Global Survey of Phosphotyrosine
Signaling Identifies Oncogenic
Kinases in Lung Cancer
Klarisa Rikova,1Ailan Guo,1Qingfu Zeng,1Anthony Possemato,1Jian Yu,1Herbert Haack,1Julie Nardone,1
Kimberly Lee,1Cynthia Reeves,1Yu Li,1Yerong Hu,3Zhiping Tan,3Matthew Stokes,1Laura Sullivan,1
Jeffrey Mitchell,1Randy Wetzel,1Joan MacNeill,1Jian Min Ren,1Jin Yuan,1Corey E. Bakalarski,2
Judit Villen,2Jon M. Kornhauser,1Bradley Smith,1Daiqiang Li,4Xinmin Zhou,3Steven P. Gygi,2
Ting-Lei Gu,1Roberto D. Polakiewicz,1John Rush,1and Michael J. Comb1,*
1Cell Signaling Technology, 3 Trask Lane, Danvers, MA 01923, USA
2Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
3Department of Cardiothoracic Surgery
4Department of Pathology
The Second Xiangya Hospital, Central South University, Changsha, Hunan, China 410011
Despite the success of tyrosine kinase-based
cancer therapeutics, for most solid tumors the
tyrosine kinases that drive disease remain un-
known, limiting our ability to identify drug tar-
gets and predict response. Here we present
the first large-scale survey of tyrosine kinase
activity in lung cancer. Using a phosphoproteo-
mic approach, we characterize tyrosine kinase
signaling across 41 non-small cell lung cancer
(NSCLC) cell lines and over 150 NSCLC tumors.
ated and analyzed to identify known oncogenic
kinases such as EGFR and c-Met as well as
novel ALK and ROS fusion proteins. Other acti-
vated tyrosine kinases such as PDGFRa and
DDR1 not previously implicated in the genesis
tivated cell circuitry, the approach outlined here
provides insight into cancer biology not avail-
able at the chromosomal and transcriptional
levels and can be applied broadly across all
Lung cancer is the leading cause of cancer mortality in the
world today. Despite decades of intensive analysis, the
majority of molecular defects that play a causal role in
the development oflung cancerremainunknown. Twoon-
cogenes important in lung cancer are K-RAS and EGFR,
mutated in 15% and 10% of NSCLC patients. Large-scale
DNA sequencing efforts have identified mutations in
PI3KCA, ERBB2, and B-RAF that together represent
another 5% of NSCLC patients (Greenman et al., 2007;
Thomas et al., 2007). Analysis of recurrent chromosomal
aberrations including amplification and deletion using
CGH and SNP arrays promises to identify many additional
genes altered in cancer (Chin et al., 2006; Neve et al.,
2006). However, genetic approaches suffer from the diffi-
culty of identifying a small number of causal changes
within a sea of changes associated with genome instabil-
ity. As a result, strategies and methods are needed to
focus analysis on the key lesions driving disease.
One such strategy involves analysis of the cellular sig-
naling pathways corrupted in cancer (Vogelstein and Kin-
zler, 2004). Although signaling pathways controlling cell
growth and differentiation are almost always altered, the
tools to survey complex signaling networks and identify
the alterations that lead to cancer have not been available.
Signaling via tyrosine kinases is often deregulated in can-
cer as these enzymes mediate most growth and survival
signaling in multicellular organisms (Blume-Jensen and
Hunter, 2001). The recent success of selective tyrosine ki-
nase inhibitors depends upon the identification of tumors
that are driven by activated kinases and are therefore
dependent upon the targeted kinase for their survival
and clinical benefit (Dowell and Minna, 2005; Weinstein,
2002). Collectively, these observations emphasize the
need for better methods to identify and understand the
role of activated tyrosine kinases in the initiation and
progression of disease.
signaling have enabled analysis of known model systems
(Rush et al., 2005; Wolf-Yadlin et al., 2006) and several
hematological cancers (Gu et al., 2006; Walters et al.,
2006). Here we broadly survey tyrosine kinase signaling
across many lung cancer cell lines and solid tumors in or-
der to classify them based upon aberrant tyrosine kinase
1190 Cell 131, 1190–1203, December 14, 2007 ª2007 Elsevier Inc.
signaling. Because tyrosine kinases are themselves phos-
phorylated on tyrosine in proportion to their activity, this
study represents the first large-scale analysis of tyrosine
kinase activity in lung cancer with the identification of
over 50 different tyrosine kinases and over 2500 down-
stream substrates. We analyze this data using hierarchal
clustering and by ranking tyrosine kinase phosphoryla-
tion/activity to identify candidate oncogenes. Using this
approach we identify known tyrosine kinases such as
EGFR and c-Met as well as novel oncogenic tyrosine
kinases not previously known to play a role in lung cancer.
Upon further biochemical, functional, and clinical analysis
we identify new subtypes of NSCLC that express acti-
vated forms of ALK, ROS, and PDGFRa.
Phosphotyrosine Profiles of NSCLC
Tumors and Cell Lines
Immunohistochemistry (IHC) and a phosphotyrosine-spe-
cific antibody were used to screen 96 paraffin-embedded,
formalin-fixed tissue samples from NSCLC patients (Fig-
ure 1A). Approximately 30% of tumors showed high levels
of phosphotyrosine expression. This group of patient
Figure 1. Heterogeneous Phosphotyrosine Expression in Human Lung Cancer Tissue and Cell Lines
(A) IHC staining of paraffin-embedded human NSCLC tumor tissues showing high, medium, and low phosphotyrosine expression.
(B) Western blot analysis of phosphotyrosine signaling in 22 different NSCLC cell lines showing different patterns of phosphotyrosine reactivity.
(C) Immunoaffinity profiling method. Cells or tissues are lysed in urea buffer and digested with protease. The resulting peptides are immunoaffinity
purified using immobilized phosphotyrosine-specific antibody (P-Tyr-100) and analyzed by LC-MS/MS as described in Experimental Procedures.
protein is a semiquantitative measure of the abundance of that protein.
(D) Western blot analysis of Met and Phospho-Met(Tyr1234/5) expression in NSCLC cell lines. Shown below is a comparison of the number of phos-
hopeptides identified by MS/MS with the immunoblotting. The number of different sites identified are shown in parenthesis.
Cell 131, 1190–1203, December 14, 2007 ª2007 Elsevier Inc. 1191
samples also showed high levels of receptor tyrosine ki-
nase (RTK) expression, suggesting that RTK activity may
play a role in the genesis of these lung tumors. Immuno-
blotting of 41 NSCLC cell lines with a phosphotyrosine-
specific antibody also showed heterogeneous reactivity
especially in the molecular weight range characteristic of
receptor tyrosine kinases (Figure 1B).
cell lines and solid tumors we used an immunoaffinity
phosphoproteomic approach. Because phosphotyrosine
represents less than 1% of the cellular phosphoproteome
as determined by tandem mass spectrometry (MS/MS)
(Olsen et al., 2006) and is difficult to analyze by conven-
tional methods, we used immunoaffinity purification with
a phosphotyrosine antibody to enrich for phosphotyro-
sine-containing peptides prior to analysis by tandem
mass spectrometry (Rush et al., 2005). All tumors were
identified as NSCLC based upon standard pathology.
cluded in the analysis. NSCLC cell lines were grown over-
night in low serum before analysis to reduce background
phosphorylation resulting from culture conditions.
Although current technology limitations may somewhat
bias our data sets, for example, tryptic phosphopeptides
that are either too small or too large to be identified by
MS/MS will not be detected, we nevertheless simulta-
neously measured the phosphorylation status of a large
number of sites (ranging between 150 and 1200 nonre-
dundant sites/cell line or tumor). Tables S1 and S2 (avail-
able online) show selected phosphotyrosine profiles from
a total of 41 NSCLC cell lines and 150 NSCLC tumors.
4551 sites of tyrosine phosphorylation were identified on
greater than 2700 different proteins, dramatically extend-
ing our knowledge of tyrosine kinase signaling in NSCLC.
When these sites were queried against PhosphoSite
(www.phosphosite.org), a comprehensive resource of
known phosphorylation sites (Hornbeck et al., 2004),
more than 85% appeared novel. These data have been
deposited as Supplemental Data on the Cell website and
in PhosphoSite and the data sets are freely available via
NSCLC Tyrosine Phosphorylation
As an initial step to screen for phosphotyrosine signaling
abnormalities and to compare NSCLC proteins based
titative approach using the number of phosphopeptide
assignments to approximate the amount of phosphopep-
tide present in the sample. Roughly speaking, the wider
the peak eluting from the LC column the more frequently
a phosphopeptide is detected by LC MS/MS and hence
the more phosphopeptide present in the sample (see
Figure 1C). For example, comparison of phosphopeptide
numbers for c-Met with the levels of phosphorylated c-
Met protein observed by western analysis are in good
agreement (Gilchrist et al., 2006; Old et al., 2005; Zybailov
et al., 2005) (see Figure 1D). We found this approach pref-
erable to other methods such as parent ion peak height
because it allowed us to simplify the analysis by combin-
ing all sites on a given protein.
We next compared the distribution of protein tyrosine
phosphorylation in NSCLC cell lines and solid tumors
based upon protein classification.
As shown in Figure 2A, protein kinases, adhesion pro-
teins, and components of the cytoskeleton are the most
highly phosphorylated protein types. When comparing
NSCLC cell lines and tumors it is important to note that
tumors represent a complex tissue ranging from 50% to
90% cancer cells. The tyrosine kinases, c-Met, EGFR,
and EphA2 show the highest levels of receptor tyrosine ki-
nase phosphorylation in cell lines while tumors show high
levels of DDR1, EGFR, DDR2, and Eph receptor tyrosine
kinase phosphorylation (Figure 2B). Fak and Src-family
kinases make up the majority of NSCLC nonreceptor tyro-
sine kinase phosphorylation (Figure 2C). Most phosphory-
lation occurs at the activation loop of these kinases (Table
(with a few exceptions such as the src family C-terminal
Jensen and Hunter, 2001; Ullrich and Schlessinger, 1990);
hence, we assume that tyrosine kinase phosphorylation is
a good readout of kinase activity in what follows.
Tyrosine Kinases Activated in NSCLC
A fraction of NSCLC tumors and cell lines exhibit high ty-
rosine phosphorylation (Figures 1A and 1B) possibly as
a result of activated/overexpressed tyrosine kinases. To
identify abnormally activated tyrosine kinases, we sub-
tracted an average signaling profile derived from either
to obtain the unsupervised hierarchal clustering results
shown in Figures 2E and 3A (see Experimental Proce-
and identifies highly phosphorylated (activated) tyrosine
kinases (compare Figures 2D and 2E). Results are consis-
tent withpreviousreportsof activated EGFR(Amann etal.,
2005), ErbB2 (Stephens et al., 2004), ErbB3 (Engelman
et al., 2005), EphA2 (Kinch et al., 2003), and c-Met (Ma
et al., 2005) receptor tyrosine kinases in NSCLC cell lines.
EGFR kinase activity is elevated in 11 cell lines (Figure 2E),
and among these, five cell lines harbor EGFR-activating
tides are observed in HCC827 (Amann et al., 2005) and
H3255 (Paez et al., 2004; Tracy et al., 2004), known to ex-
press amplified and mutated EGFR. High levels of c-Met
and ErbB2 are observed in H1993 and Calu-3 cell lines,
respectively, consistent with previous reports (Lutterbach
et al., 2007; Ma et al., 2005; Minami et al., 2007) and con-
firming known receptor tyrosine kinase activity in NSCLC
A similar analysis of NSCLC tumors is shown in Fig-
ure 3A for all tyrosine kinases and in Figure S1 for all
tyrosine kinase phosphorylation sites. Five major groups
of tumors are identified using unsupervised Pearson clus-
tering (Figure 3A). From left to right are tumors aberrantly
1192 Cell 131, 1190–1203, December 14, 2007 ª2007 Elsevier Inc.
expressing the following: only one or two highly active ty-
rosine kinases (group 1), tumors expressing active Fak to-
gether with many different Src, Abl, and receptor tyrosine
kinases (group 2), tumors expressing activated DDR1 to-
gether with src and abl kinases (group 3), tumors express-
ingSrckinases withRTKssuchasEGFR(group 4),and tu-
mors expressing predominately src and Abl tyrosine
kinases (group 5).
Tyrosine Kinase Substrates
We next separately analyzed phosphorylated substrates
(excluding tyrosine and Ser/Thr kinases) from each group
Figure 2. Comparison of Tyrosine Kinases between Lung Cancer Cell Lines and Tumors
(A) Distribution of phosphoprotein types. Each observed phosphoprotein was assigned a protein category from the PhosphoSite ontology. The num-
bers of unique proteins in each category, as a fraction of the total, are represented by the wedges of the pies.
(B)Distribution of spectral counts among receptor tyrosine kinases (RTK). Thetotalnumbers of observed spectra assignedto each RTK over all of the
cell lines (top) or the tumors (bottom) are represented as fractions of the total RTK spectra observed.
(C) Distribution of spectral counts among nonreceptor tyrosine kinases. The total numbers of observed spectra assigned to each TK (nonreceptor)
over all of the cell lines (top) or the tumors (bottom) are represented as fractions of the total TK (nonreceptor) spectra observed.
(D and E) Phosphorylation of tyrosine kinases in lung cancer cell lines. The total number of observed spectra assigned to each TK in each cell line was
used as the basis for clustering using the Pearson correlation distance metric and average linkage. In (D), no normalization has been applied; in (E),
each value in a row has had the row average subtracted.
Cell 131, 1190–1203, December 14, 2007 ª2007 Elsevier Inc. 1193
Figure 3. Phosphorylated Tyrosine Kinases and Substrates in NSCLC Patients
(A) Clustering of tumors by tyrosine kinase phosphorylation. Spectral counts for tyrosine kinases in patient tumors were normalized to the count for
GSK3band thenclustered as described inFigure 2E.Clustering produced five groups of tumorswithdifferent sets of tyrosine kinases predominating.
in (A), and spectral counts were normalized to the count for GSK3b. After all kinases were removed from the protein set, the data were clustered as in
Figure 2E and the top 30 proteins displayed. The tumors used in (B) were from group 1 in (A), those in (C) from group 2, and those in (D) from group 4.
1194 Cell 131, 1190–1203, December 14, 2007 ª2007 Elsevier Inc.
and identified the 30 most informative substrates (from
over 2500 phosphorylated proteins) for groups 1, 2, and
4 (Figures 3B–3D). Different groups have different active
kinases and different phosphorylated substrates. Group
2 tumors, with many active tyrosine kinases, show higher
levels of downstream phosphorylation than group 1 tu-
mors. For example, group 2 tumors show phosphorylation
of proteins involved in motility and cytoskeleton dynamics
as well as cell-surface receptors and glycolytic enzymes.
Overall, group 1 tumors express lower levels of substrate
phosphorylation that fall into several subgroups showing
high SHP-1, IRS-1/2, and PI3KR1/2. Group 4 tumors
show phosphorylation of different substrates including
PTEN and histones.
In general, high phosphotyrosine IHC staining is ob-
served for group 2 tumors, consistent with MS/MS results
(see Table S4). As shown in Table S4, we found no striking
correlations of hierarchal clustering groups with available
patient clinical data and tumor pathology. We also com-
pared tumor protein tyrosine phosphorylation to 48 adjacent
lung tissue samplesusing ttestcomparison(Figure S2). This
analysis identifies significant signaling differences between
tumor and normal tissue, including many cytoskeleton and
signaling proteins, and provides a rich resource for future
Ranking Activated Tyrosine Kinases
A fraction of cell lines and tumors express multiple acti-
vated tyrosine kinases (see group 2 tumors), complicating
the identification of ‘‘driver’’ kinase(s) (causally related to
disease pathogenesis) from other activated kinases func-
tioning in downstream networks. In addition, hierarchical
clustering was not useful in grouping tumors with high
EGFR phosphorylation (see Figure 3A). We instead devel-
oped an approach to identify candidate driver tyrosine ki-
nases based upon identifying unusually high levels of
tyrosine kinase activity in a subgroup of patients. We
summed total phosphorylation for each kinase across ei-
ther Figure 2E or Figure 3A and divided by the number of
cell lines or patients showing above average phosporyla-
tor tyrosine kinases ranked by average phosphorylation/
patient or cell line. This analysis identifies unusually high
tyrosine kinase phosphorylation in subsets of cell lines or
followup analysis. Of the top 20 RTKs, 15 are identified in
both cell lines and tumors. Of the top 10, Met, ALK, ROS,
PDGFRa, DDR1, and EGFR are found in both cell lines
and tumors (Table 1).
We next applied a ranking process to identify candidate
disease drivers byrankingkinases basedupon total phos-
phorylation. Among cell lines with the highest EGFR rank,
we find that EGFR is often the most highly phosphorylated
tyrosine kinase, in others it is among the top 2 or 3 kinases
(Table S3). All 5 cell lines carrying known EGFR-activating
mutations and cell lines carrying known EGFR genomic
amplifications are found among the cell lines with highest
EGFR rank (see Table S3).
We carried out a similar analysis of NSCLC tumor sam-
ples using phosphorylation rank to identify tumors show-
ing activated EGFR (Table 2). NSCLC tumors in this study
are all stage 1 or 2 and consist of 74% males, 52%
smokers, and 30% adenocarcinoma. Among the 18 tu-
mors with highest EGFR rank, 16 gave readable EGFR
kinase domain DNA sequence (Table 2); of these, 9/16 tu-
mors show kinase domain-activating mutations with 8/8
adenocarcinomas and 5/5 female nonsmokers showing
EGFR-activating mutations, consistent with previous re-
ports of enrichment for female nonsmokers and adeno-
carcinoma (Lynch et al., 2004; Pao et al., 2004) (Table 2).
Having demonstrated that tumors with EGFR-activating
mutations can be identified by EGFR phosphorylation
rank, we used the same approach to identify new candi-
date driver tyrosine kinases. As shown in Table 1, Met,
ALK, ROS, PDGFRa, DDR1, and EGFR are found in both
cell lines and tumors. C-Met was found highly phosphory-
tion as shown for H1993 cells where c-Met is a known
driver (Lutterbach et al., 2007). In contrast to EGFR and
c-Met, the kinases ALK, ROS, PDGFRa, and DDR1 have
few literature connections to lung cancer. Because cell
line models are critical to further testing the role of acti-
vated kinases in driving disease, we carefully examined
the expression of these candidates in NSCLC cell lines.
Protein expression of ROS, ALK, and PDGFRa appeared
to be highly upregulated in at least one NSCLC cell line
(Figures S3A, S3B, and S4A). Although DDR1 is active in
many tumors (Ford et al., 2007) and is a strong candidate
for followup analysis, only H1993 cells express phosphor-
ylated DDR1, and these cells are known to be driven by c-
corresponding NSCLC cell line models. Tables 2, S1, and
of ALK, c-ROS, c-Met, and PDGFRa phosphorylation. As
seen for EGFR, these RTKs are often but not always the
most highly phosphorylated tyrosine kinase (Table 2),
suggesting that they may play a role in driving disease.
We also ranked all phosphorylated proteins for cell lines
and selected tumors expressing ALK (Figure 3E), c-ROS
(Figure 3F), and PDGFRa (Figure 3G). Among the most
highly phosphorylated substrates, many are shared be-
tween cell lines and tumors and may participate in down-
stream oncogenic signaling (see arrows Figures 3E–3G).
See Table S1 for a complete list of all phosphopeptides
found in HCC78, H2228, and H1703 cell lines and six
(E–G) Most prominent phosphoproteins. Proteins were ranked, based on spectral counts, and the top 25 are shown. Before ranking the tumor pro-
teins, each protein’s counts were normalized to those for GSK3b, then the average count for that protein over all tumors was subtracted. Cell line
proteins had their average count over all cell lines subtracted. Arrows indicate proteins shared between cell lines and tumors.
Cell 131, 1190–1203, December 14, 2007 ª2007 Elsevier Inc. 1195
different NSCLC tumors expressing ROS, ALK, EGFR,
PDFGRalpha, and c-Met (over 2000 different phosphotyr-
ALK and ROS Fusion Proteins in NSCLC
Cell Lines and Tumors
High-level phosphorylation of ALK was observed in the
group of patients in the upper left corner of Figure 3A,
cell line H2228 (Figures 2E and 4A and Table 1) and ROS
in one tumor sample and HCC78 cell line (Figure 4B and
Table 1). Phosphorylation rank place ALK and ROS near
pression of ALK and ROS was restricted among the
NSCLC cell lines and exhibited a smaller than predicted
molecular weight (Figures S3A and S3B). We carried out
RT-PCR and DNA sequencing to investigate the ex-
pressed RNA transcripts. 50RACE analysis of RNA tran-
scripts derived from H2228 cells and three different tumor
samples demonstrated fusion of ALK to EML4, a microtu-
region of EML4 is fused to the kinase domain of ALK at the
precise point of fusion observed in other previously char-
acterized ALK fusions (Figure 4C), such as the NPM-ALK
nandez et al., 1999) in one tumor sample (Figure 4D). This
observed (Hernandez et al., 2002). In both EML4 and TFG
fusions,acoiled-coil domainisfusedtothekinase domain
of ALK, likely conferring dimerization/oligomerization and
constitutive kinase activity.
Similar analysis of HCC78 cells demonstrates fusion
of ROS to the transmembrane solute carrier protein
SLC34A2. The N-terminal region of SLC34A2, ending just
Table 1. Comparison of RTK Phosphorylation in Subgroups of NSCLC Cell Lines and Tumors
NSCLC Cell linesNSCLC Tumors
ROS431 43 MET8471271
ALK 361 36 ALK4647 66
MET23311 21DDR1 31366350
402 20ROS501 50
ErbB2443 15 VEGFR-26621641
FGFR12037 EGFR 12984330
EphA264 106 Axl 7612629
ErbB33866 EphB2582 29
EphB1 1025 DDR2143958 25
Axl 2464 FGFR1 934 23
EphA4 1544 EphB37933821
EphA1 1444 Mer 1991020
EphA5313 Tyro3 167 1017
Tyro31243 EphB4 26919 14
EphB21152 ErbB2 605 12
IGF1R322 Kit 14714 11
Abbreviations: RTK, receptor tyrosine kinase; NSCLC, non-small cell lung cancer.
Identifying high kinase activity (phosphorylation) in subsets of cell lines and patients. For patient samples, phosphopeptide sum
represents each protein’s spectral counts normalized to those for GSK3b and summed across all 150 tumors, minus the average
count for that protein over all tumors. Number of samples represents the number of tumors showing above average phosphopep-
tide count. For cell lines, phosphopeptide sum represents each protein’s spectral counts after subtraction of the average count for
that protein over all 41 cell lines; because the same number of cells was used in each experiment, normalization was omitted. Cell
lines and tissues are ranked in order of decreasing counts per sample.
1196 Cell 131, 1190–1203, December 14, 2007 ª2007 Elsevier Inc.
afterthefirst transmembraneregion,isfused N-terminalto
the transmembrane region of ROS producing a truncated
fusion protein with two transmembrane domains. Two
forms of this fusion protein were observed in HCC78 cells
that likely represent different splicing products produced
from the same translocation event (see Figure 4E). A sec-
ond ROS fusion was identified in the c-ROS-positive
NSCLC tumor. As shown in Figure 4F c-ROS is fused to
the N-terminal half of CD74, a type II transmembrane pro-
tein with high affinity for the MIF immune cytokine (Leng
et al., 2003). The N-terminal region of CD74 is fused to
ROS at the precise site of SLC34A2-ROS fusion (see Fig-
ure 4E) curiously creating a fusion protein with two trans-
membrane domains as found in the SLC34A2 fusion.
Expression of a tagged SLC34A2-ROS fusion protein in
mammalian cells showed constitutive kinase activity that
localized to membrane fractions (see Figures S3E and
S3F). We sequenced the kinase domains of ALK and
ROS and found no mutations.
Experiments carried out using siRNAs against ALK did
not induce cell death in H2228 cells, suggesting survival
signaling independent of ALK, such as activating muta-
tions in PI3K (Samuels et al., 2005; Samuels and Velcu-
lescu, 2004) or inactivation of PTEN (Mellinghoff et al.,
2005). Similar experiments were carried out using siRNAs
against ROS. Two different siRNAs against ROS were ef-
fective in reducing ROS protein expression and inducing
cell death in HCC78 cells (Figures S3C and S3D), de-
monstrating a strict dependence upon ROS signaling for
HCC78 cell survival.
Analysis of the most highly phosphorylated substrates
in ALK-expressing cell line and tumor samples (Figure 3E)
identified candidate downstream signaling molecules
such as SHIP2, IRS-1, and IRS-2 previously shown to be
important downstream mediators of ALK signaling in ana-
plastic large cell lymphoma. In addition, phosphorylation
of EML4, the fusion partner, is prominently seen (Fig-
ure 3E). PTPN11 and IRS-2 previously reported to be
important downstream effectors of ROS in glioblastoma
(Charest et al., 2006) were identified as highly phosphory-
lated in c-ROS-expressing samples (Figure 3F).
We prepared FISH break-apart probes to either side of
the ALK or ROS locus and identified translocations in both
c-ROS-expressing cell lines and tumors (Figure S3H). As
ALK and EML4 are located on the same arm of chromo-
some 2, deletion of the intervening DNA confirmed the ex-
pected break-apart pattern (Figure S3G). RT-PCR analy-
sis using ALK and EML4 primers from 103 NSCLC
tumors analyzed by MS/MS identified 3 positive samples
(Table 2) giving a 3% frequency for EML4-ALK; adding in
the TGF-ALK sample gives an overall frequency of ALK fu-
sions as 4% in the Chinese population.
Table 2. Patients Grouped by Receptor Tyrosine Kinase Phosphorylation
Abbreviations: AD, adenocarcinoma; SCC, squamous cell carcinoma; m1, delE746-A750; m2, L858R; m3, L747_A750>P; m4, L833V:L838V; m5,
E746_T751>IP; F*, EML4-ALK fusion; F**, TFG-ALK fusion; F***, CD74-ROS fusion.
Patients grouped byhigh EGFR, ALK, ROS, Met, and PDGFRa phosphorylation. Forpatient samples,each protein’s spectralcountswere normalized
to those for GSK3b, and the average count for that protein over all tumors was subtracted. Above average receptor tyrosine kinase phosphorylation
counts are shown. EGFR-activating mutations, ALK, and ROS translocations are indicated.
Cell 131, 1190–1203, December 14, 2007 ª2007 Elsevier Inc. 1197
1198 Cell 131, 1190–1203, December 14, 2007 ª2007 Elsevier Inc.
PDGFRa Activation in NSCLC:
Sensitivity to Imatinib
PDGFRa was identified as aberrantly activated in one
NSCLCcell line,H1703, andeight different tumorsamples
(Figure 5A and Table 1). H1703 cells also express phos-
phorylatedEGFRandFGFR1and several otherRTKs(Fig-
ure 5A). Protein expression for PDGFRa was confirmed by
western blotting (Figure S4A). We next investigated sensi-
tivity of H1703 cells to the PDGFR inhibitor Imatinib (Glee-
vec) and the EGFR inhibitor Gefitinib (Iressa). Phosphory-
lation of Akt at Ser473 was blocked by Imatinib but not by
Gefitinib treatment (Figure 5B). Imatinib dose-response
experiments (Figure S4B) indicated almost complete inhi-
bition of PDGFRa and Akt phosphorylation at 100 nM
Imatinib with little if any effect on p44/42MAPK phosphor-
Cell proliferation MTT assays were carried out to further
investigate the sensitivity of 20 NSCLC cell lines to Imati-
nib (Table S5). As shown in Figure 5C, H1703 cells show
a sensitivity profile similar to K562 cells that overexpress
Figure 4. Identification of ALK and ROS Fusions in Cell Lines and Patients
(A and B) Distribution of spectral counts among receptor tyrosine kinases in H2228 and HCC78 cell lines. The total numbers of observed spectra
assigned to each RTK are represented as fractions of the total RTK spectra observed.
(C) Schematic representation of the EML4, ALK, and EML4-ALK fusion proteins. Arrow indicates the chromosomal breakpoint.
(D) Schematic representation of the TFG, ALK, and TFG-ALK fusion proteins. Arrow indicates the chromosomal breakpoint.
(E) Schematic representation of the SLC34A2, ROS, and SLC34A2-ROS fusion proteins. Arrow indicates the chromosomal breakpoint.
(F) Schematic representation of the CD74, ROS, and CD74-ROS fusion proteins. Arrow indicates the chromosomal breakpoint.
Figure 5. Expression and Signaling of PDGFRa in H1703 Cells and Mouse Xenographs
(A) Distribution of spectral counts among receptor tyrosine kinases in H1703.
(B)Westernblotanalysisshowing theeffects ofEGFRand PDGFRinhibitors onAktphosphorylation. H1703cells wereeither untreatedortreated with
EGF, EGF with Iressa, or Gleevec for 1 hr, and the levels of EGFR, PDGFRa, Akt were determined by western blot. Phosphorylation of EGFR(Tyr1068)
and Akt(Ser473) were determined using phosphorylation-state-specific antibodies.
(C) Imatinib mesylate inhibits cell growth and induces apoptosis in H1703 cells. H1703 cells were treated with Gleevec for 72 hr, and MTS assay was
performed. Results from the means of triplicate experiments (error bars indicate SD) were shown.
