Pulmonary carcinoids are rare neuroendocrine tumours of the lung. The molecular alterations underlying the pathogenesis of these tumours have not been systematically studied so far. Here we perform gene copy number analysis (n=54), genome/exome (n=44) and transcriptome (n=69) sequencing of pulmonary carcinoids and observe frequent mutations in chromatin-remodelling genes. Covalent histone modifiers and subunits of the SWI/SNF complex are mutated in 40 and 22.2% of the cases, respectively, with MEN1, PSIP1 and ARID1A being recurrently affected. In contrast to small-cell lung cancer and large-cell neuroendocrine lung tumours, TP53 and RB1 mutations are rare events, suggesting that pulmonary carcinoids are not early progenitor lesions of the highly aggressive lung neuroendocrine tumours but arise through independent cellular mechanisms. These data also suggest that inactivation of chromatin-remodelling genes is sufficient to drive transformation in pulmonary carcinoids.


Pulmonary carcinoids are neuroendocrine tumours that account for about 2% of pulmonary neoplasms. On the basis of the WHO classification of 2004, carcinoids can be subdivided into typical or atypical, the latter ones being very rare (about 0.2%)1. Most carcinoids can be cured by surgery; however, inoperable tumours are mostly insensitive to chemo- and radiation therapies1. Apart from few low-frequency alterations, such as mutations in MEN1 (ref. 1), comprehensive genome analyses of this tumour type have so far been lacking.

Here we conduct integrated genome analyses2 on data from chromosomal gene copy number of 54 tumours, genome and exome sequencing of 29 and 15 tumour-normal pairs, respectively, as well as transcriptome sequencing of 69 tumours. Chromatin-remodelling is the most frequently mutated pathway in pulmonary carcinoids; the genes MEN1, PSIP1 and ARID1A were recurrently affected by mutations. Specifically, covalent histone modifiers and subunits of the SWI/SNF (SWich/Sucrose NonFermentable) complex are mutated in 40 and 22.2% of the cases, respectively. By contrast, mutations of TP53 and RB1 are only found in 2 out of 45 cases, suggesting that these genes are not main drivers in pulmonary carcinoids.


In total, we generated genome/exome sequencing data for 44 independent tumour-normal pairs, and for most of them, also RNAseq (n=39, 69 in total) and SNP 6.0 (n=29, 54 in total) data (Supplementary Table 1). Although no significant focal copy number alterations were observed across the tumours analysed, we detected a copy number pattern compatible with chromothripsis3 in a stage-III atypical carcinoid of a former smoker (Fig. 1a; Supplementary Fig. 1). The intensely clustered genomic structural alterations found in this sample were restricted to chromosomes 3, 12 and 13, and led to the expression of several chimeric transcripts (Fig. 1b; Supplementary Table 2). Some of these chimeric transcripts affected genes involved in chromatin-remodelling processes, including out-of-frame fusion transcripts disrupting the genes ARID2, SETD1B and STAG1. Through the analyses of genome and exome sequencing data, we detected 529 non-synonymous mutations in 494 genes, which translates to a mean somatic mutation rate of 0.4 mutations per megabase (Mb) (Fig. 1c; Supplementary Data 1), which is much lower than the rate observed in other lung tumours (Fig. 1c)2,4,5. As expected, and in contrast to small-cell lung cancer (SCLC), no smoking-related mutation signature was observed in the mutation pattern of pulmonary carcinoids (Fig. 1d).

Figure 1: Genomic characterization of pulmonary carcinoids.
Figure 1

(a) CIRCOS plot of the chromothripsis case. The outer ring shows chromosomes arranged end to end. Somatic copy number alterations (gains in red and losses in blue) detected by 6.0 SNP arrays are depicted in the inside ring. (b) Copy numbers and chimeric transcripts of affected chromosomes. Segmented copy number states (blue points) are shown together with raw copy number data averaged over 50 adjacent probes (grey points). To show the different levels of strength for the identified chimeric transcripts, all curves are scaled according to the sequencing coverage at the fusion point. (c) Mutation frequency detected by genome and exome sequencing in pulmonary carcinoids (PCA). Each blue dot represents the number of mutations (muts) per Mb in one pulmonary carcinoid sample. Average frequencies are also shown for adenocarcinomas (AD), squamous (SQ) and small-cell lung cancer (SCLC) based on previous studies2,4,5. (d) Comparison of context-independent transversion and transition rates (an overall strand symmetry is assumed) between rates derived from molecular evolution (evol)36, from a previous SCLC sequencing study2 and from the PCA genome and exome sequencing. All rates are scaled such that their overall sum is 1.

We identified MEN1, ARID1A and EIF1AX as significantly mutated genes2 (q-value <0.2, see Methods section) (Fig. 2a; Supplementary Tables 1 and 3; Supplementary Data 1). MEN1 and ARID1A play important roles in chromatin-remodelling processes. The tumour suppressor MEN1 physically interacts with MLL and MLL2 to induce gene transcription6. Specifically, MEN1 is a molecular adaptor that physically links MLL with the chromatin-associated protein PSIP1, an interaction that is required for MLL/MEN1-dependent functions7. MEN1 also acts as a transcriptional repressor through the interaction with SUV39H18. We observed mutually exclusive frame-shift and truncating mutations in MEN1 and PSIP1 in six cases (13.3%), which were almost all accompanied by loss of heterozygosity (Supplementary Fig. 2). We also detected mutations in histone methyltransferases (SETD1B, SETDB1 and NSD1) and demethylases (KDM4A, PHF8 and JMJD1C), as well as in the following members of the Polycomb complex9 (Supplementary Tables 1 and 2; Supplementary Data 1): CBX6, which belongs to the Polycomb repressive complex 1 (PRC1); EZH1, which is part of the PCR2; and YY1, a member of the PHO repressive complex 1 that recruits PRC1 and PRC2. CBX6 and EZH1 mutations were also accompanied by loss of heterozygosity (Supplementary Fig. 2). In addition, we also detected mutations in the histone modifiers BRWD3 and HDAC5 in one sample each. In total, 40% of the cases carried mutually exclusive mutations in genes that are involved in covalent histone modifications (q-value=8 × 10−7, see Methods section) (Fig. 2a; Supplementary Table 4). To evaluate the impact of these mutations on histone methylation, we compared the levels of the H3K9me3 and H3K27me3 on seven mutated and six wild-type samples, and observed a trend towards lower methylation in the mutated cases (Fig. 2b; Table 1).

Figure 2: Significant affected genes and pathways in pulmonary carcinoids.
Figure 2

(a) Significantly mutated genes and pathways identified by genome (n=29), exome (n=15) and transcriptome (n=69) sequencing. The percentage of pulmonary carcinoids with a specific gene or pathway mutated is noted at the right side. The q-values of the significantly mutated genes and pathways are shown in brackets (see Methods section). Samples are displayed as columns and arranged to emphasize mutually exclusive mutations. (b) Methylation levels of H3K9me3 and H3K27me3 in pulmonary carcinoids. Representative pictures of different degrees of methylation (high, intermediate and low) for some of the samples summarized in Table 1. The mutated gene is shown in italics at the bottom right part of the correspondent picture. Wild-type samples are denoted by WT.

Table 1: Overview of samples annotated for mutations in genes involved in histone methylation and correspondent levels of H3K9me3 and H3K27me3 detected by immunohistochemistry.

Truncating and frame-shift mutations in ARID1A were detected in three cases (6.7%). ARID1A is one of the two mutually exclusive ARID1 subunits, believed to provide specificity to the ATP-dependent SWI/SNF chromatin-remodelling complex10,11. Truncating mutations of this gene have been reported at high frequency in several primary human cancers12. In total, members of this complex were mutated in mutually exclusive fashion in 22.2% of the specimens (q-value=8 × 10−8, see Methods section) (Fig. 2a; Supplementary Table 4). Among them were the core subunits SMARCA1, SMARCA2 and SMARCA4, which carry the ATPase activity of the complex, as well as the subunits ARID2, SMARCC2, SMARCB1 and BCL11A (Fig. 2a; Supplementary Tables 1 and 2; Supplementary Data 1)13,14. Another recurrently affected pathway was sister-chromatid cohesion during cell cycle progression with the following genes mutated (Fig. 2a; Supplementary Tables 1 and 2; Supplementary Data 1; Supplementary Fig. 3): the cohesin subunit STAG1 (ref. 15), the cohesin loader NIPBL16; the ribonuclease and microRNA processor DICER, necessary for centromere establishment17; and ERCC6L, involved in sister-chromatid separation18. In addition, although only few chimeric transcripts were detected in the 69 transcriptomes analysed (Supplementary Table 5), we found one sample harbouring an inactivating chimeric transcript, leading to the loss of the mediator complex gene MED24 (Supplementary Fig. 4) that interacts both physically and functionally with cohesin and NIPBL to regulate gene expression19. In summary, we detected mutations in chromatin-remodelling genes in 23 (51.1%) of the samples analysed. The specific role of histone modifiers in the development of pulmonary carcinoids was confirmed by the lack of significance of these pathways in SCLC2 (Supplementary Table 4). This was further supported by a gene expression analysis including 49 lung adenocarcinomas (unpublished data), 43 SCLC2,20 and the 69 pulmonary carcinoids included in this study (Supplementary Data 2). Consensus k-means clustering revealed that although both SCLC and pulmonary carcinoids are lung neuroendocrine tumours, both tumour types as well as adenocarcinomas formed statistically significant separate clusters (Fig. 3a). In support of this notion, we recently reported that the early alterations in SCLC universally affect TP53 and RB12, whereas in this study these genes were only mutated in two samples (Fig. 2a; Supplementary Table 1; Supplementary Data 1). Moreover, when examining up- and downregulated pathways in SCLC versus pulmonary carcinoids by gene set enrichment analysis21, we found that in line with the pattern of mutations, the RB1 pathway was statistically significantly altered in SCLC (q-value=5 × 10−4, see Methods section) but not in pulmonary carcinoids (Fig. 3b; Supplementary Table 6).

Figure 3: Expression data analysis of pulmonary carcinois based on RNAseq data.
Figure 3

(a) Consensus k-means clustering32,33 using RNAseq expression data of 49 adenocarcinomas (AD, in blue), 43 small-cell lung cancer (SCLC, in red) and 69 pulmonary carcinoids (PCA, in purple) identified three groups using the clustering module from GenePattern31 and consensus CDF32,33 (left panel). The significance of the clustering was evaluated by using SigClust34 with a P <0.0001. Fisher’s exact test35 was used to check associations between the clusters and the histological subtypes (right panel). (b) Gene set enrichment analysis21 for SCLC versus PCA using RNAseq expression data. Low gene expression is indicated in blue and high expression, in red. On the right side are given the altered pathways in PCA (green) and SCLC (purple).

Another statistically significant mutated gene was the eukaryotic translation initiation factor 1A (EIF1AX) mutated in four cases (8.9%). In addition, SEC31A, WDR26 and the E3 ubiquitin ligase HERC2 were mutated in two samples each. Further supporting a role of E3 ubiquitin ligases in the development of pulmonary carcinoids, we found mutations or rearrangements affecting these genes in 17.8% of the samples analysed (Fig. 2a; Supplementary Tables 1 and 7; Supplementary Data 1). All together, we have identified candidate driver genes in 73.3% of the cases. Of note, we did not observe any genetic segregation between typical or atypical carcinoids, neither between the expression clusters generated from the two subtypes, nor between these clusters and the mutated pathways (Supplementary Fig. 5). However, it is worth mentioning that only nine atypical cases were included in this study. The spectrum of mutations found in the discovery cohort was further validated by transcriptome sequencing of an independent set of pulmonary carcinoid specimens (Supplementary Tables 1 and 8). Owing to the fact that many nonsense and frame-shift mutations may result in nonsense-mediated decay22,23, the mutations detected by transcriptome sequencing were only missense. Owing to this bias, accurate mutation frequencies could not be inferred from these data.


This study defines recurrently mutated sets of genes in pulmonary carcinoids. The fact that almost all of the reported genes were mutated in a mutually exclusive manner and affected a small set of cellular pathways defines these as the key pathways in this tumour type. Given the frequent mutations affecting the few signalling pathways described above and the almost universal absence of other cancer mutations, our findings support a model where pulmonary carcinoids are not early progenitor lesions of other neuroendocrine tumours, such as SCLC or large-cell neuroendocrine carcinoma, but arise through independent cellular mechanisms. More broadly, our data suggest that mutations in chromatin-remodelling genes, which in recent studies were found frequently mutated across multiple malignant tumours24, are sufficient to drive early steps in tumorigenesis in a precisely defined spectrum of required cellular pathways.


Tumour specimens

The study as well as written informed consent documents had been approved by the Institutional Review Board of the University of Cologne. Additional biospecimens for this study were obtained from the Victorian Cancer Biobank, Melbourne, Australia; the Vanderbilt-Ingram Cancer Center, Nashville, Tennessee, USA; and Roy Castle Lung Cancer Research Programme, The University of Liverpool Cancer Research Center, Liverpool, UK. The Institutional Review Board of each participating institution approved collection and use of all patient specimens in this study.

Nucleic acid extraction and sample sequencing

All samples in this study were reviewed by expert pathologists. Total RNA and DNA were obtained from fresh-frozen tumour and matched fresh-frozen normal tissue or blood. Tissue was frozen within 30 min after surgery and was stored at −80 °C. Blood was collected in tubes containing the anticoagulant EDTA and was stored at −80 °C. Total DNA and RNA were extracted from fresh-frozen lung tumour tissue containing more than 70% tumour cells. Depending on the size of the tissue, 15–30 sections, each 20 μm thick, were cut using a cryostat (Leica) at −20 °C. The matched normal sample obtained from frozen tissue was treated accordingly. DNA from sections and blood was extracted using the Puregene Extraction kit (Qiagen) according to the manufacturer’s instructions. DNA was eluted in 1 × TE buffer (Qiagen), diluted to a working concentration of 150 ng μl−1 and stored at −80 °C. For whole exome sequencing, we fragmented 1 μg of DNA with sonification technology (Bioruptor, diagenode, Liége, Belgium). The fragments were end repaired and adaptor ligated, including incorporation of sample index barcodes. After size selection, we subjected the library to an enrichment process with the SeqCap EZ Human Exome Library version 2.0 kit (Roche NimbleGen, Madison, WI, USA). The final libraries were sequenced with a paired-end 2 × 100 bp protocol. On average, 7 Gb of sequence were produced per normal, resulting in 30 × coverage of more than 80% of target sequences (44 Mb). For better sensitivity, tumours were sequenced with 12 Gb and 30 × coverage of more than 90% of target sequences. We filtered primary data according to signal purity with the Illumina Realtime Analysis software. Whole-genome sequencing was also performed using a read length of 2 × 100 bp for all samples. On average, 110 Gb of sequence were produced per sample, aiming a mean coverage of 30 × for both tumour and matched normal. RNAseq was performed on complementary DNA libraries prepared from PolyA+ RNA extracted from tumour cells using the Illumina TruSeq protocol for mRNA. The final libraries were sequenced with a paired-end 2 × 100 bp protocol aiming at 8.5 Gb per sample, resulting on a 30 × mean coverage of the annotated transcriptome. All the sequencing was carried on an Illumina HiSeq 2000 sequencing instrument (Illumina, San Diego, CA, USA).

Sequence data processing and mutation detection

Raw sequencing data are aligned to the most recent build of the human genome (NCBI build 37/hg19) using BWA (version: 0.5.9rc1)25 and possible PCR duplicates are subsequently removed from the alignments. Somatic mutations were detected using our in-house-developed sequencing analysis pipeline. In brief, the mutation-calling algorithm incorporates parameters such as local copy number profiles, estimates of tumour purity and ploidy, local sequencing depth, as well as the global sequencing error into a statistical model with which the presence of a mutated allele in the tumour is determined. Next, the absence of this variant in the matched normal is assessed by demanding that the corresponding allelic fraction is compatible with the estimated background sequencing error in the normal. In addition, we demand that the allelic fractions between tumour and normal differ significantly. To finally remove artificial mutation calls, we apply a filter that is based on the forward–reverse bias of the sequencing reads. Further details of this approach are given in Peifer et al.2

Genomic rearrangement reconstruction from paired-end data

To reconstruct rearrangements from paired-end data, we refined our initial method2 by adding breakpoint-spanning reads. Here, locations of encompassing read pairs are screened for further reads where only one pair aligns to the region and the other pair either does not align at all or is clipped by the aligner. These reads are then realigned using BLAT to a 1,000 bp region around the region defined by the encompassing reads. Rearrangements confirmed by at least one spanning read are finally reported. To filter for somatic rearrangements, we subtracted those regions where rearrangements are present in the matched normal and in all other sequenced normals within the project.

Analysis of significantly mutated genes and pathways

The analysis of significantly mutated genes is done in a way that both gene expression and the accumulation of synonymous mutations are considered to obtain robust assessments of frequently mutated, yet biologically relevant genes. To this end, the overall background mutation rate is determined first, from which the expected number of mutations for each gene is computed under the assumption of a purely random mutational process. This gene-specific expected number of mutations defines the underlying null model of our statistical test. To account for misspecifications, for example, due to a local variation of mutation rates, we also incorporated the synonymous to non-synonymous ratio into a combined statistical model to determine significantly mutated genes. Since mutation rates in non-expressed genes are often high than the genome-wide background rate2,26, genes that are having a median Fragments Per Kilobase of transcript per Million fragments mapped (FPKM) value <1 in our transcriptome sequencing data are removed prior testing. To account for multiple hypothesis testing, we are using the Benjamini–Hochberg approach27. Mutation data of the total of 44 samples, for which either whole-exome sequencing (WES) or whole-genome sequencing (WGS) was performed, were used for this analysis.

In case of the pathway analysis, gene lists of the methylation and the SWI/SNF complex were obtained from recent publications9,13,14,28. To assess whether mutations in these pathways are significantly enriched, all genes of the pathway are grouped together as if they represent a ‘single gene’ and subsequently tested if the total number of mutation exceed mutational background of the entire pathway. To this end, the same method as described above was used. Mutation data of the total of 44 samples, for which either WES or WGS was performed, were used for this analysis.

Analysis of chromosomal gene copy number data

Hybridization of the Affymetrix SNP 6.0 arrays was carried out according to the manufacturers’ instructions and analysed as follows: raw signal intensities were processed by applying a log-linear model to determine allele-specific probe affinities and probe-specific background intensities. To calibrate the model, a Gauss–Newton approach was used and the resulting raw copy number profiles are segmented by applying the circular binary segmentation method29.

Analysis of RNAseq data

For the analysis of RNAseq data, we have developed a pipeline that affords accurate and efficient mapping and downstream analysis of transcribed genes in cancer samples (Lynnette Fernandez-Cuesta and Ruping Sun, personal communication). In brief, paired-end RNAseq reads were mapped onto hg19 using a sensitive gapped aligner, GSNAP30. Possible breakpoints were called by identifying individual reads showing split-mapping to distinct locations as well as clusters of discordant read pairs. Breakpoint assembly was performed to leverage information across reads anchored around potential breakpoints. Assembled contigs were aligned back to the reference genome to confirm bona fide fusion points.

Dideoxy sequencing

All non-synonymous mutations found in the genome/exome data were checked in RNAseq data when available. Genes recurrently mutated involved in pathways statistically significantly mutated, or interesting because of their presence in other lung neuroendocrine tumours, were selected for validation. One hundred and fifty eight mutations were considered for validation: 115 validated and 43 did not (validation rate 73%). Sequencing primer pairs were designed to enclose the putative mutation (Supplementary Data 1), or to encompass the candidate rearrangement (Supplementary Table 7) or chimeric transcript (Supplementary Table 2 and 5). Sequencing was carried out using dideoxy-nucleotide chain termination (Sanger) sequencing, and electropherograms were analysed by visual inspection using four Peaks.

Gene expression data analyses

Unsupervised consensus clustering was applied to RNAseq data of 69 pulmonary carcinoids, 49 adenocarcinomas and 43 SCLC2,20 samples. The 3,000 genes with highest variation across all samples were filtered out before performing consensus clustering. We used the clustering module from GenePattern31 and the consensus CDF32,33. Significance was obtained by using SigClust34. Fisher’s exact test35 was used to check for associations between clusters and histological subtypes. gene set enrichment analysis21 were performed on 69 pulmonary carcinoids and 43 SCLC2,20 samples; and the gene set oncogenic signatures were used.

Additional information

Accession codes: Whole genome sequence data, whole exome sequence data, transcriptome sequence data and affymetrix 6.0 (copy number) data have been deposited at the European Genome-phenome Archive under the accession code EGAS00001000650.

How to cite this article: Fernandez-Cuesta, L. et al. Frequent mutations in chromatin-remodelling genes in pulmonary carcinoids. Nat. Commun. 5:3518 doi: 10.1038/ncomms4518 (2014).


  1. 1.

    , & Molecular and cellular biology of neuroendocrine lung tumors: evidence for separate biological entities. Biochim. Biophys. Acta 1826, 255–271 (2012).

  2. 2.

    et al. Integrative genome analyses identify key somatic driver mutations of small-cell lung cancer. Nat. Genet. 44, 1104–1110 (2012).

  3. 3.

    et al. Massive genomic rearrangement acquired in a single catastrophic event during cancer development. Cell 144, 27–40 (2011).

  4. 4.

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

  5. 5.

    et al. Comprehensive genomic characterization of squamous cell lung cancers. Nature 489, 519–525 (2012).

  6. 6.

    Molecular genetics of multiple endocrine neoplasia types 1 and 2. Nat. Rev. Cancer 5, 367–376 (2005).

  7. 7.

    & Menin critically links MLL proteins with LEDGF on cancer-associated target genes. Cancer Cell 8, 2469 (2008).

  8. 8.

    et al. Menin mediates epigenetic regulation via histone H3 lysine 9 methylation. Cell Death Dis. 4, e583 (2013).

  9. 9.

    & Memories from the polycomb group proteins. Annu. Rev. Genet. 46, 561–589 (2012).

  10. 10.

    & The SWI/SNF complex—chromatin and cancer. Nat. Rev. Cancer 4, 133–142 (2004).

  11. 11.

    , & Understanding the words of chromatin regulation. Cell 136, 200–206 (2009).

  12. 12.

    & Epigenetics and genetics SWI/SNF nucleosome remodellers and cancer. Nat. Rev. Cancer 11, 481–492 (2011).

  13. 13.

    , & Reprogramming human fibroblasts to neurons by recapitulating an essential microRNA-chromatin switch. Curr. Opin. Genet. Dev. 23, 591–598 (2013).

  14. 14.

    et al. Proteomic and bioinformatic analysis of mammalian SWI/SNF complexes identifies extensive roles in human malignancy. Nat. Genet. 45, 1–11 (2013).

  15. 15.

    , & The cohesin complex and its roles in chromosome biology. Genes Dev. 22, 3089–3114 (2008).

  16. 16.

    et al. Cohesin’s binding to chromosomes depends on a separate complex consisting of Scc2 and Scc4 proteins. Mol. Cell 5, 243–254 (2000).

