nature 489, 519–525 (2012)
Dulak, A. M. et al. Exome and whole-genome sequencing of esophageal adenocarcinoma identifies recurrent driver events and mutational complexity. Nature Genet. 45, 478–486 (2013)
Stransky, N. et al. The mutational landscape of head and neck squamous cell carcinoma. Science 333, 1157–1160 (2011)
Agrawal, N. et al. Comparative genomic analysis of esophageal adenocarcinoma and squamous cell carcinoma. Cancer Discov. 2, 899–905 (2012)
Imielinski, M. et al. Mapping the hallmarks of lung adenocarcinoma with massively parallel sequencing. Cell 150, 1107–1120 (2012)
Wei, X. et al. Analysis of the disintegrin-metalloproteinases family reveals ADAM29 and ADAM7 are often mutated in melanoma. Hum. Mutat. 32, E2148–E2175 (2011)
Bandla, S. et al. Comparative genomics of esophageal adenocarcinoma and squamous cell carcinoma. Ann. Thorac. Surg. 93, 1101–1106 (2012)
Ying, J. et al. Genome-wide screening for genetic alterations in esophageal cancer by aCGH identifies 11q13 amplification oncogenes associated with nodal metastasis. PLoS ONE 7, e39797 (2012)
Komatsu, Y. et al. TAOS1, a novel marker for advanced esophageal squamous cell carcinoma. Anticancer Res. 26, 2029–2032 (2006)
Parsons, D. W. et al. An integrated genomic analysis of human glioblastoma multiforme. Science 321, 1807–1812 (2008)
McLaughlin-Drubin, M. E., Meyers, J. & Munger, K. Cancer associated human papillomaviruses. Curr. Opin. Virol. 2, 459–466 (2012)
Arzumanyan, A., Reis, H. M. & Feitelson, M. A. Pathogenic mechanisms in HBV- and HCV-associated hepatocellular carcinoma. Nature Rev. Cancer 13, 123–135 (2013)
Panagiotakis, G. I. et al. Association of human herpes, papilloma and polyoma virus families with bladder cancer. Tumour Biol. 34, 71–79 (2013)
Longman, D., Arfuso, F., Viola, H. M., Hool, L. C. & Dharmarajan, A. M. The role of the cysteine-rich domain and netrin-like domain of secreted frizzled-related protein 4 in angiogenesis inhibition in vitro . Oncol. Res. 20, 1–6 (2012)
Rosenbluh, J. et al. β-Catenin-driven cancers require a YAP1 transcriptional complex for survival and tumorigenesis. Cell 151, 1457–1473 (2012)
Forde, P. M. & Kelly, R. J. Genomic alterations in advanced esophageal cancer may lead to subtype-specific therapies. Oncologist 18, 823–832 (2013)
Garnett, M. J. et al. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature 483, 570–575 (2012)
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009)
Koboldt, D. C. et al. VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res. 22, 568–576 (2012)
McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010)
Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010)
Chiang, D. Y. et al. High-resolution mapping of copy-number alterations with massively parallel sequencing. Nature Methods 6, 99–103 (2009)
Venkatraman, E. S. & Olshen, A. B. A faster circular binary segmentation algorithm for the analysis of array CGH data. Bioinformatics 23, 657–663 (2007)
Olshen, A. B., Venkatraman, E. S., Lucito, R. & Wigler, M. Circular binary segmentation for the analysis of array-based DNA copy number data. Biostatistics 5, 557–572 (2004)
Wang, J. et al. CREST maps somatic structural variation in cancer genomes with base-pair resolution. Nature Methods 8, 652–654 (2011)
Smyth, G. K. & Speed, T. Normalization of cDNA microarray data. Methods 31, 265–273 (2003)
Workman, C. et al. A new non-linear normalization method for reducing variability in DNA microarray experiments. Genome Biol. 3, research0048 (2002)
Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009)
Beroukhim, R. et al. Assessing the significance of chromosomal aberrations in cancer: methodology and application to glioma. Proc. Natl Acad. Sci. USA 104, 20007–20012 (2007)
Kan, Z. et al. Diverse somatic mutation patterns and pathway alterations in human cancers. Nature 466, 869–873 (2010)
Lawrence, M. S. et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499, 214–218 (2013)
Zhang, H. et al. Cytogenetic aberrations in immortalization of esophageal epithelial cells. Cancer Genet. Cytogenet. 165, 25–35 (2006)
Shen, Z. Y. et al. Telomere and telomerase in the initial stage of immortalization of esophageal epithelial cell. World J. Gastroenterol. 8, 357–362 (2002)
Wittchen, E. S. & Hartnett, M. E. The small GTPase Rap1 is a novel regulator of RPE cell barrier function. Invest. Ophthalmol. Vis. Sci. 52, 7455–7463 (2011)
Ou, Y. et al. Migfilin protein promotes migration and invasion in human glioma through epidermal growth factor receptor-mediated phospholipase C-gamma and STAT3 protein signaling pathways. J. Biol. Chem. 287, 32394–32405 (2012)
Acknowledgements
This work is supported by the funding from the National High Technology Research and Development Program of China (863 program no. 2012AA02A209 and 2012AA02A503), National Natural Science Foundation Fund (81021061), Guangdong Innovative Research Team Program (2009010016), the National Natural Science Foundation of China-GuangDong Joint Fund (U0932001), and the National Key Basic Research Program of China (973 program no. 2011CB911004, 2009CB521801 and 2012CB526608). The ESCC cell lines (KYSE30, KYSE70, KYSE180, KYSE410, KYSE450, KYSE140, COLO680N and KYSE510) were provided by Y. Shimada of Kyoto University. We also acknowledge International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) for sharing the EAC, HNSCC and lung SQCC data.
Author information
Authors and Affiliations
Contributions
Q.Z. and Y.S. contributed to the design of the project and Q.Z. also mainly contributed to writing the manuscript. E.L., L.X., Z.W., Jianyi Wu and B.C. provided clinical samples and relevant information. Z.G., Lin Li, X.L., Jiaqian Wang, Y.Z., G.C., J.Y., L.C., M.H., M.L., X.H., Xuehan Zhuang, K.Q., G.Y. and G.G. performed sequencing and data analysis. Lin Li and K.H. performed the validation of variations. Y.O. performed experiments and data analysis, and wrote the manuscript. W.Z. performed MIR548K assays and analysed structural variation data. X.M., Lingyan Liu, W.Z., J.F., L.D., Z.Z. and Liying Ma performed FAM135B assays. Z.G., Lin Li and X.L. edited the manuscript. Lin Li and Jiaqian Wang performed the analysis of supplementary data. Ling Ma, J.Z., Longhai Luo, M.F., B.X., T.T., M.W., Z.L., D.L., Q.F. and P.C. provided supervision and support in the project. Y. L., Xiuqing Zhang, H.Y. and Jun Wang granted as well as supervised and supported this project.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing financial interests.
Additional information
Sequencing and array-CGH data have been deposited to the European Genome-phenome Archive (EGAS00001000709) and Gene Expression Omnibus (GSE54995).
Extended data figures and tables
Extended Data Figure 1 Fold coverage of whole genome and target regions in the sequenced normal and tumour samples in ESCC.
a, The box plot depicts the distribution of mean coverage of all whole-genome sequencing samples. Lines in the two central boxes show the medians, and lines outside the two central boxes show the first and the third quartiles of the mean depths. b, The box plot depicts the distribution of fraction of whole-genome bases covered by at least 1 read, 4 reads, 10 reads and 20 reads across the 34 whole-genome sequencing samples. The lines in boxes show the medians and the lines outside the boxes show the first or third quartiles of fraction of whole-genome bases covered by reads. c, The box plot depicts the distribution of mean coverage of all whole-exome sequencing samples. d, The box plot depicts the distribution of fraction of targeted bases covered by at least 1 read, 4 reads, 10 reads and 20 reads across the 142 whole-exome sequencing samples. N, normal samples; T, tumour samples.
Extended Data Figure 2 Spectrum of somatic point mutations identified in exome regions of ESCC, EAC, HNSCC and lung SQCC.
Genomic data from 88 ESCC, 145 EAC, 74 HNSCC and 177 lung SQCC were analysed.
Extended Data Figure 3 Hierarchical clustering of 484 samples in ESCC, EAC, HNSCC and lung SQCC according to their nucleotide context-specific exonic mutation rates.
Top bar: cancer types of each sample. Genomic data from 88 ESCC, 145 EAC, 74 HNSCC and 177 lung SQCC were analysed.
Extended Data Figure 4 Mutation spectrum analysis of 88 ESCCs.
a, Context-specific mutation-based unsupervised clustering analysis in 88 ESCC cases. Heat map shows somatic mutation counts of specific mutation signatures in each case. Bottom bars: reported clusters, drinking and smoking status, and survival time. b, Top: Kaplan–Meier survival curve for three clusters of patients: pink line represents cluster 1 (n = 23); brown line represents cluster 2 (n = 18); and green line represents cluster 3 (n = 47). Cluster 1 patients had better prognosis as compared with patients of cluster 2 (P = 0.022, log-rank). Bottom: Cox proportional hazards model for cluster 1 and cluster 2 patients. P < 0.05 was considered statistically significant.
Extended Data Figure 5 Somatic mutations in TP53.
