Skip to main content

Thank you for visiting nature.com. 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.

  • Letter
  • Published:

The genomic landscape of nasopharyngeal carcinoma

Nature Genetics volume 46, pages 866–871 (2014)Cite this article

Subjects

Abstract

Nasopharyngeal carcinoma (NPC) has extremely skewed ethnic and geographic distributions, is poorly understood at the genetic level and is in need of effective therapeutic approaches. Here we determined the mutational landscape of 128 cases with NPC using whole-exome and targeted deep sequencing, as well as SNP array analysis. These approaches revealed a distinct mutational signature and nine significantly mutated genes, many of which have not been implicated previously in NPC. Notably, integrated analysis showed enrichment of genetic lesions affecting several important cellular processes and pathways, including chromatin modification, ERBB-PI3K signaling and autophagy machinery. Further functional studies suggested the biological relevance of these lesions to the NPC malignant phenotype. In addition, we uncovered a number of new druggable candidates because of their genomic alterations. Together our study provides a molecular basis for a comprehensive understanding of, and exploring new therapies for, NPC.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Somatic mutational rates and signatures and SCNVs in NPC.
Figure 2: Integrated analysis of genetic alterations in NPC.
Figure 3: Chromatin modification processes dysregulated by genomic alterations in NPC.
Figure 4: Identification of ARID1A and BAP1 as NPC tumor suppressors.
Figure 5: Actionable genomic lesions in NPC.

Similar content being viewed by others

Accession codes

Primary accessions

Gene Expression Omnibus

Sequence Read Archive

References

  1. Wei, W.I. & Sham, J.S. Nasopharyngeal carcinoma. Lancet 365, 2041–2054 (2005).

    Article  PubMed  Google Scholar 

  2. Yu, M.C. & Yuan, J.M. Epidemiology of nasopharyngeal carcinoma. Semin. Cancer Biol. 12, 421–429 (2002).

    Article  PubMed  Google Scholar 

  3. Bei, J.X. et al. A genome-wide association study of nasopharyngeal carcinoma identifies three new susceptibility loci. Nat. Genet. 42, 599–603 (2010).

    Article  CAS  PubMed  Google Scholar 

  4. Feng, B.J. et al. Genome-wide scan for familial nasopharyngeal carcinoma reveals evidence of linkage to chromosome 4. Nat. Genet. 31, 395–399 (2002).

    Article  CAS  PubMed  Google Scholar 

  5. Andersson-Anvret, M., Forsby, N., Klein, G. & Henle, W. Relationship between the Epstein-Barr virus and undifferentiated nasopharyngeal carcinoma: correlated nucleic acid hybridization and histopathological examination. Int. J. Cancer 20, 486–494 (1977).

    Article  CAS  PubMed  Google Scholar 

  6. Yu, M.C., Mo, C.C., Chong, W.X., Yeh, F.S. & Henderson, B.E. Preserved foods and nasopharyngeal carcinoma: a case-control study in Guangxi, China. Cancer Res. 48, 1954–1959 (1988).

    CAS  PubMed  Google Scholar 

  7. Ward, M.H. et al. Dietary exposure to nitrite and nitrosamines and risk of nasopharyngeal carcinoma in Taiwan. Int. J. Cancer 86, 603–609 (2000).

    Article  CAS  PubMed  Google Scholar 

  8. Chan, A.S. et al. High frequency of chromosome 3p deletion in histologically normal nasopharyngeal epithelia from southern Chinese. Cancer Res. 60, 5365–5370 (2000).

    CAS  PubMed  Google Scholar 

  9. Sheu, J.J. et al. Chromosome 3p12.3-p14.2 and 3q26.2-q26.32 are genomic markers for prognosis of advanced nasopharyngeal carcinoma. Cancer Epidemiol. Biomarkers Prev. 18, 2709–2716 (2009).

    Article  CAS  PubMed  Google Scholar 

  10. Hu, C. et al. A global view of the oncogenic landscape in nasopharyngeal carcinoma: an integrated analysis at the genetic and expression levels. PLoS ONE 7, e41055 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Burns, M.B., Temiz, N.A. & Harris, R.S. Evidence for APOBEC3B mutagenesis in multiple human cancers. Nat. Genet. 45, 977–983 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Roberts, S.A. et al. An APOBEC cytidine deaminase mutagenesis pattern is widespread in human cancers. Nat. Genet. 45, 970–976 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Burns, M.B. et al. APOBEC3B is an enzymatic source of mutation in breast cancer. Nature 494, 366–370 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Carter, H. et al. Cancer-specific high-throughput annotation of somatic mutations: computational prediction of driver missense mutations. Cancer Res. 69, 6660–6667 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Carter, H., Samayoa, J., Hruban, R.H. & Karchin, R. Prioritization of driver mutations in pancreatic cancer using cancer-specific high-throughput annotation of somatic mutations (CHASM). Cancer Biol. Ther. 10, 582–587 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Wong, W.C. et al. CHASM and SNVBox: toolkit for detecting biologically important single nucleotide mutations in cancer. Bioinformatics 27, 2147–2148 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Sun, Y. et al. An infrequent point mutation of the p53 gene in human nasopharyngeal carcinoma. Proc. Natl. Acad. Sci. USA 89, 6516–6520 (1992).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Chakrani, F. et al. Mutations clustered in exon 5 of the p53 gene in primary nasopharyngeal carcinomas from southeastern Asia. Int. J. Cancer 61, 316–320 (1995).

    Article  CAS  PubMed  Google Scholar 

  20. Or, Y.Y., Hui, A.B., To, K.F., Lam, C.N. & Lo, K.W. PIK3CA mutations in nasopharyngeal carcinoma. Int. J. Cancer 118, 1065–1067 (2006).

    Article  CAS  PubMed  Google Scholar 

  21. Chou, C.C., Chou, M.J. & Tzen, C.Y. PIK3CA mutation occurs in nasopharyngeal carcinoma but does not significantly influence the disease-specific survival. Med. Oncol. 26, 322–326 (2009).

    Article  CAS  PubMed  Google Scholar 

  22. Eden, E., Lipson, D., Yogev, S. & Yakhini, Z. Discovering motifs in ranked lists of DNA sequences. PLoS Comput. Biol. 3, e39 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Eden, E., Navon, R., Steinfeld, I., Lipson, D. & Yakhini, Z. GOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists. BMC Bioinformatics 10, 48 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Wu, J.N. & Roberts, C.W. ARID1A mutations in cancer: another epigenetic tumor suppressor? Cancer Discov. 3, 35–43 (2013).

    Article  CAS  PubMed  Google Scholar 

  25. Guan, B., Gao, M., Wu, C.H., Wang, T.L. & Shih Ie, M. Functional analysis of in-frame indel ARID1A mutations reveals new regulatory mechanisms of its tumor suppressor functions. Neoplasia 14, 986–993 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Guan, B., Wang, T.L. & Shih Ie, M. ARID1A, a factor that promotes formation of SWI/SNF-mediated chromatin remodeling, is a tumor suppressor in gynecologic cancers. Cancer Res. 71, 6718–6727 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Scheuermann, J.C. et al. Histone H2A deubiquitinase activity of the Polycomb repressive complex PR-DUB. Nature 465, 243–247 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Cancer Genome Atlas Research Network. Comprehensive molecular characterization of clear cell renal cell carcinoma. Nature 499, 43–49 (2013).

  29. Peña-Llopis, S. et al. BAP1 loss defines a new class of renal cell carcinoma. Nat. Genet. 44, 751–759 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Sato, Y. et al. Integrated molecular analysis of clear-cell renal cell carcinoma. Nat. Genet. 45, 860–867 (2013).

    Article  CAS  PubMed  Google Scholar 

  31. Harbour, J.W. et al. Frequent mutation of BAP1 in metastasizing uveal melanomas. Science 330, 1410–1413 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Chan-On, W. et al. Exome sequencing identifies distinct mutational patterns in liver fluke–related and non-infection–related bile duct cancers. Nat. Genet. 45, 1474–1478 (2013).

    Article  CAS  PubMed  Google Scholar 

  33. Jiao, Y. et al. Exome sequencing identifies frequent inactivating mutations in BAP1, ARID1A and PBRM1 in intrahepatic cholangiocarcinomas. Nat. Genet. 45, 1470–1473 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. White, E. Deconvoluting the context-dependent role for autophagy in cancer. Nat. Rev. Cancer 12, 401–410 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Amaravadi, R.K. et al. Principles and current strategies for targeting autophagy for cancer treatment. Clin. Cancer Res. 17, 654–666 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Garber, K. Inducing indigestion: companies embrace autophagy inhibitors. J. Natl. Cancer Inst. 103, 708–710 (2011).

    Article  PubMed  Google Scholar 

  37. White, E. & DiPaola, R.S. The double-edged sword of autophagy modulation in cancer. Clin. Cancer Res. 15, 5308–5316 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Amaravadi, R.K. et al. Autophagy inhibition enhances therapy-induced apoptosis in a Myc-induced model of lymphoma. J. Clin. Invest. 117, 326–336 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Bellodi, C. et al. Targeting autophagy potentiates tyrosine kinase inhibitor–induced cell death in Philadelphia chromosome–positive cells, including primary CML stem cells. J. Clin. Invest. 119, 1109–1123 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Sheen, J.H., Zoncu, R., Kim, D. & Sabatini, D.M. Defective regulation of autophagy upon leucine deprivation reveals a targetable liability of human melanoma cells in vitro and in vivo. Cancer Cell 19, 613–628 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

  42. Agrawal, N. et al. Exome sequencing of head and neck squamous cell carcinoma reveals inactivating mutations in NOTCH1. Science 333, 1154–1157 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Williams, S.E., Beronja, S., Pasolli, H.A. & Fuchs, E. Asymmetric cell divisions promote Notch-dependent epidermal differentiation. Nature 470, 353–358 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Morris, L.G. et al. Recurrent somatic mutation of FAT1 in multiple human cancers leads to aberrant Wnt activation. Nat. Genet. 45, 253–261 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Lin, D.C. et al. Genomic and molecular characterization of esophageal squamous cell carcinoma. Nat. Genet. 46, 467–473 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. de Bock, C.E. et al. The Fat1 cadherin is overexpressed and an independent prognostic factor for survival in paired diagnosis-relapse samples of precursor B-cell acute lymphoblastic leukemia. Leukemia 26, 918–926 (2012).

    Article  CAS  PubMed  Google Scholar 

  48. Dikshit, B. et al. FAT1 acts as an upstream regulator of oncogenic and inflammatory pathways, via PDCD4, in glioma cells. Oncogene 32, 3798–3808 (2013).

    Article  CAS  PubMed  Google Scholar 

  49. Adzhubei, I.A. et al. A method and server for predicting damaging missense mutations. Nat. Methods 7, 248–249 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Zack, T.I. et al. Pan-cancer patterns of somatic copy number alteration. Nat. Genet. 45, 1134–1140 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Govindan, R. et al. Genomic landscape of non-small cell lung cancer in smokers and never-smokers. Cell 150, 1121–1134 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Sakaguchi, H. et al. Exome sequencing identifies secondary mutations of SETBP1 and JAK3 in juvenile myelomonocytic leukemia. Nat. Genet. 45, 937–941 (2013).

    Article  CAS  PubMed  Google Scholar 

  53. Yoshida, K. et al. Frequent pathway mutations of splicing machinery in myelodysplasia. Nature 478, 64–69 (2011).

    Article  CAS  PubMed  Google Scholar 

  54. Yoshida, K. et al. The landscape of somatic mutations in Down syndrome–related myeloid disorders. Nat. Genet. 45, 1293–1299 (2013).

    Article  CAS  PubMed  Google Scholar 

  55. Nannya, Y. et al. A robust algorithm for copy number detection using high-density oligonucleotide single nucleotide polymorphism genotyping arrays. Cancer Res. 65, 6071–6079 (2005).

    Article  CAS  PubMed  Google Scholar 

  56. Yamamoto, G. et al. Highly sensitive method for genomewide detection of allelic composition in nonpaired, primary tumor specimens by use of Affymetrix single-nucleotide-polymorphism genotyping microarrays. Am. J. Hum. Genet. 81, 114–126 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Zhang, B., Kirov, S. & Snoddy, J. WebGestalt: an integrated system for exploring gene sets in various biological contexts. Nucleic Acids Res. 33, W741–W748 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Wang, J., Duncan, D., Shi, Z. & Zhang, B. WEB-based GEne SeT AnaLysis Toolkit (WebGestalt): update 2013. Nucleic Acids Res. 41, W77–W83 (2013).

    PubMed  PubMed Central  Google Scholar 

  59. Lin, D.C. et al. Adaptor protein Lnk binds to and inhibits normal and leukemic FLT3. Blood 120, 3310–3317 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Lin, D.C. et al. Genomic and functional characterizations of phosphodiesterase subtype 4D in human cancers. Proc. Natl. Acad. Sci. USA 110, 6109–6114 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Sowa, M.E., Bennett, E.J., Gygi, S.P. & Harper, J.W. Defining the human deubiquitinating enzyme interaction landscape. Cell 138, 389–403 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Zang, Z.J. et al. Exome sequencing of gastric adenocarcinoma identifies recurrent somatic mutations in cell adhesion and chromatin remodeling genes. Nat. Genet. 44, 570–574 (2012).

    Article  CAS  PubMed  Google Scholar 

  63. Li, L.C. & Dahiya, R. MethPrimer: designing primers for methylation PCRs. Bioinformatics 18, 1427–1431 (2002).

    Article  CAS  PubMed  Google Scholar 

  64. Douville, C. et al. CRAVAT: cancer-related analysis of variants toolkit. Bioinformatics 29, 647–648 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank M.-S. Zeng (Sun Yat-sen University Cancer Center), T. Chan (Memorial Sloan-Kettering Cancer Center), Z.J. Zang (National Cancer Centre Singapore) and P. Tan for generously sharing materials as well as relevant facilities. This work is supported by US National Institutes of Health grant R01CA026038-35 (H.P.K.), the National Research Foundation Singapore and the Singapore Ministry of Education under the Research Centres of Excellence initiative (H.P.K.) and the Singapore Ministry of Health's National Medical Research Council under its Singapore Translational Research (STaR) Investigator Award to H.P.K. This work is also supported by The Terry Fox Foundation International Run Program through funds raised by the Terry Fox Singapore Run to the National University Cancer Institute, Singapore (NCIS).

Author information

Author notes

  1. De-Chen Lin and Xuan Meng: These authors contributed equally to this work.

  2. Henry Yang, Seishi Ogawa, Kwok Seng Loh and H Phillip Koeffler: These authors jointly directed this work.

Authors and Affiliations

  1. Cancer Science Institute of Singapore, National University of Singapore, Singapore

    De-Chen Lin, Xuan Meng, Masaharu Hazawa, Ana Maria Varela, Liang Xu, Li-Zhen Liu, Ling-Wen Ding, Arjun Sharma, Boon Cher Goh, Soo Chin Lee, Henry Yang & H Phillip Koeffler

  2. Division of Hematology/Oncology, Cedars-Sinai Medical Center, University of California, Los Angeles School of Medicine, Los Angeles, California, USA

    De-Chen Lin & H Phillip Koeffler

  3. Department of Medicine, School of Medicine, National University of Singapore, Singapore

    Xuan Meng

  4. Cancer Genomics Project, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan

    Yasunobu Nagata, Yusuke Sato & Seishi Ogawa

  5. Department of Pathology and Tumor Biology, Graduate School of Medicine, Kyoto University, Kyoto, Japan

    Yasunobu Nagata, Yusuke Sato & Seishi Ogawa

  6. Department of Haematology-Oncology, National University Cancer Institute, Singapore

    Boon Cher Goh & Soo Chin Lee

  7. Department of Pathology, National University Health System, Singapore

    Bengt Fredrik Petersson

  8. Department of Otolaryngology, National University Hospital Singapore, Singapore

    Feng Gang Yu & Kwok Seng Loh

  9. Department of Immunology, National University of Singapore, Singapore

    Paul Macary & Min Zin Oo

  10. Department of Microbiology, National University of Singapore, Singapore

    Chan Soh Ha

  11. National University Cancer Institute, National University Hospital Singapore, Singapore

    H Phillip Koeffler

Authors
  1. De-Chen Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xuan Meng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Masaharu Hazawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yasunobu Nagata
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ana Maria Varela
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Liang Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yusuke Sato
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Li-Zhen Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Ling-Wen Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Arjun Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Boon Cher Goh
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Soo Chin Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Bengt Fredrik Petersson
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Feng Gang Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Paul Macary
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Min Zin Oo
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Chan Soh Ha
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Henry Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Seishi Ogawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Kwok Seng Loh
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. H Phillip Koeffler
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

D.-C.L. and H.P.K. designed the study and wrote the manuscript. D.-C.L., X.M., M.H., Y.N., Y.S., A.M.V., L.X., L.-W.D. and A.S. performed experiments. C.S.H., B.F.P., B.C.G., S.C.L., F.G.Y. and K.S.L. coordinated sample collection and processing. D.-C.L., Y.N., Y.S., L.-Z.L., H.Y. and S.O. performed bioinformatical analysis. D.-C.L., X.M., Y.N., P.M., M.Z.O., B.C.G., S.C.L., F.G.Y., H.Y., S.O., K.S.L. and H.P.K. analyzed and discussed the data.

Corresponding author

Correspondence to De-Chen Lin.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Somatic coding mutation rates of NPC and other types of human cancers

Scatter plot showing somatic coding mutation rates with each dot representing a tumor-germline pair and the horizontal bar representing median value. The number under the X-axis indicates the number of tumor-germline pairs sequenced. All of the cancers except for NPC have been published elsewhere, and were analyzed by Lawrence et al. 21. GBM, glioblastoma multiforme; CLL, chronic lymphocytic leukemia; MM, multiple myeloma; DLBCL, diffuse large B-cell lymphoma; HNSCC, head and neck squamous cell carcinoma; Lung SCC, lung squamous cell carcinoma. EAC, esophageal adenocarcinoma; Lung AC, lung adenocarcinoma.

Source data

Supplementary Figure 2 Analysis of intratumoral clonality

Intratumoral clonality plots of three representative NPC cases subjected to whole-exome sequencing. Both NPC2F and NPC15F tumors showed bi-clonal architecture and NPC7D tumor was multi-clonal. VAF was calculated with copy-number neutral variants as described previously1,2,4,22,23.

Source data

Supplementary Figure 3 Mutational spectrum and signature of NPC

(a) Frequency of the six types of base substitutions caused by somatic mutations in NPC. (b) Detailed breakdown of mutations at each group of trinucleotides. (c) Relative expression of APOBEC3B mRNA analyzed from two datasets (GDS334124 and GSE3457325) which performed cDNA microarrays on NPC cases. Horizontal bar, mean value; N.S., no significance.

Source data

Supplementary Figure 4 Tumor suppressive function of ARID1A in NPC cells

NPC cells were transiently transfected with either negative control siRNA (Scramble) or pooled siRNAs against ARID1A (siARID1A). 48 hours later, cells were analyzed by (a) WB with indicated antibodies, and (b) migration assay. (c) NPC cells stably expressing either negative control shRNA (Scramble) or pooled shRNAs against ARID1A (shARID1A) were subjected to WB analysis. (d) HONE1 cells stably expressing either negative control shRNA (Scramble) or pooled shRNAs against ARID1A (shARID1A) were subjected to migration assay, (e) anchorage-independent colony formation assay and (f) xenograft growth assay. Horizontal bar, mean value. (g) Quantification of c-Myc protein levels of Fig. 4d. (h) SNP-array data of chromosome 1p of HONE1 cells. Blue line, total gene dosage; green and red lines, alleles-specific gene dosage. The value on the left indicates copy number. (i) MSP analysis of ARID1A promoter region in HONE1 cells. Upper panel, potential CpG sites were predicted by MethPrimer17. Vertical orange bars, CpG sites. Lower panel, PCR products were amplified by either methylated-specific (“M”) or unmethylated-specific (“U”) primers. (j) Relative ARID1A mRNA level of NPC cells was measured by real-time RT-PCR and normalized with HONE1 cells. (k) HONE1 cells were treated with either DMSO (Control), Vorinostat (5 μM, histone deacetylase inhibitor), or MG-132 (10 μM, proteasome inhibitor) for 12 hours and subject to WB analysis with indicated antibodies. Both β-Actin and α-Tubulin were used as loading controls for WB. Data from (b, d, e, g, j) represent mean ± SD; N = 3. *, P < 0.05; **, P < 0.01. N.S., no significance.

Source data

Supplementary Figure 5 Exploration of the function of autophagy pathway and FAT1 in NPC cells

(a) SUNE1 cell were treated with either DMSO (control), chloroquine (15 μM), cisplatin (20 μM), or chloroquine (15 μM) together with cisplatin (20 μM) for 48 hours, and apoptotic cells were detected with Annexin-V staining. (b) NPC cells were transfected with control siRNAs (Scramble) or siRNAs against FAT1 and subjected to either short-term proliferation assay or WB analysis with indicated antibodies. β-Actin was examined as a loading control. (c) A panel of isogenic NPC cells stably over-expressing either GFP or FAT1 (FAT1-OE) were examined by xenograft growth assay. Data of (a, b) represent mean ± SD; N = 3. Horizontal bar, mean value; *, P < 0.05; N.S., no significance.

Source data

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–5 and Supplementary Tables 4,5 and 7–10. (PDF 1905 kb)

Supplementary Table 1

(a) Coverage analysis of whole exome sequencing (Discovery Cohort and 5 NPC cell lines). (b) Coverage analysis of targeted sequencing (Prevalence Cohort). (XLSX 24 kb)

Supplementary Table 2

(a) Clinical parameters of NPCs analyzed with whole exome sequencing and SNP-array (Discovery Cohort). (b) Clinical parameters of NPCs analyzed with targeted sequencing (Prevalence Cohort). (XLSX 23 kb)

Supplementary Table 3

(a) Summary of somatic mutations of 56 NPCs analyzed with whole exome sequencing. (b) Summary of probable somatic mutations in NPC cell lines. (c) Summary of somatic mutations of 66 NPCs analyzed with targeted sequencing (XLSX 170 kb)

Supplementary Table 6

Genes for targeted capture in Prevalence Cohort (XLSX 27 kb)

Source data

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lin, DC., Meng, X., Hazawa, M. et al. The genomic landscape of nasopharyngeal carcinoma. Nat Genet 46, 866–871 (2014). https://doi.org/10.1038/ng.3006

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ng.3006

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer