Frequent mutations in chromatin-remodelling genes in pulmonary carcinoids

Abstract

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.

ARTICLE

Received 13 Aug 2013 | Accepted 26 Feb 2014 | Published 27 Mar 2014

DOI: 10.1038/ncomms4518

Frequent mutations in chromatin-remodelling genes in pulmonary carcinoids

Lynnette Fernandez-Cuesta1,*, Martin Peifer1,2,*, Xin Lu1, Ruping Sun3, Luka Ozreti4, Danila Seidel1,5,Thomas Zander1,6,7, Frauke Leenders1,5, Julie George1, Christian Mller1, Ilona Dahmen1, Berit Pinther1,Graziella Bosco1, Kathryn Konrad8, Janine Altmller8,9,10, Peter Nrnberg2,8,9, Viktor Achter11, Ulrich Lang11,12, Peter M. Schneider13, Magdalena Bogus13, Alex Soltermann14, Odd Terje Brustugun15,16, slaug Helland15,16, Steinar Solberg17, Marius Lund-Iversen18, Sascha Ansn6, Erich Stoelben19, Gavin M. Wright20, Prudence Russell21, Zoe Wainer20, Benjamin Solomon22, John K. Field23, Russell Hyde23, Michael P.A. Davies23, Lukas C. Heukamp4,7, Iver Petersen24, Sven Perner25, Christine M. Lovly26, Federico Cappuzzo27, William D. Travis28, Jrgen Wolf5,6,7, Martin Vingron3, Elisabeth Brambilla29, Stefan A. Haas3, Reinhard Buettner4,5,7 & Roman K. Thomas1,4,5

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 modiers 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 sufcient to drive transformation in pulmonary carcinoids.

1 Department of Translational Genomics, Center of Integrated Oncology CologneBonn, Medical Faculty, University of Cologne, 50924 Cologne, Germany. 2 Center for Molecular Medicine Cologne (CMMC), University of Cologne, 50931 Cologne, Germany. 3 Computational Molecular Biology Group, Max Planck Institute for Molecular Genetics, D-14195 Berlin, Germany. 4 Department of Pathology, University Hospital Medical Center, University of Cologne, 50937 Cologne, Germany.

5 Laboratory of Translational Cancer Genomics, Center of Integrated Oncology CologneBonn, University of Cologne, 50924 Cologne, Germany. 6 Department I of Internal Medicine, Center of Integrated Oncology Kln-Bonn, University of Cologne, 50924 Cologne, Germany. 7 Network Genomic Medicine, University Hospital Cologne, Center of Integrated Oncology Cologne Bonn, 50924 Cologne, Germany. 8 Cologne Center for Genomics (CCG), University of Cologne, 50931 Cologne, Germany. 9 Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, 50931 Cologne, Germany.

10 Institute of Human Genetics, University of Cologne, Cologne 50931, Germany. 11 Computing Center, University of Cologne, 50931 Cologne, Germany.

12 Department of Informatics, University of Cologne, 50931 Cologne, Germany. 13 Institute of Legal Medicine, University of Cologne, 50823 Cologne, Germany.

14 Institute for Surgical Pathology, University Hospital Zurich, 8091 Zurich, Switzerland. 15 Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, N-0424 Oslo, Norway. 16 Department of Oncology, Norwegian Radium Hospital, Oslo University Hospital, N-0310 Oslo, Norway. 17 Department of Thoracic Surgery, Rikshospitalet, Oslo University Hospital, N-0027 Oslo, Norway. 18 Department of pathology, Norwegian Radium Hospital, Oslo University Hospital, N-0310 Oslo, Norway. 19 Thoracic Surgery, Lungenklinik Merheim, Kliniken der Stadt Kln gGmbH, 51109 Cologne, Germany. 20 Department of Surgery, St Vincents Hospital, University of Melbourne, Melbourne, Victoria 3065, Australia. 21 Department of Pathology, St Vincents Hospital, Melbourne, Victoria 3065, Australia.

22 Department of Haematology and Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria 3002, Australia. 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. 24 Institute of Pathology, Jena University Hospital, Friedrich-Schiller-University, 07743 Jena, Germany. 25 Department of Prostate Cancer Research, Institute of Pathology, University Hospital of Bonn, 53127 Bonn, Germany. 26 Vanderbilt-Ingram Cancer Center, Nashville, Tennessee 37232, USA. 27 Department of Medical Oncology, Istituto Toscano Tumouri, 57100 Livorno, Italy. 28 Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York 10065, USA. 29 Department of Pathology, CHU Grenoble INSERM U823, Institute Albert Bonniot 38043, CS10217 Grenoble, France. * These authors contributed equally to this work. Correspondence and requests for materials should be addressed to R.K.T., (email: mailto:roman.thomas@uni-Köln.de

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Pulmonary carcinoids are neuroendocrine tumours that account for about 2% of pulmonary neoplasms. On the basis of the WHO classication 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. Specically, covalent histone modiers 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.

ResultsIn 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 signicant 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

a b

4

3

2

1

0

22

1

19

20

21

18

Copy number

17

2

15 16

3

4

14

13

12

5

0 0

40 40

80 80

120 120 0 40 80 120

160

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11

chr3 (Mbp) chr12 (Mbp) chr13 (Mbp)

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Copy numbersegments Raw copy number

Intra-chromosomal chimeric transcript

Inter-chromosomal chimeric transcript

c d

12

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SQ

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0.8

AD

8

SCLC

0.6

Muts/Mb

6

Mutation rate

G:C>A:T

A:T>G:C

C:G>A:T

C:G>G:C

A:T>T:A

A:T>C:G

0.4

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0.2

2

0

0.0

SCLC Evol PCA

Figure 1 | Genomic characterization of pulmonary carcinoids. (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 identied 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.

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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 of0.4 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).

We identied MEN1, ARID1A and EIF1AX as signicantly mutated genes2 (q-value o0.2, 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. Specically,

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 modiers BRWD3 and HDAC5 in one sample each. In total, 40% of the cases carried mutually exclusive mutations in genes that are

a

Samples

SNP 6.0 WGS

WES RNAseq

Typical Atypical

(%)

Histone covalent

modiers

Histone methylation

(q=8x107)

MEN1*(q=5x105)/PSIP1*

Polycomb complex*

Histone Lys methyltransf.

Histone Lys demethylases

ARID1A (q=0.14)

ATPase activity

Other subunits

Histone acetylation

BRWD3-HDAC5

WDR26

ATP-dependent chromatin remodelling SWI/SNF complex

(q=8x108)

Sister-chromatid cohesion

EIF1AX (q=6x107)

SEC31A

E3 Ub ligases

ATM*-APC-TP53*-RB1

* Mutations frequently accompanied by LOH

0 20 40 60 80 100

Missense Nonsense Indel Splice Rearrangement

b

High Intermediate Low

H3K9 H3K27

WT

WT

JMJD1C CBX6

NSD1

EZH1

Figure 2 | Signicant affected genes and pathways in pulmonary carcinoids. (a) Signicantly mutated genes and pathways identied by genome(n 29), exome (n 15) and transcriptome (n 69) sequencing. The percentage of pulmonary carcinoids with a specic gene or pathway mutated is

noted at the right side. The q-values of the signicantly 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.

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involved in covalent histone modications (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).

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 speci-city 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 specic role of histone modiers in the development of pulmonary carcinoids was conrmed by the lack of signicance 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 signicant 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 signicantly altered in SCLC (q-value 5 10 4, see Methods section) but not in

pulmonary carcinoids (Fig. 3b; Supplementary Table 6).

Another statistically signicant 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 identied 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.

DiscussionThis study denes 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 denes 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 ndings 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 sufcient to drive early steps in tumorigenesis in a precisely dened spectrum of required cellular pathways.

Methods

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

Table 1 | Overview of samples annotated for mutations in

genes involved in histone methylation and correspondent

levels of H3K9me3 and H3K27me3 detected by

immunohistochemistry.

Sample Mutation H3K9me3 H3K27me3 S02333 JMJD1C_H954N Intermediate Low

S01502 KDM4A_I168T Intermediate NA S02323 MEN1_e3 1 and LOH Low Low

S02339 NSD1_A1047G Intermediate Low S02327 CBX6_P302S and LOH Low Low S01746 EZH1_R728G and LOH Low Intermediate S02325 YY1_E253K Low Intermediate S01501 Wild type NA High S01731 Wild type Low Low S01742 Wild type High High S02334 Wild type Intermediate High S02337 Wild type High High S02338 Wild type High Intermediate

LOH, loss of heterozygosity; NA, not applicable.

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a

G1 G2 G3

Positive association (q < 0.05)

Negative association (q < 0.05)

q > 0.05

SCLC

CA

AD

b

ERB2_UP.V1_UP

BMI1_DN_MEL18_DN.V1_DN

PCA

SCLC

0

CAHOY_NEURONAL

KRAS.KIDNEY_UP.V1_UP

EGFR_UP.V1_DN

ERB2_UP.V1_DN

NFE2L2.V2

CSR_EARLY_UP.V1_UP

PRC2_EDD_UP.V1_UP

CORDENONSI_YAP_CONSERVED_SIGNATURE

SNF5_DN.V1_UP

MTOR_UP.V1_UP

E2F3_UP.V1_UP

RB_P130_DN.V1_UP

GCNP_SHH_UP_EARLY.V1_UP

VEGF_A_UP.V1_DN

RB_DN.V1_UP

GCNP_SHH_UP_LATE.V1_UP

RPS14_DN.V1_DN

1

1

HOXA9_DN.V1_DN

E2F1_UP.V1_UP

RB_P107_DN.V1_UP

CSR_LATE_UP.V1_UP

PRC2_EZH2_UP.V1_UP

Figure 3 | Expression data analysis of pulmonary carcinois based on RNAseq data. (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) identied three groups using the clustering module from GenePattern31 and consensus CDF32,33 (left panel). The signicance of the clustering was evaluated by using SigClust34 with a P o0.0001. Fishers 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).

70% tumour cells. Depending on the size of the tissue, 1530 sections, each 20 mm 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 manufacturers instructions. DNA was eluted in 1 TE buffer (Qiagen), diluted to a

working concentration of 150 ng ml 1 and stored at 80 C. For whole exome

sequencing, we fragmented 1 mg of DNA with sonication technology (Bioruptor, diagenode, Lige, 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 nal 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 ltered 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 per

formed on complementary DNA libraries prepared from PolyA RNA extracted

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from tumour cells using the Illumina TruSeq protocol for mRNA. The nal 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 proles, 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 signicantly. To nally remove articial mutation calls, we apply a lter that is based on the forwardreverse 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 rened 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 dened by the encompassing reads. Rearrangements conrmed by at least one spanning read are nally reported. To lter 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 signicantly mutated genes and pathways. The analysis of signicantly 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 rst, from which the expected number of mutations for each gene is computed under the assumption of a purely random mutational process. This gene-specic expected number of mutations denes the underlying null model of our statistical test. To account for misspecications, 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 signicantly 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 o1 in our transcriptome sequencing data are removed prior testing.

To account for multiple hypothesis testing, we are using the BenjaminiHochberg 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 signicantly 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-specic probe afnities and probe-specic background intensities. To calibrate the model, a GaussNewton approach was used and the resulting raw copy number proles 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 efcient 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 conrm bona de 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 signicantly mutated, or interesting because of their presence in other lung neuroendocrine tumours, were selected for validation. One hundred and fty 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 ltered out before performing consensus clustering. We used the clustering module from GenePattern31 and the consensus CDF32,33. Signicance was obtained by using SigClust34. Fishers exact test35 was used to check for associations between clusters and histological subtypes. gene set enrichment analysis21 were performed on69 pulmonary carcinoids and 43 SCLC2,20 samples; and the gene set oncogenic signatures were used.

References

1. Swarts, D. R., Ramaekers, F. C. & Speel, E.-J. Molecular and cellular biology of neuroendocrine lung tumors: evidence for separate biological entities. Biochim. Biophys. Acta 1826, 255271 (2012).

2. Peifer, M. et al. Integrative genome analyses identify key somatic driver mutations of small-cell lung cancer. Nat. Genet. 44, 11041110 (2012).

3. Stephens, P. J. et al. Massive genomic rearrangement acquired in a single catastrophic event during cancer development. Cell 144, 2740 (2011).4. Imielinski, M. et al. Mapping the hallmarks of lung adenocarcinoma with massively parallel sequencing. Cell 150, 11071120 (2012).

5. Hammerman, P. S. et al. Comprehensive genomic characterization of squamous cell lung cancers. Nature 489, 519525 (2012).

6. Marx, S. J. Molecular genetics of multiple endocrine neoplasia types 1 and 2. Nat. Rev. Cancer 5, 367376 (2005).

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

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

9. Lanzuolo, C. & Orlando, V. Memories from the polycomb group proteins. Annu. Rev. Genet. 46, 561589 (2012).

10. Roberts, C. W. M. & Orkin, S. H. The SWI/SNF complexchromatin and cancer. Nat. Rev. Cancer 4, 133142 (2004).

11. Wu, J. I., Lessard, J. & Crabtree, G. R. Understanding the words of chromatin regulation. Cell 136, 200206 (2009).

12. Wilson, B. G. & Roberts, C. W. M. Epigenetics and genetics SWI/SNF nucleosome remodellers and cancer. Nat. Rev. Cancer 11, 481492 (2011).

13. Tang, J., Yoo, A. S. & Crabtree, G. R. Reprogramming human broblasts to neurons by recapitulating an essential microRNA-chromatin switch. Curr. Opin. Genet. Dev. 23, 591598 (2013).

