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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.

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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.

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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.

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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)

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Song, Y., Li, L., Ou, Y. et al. Identification of genomic alterations in oesophageal squamous cell cancer. Nature 509, 91–95 (2014).

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