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Cited by 174Lung Cancer Treatments and MutationsCancer Genomics and DiagnosticsLung Cancer Research Studies

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Elucidating the genomic architecture of Asian EGFR-mutant lung adenocarcinoma through multi-region exome sequencing

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Nahar, Rahul, Zhai, Weiwei, Zhang, Tong, Takano, Angela, Khng, Alexis J., Lee, Yin Yeng, Liu, Xingliang, Lim, Chong Hee, Koh, Tina P. T., Aung, Zaw Win, Lim, Tony Kiat Hon, Veeravalli, Lavanya, Yuan, Ju, Teo, Audrey S. M., Chan, Cheryl X., Poh, Huay Mei, Chua, Ivan M. L., Liew, Audrey Ann, Lau, Dawn Ping Xi, Kwang, Xue Lin, Toh, Chee Keong, Lim, Wan-Teck, Lim, Bing, Tam, Wai Leong, Tan, Eng-Huat, Hillmer, Axel M., Tan, Daniel S. W.·2018·Nature Communications
DOI 10.1038/s41467-017-02584-zPMC PMC5768770
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Abstract

EGFR-mutant lung adenocarcinomas (LUAD) display diverse clinical trajectories and are characterized by rapid but short-lived responses to EGFR tyrosine kinase inhibitors (TKIs). Through sequencing of 79 spatially distinct regions from 16 early stage tumors, we show that despite low mutation burdens, EGFR-mutant Asian LUADs unexpectedly exhibit a complex genomic landscape with frequent and early whole-genome doubling, aneuploidy, and high clonal diversity. Multiple truncal alterations, including TP53 mutations and loss of CDKN2A and RB1, converge on cell cycle dysregulation, with late sector-specific high-amplitude amplifications and deletions that potentially beget drug resistant clones. We highlight the association between genomic architecture and clinical phenotypes, such as co-occurring truncal drivers and primary TKI resistance. Through comparative analysis with published smoking-related LUAD, we postulate that the high intra-tumor heterogeneity observed in Asian EGFR-mutant LUAD may be contributed by an early dominant driver, genomic instability, and low background mutation rates.

