Small Cell Lung Cancer and Other Cancer Types with Deﬁned ... · therapeutic target for cancer therapy (1). Alterations of MET that are thought to be oncogenic include activating

Translational Cancer Mechanisms and Therapy

Capmatinib (INC280) Is Active Against Models ofNon–Small Cell Lung Cancer and Other CancerTypes with Defined Mechanisms of METActivationSabrina Baltschukat1, Barbara Schacher Engstler1, Alan Huang2, Huai-Xiang Hao2,Angela Tam2, Hui Qin Wang2, Jinsheng Liang2, Matthew T. DiMare2,Hyo-Eun Carrie Bhang2, Youzhen Wang2, Pascal Furet3,William R. Sellers2,Francesco Hofmann1, Joseph Schoepfer3, and Ralph Tiedt1

Abstract

Purpose: The selective MET inhibitor capmatinib is beinginvestigated in multiple clinical trials, both as a single agentand in combination. Here, we describe the preclinical data ofcapmatinib, which supported the clinical biomarker strategyfor rational patient selection.

Experimental Design: The selectivity and cellular activity ofcapmatinib were assessed in large cellular screening panels.Antitumor efficacy was quantified in a large set of cell line– orpatient-derived xenograft models, testing single-agent or com-bination treatment depending on the genomic profile of therespective models.

Results: Capmatinib was found to be highly selective forMET over other kinases. It was active against cancer modelsthat are characterized by MET amplification, marked METoverexpression, MET exon 14 skipping mutations, or METactivation via expression of the ligand hepatocyte growth

factor (HGF). In cancer models where MET is the dominantoncogenic driver, anticancer activity couldbe further enhancedby combination treatments, for example, by the addition ofapoptosis-inducing BH3 mimetics. The combinations of cap-matinib and other kinase inhibitors resulted in enhancedanticancer activity against models where MET activation co-occurred with other oncogenic drivers, for example EGFRactivating mutations.

Conclusions:Activity of capmatinib inpreclinicalmodels isassociated with a small number of plausible genomic features.The low fraction of cancer models that respond to capmatinibas a single agent suggests that the implementation of patientselection strategies based on these biomarkers is critical forclinical development. Capmatinib is also a rational combina-tion partner for other kinase inhibitors to combat MET-drivenresistance.

IntroductionA 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 attractivetherapeutic target for cancer therapy (1). Alterations of MET thatare thought to be oncogenic include activating mutations, over-expression, 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 targetingMET orHGFhave been discovered andclinically developed to various stages (2). However, the establish-mentofpredictivebiomarkers for efficient clinical developmentofsuch agents has proven challenging (3). One factor impedingprogress in this area is that some clinically studied agents are notMET selective. For example, tivantinib was initially described as aselectiveMET inhibitor,while later studies revealed that it also actsas a microtubule-disrupting agent, substantially complicating theinterpretation of clinical data (4). Likewise, several multikinaseinhibitors such as cabozantinib inhibit multiple relevant cancertargets alongwithMET, such as vascular endothelial growth factor2 [VEGFR2 (KDR); ref. 5], making it difficult to dissect thecontribution of MET inhibition to any observed effects. In addi-tion, multiple mechanisms of MET activation (including muta-tion, amplification, overexpression, ligand-mediated activation)have been associated with MET dependency in the preclinicalliterature, some of which are overlapping. Thus, evaluation ofmultiple biomarkers and definition of appropriate cutoffs isrequired to predict response to MET inhibitors.

Crizotinib was among the first MET kinase inhibitors thathelped gain a clearer understanding of the therapeutic potentialof MET inhibition, because its other primary targets such asanaplastic lymphoma kinase (ALK) and ROS1 are only relevant

1Novartis Institutes for BioMedical Research, Oncology Disease Area, Basel,Switzerland. 2Novartis Institutes for BioMedical Research, Oncology DiseaseArea, Cambridge, Massachusetts. 3Novartis Institutes for BioMedical Research,Global Discovery Chemistry, Basel, Switzerland.

Note: Supplementary data for this article are available at Clinical CancerResearch Online (http://clincancerres.aacrjournals.org/).

Current address for A. Huang: Tango Therapeutics, Cambridge, Massachusetts;and W.R. Sellers, Broad Institute, Cambridge, Massachusetts.

Corresponding Author: Ralph Tiedt, Novartis Institutes for BioMedicalResearch, Klybeckstrasse 141, 4057 Basel, Switzerland. Phone: 41795721480;Fax: 41616966242; E-mail: [email protected]

doi: 10.1158/1078-0432.CCR-18-2814

�2019 American Association for Cancer Research.

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in rare and translocation-defined cancers that generally do notoverlap with cancers in which MET is the dominant oncogenicdriver (6). Meanwhile, the clinical activity of crizotinib in METactivated lung cancer is well documented, and the acquisition ofMET resistance mutations in initially responsive tumors demon-strated conclusively that this activity was indeed due to METinhibition (7–9).

Capmatinib (INC280, formerly INCB28060) is a highly selec-tive and potent MET inhibitor with in vitro and in vivo activitiesagainst preclinical cancer models with MET activation (10). Cap-matinib is being tested both as a single agent and in combinationin multiple clinical trials that are guided by biomarker-basedpatient selection criteria. Here, we further elaborate on the pre-clinical profile of capmatinib and describe data guiding theclinical biomarker strategy.

Materials and MethodsCompounds

Capmatinib hydrochloride [2-fluoro-N-methyl-4-(7-(quino-lin-6-ylmethyl)imidazo[1,2-b][1,2,4]triazin-2-yl)benzamidedihydrochloride monohydrate, C23H17FN6O�2ClH�H2O] wassynthesized atNovartis. All other compoundswere obtained fromcommercial sources.

High-throughput cell line screenAll cell lines were obtained from commercial sources and

screened for compound sensitivity in the context of the Novar-tis/Broad Institute Cancer Cell Line Encyclopedia project (11).The details can be found in the Supplementary Materials andMethods.

Quantification of live and dead EBC-1 and NCI-H1993 cellsCells were seeded at 2,000 cells per well in 96-well plates in 100

mL per well and incubated for 24 hours at 37�C in 5% CO2.Capmatinib was then added from a 10 mmol/L DMSO stocksolution using a HP D300 Digital Dispenser (Tecan). After 5 daysof incubation, Hoechst 33342 and propidium iodide were addedto the culture medium at final concentrations of 1 and 2 mg/mL,respectively, and incubated for 45 minutes at 37�C and 5% CO2.The number of Hoechst 33342-stained nuclei and propidiumiodide-stained dead cells was then quantified following image

acquisition on a Cellomics VTi automated immunofluorescencemicroscope (ThermoFisher Scientific) using the appropriate exci-tation/emission filter sets.

Animals and maintenance conditionsFor all studies, animals were housed in a 12-hour light/dark

cycle facility and had access to food and water ad libitum. Micewere maintained and handled in accordance with Novartis Insti-tutes for BioMedical Research (NIBR) Institutional Animal Careand Use Committee (IACUC) regulations and guidelines. Allstudies were approved by the NIBR IACUC.

Drug combination dose–response matrixA detailed description of experimental procedures and calcula-

tions 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 normal-ized and randomized to avoid systematic position effects. Afterincubation with the drugs, effects were quantified either bystaining with propidium iodide and Hoechst 33342 or by Cell-Titer-Glo (CTG; Promega) including a readout for untreated cells("day 000). Both methods allowed to quantify the extent of cellkilling in the respective experiments.

Modeling of the structure of capmatinib bound to the METkinase domain

A model of capmatinib bound to the ATP site was constructedbased 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; ref. 12) representativeof the binding mode of the class of highly selective MET inhibi-tors, to which capmatinib belongs. In this binding mode, theimidazotriazine core of capmatinib makes an aromatic stackinginteraction with MET residue Y1230 while its quinoline moietyinteracts with the hinge region of the kinase. The stacking inter-action ismade possible by a particular conformation of the kinaseactivation loop (A-loop) stabilized by a salt bridge betweenresidues D1228 and K1110. An intramolecular hydrogen bondbetween the amide nitrogen and the fluoro atom of capmatinib ispostulated. Additional information can be found in the Supple-mentary Materials and Methods.

ResultsCapmatinib is highly selective for MET compared with otherkinases

Capmatinib (Fig. 1A) had previously been screened against 57human kinases and was found to be selective for MET within thispanel (10). To extend this kinase selectivity profiling, we mea-sured the affinity of capmatinib in a set of 442 kinases anddisease-relevant variants using the KINOMEscan selectivity screeningplatform. At a screening concentration of 10 mmol/L, which ismore than a 1,000-fold above the reported on-target IC50 inbiochemical assays (10), nine kinases scored as hits with thepredefined cutoff of �65% reduction in binding to the capturematrix compared with a vehicle control (Fig. 1B). These hitsincluded MET and two mutant variants thereof. Given that thekinase panel was screened at a concentration of capmatinib that ismuch higher than its active concentration against MET, we deter-mined the binding constants (Kd) for all nine hits (Fig. 1C). TheKd

values forMET and twomutant variants were subnanomolar, and

Translational Relevance

The clinical development of MET inhibitors has been chal-lenging as is indicated by several failed clinical trials. Con-tributing factors likely include the use of nonselective agents,for which predictive biomarkers of response are difficult toidentify, as well as the failure to implement a stringent bio-marker-based patient selection strategy during the develop-ment of selective MET-targeting agents. The activity of thehighly selective and potent MET inhibitor capmatinib is asso-ciated with a small set of specific genomic parameters. Thisinsight has given rise to a series of single-agent and combi-nation trials of capmatinib in lung cancer and other cancerindications that are guided by these potential predictive bio-markers. The underlying preclinical data are described in thispaper.

Preclinical Profile of the MET Inhibitor Capmatinib

www.aacrjournals.org Clin Cancer Res; 25(10) May 15, 2019 3165



were lower by a factor of approximately 1,000 or more comparedwith all other hits. Of note, the MET mutations M1250T andY1235Ddid not have a notable impact on capmatinib binding. Insummary, these data confirm that capmatinib is a highly selectiveMET inhibitor.

High selectivity of capmatinib is explained by its bindingmodeto MET

Structural modeling of the MET kinase domain bound withcapmatinib revealed that the phenol moiety of Y1230 directly

binds to the central aromatic ring of capmatinib in a pi stackinginteraction, while D1228 forms a salt bridge with K1110 thatstabilizes the MET activation loop in a conformation that isnecessary to support the Y1230–capmatinib interaction(Fig. 1D). This binding interaction is similar to crizotinib andother selective MET inhibitors, and although Y1230 and D1228are conserved in other tyrosine kinases such as IGF1-R and KDR,the required conformation of the activation loop is also stabilizedby multiple hydrophobic interactions between residues of theactivation loop and residues of helix C that are specific to theMET

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Capmatinib is a highly selective MET inhibitor. A, Chemical structure of capmatinib (INC280, INCB28060). B, TREEspot view of KINOMEscan selectivity panel forcapmatinib at 10 mmol/L. Kinases that bind are marked with circles if <35% of the respective recombinant kinase remained captured on the immobilized ligand inthe presence of the indicated concentration of capmatinib relative to a DMSO control. Circle sizes reflect the "% remaining" values, which are expected to roughlycorrelate with binding affinities. MET or twomutant variants thereof are depicted in blue, all other kinases (total of 6) are depicted in red. Wild-type MET isdepicted twice, once in the "TK" section and once in the "MUTANT" section of the plot. C, Binding constants (Kd) measured in dose–response experiments. EachKd is the average result of two determinations. D, Structural model of capmatinib bound to the MET kinase domain. The model is based on the crystal structure ofMET 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 bindingmode of the class of highly selective MET inhibitors to which capmatinib belongs. In this bindingmode, the imidazotriazine core of capmatinib makes an aromaticstacking interaction with MET residue Y1230, whereas its quinoline moiety interacts with the hinge region of the kinase. The stacking interaction is made possibleby 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 dataare available in Supplementary Table S1.

Baltschukat et al.

Clin Cancer Res; 25(10) May 15, 2019 Clinical Cancer Research3166



kinase (13, 14). To validate the structural model experimentally,we made use of a panel of BaF3 cells transformed with TPR-METconstructs bearing MET kinase domain mutations. Some of thesemutants had been obtained in an unbiased cellular resistancescreen with a selective MET inhibitor that is structurally relatedto capmatinib (13). As expected, significant resistance wasobserved when BaF3 cells bearing MET D1228 and Y1230 muta-tions were treated with capmatinib, while much smaller shifts inthe IC50, if any, were seen with other variants (Fig. 1E; Supple-mentary Table S1). These observations are in line with the pro-posed structural model of the MET–capmatinib interaction.Importantly, recent clinical case reports documented METD1228 or Y1230 mutations in lung cancers with acquired resis-tance to MET inhibitors (7–9, 15).

MET amplification and HGF expression are associated withcapmatinib sensitivity in vitro

MET gene amplification, leading to overexpression and autop-hosphorylation of the MET protein, has been linked to METinhibitor sensitivity in cell lines (16–19). In addition, responseto capmatinib has also been reported in two preclinical modelsthat express both MET and its ligand HGF (10). To assess pre-dictors of response to capmatinib in an unbiased and systematicmanner, we tested the activity of capmatinib against more than600 well-characterized cancer cell lines in the Cancer Cell LineEncyclopedia (CCLE) project (11). Cell line screens were con-ducted twice independently in a high throughput format, wheredose–response curves were generated for capmatinib after a 3-dayincubation period. After quality control, we obtained interpret-able results for a total of 605 cell lines (458 in the first screen and364 in the second screen, with an overlap of 217 cell lines;Supplementary Table S2). We considered both themaximal effect(Amax) and the EC50 (inflection point) of the fitted sigmoid dose–response curve to determine sensitivity (Supplementary Fig. S1A).With a low stringency (Amax � �25% and inflection point �100nmol/L), we observed a total of 13 responders or partial respon-ders among all tested cell lines (Fig. 2A). The two screens werelargely concordant in terms of capmatinib response for the 217cell lines tested in both occasions, with the exception of two celllines that scored asmodestly sensitive in one screen and complete-ly resistant in the other. Interestingly, all responsive cell linesexcept these two discordant lines were characterized by one of twogenomic profiles: (i) MET gene amplification, leading to pro-nouncedMETmRNA overexpression (Fig. 2B) or (ii) high expres-sion of theMET ligand HGF (Fig. 2C). The expression of HGF bycancer cell lines may be indicative of an autocrine loop thatactivates MET in these cells. Indeed, we found a good correlationbetween HGFmRNA expression and the amount of HGF proteinin cell culture supernatants (Supplementary Fig. S1B). Four of theseven cell lines in the autocrine category were derived fromglioblastoma, presumably related to the observation that glio-blastoma shows frequent gain of chromosome 7 regions encom-passing both MET and HGF (20).

Only twoMET-amplified cell lines with known dependence onMET (17, 19) displayed profound responses to capmatinib (Amax

close to �100%) at low concentrations (inflection point <10nmol/L). All HGF-expressing cell lines and two of the MET-amplified cell lines showed partial responses (Amax > �60%). Insome of the cell lines expressing HGF, the dose–response curvewas very shallow, suggesting only amoderate reduction in growthupon MET inhibition under the screening conditions.

