Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Identification of genomic alterations in oesophageal squamous cell cancer



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.

Your institute does not have access to this article

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Figure 1: Significantly mutated genes in ESCC.
Figure 2: FAM135B positively modulates ESCC cellular malignant phenotypes.
Figure 3: Landscape of genomic copy number alterations in ESCC and oncogenic MIR548K identified from significantly amplified region.
Figure 4: Somatically altered pathways in ESCC.

Accession codes

Primary accessions

Gene Expression Omnibus


  1. 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)

    Article  Google Scholar 

  2. 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)

    CAS  Article  Google Scholar 

  3. Zhang, S. W. et al. An analysis of incidence and mortality of esophageal cancer in China, 2003–2007. China Cancer 21, 241–247 (2012)

    Google Scholar 

  4. The Caner Genome Atlas Research Network Comprehensive genomic characterization of squamous cell lung cancers. Nature 489, 519–525 (2012)

    ADS  Article  Google Scholar 

  5. 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)

    CAS  Article  Google Scholar 

  6. Stransky, N. et al. The mutational landscape of head and neck squamous cell carcinoma. Science 333, 1157–1160 (2011)

    ADS  CAS  Article  Google Scholar 

  7. Agrawal, N. et al. Comparative genomic analysis of esophageal adenocarcinoma and squamous cell carcinoma. Cancer Discov. 2, 899–905 (2012)

    CAS  Article  Google Scholar 

  8. Imielinski, M. et al. Mapping the hallmarks of lung adenocarcinoma with massively parallel sequencing. Cell 150, 1107–1120 (2012)

    CAS  Article  Google Scholar 

  9. 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)

    CAS  Article  Google Scholar 

  10. Bandla, S. et al. Comparative genomics of esophageal adenocarcinoma and squamous cell carcinoma. Ann. Thorac. Surg. 93, 1101–1106 (2012)

    Article  Google Scholar 

  11. 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)

    ADS  CAS  Article  Google Scholar 

  12. Komatsu, Y. et al. TAOS1, a novel marker for advanced esophageal squamous cell carcinoma. Anticancer Res. 26, 2029–2032 (2006)

    CAS  PubMed  Google Scholar 

  13. Parsons, D. W. et al. An integrated genomic analysis of human glioblastoma multiforme. Science 321, 1807–1812 (2008)

    ADS  CAS  Article  Google Scholar 

  14. McLaughlin-Drubin, M. E., Meyers, J. & Munger, K. Cancer associated human papillomaviruses. Curr. Opin. Virol. 2, 459–466 (2012)

    CAS  Article  Google Scholar 

  15. Arzumanyan, A., Reis, H. M. & Feitelson, M. A. Pathogenic mechanisms in HBV- and HCV-associated hepatocellular carcinoma. Nature Rev. Cancer 13, 123–135 (2013)

    CAS  Article  Google Scholar 

  16. Panagiotakis, G. I. et al. Association of human herpes, papilloma and polyoma virus families with bladder cancer. Tumour Biol. 34, 71–79 (2013)

    CAS  Article  Google Scholar 

  17. 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)

    Article  Google Scholar 

  18. Rosenbluh, J. et al. β-Catenin-driven cancers require a YAP1 transcriptional complex for survival and tumorigenesis. Cell 151, 1457–1473 (2012)

    CAS  Article  Google Scholar 

  19. Forde, P. M. & Kelly, R. J. Genomic alterations in advanced esophageal cancer may lead to subtype-specific therapies. Oncologist 18, 823–832 (2013)

    CAS  Article  Google Scholar 

  20. Garnett, M. J. et al. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature 483, 570–575 (2012)

    ADS  CAS  Article  Google Scholar 

  21. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009)

    CAS  Article  Google Scholar 

  22. 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)

    CAS  Article  Google Scholar 

  23. McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010)

    CAS  Article  Google Scholar 

  24. Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010)

    Article  Google Scholar 

  25. Chiang, D. Y. et al. High-resolution mapping of copy-number alterations with massively parallel sequencing. Nature Methods 6, 99–103 (2009)

    CAS  Article  Google Scholar 

  26. Venkatraman, E. S. & Olshen, A. B. A faster circular binary segmentation algorithm for the analysis of array CGH data. Bioinformatics 23, 657–663 (2007)

    CAS  Article  Google Scholar 

  27. 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)

    Article  Google Scholar 

  28. Wang, J. et al. CREST maps somatic structural variation in cancer genomes with base-pair resolution. Nature Methods 8, 652–654 (2011)

    CAS  Article  Google Scholar 

  29. Smyth, G. K. & Speed, T. Normalization of cDNA microarray data. Methods 31, 265–273 (2003)

    CAS  Article  Google Scholar 

  30. Workman, C. et al. A new non-linear normalization method for reducing variability in DNA microarray experiments. Genome Biol. 3, research0048 (2002)

    Article  Google Scholar 

  31. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009)

    Article  Google Scholar 

  32. 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)

    ADS  CAS  Article  Google Scholar 

  33. Kan, Z. et al. Diverse somatic mutation patterns and pathway alterations in human cancers. Nature 466, 869–873 (2010)

    ADS  CAS  Article  Google Scholar 

  34. Lawrence, M. S. et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499, 214–218 (2013)

    ADS  CAS  Article  Google Scholar 

  35. Zhang, H. et al. Cytogenetic aberrations in immortalization of esophageal epithelial cells. Cancer Genet. Cytogenet. 165, 25–35 (2006)

    CAS  Article  Google Scholar 

  36. 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)

    CAS  Article  Google Scholar 

  37. 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)

    CAS  Article  Google Scholar 

  38. 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)

    CAS  Article  Google Scholar 

Download references


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



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

Correspondence to Qimin Zhan.

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)

PowerPoint slides

Rights and permissions

Reprints 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).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

Further reading


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.


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing