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.

Genomic landscape of lung adenocarcinoma in East Asians

Nature Genetics volume 52, pages 177–186 (2020)Cite this article

Subjects

Abstract

Lung cancer is the world’s leading cause of cancer death and shows strong ancestry disparities. By sequencing and assembling a large genomic and transcriptomic dataset of lung adenocarcinoma (LUAD) in individuals of East Asian ancestry (EAS; n = 305), we found that East Asian LUADs had more stable genomes characterized by fewer mutations and fewer copy number alterations than LUADs from individuals of European ancestry. This difference is much stronger in smokers as compared to nonsmokers. Transcriptomic clustering identified a new EAS-specific LUAD subgroup with a less complex genomic profile and upregulated immune-related genes, allowing the possibility of immunotherapy-based approaches. Integrative analysis across clinical and molecular features showed the importance of molecular phenotypes in patient prognostic stratification. EAS LUADs had better prediction accuracy than those of European ancestry, potentially due to their less complex genomic architecture. This study elucidated a comprehensive genomic landscape of EAS LUADs and highlighted important ancestry differences between the two cohorts.

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

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

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

Fig. 1: Driver genes for EAS LUADs.
Fig. 2: CNVs and mutation signature analysis.
Fig. 3: Transcriptomic clusters in EAS and EUR cohorts.
Fig. 4: Ancestry differences in therapeutic opportunities.
Fig. 5: Survival groups and cohort differences.

Data availability

Raw sequencing data have been deposited in the European Genome-phenome Archive (EGA, http://www.ebi.ac.uk/ega/) under accession codes EGAD00001004421 and EGAD00001004422. All clinical records, somatic mutations, copy number variations and histological images from our study are hosted in OncoSG (https://src.gisapps.org/OncoSG/) under dataset ‘Lung Adenocarcinoma (GIS, 2019)’ which is publicly available (Supplementary Note).

References

  1. Bray, F. et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 68, 394–424 (2018).

    PubMed  Google Scholar 

  2. Cheng, T.-Y. D. et al. The international epidemiology of lung cancer: latest trends, disparities, and tumor characteristics. J. Thorac. Oncol. 11, 1653–1671 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. The Cancer Genome Atlas Research Network. Comprehensive molecular profiling of lung adenocarcinoma. Nature 511, 543–550 (2014).

    Article  CAS  PubMed Central  Google Scholar 

  5. Campbell, J. D. et al. Distinct patterns of somatic genome alterations in lung adenocarcinomas and squamous cell carcinomas. Nat. Genet. 48, 607–616 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Kadara, H. et al. Whole-exome sequencing and immune profiling of early-stage lung adenocarcinoma with fully annotated clinical follow-up. Ann. Oncol. 28, 75–82 (2017).

    Article  CAS  PubMed  Google Scholar 

  7. Garon, E. B. et al. Pembrolizumab for the treatment of non-small-cell lung cancer. N. Engl. J. Med. 372, 2018–2028 (2015).

    Article  PubMed  Google Scholar 

  8. Borghaei, H. et al. Nivolumab versus docetaxel in advanced nonsquamous non–small-cell lung cancer. N. Engl. J. Med. 373, 1627–1639 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Fehrenbacher, L. et al. Updated efficacy analysis including secondary population results for OAK: a randomized phase III study of Atezolizumab versus Docetaxel in patients with previously treated advanced non-small cell lung cancer. J. Thorac. Oncol. 13, 1156–1170 (2018).

    Article  PubMed  Google Scholar 

  10. Ayers, M. et al. IFN-γ-related mRNA profile predicts clinical response to PD-1 blockade. J. Clin. Invest. 127, 2930–2940 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Cristescu, R. et al. Pan-tumor genomic biomarkers for PD-1 checkpoint blockade-based immunotherapy. Science 362, eaar3593 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Tan, W.-L. et al. Novel therapeutic targets on the horizon for lung cancer. Lancet Oncol. 17, e347–e362 (2016).

    Article  CAS  PubMed  Google Scholar 

  13. Shigematsu, H. et al. Clinical and biological features associated with epidermal growth factor receptor gene mutations in lung cancers. J. Natl. Cancer Inst. 97, 339–346 (2005).

    Article  CAS  PubMed  Google Scholar 

  14. Jain, A. et al. Impact of smoking and brain metastasis on outcomes of advanced EGFR mutation lung adenocarcinoma patients treated with first line epidermal growth factor receptor tyrosine kinase inhibitors. PloS ONE 10, e0123587 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Kris, M. G. et al. Using multiplexed assays of oncogenic drivers in lung cancers to select targeted drugs. JAMA 311, 1998–2006 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Clinical Lung Cancer Genome Project (CLCGP) & Network Genomic Medicine (NGM). A genomics-based classification of human lung tumors. Sci. Transl. Med. 5, 209ra153 (2013).

  17. Wu, K. et al. Frequent alterations in cytoskeleton remodelling genes in primary and metastatic lung adenocarcinomas. Nat. Commun. 6, 10131 (2015).

    Article  CAS  PubMed  Google Scholar 

  18. Wang, C. et al. Whole-genome sequencing reveals genomic signatures associated with the inflammatory microenvironments in Chinese NSCLC patients. Nat. Commun. 9, 2054 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Luo, W. et al. Characteristics of genomic alterations of lung adenocarcinoma in young never-smokers. Int. J. Cancer 143, 1696–1705 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Nahar, R. et al. Elucidating the genomic architecture of Asian EGFR-mutant lung adenocarcinoma through multi-region exome sequencing. Nat. Commun. 9, 216 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. 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  PubMed  PubMed Central  Google Scholar 

  22. DePristo, M. A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Ramos, A. H. et al. Oncotator: cancer variant annotation tool. Hum. Mutat. 36, E2423–E2429 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Google Scholar 

  25. Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Saunders, C. T. et al. Strelka: accurate somatic small-variant calling from sequenced tumor–normal sample pairs. Bioinformatics 28, 1811–1817 (2012).

    Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Tokheim, C. J., Papadopoulos, N., Kinzler, K. W., Vogelstein, B. & Karchin, R. Evaluating the evaluation of cancer driver genes. Proc. Natl Acad. Sci. USA 113, 14330–14335 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Nicorici, D. et al. FusionCatcher—a tool for finding somatic fusion genes in paired-end RNA-sequencing data. Preprint at bioRxiv https://doi.org/10.1101/011650 (2014).

  31. Liu, L. et al. Comprehensive genomic profiling of lung cancer using a validated panel to explore therapeutic targets in East Asian patients. Cancer Sci. 108, 2487–2494 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Hu, X. et al. TumorFusions: an integrative resource for cancer-associated transcript fusions. Nucleic Acids Res. 46, D1144–D1149 (2018).

    Article  CAS  PubMed  Google Scholar 

  33. Weischenfeldt, J. et al. Integrative genomic analyses reveal an androgen-driven somatic alteration landscape in early-onset prostate cancer. Cancer Cell 23, 159–170 (2013).

    Article  CAS  PubMed  Google Scholar 

  34. Liao, S. et al. A genetic interaction analysis identifies cancer drivers that modify EGFR dependency. Genes Dev. 31, 184–196 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Gibson, B. A. & Kraus, W. L. New insights into the molecular and cellular functions of poly(ADP-ribose) and PARPs. Nat. Rev. Mol. Cell Biol. 13, 411–424 (2012).

    Article  CAS  PubMed  Google Scholar 

  36. Ikeda, Y. et al. Germline PARP4 mutations in patients with primary thyroid and breast cancers. Endocr. Relat. Cancer 23, 171–179 (2016).

    Article  CAS  PubMed  Google Scholar 

  37. Long, N. P. et al. Novel biomarker candidates for colorectal cancer metastasis: a meta-analysis of in vitro studies. Cancer Inform. 15, 11–17 (2016).

    PubMed  PubMed Central  Google Scholar 

  38. Katsyv, I. et al. EPRS is a critical regulator of cell proliferation and estrogen signaling in ER+ breast cancer. Oncotarget 7, 69592–69605 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Wang, J.-W. et al. Deregulated expression of LRBA facilitates cancer cell growth. Oncogene 23, 4089–4097 (2004).

    Article  CAS  PubMed  Google Scholar 

  40. Sung, H. et al. Inactivation of RASA1 promotes melanoma tumorigenesis via R-Ras activation. Oncotarget 7, 23885–23896 (2016).

    PubMed  PubMed Central  Google Scholar 

  41. Zhu, Y.-J., Xu, B. & Xia, W. Hsa-mir-182 downregulates RASA1 and suppresses lung squamous cell carcinoma cell proliferation. Clin. Lab. 60, 155–159 (2014).

    CAS  PubMed  Google Scholar 

  42. Wang, W.-H., Studach, L. L. & Andrisani, O. M. Proteins ZNF198 and SUZ12 are down-regulated in hepatitis B virus (HBV) X protein-mediated hepatocyte transformation and in HBV replication. Hepatology 53, 1137–1147 (2011).

    Article  CAS  PubMed  Google Scholar 

  43. Tamborero, D. et al. Cancer genome interpreter annotates the biological and clinical relevance of tumor alterations. Genome Med. 10, 25 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Lee, Y. J. et al. Activating mutations within the EGFR kinase domain: a molecular predictor of disease-free survival in resected pulmonary adenocarcinoma. J. Cancer Res. Clin. Oncol. 135, 1647–1654 (2009).

    Article  CAS  PubMed  Google Scholar 

  45. D’Angelo, S. P. et al. Distinct clinical course of EGFR-mutant resected lung cancers: results of testing of 1118 surgical specimens and effects of adjuvant Gefitinib and Erlotinib. J. Thorac. Oncol. 7, 1815–1822 (2012).

    Article  CAS  PubMed  Google Scholar 

  46. Jeon, J. H. et al. Prognostic and predictive role of epidermal growth factor receptor mutation in recurrent pulmonary adenocarcinoma after curative resection. Eur. J. Cardiothorac. Surg. 47, 556–562 (2015).

    Article  PubMed  Google Scholar 

  47. Favero, F. et al. Sequenza: allele-specific copy number and mutation profiles from tumor sequencing data. Ann. Oncol. 26, 64–70 (2015).

    CAS  PubMed  Google Scholar 

  48. Mermel, C. H. et al. GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers. Genome Biol. 12, R41 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Gaujoux, R. & Seoighe, C. A flexible R package for nonnegative matrix factorization. BMC Bioinformatics 11, 367 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Blokzijl, F., Janssen, R., van Boxtel, R. & Cuppen, E. MutationalPatterns: comprehensive genome-wide analysis of mutational processes. Genome Med. 10, 33 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Haradhvala, N. J. et al. Mutational strand asymmetries in cancer genomes reveal mechanisms of DNA damage and repair. Cell 164, 538–549 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Hayes, D. N. et al. Gene expression profiling reveals reproducible human lung adenocarcinoma subtypes in multiple independent patient cohorts. J. Clin. Oncol. 24, 5079–5090 (2006).

    Article  CAS  PubMed  Google Scholar 

  55. Wilkerson, M. D. et al. Differential pathogenesis of lung adenocarcinoma subtypes involving sequence mutations, copy number, chromosomal instability, and methylation. PLoS ONE 7, e36530 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Hoshida, Y., Brunet, J.-P., Tamayo, P., Golub, T. R. & Mesirov, J. P. Subclass mapping: identifying common subtypes in independent disease data sets. PLoS ONE 2, e1195 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Nirmal, A. J. et al. Immune cell gene signatures for profiling the microenvironment of solid tumors. Cancer Immunol. Res. 6, 1388–1400 (2018).

    Article  PubMed  Google Scholar 

  58. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Liberzon, A. et al. The molecular signatures database (MSigDB) hallmark gene set collection. Cell Syst. 1, 417–425 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Yoshihara, K. et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat. Commun. 4, 2612 (2013).

    Article  CAS  PubMed  Google Scholar 

  61. Sanchez-Vega, F. et al. Oncogenic signaling pathways in the cancer genome atlas. Cell 173, 321–337.e10 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Chakravarty, D. et al. OncoKB: A precision oncology knowledge base. JCO Precis. Oncol. https://doi.org/10.1200/PO.17.00011 (2017).

  63. Wu, Y.-L. et al. A consensus on immunotherapy from the 2017 Chinese Lung Cancer Summit expert panel. Transl. Lung Cancer Res. 7, 436–436 (2018). 428-.

    Article  CAS  Google Scholar 

  64. Morris, L. G. T. et al. Pan-cancer analysis of intratumor heterogeneity as a prognostic determinant of survival. Oncotarget 7, 10051–10063 (2016).

    PubMed  PubMed Central  Google Scholar 

  65. Yuan, Y. et al. Assessing the clinical utility of cancer genomic and proteomic data across tumor types. Nat. Biotechnol. 32, 644 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Jamal-Hanjani, M. et al. Tracking the evolution of non-small-cell lung cancer. N. Engl. J. Med. 376, 2109–2121 (2017).

    Article  CAS  PubMed  Google Scholar 

  67. Tan, D. S. W., Mok, T. S. K. & Rebbeck, T. R. Cancer genomics: diversity and disparity across ethnicity and geography. J. Clin. Oncol. 34, 91–101 (2016).

    Article  CAS  PubMed  Google Scholar 

  68. Haiman, C. A. et al. Genome-wide association study of prostate cancer in men of African ancestry identifies a susceptibility locus at 17q21. Nat. Genet. 43, 570–573 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Haiman, C. A. et al. A common variant at the TERT-CLPTM1L locus is associated with estrogen receptor-negative breast cancer. Nat. Genet. 43, 1210–1214 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Ziegler, R. G. et al. Migration patterns and breast cancer risk in Asian-American women. J. Natl Cancer Inst. 85, 1819–1827 (1993).

    Article  CAS  PubMed  Google Scholar 

  71. Kuniholm, M. H. et al. Prevalence of hepatitis C virus infection in US Hispanic/Latino adults: results from the NHANES 2007-2010 and HCHS/SOL studies. J. Infect. Dis. 209, 1585–1590 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  72. Teng, A. M., Blakely, T., Baker, M. G. & Sarfati, D. The contribution of Helicobacter pylori to excess gastric cancer in Indigenous and Pacific men: a birth cohort estimate. Gastric Cancer 20, 752–755 (2017).

    Article  PubMed  Google Scholar 

  73. Haiman, C. A. et al. Ethnic and racial differences in the smoking-related risk of lung cancer. N. Engl. J. Med. 354, 333–342 (2006).

    Article  CAS  PubMed  Google Scholar 

  74. Wu, C. et al. Genetic variants on chromosome 15q25 associated with lung cancer risk in Chinese populations. Cancer Res. 69, 5065–5072 (2009).

    Article  CAS  PubMed  Google Scholar 

  75. Wang, J. et al. Genetic predisposition to lung cancer: comprehensive literature integration, meta-analysis, and multiple evidence assessment of candidate-gene association studies. Sci. Rep. 7, 8371 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Seow, A. et al. Fumes from meat cooking and lung cancer risk in Chinese women. Cancer Epidemiol. Biomarkers Prev. 9, 1215–1221 (2000).

    CAS  PubMed  Google Scholar 

  77. Lee, T. & Gany, F. Cooking oil fumes and lung cancer: a review of the literature in the context of the U.S. population. J. Immigr. Minor. Health 15, 646–652 (2013).

    Article  PubMed  Google Scholar 

  78. Lortet-Tieulent, J. et al. Convergence of decreasing male and increasing female incidence rates in major tobacco-related cancers in Europe in 1988–2010. Eur. J. Cancer 51, 1144–1163 (2015).

    Article  PubMed  Google Scholar 

  79. Jemal, A. et al. Higher lung cancer incidence in young women than young men in the United States. N. Engl. J. Med. 378, 1999–2009 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  80. Takeuchi, T. et al. Expression profile-defined classification of lung adenocarcinoma shows close relationship with underlying major genetic changes and clinicopathologic behaviors. J. Clin. Oncol. 24, 1679–1688 (2006).

    Article  CAS  PubMed  Google Scholar 

  81. Davoli, T., Uno, H., Wooten, E. C. & Elledge, S. J. Tumor aneuploidy correlates with markers of immune evasion and with reduced response to immunotherapy. Science 355, eaaf8399 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Taylor, A. M. et al. Genomic and functional approaches to understanding cancer aneuploidy. Cancer Cell 33, 676–689.e3 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Coudray, N. et al. Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat. Med. 24, 1559–1567 (2018).

    Article  CAS  PubMed  Google Scholar 

  84. Cann, H. M. et al. A human genome diversity cell line panel. Science 296, 261–262 (2002).

    Article  CAS  PubMed  Google Scholar 

  85. Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint at https://arxiv.org/abs/1303.3997v2 (2013).

  86. Ding, L. et al. Somatic mutations affect key pathways in lung adenocarcinoma. Nature 455, 1069–1075 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. 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 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Kandoth, C. et al. Mutational landscape and significance across 12 major cancer types. Nature 502, 333–339 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Bailey, M. H. et al. Comprehensive characterization of cancer driver genes and mutations. Cell 173, 371–385.e18 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Tan, V. Y. F. & Févotte, C. Automatic relevance determination in nonnegative matrix factorization with the /spl beta/-divergence. IEEE Trans. Pattern Anal. Mach. Intell. 35, 1592–1605 (2013).

    Article  PubMed  Google Scholar 

  92. Kim, J. et al. Somatic ERCC2 mutations are associated with a distinct genomic signature in urothelial tumors. Nat. Genet. 48, 600–606 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Korotkevich, G., Sukhov, V. & Sergushichev, A. Fast gene set enrichment analysis. Preprint at bioRxiv https://doi.org/10.1101/060012 (2019).

  95. Liberzon, A. et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739–1740 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Andor, N., Harness, J. V., Müller, S., Mewes, H. W. & Petritsch, C. EXPANDS: expanding ploidy and allele frequency on nested subpopulations. Bioinformatics 30, 50–60 (2014).

    Article  CAS  PubMed  Google Scholar 

  97. Merlo, L. M. F. et al. A comprehensive survey of clonal diversity measures in Barrett’s esophagus as biomarkers of progression to esophageal adenocarcinoma. Cancer Prev. Res. (Phila) 3, 1388–1397 (2010).

    Article  Google Scholar 

  98. Mroz, E. A. & Rocco, J. W. MATH, a novel measure of intratumor genetic heterogeneity, is high in poor-outcome classes of head and neck squamous cell carcinoma. Oral Oncol. 49, 211–215 (2013).

    Article  CAS  PubMed  Google Scholar 

  99. Harrell, F. Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis (Springer, 2001).

  100. Harrell, F. E., Lee, K. L. & Mark, D. B. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat. Med. 15, 361–387 (1996).

    Article  PubMed  Google Scholar 

  101. Schröder, M. S., Culhane, A. C., Quackenbush, J. & Haibe-Kains, B. survcomp: an R/Bioconductor package for performance assessment and comparison of survival models. Bioinformatics 27, 3206–3208 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This work was funded by Glaxo Wellcome Manufacturing Pte Ltd, the Agency for Science Technology and Research (A*STAR) (grant no. GIS/15-IAF100), the National Medical Research Council, Singapore (grant nos. NMRC/OFLCG/002c/2018, NMRC/OFIRG/0064/2017 and NMRC/TCR/007-NCC/2013), the National Research Foundation, Singapore (grant no. NRF-NRFF2015–04), German Cancer Aid (grant no. 70113510) and Lung Cancer Consortium Singapore (LCCS). LCCS is jointly supported by philanthropy (including Singapore Millennium Foundation), and institutional and industrial grants. W.Z. is supported in part by the National Key R&D program of China (grant nos. 2018YFC0910400 and 2018YFC1406902) and the National Science Foundation of China (grant no. 31970566). We thank Beijing Genomics Institute for providing the published sequencing data. We thank T. Zhang for contribution to genomic data analysis, C. T. J. Ong, Y. L. Lee, I. M. L. Chua and W. W. J. Soon for the next-generation sequencing work, the GIS Research Pipeline Development team for support with analysis pipelines and Y. Matsuoka for administrative support. We thank Y. Cun for helpful discussions.

Author information

Authors and Affiliations

  1. Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore

    Jianbin Chen, Hechuan Yang, Audrey Su Min Teo, Lidyana Bte Amer, Faranak Ghazi Sherbaf, Chu Quan Tan, Jacob Josiah Santiago Alvarez, Bingxin Lu, Jia Qi Lim, Rahul Nahar, Yin Yeng Lee, Cheryl Zi Jin Phua, Khi Pin Chua, Pauline Jieqi Chen, Mei Mei Chang, Yi Fei Lee, Andreas Wilm, Poh Sum Choi, Poh Yong Ng, Siming Ma, Bing Lim, Wai Leong Tam, Anders Jacobsen Skanderup, Daniel Shao Weng Tan, Axel M. Hillmer & Weiwei Zhai

  2. Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing, China

    Hechuan Yang & Weiwei Zhai

  3. Department of Anatomical Pathology, Singapore General Hospital, Singapore, Singapore

    Angela Takano, Tony Kiat Hon Lim & Joe Poh Sheng Yeong

  4. Division of Medical Oncology, National Cancer Centre Singapore, Singapore, Singapore

    Lisda Suteja, Chee Keong Toh, Wan-Teck Lim, Eng-Huat Tan & Daniel Shao Weng Tan

  5. Surgical Oncology, National Cancer Centre Singapore, Singapore, Singapore

    Tina Puay Theng Koh

  6. Department of Cardiothoracic Surgery, National Heart Centre Singapore, Singapore, Singapore

    Boon-Hean Ong

  7. Department of Respiratory & Critical Care Medicine, Singapore General Hospital, Singapore, Singapore

    Devanand Anantham & Anne Ann Ling Hsu

  8. Department of Vascular & Interventional Radiology, Singapore General Hospital, Singapore, Singapore

    Apoorva Gogna & Chow Wei Too

  9. Division of Clinical Trials and Epidemiological Sciences, National Cancer Centre Singapore, Singapore, Singapore

    Zaw Win Aung, Lanying Wang & Eng-Huat Tan

  10. School of Biological Sciences, Nanyang Technological University, Singapore, Singapore

    Yi Fei Lee, Wai Leong Tam & Weiwei Zhai

  11. Institute of Molecular and Cell Biology, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore

    Wan-Teck Lim & Joe Poh Sheng Yeong

  12. Centre for Quantitative Medicine, Program in Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore

    Jin Liu

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

    Wai Leong Tam

  14. Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

    Wai Leong Tam

  15. Oncology R&D, GlaxoSmithKline, Collegeville, PA, USA

    Caretha L. Creasy

  16. Cancer Therapeutics Research Laboratory, Division of Medical Sciences, National Cancer Centre Singapore, Singapore, Singapore

    Daniel Shao Weng Tan

  17. Institute of Pathology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany

    Axel M. Hillmer

  18. Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China

    Weiwei Zhai

Authors
  1. Jianbin Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hechuan Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Audrey Su Min Teo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lidyana Bte Amer
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Faranak Ghazi Sherbaf
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Chu Quan Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jacob Josiah Santiago Alvarez
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Bingxin Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Jia Qi Lim
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Angela Takano
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Rahul Nahar
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Yin Yeng Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Cheryl Zi Jin Phua
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Khi Pin Chua
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Lisda Suteja
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Pauline Jieqi Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Mei Mei Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Tina Puay Theng Koh
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Boon-Hean Ong
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Devanand Anantham
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Anne Ann Ling Hsu
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Apoorva Gogna
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Chow Wei Too
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Zaw Win Aung
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Yi Fei Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. Lanying Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  27. Tony Kiat Hon Lim
    View author publications

    You can also search for this author in PubMed Google Scholar

  28. Andreas Wilm
    View author publications

    You can also search for this author in PubMed Google Scholar

  29. Poh Sum Choi
    View author publications

    You can also search for this author in PubMed Google Scholar

  30. Poh Yong Ng
    View author publications

    You can also search for this author in PubMed Google Scholar

  31. Chee Keong Toh
    View author publications

    You can also search for this author in PubMed Google Scholar

  32. Wan-Teck Lim
    View author publications

    You can also search for this author in PubMed Google Scholar

  33. Siming Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  34. Bing Lim
    View author publications

    You can also search for this author in PubMed Google Scholar

  35. Jin Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  36. Wai Leong Tam
    View author publications

    You can also search for this author in PubMed Google Scholar

  37. Anders Jacobsen Skanderup
    View author publications

    You can also search for this author in PubMed Google Scholar

  38. Joe Poh Sheng Yeong
    View author publications

    You can also search for this author in PubMed Google Scholar

  39. Eng-Huat Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

  40. Caretha L. Creasy
    View author publications

    You can also search for this author in PubMed Google Scholar

  41. Daniel Shao Weng Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

  42. Axel M. Hillmer
    View author publications

    You can also search for this author in PubMed Google Scholar

  43. Weiwei Zhai
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

A.M.H., W.Z. and D.S.W.T. conceived the study, and B. Lim, W.L.T. and E.-H.T. contributed. A.M.H. coordinated the genomics work. D.S.W.T. coordinated the clinical work. J.C. and W.Z. coordinated work on data analysis. J.C. performed genomic data analysis with contributions from H.Y., C.Q.T., B. Lu, J.J.S.A., J.Q.L., F.G.S., R.N., Y.Y.L., C.Z.J.P., K.P.C., Y.F.L. and J.L. A.W. contributed to analysis pipeline development. A.S.M.T. and L.B.A. performed nucleic acid extraction, exome library preparations and fusion gene validation, with contributions from F.G.S. who also performed SNV validation. A.T., with assistance from Z.W.A. and T.K.H.L., performed sectoring and histology studies and led the pathological work. P.S.C. and P.Y.N. contributed to RNA-seq library preparations and sequencing. T.P.T.K., B.-H.O., D.A., A.A.L.H., A.G. and C.W.T. performed surgery and biopsy procedures. D.S.W.T., A.T., W.-T.L., C.K.T., L.W. and E.-H.T. coordinated patient tissue banking, specimen transfer and clinical data curation. P.J.C., M.M.C., J.J.S.A. and A.J.S. implemented the OncoSG data portal. L.S., Z.W.A. and J.P.S.Y. performed multiplex immunohistochemistry. J.C., W.Z., A.M.H., D.S.W.T., C.L.C. and E.-H.T. interpreted the data and conceptualized the manuscript. J.C. created figures with contributions from F.G.S., C.Q.T., J.J.S.A., S.M., K.P.C. and W.Z. J.C. and W.Z. wrote the manuscript, with contributions from D.S.W.T., A.M.H., C.L.C., S.M., A.S.M.T., J.J.S.A., H.Y., B. Lu, K.P.C. and E.-H.T.

Corresponding authors

Correspondence to Daniel Shao Weng Tan, Axel M. Hillmer or Weiwei Zhai.

