Characterization of primary lung carcinomas using tiling resolution
bacterial artificial chromosome microarrays
Maria Planck, MD, PhD1*, Johan Staaf, PhD1, Mats Jönsson, PhD1, Pär-Ola Bendahl, PhD1,
Anna Karlsson, MSc1, Sofi Isaksson1, Annette Salomonsson1, Maria Soller, MD, PhD, SvenBörje Ewers MD, PhD1, Leif Johansson, MD, PhD2, Per Jönsson, MD, PhD3, Göran Jönsson,
PhD1
Departments of Oncology1, Pathology2, and Thoracic Surgery3 at Skåne University Hospital,
Lund, Sweden.
* Correspondence to: Maria Planck, Lund University, Clinical Sciences, Lund, Department of
Oncology, Barngatan 2:1, SE-221 85 Lund, Sweden
Phone +46-46-177501, Fax +46-46-147327, E-mail maria.planck@med.lu.se
Keywords:
Lung cancer, KRAS, EGFR, array-based CGH, amplification, deletion, whole genome,
smoking
Introduction
Due to high incidence and poor survival, lung cancer is the world-wide leading cause of death
from cancer. Small cell lung cancer (SCLC) accounts for about 15% of all diagnoses whereas
non small cell lung cancer (NSCLC) constitutes the majority of cases, primarily including
adenocarcinoma (AC) and squamous cell carcinoma (SqCC). About one fourth of NSCLC,
but very few SCLC, constitutes early stage tumors that are accessible for surgery.
The outcome after surgical resection is heterogeneous, also within the same histology and
clinico-pathological stage, and the tumor biology behind these differences are not fully
elucidated (Chen, Liang et al. 2007, Huang, Heist et al 2009). Furthermore, although response
to several medical therapies in clinical trials varies by histological subtype of lung carcinoma,
the molecular alterations that determine histology remain largely unknown (Hirsch et
al.2008). Moreover, although the use of cigarettes is the major pathogenic factor, not all cases
of lung cancer can be attributable to smoking and the proportion of never-smokers among
lung cancer patients is increasing (Ref Svenska kvalitetsregistret + D´Addario 2009). The
genetic aberrations that differ between smokers´ and non-smokers´ lung carcinomas are not
completely clear (Sun et al. Nat Rev Cancer 2007).
Thus, further characterization of tumors at the gene level may have future implications for
improved diagnostic, prognostic and predictive tools. The aim of the present study was
therefore to characterize the genomic profiles of clinical specimens of lung carcinomas. Using
whole-genome tiling resolution bacterial artificial chromosome (BAC) microarrays, a total of
74 early stage primary carcinomas (AC, SqCC, and SCLC) were profiled to allow for
identification of recurrent alterations and for subclassifications corresponding to
histopathological, molecular, and clinical data.
All major histological types of invasive lung carcinoma display multiple genetic alterations,
generally believed to have accumulated during a multistep carcinogenesis. The tumors
frequently exhibit multiple karyotypic changes with both numerical and structural aberrations
that may involve any chromosome (Mitelman, et al. 2006). Studies of recurrent alterations by
comparative genomic hybridization (CGH) have shown multiple genomic imbalances in
NSCLC, including gains of chromosome arms 1q, 3q, 5p and 8q, and loss of 3p, 8p, 9p, 13q,
and 17p (Balsara and Testa 2002; Samuels and Velculescu 2004). In SCLC, our knowledge of
genomic imbalances and their oncogenic consequences is more limited, due to shortage of
resected tumor material. However, losses of 3p, 5 q and 17p have been reported as prominent
changes, as well as amplifications of the MYC family genes (Reviewed in Balsara and Testa
2002).
The use of whole-genome tiling resolution BAC microarrays allows for characterization of
DNA copy number changes at a resolution only limited by the number of BAC clones used
for the arrays (Ishkanian, et al. 2004). Whole genome profiling of 28 NSCLC cell lines using
tiling BAC microarrays has demonstrated frequent amplifications in multiple regions across
chromosome 7 and also other novel frequent gains and losses throughout the genome (Garnis,
et al. 2006). Previous genome-wide studies of DNA copy number alterations (CNAs) that
includes clinical specimens of lung cancer have used SNP arrays on 371 primary AC (Weir, et
al. 2007), SNP arrays on 70 primary tumors of either the NSCLC or the SCLC type (Zhao, et
al. 2005), or array-based CGH in 19 primary SCLC (Voortman, et al. 2010). These whole
genome-lung cancer studies, using platforms with resolutions comparable to ours, show
consistently that most chromosomal arms undergo either amplification or deletion in a large
proportion of tumors. Regarding the limited investigations of the SCLC subtype, no high level
amplifications or deletions overlapped between primary tumor specimens in the two previous
studies (Zhao 2005, Voortman 2010). For NSCLC, high level and focal alterations that
overlapped between previous studies included amplifications of EGFR, MDM2, and CCNE1 (
Zhao, et al. 2005, Weir, et al. 2007). Herein we describe these and other recurrent
amplifications and deletions of early stage primary NSCLC and SCLC. We also report
associations between CNAs and clinical, histopathological, and molecular data.
Materials and Methods
Tumors
46 AC, 12 SqCC, and 16 SCLC were obtained from patients selected for surgery of early
stage, primary lung cancer 1989-2003 at the Lund University Hospital, Sweden. With five
exceptions (N+), all cases were T1-4N0M0. None of the patients had received chemo- or
radiotherapy. The tumor histology of all samples was confirmed by a pathologist (LJ). The
total follow-up time was at least 8 years for 67 patients, 5-7 years for 3 patients, and 2-4 years
for 4 patients. Clinical and histopathological data are summarized in table 1, but for detailed
characteristics of the tumors, see Supplementary table …. The study was approved by the
regional Ethical Committee in Lund, Sweden (Registration no. 2004/762 and 2008/702).
Tumor biopsies from all cases were freshly frozen and DNA was extracted using Proteinase K
(20mg/µl) digestion followed by phenol-chloroform purification according to published
protocols (Ref Jönsson GCC 2007??).
Array based CGH
With array based comparative genomic hybridization (aCGH), genomic copy numbers are
determined from the intensity ratio between the tumor DNA and an average of normal DNAs.
DNA copy number changes are detected with a resolution only limited by the number of DNA
probes on the array. Here we describe a 70kbp resolution mapping by whole genome tiling
aCGH; Microarrays used for the present aCGH investigations comprised a total of 32 433
BAC clones and were produced at the SCIBLU Genomics Centre, Lund University, Sweden,
as previously described (Ref Jönsson GCC 2007). For all samples, 2µg of tumor DNA and
1.5µg reference DNA (Promega Corporation, Madison, USA) was labeled and hybridized to
the arrays as described previously (Jönsson 2007), followed by scanning, image analysis and
initial data handling performed as in previous studies (Jönsson 2007, Staaf BMC 2007 JCO
2010). Gain/loss limits were set to log2 +/-0.1 and recurrent high-level amplification were
defined as a log 2 ratio > 0.9 and present in at least 2/74 samples.
Fluorescence In Situ Hybridization
Fluorescence In Situ Hybridization (FISH) analysis was performed to confirm the presence of
some of the homozygous deletions revealed by aCGH (9p21.3 in four samples, 21q21.1 in
two samples). Interphase FISH analysis was performed on touche/imprint preparations made
from the same fresh-frozen tumor samples that had been subjected to aCGH. Based on the
aCGH findings, BAC clones within the relevant deletions were selected as probes for FISH
analysis. For detailed methodology, see Supplements.
GISTIC
Using Genomic Identification of Significant Targets In Cancer (GISTIC), the most
significantly amplified or deleted peaks in the 74 tumor genomes were identified (Ref??).
Student`s t-test was performed on average log2 ratios for the GISTIC peaks in order to
identify those amplifications or deletions that were significantly associated with clinical,
morphological or molecular variables.
Hierarchical clustering
Hierarchical clustering of significant peaks was generated by using Pearson correlation for
linkage based on the average log2 ratios for every GISTIC region. Fisher`s exact test was
used to identify clinical, morphological or molecular variables that were significantly
associated with either sub-cluster.
Immunohistochemistry?
Quantitative real time PCR?
Mutation analysis?
Results
Generally, the tumors displayed complex DNA copy number profiles, with numerous
recurrent gains and losses observed in all chromosomes. Gains or losses of whole
chromosome arms occurred in a considerable no. of tumors, with amplifications of 1q seen in
30% of the tumors, amplifications of 5p in 38%, 8q in 34%, 17q in 31%, and amplifications of
20q in 32% of the tumors. Most frequent whole arm deletions were those of 3p and 5q,
observed in 31% and 28% of the tumors, respectively. The overall frequency of gene copy
alterations varied between the tumors, with FGA levels (Fraction of the Genome Altered)
from 17% to 55%.
GISTIC identified 61 peaks (focal events or broad regions) containing the most significant
CNAs. The size of the lost/gained regions identified by GISTIC ranged from… bp to …bp.
All these peaks are described in supplementary table …. The most common amplification
peaks identified by GISTIC (Fig freq plot) were those of 1q22, 3q27.1 (in SqCC), 5p15.33,
6p21.1, 8q24.3, 16p13.3, 17q25.3, 19p13.3, 19q13.11 (in SCLC), and 20q13.33. The most
common deletion peaks (Fig freq plot) were those of 3p14.3 (in SqCC), 5q11.2, 5q35.2,
8p23.2, 16q21 (in SCLC), and 17p13.1. The most frequently altered GISTIC peaks in each
histology are summarized in table x ..but for complete lists, see supplementary table….
GISTIC events that were significantly associated with histological subtypes included ….
In addition to the alterations identified as most significant according to GISTIC described
above, the tumors showed various other frequent CNAs involving genes implicated in cancer,
e.g. PIK3CA (amp 3q26.32) in 50% of the tumors, BCL6 (amp 3q27.3) in 51%, TP73L (amp
3q28) in 41%, ERBB2 (amp17q12) in 58%, CCNE1 (amp19q12) in 46%, CTNNB1 (del
3p22.1) in 59%, and PTEN (del 10q23) in 43% of the tumors.
High level amplifications (log2 ratios ≥0.9) could be identified in 14 of the 38 gained GISTIC
regions. High-level amplifications within GISTIC regions were observed in 18 of the 74
tumor samples and were significantly more common in tumors of the SqCC or SCLC
subtypes (p= 0.004, chi square test). Of the 7 GISTIC high level amplifications that were
recurrent in our tumor material, all harbored known or potential oncogenes, e.g. MYCL1, KIT,
EGFR, ZNF703, CCND1, KRAS, and MDM 2 (Table 2). Furthermore, we observed recurrent
high level amplifications that were outside the 38 amplification peaks identified by GISTIC
but harbored known oncogenes, e.g. TP73L, BCL6, PIK3CA, ERBB2, CCNE1 (Table 2).
Moreover, a homozygous deletion (defined as log2 ratio < -1.3) of 9p21.3 was observed in
one AC. This tumor, together with one other AC (also with 9p21.3 deletion but of borderline
log2 ratio), were subjected to interphase FISH, which in both cases could confirm deletion of
the gene CDKN2A within this region. Furthermore, modest CDKN2A deletions (log2ratio -0.1
< -1.3) were observed in 43% of the tumors.
Unsupervised hierarchical clustering divided the samples into two subgroups displaying
differences in both FGA levels and patterns of preferred CNAs. The clusters showed
significant associations to histopathology (p<0.001, chi square test), with all SqCCs and
SCLCs conferred to one subgroup and the ACs distributed across both groups. The
SqCC/SCLC subcluster was significantly associated with occurrence of specific
amplification/deletion peaks (Table t-test) and higher FGA levels (p= Fig x).
Adenocarcinomas in subcluster 1 showed higher FGA compared with adenocarcinomas in
subcluster 2 (p=….., t-test)
Amplification of 7p11.2, harboring the EGFR gene, was significantly more common (p=….,
t-test) in adenocarcinomas from never-smokers (60% of AC) compared to smokers (28% of
AC). Amplifications of 12p13.31 and 12q24.31 were also significantly correlated to smoking
(p=…., t-test) as were deletions of 10q24.32 (p=…., t-test), which was observed in 80% of
AC from non-smokers but in only 14% of AC from smokers, and of 15q13.1 (p=…., t-test),
observed in 70% and 31% of AC from never-smokers and smokers, respectively (Table ttest). However, associations of GISTIC peaks to other clinical factors (stage, sex, age, or
prognosis) were not statistically significant.
Discussion
The genetic basis for initiation and development of lung carcinoma has a clinical impact
through targeted therapeutics, diagnostic tools, prognostics, and predictive markers. We used
whole-genome tiling resolution BAC microarrays for characterization of the lung cancer
genome and demonstrated a great complexity and variability of tumor gene copy numbers in
early stage primary tumors. The high number of recurrent chromosomal aberrations observed
in this and previous studies suggests that the majority of genes with an impact on lung
tumorigenesis remain to be determined.
Insert: Comparison between our study and literature regarding large-scale alterations
(Garnis, Luk, Balsara, Bjorkqvist, Petersen, Testa)
…………………………….…………….…………………………………………………………………
Many of the most significant copy number altered regions (identified by GISTIC) harbored
previously described oncogenes (Supplementary table + Fig frequency plot + Table Top Copy
No Alt). Oncogene amplification was stated as a causal mechanism in lung cancer
development by a study of NSCLC cell lines where enhanced gene expression was
demonstrated for more than 50% of the genes within the amplification hotspots demonstrated
(Ref Lockwood et al. 2008). Several of these genes are located within amplified GISTIC
regions in our study, e.g. EGFR, MYC, AKT1, NTRK1 and NKX2-1, confirming the usefulness
of whole-genome tiling resolution BAC microarrays for identification of candidate genes in
lung cancer development. Accordingly, of the focal high level CNAs that overlap between
previous studies (i.e. studies using platforms with comparable resolutions and performed on
clinical specimens of lung cancer), all were demonstrated herein; Focal high level
amplification of 8p12 and high level amplification peaks harboring EGFR and MDM2 were
among GISTIC events in the present study and focal high level amplifications within 19q12,
harboring CCNE1, were also recurrent in the present study (Ref Weir, Zhao, Garnis).
Hierarchical clustering of GISTIC data demonstrated significant correlation between the
subgroups generated and the histological subtypes (Fig. ).This stands in some contrast to
earlier findings of highly overlapping genomic profiles of AC and SqCC according to
supervised and unsupervised CGH profile clustering (Tonon et al. 2005 Jfr också övriga tiling
copy no. studies). However, by using gene expression profiling, several studies successfully
stratified lung carcinomas into profiles that correlate to histological subtypes. (Ref x flera)
Diskutera AC som skilt från övriga i vårt material. AC fördelat på två grupper och hur dessa
skiljer sig i vårt mat. Inget samband rökstatus.
In the literature, some chromosomal imbalances, e.g. gains of 1q22-32, have been reported at
higher rates in AC (Ref). However, no CNAs herein were significantly more frequently
observed in AC compared to the other histologies, perhaps implicating that chromosomal
instability drives AC progression to a lesser extent compared to SqCC and SCLC. In contrast,
mutation-driven tumorigenesis may be more frequent in AC, as illustrated by the EGFR and
KRAS mutations observed in this subtype (Ref). Accordingly, targeting EGFR has been a
successful approach in therapy for NSCLC, mainly AC, whereas corresponding progress has
not been made in SCLC. Being the most aggressive subtype of lung cancer, molecular
characterization of SCLC, enabling future development of targeted tools for its management,
is highly relevant. Few high resolution-studies have investigated whole genome copy number
alterations in SCLC (Ref Zhao, Voortman). However, based on the most frequent CNAs found
in 46 primary SCLC specimens by using only 2464 BAC clones for aCGH, two pathways of
SCLC tumorigenesis have been proposed; the focal adhesion pathway and the neuroendocrine
ligand-receptor pathway (Ocak et al. 2010). The significantly enriched or reduced genes in
their study were also involved in recurrent amplifications or deletions among SCLC in our
study, e.g. PIK3CA, MAPK10, FAK1,GRIN3B …Utvidga, kolla samtliga i Ocak……. Jfr
också med Zhao Voortman här …… However, all these CNAs occurred across histological
subtypes in our material and only amplification of 19p13.3 was significantly correlated to
SCLC histology. Across histological subtypes, the most frequent GISTIC event seen in our
study was amplification of 5p15.33, containing TERT. This region has frequently been
reported as a major susceptibility locus in NSCLC (Kang et al. 2008 mfl) but was in our
material amplified also in 100% of SCLC, thus pointing out the 5p15.33 amplicon as relevant
to development of all types of lung carcinoma. Copy number gain of 3q26-29 has been
reported twice as often in SqCCs compared with AC, with PIK3CA as a presumed target of
amplification (Balsara and Testa 2002; Samuels and Velculescu 2004, Petersen1997; Tonon
2005.). In accordance with previous aCGH profiling of NSCLC, we could significantly differ
between SqCC and AC by the presence of 3q27.1 amplification (p=0.006 , t-test). However,
also 81% of SCLC showed amplification of 3q27.1, thus suggesting rejection of the previous
hypothesis that genes residing within the 3q26-27 amplicon should selectively induce a
squamous cell phenotype (ref Tonon et al 2005). Rather, we suggest herein that features of the
tumor genome are shared between the non-AC subtypes of lung cancer, illustrated by the
higher FGA-levels, more frequent high level amplifications, and specific CNAs shared
between SqCC and SCLC in this study.
Diskutera fynd angående rökning.
Insert molecular discussion (?) –
Although non-significant, also molecular parameters, e.g. EGFR mutation status, were
reflected in whole genome analysis.
Use of IHC and mutation data???
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Acknowledgements
We thank Linda Jansson for performing valuable laboratory work, Erik Gyllstedt and Leif
Hagman for including patients, the Personnel at Ward no. 1 at Skåne University
Hospital in Lund for their excellent involvement in the Southern Lung Cancer Study,
and Birgitta Sjögren for valuable administrative help.
The study was directly supported by the Mrs Berta Kamprad Foundation, the Gunnar Nilsson
Cancer Foundation, the Lund University Hospital Research Funds (??), ALF (??),
Fysiografiska (??), Lundgrens (??), Onkologens forskningsstiftelse (Fråga Drott
mfl??), FoU (???), Jubileumsfonden (??)
The SCIBLU genomics center is supported by governmental funding (ALF) and by Lund
University.
TABLE ... TOP COPY NUMBER ALTERATIONS IN DIFFERENT HISTOLOGICAL SUBTYPES OF LUNG CANCER - DESCRIPTION OF PEAKS
FREQUENCIES AND EXAMPLES OF GENES WITHIN RESPECTIVE PEAKS
Top copy number alterations in primary adenocarcinomas
Event
Peak limits
Frequency
Other histologies
Genes
Amp 1q22
chr1:153078485-153585969
35/46 (76%)
SqCC 42%, SCLC 81%
NTRK1
Amp 5p15.33
chr5:1-1816605
30/46 (65%)
SqCC 75%, SCLC 100%
TERT
Amp 8q24.3
chr8:144630897-146274826
30/46 (65%)
SqCC 67%, SCLC 75%
BOP1
Amp 19p13.3
chr19:1-1851752
30/46 (65%)
SqCC 75%, SCLC 94%
PTBP1
Amp 20q13.33
chr20:61156754-62435964
28/46 (61%)
SqCC 50%, SCLC 88%
TNFRSF6B
Del 9p21.3
chr9:21739341-22219917
26/46 (57%)
SqCC 25%, SCLC 13%
CDKN2A/B
Top copy number alterations in primary squamous cell carcinomas
Event
Peak limits
Frequency
Other histologies
Genes
Amp 3q27.1
chr3:185309230-185477405
12/12 (100%)
AC 7%, SCLC 81%
EIF4G1
Amp 5p15.33
chr5:1-1816605
9/12 (75%)
AC 65%, SCLC 100%
TERT
Amp 5p13.2
chr5:35522131-36258193
9/12 (75%)
AC 43%, SCLC 63%
SKP2
Amp 19p13.3
chr19:1-1851752
9/12 (75%)
AC 65%, SCLC 94%
PTBP1
Del 3p14.3
chr3:56645203-58524351
10/12 (83%)
AC 37%, SCLC 63%
DNASE1L3
Del 4p14
chr4:39055793-40980025
9/12 (75%)
AC 28%, SCLC 31%
RFC1
Del 4q35.2
chr4:189034075-191411218
9/12 (75%)
AC 33%, SCLC 44%
ZFP42 (REX1)
Del 5q11.2
chr5:56289181-56744443
11/12 (92%)
AC 39%, SCLC 69%
Del 10q24.32
chr10:103948045-105382091
9/12 (75%)
AC 28%, SCLC 69%
SUFU, TRIM8, Ldb1, BTRC, NEURL
Del 16q23.1
chr16:77161953-77474570
9/12 (75%)
AC 33%, SCLC 63%
WWOX
Del 17p13.1
chr17:9580037-10054997
10/12 (83%)
AC 37%, SCLC 50%
GAS7
-
Top copy number alterations in primary small cell lung carcinomas
Event
Peak limits
Frequency
Other histologies
Genes
Amp1p34.2
chr1:39706829-40460057
13/16 (81%)
AC 13%, SqCC 17%
MYCL1
Amp1q22
chr1:153078485-153585969
13/16 (81%)
AC 76%, SqCC 42%
NTRK1
Amp 3q27.1
chr3:185309230-185477405
13/16 (81%)
AC 7%, SqCC 100%
EIF4G1
Amp 5p15.33
chr5:1-1816605
16/16 (100%)
AC 65%, SqCC 75%
TERT
Amp12p13.31
chr12:6229311-6650325
13/16 (81%)
AC 22%, SqCC 67%
LTBR
Amp19p13.3
chr19:1-1851752
15/16 (94%)
AC 65%, SqCC 75%
PTBP1
Amp19q13.11
chr19:40209391-40742439
14/16 (88%)
AC 37%, SqCC 42%
MAG
Amp19q13.42
chr19:59164051-61182227
13/16 (81%)
AC 33%, SqCC 42%
(miRNA cluster)
Amp 20q11.21
chr20:29616800-29843737
13/16 (81%)
AC 35%, SqCC 42%
BCL2L1
Amp 20q13.33
chr20:61156754-62435964
14/16 (88%)
AC 61%, SqCC 50%
TNFRSF6B
Del 16q21
chr16:58416883-58826177
14/16 (88%)
AC 26%, SqCC 50%
NDRG4
Table 1. Patient and tumor characteristics
Primary lung carcinoma, n (%)
74
(100)
Smokers, n (%)
56
(76)
Non-smokers, n (%)
11
(15)
7
(9)
Females, n (%)
40
(54)
Males, n (%)
34
(46)
Mean age, years (range)
66
(36-85)
patients ≥ 70 years, n (%)
32
(43)
patients < 70 years, n (%)
42
(57)
Adenocarcinomas, n (%)
44
(59)
Squamous cell carcinomas, n (%)
14
(19)
Small cell lung cancers, n (%)
16
(22)
T1 tumors*, n (%)
22
(30)
T2 tumors*, n (%)
40
(54)
T3 tumors*, n (%)
7
(9)
T4 tumors*, n (%)
3
(4)
Tx tumors*, n (%)
2
(3)
N0 tumors*, n (%)
67
(90)
N1 tumors*, n (%)
3
(4)
N2 tumors*, n (%)
2
(3)
NX tumors*, n (%)
2
(3)
M0 tumors*, n (%)
74
(100)
EGFR amplification
7
(9)
EGFR mutation
7
(9)
34
(46)
18
(24)
6
(8)
Alive > 8 years after surgery, n (%)
27
(36)
Death from other cause within 8 years, n (%)
16
(22)
7
(9)
Unknown smoking status, n (%)
EGFR protein overexpression
KRAS mutation
KRAS amplification
Overall survival, years (range)
Death from lung cancer < 3 years after surgery, n (%)
Death from lung cancer 3 ≤ 8 years after surgery, n (%)
Follow-up time <8 years, n (%)
*TNM classification according to IASLC 7th edition was reconstructed from patient charts and pathological diagnoses
a)
Cluster SqCC/SCLC/AC
Amp 3q27.1
p=0.0008
Amp 12p13.31
p=0.007
Del 3p14.3
p=0.005
Del 5q11.2
p=0.009
Del 5q35.2
p=0.00014
b)
c)
Smokers/Former smokers
Amp 12p13.31
p=0.009
Amp q24.31
p=0.001
AC
Cluster AC only
Never-smokers
SqCC
Table … GISTIC regions significantly associated (t-test) with a) Subtypes identified by unsupervised
hierarchical clustering of significant GISTIC peaks b) Smoking status c) Histology
SCLC