capmatinib (inc280) is active against models of non-small cell … · biomarkers of response are...

Capmatinib (INC280) Is Active Against Models of Non–Small Cell Lung Cancer

and Other Cancer Types with Defined Mechanisms of MET Activation

Sabrina Baltschukat1, Barbara Schacher Engstler

1, Alan Huang

2*, Huai-Xiang Hao

2, Angela Tam

2, Hui-

Qin Wang2, Jinsheng Liang

2, Matthew T. DiMare

2, Hyo-Eun Carrie Bhang

2, Youzhen Wang

2,

Pascal Furet3, William R. Sellers

2*, Francesco Hofmann

1, Joseph Schoepfer

3, and Ralph Tiedt

1

1Novartis Institutes for BioMedical Research, Oncology Disease Area, Basel, Switzerland.

2Novartis

Institutes for BioMedical Research, Oncology Disease Area, Cambridge, Massachusetts, USA. 3Novartis

Institutes for BioMedical Research, Global Discovery Chemistry, Basel, Switzerland.

*Current address:

Alan Huang, Tango Therapeutics, 100 Binney Street, Suite 700, Cambridge, MA 02142, USA.

William R. Sellers, Broad Institute, 415 Main Street, Cambridge, MA 02142, USA.

Corresponding Author: Ralph Tiedt, Novartis Institutes for Biomedical Research, Klybeckstrasse 141,

4057 Basel, Switzerland. Phone Number: +41 79 572 14 80; Fax Number: +41 61 696 62 42;

E-mail: [email protected]

Running title (character limit [with spaces]: 60): Preclinical Profile of the MET Inhibitor Capmatinib

Keywords: lung cancer, tyrosine kinase inhibitors, biomarkers

Funded by: These studies were sponsored by Novartis.

Disclosure of potential conflict of interest: S. Baltschukat and B. Schacher Engstler are employees

and shareholders of Novartis. A. Huang was employee of Novartis during the conduct of the study. W.R.

Sellers was employee of Novartis during the conduct of the study and is a current shareholder. He is also

a Board member, SAB member and shareholder of Peloton Therapeutics; a SAB member and

shareholder of Ideaya Biosciences; a SAB member of Epidarex Capital and has consulted for Array

Pharmaceuticals, Astex Pharmaceuticals, Ipsen, Servier, and Sanofi. H.-X. Hao, A. Tam, J. Liang, M.T.

Research. on March 19, 2020. © 2019 American Association for Cancerclincancerres.aacrjournals.org Downloaded from

Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on January 23, 2019; DOI: 10.1158/1078-0432.CCR-18-2814

mailto:[email protected]

http://clincancerres.aacrjournals.org/

DiMare, H.C. Bhang, Y. Wang, P. Furet, F. Hofmann, and J. Schoepfer are employees of Novartis. H.-Q.

Wang is an employee of Novartis and has a patent CA2879704 issued. R. Tiedt is an employee and

shareholder of Novartis and is named inventor on the patents WO/2013/149581 and WO/2013/151913.




Translational Relevance

The clinical development of MET inhibitors has been challenging as is indicated by several failed

clinical trials. Contributing factors likely include the use of non-selective agents, for which predictive

biomarkers of response are difficult to identify, as well as the failure to implement a stringent biomarker-

based patient selection strategy during the development of selective MET-targeting agents. The activity of

the highly selective and potent MET inhibitor capmatinib is associated with a small set of specific genomic

parameters. This insight has given rise to a series of single agent and combination trials of capmatinib in

lung cancer and other cancer indications that are guided by these potential predictive biomarkers. The

underlying preclinical data are described in this paper.




Abstract

Purpose: The selective MET inhibitor capmatinib is being investigated in multiple clinical trials,

both as a single agent and in combination. Here, we describe the preclinical data of capmatinib that

supported the clinical biomarker strategy for rational patient selection.

Experimental Design: The selectivity and cellular activity of capmatinib were assessed in large

cellular screening panels. Antitumor efficacy was quantified in a large set of cell line- or patient-derived

xenograft models, testing single agent or combination treatment depending on the genomic profile of the

respective models.

Results: Capmatinib was found to be highly selective for MET over other kinases. It was active

against cancer models that are characterized by MET amplification, marked MET overexpression, MET

exon 14 skipping mutations, or MET activation via expression of the ligand hepatocyte growth factor

(HGF). In cancer models where MET is the dominant oncogenic driver, anticancer activity could be further

enhanced by combination treatments, for example, by the addition of apoptosis-inducing BH3 mimetics.

The combinations of capmatinib and other kinase inhibitors resulted in enhanced anticancer activity

against models where MET activation co-occurred with other oncogenic drivers, for example EGFR

activating mutations.

Conclusions: Activity of capmatinib in preclinical models is associated with a small number of

plausible genomic features. The low fraction of cancer models that respond to capmatinib as a single

agent suggests that the implementation of patient selection strategies based on these biomarkers is

critical for clinical development. Capmatinib is also a rational combination partner for other kinase

inhibitors to combat MET-driven resistance.




Introduction

A plethora of preclinical and clinical observations spanning several decades has

established the receptor tyrosine kinase (RTK) MET (c-Met, cMET, or c-MET) as an oncogene

and attractive therapeutic target for cancer therapy (1). Alterations of MET that are thought to be

oncogenic include activating mutations, overexpression, gene amplification, and translocations.

Furthermore, MET is aberrantly activated in cancer through its only ligand hepatocyte growth

factor (HGF). Based on these observations, numerous agents targeting MET or HGF have been

discovered and clinically developed to various stages (2). However, the establishment of

predictive biomarkers for efficient clinical development of such agents has proven challenging

(3). One factor impeding progress in this area is that some clinically studied agents are not MET

selective. For example, tivantinib was initially described as a selective MET inhibitor, while later

studies revealed that it also acts as a microtubule-disrupting agent, substantially complicating

the interpretation of clinical data (4). Likewise, several multikinase inhibitors such as

cabozantinib inhibit multiple relevant cancer targets along with MET, such as vascular

endothelial growth factor 2 (VEGFR2) (5), making it difficult to dissect the contribution of MET

inhibition to any observed effects. In addition, multiple mechanisms of MET activation (including

mutation, amplification, overexpression, ligand-mediated activation) have been associated with

MET dependency in the preclinical literature, some of which are overlapping. Thus, evaluation

of multiple biomarkers and definition of appropriate cut-offs is required to predict response to

MET inhibitor.