Ethics declarations

Competing interests

A.M.H, D.S.W.T., and W.Z. received research funding from Glaxo Wellcome Manufacturing Pte Ltd.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Kaplan-Meier plots of driver genes that can stratify patient survival outcome.

Survival outcomes of patients harbouring the driver mutation were compared against those did not in the (a) East Asian- ancestry (EAS, n=293) and (b) European-ancestry (EUR, n=225) cohort. Genes tested are from a curated list of LUAD drivers (Methods), and the plotted genes are those show significant coefficients (FDR<0.2 in EAS and p-values<0.05 in EUR, two-sided t-test) in a multivariate Cox model including stage, age, gender and smoker in any cohort. (c) Comparision of the survival outcomes of mutant or wildtype EGFR carriers among the EAS non-smokers (n=185) and smokers (n=110). EGFR mutant carriers showed better outcome, especially among non-smokers. Mut, mutant; WT, wildtype.

Extended Data Fig. 2 PCA of RNA profiles using both tumor and normal samples.

PCA of (a) East Asian-ancestry (EAS) and (b) European-ancestry (EUR) tumor (EAS, n=172; EUR, n=249) and normal (EAS, n=88; EUR, n=42) samples to illustrate the relationship of LUAD transcriptomic subtypes and the normal samples. In the two-group partitions, the TRU clusters were closer to the normal samples in both cohorts. In the three-group partitions, the TRU and TRU-I sub-clusters in the Asian were closer to the normal samples, while the TRU sub-cluster in the EUR was closer to the normal samples.

Extended Data Fig. 3 Phenotypes of the RNA sub-clusters in the EUR cohort.

The top two rows indicate the cluster assignment of the patients. The following rows show the normalized mean expression of GSEA enriched gene sets from the differential expressed genes between the TRU and non-TRU clusters and between the PI and PP sub-clusters, and the values of immune-related signatures. High values were shown in red and low in blue. The oncoprint plot shows major driver mutations across sub-clusters. The clinical and other genomic phenotypes are shown at the bottom.

Extended Data Fig. 4 Kaplan-Meier plots of the survival groups derived from genomic features only.

Using only genomic features (driver genes, molecular and ITH features), patients were divided evenly into three survival groups based on the predicted hazard from the multivariate Cox model. For both East Asian-ancestry (a) and European-ancestry (b) cohort, these survival models can clearly stratify patient survival outcome. They could stratify survival outcome even within early or late stage patients, indicating the prediction power of genomic features independent of clinical features. Statistical test used can be found under Methods section “Statistics and Reproducibility”.

Extended Data Fig. 5 Comparison of the prediction accuracies between the balanced EAS and EUR cohorts.

Related to Fig. 5c, box plots showing prediction accuracy calculated as Harrell’s concordance index (c-index) from the multivariate Cox models with different set of predictors. For fair comparisons across cohorts, the proportion of smokers were balanced by randomly down-sampling non-smokers in the East Asian-ancestry cohort and smokers in the European-ancestry cohort (a). To rule out the effect of EGFR mutation and possible TKI treatment on patient survival, the comparison was narrowed down to only patients with wildtyp EGFR (b). Statistical test used and the definition of boxplot elements can be found under Methods section “Statistics and Reproducibility”.

Extended Data Fig. 6 Summary of the ancestry differences.

A summary of major ancestry differences across the two cohorts in this study (top), and the differences seen when comparing among smokers and non-smokers (bottom). Red, higher/more; blue, lower/less; ≈, similar; ♂, male; ♀, female; IO, immuno-oncology; NA, not available.

Supplementary information

Supplementary Information

Supplementary Notes 1–7 and Figs. 1–41

Reporting Summary

Supplementary Tables

Supplementary Tables 1–12

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, J., Yang, H., Teo, A.S.M. et al. Genomic landscape of lung adenocarcinoma in East Asians. Nat Genet 52, 177–186 (2020). https://doi.org/10.1038/s41588-019-0569-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41588-019-0569-6

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research