Assessing the significance of chromosomal
aberrations in cancer: Methodology and
application to glioma
Rameen Beroukhima,b,c,d, Gad Getza, Leia Nghiemphue, Jordi Barretinaa,b, Teli Hsuehe, David Linharta,b, Igor Vivancoe,
Jeffrey C. Leea,b, Julie H. Huange, Sethu Alexandera,b, Jinyan Dua,b, Tweeny Kaue, Roman K. Thomasa,b,f,g, Kinjal Shaha,b,
Horacio Sotoe, Sven Pernerc,h, John Prensnera,b, Ralph M. Debiasia,b, Francesca Demichelisc, Charlie Hattona,b,
Mark A. Rubina,c,d, Levi A. Garrawaya,b,c,d, Stan F. Nelsone, Linda Liaue, Paul S. Mischele, Tim F. Cloughesye,
Matthew Meyersona,b,d, Todd A. Goluba,b,d,i,j, Eric S. Landera,d,k,l, Ingo K. Mellinghoffl,m, and William R. Sellersa,b,c,d,l,n
aBroad Institute, Massachusetts Institute of Technology and Harvard University, 7 Cambridge Center, Cambridge, MA 02142;bDepartments of Medical
Oncology and Pediatric Oncology and Center for Cancer Genome Discovery, Dana–Farber Cancer Institute, 44 Binney Street, Boston, MA 02115;
cDepartments of Medicine and Pathology, Brigham and Women’s Hospital, 75 Francis Street, Boston, MA 02115;dDepartments of Medicine,
Pathology, and Pediatrics, Harvard Medical School, Boston, MA 02115;eDepartments of Molecular and Medical Pharmacology, Neurology,
Pathology, Human Genetics, and Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, CA 90095;fMax Planck
Institute for Neurological Research and Klaus-Joachim Zu ¨lch Laboratories, Max Planck Society and Medical Faculty, University of Cologne,
Gleueler Strasse 50, 50931 Cologne, Germany;gCenter for Integrated Oncology and Department I for Internal Medicine, University of
Cologne, 50931 Cologne, Germany;hDepartment of Pathology, University of Ulm, D-89070 Ulm, Germany;iHoward Hughes Medical
Institute, Chevy Chase, MD 20815;jDepartment of Medicine, Children’s Hospital Boston, Boston, MA 02115;kWhitehead Institute for
Biomedical Research, Massachusetts Institute of Technology, 9 Cambridge Center, Cambridge, MA 02142;mHuman Oncology and
Pathogenesis Program and Department of Neurology, Memorial Sloan–Kettering Cancer Center, 1275 York Avenue, New York,
NY 10021; andnNovartis Institutes for BioMedical Research, 250 Massachusetts Avenue, Cambridge, MA 02139
Contributed by Eric S. Lander, October 22, 2007 (sent for review July 23, 2007)
Comprehensive knowledge of the genomic alterations that under-
lie cancer is a critical foundation for diagnostics, prognostics, and
targeted therapeutics. Systematic efforts to analyze cancer ge-
nomes are underway, but the analysis is hampered by the lack of
a statistical framework to distinguish meaningful events from
random background aberrations. Here we describe a systematic
method, called Genomic Identification of Significant Targets in
Cancer (GISTIC), designed for analyzing chromosomal aberrations
in cancer. We use it to study chromosomal aberrations in 141
methods highlight hundreds of altered regions with little concor-
dance between studies. The new approach reveals a highly con-
cordant picture involving ?35 significant events, including 16–18
broad events near chromosome-arm size and 16–21 focal events.
Approximately half of these events correspond to known cancer-
related genes, only some of which have been previously tied to
glioma. We also show that superimposed broad and focal events
may have different biological consequences. Specifically, gliomas
with broad amplification of chromosome 7 have properties differ-
ent from those with overlapping focal EGFR amplification: the
broad events act in part through effects on MET and its ligand HGF
and correlate with MET dependence in vitro. Our results support
the feasibility and utility of systematic characterization of the
bioinformatics ? comparative genomic hybridization ? glioblastoma ? copy-
number alterations ? single-nucleotide polymorphism arrays
prognostics, and targeted therapeutics. Various efforts are now
underway aimed at systematically obtaining this information.
The first challenge in such a program is to study large collections
of tumors to characterize the alterations that have occurred in
their genomes. With recent advances in genomic technology, this
is becoming increasingly feasible. For example, DNA arrays
containing probes for hundreds of thousands of genetic loci have
made it possible to detect regional amplifications and deletions
with high resolution. Once the genomic alterations have been
detected, the second challenge is to distinguish between ‘‘driver’’
mutations that are functionally important changes (that is, that
omprehensive knowledge of the mutational events respon-
sible for cancer is a critical foundation for future diagnostics,
confer a biological property that allows the tumor to initiate,
grow, or persist) and ‘‘passenger’’ mutations that represent
random somatic events (that is, changes that occurred before a
clonal expansion and are simply carried along despite conferring
no selective advantage).
The importance of this second challenge is evident from
recent studies of chromosomal aberrations in cancer. Strikingly,
different studies of the same tumor type often report ‘‘regions of
interest’’ that are highly discordant. For example, two recent
studies of lung cancer, with similar sample sizes and analytic
methods, reported 48 and 93 regions of interest, respectively (1,
2); the overlap between the lists was ?5%.
Although perfect agreement should not be expected (in part
because of differences in analytic methods), such a high level of
discordance is disconcerting. There are two potential explana-
tions. One possibility is that the true number of cancer-related
regions is extremely large, with each tumor containing only a
small and variable subset of the alterations and each study
detecting only a small subset of the regions. An alternative
possibility is that many of the regions of interest reported in
current studies are random events of no biologic significance,
such as random passenger mutations. Current analysis methods
do not explicitly account for the background rate of random
chromosomal aberrations. Similar issues arise in interpreting
studies of point mutations in cancer resequencing projects (3–6).
In this article we describe a statistical approach, called
Genomic Identification of Significant Targets in Cancer
Author contributions: R.B. and G.G. contributed equally to this work; R.B., G.G., F.D.,
T.H., D.L., I.V., J.C.L., J.H.H., S.A., J.D., T.K., R.K.T., K.S., H.S., S.P., J.P., R.M.D., S.F.N., L.L.,
P.S.M., T.F.C., and I.K.M. performed research; R.B., G.G., and I.K.M. contributed new
reagents/analytic tools; R.B., G.G., R.M.D., C.H., and I.K.M. analyzed data; and R.B., G.G.,
R.K.T., M.M., T.A.G., E.S.L., I.K.M., and W.R.S. wrote the paper.
The authors declare no conflict of interest.
Data deposition: The data reported in this paper have been deposited in the Gene
Expression Omnibus (GEO) database, www.ncbi.nlm.nih.gov/projects/geo (accession no.
firstname.lastname@example.org, or email@example.com.
whom correspondencemaybe addressed. E-mail: firstname.lastname@example.org,
This article contains supporting information online at www.pnas.org/cgi/content/full/
© 2007 by The National Academy of Sciences of the USA
December 11, 2007 ?
vol. 104 ?
no. 50 ?
(GISTIC), for identifying regions of aberration that are more
likely to drive cancer pathogenesis. The method identifies those
regions of the genome that are aberrant more often than would
be expected by chance, with greater weight given to high-
amplitude events (high-level copy-number gains or homozygous
deletions) that are less likely to represent random aberrations.
We then apply GISTIC to a newly generated, high-resolution
data set of chromosomal aberrations in 141 gliomas. Glioma is
an excellent model in which to test the approach because the
functional roles of a substantial number of copy number alter-
ations have already been validated in preclinical models (7, 8).
We find 32 statistically significant events of genomic amplifica-
tion or loss. Using standard analytic methods, the regions of
interest found in this study and those reported in two other
recent studies of glioma are highly discordant. Strikingly, we find
that the discordance largely vanishes when the GISTIC meth-
odology is applied to the underlying data from all three studies.
Moreover, the regions we find contain nearly all cancer genes
previously known to be involved in glioma.
The significant aberrations in the glioma genome fall into two
types: focal and broad (near the size of a chromosome arm). By
studying the biological properties of tumors, we find evidence
that overlapping focal and broad events can have very different
consequences. Focusing on chromosome 7 (chr7), we show that
focal high-level amplification at the EGFR gene is associated
with activation of EGFR itself whereas broad lower-level am-
plification of the whole chromosome often activates the MET
axis by increasing the dosage of both MET and its ligand HGF,
suggesting that a subset of glioblastoma (GBM) patients with
polysomy 7 might benefit from MET inhibition.
GISTIC Methodology. GISTIC identifies significant aberrations
through two key steps [Fig. 1 and supporting information (SI)
Text]. First, the method calculates a statistic (G score) that
involves both the frequency of occurrence and the amplitude of
the aberration. Second, it assesses the statistical significance of
that would be expected by chance, using a permutation test that
is based on the overall pattern of aberrations seen across the
genome. The method accounts for multiple-hypothesis testing
using the false-discovery rate (FDR) framework (9) and assigns
a q value to each result, reflecting the probability that the event
is due to chance fluctuation. For each significant region, the
method defines a ‘‘peak region’’ with the greatest frequency and
amplitude of aberration. Each peak is tested to determine
whether the signal is due primarily to broad events, focal events,
or overlapping events of both types.
Application to Glioma. We applied the method to a collection of
141 gliomas, including 107 primary GBMs, 15 secondary GBMs,
and 19 lower-grade gliomas (SI Table 2). We hybridized genomic
DNA to microarrays containing probes for ?100,000 SNPs to
identify copy-number changes and loss of heterozygosity (LOH).
A genome-wide view of the copy-number alterations is shown in
Fig. 2a (LOH results are described in SI Note 1 in SI Text). The
overall pattern is complex, with almost every region of the
genome being altered in at least one tumor. Nonetheless, only 16
broad and 16 focal events are significant. Focal events are
superimposed on four broad events (including two focal events
on chromosome 7 and single events on chromosomes 9, 10, and
13), resulting in a total of 28 peak regions of amplification,
deletion, and LOH.
The 16 broad events include six amplifications (chromosomes
7, 8q, 12p, 17q, 19p, and 20), nine deletions (6q, 9p, 10, 11p, 13,
14, 16q, 19q, and 22), and one region of copy-neutral LOH (17p)
(Fig. 2b, SI Table 3, and SI Note 1 in SI Text). These events occur
at high frequency (range 10–70%, median 27%). In particular,
amplification of chr7 and deletion of chr10 each affect ?60% of
our samples, including ?80% of our primary GBMs. For broad
regions without superimposed focal events, the peak regions are
large (median of 110 genes) (SI Table 3).
The 16 focal events tend to occur at lower frequencies than the
broad aberrations (range 6–49%, median 14%) (SI Table 3).
Among these, amplifications of 4q12 and 7p11.2 (18–26% of
samples) and deletions of 1p36.31 and 9p21.3 (35–49%) are the
most frequent. In some cases, a high degree of amplification
renders amplifications highly significant even though they occur
in only 6–7% of samples (for example, the regions containing
CDK4 and MDM2 on chr12). Because the background rate of
deletions across the genome is higher, deletions usually must
occur at higher frequencies than amplifications to attain similar
levels of significance (SI Table 3). The peak regions for the focal
events can be localized to small regions (median of four genes).
Analysis Confirms Known Genes and Identifies New Loci. We com-
pared the 28 peak regions to the locations of oncogenes and
tumor-suppressor genes previously implicated in the pathogen-
esis of glioma. A recent review (10) lists 12 such genes reported
to be altered in multiple studies of glioma (TP53, RB1,
CDKN2A/B, PTEN, EGFR, PDGFRA, MET, CDK4, CDK6,
MDM2, MDM4, and MYC). We found that 11 of these 12 genes
each correspond with one of the 28 peak regions (with one of
at each location (Upper Center). In addition, by permuting the locations in each tumor, GISTIC determines the frequency with which a given score would be
attained if the events were due to chance and therefore randomly distributed (Lower Center). A significance threshold (green line) is determined such that
significant scores are unlikely to occur by chance alone. Alterations are deemed significant if they occur in regions that surpass this threshold (Right). For more
details see SI Text.
Overview of the GISTIC method. After identifying the locations and, in the case of copy-number alterations, magnitudes (as log2signal intensity ratios)
www.pnas.org?cgi?doi?10.1073?pnas.0710052104Beroukhim et al.
these, MYC, lying just beyond the boundaries defined by GIS-
TIC; as described below, this slight discrepancy is resolved with
additional data) (SI Table 3). The 12th gene (CDK6) lies within
the broad region of significant amplification on chr7, although it
does not correspond to a peak. Interestingly, TP53 is within the
single peak region of LOH that is not reflected in a peak of
copy-number change, suggesting that it is primarily inactivated
through copy-neutral LOH (SI Note 1 in SI Text).
An additional five peak regions contain genes that are known
to play a role in other cancers (MYCN, PIK3CA, CCND2, KRAS,
and CHD5) (11, 12). Our analysis suggests that chromosomal
aberrations involving these genes are also relevant for glioma
pathogenesis. These genes should therefore be carefully char-
acterized in glioma.
The remaining 12 regions (43% of the total) are not associated
with known cancer-related genes. These events occur at sub-
stantial frequencies (6–37%), but nine are due to broad events
and two others rarely reach high amplitude. The final region
(12q14.3) undergoes high-level amplifications, but always in
concert with amplifications of either of two neighboring regions
containing CDK4 and MDM2 (data not shown), suggesting that
it may be due to structural features required to amplify these
genes [as is the case in dedifferentiated liposarcoma (13, 14)].
The fact that nearly half the regions (including regions affected
by highly prevalent aberrations) are not yet associated with
known cancer-related genes underscores the importance of
systematic analysis of the cancer genome.
Previous studies of copy-number alterations in glioma have
shown distinct patterns for certain subtypes such as primary vs.
secondary GBMs (15) or astrocytic vs. oligodendroglial tumors
(16). To explore whether our combined analysis of these glioma
subtypes prevented the detection of alterations specific to pri-
mary GBMs, we performed a separate analysis on only the 107
primary GBMs in our sample set. No additional statistically
significant alterations were identified (SI Fig. 4).
Consistency Across Independent Data Sets. We then sought to
compare our results with two previous studies of copy-number
alterations in glioma (178 samples on 100K SNP arrays; 37
samples on a 16K CGH arrays) (15, 17). At first glance, there
appear to be striking differences. The previous studies reported
many more regions of interest (208 and 97) (Table 1), but the
regions included fewer of the known glioma-associated genes
attributable to the methodology used in these studies, in which
minimal common regions of copy-number change are reported,
without explicitly taking into account the degree of background
noise (see SI Note 2 in SI Text). Applying a similar analysis to our
own data identifies a similarly large number of regions (144) (see
SI Note 2 in SI Text), but these include fewer known glioma-
associated genes and show low concordance with the other
studies. By contrast, applying GISTIC to the raw data from the
other two studies identifies 24 and 26 significant regions each.
Importantly, these regions agree closely with the 27 regions
(excluding copy-neutral LOH of 17p, because it is not observed
in the copy-number analyses) identified above (SI Fig. 5), and
they include essentially the same glioma-associated genes. More-
over, these results are specific to glioma, as seen in a comparison
to lung cancer (SI Fig. 6) (2). The strong concordance across
three independent data sets and two different platforms sup-
analysis of normalized signal intensities from 100K SNP arrays (see SI Text), are displayed across the genome (chromosome positions, indicated along the y axis,
are proportional to marker density) for 141 gliomas (x axis; diagnosis is displayed on top, and gliomas with low purity are segregated to the right). Broad events
near the size of a chromosome arm are the most prominent, including amplifications of chr7 and deletions of chr10 observed among ?80% of GBMs. (b) GISTIC
analysis of copy-number changes in glioma. The statistical significance of the aberrations identified in a are displayed as FDR q values (9) to account for
multiple-hypothesis testing. Chromosome positions are indicated along the y axis with centromere positions indicated by dotted lines. Fifteen broad events
(indicated by red bars for amplifications and blue bars for deletions) and 16 focal events (indicated by dashes) surpass the significance threshold (green line).
The locations of the peak regions and the known cancer-related genes within those peaks are indicated to the right of each panel. Several broad regions,
including chr7 and chr10, contain superimposed focal events, leading to needle-shaped peaks superimposed on highly significant plateaus.
Significant broad and focal copy-number alterations in the glioma genome. (a) Amplifications (red) and deletions (blue), determined by segmentation
Beroukhim et al. PNAS ?
December 11, 2007 ?
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ports the validity of both the GISTIC methodology and our
results for glioma.
our initial data set with the data from the prior study performed
on the same platform to obtain a pooled data set with 319 glioma
samples. This analysis identified 34 significant regions, including
27 of the 28 identified in the initial data set (SI Table 3). For the
additional regions, the prevalence of aberrations is similar in the
initial and combined data sets (11.0% vs. 11.3%), but they now
exceed the significance threshold due to the larger sample size.
Increasing the sample size also leads to narrower peak regions,
with the median number of genes decreasing from 12 to five per
region. Inclusion of additional data sets may define these regions
with even greater precision and facilitate identification of the
Overlapping Broad and Focal Events May Have Different Conse-
quences. Having identified various instances of overlapping
broad and focal events, we explored whether they have distinct
functional consequences, using chr7 as an example (SI Fig. 7).
We first compared copy-number profiles to gene expression
among a group of 43 primary GBMs for which we had sufficient
material for a combined analysis. EGFR was overexpressed in
most GBMs with focal EGFR amplification (7gainEGFRamp) but
in none of the GBMs with broad amplification of chromosome
Table 1. Comparison of results between copy-number analyses of the glioma genome
MCR analysis* GISTIC analysis
Data setPlatform No. of tumorsNo. of MCRs No. of glioma genes in MCRs†
No. of peaks No. of glioma genes in peaks†
Kotliarov et al. (17)
Maher et al. (15)
*Minimal common regions (MCR) analysis, as presented in cited publications.
†Eleven glioma genes affected by copy-number aberrations are considered ?known?: PTEN, RB1, CDKN2A/B, EGFR, PDGFRA, MET, CDK4, CDK6, MDM2, MDM4,
and MYC (10).
in primary GBMs. These data are log2-transformed signal intensities from all concordant probe sets for each gene from Affymetrix U133 arrays, centered and
in red) relative to 7norm. Conversely, a subset of tumors with 7gainoverexpress MET or its ligand HGF, even in the absence of focal amplification. (b) A subset of
(35). (c) Constitutive phosphorylation of MET in MET/HGF?lines. Immunoblots to the indicated epitopes were performed on whole-cell lysates prepared after
were included as positive controls (35). (d) Decreased viability of MET/HGF?cell lines (red) compared with non-MET/HGF?lines (black) when treated with the
MET inhibitor SU11274. Viability was measured by using Trypan blue exclusion after exposure to inhibitor at the indicated concentrations for 96 h. MKN-45 cells
(blue) were included as positive controls.
Broad gains of chromosome 7 often activate the MET pathway but not EGFR. (a) Expression levels of EGFR, MET, and its ligand HGF (all located on chr7)
www.pnas.org?cgi?doi?10.1073?pnas.0710052104Beroukhim et al.
found three additional sources of evidence supporting the bio-
logic distinction between these two classes of tumors. First,
recognizing that EGFR-amplified tumors are known to carry a
low rate of mutations in the TP53 gene (18), we sequenced TP53
and found these mutations more frequently associated with 7gain
compared with 7gainEGFRamp(two-sided Fisher’s exact test, P ?
0.03). Second, we found that 7gainis less frequently associated
with EGFR point mutations or expression of the EGFRvIII
deletion mutant [determined for many of our tumors in a prior
study (19)] (P ? 0.001). Third, we found that 7gainbut not
7gainEGFRampoccurs frequently in secondary GBMs (P ? 0.16).
These three findings are consistent with earlier observations
regarding 7gainEGFRamp(20, 21) and further suggest that 7gain
has distinct functional consequences.
To explore the function of 7gain, we identified genes on the
chromosome that show extreme outliers in expression in at least
10% of tumors with 7gain, compared with 7normal(SI Note 3 in SI
Text). The notion behind this ‘‘comparative outlier analysis’’ is
that broad events such as 7gainmay have heterogeneous effects
are strongly up-regulated in even a subset of the samples.
Strikingly, two of the top four genes in this analysis are those
encoding the receptor MET and its ligand HGF (SI Table 4).
Approximately one-third of 7gainevents are associated with either
one tend to overexpress both (P ? 0.06) (data not shown). The
overexpression of MET and HGF appears to be functionally
relevant: we studied glioma cell lines with 7gainand increased
expression of MET and HGF (Fig. 3b) and found phosphorylation
and activation of the MET receptor even under serum-starved
conditions (Fig. 3c). These cell lines showed enhanced responsive-
ness to the MET kinase inhibitor SU11274 (22) (Fig. 3d) at drug
concentrations that inhibit the MET signaling pathway (SI Fig. 8 a
and b). None of the GBM cell lines with 7gainshowed constitutive
activation of EGFR or were responsive to the EGFR kinase
inhibitor erlotinib (SI Fig. 8 c and d). Compared with the relatively
7gainwith overexpression of MET and HGF may provide a more
common mechanism for cell-autonomous activation of the MET
signaling pathway in glioma. This finding may be relevant for the
clinical deployment of inhibitors targeting this network in glioma
and other cancers (23, 24).
Statistical approaches such as GISTIC identify those recurrent
changes that are concordant across data sets and less likely to
represent random passenger events. Indeed, we have now suc-
cessfully used this approach to identify biologically significant
aberrations in lymphoma (25), melanoma (W. M. Lin, A. C.
Baker, R.B., W. Winckler, W. Feng, et al., unpublished work),
and lung cancer (26). Although we believe it is likely that most
events identified by GISTIC are drivers that recur because of
because of biases in the DNA mutation or repair machinery. In
addition, some driver aberrations may occur at low frequency
and therefore may be missed. The design of future experiments
should consider the number of tumors needed to power detec-
tion of such rare events.
Ultimately, the utility of systematic efforts to characterize the
cancer genome is an empirical question. There are at least two
potential concerns: on one hand, that the vast majority of
cancer-related genes are already known with little left to learn;
on the other hand, that cancer is hopelessly complicated, with a
large number of cancer genes, each altered in a small fraction of
tumors. The results here suggest a more favorable situation, at
least for copy-number alterations. With appropriate statistical
methodology, three studies reveal a concordant picture of the
glioma genome. There appears to be a tractable number of
recurrent events, in the range of 40. Larger tumor collections
may identify some additional low-prevalence events, but it seems
likely that the majority of significant recurrent copy-number
alterations at this scale have been found. Approximately half are
likely to involve known cancer-related genes, with some not
having previously been established to be involved in glioma; all
of these genes should be systematically characterized in glioma.
The remaining events likely point to cancer-related genes and
other functional elements that remain to be discovered; the
identification of the genes associated with the broad events is
particularly important and will likely require the application of
orthogonal approaches, such as expression profiling, mutational
analysis, and RNA interference. Finally, copy-number aberra-
tions are only one form of the genomic changes in glioma.
Identification of other cancer-associated events, including mu-
tations, rearrangements, and epigenetic alterations, will require
similar statistical approaches and large data sets, as presented
Clinical Samples and Cell Lines. Genomic DNA was extracted from
fresh-frozen tumors samples using DNeasy (Qiagen). Non-
tumor tissue, including paired normal brain corresponding to 10
gliomas, was used for germ-line control DNA. Collection and
analysis of all clinical samples were approved by the University
of California Institutional Review Board. RNA and DNA were
also obtained from the GBM cell lines 8-MG-BA, A172, DK-
MG, GAMG, HS683, LN-18, SF-268, SF-295, SNB-75, T98G,
and U251. Gastric and lung cancer cell lines MKN-45 (high level
MET amplified) and H3255 (L858R EGFR mutant) were in-
cluded as positive controls in experiments with the MET kinase
inhibitor SU11274 and the EGFR kinase inhibitor erlotinib,
SNP Arrays. Genomic DNA was applied according to the manu-
facturer’s instructions to oligonucleotide arrays (Affymetrix)
interrogating 116,204 SNP loci on all chromosomes except Y.