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Figures

Fig. 1 Landscape of clonal and subclonal mutations in Asian EGFR-mutant tumors. a Phylogenetic trees generated for the 16 Asian EGFR-mutant LUADs. Trunks, branches and tips are depicted in blue, green, and red, respectively, while non-silent mutations carrying LUAD specific drivers are in red and other cancer drivers are in blue (Methods section). All patients are never-smokers except A103 (marked *) who was a light ex-smoker. Truncal mutation burden followed by total mutation burden for all sectors is indicated below the trees. b Bar plot representing truncal and non-truncal mutation burden per sector. c Oncoprint heatmap for mutations in LUAD drivers depicting the presence (see color legend) or absence (gray box) and type of non-silent mutation. d Proportions of the three mutation signatures identified for each sector. Signature numbers are according to the COSMIC nomenclature. e Pie charts representing contribution of the three mutation signatures in early (trunk) and late (branch/private) mutations
Fig. 1 Landscape of clonal and subclonal mutations in Asian EGFR-mutant tumors. a Phylogenetic trees generated for the 16 Asian EGFR-mutant LUADs. Trunks, branches and tips are depicted in blue, green, and red, respectively, while non-silent mutations carrying LUAD specific drivers are in red and other cancer drivers are in blue (Methods section). All patients are never-smokers except A103 (marked *) who was a light ex-smoker. Truncal mutation burden followed by total mutation burden for all sectors is indicated below the trees. b Bar plot representing truncal and non-truncal mutation burden per sector. c Oncoprint heatmap for mutations in LUAD drivers depicting the presence (see color legend) or absence (gray box) and type of non-silent mutation. d Proportions of the three mutation signatures identified for each sector. Signature numbers are according to the COSMIC nomenclature. e Pie charts representing contribution of the three mutation signatures in early (trunk) and late (branch/private) mutationsCC BY · PubMed Central
Fig. 2 Genomic instability and variegated copy number landscape of EGFR-mutant tumors. a Bar plot representing the fraction of genome altered by copy number alterations relative to ploidy of the sector, which is termed as the genomic instability index (GII). b Bar showing genome doubling status. Blue indicates significant evidence for genome doubling (Supplementary Data 3, Methods section) and gray indicates no genome doubling. c Heatmap depicting gains in known driver or recurrently amplified cytobands in LUAD (Methods section). Light red represents gain in one copy beyond the ploidy while dark red represents gain in ≥2 copies beyond the ploidy. d Heatmap depicting losses in known driver or recurrently amplified cytobands in LUAD (Methods section). Light blue represents loss of one copy relative to the ploidy while dark blue represents loss of ≥2 copies beyond the ploidy. Samples for all panels are as depicted in d. TP53 wild-type samples are depicted in gray
Fig. 2 Genomic instability and variegated copy number landscape of EGFR-mutant tumors. a Bar plot representing the fraction of genome altered by copy number alterations relative to ploidy of the sector, which is termed as the genomic instability index (GII). b Bar showing genome doubling status. Blue indicates significant evidence for genome doubling (Supplementary Data 3, Methods section) and gray indicates no genome doubling. c Heatmap depicting gains in known driver or recurrently amplified cytobands in LUAD (Methods section). Light red represents gain in one copy beyond the ploidy while dark red represents gain in ≥2 copies beyond the ploidy. d Heatmap depicting losses in known driver or recurrently amplified cytobands in LUAD (Methods section). Light blue represents loss of one copy relative to the ploidy while dark blue represents loss of ≥2 copies beyond the ploidy. Samples for all panels are as depicted in d. TP53 wild-type samples are depicted in grayCC BY · PubMed Central
Fig. 3 EGFR is a dominant driver with few co-drivers. Dot plots comparing mutation burden on a trunk and b branches between the Asian EGFR-mutant and smoker dominated Caucasian cohorts19, 20. c Dot plot comparing number of branch/private mutated drivers (extended driver list) between the EGFR-mutant and smoker dominated Caucasian cohorts19, 20. Welch’s t-test was used to compare the two groups. d Dot plot showing that EGFR-mutant LUADs have significantly fewer truncal drivers (extended driver list; Welch’s t-test) compared to smoker Caucasian LUADs. Three random sectors were picked 20 times iteratively and averages of the iterations are represented as circles in a–d. Horizontal line indicates the median for that cohort. e “Driver dominance score” measuring driver self-sufficiency for each of 78 LUAD driver genes calculated across published 412 tumors3, 4 is plotted against the fraction of patients carrying the mutated driver
Fig. 3 EGFR is a dominant driver with few co-drivers. Dot plots comparing mutation burden on a trunk and b branches between the Asian EGFR-mutant and smoker dominated Caucasian cohorts19, 20. c Dot plot comparing number of branch/private mutated drivers (extended driver list) between the EGFR-mutant and smoker dominated Caucasian cohorts19, 20. Welch’s t-test was used to compare the two groups. d Dot plot showing that EGFR-mutant LUADs have significantly fewer truncal drivers (extended driver list; Welch’s t-test) compared to smoker Caucasian LUADs. Three random sectors were picked 20 times iteratively and averages of the iterations are represented as circles in a–d. Horizontal line indicates the median for that cohort. e “Driver dominance score” measuring driver self-sufficiency for each of 78 LUAD driver genes calculated across published 412 tumors3, 4 is plotted against the fraction of patients carrying the mutated driverCC BY · PubMed Central
Fig. 4 TP53 mutations, genomic instability, high-driver burden lead to poor outcome. a Lower panel is a heatmap representing number of copies for selected genes involved in EGFR TKI resistance or associated with prognosis. Upper panel represents features of a tumor which are associated with patient outcome like TP53 mutation status, genomic instability index, presence of whole-genome doubling, above and below median number of drivers (LUAD specific or extended driver list) and the relapse status. All these features tend to coincide in many tumors. b Total mutation burdens and c driver burdens (extended driver list) are compared between TP53 mutant (mt) and wild-type (wt) tumors. Three random sectors were picked iteratively (n = 20) and averages across iterations are represented in b and c. The first p-value is taking all 16 patients into consideration and the second p-value is after eliminating the outlier A102 in the analysis. P-values are calculated using Welch’s t-test. d Survival plots using TCGA LUAD EGFR-mutant cases (those with non-silent mutations in tyrosine kinase domain, n = 26)4 after stratifying above or below median number of LUAD drivers (median = 3). P-value from χ2-test is indicated
Fig. 4 TP53 mutations, genomic instability, high-driver burden lead to poor outcome. a Lower panel is a heatmap representing number of copies for selected genes involved in EGFR TKI resistance or associated with prognosis. Upper panel represents features of a tumor which are associated with patient outcome like TP53 mutation status, genomic instability index, presence of whole-genome doubling, above and below median number of drivers (LUAD specific or extended driver list) and the relapse status. All these features tend to coincide in many tumors. b Total mutation burdens and c driver burdens (extended driver list) are compared between TP53 mutant (mt) and wild-type (wt) tumors. Three random sectors were picked iteratively (n = 20) and averages across iterations are represented in b and c. The first p-value is taking all 16 patients into consideration and the second p-value is after eliminating the outlier A102 in the analysis. P-values are calculated using Welch’s t-test. d Survival plots using TCGA LUAD EGFR-mutant cases (those with non-silent mutations in tyrosine kinase domain, n = 26)4 after stratifying above or below median number of LUAD drivers (median = 3). P-value from χ2-test is indicatedCC BY · PubMed Central
Fig. 5 Schematic of evolutionary trajectories in smokers and EGFR-mutant non-smoker tumors. a In smokers, accumulation of many truncal mutations and drivers before branched evolution. Top: driver mutations are represented as colored rectangles. The dashed line indicates the point of diversification on the tree or the last common ancestral clone (carrying the last truncal driver mutation). Middle: shaded background represents clonal expansion (y-axis) as mutations accumulate (x-axis) in individual clones (colors). Bottom: schematic representation of phylogenetic mutation tree. b The non-smoker scenario where a dominant driver like EGFR is hit early leading to a big fitness advantage, fewer clonal sweeps and early diversification. Panel structure as in a. In both scenarios, evolutionary trajectory is influenced by clonal dynamics, which in turn is related to competitive fitness of the individual cell populations. Example of such factors include but are not limited to mutations rates, driver nature, cytokine milieu, immune cell infiltration, and metabolic conditions
Fig. 5 Schematic of evolutionary trajectories in smokers and EGFR-mutant non-smoker tumors. a In smokers, accumulation of many truncal mutations and drivers before branched evolution. Top: driver mutations are represented as colored rectangles. The dashed line indicates the point of diversification on the tree or the last common ancestral clone (carrying the last truncal driver mutation). Middle: shaded background represents clonal expansion (y-axis) as mutations accumulate (x-axis) in individual clones (colors). Bottom: schematic representation of phylogenetic mutation tree. b The non-smoker scenario where a dominant driver like EGFR is hit early leading to a big fitness advantage, fewer clonal sweeps and early diversification. Panel structure as in a. In both scenarios, evolutionary trajectory is influenced by clonal dynamics, which in turn is related to competitive fitness of the individual cell populations. Example of such factors include but are not limited to mutations rates, driver nature, cytokine milieu, immune cell infiltration, and metabolic conditionsCC BY · PubMed Central

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Metadata

Authors
Nahar, R., Zhai, W., Zhang, T., Takano, A., Khng, A. J., Lee, Y. Y., Liu, X., Lim, C. H., Koh, T. P. T., Aung, Z. W., Lim, T. K. H., Veeravalli, L., Yuan, J., Teo, A. S. M., Chan, C. X., Poh, H. M., Chua, I. M. L., Liew, A. A., Lau, D. P. X., Kwang, X. L., Toh, C. K., Lim, W.-T., Lim, B., Tam, W. L., Tan, E.-H., Hillmer, A. M., & Tan, D. S. W.
Affiliations
  • Genome Institute of Singapore (SG)
  • Nanyang Technological University (SG)
  • Singapore General Hospital (SG)
  • National Heart Centre Singapore (SG)
  • National Cancer Centre Singapore (SG)
  • University Hospital Cologne (DE)
Corresponding author
  • Hillmer, Axel M.
  • Tan, Daniel S. W.
ORCID
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2018-01-15
Volume
9
Issue
1
Pages
216-216
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-017-02584-z
PMC
PMC5768770
OpenAlex
W2783397995
Cited by
173
References
65
Citations by year
2026: 10 · 2025: 7 · 2024: 12 · 2023: 26 · 2022: 26 · 2021: 34 · 2020: 25 · 2019: 23 · 2018: 10
FWCI
17.32
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Communications (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Kölner Universitäts PublikationsServer (Universität zu Köln) (publishedVersion)
  • National University of Singapore (submittedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • DR-NTU (Nanyang Technological University) (submittedVersion, cc-by)
Funders
  • Francis Crick Institute (10112)
  • National Cancer Centre of Singapore
  • Nanomaterials Microdevices Research Center, Osaka Institute of Technology
  • Singapore General Hospital
  • Genome Institute of Singapore
  • Medical Research Council
  • National Medical Research Council (NMRC/TCR/007-NCC/2013)
  • Biomedical Research Council
Topics
  • Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Lung Cancer Treatments and Mutations
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
  • Health Sciences › Medicine › Oncology › Lung Cancer Research Studies
Keywords
EGFR-mutant lung adenocarcinoma, Asian patients, whole-genome duplication, aneuploidy, clonal diversity, TP53 mutation, CDKN2A loss, RB1 loss, cell cycle dysregulation, gene amplification, gene deletion, EGFR tyrosine kinase inhibitor resistance, intratumoral heterogeneity, genomic instability, smoking-related lung cancer
MeSH terms
Exome Sequencing, Adenocarcinoma, Humans, Lung Neoplasms, Mutation, Drug Resistance, Neoplasm, Genetic Predisposition to Disease, Genomics, Asian People, Cell Line, Tumor, Protein Kinase Inhibitors, ErbB Receptors

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