The types and relative positions of confirmed somatic mutations are shown in the transcript of TP53. Red stars, nonsense mutations (n = 17); bullets, missense mutations (n = 53); red triangles, indels (n = 7); and diamond, mutations at splice sites (n = 3). P53_TAD, p53 transactivation domain; P53, p53 DNA-binding domain; P53_tetramer, p53 tetramerization motif.
Extended Data Figure 6 The relationship between survival time and mutations of FAM135B in ESCC patients.
Top: Kaplan–Meier survival curve for wild-type and FAM135B mutant (P = 0.026, log-rank). Blue line, FAM135B wide type (n = 82); green line, FAM135B mutant (n = 6). Bottom: Cox proportional hazards model for wild-type and mutations of FAM135B. P < 0.05 was considered statistically significant.
Extended Data Figure 7 Histone-modifying genes recurrently mutated in 88 ESCCs.
The sites for modification are marked in colour. Histone octamer with main methylation (blue), acetylation (red) and phosphorylation (green) genes on specific histone residues mutated in more than one sample are shown.
Extended Data Figure 8 Comparative analysis of genomic copy number alterations among ESCC, EAC, HNSCC and lung SQCC.
Genomic data from 140 ESCC, 70 EAC, 312 HNSCC, 663 lung SQCC were analysed. Figure shows the amplification (AMP) and deletion (DEL) for chromosomes 1–22 and X. High-frequency differences occurring in four cancer types are indicated in respective curves.
Extended Data Figure 9 Copy number alterations with similar frequency identified between ESCC and HNSCC in JAK–STAT signalling, RTK–Ras signalling and cell cycle pathways.
Frequency of copy number alterations are shown under genes. Rectangle, ESCC; ellipse, HNSCC.
Extended Data Figure 10 Circos plot of intra- and inter-chromosomal translocations in all 17 WGS cases.
Intra-chromosomal, green; inter-chromosomal, black.
Supplementary information
Supplementary Tables 1-7
This zipped file contains Supplementary Tables 1-7: Table 1-Clinical features of 158 ESCC cases; Table 2-Summary of whole genome sequencing in 17 ESCC cases; Table 3-Summary of whole exome sequencing in 71 ESCC cases; Table 4-Somatic SNVs and indels of coding regions in 88 ESCC cases; Table 5- Summary of mutation in 88 ESCC cases; Table 6-Somatic mutation rate in 88 ESCC cases; and Table 7-Validation results of somatic SNVs and indels. (ZIP 647 kb)
Supplementary Tables 8-14
This zipped file contains Supplementary Tables 8-14: Table 8-Summary of copy number alterations in 140 ESCC cases; Table 9-Summary of 58 significant regions of CNA; Table 10. Structure variations in 17 ESCC cases; Table 11-Validation results of somatic SVs; Table 12-Context-specific mutation spectrum; Table 13- Kaplan-Meier survival analysis of mutation spectrum clusters; and Table 14-Kaplan-Meier survival analysis of 8 significant mutated genes. (ZIP 191 kb)
Supplementary Tables 15-22
This zippedfile contains Supplementary Tables 15-22: Table 15-Mutated histone-modifying genes; Table 16-Classification of mutated histone-modifying genes; Table 17- Table 17. Correlations of regional lymph nodes metastasis and survival with 58 significant regions of CNA; Table 18- Frequency of SV breakpoints effected genes in 17 ESCC cases; Table 19-Virus integration analysis in 17 ESCC cases; Table 20-Mutated pathways in 88 ESCC cases; Table 21-Potential therapeutic target genes; and Table 22-Summary of pathway affected by potential therapeutic target genes. (ZIP 134 kb)
Rights and permissions
About this article
Cite this article
Song, Y., Li, L., Ou, Y. et al. Identification of genomic alterations in oesophageal squamous cell cancer. Nature 509, 91–95 (2014). https://doi.org/10.1038/nature13176
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/nature13176
This article is cited by
-
Understanding PI3K/Akt/mTOR signaling in squamous cell carcinoma: mutated PIK3CA as an example
Molecular Biomedicine (2024)
-
Metabolic subtypes and immune landscapes in esophageal squamous cell carcinoma: prognostic implications and potential for personalized therapies
BMC Cancer (2024)
-
Genetic and molecular characterization of metabolic pathway-based clusters in esophageal squamous cell carcinoma
Scientific Reports (2024)
-
Concordance of assessments of four PD-L1 immunohistochemical assays in esophageal squamous cell carcinoma (ESCC)
Journal of Cancer Research and Clinical Oncology (2024)
-
Genomic and transcriptional characterization of early esophageal squamous cell carcinoma
BMC Medical Genomics (2023)
Comments
By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.