Crizotinib was among the first MET kinase inhibitors that helped gain a clearer

understanding of the therapeutic potential of MET inhibition, because its other primary targets

such as anaplastic lymphoma kinase (ALK) and ROS1 are only relevant in rare and

translocation-defined cancers that generally do not overlap with cancers in which MET is the

dominant oncogenic driver (6). Meanwhile, the clinical activity of crizotinib in MET activated lung




cancer is well documented, and the acquisition of MET resistance mutations in initially

responsive tumors demonstrated conclusively that this activity was indeed due to MET inhibition

(7-9).

Capmatinib (INC280, formerly INCB28060) is a highly selective and potent MET inhibitor

with in vitro and in vivo activities against preclinical cancer models with MET activation (10).

Capmatinib is being tested both as a single agent and in combination in multiple clinical trials

that are guided by biomarker-based patient selection criteria. Here, we further elaborate on the

preclinical profile of capmatinib and describe data guiding the clinical biomarker strategy.




Materials and Methods

Compounds

Capmatinib hydrochloride (2-fluoro-N-methyl-4-(7-(quinolin-6-ylmethyl)imidazo[1,2-

b][1,2,4]triazin-2-yl)benzamide dihydrochloride monohydrate, C23H17FN6O.2ClH.H2O) was

synthesized at Novartis. All other compounds were obtained from commercial sources.

High-throughput cell line screen

All cell lines were obtained from commercial sources and screened for compound

sensitivity in the context of the Novartis/Broad Institute Cancer Cell Line Encyclopedia project

(11). The details can be found in the Supplementary Materials and Methods.

Quantification of live and dead EBC-1 and NCI-H1993 cells

Cells were seeded at 2000 cells per well in 96-well plates in 100 L per well and

incubated for 24 hours at 37°C in 5% CO2. Capmatinib was then added from a 10 mM dimethyl

sulfoxide (DMSO) stock solution using a HP D300 Digital Dispenser (Tecan). After 5 days of

incubation, Hoechst 33342 and propidium iodide were added to the culture medium at final

concentrations of 1 µg/mL and 2 µg/mL, respectively, and incubated for 45 minutes at 37°C and

5% CO2. The number of Hoechst 33342-stained nuclei and propidium iodide-stained dead cells

was then quantified following image acquisition on a Cellomics VTi automated

immunofluorescence microscope (ThermoFisher) using the appropriate excitation/emission filter

sets.

Animals and maintenance conditions

For all studies, animals were housed in a 12-hour light/dark cycle facility and had access

to food and water ad libitum. Mice were maintained and handled in accordance with Novartis

Institutes for BioMedical Research (NIBR) Institutional Animal Care and Use Committee (IACUC)

regulations and guidelines. All studies were approved by the NIBR IACUC.

Drug combination dose-response matrix




A detailed description of experimental procedures and calculations can be found in the

Supplementary Materials and Methods. In brief, dose matrices were set up in multiwell plates

(96 or 384) using a HP D300 Digital Dispenser. Wells were DMSO normalized and randomized

to avoid systematic position effects. After incubation with the drugs, effects were quantified

either by staining with propidium iodide and Hoechst 33342 or by CellTiter-Glo (CTG, Promega)

including a readout for untreated cells (“day 0”). Both methods allowed to quantify the extent of

cell killing in the respective experiments.

Modeling of the structure of capmatinib bound to the MET kinase domain

A model of capmatinib bound to the ATP site was constructed based on the crystal

structure of MET in complex with 6-(difluoro(6-(4-fluorophenyl)-[1,2,4]triazolo[4,3-b][1,2,4]triazin-

3-yl)methyl)quinoline (PDB code: 5EOB) (12) representative of the binding mode of the class of

highly selective MET inhibitors, to which capmatinib belongs. In this binding mode, the

imidazotriazine core of capmatinib makes an aromatic stacking interaction with MET residue

Y1230 while its quinoline moiety interacts with the hinge region of the kinase. The stacking

interaction is made possible by a particular conformation of the kinase activation loop (A-loop)

stabilized by a salt bridge between residues D1228 and K1110. An intramolecular hydrogen

bond between the amide nitrogen and the fluoro atom of capmatinib is postulated. Additional

information can be found in the Supplementary Materials and Methods.




Results

Capmatinib is highly selective for MET compared to other kinases

Capmatinib (Fig. 1A) had previously been screened against 57 human kinases and was

found to be selective for MET within this panel (10). To extend this kinase selectivity profiling,

we measured the affinity of capmatinib in a set of 442 kinases and disease-relevant variants

using the KINOMEscan selectivity screening platform. At a screening concentration of 10 M,

which is more than a 1000-fold above the reported on-target IC50 in biochemical assays (10), 9

kinases scored as hits with the predefined cutoff of ≥ 65% reduction in binding to the capture

matrix compared to a vehicle control (Fig. 1B). These hits included MET and 2 mutant variants

thereof. Given that the kinase panel was screened at a concentration of capmatinib that is much

higher than its active concentration against MET, we determined the binding constants (Kd) for

all 9 hits (Fig. 1C). The Kd values for MET and 2 mutant variants were sub-nanomolar, and

were lower by a factor of approximately 1000 or more compared to all other hits. Of note, the

MET mutations M1250T and Y1235D did not have a notable impact on capmatinib binding. In

summary, these data confirm that capmatinib is a highly selective MET inhibitor.

High selectivity of capmatinib is explained by its binding mode to MET

Structural modeling of the MET kinase domain bound with capmatinib revealed that the

phenol moiety of Y1230 directly binds to the central aromatic ring of capmatinib in a pi stacking

interaction, while D1228 forms a salt bridge with K1110 that stabilizes the MET activation loop in

a conformation that is necessary to support the Y1230 – capmatinib interaction (Fig. 1D). This

binding interaction is similar to crizotinib and other selective MET inhibitors, and although Y1230

and D1228 are conserved in other tyrosine kinases such as IGF1-R and KDR, the required

conformation of the activation loop is also stabilized by multiple hydrophobic interactions

between residues of the activation loop and residues of helix C that are specific to the MET

kinase (13,14). To validate the structural model experimentally, we made use of a panel of BaF3




cells transformed with TPR-MET constructs bearing MET kinase domain mutations. Some of

these mutants had been obtained in an unbiased cellular resistance screen with a selective

MET inhibitor that is structurally related to capmatinib (13). As expected, significant resistance

was observed when BaF3 cells bearing MET D1228 and Y1230 mutations were treated with

capmatinib, while much smaller shifts in the IC50, if any, were seen with other variants (Fig. 1E

and Supplementary Table 1). These observations are in line with the proposed structural model

of the MET-capmatinib interaction. Importantly, recent clinical case reports documented MET

D1228 or Y1230 mutations in lung cancers with acquired resistance to MET inhibitors (7-9,15).

MET amplification and HGF expression are associated with capmatinib sensitivity

in vitro

MET gene amplification, leading to overexpression and autophosphorylation of the MET

protein, has been linked to MET inhibitor sensitivity in cell lines (16-19). In addition, response to

capmatinib has also been reported in 2 preclinical models that express both MET and its ligand

HGF (10). To assess predictors of response to capmatinib in an unbiased and systematic

manner, we tested the activity of capmatinib against more than 600 well-characterized cancer

cell lines in the Cancer Cell Line Encyclopedia (CCLE) project (11). Cell line screens were

conducted twice independently in a high throughput format, where dose-response curves were

generated for capmatinib after a 3-day incubation period. After quality control, we obtained

interpretable results for a total of 605 cell lines (458 in the first screen and 364 in the second

screen, with an overlap of 217 cell lines, Supplementary Table 2). We considered both the

maximal effect (Amax) and the EC50 (inflection point) of the fitted sigmoid dose-response curve to

determine sensitivity (Supplementary Fig. 1A). With a low stringency (Amax ≤ −25% and inflection

point ≤ 100 nM), we observed a total of 13 responders or partial responders among all tested

cell lines (Fig. 2A). The 2 screens were largely concordant in terms of capmatinib response for

the 217 cell lines tested in both occasions, with the exception of two cell lines that scored as




modestly sensitive in one screen and completely resistant in the other. Interestingly, all

responsive cell lines except these 2 discordant lines were characterized by 1 of 2 genomic

profiles: (1) MET gene amplification, leading to pronounced MET mRNA overexpression (Fig.

2B) or (2) high expression of the MET ligand HGF (Fig. 2C). The expression of HGF by cancer

cell lines may be indicative of an autocrine loop that activates MET in these cells. Indeed, we

found a good correlation between HGF mRNA expression and the amount of HGF protein in cell

culture supernatants (Supplementary Fig. 1B). Four of the 7 cell lines in the autocrine category

were derived from glioblastoma, presumably related to the observation that glioblastoma shows

frequent gain of chromosome 7 regions encompassing both MET and HGF (20).

Only 2 MET-amplified cell lines with known dependence on MET (17,19) displayed

profound responses to capmatinib (Amax close to −100%) at low concentrations (inflection point

< 10 nM). All HGF-expressing cell lines and 2 of the MET-amplified cell lines showed partial

responses (Amax > −60%). In some of the cell lines expressing HGF, the dose-response curve

was very shallow, suggesting only a moderate reduction in growth upon MET inhibition under

the screening conditions.

To investigate whether these observations are generalizable to selective MET inhibitors,

we combined the CCLE screening results of capmatinib with results from 3 other MET inhibitors

in the same screening format, each tested twice independently like capmatinib: crizotinib, JNJ-

38877605 (2), and PF-4217903 (14). The latter 2 compounds are highly selective MET inhibitors

with chemical structures similar to capmatinib. For crizotinib, cellular activity explainable by ALK

translocations was disregarded for this combined analysis. An overall number of 709 cell lines

could be analyzed that had been tested in more than 1 screen. Sensitive cell lines (“hits”) were

scored as for capmatinib, but adapting the inflection point cutoff to the relatively lower potency

of the other inhibitors. A total of 16 hits were observed that scored with more than 1 MET

inhibitor and included 10 of the hits previously identified with capmatinib alone (overall hit rate

16/709 = 2%; Supplementary Fig. 1C and Supplementary Table 3). All hits were associated with




high expression and/or copy number of MET or they co-expressed MET and HGF. When

defining thresholds for those biomarkers guided by the hit with the respective lowest value, we

noted that the hit rate among cell lines with high MET copy number (amplified) was relatively

high (4/6 = 67%), followed by cell lines showing MET overexpression (5/9 = 56%, 4 of these 5

also amplified), suggesting that these biomarkers, which are largely overlapping, might be

suitable predictive markers for a selective MET inhibitor (Supplementary Fig. 1C). Conversely,

among the cell lines with MET/HGF co-expression (putative autocrine), the hit rate was lower

(11/32 = 34%), which could be due to at least 2 factors: (1) maximal growth inhibition in this

category was mostly modest, which makes detection in a high-throughput screen less likely. (2)

HGF-mediated MET activation does not lead to MET-dependent growth in a fraction of these

cell lines.

Clinically, response to MET inhibitors has been observed in lung cancer patients whose

tumors contained mutations leading to MET exon 14 skipping (21). In our tested cell line panel,

2 models contained such mutations: the gastric cancer cell line Hs 746.T and the lung cancer

cell line NCI-H596. Hs 746.T responded to capmatinib treatment in vitro, but MET is also highly

amplified in this cell line. Thus, it is difficult to assess the contribution of MET exon 14 skipping

to capmatinib sensitivity in this model. NCI-H596 cells were resistant to MET inhibition in vitro.

However, in this cell line, we observed more persistent MET phosphorylation in response to

HGF stimulation (Supplementary Fig. 1D), which is consistent with the reported functional

consequence of MET exon 14 deletion (22).

Associated genomic features of capmatinib sensitivity are recapitulated by the

MET-dependency profile in genetic screens

Dependency on MET was evaluated genetically in a large-scale pooled short hairpin

RNA (shRNA) screen across 398 cell lines interrogating cell-autonomous dependencies of 7837

genes each targeted by 20 shRNAs (23). As in the screen with capmatinib, only the 2 MET-




amplified cell lines EBC-1 and MKN-45 showed strong dropout that was clearly distinct from the

rest of the screened cell lines (Fig. 2D). Autocrine lines were enriched among the cell lines with

MET-dependent growth, but the signal was less pronounced. No clear dependencies were

detected upon HGF knockdown (data not shown). This is generally expected for genes

encoding secreted factors, since in a pooled shRNA screening format only a tiny fraction of cells

will bear shRNAs that target HGF, with negligible impact on the total level of HGF protein in the

cell culture medium.

Combining our pooled shRNA screening data with 2 additional published screens

strengthened the link between MET amplification and MET dependency (Supplementary Fig.

2A). Interestingly, a publicly available genome-wide CRISPR screen revealed a marked

MET-dependency signal for several cancer cell lines expressing HGF, unlike the RNAi data sets

(Supplementary Fig. 2B). This finding recapitulates the previously observed responses to

capmatinib and other MET inhibitors seen in autocrine cell lines. Conversely, the apparent MET

dependency of MET-amplified cell lines was much less pronounced in the CRISPR screen,

which is likely explained by the need to computationally adjust dependency scores for amplified

genes (24,25). Conversely, the more sensitive detection of dependencies in HGF-expressing

cell lines may be related to a superior signal-to-noise ratio of CRISPR vs RNAi, enabling the

detection of more subtle effects on growth.

In summary, all genetic MET dependencies and responses of cell lines to capmatinib

and other selective MET inhibitors can be explained by either very strong MET overexpression,

mostly as a consequence of MET gene amplification or by co-expression of MET and its ligand

HGF.

Capmatinib is active against cell line-derived and patient-derived xenograft

models with MET-activating alterations including exon 14 skipping mutation




The MET-amplified lung cancer cell line EBC-1 was found to be exquisitely sensitive to

MET inhibition in our cellular screens. This was confirmed by measuring the impact of a diverse

series of clinically relevant MET inhibitors on proliferation of this cell line (Supplementary

Fig. 3A). Each of the MET inhibitors caused profound inhibition of proliferation though with

different potencies.

We then confirmed the capmatinib sensitivity of the EBC-1 cell line in vivo (Fig. 3A).

Remarkably, even large EBC-1 xenograft tumors underwent pronounced regression upon

treatment. To further characterize the activity of capmatinib in lung cancer in vivo, we first

analyzed the Novartis patient-derived xenograft models (PDX) collection (26), but did not

identify any lung cancer models with MET amplification or exon 14 skipping mutations (data not

shown). Therefore, we turned to an external well-annotated PDX collection (27) of 66 lung

cancer PDX models with gene expression data (by Affymetrix HG U133 plus 2.0 array and

RNA-seq), gene copy number (by Affymetrix SNP 6.0 array) and whole exome sequencing data

(Supplementary Table 4). The measurements of MET mRNA expression by Affymetrix array

and RNA-seq were in excellent agreement, and we chose the 3 lung adenocarcinoma models

with highest MET expression for further studies (Supplementary Fig. 3B). High total and

phospho-MET protein levels had also been observed for 2 of those models (27). Interestingly,

MET gene copy numbers were more distinct, with high-level amplification in 2 models (14 and

11 copies in LXFA 526 and LXFA 1647, respectively, as part of 1-2 Mb amplicons) and only

moderate, very broad copy number gain in the third model (LXFA 623; Supplementary Fig. 3C).

This constellation enabled us to investigate whether high MET expression in the absence of

amplification could be sufficient to predict response to capmatinib. Indeed, all 3 models

underwent profound regression upon MET inhibition with capmatinib (Fig. 3B), including

complete responses in a subset of mice for 2 models (Supplementary Fig. 3D). Treatments

were well tolerated as far as determined by body weight monitoring (Supplementary Fig. 3E).




However, all tumors grew back after cessation of treatment on day 21, indicating persistent

disease.

The pharmacodynamic effect of capmatinib was measured at the end of the study by

quantifying total MET and phospho-MET in tumor lysates using a multispot ELISA. LXFA 623

tumors showed markedly lower total and phospho-MET levels than the 2 MET-amplified models

(Fig. 3C and Supplementary Fig. 3F). MET inhibition was clearly detectable at 2 hours after the

last dosing, with some degree of phospho-MET recovery in 2 out of 3 models at 12 hours after

dosing.

In a third PDX model collection, a lung cancer model named LU5381 with MET exon 14

skipping mutation and moderate MET copy number gain (~5) was identified, thus dissociating

MET exon 14 skipping from high-level MET amplification. When treating mice bearing LU5381

xenografts with capmatinib, we observed tumor regression (Fig. 3D and Supplementary Fig. 3G).

Notably, capmatinib was also active against a liver cancer xenograft model, in which the MET

gene is amplified (16) (Fig. 3E).

In vivo activity of capmatinib is observed in autocrine models

In the in vitro screens, putative autocrine cell lines generally showed relatively subtle

responses to capmatinib treatment (Fig. 2A and 2C). Yet, the in vivo response of xenografts

derived from such models was much more dramatic, as exemplified by the glioblastoma cell line

U87-MG (10). Thus, experimental conditions can have a strong impact on the apparent

sensitivity of such preclinical models. Regression of additional MET/HGF autocrine glioblastoma

xenografts in response to MET inhibitors had been reported previously (28). When we treated

xenografts of the gastric cancer cell line IM95, which expresses higher levels of HGF mRNA

than U87-MG and produced comparable amounts of HGF as detected in cell culture

supernatants (Supplementary Fig. 1C), a significant growth reduction but no regression was

observed (Supplementary Fig. 3F). This result confirms that HGF-expressing cancer models




can show pronounced responses to capmatinib in vivo, but the level of HGF expression does

not appear to be sufficient to make quantitative predictions about response depth.

Impact of capmatinib on viability in MET-amplified EGFR wild type lung cancer

cell lines can be enhanced by combinations

We analyzed the response of 2 MET-amplified lung cancer cell lines EBC-1 and NCI-

H1993 (17) to capmatinib in more detail, aiming to distinguish growth arrest from cell death. To

this end, we quantified total and dead cells by automated microscopy using specific fluorescent

dyes. Interestingly, EBC-1 cells displayed a markedly higher rate of cell death upon capmatinib

treatment, albeit not reaching 100%, while the effect in NCI-H1993 was largely restricted to

inhibition of proliferation (Fig. 4A). This observation indicates that the reductions of growth and

viability following MET inhibition are not always strictly coupled. Next, we studied the effect of

MET inhibition on cellular signaling in these 2 MET-amplified lung cancer cell lines. As expected,

MET phosphorylation as well as phosphorylation of AKT and ERK were suppressed at low

single-digit nanomolar concentrations of capmatinib in both cell lines (Fig. 4B). In line with the

effects on cellular proliferation, suppression of protein phosphorylation occurred at slightly lower

concentrations in EBC-1 than in NCI-H1993, but the maximally achievable effects were

comparable. Thus, the cellular phosphorylation events studied here do not provide an obvious

explanation for the observed differences in cell death upon capmatinib treatment.

Intrigued by the observation that capmatinib arrests growth of MET-amplified NCI-H1993

cells but failed to induce cell death, we tried to improve this outcome using combination

treatments. We reasoned that co-targeting members of the BCL2 family of antiapoptotic proteins

might be a good starting point. We used previously described selective inhibitors of BCL2,

BCL2L1 (BCL-xL), or MCL1 (29-31) and combined them with capmatinib in a concentration

matrix followed by direct quantification of cell death using propidium iodide and Hoechst 33342

staining. Combined inhibition of MET and either MCL1 or BCL2L1 led to synergistic killing of a




substantial fraction of cancer cells (Fig. 4C and Supplementary Fig. 4A), while combined BCL2

inhibition was inactive (Supplementary Fig. 4A). Yet, under the tested conditions not all cancer

cells were killed even with combination treatment. We also examined the effect of the same

combinations in EBC-1 cells, although in those cells capmatinib on its own is already inducing

pronounced cell death. Interestingly, however, the fraction of dead cells was further increased

by concomitant MCL1 or BCL2L1 inhibition (Supplementary Fig. 4B).

The combination of a selective MET inhibitor with the microtubule-stabilizing

chemotherapeutic docetaxel was found to be active against MET-amplified gastric cancer

models (32). Independently, we observed during a systematic combination screen that

docetaxel and chemotherapeutics with related mode of action were active in combination with

the EGFR tyrosine kinase inhibitor nazartinib in EGFR-mutant lung cancer models (manuscript

in preparation). Therefore, we tested the combination of capmatinib and docetaxel in the 2

available MET-amplified lung cancer cell lines, EBC-1 and NCI-H1993 (Fig. 4D and

Supplementary Fig. 4C). In both cell lines, a synergistic boost of cell killing was observed. The

EGFR inhibitor erlotinib had previously been reported to prevent outgrowth of resistant EBC-1

cells upon prolonged MET inhibition (33). In line with this report, the combined treatment of

EBC-1 cells with erlotinib and capmatinib further increased cell killing similar to the docetaxel

combination (Supplementary Fig. 4D), while the added benefit of erlotinib against NCI-H1993

was modest (data not shown). In summary, the activity of capmatinib against MET-amplified

tumors can be further enhanced by several combination partners with distinct mode of action.

Capmatinib can revert MET-driven resistance to other kinase inhibitors

While cancer models that depend primarily on MET alone are relatively infrequent (Fig.

2A and Supplementary Fig. 1C), MET has also been reported to cause acquired or adaptive

resistance to other targeted therapies, which is the basis for an important additional clinical

application of MET inhibitors. For example, in lung cancer with EGFR activating mutations, the

activation of MET can bypass EGFR dependency, causing resistance to EGFR inhibitors. This




was first discovered in an EGFR-mutant lung cancer cell line named HCC827, which contains a

minute fraction of MET-amplified subclones that grow out under treatment with EGFR inhibitors

(34,35). The clinical relevance of this resistance mechanism has hence been confirmed in

numerous clinical studies.

Using parental HCC827 cells and gefitinib-resistant derivatives (GR) bearing MET

amplification, we confirmed that capmatinib can revert gefitinib resistance in the GR variant in

vitro, while not adding to the effect of gefitinib in parental cells (Supplementary Fig. 5A).

Capmatinib also had a subtle but measurable effect on the growth of HCC827 GR cells as a

single agent. Interestingly, when testing the same combination in vivo using HCC827 GR

xenografts, we observed a relatively strong antitumor effect of capmatinib even as a single

agent, leading to stasis for more than 3 weeks until tumors started to progress again (Fig. 5A).

However, combination treatment led to profound and sustained tumor regression. Similar results

were obtained when treating a MET-activated HCC827 xenograft derivative with a combination

of capmatinib and the third-generation EGFR inhibitor nazartinib (EGF816) (36). Besides MET

amplification, activation of MET via its ligand HGF has been proposed as another potential

mechanism of resistance to EGFR inhibitors in lung cancer (37), and indeed we confirmed that

addition of exogenous HGF to 2 EGFR-mutant lung cancer cell lines could substantially reduce

growth inhibition by gefitinib (Supplementary Fig. 5B).

We hypothesized that – analogous to EGFR-mutant lung cancer – MET may also drive

resistance to ALK inhibition in ALK-translocated lung cancer. While this potential resistance

mechanism is not expected in patients treated with the dual MET/ALK inhibitor crizotinib, it may

be relevant in patients treated with second-generation selective ALK inhibitors. In support of this

hypothesis, we noted in our PDX collection a lung cancer model with EML4-ALK translocation

that expressed very high MET mRNA levels without MET amplification, and high phospho-MET

protein levels (Supplementary Fig. 5C). While this model was responsive to crizotinib




(Supplementary Fig. 5D), it did not respond to the second-generation ALK inhibitor ceritinib, but

regressed when ceritinib was combined with capmatinib (Fig. 5B).

The ability of HGF to diminish the effect of kinase inhibition through MET activation has

also been described in several other contexts beyond EGFR-mutant lung cancer (38-40). For

example, HGF can reduce the effect of ERBB2 inhibition in ERBB2-amplified cancers. In

keeping with a previous report (40), we observed no or partial rescue by exogenous HGF in 4

ERBB2-amplified breast cancer cell lines (data not shown). In an ERBB2-amplified lung and

gastric cancer cell line, however, which were both sensitive to lapatinib, the effect of HGF was

more pronounced, in particular by enhancing overall growth but also reducing the maximal

inhibitory effect of lapatinib (Fig. 5C). Interestingly, the esophageal cancer cell line OE33, which

is MET-amplified and partially sensitive to capmatinib (Fig. 2A), also displays ERBB2

amplification and high ERBB2 mRNA expression, suggesting that both RTKs could be activated

(41). In support of this hypothesis, combined treatment with capmatinib and lapatinib resulted in

more pronounced growth inhibition than either single agent alone (Fig. 5D).

Another example where HGF was reported to drive resistance is BRAF-mutant melanoma

treated with BRAF inhibitors (39,40). While HGF may be produced by noncancer cells in the

tumor microenvironment, such as cancer-associated fibroblasts, we also identified a BRAF-

mutant colorectal cancer (CRC) cell line RKO where autocrine MET activation may play a role:

the modest growth inhibitory effect upon targeting mutant BRAF signaling with dabrafenib plus

trametinib in these cells could be enhanced by capmatinib treatment, albeit not to an extent that

resulted in cell killing (Supplementary Fig. 5E).

In summary, activation of MET, either by direct alterations of the MET gene itself or

through HGF, can cause resistance to various kinase inhibitors, which may substantially expand

the clinical utility of a MET inhibitor like capmatinib in combination therapies.




Discussion

Systematic screening across broad cancer cell line panels revealed that sensitivity to the

selective and highly potent MET inhibitor capmatinib and/or genetic MET dependency can be

explained by distinct mechanisms of MET activation that could serve as predictive biomarkers.

Among those, MET amplification and pronounced MET overexpression were associated with

robust sensitivity to capmatinib in vitro and in vivo. The percentage of models with these two

MET-activating features is low across cancer types, indicating that a very stringent patient

selection approach might be needed in contrast to the approach taken in several previous

negative clinical trials with MET-targeting agents. Furthermore, MET-amplified models generally

also displayed overexpression, while the reverse was not always true, raising the question

whether MET expression or MET gene copy number (GCN) is the more efficient predictive

biomarker.

These observations formed the basis for clinical exploration of capmatinib with an initial

focus on patient selection markers and cut-offs. A phase I study examined the predictive value

of MET expression (immunohistochemistry) versus MET GCN (fluorescence in situ hybridization)

in a lung cancer expansion cohort and reached the conclusion that GCN-based selection will

likely results in a higher response rate (42)(manuscript in preparation). GCN-based selection is

now further refined in a phase II study with cohorts covering several GCN ranges. This study is

also recruiting lung cancer patients whose tumors bear MET exon 14 skipping mutations

(METex14), which partially overlaps with MET amplification (43). The predictive value of

METex14 has likely been underestimated preclinically due to the lack of models and overlap

with amplification, and only emerged as a potential stratifier based on clinical evidence and

exome sequencing data from very large cancer sample sets (21). This case illustrates that even

the most extensive cancer model collections (e.g. CCLE) do not cover every possible cancer

dependency. The incidence of this genetic alteration in lung cancer is ~3-4% (21,44), while the




incidence of “MET amplification” is a function of the determined copy number cut-off, and will

need to be defined in ongoing trials. Additional candidate biomarkers that require clinical

exploration for lack of preclinical models are MET activating kinase domain mutations (45) and

MET chromosomal rearrangements (46,47).

Capmatinib was also investigated clinically as single agent in liver cancer, revealing that

both MET amplification and MET overexpression can contribute to the pre-selection of

responding tumors (48)(manuscript submitted). No clinical trials with capmatinib have yet been

performed that utilized HGF as selection marker, in part due to the finding that the majority of

presumable autocrine models displayed only minor growth reductions under treatment in vitro.

(45)

Not all models bearing predictive MET alterations respond to capmatinib to the same

extent. This is illustrated by the MET-amplified NCI-H1993 cell line that fails to undergo cell

death upon MET inhibition. Of note, NCI-H1993 was derived from a metastasis, whereas

another cell line NCI-H2073 was derived from the primary tumor of the same patient and lacks

MET amplification (49), highlighting that MET amplification is not always a truncal event, and

that it may be important to determine whether it is present as a clonal rather than sub-clonal

event in enrolling patients. In support of this notion, a recent clinical report on the activity of the

MET inhibitor AMG337 in esophagogastric cancer described that MET amplification was

detected solely in a metastasis but not the primary tumor in 2 out of 6 cases, where it appeared

to be associated with less clinical benefit (51).

Several capmatinib combinations are being tested in clinical trials. The concept of

combining capmatinib and EGFR inhibitors in EGFR-mutant lung cancer with MET

dysregulation is clinically validated (52) and has been explored in further trials (NCT02468661,

NCT02335944). However, our preclinical data suggest that capmatinib combinations can be

effective beyond EGFR-targeting agents, both in tumors where MET is the dominant oncogenic

driver, and in tumors with other co-occurring drivers. Exemplifying the former category, we




observed that combinations with BH3 mimetics as well as docetaxel enhance the anticancer

activity of capmatinib in MET-amplified lung cancer models. In addition to the role of MET as a

cancer cell-autonomous driver, MET activation in immune cells has been linked to immune

suppression via various mechanisms (54), and a recent study showed that capmatinib can

enhance the activity of various cancer immune therapies (55) (manuscript in preparation). The

combination of capmatinib with anti-PD1 antibodies is currently being evaluated in 2 clinical

trials (NCT02323126, NCT02795429).




Authors' Contributions

Conception and design: A. Huang, H.-X. Hao, W.R. Sellers, F. Hofmann, J. Schoepfer, R.

Tiedt

Acquisition of data: S. Baltschukat, B. Schacher Engstler, A. Tam, J. Liang, M.T. DiMare

Analysis and interpretation of data: S. Baltschukat, B. Schacher Engstler, A. Huang, H.-X.

Hao, A. Tam, H.-Q. Wang, J. Liang, M.T. DiMare, H.C. Bhang, Y. Wang, P. Furet, W.R. Sellers,

F. Hofmann, J. Schoepfer, R. Tiedt

Writing, review, and/or revision of the manuscript: S. Baltschukat, B. Schacher Engstler, A.

Huang, H.-X. Hao, A. Tam, H.-Q. Wang, J. Liang, M.T. DiMare, H.C. Bhang, Y. Wang, P. Furet,

W.R. Sellers, F. Hofmann, J. Schoepfer, R. Tiedt

Study supervision: A. Huang, H.-X. Hao, H.-Q. Wang, H.C. Bhang, Y. Wang, R. Tiedt

Acknowledgments

We would like to thank Christopher J. Wilson and team for conducting the large-scale cancer

cell line screens with capmatinib and the Novartis DRIVE team for conducting the pooled

shRNA screen. We would like to thank Chen Liu for technical assistance in the RKO

experiments, Markus Wartmann and Andreas Hueber for help with live/dead cell imaging, and

Sabine Zumstein-Mecker for help with EBC-1 combination experiments. PDX studies with the

models LXFA 526, LXFA 623 and LXFA 1647 were conducted at Charles River Laboratories

(former Oncotest), Freiburg, Germany. The LU5381 PDX study was conducted at Crown

Biosciences, San Diego, California, USA. The HCC827GR derivatives used in this study were

kindly provided by Jeffery Engelman and Pasi Jänne. We thank the capmatinib global project

team as well as Peter Hammerman for review and helpful comments on this manuscript and

Pushkar Narvilkar, Novartis Healthcare Pvt. Ltd. for providing medical editorial assistance.




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Figure Legends

Figure 1.

Capmatinib is a highly selective MET inhibitor. A, chemical structure of capmatinib (INC280,

INCB28060). B, TREEspot view of KINOMEscan selectivity panel for capmatinib at 10 μM.

Kinases that bind are marked with circles if < 35% of the respective recombinant kinase

remained captured on the immobilized ligand in the presence of the indicated concentration of

capmatinib relative to a DMSO control. Circle sizes reflect the “% remaining” values, which are

expected to roughly correlate with binding affinities. MET or two mutant variants thereof are

depicted in blue, all other kinases (total of 6) are depicted in red. Wild type MET is depicted

twice, once in the “TK” section and once in the “MUTANT” section of the plot. C, Binding

constants (Kd) measured in dose-response experiments. Each Kd is the average result of 2

determinations. D, structural model of capmatinib bound to the MET kinase domain. The model

is based on the crystal structure of MET in complex with 6-(difluoro(6-(4-fluorophenyl)-

[1,2,4]triazolo[4,3-b][1,2,4]triazin-3-yl)methyl)quinoline (PDB code: 5EOB) representative of the

binding mode of the class of highly selective MET inhibitors to which capmatinib belongs. In this

binding mode, the imidazotriazine core of capmatinib makes an aromatic stacking interaction

with MET residue Y1230 while its quinoline moiety interacts with the hinge region of the kinase.

The stacking interaction is made possible by a particular conformation of the kinase activation

loop (A-loop) stabilized by a salt bridge between residues D1228 and K1110. E, representative

dose response curves of BaF3 TPR-MET cells and mutant variants as indicated after incubation

with capmatinib for 3 days followed by resazurin readout. More data are available in

Supplementary Table 2.

Figure 2.

Sensitivity of cancer cell lines to capmatinib in vitro is associated with MET amplification or HGF

expression. A, results of 2 high-throughput cancer cell line screens with capmatinib. Dose-

response curves were obtained after incubation of cells with capmatinib for 72 hours and using

a CellTiter-Glo readout. The plots indicate the inflection point (EC50) of the fitted sigmoid curve

vs the maximal effect (lower plateau; Amax) relative to a proteasome inhibitor treatment that

was assumed to be pan-lethal and defines −100% (Supplementary Fig. 1A). If no sigmoid curve

could be fitted, the maximally tested concentration (8 M on the left, 30 M on the right) is

shown as EC50. The first CCLE screen (left panel) covered 458 cell lines, the second (right)

covered 364 cell lines, for a total of 605 with 217 cell lines overlapping in both screens.

Sensitive cell lines in either screen, defined as Amax ≤ −25 and inflection point ≤ 0.1 M, are

labeled. Cancer types (tissue of origin) are shown by color as indicated. Hits in the two screens

are concordant for those lines that were part of both screens, except for the cell lines SJRH30

and MSTO211H. B, Affymetrix human genome U133 Plus 2.0 gene expression data for MET

(probeset 213807_x) on the x axis vs MET copy numbers derived from Affymetrix SNP 6.0

arrays on the y axis. A total of 587 CCLE cell lines with available data that were part of either

screen are shown. Gene expression data are RMA-normalized and shown in log2 scale. The

same cell lines as in A are labeled. C, as in B, but showing HGF mRNA expression (probeset

209960_at) on the y axis. 598 cell lines of the CCLE with available expression data are shown.

D, profile of MET in pooled shRNA screen “Project DRIVE” (23). MET amplified and autocrine

cell lines (Supplementary Fig. 1C) are indicated by color.




Figure 3.

Capmatinib shows antitumor efficacy in several mouse xenograft models of lung and liver

cancer in which MET is amplified, overexpressed, or mutated. A, experiment with xenografts of

the MET-amplified lung cancer cell line EBC-1. Capmatinib was dosed at 10 mg/kg twice daily.

Treatment was started in 1 group (red) when tumors reached an average size of around

400 mm3, and in another group when average size was around 800 mm3. B, activity of

capmatinib (10 mg/kg twice daily) against 3 different lung cancer PDX models, all expressing

very high MET mRNA levels. MET gene copy numbers are indicated. C, inhibition of MET

phosphorylation in PDX tumors 2 hours or 12 hours after the last capmatinib dose, as measured

by multispot ELISA assessing both phospho-MET and total MET. D, antitumor efficacy of

capmatinib (10 mg/kg twice daily) against lung PDX tumors bearing a MET exon 14 skipping

mutation but not high level MET amplification. E, capmatinib (5 mg/kg daily) activity against

xenografts of the MET-amplified liver cancer cell line HCCLM3.

Figure 4.

Effect of capmatinib in MET-amplified lung cancer cells can be enhanced by combinations. A,

effect of capmatinib on cell proliferation and viability in 2 MET-amplified lung cancer cell lines.

Total cells and dead cells were quantified after 5 days of drug exposure by staining with

Hoechst 33342 and propidium iodide followed by automated imaging. Mean ± standard

deviation (n = 3) are shown. Dashed lines indicate the percent of dead cells after treatment with

1 M staurosporine (a pan-kinase inhibitor known to kill most cell lines in vitro). B, Western blots

showing effects of capmatinib on phosphorylation of the indicated proteins after 4 hours of drug

exposure. C, dose matrices of capmatinib in combination with either the selective MCL1 inhibitor

S63845 (left), or the selective BCL-xL inhibitor A-1155463 (right). NCI-H1993 cells were treated

for 7 days, and killed cells were quantified by concomitant staining with propidium iodide and

Hoechst 33342 at the end of the assay. Percent dead cells are indicated in the matrix, areas of

more extensive cell killing are highlighted in green. D, treatment of EBC-1 or NCI-H1993 cells

with the indicated dose matrix of capmatinib and docetaxel for 7 days followed by CellTiter-Glo

readout. A read for seeded cells (day 0) was also obtained. Effects were calculated considering

both the day 0 and the end-of-assay values as described in the Supplements. A value of 0

indicates no inhibition, 100 indicates complete growth arrest, and 200 represents complete cell

killing. Areas of more extensive cell killing are highlighted in darker red or black.

Figure 5.

Capmatinib is active in combination with other kinase inhibitors in several preclinical cancer

models. A, antitumor efficacy of capmatinib and gefitinib as single agents or in combination

against an EGFR-mutant lung cancer model with concomitant MET amplification (HCC827 GR).