Arrays were scanned by using the GeneChip Scanner 3000, and
genotyping was performed by using Affymetrix Genotyping
Tools Version 2.0. Probe-level signal intensities were normalized
to a baseline array with median intensity using invariant set
normalization (27). SNP-level signal intensities were obtained by
using a model-based (PM/MM) method (28). Further analytic
steps are described in SI Text. SNP, gene, and cytogenetic band
locations are based on the hg16 (July 2003) genome build
(http://genome.ucsc.edu). Data from SF-268, SF-295, SNB-75,
and six gliomas (along with paired normals) were previously
published (29, 30). Data are available at www.broad.mit.edu/
cancer/pub/GISTIC and through the Gene Expression Omnibus
(www.ncbi.nlm.nih.gov/projects/geo, accession no. GSE9635).
Mutation Detection.PTEN and TP53 were sequenced in 134 of the
141 samples undergoing SNP analysis as previously described
(19). All exons were covered in ?70% of samples except exons
1, 8, and 9 in PTEN and exons 7 and 10 in TP53. EGFR point
mutations and vIII expression were determined for 133 and 58
samples, respectively, in a prior study (19).
Expression Arrays. Expression data were obtained by using Af-
fymetrix U133A/B and plus 2 arrays from 43 primary GBMs
undergoing SNP array analysis. CEL files from U133A and plus
2 arrays were preprocessed separately by using RMA (31). Probe
sets common to both arrays were used after equalizing the mean
and standard deviation of the U133A and plus 2 arrays. Expres-
sion data from GBM cell lines were generated by Affymetrix
cartridge arrays, except SF-268, SF-295, and SNB-75, where
available U133A data were used (http://wombat.gnf.org/
Beroukhim et al.PNAS ?
December 11, 2007 ?
vol. 104 ?
no. 50 ?
EGFR and MET Inhibition. Cell proliferation assays. Cell lines were Download full-text
maintained in RPMI medium 1640 containing 10% serum and
1% penicillin/streptomycin. Stock solutions of erlotinib (10 mM;
WuXi Pharmatech) and SU11274 (1 mM; Calbiochem) were
prepared in DMSO and maintained at ?20°C or 4°C according
to the manufacturer’s instructions. Drugs were diluted in fresh
medium before each experiment. Cells were cultured in the
presence of drug or vehicle for 4 days, and viability was deter-
mined by using the WST assay (Roche) or Trypan blue exclusion
assay as previously described (32, 33).
Western blot analysis. To examine basal MET and EGFR phos-
phorylation, cells were grown in serum-free DMEM with 1%
L-glutamine 200 mM and 1% penicillin/streptomycin for 24 h.
Cells were harvested and lysed by using cell lysis buffer (Cell
Signaling Technology) with 1% protease and 1% phosphatase
inhibitors (Calbiochem). Total protein concentration was deter-
mined by using Bio-Rad Protein Assay Standard I (Bio-Rad).
Equal protein amounts were resolved by SDS/PAGE and elec-
trotransferred to nitrocellulose membrane blots (34). Blots were
probed with antibodies against MET (25H2), P-MET (P-Tyr
1234/5), P-EGFR (P-Tyr 1173) (Cell Signaling Technology), and
EGFR (sc-03; Santa Cruz Biotechnology) and exposed to stan-
dard x-ray film after application of peroxidase-conjugated sec-
ondary antibodies (Jackson ImmunoResearch) and ECL West-
ern blotting detection reagents (GE Healthcare).
Nadav Kupiec and Bang Wong provided help with illustrations. Howard
Fine, Elizabeth Maher, Cameron Brennan, Ron Depinho, and Lynda
Chin provided data. This work was supported by funds from the Broad
Institute of Harvard University and Massachusetts Institute of Technol-
ogy, the Dana–Farber/Harvard Cancer Center Prostate Specialized
Program of Research Excellence (R.B.), Department of Defense Grant
PC040638 (to R.B.), National Cancer Institute Grants CA109038 and
CA126546 (to M.M.), the Henry E. Singleton Brain Cancer Program at
the Brain Tumor Funders’ Collaborative. J.B. is a Beatriu de Pinos
Fellow of the Departament d’Educacio ´ i Universitats de la Generalitat
de Catalunya. J.D. and R.K.T. are supported by fellowships of the
Leukemia and Lymphoma Society and the International Association for
the Study of Lung Cancer, and R.K.T. is a Mildred Scheel Fellow of the
Deutsche Krebshilfe. I.K.M. is a Sontag Foundation Distinguished
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