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:

Whole-exome and targeted gene sequencing of gallbladder carcinoma identifies recurrent mutations in the ErbB pathway

Abstract

Individuals with gallbladder carcinoma (GBC), the most aggressive malignancy of the biliary tract, have a poor prognosis. Here we report the identification of somatic mutations for GBC in 57 tumor-normal pairs through a combination of exome sequencing and ultra-deep sequencing of cancer-related genes. The mutation pattern is defined by a dominant prevalence of C>T mutations at TCN sites. Genes with a significant frequency (false discovery rate (FDR) < 0.05) of non-silent mutations include TP53 (47.1%), KRAS (7.8%) and ERBB3 (11.8%). Moreover, ErbB signaling (including EGFR, ERBB2, ERBB3, ERBB4 and their downstream genes) is the most extensively mutated pathway, affecting 36.8% (21/57) of the GBC samples. Multivariate analyses further show that cases with ErbB pathway mutations have a worse outcome (P = 0.001). These findings provide insight into the somatic mutational landscape in GBC and highlight the key role of the ErbB signaling pathway in GBC pathogenesis.

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

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

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

Figure 1: Somatic SNV signature in GBC.
Figure 2: Significantly mutated genes in GBC.
Figure 3: Somatic alterations of ERBB3 and ERBB2 and their oncogenic effects on normal and GBC cells.
Figure 4: Somatic mutations of the ErbB signaling pathway in GBC.

Similar content being viewed by others

References

  1. Randi, G., Franceschi, S. & La Vecchia, C. Gallbladder cancer worldwide: geographical distribution and risk factors. Int. J. Cancer 118, 1591–1602 (2006).

    Article  CAS  Google Scholar 

  2. Srivastava, K., Srivastava, A., Sharma, K.L. & Mittal, B. Candidate gene studies in gallbladder cancer: a systematic review and meta-analysis. Mutat. Res. 728, 67–79 (2011).

    Article  CAS  Google Scholar 

  3. Wolpin, B.M. & Mayer, R.J. A step forward in the treatment of advanced biliary tract cancer. N. Engl. J. Med. 362, 1335–1337 (2010).

    Article  CAS  Google Scholar 

  4. Boutros, C., Gary, M., Baldwin, K. & Somasundar, P. Gallbladder cancer: past, present and an uncertain future. Surg. Oncol. 21, e183–e191 (2012).

    Article  CAS  Google Scholar 

  5. Maurya, S.K., Tewari, M., Mishra, R.R. & Shukla, H.S. Genetic aberrations in gallbladder cancer. Surg. Oncol. 21, 37–43 (2012).

    Article  Google Scholar 

  6. 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  Google Scholar 

  7. Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly (Austin) 6, 80–92 (2012).

    Article  CAS  Google Scholar 

  8. Alexandrov, L.B. et al. Signatures of mutational processes in human cancer. Nature 500, 415–421 (2013).

    Article  CAS  Google Scholar 

  9. Li, M. & Liu, Y. The applications of exome sequencing in the study of gastrointestinal cancer. Chin. J. Pract. Surg. 33, 414–416 (2013).

    CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  12. Kuong, K.J. & Loeb, L.A. APOBEC3B mutagenesis in cancer. Nat. Genet. 45, 964–965 (2013).

    Article  CAS  Google Scholar 

  13. Dees, N.D. et al. MuSiC: identifying mutational significance in cancer genomes. Genome Res. 22, 1589–1598 (2012).

    Article  CAS  Google Scholar 

  14. Wong, K.M., Hudson, T.J. & McPherson, J.D. Unraveling the genetics of cancer: genome sequencing and beyond. Annu. Rev. Genomics Hum. Genet. 12, 407–430 (2011).

    Article  CAS  Google Scholar 

  15. Wistuba, I.I. & Gazdar, A.F. Gallbladder cancer: lessons from a rare tumour. Nat. Rev. Cancer 4, 695–706 (2004).

    Article  CAS  Google Scholar 

  16. Croce, C.M. Oncogenes and cancer. N. Engl. J. Med. 358, 502–511 (2008).

    Article  CAS  Google Scholar 

  17. Jaiswal, B.S. et al. Oncogenic ERBB3 mutations in human cancers. Cancer Cell 23, 603–617 (2013).

    Article  CAS  Google Scholar 

  18. Desai, M.D., Saroya, B.S. & Lockhart, A.C. Investigational therapies targeting the ErbB (EGFR, HER2, HER3, HER4) family in GI cancers. Expert Opin. Investig. Drugs 22, 341–356 (2013).

