Article | Published:

Spatial intratumoral heterogeneity and temporal clonal evolution in esophageal squamous cell carcinoma

Nature Genetics volume 48, pages 15001507 (2016) | Download Citation

Abstract

Esophageal squamous cell carcinoma (ESCC) is among the most common malignancies, but little is known about its spatial intratumoral heterogeneity (ITH) and temporal clonal evolutionary processes. To address this, we performed multiregion whole-exome sequencing on 51 tumor regions from 13 ESCC cases and multiregion global methylation profiling for 3 of these 13 cases. We found an average of 35.8% heterogeneous somatic mutations with strong evidence of ITH. Half of the driver mutations located on the branches of tumor phylogenetic trees targeted oncogenes, including PIK3CA, NFE2L2 and MTOR, among others. By contrast, the majority of truncal and clonal driver mutations occurred in tumor-suppressor genes, including TP53, KMT2D and ZNF750, among others. Interestingly, phyloepigenetic trees robustly recapitulated the topological structures of the phylogenetic trees, indicating a possible relationship between genetic and epigenetic alterations. Our integrated investigations of spatial ITH and clonal evolution provide an important molecular foundation for enhanced understanding of tumorigenesis and progression in ESCC.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Accessions

Primary accessions

Gene Expression Omnibus

Sequence Read Archive

References

  1. 1.

    et al. Global cancer statistics, 2012. CA Cancer J. Clin. 65, 87–108 (2015).

  2. 2.

    et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int. J. Cancer 136, E359–E386 (2015).

  3. 3.

    & Esophageal cancer. N. Engl. J. Med. 349, 2241–2252 (2003).

  4. 4.

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

  5. 5.

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

  6. 6.

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

  7. 7.

    et al. Genetic landscape of esophageal squamous cell carcinoma. Nat. Genet. 46, 1097–1102 (2014).

  8. 8.

    et al. Genomic analyses reveal mutational signatures and frequently altered genes in esophageal squamous cell carcinoma. Am. J. Hum. Genet. 96, 597–611 (2015).

  9. 9.

    et al. Whole-genome sequencing reveals diverse models of structural variations in esophageal squamous cell carcinoma. Am. J. Hum. Genet. 98, 256–274 (2016).

  10. 10.

    & Biological and therapeutic impact of intratumor heterogeneity in cancer evolution. Cancer Cell 27, 15–26 (2015).

  11. 11.

    et al. A census of human cancer genes. Nat. Rev. Cancer 4, 177–183 (2004).

  12. 12.

    et al. Discovery and saturation analysis of cancer genes across 21 tumour types. Nature 505, 495–501 (2014).

  13. 13.

    et al. Clonal status of actionable driver events and the timing of mutational processes in cancer evolution. Sci. Transl. Med. 7, 283ra54 (2015).

  14. 14.

    et al. Temporal dissection of tumorigenesis in primary cancers. Cancer Discov. 1, 137–143 (2011).

  15. 15.

    et al. The clonal and mutational evolution spectrum of primary triple-negative breast cancers. Nature 486, 395–399 (2012).

  16. 16.

    et al. Widespread genetic heterogeneity in multiple myeloma: implications for targeted therapy. Cancer Cell 25, 91–101 (2014).

  17. 17.

    et al. Evolution and impact of subclonal mutations in chronic lymphocytic leukemia. Cell 152, 714–726 (2013).

  18. 18.

    et al. Consistent and differential genetic aberrations between esophageal dysplasia and squamous cell carcinoma detected by array comparative genomic hybridization. Clin. Cancer Res. 19, 5867–5878 (2013).

  19. 19.

    et al. ANO1 protein as a potential biomarker for esophageal cancer prognosis and precancerous lesion development prediction. Oncotarget 7, 24374–24382 (2016).

  20. 20.

    et al. Calcium-activated chloride channel ANO1 promotes breast cancer progression by activating EGFR and CAMK signaling. Proc. Natl. Acad. Sci. USA 110, E1026–E1034 (2013).

  21. 21.

    et al. Amplification and overexpression of CTTN (EMS1) contribute to the metastasis of esophageal squamous cell carcinoma by promoting cell migration and anoikis resistance. Cancer Res. 66, 11690–11699 (2006).

  22. 22.

    et al. Genome-wide gene expression profile analyses identify CTTN as a potential prognostic marker in esophageal cancer. PLoS One 9, e88918 (2014).

  23. 23.

    et al. Spatial and temporal diversity in genomic instability processes defines lung cancer evolution. Science 346, 251–256 (2014).