Capmatinib was dosed at 3 mg/kg once daily and gefitinib at 25 mg/kg once daily. B,

combinatorial efficacy of capmatinib and the ALK inhibitor ceritinib against a PDX model (X-

1787) with EML4-ALK translocation and high MET mRNA expression. Capmatinib was dosed at

25 mg/kg once daily and ceritinib was dosed at 25 mg/kg once daily. Each study arm contained

4 animals. C, ERBB2-amplified cell lines NCI-H2170 or NCI-N87 were treated with a dilution

series of lapatinib in the presence or absence of 50 ng/mL recombinant HGF. Cell viability was

measured after 96 hours using a resazurin assay. The initial amount of viable cells was

quantified at the time of compound addition (dashed line), and cell growth on the y axis is




expressed as a multiple of this value. D, the MET and ERBB2 co-amplified esophageal cell line

OE33 was exposed to lapatinib, capmatinib, or combinations in a fixed ratio for 72 hours before

measuring cell viability using a resazurin assay. The x axis label corresponds to capmatinib

concentrations, while lapatinib was used at 10-fold higher concentrations due to its relative

lower potency. In combination, lapatinib and capmatinib were mixed at a ratio of 10:1.




MET

AXL

IRAK1

CDK11

YSK4

ABL1 (H396P)-nonphos.

MET

MET (Y1235D)MET (M1250T)

PIP5K2C

F

O

NH

N

N

NN

N

Figure 1

A C

B

D

K1110

D1228Y1230

A-loop

E

-2 -1 0 1 2 3 40

20

40

60

80

100

120

no mutationY1230HD1228AV1155LF1200IL1195V

Log [capmatinib] in nM

% o

f con

trol

SD

Kinase Kd (nM)

MET 0.31

MET (M1250T) 0.69

MET (Y1235D) 0.53

ABL1 (H396P) nonphosphorylated 3200

AXL 1100

CDK11 5700

IRAK1 500

PIP5K2C >10000

YSK4 2100




Figure 2

A

Amax

(%)

0.001 0.01 0.1 1 10

20

0

-20

-40

-60

-80

-100

ebc1

mkn45

oe33

dkmg sf295

mogguvwmsto211h

sjrh30

inflection point ( M)0.004 0.01 0.04 0.1 0.4 1 4

2010

0-10-20-30-40-50-60-70-80-90

kp4oe33

u87mgdkmg

mogguvwmsto211h

sjrh30

mkn45ebc1

hs746t

chp212

sf295

Amax

(%)

inflection point ( M)

first CCLE screen second CCLE screen

4 5 6 7 8 9 10 11 12

24

20

16

12

8

4

0

ebc1

mkn45

hs746t

oe33

chp212

sjrh30

dkmg

mogguvwu87mg

msto211h

sf295

kp4

B C

MET

cop

y nu

mbe

r

MET mRNA expression MET mRNA expression

HG

F m

RN

A ex

pres

sion

4 5 6 7 8 9 10 11 12

11

10

9

8

7

6

5

4

3

ebc1

kp4

hs746t

oe33

dkmg sf295

mogguvw

chp212

msto211h

sjrh30

u87mg

mkn45

autonomic_gangliacentral_nervous_systemlungoesophaguspancreaspleurasoft_tissuestomach

D 1.00

0.50

0.00

-0.50

-1.00

-1.50

-2.00

-2.50

amplifiedautocrine

MET

dep

ende

ncy

scor

e Tissue of origin for labeled cell lines




Figure 3

A

B

0 5 10 15 20 25 30 350

400

800

1200

1600

2000

days post implantation

tum

or v

olum

e (m

m3

SEM

)vehicle (small)INC280 (small)vehicle (large)INC280 (large)

0 10 20 30 40 50 60 700

250

500

750

1000

1250

vehicleINC280

days of treatment

tum

or v

olum

e (m

m3

SEM

) LXFA 623 (MET GCN 4)

tum

or v

olum

e (m

m3

SEM

)

0 10 20 30 40 500

250

500

750

1000

1250

days of treatment

vehicleINC280

LXFA 526 (MET GCN 14)

tum

or v

olum

e (m

m3

SEM

)

0 10 20 30 40 50 600

100

200

300

400

500

days of treatment

vehicleINC280

LXFA 1647 (MET GCN 11)

C

vehicleINC280

D LU5381(MET exon 14 skipping)

0 1 2 3 4 50

100

200

300

400

500

days of treatment

tum

or v

olum

e (m

m3

SEM

)

E

20 25 300

500

1000

1500

2000

2500

days of treatment

tum

or v

olum

e (m

m3

SEM

) HCCLM3

vehicleINC280

2 h 12h0.1

1

10

100

time after last dose

phos

pho-

Met

/ to

tal M

et

LXFA623, vehicleLXFA623, INC280






A

Figure 4

NCI-H1993EBC-1

100 33 10 3.3 1

0.3

0.1 0

100 33 10 3.3 1

0.3

0.1 0

phospho-MET(Y1234/1235)

total MET

phospho-AKT

phospho-ERK

total AKTtotal ERK

[capmatinib] in nM

0.01 0.1 1 10 100 10000

20

40

60

80

0

20

40

60

80

100

120

[capmatinib] in nM

% d

ead

cells

total cells (% of control)

total cellsdead cells

EBC-1

0.01 0.1 1 10 100 10000

20

40

60

80

0

20

40

60

80

100

120

[capmatinib] in nM

% d

ead

cells

total cells (% of control)

total cellsdead cells

NCI-H1993

B

C

[capmatinib] in M [capmatinib] in M

[S63

845]

in

M

[A-1

1554

63] i

n M

MCL1 BCL1L1

[capmatinib] in M [capmatinib] in M

[doc

etax

el] i

n M

[doc

etax

el] i

n M

EBC-1 NCI-H1993D




Figure 5

-2 0 2 40

2

4

6

8

Log [lapatinib] in nM

fold

see

ded

cells

SD

-2 0 2 4Log [lapatinib] in nM

NCI-H2170 (lung) NCI-H87 (gastric)

no HGF50 ng/mL HGF

A

C

D

10 20 30 400

200

400

600

800

1000 vehicle control3 mpk capmatinb qd

days after cell injection

tum

or v

olum

e (m

m3 )

25 mpk gefitinib qdcombination

25 30 350

300

600

900

1200

1500 vehicle control

25 mpk ceritinib qd15 mpk capmatinib qd

combination

days after implantation

tum

or v

olum

e (m

m3 )

B

0.001 0.01 0.1 1 10 1000

25

50

75

100

[compound] in nM

% o

f con

trol

SD

capmatiniblapatinibcombination




Published OnlineFirst January 23, 2019.Clin Cancer Res Sabrina Baltschukat, Barbara Schacher Engstler, Alan Huang, et al.

ActivationMETof MechanismsLung Cancer and Other Cancer Types with Defined

Capmatinib (INC280) Is Active Against Models of Non-Small Cell

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