(D) Treatment of Imatinib on H1703 mouse xenographs. Mice with similar tumor size were divided to two groups, one group (5 mice) was treated with
Gleevec, the other group (5 mice) was not treated. After 7 days of treatment, the size (mm length x mm width) of each tumor was measured.
(E) Regulation of PDGFRa phosphorylation in H1703 cells by Imatinib. H1703 cell were labeled with light and heavy amino acids and analyzed by
LC-MS/MS tandem mass spectrometry as described for SILAC. PDGFRa phosphorylation sites detected by mass spectrometry were indicated
as well as the fold change measured after a 3 hr treatment with Imatinib.
(F) Regulation of PDGFRa downstream signaling in H1703 cells as determined by SILAC and LC-MS/MS. Red circles depict proteins with decreased
phosphorylation following Imatinib treatment. Black and red arrows indicate known and predicted (scansite and netphosK) substrates, respectively.
Cell 131, 1190–1203, December 14, 2007 ª2007 Elsevier Inc. 1199
Bcr-Abl fusion protein (Druker et al., 2001; Mahon et al.,
2000). In contrast, 19 NSCLC cell lines (A549, H1373,
H441, and many others negative for PDGFRa expression)
lating drug sensitivity with kinase phosphorylation. Our Im-
atinib sensitivity profile differs from a previous report that
identified PDGFRa expression in A549 cells and showed
sensitivity to Imatinib (Zhang et al., 2003). To examine the
effects of Imatinib on apoptosis, H1703 cells were treated
with Imatinib and cleavage of PARP and caspase 3 exam-
ined by western blotting and flow cytometry, respectively.
Imatinib (0.1 mM) significantly increased cleaved caspase
3 and cleaved PARP expression in H1703 cells (Figures
S4C and S4D). We next examined the effects of Imatinib
in vivo using mouse xenograft models. Nude mice were
tion monitored over a period of several weeks. Upon ap-
pearanceofthefirstvisibletumors, mice were treated daily
with Imatinib (50 mg/kg) or vehicle for a 2 week period.
Imatinib-treated mice showed immediate and profound
effects on tumor growth, while tumor growth continued
in control mice (Figures 5D and S4F). Tumor growth was
quantified in control and Imatinib-treated animals (Fig-
ure 5D), demonstrating exquisite sensitivity to Imatinib
even in the complex tumor environment.
naling, H1703 cells were grown in heavy and light amino
acid-labeled media, treated with and without Imatinib, and
phosphopeptides analyzed by mass spectrometry/SILAC
ment with Imatinib. Treatment of H1703 cells with Imatinib
tor (Figure 5E). Ten sites of tyrosine phosphorylation were
observed and three new sites were identified (Tyr613, 926,
and 962). Imatinib also suppressed tyrosine phosphoryla-
including phospholipase Cg 1, the regulatory subunit of
teins regulating the cytoskeleton and actin reorganization
and signaling molecules involved in membrane recycling
Adam 9 (Mazzocca et al., 2005) known to liberate ligands
for EGFR and FGFR (Peduto et al., 2005) was found to
be highly phosphorylated in H1703 cells. Imatinib also in-
hibited phosphorylation of the ras effector Rin1 (Hu et al.,
2005) and inhibited phosphorylation of SMS2, an enzyme
involved in ceramide synthesis (Taguchi et al., 2004) (see
Table S6 for a complete list). Western analysis confirmed
selected SILAC results (Figure S4E). A complete list of SI-
LAC results is shown in Table S6 for one experiment. This
experiment was repeated on three different occasions with
PDGFRa in NSCLC Tumor Samples
found in group 2 (Figure 3A) and also express FAK, Abl,
DDR1/2, and VEGF1/2 in addition to many other active ty-
rosine kinases. Similar to H1703 cells, these NSCLC tu-
skeleton proteins (Figure 3G), suggesting engagement of
ing a PDGFRa-specific antibody to screen NSCLC tumor
samples identified strong PDGFRa staining in 2%–3% of
alsodiffer fromthe report (Zhang etal.,2003) that100% of
NSCLC adenocarcinomas express PDGFRa. Amplifica-
tion at the PDGFRa locus was observed by fluorescence
in situ hybridization (FISH) analysis in one of the IHC-pos-
itive NSCLC samples (Figure S4H).
In this study we apply an unbiased and global phospho-
proteomic approach to the analysis of signaling defects
in lung cancer. The approach generates a deep and ex-
pansive view of tyrosine kinase activity and downstream
signaling networks. Because these studies represent an
early phase in the characterization of tyrosine kinase sig-
naling, most of the thousands of phosphotyrosine sites
identified are novel and their positions in cell circuits and
roles in regulating cell function have yet to be established.
Given these limitations, can we identify activated circuitry
associated with NSCLC and trace it to the kinases driving
tyrosine signaling across many different NSCLC samples
to identify and group samples based upon activated tyro-
Classifying Tumors by Activated Tyrosine Kinases
As a test, we first attempted to identify NSCLC tumors
driven by EGFR-activating mutations. By ranking EGFR
tyrosine kinase activity across cell lines and tumors, we
found that high EGFR rank dramatically enriched for
EGFR-activating mutations. Of 11 cell lines with high
rank, 5 contained known EGFR-activating mutations, and
of the 16 EGFR tumors from which we obtained sequence
information, 8/9 were adenocarcinomas and 9 contained
kinase domain-activating mutations. The remaining squa-
mous cell carcinoma (SCC) patients showing high EGFR
activity, perhaps with EGFR genomic amplifications,
should be further investigated and may represent a group
of candidates for EGFRI therapy.
Our finding that roughly half of the high ranking EGFR
cell lines and tumors carry EGFR-activating mutations
led us to group tumors based upon tyrosine kinase rank,
leading to the identification of tumors expressing kinases
activated above mean levels. The RTKs (Met, ALK, DDR1,
ROS, VEGFR-2, IGF1R, PDGFRa, EGFR, and Axl) and the
non-RTKs (FAK, LYN, FYN, HCK, FRK, BRK, and others
shown in Figure 3A) were found to be highly phosphory-
lated in NSCLC. Their role in the pathogenesis of NSCLC
is largely unexplored and deserves further investigation.
Recent large-scale cancer sequencing efforts (Greenman
1200 Cell 131, 1190–1203, December 14, 2007 ª2007 Elsevier Inc.
et al., 2007; Thomas et al., 2007) have identified mutations
inthe activation loop of manyof thesekinases, suggesting
roles as disease drivers.
One important observation from our study is that cell
lines and tumors often express many different activated
kinases (see Figure 3A, group 2) that may participate to-
gether in hierarchical networks or independently in sepa-
rate parallel pathways. A better understanding of these
relationships will help identify the best drug targets and
predict when multiple inhibitors may be necessary to drive
cell death and may also help predict drug resistance. For
example, HCC827 cell death induced by EGFR inhibitors
(Amann et al., 2005) can be overcome by amplification
of coexpressed c-Met (see Table S3) and is observed in
a fraction of patients that develop resistance to EGFRIs
(Engelman et al., 2007). A similar resistance mechanism
has recently beenobserved for c-Kit- and PDGFRa-driven
relapsed patients (Mahadevan et al., 2007). This suggests
that kinase switching might be a general problem in drug
resistance and knowledge of coexpressed RTKs may
help predict drug resistance and more effective combina-
tion drug therapies. Finally, our finding that most NSCLC
tumors express different combinations of active tyrosine
kinases suggests the need for individualized therapies.
The rank order of kinase activity may provide important
clues as to network relationships, drug sensitivities, and
drug resistance and when combined with information
about genetic and chromosomal abnormalities will help
guide future therapies.
Gene Fusions in Lung Cancer
A recent report (Soda et al., 2007) identified EML4-ALK
fusions in 6.7% of Japanese patients consistent with
growing evidence for chromosomal translocations in solid
tumors (Mitelman et al., 2004), particularly prostate can-
cer (Tomlins et al., 2005). Our MS/MS findings in the Chi-
nese population indicate a lower frequency (6/137 pa-
tients positive for ALK) of approximately 4.4%. Of these
6 patients, 3 were shown to have fusions with EML4 at
2 different breakpoints. One patient was shown to have
a fusion with TFG and very high ALK phosphorylation (Ta-
ble 2); for the remaining two patients we lacked sufficient
material to identify the fusion partner. ROS was found to
be activated by gene rearrangements resulting in novel
chimeric fusion proteins: SLC34A2-ROS, CD74-ROS.
Although ROS translocation and overexpression have
been previously identified in glioblastoma, to the best of
our knowledge ROS translocations have not been previ-
ously implicated in epithelial tumors (carcinomas) and
appear to be the first examples of transmembrane fusion.
PDGFRa is activated by kinase domain mutations in mes-
enchymal tumors of the GIST along with C-Kit but not in
other tumors where overexpression and autocrine or
paracrine activation of PDGFRa is observed, including in
glioblastoma and ovarian tumors. An important next
step will be to identify NSCLC patients with ALK-, ROS-,
and PDGFR-positive tumors and to determine their sensi-
tivity to selective tyrosine kinase inhibitors such as
The NSCLC tyrosine phosphorylation data sets reported
a unique view of tumor biology. The large number of acti-
intervene to block pathway and network activity using mul-
of the activatedtyrosinekinasesidentified here, but in each
case, we identified new disease mechanisms in a field well
studied by other methods. By combining multiple levels of
analysis including genetic, epigenetic, and signaling we
can begin to understand the formation of solid tumors and
how to identify and target their Achilles heel.
A detailed description of the experimental procedures can be found in
the Supplemental Data.
Cell Culture, Reagents, Western Blot,
and Immunoprecipitation Analysis
All cell culture reagents were purchased from Invitrogen. Human
NSCLC cell lines were obtained from American Type Culture Collec-
tion. ROS and phospho-PDGFRa antibodies were purchased from
Santa Cruz, all other antibodies were from Cell Signaling Technology
(CST). Western blot and Immunoprecipitation analyses were per-
formed following CST protocols.
Phosphopeptide Immunoprecipitation and Analysis
by LC-MS/MS Mass Spectrometry
Phosphopeptide immunoprecipitation from different cell lines was
performed as described previously(Rush et al., 2005) using the Phos-
phoScan Kit (P-Tyr-100) from CST.
Analysis of Phosphorylation Site Data Sets
To assign peptide sequences, we used the hash string-matching algo-
rithm implemented in Biofacet (Gene-IT) to search proteins in Phos-
phoSite. If the peptide sequence matched multiple proteins, the pro-
tein with the first accession number in alphabetical order was
chosen as a representative. For example, GASQAGM#TGY*GMPR
matches both SM22-alpha (P37802) and TAGLN3 (Q9UI15) and would
be assigned to SM22-alpha. For a few peptides, we manually chose
the best studied protein of a set to be the representative. In the case
of the peptide GEPNVSY*ICSR matching both GSK3a (P49840) and
GSK3b (P49841), GSK3b was assigned as the representative.
We counted the number of spectra observed for each peptide
sequence in a mass spectrometry run (Liu et al., 2004). Spectra were
subjected to the quality criteria described below (i.e., Supplemental
Experimental Procedures, LTQ-FT MS, Sequest searches, and Vista).
To calculate a protein spectrum count, we summed the numbers for
all of the peptides assigned to each protein in that run. Hierarchal clus-
tering was carried out using TIGR’s MeV program (Saeed et al., 2003)
with Pearson Correlation Distance and Average linkage clustering. The
number of times a given phosphoprotein was identified (sum of all
observed spectra assigned to that protein) was imported into MeV
and used to assemble heat maps. A protein with 15 or more phospho-
peptides is shown in red.
Cell 131, 1190–1203, December 14, 2007 ª2007 Elsevier Inc. 1201
For each patient sample, each protein’s spectral counts were nor-
malized to those for GSK3b, and the average count for that protein
over all tumors was subtracted.
Growth Inhibition Assay
Cell growth inhibition assay was performed with CellTiter 96 AQueous
One Solution Cell Proliferation Assay (Promega) according to manu-
Immunohistochemical Staining and Fluorescence
In Situ Hybridization
Supplemental Data include Supplemental Experimental Procedures,
four figures, and seven tables and can be found with this article online
We thank members of Cell Signaling Technology, Inc. (CST) for help
and fruitful discussions, especially Jessica Cherry, Corinne Michaud,
Katie Crosby, Yi Wang, Vicky Yang, and Jason Beaudet for technical
reading the manuscript; and Peter Hornbeck for critical review of the
manuscript.This paper is dedicated to the memories of Dr. Yi Tan,
Vice President of CST, and Dr. Qingfu Zeng, author and pathologist
at CST who passed at a young age from cancer.
Received: June 21, 2007
Revised: September 19, 2007
Accepted: November 1, 2007
Published: December 13, 2007
Amann, J., Kalyankrishna, S., Massion, P.P., Ohm, J.E., Girard, L.,
Shigematsu, H., Peyton, M., Juroske, D., Huang, Y., Stuart Salmon,
J., et al. (2005). Aberrant epidermal growth factor receptor signaling
and enhanced sensitivity to EGFR inhibitors in lung cancer. Cancer
Res. 65, 226–235.
Blume-Jensen, P., and Hunter, T. (2001). Oncogenic kinase signalling.
Nature 411, 355–365.
Charest, A., Wilker, E.W., McLaughlin, M.E., Lane, K., Gowda, R.,
Coven, S., McMahon, K., Kovach, S., Feng, Y., Yaffe, M.B., et al.
(2006). ROS fusion tyrosine kinase activates a SH2 domain-containing
phosphatase-2/phosphatidylinositol 3-kinase/mammalian target of
rapamycin signaling axis to form glioblastoma in mice. Cancer Res.
Chin, K., DeVries, S., Fridlyand, J., Spellman, P.T., Roydasgupta, R.,
Kuo, W.L., Lapuk, A., Neve, R.M., Qian, Z., Ryder, T., et al. (2006).
Genomic and transcriptional aberrations linked to breast cancer
pathophysiologies. Cancer Cell 10, 529–541.
Dowell, J.E., and Minna, J.D. (2005). Chasing mutations in the epider-
mal growth factor in lung cancer. N. Engl. J. Med. 352, 830–832.
Druker, B.J., Sawyers, C.L., Kantarjian, H., Resta, D.J., Reese, S.F.,
Ford, J.M., Capdeville, R., and Talpaz, M. (2001). Activity of a specific
inhibitor of the BCR-ABL tyrosine kinase in the blast crisis of chronic
myeloid leukemia and acute lymphoblastic leukemia with the Philadel-
phia chromosome. N. Engl. J. Med. 344, 1038–1042.
Engelman, J.A., Janne, P.A., Mermel, C., Pearlberg, J., Mukohara, T.,
Fleet, C., Cichowski, K., Johnson, B.E., and Cantley, L.C. (2005). ErbB-3
mediates phosphoinositide 3-kinase activity in gefitinib-sensitive non-
small cell lung cancer cell lines. Proc. Natl. Acad. Sci. USA 102,
Engelman, J.A., Zejnullahu, K., Mitsudomi, T., Song, Y., Hyland, C.,
Park, J.O., Lindeman, N., Gale, C.M., Zhao, X., Christensen, J., et al.
(2007). MET amplification leads to gefitinib resistance in lung cancer
by activating ERBB3 signaling. Science 316, 1039–1043.
Everley, P.A., Bakalarski, C.E., Elias, J.E., Waghorne, C.G., Beausoleil,
S.A., Gerber, S.A., Faherty, B.K., Zetter, B.R., and Gygi, S.P. (2006).
Enhanced analysisofmetastaticprostate cancerusingstableisotopes
and high mass accuracy instrumentation. J. Proteome Res. 5, 1224–
Ford, C.E., Lau, S.K., Zhu, C.Q., Andersson, T., Tsao, M.S., and Vogel,
W.F. (2007). Expression and mutation analysis of the discoidin domain
receptors 1 and 2 in non-small cell lung carcinoma. Br. J. Cancer 96,
Gilchrist, A., Au, C.E., Hiding, J., Bell, A.W., Fernandez-Rodriguez, J.,
Lesimple, S., Nagaya, H., Roy, L., Gosline, S.J., Hallett, M., et al.
(2006). Quantitative proteomics analysis of the secretory pathway.
Cell 127, 1265–1281.
Greenman, C., Stephens, P., Smith, R., Dalgliesh, G.L., Hunter, C.,
Bignell, G., Davies, H., Teague, J., Butler, A., Stevens, C., et al.
(2007). Patterns of somatic mutation in human cancer genomes.
Nature 446, 153–158.
Gu, T.L., Goss, V.L., Reeves, C., Popova, L., Nardone, J., Macneill, J.,
Walters, D.K., Wang, Y., Rush, J., Comb, M.J., et al. (2006). Phospho-
tyrosine profiling identifies theKG-1cell line as amodel for the study of
FGFR1 fusions in acute myeloid leukemia. Blood 108, 4202–4204.
Hernandez, L., Bea, S.,Bellosillo, B.,Pinyol, M.,Falini, B.,Carbone, A.,
Ott, G., Rosenwald, A., Fernandez, A., Pulford, K., et al. (2002). Diver-
sity of genomic breakpoints in TFG-ALK translocations in anaplastic
large cell lymphomas: identification of a new TFG-ALK(XL) chimeric
gene with transforming activity. Am. J. Pathol. 160, 1487–1494.
Hernandez, L., Pinyol, M., Hernandez, S., Bea, S., Pulford, K.,
Rosenwald, A., Lamant, L., Falini, B., Ott, G., Mason, D.Y., et al.
(1999). TRK-fused gene (TFG) is a new partner of ALK in anaplastic
large cell lymphoma producing two structurally different TFG-ALK
translocations. Blood 94, 3265–3268.
Hornbeck, P.V., Chabra, I., Kornhauser, J.M., Skrzypek, E., and
Zhang, B. (2004). PhosphoSite: A bioinformatics resource dedicated
to physiological protein phosphorylation. Proteomics 4, 1551–1561.
Hu, H., Bliss, J.M., Wang, Y., and Colicelli, J. (2005). RIN1 is an ABL
tyrosine kinase activator and a regulator of epithelial-cell adhesion
and migration. Curr. Biol. 15, 815–823.
Kinch, M.S., Moore, M.B., and Harpole, D.H., Jr. (2003). Predictive
value of the EphA2 receptor tyrosine kinase in lung cancer recurrence
and survival. Clin. Cancer Res. 9, 613–618.
T., Chen, Y.,Mitchell, R.A., and Bucala, R. (2003). MIF signal transduc-
tion initiated by binding to CD74. J. Exp. Med. 197, 1467–1476.
Liu, H., Sadygov, R.G., and Yates, J.R., 3rd. (2004). A model for
random sampling and estimation of relative protein abundance in
shotgun proteomics. Anal. Chem. 76, 4193–4201.
Lutterbach, B., Zeng, Q., Davis, L.J., Hatch, H., Hang, G., Kohl, N.E.,
Gibbs, J.B., and Pan, B.S. (2007). Lung cancer cell lines harboring
MET gene amplification are dependent on Met for growth and survival.
Cancer Res. 67, 2081–2088.
Lynch, T.J., Bell, D.W., Sordella, R., Gurubhagavatula, S., Okimoto,
R.A., Brannigan, B.W., Harris, P.L., Haserlat, S.M., Supko, J.G.,
Haluska, F.G., et al. (2004). Activating mutations in the epidermal
growth factor receptor underlying responsiveness of non-small-cell
lung cancer to gefitinib. N. Engl. J. Med. 350, 2129–2139.
Ma, P.C., Jagadeeswaran, R., Jagadeesh, S., Tretiakova, M.S.,
Nallasura, V., Fox, E.A., Hansen, M., Schaefer, E., Naoki, K., Lader,
1202 Cell 131, 1190–1203, December 14, 2007 ª2007 Elsevier Inc.
A., et al. (2005). Functional expression and mutations of c-Met and its
therapeutic inhibition with SU11274 and small interfering RNA in non-
small cell lung cancer. Cancer Res. 65, 1479–1488.
Mahadevan, D., Cooke, L., Riley, C., Swart, R., Simons, B., Della
Croce, K., Wisner, L., Iorio, M., Shakalya, K., Garewal, H., et al.
(2007). A novel tyrosine kinase switch is a mechanism of imatinib resis-
tance in gastrointestinal stromal tumors. Oncogene 26, 3909–3919.
Mahon, F.X., Deininger, M.W., Schultheis, B., Chabrol, J., Reiffers, J.,
Goldman, J.M., and Melo, J.V. (2000). Selection and characterization
kinase inhibitor STI571: diverse mechanisms of resistance. Blood 96,
Mazzocca, A., Coppari, R., De Franco, R., Cho, J.Y., Libermann, T.A.,
Pinzani, M., and Toker, A. (2005). A secreted form of ADAM9 promotes
carcinoma invasion through tumor-stromal interactions. Cancer Res.
Mellinghoff, I.K., Wang, M.Y., Vivanco, I., Haas-Kogan, D.A., Zhu, S.,
Dia, E.Q., Lu, K.V., Yoshimoto, K., Huang, J.H., Chute, D.J., et al.
(2005). Molecular determinants of the response of glioblastomas to
EGFR kinase inhibitors. N. Engl. J. Med. 353, 2012–2024.
Minami, Y., Shimamura, T., Shah, K., Laframboise, T., Glatt, K.A.,
Liniker, E., Borgman, C.L., Haringsma, H.J., Feng, W., Weir, B.A.,
et al. (2007). The major lung cancer-derived mutants of ERBB2 are on-
cogenic and are associated with sensitivity to the irreversible EGFR/
ERBB2 inhibitor HKI-272. Oncogene 26, 5023–5027.
Mitelman, F., Johansson, B., and Mertens, F. (2004).Fusion genes and
rearranged genes as a linear function of chromosome aberrations in
cancer. Nat. Genet. 36, 331–334.
Morris, S.W., Kirstein, M.N., Valentine, M.B., Dittmer, K.G., Shapiro,
D.N., Saltman, D.L., and Look, A.T. (1994). Fusion of a kinase gene,
ALK, to a nucleolar protein gene, NPM, in non-Hodgkin’s lymphoma.
Science 263, 1281–1284.
Neve, R.M., Chin, K., Fridlyand, J., Yeh, J., Baehner, F.L., Fevr, T.,
Clark, L., Bayani, N., Coppe, J.P., Tong, F., et al. (2006). A collection
of breast cancer cell lines for the study of functionally distinct cancer
subtypes. Cancer Cell 10, 515–527.
Old, W.M., Meyer-Arendt, K., Aveline-Wolf, L., Pierce, K.G., Mendoza,
A., Sevinsky, J.R., Resing, K.A., and Ahn, N.G. (2005). Comparison of
label-free methods for quantifying human proteins by shotgun proteo-
mics. Mol. Cell. Proteomics 4, 1487–1502.
Olsen, J.V., Blagoev, B., Gnad, F., Macek, B., Kumar, C., Mortensen,
P., and Mann, M. (2006). Global, in vivo, and site-specific phosphory-
lation dynamics in signaling networks. Cell 127, 635–648.
Ong, S.E., Blagoev, B., Kratchmarova, I., Kristensen, D.B., Steen, H.,
Pandey, A., and Mann, M. (2002). Stable isotope labeling by amino
acids in cell culture, SILAC, as a simple and accurate approach to
expression proteomics. Mol. Cell. Proteomics 1, 376–386.
Paez, J.G., Janne, P.A., Lee, J.C., Tracy, S., Greulich, H., Gabriel, S.,
Herman, P., Kaye, F.J., Lindeman, N., Boggon, T.J., et al. (2004).
EGFR mutations in lung cancer: correlation with clinical response to
gefitinib therapy. Science 304, 1497–1500.
Pao, W., Miller, V., Zakowski, M., Doherty, J., Politi, K., Sarkaria, I.,
Singh, B., Heelan, R., Rusch, V., Fulton, L., et al. (2004). EGF receptor
gene mutations are common in lung cancers from ‘‘never smokers’’
and are associated with sensitivity of tumors to gefitinib and erlotinib.
Proc. Natl. Acad. Sci. USA 101, 13306–13311.
Peduto, L., Reuter, V.E., Shaffer, D.R., Scher, H.I., and Blobel, C.P.
(2005). Critical function for ADAM9 in mouse prostate cancer. Cancer
Res. 65, 9312–9319.
Rush, J., Moritz, A., Lee, K.A., Guo, A., Goss, V.L., Spek, E.J., Zhang,
H.,Zha, X.M., Polakiewicz, R.D., and Comb,M.J. (2005).Immunoaffin-
ity profiling of tyrosine phosphorylation in cancer cells. Nat. Biotech-
nol. 23, 94–101.
Saeed, A.I., Sharov, V., White, J., Li, J., Liang, W., Bhagabati, N.,
Braisted, J., Klapa, M., Currier, T., Thiagarajan, M., et al. (2003).
TM4: a free, open-source system for microarray data management
and analysis. Biotechniques 34, 374–378.
Samuels, Y., and Velculescu, V.E. (2004). Oncogenic mutations of
PIK3CA in human cancers. Cell Cycle 3, 1221–1224.
Samuels, Y., Diaz, L.A., Jr., Schmidt-Kittler, O., Cummins, J.M.,
Delong, L., Cheong, I., Rago, C., Huso, D.L., Lengauer, C., Kinzler,
K.W., et al. (2005). Mutant PIK3CA promotes cell growth and invasion
of human cancer cells. Cancer Cell 7, 561–573.
Soda, M., Choi, Y.L., Enomoto, M., Takada, S., Yamashita, Y.,
Ishikawa, S., Fujiwara, S., Watanabe, H., Kurashina, K., Hatanaka,
H., et al. (2007). Identification of the transforming EML4-ALK fusion
gene in non-small-cell lung cancer. Nature 448, 561–566.
Stephens, P., Hunter, C., Bignell, G., Edkins, S., Davies, H., Teague, J.,
intragenic ERBB2 kinase mutations in tumours. Nature 431, 525–526.
Taguchi, Y., Kondo, T., Watanabe, M., Miyaji, M., Umehara, H.,
Kozutsumi, Y., and Okazaki, T. (2004). Interleukin-2-induced survival
of natural killer (NK) cells involving phosphatidylinositol-3 kinase-
dependent reduction of ceramide through acid sphingomyelinase,
sphingomyelin synthase, and glucosylceramide synthase. Blood 104,
Thomas, R.K., Baker, A.C., Debiasi, R.M., Winckler, W., Laframboise,
T., Lin, W.M., Wang, M., Feng, W., Zander, T., Macconnaill, L.E., et al.
(2007). High-throughput oncogenemutation profiling inhumancancer.
Nat. Genet. 39, 347–351.
Tomlins, S.A., Rhodes, D.R., Perner, S., Dhanasekaran, S.M., Mehra,
R., Sun, X.W., Varambally, S., Cao, X., Tchinda, J., Kuefer, R., et al.
(2005). Recurrent fusion of TMPRSS2 and ETS transcription factor
genes in prostate cancer. Science 310, 644–648.
Janne, P.A. (2004). Gefitinib induces apoptosis in the EGFRL858R
Ullrich, A., and Schlessinger, J. (1990). Signal transduction by
receptors with tyrosine kinase activity. Cell 61, 203–212.
Vogelstein, B., and Kinzler, K.W. (2004). Cancer genes and the
pathways they control. Nat. Med. 10, 789–799.
Walters, D.K., Mercher, T., Gu, T.L., O’Hare, T., Tyner, J.W., Loriaux,
M., Goss, V.L., Lee, K.A., Eide, C.A., Wong, M.J., et al. (2006). Activat-
ing alleles of JAK3 in acute megakaryoblastic leukemia. Cancer Cell
Weinstein, I.B. (2002). Cancer. Addiction to oncogenes–the Achilles
heal of cancer. Science 297, 63–64.
Wolf-Yadlin,A.,Kumar, N.,Zhang, Y.,Hautaniemi, S.,Zaman,M.,Kim,
H.D., Grantcharova, V., Lauffenburger, D.A., and White, F.M. (2006).
Effects of HER2 overexpression on cell signaling networks governing
proliferation and migration. Mol. Syst. Biol. 2, 54.
Zhang, P., Gao, W.Y., Turner, S., and Ducatman, B.S. (2003). Gleevec
(STI-571) inhibits lung cancer cell growth (A549) and potentiates the
cisplatin effect in vitro. Mol. Cancer 2, 1.
Zybailov, B., Coleman, M.K., Florens, L., and Washburn, M.P. (2005).
Correlation of relative abundance ratios derived from peptide ion chro-
matograms and spectrum counting for quantitative proteomic analysis
using stable isotope labeling. Anal. Chem. 77, 6218–6224.
The nucleotide sequences of CD74-ROS: EU236945, SLC34A2-ROS
(long): EU236946, SLC34A2-ROS (short): EU236947, EML4-ALK:
EU236948 and protein sequences CD74/ROS: ABX59671, SLC34A2/
ROS fusion protein long isoform: ABX59672 , SLC34A2/ROS fusion
protein short isoform: ABX59673, EML4/ALK: ABX59674 were depos-
ited in GenBank.
Cell 131, 1190–1203, December 14, 2007 ª2007 Elsevier Inc. 1203