14. Kadoch, C. et al. Proteomic and bioinformatic analysis of mammalian SWI/ SNF complexes identies extensive roles in human malignancy. Nat. Genet. 45, 111 (2013).

15. Peters, J., Tedeschi, A. & Schmitz, J. The cohesin complex and its roles in chromosome biology. Genes Dev. 22, 30893114 (2008).

16. Ciosk, R. et al. Cohesins binding to chromosomes depends on a separate complex consisting of Scc2 and Scc4 proteins. Mol. Cell 5, 243254 (2000).

17. Fukagawa, T. et al. Dicer is essential for formation of the heterochromatin structure in vertebrate cells. Nat. Cell Biol. 6, 784791 (2004).

18. Baumann, C., Krner, R., Hofmann, K. & Nigg, E. A. PICH, a centromere-associated SNF2 family ATPase, is regulated by Plk1 and required for the spindle checkpoint. Cell 128, 101114 (2007).

19. Kagey, M. H. et al. Mediator and cohesin connect gene expression and chromatin architecture. Nature 467, 430435 (2010).

20. Rudin, C. M. et al. Comprehensive genomic analysis identies SOX2 as a frequently amplied gene in small-cell lung cancer. Nat. Genet. 44, 11111116 (2012).

21. Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S. & Ebert, B. L. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression proles. Proc. Natl Acad. Sci. USA 102, 1554515550 (2005).

22. Nicholson, P. 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, 677700 (2010).

23. Yepiskoposyan, H., Aeschimann, F., Nilsson, D., Okoniewski, M. & Mhlemann, O. Autoregulation of the nonsense-mediated mRNA decay pathway in human cells. RNA 17, 21082118 (2011).

6 NATURE COMMUNICATIONS | 5:3518 | DOI: 10.1038/ncomms4518 | http://www.nature.com/naturecommunications

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NATURE COMMUNICATIONS | DOI: 10.1038/ncomms4518 ARTICLE

24. Timp, W. & Feinberg, A. P. Cancer as a dysregulated epigenome allowing cellular growth advantage at the expense of the host. Nat. Rev. Cancer 13, 497510 (2013).

25. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 17541760 (2009).

26. Lawrence, M. S. et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499, 214218 (2013).

27. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Statist. Soc. B 57, 289300 (1995).

28. Black, J. C., Van Rechem, C. & Whetstine, J. R. Histone lysine methylation dynamics: establishment, regulation, and biological impact. Mol. Cell 48, 491507 (2012).

29. Olshen, A. B., Venkatraman, E. S., Lucito, R. & Wigler, M. Circular binary segmentation for the analysis of array-based DNA copy number data. Biostatistics 5, 557572 (2004).

30. Wu, T. D. & Nacu, S. Fast and SNP-tolerant detection of complex variants and splicing in short reads. Bioinformatics 26, 873881 (2010).

31. Kuehn, H., Liberzon, A., Reich, M. & Mesirov, J. P. Using genepattern for gene expression analysis. Curr. Protoc. Bioinformatics 22, 7.12.17.12.39 (2008).32. Wilkerson, M. D. & Hayes, D. N. ConsensusClusterPlus: a class discovery tool with condence assessments and item tracking. Bioinformatics 26, 15721573 (2010).

33. Monti, S. et al. Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data. Mach. Learn. 52, 91118 (2003).

34. Liu, Y. et al. Statistical signicance of clustering for high-dimension, low-sample size data. J. Am. Stat. Assoc. 103, 12811293 (2008).

35. Fisher, R. A. Statistical Methods for Research Workers (Oliver and Boyd, 1954).36. Karro, J. E. et al. Exponential decay of GC content detected by strand-symmetric substitution rates inuences the evolution of isochore structure. Mol. Biol. Evol. 25, 362374 (2008).

Acknowledgements

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 Mller 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-AACRIR60109 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 contributions

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 scientic 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.

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.

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Competing nancial interests: R.K.T. and M.P. are founders and shareholders of Blackeld AG. R.K.T. received consulting and lecture fees (Sano-Aventis, Merck, Roche, Lilly, Boehringer Ingelheim, AstraZeneca, Atlas-Biolabs, Daiichi-Sankyo, MSD, Black-eld AG, Puma) as well as research support (Merck, EOS and AstraZeneca). M.P. received consulting fees from Blackeld 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 Pzer. J.W. received consulting and lecture fees from Roche, Novartis, Boehringer Ingelheim, AstraZeneca, Bayer, Lilly, Merck, Amgen and research support from Roche, Bayer, Novartis, Boeh-ringer 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 Pzer and has served as a speaker for Abbott and Qiagen. The remaining authors declare no competing nancial interests.

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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).

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