    Article  CAS  Google Scholar 

  19. Nakazawa, K. et al. Amplification and overexpression of c-erbB-2, epidermal growth factor receptor, and c-met in biliary tract cancers. J. Pathol. 206, 356–365 (2005).

    Article  CAS  Google Scholar 

  20. Kiguchi, K. et al. Constitutive expression of ErbB-2 in gallbladder epithelium results in development of adenocarcinoma. Cancer Res. 61, 6971–6976 (2001).

    CAS  PubMed  Google Scholar 

  21. Baselga, J. & Swain, S.M. Novel anticancer targets: revisiting ERBB2 and discovering ERBB3. Nat. Rev. Cancer 9, 463–475 (2009).

    Article  CAS  Google Scholar 

  22. Sliwkowski, M.X. Ready to partner. Nat. Struct. Biol. 10, 158–159 (2003).

    Article  CAS  Google Scholar 

  23. Franklin, M.C. et al. Insights into ErbB signaling from the structure of the ErbB2-pertuzumab complex. Cancer Cell 5, 317–328 (2004).

    Article  CAS  Google Scholar 

  24. Macdonald-Obermann, J.L., Adak, S., Landgraf, R., Piwnica-Worms, D. & Pike, L.J. Dynamic analysis of the epidermal growth factor (EGF) receptor–ErbB2-ErbB3 protein network by luciferase fragment complementation imaging. J. Biol. Chem. 288, 30773–30784 (2013).

    Article  CAS  Google Scholar 

  25. Pignochino, Y. et al. Targeting EGFR/HER2 pathways enhances the antiproliferative effect of gemcitabine in biliary tract and gallbladder carcinomas. BMC Cancer 10, 631 (2010).

    Article  CAS  Google Scholar 

  26. Goldin, R.D. & Roa, J.C. Gallbladder cancer: a morphological and molecular update. Histopathology 55, 218–229 (2009).

    Article  Google Scholar 

  27. Lohse, M. et al. RobiNA: a user-friendly, integrated software solution for RNA-Seq–based transcriptomics. Nucleic Acids Res. 40, W622–W627 (2012).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  30. Cibulskis, K. et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat. Biotechnol. 31, 213–219 (2013).

    Article  CAS  Google Scholar 

  31. Ye, K., Schulz, M.H., Long, Q., Apweiler, R. & Ning, Z. Pindel: a pattern growth approach to detect break points of large deletions and medium sized insertions from paired-end short reads. Bioinformatics 25, 2865–2871 (2009).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  33. Choi, Y., Sims, G.E., Murphy, S., Miller, J.R. & Chan, A.P. Predicting the functional effect of amino acid substitutions and indels. PLoS ONE 7, e46688 (2012).

    Article  CAS  Google Scholar 

  34. Wendl, M.C. et al. PathScan: a tool for discerning mutational significance in groups of putative cancer genes. Bioinformatics 27, 1595–1602 (2011).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This study was supported by the National Natural Science Foundation of China (81172026, 81272402, 81301816, 81172029, 81370728, 81125020, 81328022 and 81302507), the National High-Technology Research and Development Program (863 Program, 2012AA022606; 2012BAK01B00), the Foundation for Interdisciplinary Research of Shanghai Jiao Tong University (YG2011ZD07), the Shanghai Science and Technology Commission Intergovernmental International Cooperation Project (12410705900), the Shanghai Science and Technology Commission Medical-Guiding Project (12401905800), the China Postdoctoral Science Foundation (2013M541513), the Program for Changjiang Scholars and the Leading Talent program of Shanghai.

