Co-occurring alterations in the RAS-MAPK pathway limit response to MET inhibitor treatment in MET exon 14 skipping mutation-positive lung cancer

Abstract

PURPOSE:

While patients with advanced-stage non-small cell lung cancers (NSCLCs) harboring MET exon 14 skipping mutations (METex14) often benefit from MET tyrosine kinase inhibitor (TKI) treatment, clinical benefit is limited by primary and acquired drug resistance. The molecular basis for this resistance remains incompletely understood.

METHODS:

Targeted sequencing analysis was performed on cell-free circulating tumor DNA obtained from 289 patients with advanced-stage METex14-mutated NSCLC.

RESULTS:

Prominent co-occurring RAS-MAPK pathway gene alterations (e.g. in KRAS, NF1) were detected in NSCLCs with METex14 skipping alterations as compared to EGFR-mutated NSCLCs. There was an association between decreased MET TKI treatment response and RAS-MAPK pathway co-occurring alterations. In a preclinical model expressing a canonical METex14 mutation, KRAS overexpression or NF1 downregulation hyperactivated MAPK signaling to promote MET TKI resistance. This resistance was overcome by co-treatment with crizotinib and the MEK inhibitor trametinib.

CONCLUSION:

Our study provides a genomic landscape of co-occurring alterations in advanced-stage METex14-mutated NSCLC and suggests a potential combination therapy strategy targeting MAPK pathway signaling to enhance clinical outcomes.

Introduction:

Somatic MET mutations leading to splicing-mediated loss of exon 14 and subsequent MET overexpression are an emerging therapeutic target present in 2–4% of lung adenocarcinomas (1,2). MET tyrosine kinase inhibitor (TKI) treatment was associated with improved overall survival in a retrospective study of patients with METex14-mutated NSCLC (3). In ongoing prospective studies, response rates of 32% to the multi-kinase inhibitor crizotinib, 42% to the MET TKI tepotinib, and up to 71% for treatment-naïve patients to the MET TKI capmatinib have been reported (4–6). Crizotinib has recently received FDA breakthrough designation for use in the treatment of METex14-mutated NSCLC.

While MET remains an attractive therapeutic target, both primary and acquired resistance limit the long-term survival of patients with METex14-mutated NSCLC. