Comprehensive molecular profiling of lung adenocarcinoma

Cancer Genome Atlas Research Network

Abstract

Adenocarcinoma of the lung is the leading cause of cancer death worldwide. Here we report molecular profiling of 230 resected lung adenocarcinomas using messenger RNA, microRNA and DNA sequencing integrated with copy number, methylation and proteomic analyses. High rates of somatic mutation were seen (mean 8.9 mutations per megabase). Eighteen genes were statistically significantly mutated, including RIT1 activating mutations and newly described loss-of-function MGA mutations which are mutually exclusive with focal MYC amplification. EGFR mutations were more frequent in female patients, whereas mutations in RBM10 were more common in males. Aberrations in NF1, MET, ERBB2 and RIT1 occurred in 13% of cases and were enriched in samples otherwise lacking an activated oncogene, suggesting a driver role for these events in certain tumours. DNA and mRNA sequence from the same tumour highlighted splicing alterations driven by somatic genomic changes, including exon 14 skipping in MET mRNA in 4% of cases. MAPK and PI(3)K pathway activity, when measured at the protein level, was explained by known mutations in only a fraction of cases, suggesting additional, unexplained mechanisms of pathway activation. These data establish a foundation for classification and further investigations of lung adenocarcinoma molecular pathogenesis.

Figures

Figure 1. Somatic mutations in lung adenocarcinoma
Figure 1. Somatic mutations in lung adenocarcinoma
a, Co-mutation plot from whole exome sequencing of 230 lung adenocarcinomas. Data from TCGA samples were combined with previously published data for statistical analysis. Co-mutation plot for all samples used in the statistical analysis (n =412) can be found in Supplementary Fig. 2. Significant genes with a corrected P value less than 0.025 were identified using the MutSig2CV algorithm and are ranked in order of decreasing prevalence. b, c, The differential patterns of mutation between samples classified as transversion high and transversion low samples (b) or male and female patients (c) are shown for all samples used in the statistical analysis (n =412). Stars indicate statistical significance using the Fisher’s exact test (black stars: q <0.05, grey stars: P <0.05) and are adjacent to the sample set with the higher percentage of mutated samples.
Figure 2. Aberrant RNA transcripts in lung…
Figure 2. Aberrant RNA transcripts in lung adenocarcinoma associated with somatic DNA translocation or mutation
a, Normalized exon level RNA expression across fusion gene partners. Grey boxes around genes mark the regions that are removed as a consequence of the fusion. Junction points of the fusion events are also listed in Supplementary Table 9. Exon numbers refer to reference transcripts listed in Supplementary Table 9. b,MET exon 14 skipping observed in the presence of exon 14 splice site mutation (ss mut), splice site deletion (ss del) or a Y1003* mutation. A total of 22 samples had insufficient coverage around exon 14 for quantification. The percentage skipping is (total expression minus exon 14 expression)/total expression. c, Significant differences in the frequency of 129 alternative splicing events in mRNA from tumours with U2AF1 S34F tumours compared to U2AF1 WT tumours (q value <0.05). Consistent with the function of U2AF1 in 3′ splice site recognition, most splicing differences involved cassette exon and alternative 3′ splice site events (chi-squared test, P <0.001).
Figure 3. Identification of novel candidate driver…
Figure 3. Identification of novel candidate driver genes
a, GISTIC analysis of focal amplifications in oncogene-negative (n =87) and oncogene-positive (n =143) TCGA samples identifies focal gains of MET and ERBB2 that are specific to the oncogene-negative set (purple). b,TP53, KEAP1, NF1 and RIT1 mutations are significantly enriched in samples otherwise lacking oncogene mutations (adjusted P <0.05 by Fisher’s exact test). c, Co-mutation plot of variants of known significance within the RTK/RAS/RAF pathway in lung adenocarcinoma. Not shown are the 63 tumours lacking an identifiable driver lesion. Only canonical driver events, as defined in Supplementary Fig. 9, and proposed driver events, are shown; hence not every alteration found is displayed. d, New candidate driver oncogenes (blue: 13% of cases) and known somatically activated drivers events (red: 63%) that activate the RTK/RAS/RAF pathway can be found in the majority of the 230 lung adenocarcinomas.
Figure 4. Pathway alterations in lung adenocarcinoma
Figure 4. Pathway alterations in lung adenocarcinoma
a, Somatic alterations involving key pathway components for RTK signalling, mTOR signalling, oxidative stress response, proliferation and cell cycle progression, nucleosome remodelling, histone methylation, and RNA splicing/processing. b, c, Proteomic analysis by RPPA (n =181) P values by two-sided t-test. Box plots represent 5%, 25%, 75%, median, and 95%. PP, proximal proliferative; TRU, terminal respiratory unit; PI, proximal inflammatory. c, mTOR signalling may be activated, by either Akt (for example, via PI(3)K) or inactivation of AMPK (for example, via STK11 loss). Tumours were separated into three main groups: those with PI(3)K-AKT activation, through either PIK3CA activating mutation or unknown mechanism (high p-AKT); those with LKB1-AMPK inactivation, through either STK11 mutation or unknown mechanism with low levels of LKB1 and p-AMPK; and those showing none of the above features.
Figure 5. Integrative analysis
Figure 5. Integrative analysis
ac, Integrating unsupervised analyses of 230 lung adenocarcinomas reveals significant interactions between molecular subtypes. Tumours are displayed as columns, grouped by mRNA expression subtypes (a), DNA methylation subtypes (b), and integrated subtypes by iCluster analysis (c). All displayed features are significantly associated with subtypes depicted. The CIMP phenotype is defined by the most variable CpG island and promoter probes.

References

    1. Paez JG, et al. EGFR mutations in lung cancer: correlation with clinical response to gefitinib therapy. Science. 2004;304:1497–1500.
    1. Kwak EL, et al. Anaplastic lymphoma kinase inhibition in non-small-cell lung cancer. N Engl J Med. 2010;363:1693–1703.
    1. Bergethon K, et al. ROS1 rearrangements define a unique molecular class of lung cancers. J Clin Oncol. 2012;30:863–870.
    1. Drilon A, et al. Response to cabozantinib in patients with RET fusion-positive lung adenocarcinomas. Cancer Discov. 2013;3:630–635.
    1. Stephens P, et al. Lung cancer: intragenic ERBB2 kinase mutations in tumours. Nature. 2004;431:525–526.
    1. Takahashi T, et al. p53: a frequent target for genetic abnormalities in lung cancer. Science. 1989;246:491–494.
    1. Sanchez-Cespedes M, et al. Inactivation of LKB1/STK11 is a common event in adenocarcinomas of the lung. Cancer Res. 2002;62:3659–3662.
    1. Shapiro GI, et al. Reciprocal Rb inactivation and p16INK4 expression in primary lung cancers and cell lines. Cancer Res. 1995;55:505–509.
    1. Singh A, et al. Dysfunctional KEAP1–NRF2 interaction in non-small-cell lung cancer. PLoS Med. 2006;3:e420.
    1. Medina PP, et al. Frequent BRG1/SMARCA4-inactivating mutations in human lung cancer cell lines. Hum Mutat. 2008;29:617–622.
    1. Ding L, et al. Somatic mutations affect key pathways in lung adenocarcinoma. Nature. 2008;455:1069–1075.
    1. Imielinski M, et al. Mapping the hallmarks of lung adenocarcinoma with massively parallel sequencing. Cell. 2012;150:1107–1120.
    1. Govindan R, et al. Genomic landscape of non-small cell lung cancer in smokers and never-smokers. Cell. 2012;150:1121–1134.
    1. Travis WD, Brambilla E, Riely GJ. New pathologic classification of lung cancer: relevance for clinical practice and clinical trials. J Clin Oncol. 2013;31:992–1001.
    1. The Cancer Genome Atlas Research Network. Comprehensive genomic characterization of squamous cell lung cancers. Nature. 2012;489:519–525.
    1. Carter SL, et al. Absolute quantification of somatic DNA alterations in human cancer. Nature Biotechnol. 2012;30:413–421.
    1. Cibulskis K, et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nature Biotechnol. 2013;31:213–219.
    1. Lawrence MS, et al. Discovery and saturation analysis of cancer genes across 21 tumour types. Nature. 2014;505:495–501.
    1. Hurlin PJ, Steingrimsson E, Copeland NG, Jenkins NA, Eisenman RN. Mga, a dual-specificity transcription factor that interacts with Max and contains a T-domain DNA-binding motif. EMBO J. 1999;18:7019–7028.
    1. Peifer M, et al. Integrative genome analyses identify key somatic driver mutations of small-cell lung cancer. Nature Genet. 2012;44:1104–1110.
    1. Rudin CM, et al. Comprehensive genomic analysis identifies SOX2 as a frequently amplified gene in small-cell lung cancer. Nature Genet. 2012;44:1111–1116.
    1. Tokumo M, et al. The relationship between epidermal growth factor receptor mutations and clinicopathologic features in non-small cell lung cancers. Clin Cancer Res. 2005;11:1167–1173.