Author information

Authors and Affiliations

Authors

Contributions

H. Wang, Yun Liu and Yingbin Liu conceived the study. C.L., B.S., B.L., Y.E.C., L.H., H. Wang, Yun Liu and Yingbin Liu directed the study. M.L., Z.Z., X.L., H. Wang, Yun Liu and Yingbin Liu contributed to the project design. M.L., X.L., D. Zhou, T.W., X. Wu, X.-A.W. and Qichen Ding performed experiments. Z.Z., J.Y., D. Zhang, X. Weng and H.Z. performed bioinformatics data analysis. W.W., K.Q., H. Weng, Qian Ding, P.C., T.L., Y.H. and W.L. contributed samples, data and comments on the manuscript. M.L., Z.Z., X.L., Z. Tan, J.M., W.G., W.T. and Y. Zheng analyzed and interpreted data. Y.S., R.B., Y.C., L.J., P.D., J.G., W.S., J.L., Z. Tang, Y. Zhang and X. Wang contributed reagents, materials and/or analysis tools. M.L., Z.Z. and X.L. wrote the manuscript.

Corresponding authors

Correspondence to Hui Wang, Yun Liu or Yingbin Liu.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Research strategy of this study.

Whole-exome sequencing was performed for 32 GBC pairs, and ultra-deep targeted gene sequencing was performed for 51 GBC pairs. The recurrently mutated genes, as determined from the exome data, were included in the targeted gene panels. Recurrently mutated genes and pathways were evaluated for the two data sets. By combining the 2 data sets, 21 of 57 GBC patients carrying non-silent somatic mutation(s) of ErbB pathways were identified. Oncology studies of the ErbB family on GBC cell lines were conducted, and associations between ErbB pathway mutations and prognosis were assessed.

Supplementary Figure 2 Comparison between exome and targeted sequencing.

(a) The samples included in the exome and targeted sequencing are shown in a Venn diagram. In total, 57 samples were utilized in this study, 26 of which were processed with both exome and targeted sequencing. (b) In the 26 overlapped samples, somatic non-silent mutations found in the shared coding region of exome and targeted sequencing are compared and shown in a Venn diagram. Seventy-one somatic mutations were identified by both methods; 22 and 96 were discovered only by exome or targeted sequencing, respectively.

Supplementary Figure 3 Schematic diagram of TP53 and KRAS somatic mutations.

(a,b) The relative positions of somatic mutations in TP53 and KRAS are shown with the gene structure. An orange box indicates a non-silent mutation, and a blue box indicates a mutation in an intron or UTR.

Supplementary Figure 4 The oncogenic effect of ERBB3 mutants in OCUG-1 cells.

OCUG-1 cells were transiently transfected with vector expressing ERBB3-WT or mutants, and cell viability was determined by MTT assay. Data represent the means ± s.e.m. of three independent experiments (*P < 0.05, **P < 0.01 compared to cells transfected with the control vector; §P < 0.05, §§P < 0.01 compared to cells expressing ERBB3 WT).

Supplementary Figure 5 The efficacies of RNA interference against ERBB1, ERBB2 and ERBB3 in GBC cells as determined by RT-PCR.

(a–d) GBC cells were respectively transfected with RNAi against two independent regions of ERBB1, ERBB2 and ERBB3, and the mRNA expressions of these genes was determined with real-time quantitative PCR 48 h later. β-actin was used as an internal control.

Supplementary Figure 6 Analysis of cell viability of GBC cells after knocking down ERBB1, ERBB2 and ERBB3.

(a–d) GBC cells were transfected with two siRNAs against ERBB1, ERBB2 and ERBB3, and cell viability was determined at days 0, 2, 4 and 6. The data shown are representative of values from three independent experiments (mean ± s.e.m.; *P < 0.05, **P < 0.01 versus the control siRNA group).

Supplementary Figure 7 Knocking down of ERBB3 and ERBB2 impaired GBC cell migration.

The effect of ERBB3 and ERBB2 on cell migration was determined for NOZ and OCUG-1 cells using RNAi. Representative images indicate three independent experiments with similar results.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–7 and Supplementary Tables 1–3, 5–9 and 11–18. (PDF 3308 kb)

Supplementary Tables 4 and 10

Supplementary Tables 4 and 10. (XLS 1688 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, M., Zhang, Z., Li, X. et al. Whole-exome and targeted gene sequencing of gallbladder carcinoma identifies recurrent mutations in the ErbB pathway. Nat Genet 46, 872–876 (2014). https://doi.org/10.1038/ng.3030

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ng.3030

This article is cited by

Search

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