  17. 17.

    et al. Dicer is essential for formation of the heterochromatin structure in vertebrate cells. Nat. Cell Biol. 6, 784–791 (2004).

  18. 18.

    , , & PICH, a centromere-associated SNF2 family ATPase, is regulated by Plk1 and required for the spindle checkpoint. Cell 128, 101–114 (2007).

  19. 19.

    et al. Mediator and cohesin connect gene expression and chromatin architecture. Nature 467, 430–435 (2010).

  20. 20.

    et al. Comprehensive genomic analysis identifies SOX2 as a frequently amplified gene in small-cell lung cancer. Nat. Genet. 44, 1111–1116 (2012).

  21. 21.

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

  22. 22.

    et al. Nonsense-mediated mRNA decay in human cells: mechanistic insights, functions beyond quality control and the double-life of NMD factors. Cell. Mol. Life Sci. 67, 677–700 (2010).

  23. 23.

    , , , & Autoregulation of the nonsense-mediated mRNA decay pathway in human cells. RNA 17, 2108–2118 (2011).

  24. 24.

    & Cancer as a dysregulated epigenome allowing cellular growth advantage at the expense of the host. Nat. Rev. Cancer 13, 497–510 (2013).

  25. 25.

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

  26. 26.

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

  27. 27.

    & Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Statist. Soc. B 57, 289–300 (1995).

  28. 28.

    , & Histone lysine methylation dynamics: establishment, regulation, and biological impact. Mol. Cell 48, 491–507 (2012).

  29. 29.

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

  30. 30.

    & Fast and SNP-tolerant detection of complex variants and splicing in short reads. Bioinformatics 26, 873–881 (2010).

  31. 31.

    , , & Using genepattern for gene expression analysis. Curr. Protoc. Bioinformatics 22, 7.12.1–7.12.39 (2008).

  32. 32.

    & ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinformatics 26, 1572–1573 (2010).

  33. 33.

    et al. Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data. Mach. Learn. 52, 91–118 (2003).

  34. 34.

    et al. Statistical significance of clustering for high-dimension, low-sample size data. J. Am. Stat. Assoc. 103, 1281–1293 (2008).

  35. 35.

    Statistical Methods for Research Workers Oliver and Boyd (1954).

  36. 36.

    et al. Exponential decay of GC content detected by strand-symmetric substitution rates influences the evolution of isochore structure. Mol. Biol. Evol. 25, 362–374 (2008).

Download references


We are indebted to the patients donating their tumour specimens as part of the Clinical Lung Cancer Genome Project initiative. We thank Philipp Lorimier, Elisabeth Kirst, Emilia Müller and Juana Cuesta Valdes for their technical assistance. We furthermore thank the regional computing centre of the University of Cologne (RRZK) for providing the CPU time on the DFG-funded supercomputer ‘CHEOPS’, as well as the support. This work was supported by the Deutsche Krebshilfe as part of the small-cell lung cancer genome-sequencing consortium (grant ID: 109679 to R.K.T., M.P., R.B., P.N., M.V. and S.A.H.). Additional funds were provided by the EU-Framework program CURELUNG (HEALTH-F2-2010-258677 to R.K.T., J.W., J.K.F. and E.B.); by the German federal state North Rhine Westphalia (NRW) and the European Union (European Regional Development Fund: Investing In Your Future) within PerMed NRW (grant 005-1111-0025 to R.K.T., J.W., R.B.); by the Deutsche Forschungsgemeinschaft through TH1386/3-1 (to R.K.T.) and through SFB832 (TP6 to R.K.T. and J.W.; TP5 to L.C.H.); by the German Ministry of Science and Education (BMBF) as part of the NGFNplus program (grant 01GS08101 to R.K.T., J.W., P.N.); by the Deutsche Krebshilfe as part of the Oncology Centers of Excellence funding program (R.K.T., R.B., J.W.); by Stand Up To Cancer–American Association for Cancer Research Innovative Research Grant (SU2C-AACR-IR60109 to R.K.T.); by an NIH K12 training grant (K12 CA9060625) and by an Uniting Against Lung Cancer grant, and a Damon Runyon Clinical Investigator Award (to C.M.L.); and by AIRC and Istituto Toscano Tumori project F13/16 (to F.C.).

Author information

Author notes

    • Lynnette Fernandez-Cuesta
    •  & Martin Peifer

    These authors contributed equally to this work


  1. Department of Translational Genomics, Center of Integrated Oncology Cologne–Bonn, Medical Faculty, University of Cologne, 50924 Cologne, Germany

    • Lynnette Fernandez-Cuesta
    • , Martin Peifer
    • , Xin Lu
    • , Danila Seidel
    • , Thomas Zander
    • , Frauke Leenders
    • , Julie George
    • , Christian Müller
    • , Ilona Dahmen
    • , Berit Pinther
    • , Graziella Bosco
    •  & Roman K. Thomas
  2. Center for Molecular Medicine Cologne (CMMC), University of Cologne, 50931 Cologne, Germany

    • Martin Peifer
    •  & Peter Nürnberg
  3. Computational Molecular Biology Group, Max Planck Institute for Molecular Genetics, D-14195 Berlin, Germany

    • Ruping Sun
    • , Martin Vingron
    •  & Stefan A. Haas
  4. Department of Pathology, University Hospital Medical Center, University of Cologne, 50937 Cologne, Germany

    • Luka Ozretić
    • , Lukas C. Heukamp
    • , Reinhard Buettner
    •  & Roman K. Thomas
  5. Laboratory of Translational Cancer Genomics, Center of Integrated Oncology Cologne—Bonn, University of Cologne, 50924 Cologne, Germany

    • Danila Seidel
    • , Frauke Leenders
    • , Jürgen Wolf
    • , Reinhard Buettner
    •  & Roman K. Thomas
  6. Department I of Internal Medicine, Center of Integrated Oncology Ko¨ln-Bonn, University of Cologne, 50924 Cologne, Germany

    • Thomas Zander
    • , Sascha Ansén
    •  & Jürgen Wolf
  7. Network Genomic Medicine, University Hospital Cologne, Center of Integrated Oncology Cologne Bonn, 50924 Cologne, Germany

    • Thomas Zander
    • , Lukas C. Heukamp
    • , Jürgen Wolf
    •  & Reinhard Buettner
  8. Cologne Center for Genomics (CCG), University of Cologne, 50931 Cologne, Germany

    • Kathryn Konrad
    • , Janine Altmüller
    •  & Peter Nürnberg
  9. Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, 50931 Cologne, Germany

    • Janine Altmüller
    •  & Peter Nürnberg
  10. Institute of Human Genetics, University of Cologne, Cologne 50931, Germany

    • Janine Altmüller
  11. Computing Center, University of Cologne, 50931 Cologne, Germany

    • Viktor Achter
    •  & Ulrich Lang
  12. Department of Informatics, University of Cologne, 50931 Cologne, Germany

    • Ulrich Lang
  13. Institute of Legal Medicine, University of Cologne, 50823 Cologne, Germany

    • Peter M. Schneider
    •  & Magdalena Bogus
  14. Institute for Surgical Pathology, University Hospital Zurich, 8091 Zurich, Switzerland

    • Alex Soltermann
  15. Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, N-0424 Oslo, Norway

    • Odd Terje Brustugun
    •  & Åslaug Helland
  16. Department of Oncology, Norwegian Radium Hospital, Oslo University Hospital, N-0310 Oslo, Norway

    • Odd Terje Brustugun
    •  & Åslaug Helland
  17. Department of Thoracic Surgery, Rikshospitalet, Oslo University Hospital, N-0027 Oslo, Norway

    • Steinar Solberg
  18. Department of pathology, Norwegian Radium Hospital, Oslo University Hospital, N-0310 Oslo, Norway

    • Marius Lund-Iversen
  19. Thoracic Surgery, Lungenklinik Merheim, Kliniken der Stadt Ko¨ln gGmbH, 51109 Cologne, Germany

    • Erich Stoelben
  20. Department of Surgery, St Vincent’s Hospital, University of Melbourne, Melbourne, Victoria 3065, Australia

    • Gavin M. Wright
    •  & Zoe Wainer
  21. Department of Pathology, St Vincent’s Hospital, Melbourne, Victoria 3065, Australia

    • Prudence Russell
  22. Department of Haematology and Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria 3002, Australia

    • Benjamin Solomon
  23. Roy Castle Lung Cancer Research Programme, Department of Molecular and Clinical Cancer Medicine, Institute of Translational Medicine, University of Liverpool Cancer Research Centre, Liverpool L3 9TA, UK

    • John K. Field
    • , Russell Hyde
    •  & Michael P. A. Davies
  24. Institute of Pathology, Jena University Hospital, Friedrich-Schiller-University, 07743 Jena, Germany

    • Iver Petersen
  25. Department of Prostate Cancer Research, Institute of Pathology, University Hospital of Bonn, 53127 Bonn, Germany

    • Sven Perner
  26. Vanderbilt-Ingram Cancer Center, Nashville, Tennessee 37232, USA

    • Christine M. Lovly
  27. Department of Medical Oncology, Istituto Toscano Tumouri, 57100 Livorno, Italy

    • Federico Cappuzzo
  28. Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York 10065, USA

    • William D. Travis
  29. Department of Pathology, CHU Grenoble INSERM U823, Institute Albert Bonniot 38043, CS10217 Grenoble, France

    • Elisabeth Brambilla


  1. Search for Lynnette Fernandez-Cuesta in:

  2. Search for Martin Peifer in:

  3. Search for Xin Lu in:

  4. Search for Ruping Sun in:

  5. Search for Luka Ozretić in:

  6. Search for Danila Seidel in:

  7. Search for Thomas Zander in:

  8. Search for Frauke Leenders in:

  9. Search for Julie George in:

  10. Search for Christian Müller in:

  11. Search for Ilona Dahmen in:

  12. Search for Berit Pinther in:

  13. Search for Graziella Bosco in:

  14. Search for Kathryn Konrad in:

  15. Search for Janine Altmüller in:

  16. Search for Peter Nürnberg in:

  17. Search for Viktor Achter in:

  18. Search for Ulrich Lang in:

  19. Search for Peter M. Schneider in:

  20. Search for Magdalena Bogus in:

  21. Search for Alex Soltermann in:

  22. Search for Odd Terje Brustugun in:

  23. Search for Åslaug Helland in:

  24. Search for Steinar Solberg in:

  25. Search for Marius Lund-Iversen in:

  26. Search for Sascha Ansén in:

  27. Search for Erich Stoelben in:

  28. Search for Gavin M. Wright in:

  29. Search for Prudence Russell in:

  30. Search for Zoe Wainer in:

  31. Search for Benjamin Solomon in:

  32. Search for John K. Field in:

  33. Search for Russell Hyde in:

  34. Search for Michael P. A. Davies in:

  35. Search for Lukas C. Heukamp in:

  36. Search for Iver Petersen in:

  37. Search for Sven Perner in:

  38. Search for Christine M. Lovly in:

  39. Search for Federico Cappuzzo in:

  40. Search for William D. Travis in:

  41. Search for Jürgen Wolf in:

  42. Search for Martin Vingron in:

  43. Search for Elisabeth Brambilla in:

  44. Search for Stefan A. Haas in:

  45. Search for Reinhard Buettner in:

  46. Search for Roman K. Thomas in:


L.F.-C. and R.K.T. conceived the project. L.F.-C., M.P. and R.K.T. analysed, interpreted the data and wrote the manuscript. L.O., C.M., I.D., B.P., K.K., J.A. and M.B. performed experiments. L.F.-C., M.P. and X.L. performed computational analysis. M.P., R.S. and S.A.H. provided unpublished algorithms. L.F.-C., M.P., T.Z., R.B. and R.K.T. gave scientific input. A.S., O.T.B., A.H., S.S., M.L.-I., S.A., E.S., G.M.W., P.R., Z.W., B.S., J.K.F., R.H., M.P.A.D., L.C.H., I.P., S.P., C.L., F.C., E.B. and R.B. contributed with samples. L.O., W.D.T., E.B. and R.B. performed pathology review. V.A. and U.L. provided and optimized compute and data infrastructure. D.S., F.L., J.G., G.B., P.N., P.M.S., S.A., J.W. and M.V. helped with logistics. All the co-authors reviewed the manuscript.

Competing interests

R.K.T. and M.P. are founders and shareholders of Blackfield AG. R.K.T. received consulting and lecture fees (Sanofi-Aventis, Merck, Roche, Lilly, Boehringer Ingelheim, AstraZeneca, Atlas-Biolabs, Daiichi-Sankyo, MSD, Blackfield AG, Puma) as well as research support (Merck, EOS and AstraZeneca). M.P. received consulting fees from Blackfield AG. R.B. is a cofounder and owner of Targos Molecular Diagnostics and received honoraria for consulting and lecturing from Astra-Zeneca, Boehringer Ingelheim, Merck, Roche, Novartis, Lilly and Pfizer. J.W. received consulting and lecture fees from Roche, Novartis, Boehringer Ingelheim, AstraZeneca, Bayer, Lilly, Merck, Amgen and research support from Roche, Bayer, Novartis, Boehringer Ingelheim. T.Z. received honoraria from Roche, Novartis, Boehringer Ingelheim, Lilly, Merck, Amgen and research support from Novartis. C.M.L. has served on an Advisory Board for Pfizer and has served as a speaker for Abbott and Qiagen. The remaining authors declare no competing financial interests.

Corresponding author

Correspondence to Roman K. Thomas.

Supplementary information

PDF files

  1. 1.

    Supplementary Figures and Tables

    Supplementary Figures 1-5, Supplementary Tables 1-8

Excel files

  1. 1.

    Supplementary Data 1

    Mutation calls

  2. 2.

    Supplementary Data 2

    RNAseq gene expression data on PCA

About this article

Publication history






Further reading


By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.