    1. Coleman MP, et al. A novel gene, DXS8237E, lies within 20 kb upstream of UBE1 in Xp11.23 and has a different X inactivation status. Genomics. 1996;31:135–138.
    1. Weir BA, et al. Characterizing the cancer genome in lung adenocarcinoma. Nature. 2007;450:893–898.
    1. Stephens PJ, et al. Massive genomic rearrangement acquired in a single catastrophic event during cancer development. Cell. 2011;144:27–40.
    1. Kong-Beltran M, et al. Somatic mutations lead to an oncogenic deletion of Met in lung cancer. Cancer Res. 2006;66:283–289.
    1. Seo JS, et al. The transcriptional landscape and mutational profile of lung adenocarcinoma. Genome Res. 2012;22:2109–2119.
    1. Wu S, Romfo CM, Nilsen TW, Green MR. Functional recognition of the 3′ splice site AG by the splicing factor U2AF35. Nature. 1999;402:832–835.
    1. Brooks AN, et al. A pan-cancer analysis of transcriptome changes associated with somatic mutations in U2AF1 reveals commonly altered splicing events. PLoS ONE. 2014;9:e87361.
    1. Pao W, Hutchinson KE. Chipping away at the lung cancer genome. Nature Med. 2012;18:349–351.
    1. Beroukhim R, et al. Assessing the significance of chromosomal aberrations in cancer: methodology and application to glioma. Proc Natl Acad Sci USA. 2007;104:20007–20012.
    1. Berger AH, et al. Oncogenic RIT1 mutations in lung adenocarcinoma. Oncogene. 2014 .
    1. Creighton CJ, et al. Proteomic and transcriptomic profiling reveals a link between the PI3K pathway and lower estrogen-receptor (ER) levels and activity in ER+ breast cancer. Breast Cancer Res. 2010;12:R40.
    1. Wilkerson MD, et al. Differential pathogenesis of lung adenocarcinoma subtypes involving sequence mutations, copy number, chromosomal instability, and methylation. PLoS ONE. 2012;7:e36530.
    1. Beer DG, et al. Gene-expression profiles predict survival of patients with lung adenocarcinoma. Nature Med. 2002;8:816–824.
    1. Hayes DN, et al. Gene expression profiling reveals reproducible human lung adenocarcinoma subtypes in multiple independent patient cohorts. J Clin Oncol. 2006;24:5079–5090.
    1. Bhattacharjee A, et al. Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proc Natl Acad Sci USA. 2001;98:13790–13795.
    1. Travis WD, et al. International association for the study of lung cancer/American Thoracic Society/European Respiratory Society international multidisciplinary classification of lung adenocarcinoma. J Thoracic Oncol. 2011;6:244–285.
    1. Yatabe Y, Mitsudomi T, Takahashi T. TTF-1 expression in pulmonary adenocarcinomas. Am J Surg Pathol. 2002;26:767–773.
    1. Shinjo K, et al. Integrated analysis of genetic and epigenetic alterations reveals CpG island methylator phenotype associated with distinct clinical characters of lung adenocarcinoma. Carcinogenesis. 2012;33:1277–1285.
    1. Mo Q, et al. Pattern discovery and cancer gene identification in integrated cancer genomic data. Proc Natl Acad Sci USA. 2013;110:4245–4250.
    1. Lawrence MS, et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature. 2013;499:214–218.

Source: PubMed

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