  24. 24.

    et al. Genomic architecture and evolution of clear cell renal cell carcinomas defined by multiregion sequencing. Nat. Genet. 46, 225–233 (2014).

  25. 25.

    et al. Tracking the genomic evolution of esophageal adenocarcinoma through neoadjuvant chemotherapy. Cancer Discov. 5, 821–831 (2015).

  26. 26.

    , , , & DeconstructSigs: delineating mutational processes in single tumors distinguishes DNA repair deficiencies and patterns of carcinoma evolution. Genome Biol. 17, 31 (2016).

  27. 27.

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

  28. 28.

    , & Epigenetic alterations and their clinical implications in esophageal squamous cell carcinoma. Gen. Thorac. Cardiovasc. Surg. 61, 262–269 (2013).

  29. 29.

    et al. Epigenomic program of Barrett's-associated neoplastic progression reveals possible involvement of insulin signaling pathways. Endocr. Relat. Cancer 19, L5–L9 (2012).

  30. 30.

    et al. Widespread hypomethylation occurs early and synergizes with gene amplification during esophageal carcinogenesis. PLoS Genet. 7, e1001356 (2011).

  31. 31.

    et al. Intratumor DNA methylation heterogeneity reflects clonal evolution in aggressive prostate cancer. Cell Rep. 8, 798–806 (2014).

  32. 32.

    et al. DNA methylation and somatic mutations converge on the cell cycle and define similar evolutionary histories in brain tumors. Cancer Cell 28, 307–317 (2015).

  33. 33.

    & Comparison of phylogenetic trees. Math. Biosci. 53, 131–147 (1981).

  34. 34.

    et al. The Eph-receptor A7 is a soluble tumor suppressor for follicular lymphoma. Cell 147, 554–564 (2011).

  35. 35.

    et al. EPHA7, a new target gene for 6q deletion in T-cell lymphoblastic lymphomas. Carcinogenesis 33, 452–458 (2012).

  36. 36.

    et al. Methylation of protocadherin 10, a novel tumor suppressor, is associated with poor prognosis in patients with gastric cancer. Gastroenterology 136, 640–651.e1 (2009).

  37. 37.

    et al. A novel Wnt regulatory axis in endometrioid endometrial cancer. Cancer Res. 74, 5103–5117 (2014).

  38. 38.

    et al. Inactivation of the putative suppressor gene DOK1 by promoter hypermethylation in primary human cancers. Int. J. Cancer 130, 2484–2494 (2012).

  39. 39.

    et al. Characterization of DOK1, a candidate tumor suppressor gene, in epithelial ovarian cancer. Mol. Oncol. 5, 438–453 (2011).

  40. 40.

    & DNA methylation dynamics in health and disease. Nat. Struct. Mol. Biol. 20, 274–281 (2013).

  41. 41.

    & Do short, frequent DNA sequence motifs mould the epigenome? Nat. Rev. Mol. Cell Biol. 17, 257–262 (2016).

  42. 42.

    & A decade of exploring the cancer epigenome —biological and translational implications. Nat. Rev. Cancer 11, 726–734 (2011).

  43. 43.

    et al. The role of DNA methylation in directing the functional organization of the cancer epigenome. Genome Res. 25, 467–477 (2015).

  44. 44.

    et al. The human colon cancer methylome shows similar hypo- and hypermethylation at conserved tissue-specific CpG island shores. Nat. Genet. 41, 178–186 (2009).

  45. 45.

    et al. Differential methylation of tissue- and cancer-specific CpG island shores distinguishes human induced pluripotent stem cells, embryonic stem cells and fibroblasts. Nat. Genet. 41, 1350–1353 (2009).

  46. 46.

    & Kinase inhibitors and monoclonal antibodies in oncology: clinical implications. Nat. Rev. Clin. Oncol. 13, 209–227 (2016).

  47. 47.

    et al. Cancer: evolution within a lifetime. Annu. Rev. Genet. 48, 215–236 (2014).

  48. 48.

    et al. Multiple region whole-exome sequencing reveals dramatically evolving intratumor genomic heterogeneity in esophageal squamous cell carcinoma. Oncogenesis 4, e175 (2015).

  49. 49.

    , , , & Intratumor heterogeneity: seeing the wood for the trees. Sci. Transl. Med. 4, 127ps10 (2012).

  50. 50.

    et al. Downregulation of EphA7 by hypermethylation in colorectal cancer. Oncogene 24, 5637–5647 (2005).

  51. 51.

    et al. ABCB4 is frequently epigenetically silenced in human cancers and inhibits tumor growth. Sci. Rep. 4, 6899 (2014).