To investigate whether these observations are generalizableto selective MET inhibitors, we combined the CCLE screeningresults of capmatinib with results from three other MET inhi-bitors in the same screening format, each tested twice inde-pendently like capmatinib: crizotinib, JNJ-38877605 (2), andPF-4217903 (14). The latter two compounds are highly selec-tive MET inhibitors with chemical structures similar to capma-tinib. For crizotinib, cellular activity explainable by ALK trans-locations was disregarded for this combined analysis. An over-all number of 709 cell lines could be analyzed that had beentested in more than one screen. Sensitive cell lines ("hits") werescored as for capmatinib, but adapting the inflection pointcutoff to the relatively lower potency of the other inhibitors. Atotal of 16 hits were observed that scored with more than oneMET inhibitor and included 10 of the hits previously identifiedwith capmatinib alone (overall hit rate 16/709 ¼ 2%; Supple-mentary Fig. S1C; Supplementary Table S3). All hits wereassociated with high expression and/or copy number of METor they coexpressed MET and HGF. When defining thresholdsfor those biomarkers guided by the hit with the respectivelowest value, we noted that the hit rate among cell lines withhigh MET copy number (amplified) was relatively high (4/6 ¼67%), followed by cell lines showing MET overexpression (5/9¼ 56%, four of these five also amplified), suggesting that thesebiomarkers, which are largely overlapping, might be suitablepredictive markers for a selective MET inhibitor (Supplemen-tary Fig. S1C). Conversely, among the cell lines with MET/HGFcoexpression (putative autocrine), the hit rate was lower (11/32¼ 34%), which could be due to at least two factors: (i) maximalgrowth inhibition in this category was mostly modest, whichmakes detection in a high-throughput screen less likely. (ii)HGF-mediated MET activation does not lead to MET-depen-dent growth in a fraction of these cell lines.

Clinically, response to MET inhibitors has been observed inpatients with lung cancer whose tumors contained mutationsleading toMET exon14 skipping (21). In our tested cell linepanel,two models contained such mutations: the gastric cancer cell lineHs 746.T and the lung cancer cell line NCI-H596. Hs 746.Tresponded to capmatinib treatment in vitro, butMET is also highlyamplified in this cell line. Thus, it is difficult to assess thecontribution of MET exon 14 skipping to capmatinib sensitivityin this model. NCI-H596 cells were resistant to MET inhibition invitro. However, in this cell line, we observed more persistent METphosphorylation in response to HGF stimulation (Supplemen-tary Fig. S1D), which is consistent with the reported functionalconsequence of MET exon 14 deletion (22).

Associated genomic features of capmatinib sensitivity arerecapitulated by theMET-dependency profile in genetic screens

Dependency onMET was evaluated genetically in a large-scalepooled short hairpin RNA (shRNA) screen across 398 cell linesinterrogating cell-autonomous dependencies of 7,837 genes eachtargeted by 20 shRNAs (23). As in the screen with capmatinib,only the twoMET-amplified cell lines EBC-1 andMKN-45 showedstrong dropout that was clearly distinct from the rest of thescreened cell lines (Fig. 2D). Autocrine lines were enriched amongthe cell lines withMET-dependent growth, but the signal was lesspronounced. No clear dependencies were detected upon HGFknockdown (data not shown). This is generally expected for genesencoding secreted factors, because in a pooled shRNA screeningformat only a tiny fraction of cells will bear shRNAs that target





HGF, with negligible impact on the total level of HGF protein inthe cell culture medium.

Combining our pooled shRNA screening data with two addi-tional published screens strengthened the link between METamplification and MET dependency (Supplementary Fig. S2A).Interestingly, a publicly available genome-wide CRISPR screenrevealed a marked MET-dependency signal for several cancer celllines expressing HGF, unlike the RNAi data sets (SupplementaryFig. S2B). This finding recapitulates the previously observed

responses to capmatinib and other MET inhibitors seen in auto-crine cell lines. Conversely, the apparent MET dependency ofMET-amplified cell lines was much less pronounced in theCRISPR screen, which is likely explained by the need to compu-tationally adjust dependency scores for amplified genes (24, 25).The more sensitive detection of dependencies in HGF-expressingcell lines may be related to a superior signal-to-noise ratio ofCRISPR versus RNAi, enabling the detection ofmore subtle effectson growth.

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Sensitivity of cancer cell lines to capmatinib in vitro is associated withMET amplification or HGF expression. A, Results of two high-throughput cancer cell linescreens with capmatinib. Dose–response curves were obtained after incubation of cells with capmatinib for 72 hours and using a CellTiter-Glo readout. The plotsindicate the inflection point (EC50) of the fitted sigmoid curve versus the maximal effect (lower plateau; Amax) relative to a proteasome inhibitor treatment thatwas assumed to be pan-lethal and defines�100% (Supplementary Fig. S1A). If no sigmoid curve could be fitted, the maximally tested concentration (8 mmol/L onthe left, 30 mmol/L on the right) is shown as EC50. The first CCLE screen (left) covered 458 cell lines, the second (right) covered 364 cell lines, for a total of 605with 217 cell lines overlapping in both screens. Sensitive cell lines in either screen, defined as Amax��25 and inflection point�0.1 mmol/L, are labeled. Cancertypes (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 celllines SJRH30 and MSTO211H. B, Affymetrix human genome U133 Plus 2.0 gene expression data forMET (probeset 213807_x) on the x-axis versusMET copynumbers 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. Geneexpression data are RMA-normalized and shown in log2 scale. The same cell lines as in A are labeled. C, as in B, but showing HGFmRNA expression (probeset209960_at) on the y-axis. A total of 598 cell lines of the CCLE with available expression data are shown. D, Profile ofMET in pooled shRNA screen "ProjectDRIVE" (23).MET amplified and autocrine cell lines (Supplementary Fig. S1C) are indicated by color.

Baltschukat et al.




In summary, all genetic MET dependencies and responses ofcell lines to capmatinib and other selective MET inhibitors can beexplained by either very strong MET overexpression, mostly as aconsequence of MET gene amplification, or by coexpression ofMET and its ligand HGF.

Capmatinib is active against cell line–derived and patient-derived xenograft models with MET-activating alterationsincluding exon 14 skipping mutation

TheMET-amplified lung cancer cell line EBC-1 was found to beexquisitely sensitive toMET inhibition in our cellular screens. Thiswas confirmed by measuring the impact of a diverse series of

clinically relevant MET inhibitors on proliferation of this cell line(Supplementary Fig. S3A). Each of the MET inhibitors causedprofound inhibition of proliferation though with differentpotencies.

We then confirmed the capmatinib sensitivity of the EBC-1 cellline in vivo (Fig. 3A). Remarkably, even large EBC-1 xenografttumors underwent pronounced regression upon treatment. Tofurther characterize the activity of capmatinib in lung cancer invivo, we first analyzed the Novartis patient-derived xenograftmodels (PDX) collection (26), but did not identify any lungcancer models with MET amplification or exon 14 skippingmutations (data not shown). Therefore, we turned to an external

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Capmatinib shows antitumorefficacy in several mousexenograft models of lung and livercancer in whichMET is amplified,overexpressed, or mutated.A,Experiment with xenografts of theMET-amplified lung cancer cell lineEBC-1. Capmatinib was dosed at 10mg/kg twice daily. Treatment wasstarted in one group (red) whentumors reached an average size ofaround 400mm3, and in anothergroup when average size wasaround 800mm3. B,Activity ofcapmatinib (10 mg/kg twice daily)against three different lung cancerPDXmodels, all expressing veryhighMETmRNA levels.MET genecopy numbers are indicated. C,Inhibition of MET phosphorylationin PDX tumors 2 or 12 hours afterthe last capmatinib dose, asmeasured by multispot ELISAassessing both phospho-MET andtotal MET. D, Antitumor efficacy ofcapmatinib (10 mg/kg twice daily)against lung PDX tumors bearing aMET exon 14 skipping mutation butnot high-levelMET amplification.E, Capmatinib (5 mg/kg daily)activity against xenografts of theMET-amplified liver cancer cell lineHCCLM3.





well-annotated PDX collection (27) of 66 lung cancer PDXmodels with gene expression data (by Affymetrix HG U133 plus2.0 array and RNA-seq), gene copy number (GCN; by AffymetrixSNP 6.0 array), and whole exome sequencing data (Supplemen-tary Table S4). The measurements of MET mRNA expressionby Affymetrix array and RNA-seq were in excellent agreement,andwe chose the three lung adenocarcinomamodels with highestMET expression for further studies (Supplementary Fig. S3B).High total and phospho-MET protein levels had also beenobserved for two of those models (27). Interestingly, MET genecopynumbersweremore distinct,withhigh-level amplification intwo 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. S3C). This constellation enabled us to inves-tigate whether high MET expression in the absence of amplifica-tion could be sufficient to predict response to capmatinib. Indeed,all three models underwent profound regression upon METinhibition with capmatinib (Fig. 3B), including completeresponses in a subset of mice for two models (SupplementaryFig. S3D). Treatments were well tolerated as far as determined bybody weight monitoring (Supplementary Fig. S3E). However, alltumors grew back after cessation of treatment on day 21, indi-cating persistent disease.

The pharmacodynamic effect of capmatinib was measured atthe end of the study by quantifying total MET and phospho-METin tumor lysates using a multispot ELISA. LXFA 623 tumorsshowed markedly lower total and phospho-MET levels than the2MET-amplified models (Fig. 3C; Supplementary Fig. S3F). METinhibition was clearly detectable at 2 hours after the last dosing,with some degree of phospho-MET recovery in two of threemodels at 12 hours after dosing.

In a third PDX model collection, a lung cancer model namedLU5381withMET exon 14 skippingmutation andmoderateMETcopy number gain (�5) was identified, thus dissociating METexon 14 skipping from high-level MET amplification. Whentreating mice bearing LU5381 xenografts with capmatinib, weobserved tumor regression (Fig. 3D; Supplementary Fig. S3G).

Notably, capmatinib was also active against a liver cancerxenograft model, in which the MET gene is amplified (Fig. 3E;ref. 16).

In vivo activity of capmatinib is observed in autocrine modelsIn the in vitro screens, putative autocrine cell lines generally

showed relatively subtle responses to capmatinib treatment(Figs. 2A–C). Yet, the in vivo response of xenografts derivedfrom such models was much more dramatic, as exemplified bythe glioblastoma cell line U87-MG (10). Thus, experimentalconditions can have a strong impact on the apparent sensitivityof such preclinical models. Regression of additional MET/HGFautocrine glioblastoma xenografts in response to MET inhibi-tors had been reported previously (28). When we treatedxenografts of the gastric cancer cell line IM95, which expresseshigher levels of HGF mRNA than U87-MG and producedcomparable amounts of HGF as detected in cell culture super-natants (Supplementary Fig. S1C), a significant growth reduc-tion but no regression was observed (Supplementary Fig. S3F).This result confirms that HGF-expressing cancer models canshow pronounced responses to capmatinib in vivo, but the levelof HGF expression does not appear to be sufficient to makequantitative predictions about response depth.

Impact of capmatinib on viability inMET-amplified EGFRwild-type lung cancer cell lines can be enhanced by combinations

We analyzed the response of two MET-amplified lung cancercell lines EBC-1 and NCI-H1993 (17) to capmatinib in moredetail, aiming to distinguish growth arrest from cell death. To thisend, we quantified total and dead cells by automated microscopyusing specificfluorescent dyes. Interestingly, EBC-1 cells displayeda markedly higher rate of cell death upon capmatinib treatment,albeit not reaching 100%, whereas the effect in NCI-H1993 waslargely restricted to inhibition of proliferation (Fig. 4A). Thisobservation indicates that the reductions of growth and viabilityfollowingMET inhibition are not always strictly coupled.Next, westudied the effect of MET inhibition on cellular signaling in thesetwo MET-amplified lung cancer cell lines. As expected, METphosphorylation as well as phosphorylation of AKT and ERKwere suppressed at low single-digit nanomolar concentrations ofcapmatinib in both cell lines (Fig. 4B). In line with the effects oncellular proliferation, suppression of protein phosphorylationoccurred 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 notprovide an obvious explanation for the observed differences incell death upon capmatinib treatment.

Intrigued by the observation that capmatinib arrests growthof MET-amplified NCI-H1993 cells but failed to induce celldeath, we tried to improve this outcome using combinationtreatments. We reasoned that cotargeting members of the BCL2family 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 withcapmatinib in a concentration matrix followed by direct quan-tification of cell death using propidium iodide and Hoechst33342 staining. Combined inhibition of MET and either MCL1or BCL2L1 led to synergistic killing of a substantial fraction ofcancer cells (Fig. 4C; Supplementary Fig. S4A), whereas com-bined BCL2 inhibition was inactive (Supplementary Fig. S4A).Yet, under the tested conditions not all cancer cells were killedeven with combination treatment. We also examined the effectof the same combinations in EBC-1 cells, although in thosecells capmatinib on its own is already inducing pronounced celldeath. Interestingly, however, the fraction of dead cells wasfurther increased by concomitant MCL1 or BCL2L1 inhibition(Supplementary Fig. S4B).

The combination of a selective MET inhibitor with the micro-tubule-stabilizing chemotherapeutic docetaxel was found to beactive against MET-amplified gastric cancer models (32). Inde-pendently, we observed during a systematic combination screenthat docetaxel and chemotherapeuticswith relatedmodeof actionwere active in combination with the EGFR tyrosine kinase inhib-itor nazartinib in EGFR-mutant lung cancer models (manuscriptin preparation). Therefore, we tested the combination of capma-tinib and docetaxel in the two available MET-amplified lungcancer cell lines, EBC-1 and NCI-H1993 (Fig. 4D; SupplementaryFig. S4C). In both cell lines, a synergistic boost of cell killing wasobserved. The EGFR inhibitor erlotinib had previously beenreported to prevent outgrowth of resistant EBC-1 cells uponprolonged MET inhibition (33). In line with this report, thecombined treatment of EBC-1 cells with erlotinib and capmatinibfurther increased cell killing similar to the docetaxel combination(Supplementary Fig. S4D), whereas the added benefit of erlotinibagainst NCI-H1993 was modest (data not shown). In summary,

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the activity of capmatinib against MET-amplified tumors can befurther enhanced by several combination partners with distinctmode of action.

Capmatinib can revert MET-driven resistance to other kinaseinhibitors

Although cancer models that depend primarily on METalone are relatively infrequent (Fig. 2A and SupplementaryFig. S1C), MET has also been reported to cause acquired oradaptive resistance to other targeted therapies, which is the

basis for an important additional clinical application of METinhibitors. For example, in lung cancer with EGFR activatingmutations, the activation of MET can bypass EGFR depen-dency, causing resistance to EGFR inhibitors. This was firstdiscovered in an EGFR-mutant lung cancer cell line namedHCC827, which contains a minute fraction of MET-amplifiedsubclones that grow out under treatment with EGFR inhibi-tors (34, 35). The clinical relevance of this resistance mech-anism has hence been confirmed in numerous clinicalstudies.

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Effect of capmatinib inMET-amplified lung cancer cells can be enhanced by combinations. A, Effect of capmatinib on cell proliferation and viability in twoMET-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 iodidefollowed by automated imaging. Mean� SD (n¼ 3) are shown. Dashed lines indicate the percent of dead cells after treatment with 1 mmol/L staurosporine (apan-kinase inhibitor known to kill most cell lines in vitro). B,Western blots showing effects of capmatinib on phosphorylation of the indicated proteins after 4hours 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 theend 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-H1993cells 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 Supplementary Material. 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.





Using parental HCC827 cells and gefitinib-resistant derivatives(GR) bearing MET amplification, we confirmed that capmatinibcan revert gefitinib resistance in the GR variant in vitro, while notadding to the effect of gefitinib in parental cells (SupplementaryFig. S5A). Capmatinib also had a subtle but measurable effect onthe growth of HCC827 GR cells as a single agent. Interestingly,when testing the same combination in vivo using HCC827 GRxenografts, we observed a relatively strong antitumor effect ofcapmatinib even as a single agent, leading to stasis formore than 3weeks until tumors started to progress again (Fig. 5A). However,combination treatment led to profound and sustained tumorregression. Similar results were obtained when treating a MET-activated HCC827 xenograft derivative with a combination ofcapmatinib and the third-generation EGFR inhibitor nazartinib(EGF816; ref. 36). Besides MET amplification, activation of METvia its ligand HGF has been proposed as another potentialmechanism of resistance to EGFR inhibitors in lung cancer (37),and indeed we confirmed that addition of exogenous HGF to twoEGFR-mutant lung cancer cell lines could substantially reducegrowth inhibition by gefitinib (Supplementary Fig. S5B).

We hypothesized that—analogous to EGFR-mutant lungcancer—MET may also drive resistance to ALK inhibition inALK-translocated lung cancer. Although this potential resis-tance mechanism is not expected in patients treated with thedual MET/ALK inhibitor crizotinib, it may be relevant inpatients treated with second-generation selective ALK inhibi-tors. In support of this hypothesis, we noted in our PDXcollection a lung cancer model with EML4-ALK translocationthat expressed very high MET mRNA levels without METamplification, and high phospho-MET protein levels (Supple-mentary Fig. S5C). Although this model was responsive tocrizotinib (Supplementary Fig. S5D), it did not respond to thesecond-generation ALK inhibitor ceritinib, but regressed whenceritinib was combined with capmatinib (Fig. 5B).

The ability of HGF to diminish the effect of kinase inhibitionthrough MET activation has also been described in several othercontexts beyond EGFR-mutant lung cancer (38–40). For example,HGF can reduce the effect of ERBB2 inhibition in ERBB2-ampli-fied cancers. In keeping with a previous report (40), we observedno or partial rescue by exogenous HGF in four ERBB2-amplifiedbreast cancer cell lines (data not shown). In an ERBB2-amplified

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lung and gastric cancer cell line, however, which were bothsensitive to lapatinib, the effect of HGF was more pronounced,in particular by enhancing overall growth but also reducing themaximal inhibitory effect of lapatinib (Fig. 5C). Interestingly, theesophageal cancer cell line OE33, which is MET-amplified andpartially sensitive to capmatinib (Fig. 2A), also displays ERBB2amplification and high ERBB2mRNA expression, suggesting thatboth RTKs could be activated (41). In support of this hypothesis,combined treatment with capmatinib and lapatinib resulted inmore pronounced growth inhibition than either single agentalone (Fig. 5D).

Another example where HGFwas reported to drive resistance isBRAF-mutant melanoma treated with BRAF inhibitors (39, 40).Although HGF may be produced by noncancer cells in the tumormicroenvironment, such as cancer-associated fibroblasts, we alsoidentified a BRAF-mutant colorectal cancer cell line (RKO) whereautocrine MET activation may play a role: the modest growthinhibitory effect upon targeting mutant BRAF signaling withdabrafenib plus trametinib in these cells could be enhanced bycapmatinib treatment, albeit not to an extent that resulted in cellkilling (Supplementary Fig. S5E).

In summary, activation of MET, either by direct alterations ofthe MET gene itself or through HGF, can cause resistance tovarious kinase inhibitors, which may substantially expand theclinical utility of a MET inhibitor like capmatinib in combinationtherapies.

DiscussionSystematic screening across broad cancer cell line panels

revealed that sensitivity to the selective and highly potent METinhibitor capmatinib and/or genetic MET dependency can beexplained by distinct mechanisms of MET activation that couldserve as predictive biomarkers. Among those, MET amplifica-tion and pronounced MET overexpression were associated withrobust sensitivity to capmatinib in vitro and in vivo. The per-centage of models with these two MET-activating features is lowacross cancer types, indicating that a very stringent patientselection approach might be needed in contrast to the approachtaken in several previous negative clinical trials with MET-targeting agents. Furthermore,MET-amplified models generallyalso displayed overexpression, whereas the reverse was notalways true, raising the question whether MET expression orMET GCN is the more efficient predictive biomarker.

These observations formed the basis for clinical exploration ofcapmatinib with an initial focus on patient selection markers andcutoffs. A phase I study examined the predictive value of METexpression (IHC) versus MET GCN (FISH) in a lung cancerexpansion cohort and reached the conclusion that GCN-basedselection will likely result in a higher response rate (manuscriptin preparation; ref. 42). GCN-based selection is now furtherrefined in a phase II study with cohorts covering several GCNranges. This study is also recruiting lung cancer patients whosetumors bearMET exon 14 skipping mutations (METex14), whichpartially overlaps with MET amplification (43). The predictivevalue of METex14 has likely been underestimated preclinicallydue to the lack of models and overlap with amplification, andonly emerged as a potential stratifier based on clinical evidenceand exome sequencing data from very large cancer samplesets (21). This case illustrates that even the most extensive cancermodel collections (e.g., CCLE) do not cover every possible cancer

dependency. The incidence of this genetic alteration in lung canceris nearly 3% (21, 44), whereas the incidence of "MET amplifica-tion" is a function of the determined copy number cut-off, andwill need to be defined in ongoing trials. Additional candidatebiomarkers that require clinical exploration for lack of preclinicalmodels are MET activating kinase domain mutations (45) andMET chromosomal rearrangements (46, 47).

Capmatinib was also investigated clinically as single agent inliver cancer, revealing that both MET amplification and METoverexpression can contribute to the pre-selection of respondingtumors (manuscript submitted; ref. 48). No clinical trials withcapmatinib have yet been performed that utilized HGF as selec-tion marker, in part due to the finding that the majority ofpresumable autocrine models displayed only minor growthreductions under treatment in vitro (45).

Not all models bearing predictive MET alterations respond tocapmatinib to the same extent. This is illustrated by the MET-amplified NCI-H1993 cell line that fails to undergo cell deathupon MET inhibition. Of note, NCI-H1993 was derived from ametastasis, whereas another cell line (NCI-H2073) was derivedfrom the primary tumor of the same patient and lacks METamplification (49), highlighting that MET amplification is notalways a truncal event, and that it may be important to determinewhether it is present as a clonal rather than subclonal event inenrolling patients. In support of this notion, a recent clinicalreport on the activity of the MET inhibitor AMG337 in esopha-gogastric cancer described that MET amplification was detectedsolely in ametastasis but not the primary tumor in twoof six cases,where it appeared to be associated with less clinical benefit (51).

Several capmatinib combinations are being tested in clinicaltrials. The concept of combining capmatinib and EGFRinhibitors in EGFR-mutant lung cancer with MET dysregulationis clinically validated (52) and has been explored in further trials(NCT02468661, NCT02335944). However, our preclinical datasuggest that capmatinib combinations can be effective beyondEGFR-targeting agents, both in tumors where MET is the domi-nant oncogenic driver, and in tumors with other co-occurringdrivers. Exemplifying the former category, we observed thatcombinations with BH3 mimetics and docetaxel enhance theanticancer activity of capmatinib in MET-amplified lung cancermodels. In addition to the role of MET as a cancer cell-autono-mous driver, MET activation in immune cells has been linked toimmune suppression via various mechanisms (54), and a recentstudy showed that capmatinib can enhance the activity of variouscancer immune therapies (manuscript in preparation; ref. 55).The combination of capmatinib with anti-PD1 antibodies iscurrently being evaluated in two clinical trials (NCT02323126,NCT02795429).

Disclosure of Potential Conflicts of InterestS. Baltschukat, B.S. Engstler, H.-E.C. Bhang, F. Hofmann, and R. Tiedt hold

ownership interest (including patents) in Novartis. W.R. Sellers holds owner-ship interest (including patents) in Novartis and is a consultant/advisory boardmember for Servier Pharmaceuticals, Sanofi Pharmaceuticals, and Array Bio-pharma. No potential conflicts of interest were disclosed by the other authors.

Authors' ContributionsConception and design: A. Huang, J. Liang, H.-E.C. Bhang, Y. Wang, R. TiedtDevelopment of methodology: H.Q. Wang, J. Liang, R. TiedtAcquisition of data (provided animals, acquired and managed patients,provided facilities, etc.): S. Baltschukat, A. Tam, H.Q. Wang, J. Liang,M.T. DiMare, H.-E.C. Bhang





Analysis and interpretation of data (e.g., statistical analysis, biostatistics,computational analysis): S. Baltschukat, A. Huang, H.-X. Hao, H.Q. Wang,J. Liang, H.-E.C. Bhang, Y. Wang, P. Furet, R. TiedtWriting, review, and/or revision of the manuscript: S. Baltschukat, A. Huang,H.-X. Hao, H.Q. Wang, J. Liang, M.T. DiMare, H.-E.C. Bhang, Y. Wang, P. Furet,W.R. Sellers, F. Hofmann, J. Schoepfer, R. TiedtAdministrative, technical, or material support (i.e., reporting or organizingdata, constructing databases): B.S. Engstler, J. SchoepferStudy supervision:A. Huang, J. Liang, H.-E.C. Bhang, W.R. Sellers, F. Hofmann,R. Tiedt

AcknowledgmentsThe authors thank Christopher J. Wilson and team for conducting the large-

scale cancer cell line screens with capmatinib and the Novartis DRIVE team forconducting the pooled shRNA screen. They also thank Chen Liu for technicalassistance in the RKO experiments, Markus Wartmann and Andreas Hueber forhelp with live/dead cell imaging, and Sabine Zumstein-Mecker for help with

EBC-1 combination experiments. PDX studies with themodels LXFA 526, LXFA623, and LXFA 1647 were conducted at Charles River Laboratories (formerOncotest), Freiburg, Germany. The LU5381 PDX studywas conducted at CrownBiosciences, San Diego, California. The HCC827GR derivatives used in thisstudy were kindly provided by Jeffery Engelman and Pasi J€anne. The authorsthank the capmatinib global project team as well as Peter Hammerman forreview and helpful comments on this manuscript and Pushkar Narvilkar,Novartis Healthcare Pvt. Ltd., for providing medical editorial assistance. Thesestudies were sponsored by Novartis.

The costs of publication of this article were defrayed in part by the paymentof page charges. This article must therefore be hereby marked advertisement inaccordance with 18 U.S.C. Section 1734 solely to indicate this fact.

Received September 17, 2018; revised December 12, 2018; accepted January18, 2019; published first January 23, 2019.

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Small Cell Lung Cancer and Other Cancer Types with Deﬁned ... · therapeutic target for cancer therapy (1). Alterations of MET that are thought to be oncogenic include activating