  52. 52.

    et al. Protocadherin 10 suppresses tumorigenesis and metastasis in colorectal cancer and its genetic loss predicts adverse prognosis. Int. J. Cancer 135, 2593–2603 (2014).

  53. 53.

    et al. PCDH10 promoter hypermethylation is frequent in most histologic subtypes of mature lymphoid malignancies and occurs early in lymphomagenesis. Genes Chromosom. Cancer 52, 1030–1041 (2013).

  54. 54.

    et al. Cancer statistics in China, 2015. CA Cancer J. Clin. 66, 115–132 (2016).

  55. 55.

    & SAMBLASTER: fast duplicate marking and structural variant read extraction. Bioinformatics 30, 2503–2505 (2014).

  56. 56.

    et al. From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline. Curr. Protoc. Bioinformatics 43, 11.10.1–11.10.33 (2013).

  57. 57.

    et al. VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res. 22, 568–576 (2012).

  58. 58.

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

  59. 59.

    , & ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).

  60. 60.

    et al. Paired exome analysis of Barrett's esophagus and adenocarcinoma. Nat. Genet. 47, 1047–1055 (2015).

  61. 61.

    , , & Circular binary segmentation for the analysis of array-based DNA copy number data. Biostatistics 5, 557–572 (2004).

  62. 62.

    et al. Absolute quantification of somatic DNA alterations in human cancer. Nat. Biotechnol. 30, 413–421 (2012).

  63. 63.

    , & Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat. Protoc. 4, 1073–1081 (2009).

  64. 64.

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

  65. 65.

    , & Predicting functional effect of human missense mutations using PolyPhen-2. Curr. Protoc. Hum. Genet. Chapter 7, Unit 7.20 (2013).

  66. 66.

    , , , & Low-level processing of Illumina Infinium DNA Methylation BeadArrays. Nucleic Acids Res. 41, e90 (2013).

  67. 67.

    & Fast and accurate phylogeny reconstruction algorithms based on the minimum-evolution principle. J. Comput. Biol. 9, 687–705 (2002).

  68. 68.

    et al. Estimation of the fraction of cancer cells in a tumor DNA sample using DNA methylation. PLoS One 8, e82302 (2013).

  69. 69.

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

  70. 70.

    et al. In situ cytotoxic and memory T cells predict outcome in patients with early-stage colorectal cancer. J. Clin. Oncol. 27, 5944–5951 (2009).

  71. 71.

    et al. Infiltration of Lynch colorectal cancers by activated immune cells associates with early staging of the primary tumor and absence of lymph node metastases. Clin. Cancer Res. 18, 1237–1245 (2012).

  72. 72.

    et al. Whole-transcriptome analysis of flow-sorted cervical cancer samples reveals that B cell expressed TCL1A is correlated with improved survival. Oncotarget 6, 38681–38694 (2015).

  73. 73.

    , , , & The tumor area occupied by Tbet+ cells in deeply invading cervical cancer predicts clinical outcome. J. Transl. Med. 13, 295 (2015).

  74. 74.

    et al. Differential DNA methylation in purified human blood cells: implications for cell lineage and studies on disease susceptibility. PLoS One 7, e41361 (2012).

  75. 75.

    Cancer Genome Atlas Research Network. Comprehensive molecular characterization of gastric adenocarcinoma. Nature 513, 202–209 (2014).

  76. 76.

    et al. Molecular profiling reveals biologically discrete subsets and pathways of progression in diffuse glioma. Cell 164, 550–563 (2016).

  77. 77.

    Cancer Genome Atlas Research Network. The molecular taxonomy of primary prostate cancer. Cell 163, 1011–1025 (2015).

  78. 78.

    , , , & Redefining CpG islands using hidden Markov models. Biostatistics 11, 499–514 (2010).

  79. 79.

    et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

  80. 80.

    et al. A reference methylome database and analysis pipeline to facilitate integrative and comparative epigenomics. PLoS One 8, e81148 (2013).

Download references

Acknowledgements

We thank H. Shen and D. Weisenberger as well as A.D. Jeyasekharan for their kind help on analysis and discussion. This work was funded by the Singapore Ministry of Health's National Medical Research Council (NMRC) through its Singapore Translational Research (STaR) Investigator Award to H.P.K., an NMRC Individual Research Grant (NMRC/1311/2011) and the NMRC Centre Grant awarded to the National University Cancer Institute of Singapore, the National Research Foundation Singapore and the Singapore Ministry of Education under its Research Centres of Excellence initiatives to H.P.K. D.-C.L. was supported by the American Society of Hematology Fellow Scholar Award, the National Natural Science Foundation of China (81672786) and National Center for Advancing Translational Sciences UCLA CTSI Grant UL1TR000124. M.-R.W. was supported by the National Natural Science Foundation of China (81330052, 81520108023 and 81321091). Y.Z. was supported by the Beijing Natural Science Foundation (7151008). This study was partially supported by a generous donation from the Melamed family and NIH/NCI grant 1U01CA184826 as well as institutional support from the Samuel Oschin Comprehensive Cancer Institute to B.P.B. and H.Q.D.

Author information

Author notes

    • Jia-Jie Hao
    • , De-Chen Lin
    • , Huy Q Dinh
    • , Anand Mayakonda
    •  & Yan-Yi Jiang

    These authors contributed equally to this work.

Affiliations

  1. State Key Laboratory of Molecular Oncology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

    • Jia-Jie Hao
    • , Chen Chang
    • , Ye Jiang
    • , Chen-Chen Lu
    • , Xin Xu
    • , Yu Zhang
    • , Yan Cai
    • , Qi-Min Zhan
    •  & Ming-Rong Wang
  2. Division of Hematology/Oncology, Cedars-Sinai Medical Center, UCLA School of Medicine, Los Angeles, California, USA.

    • De-Chen Lin
    •  & H Phillip Koeffler
  3. Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China.

    • De-Chen Lin
  4. Center for Bioinformatics and Functional Genomics, Biomedical Sciences, Cedars-Sinai Medical Center, UCLA School of Medicine, Los Angeles, California, USA.

    • Huy Q Dinh
    •  & Benjamin P Berman
  5. Cancer Science Institute of Singapore, National University of Singapore, Singapore.

    • Anand Mayakonda
    • , Yan-Yi Jiang
    •  & H Phillip Koeffler
  6. Faculty of Medicine, Kunming University of Science and Technology, Kunming, China.

    • Zhi-Zhou Shi
  7. Department of Pathology, Linzhou Cancer Hospital, Henan, China.

    • Jin-Wu Wang
  8. Department of Cancer Epidemiology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

    • Wen-Qiang Wei
  9. National University Cancer Institute, National University Hospital Singapore, Singapore.

    • H Phillip Koeffler

Authors

  1. Search for Jia-Jie Hao in:

  2. Search for De-Chen Lin in:

  3. Search for Huy Q Dinh in:

  4. Search for Anand Mayakonda in:

  5. Search for Yan-Yi Jiang in:

  6. Search for Chen Chang in:

  7. Search for Ye Jiang in:

  8. Search for Chen-Chen Lu in:

  9. Search for Zhi-Zhou Shi in:

  10. Search for Xin Xu in:

  11. Search for Yu Zhang in:

  12. Search for Yan Cai in:

  13. Search for Jin-Wu Wang in:

  14. Search for Qi-Min Zhan in:

  15. Search for Wen-Qiang Wei in:

  16. Search for Benjamin P Berman in:

  17. Search for Ming-Rong Wang in:

  18. Search for H Phillip Koeffler in:

Contributions

M.-R.W., D.-C.L., B.P.B. and H.P.K. conceived and designed the experiments. J.-J.H., D.-C.L., H.Q.D., W.-Q.W., B.P.B., M.-R.W. and H.P.K. wrote the manuscript. J.-J.H., D.-C.L., Y.J., C.C., C.-C.L., X.X. and Y.C. performed the experiments. J.-J.H., H.Q.D., A.M., B.P.B. and Z.-Z.S. performed statistical analysis. J.-J.H., D.-C.L., H.Q.D., Y.-Y.J., B.P.B. and H.P.K. analyzed the data. X.X. contributed reagents. W.-Q.W. contributed materials. J.-W.W. and J.-J.H. read slides with hematoxylin and eosin staining. D.-C.L., Y.Z., Q.-M.Z. and H.P.K. jointly supervised research.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to De-Chen Lin or Wen-Qiang Wei or Benjamin P Berman or Ming-Rong Wang.

Integrated supplementary information

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–10 and Supplementary Tables 1, 5 and 7.

Excel files

  1. 1.

    Supplementary Table 2

    Detailed information of all somatic mutations in 51 tumor regions from 13 patients with ESCC.

  2. 2.

    Supplementary Table 3

    Validation by PCR and Sanger sequencing.

  3. 3.

    Supplementary Table 4

    Copy number of each chromosomal segment in 51 tumor regions from 13 patients with ESCC.

  4. 4.

    Supplementary Table 6

    Mutations incompatible with the phylogenetic tree.

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/ng.3683

Further reading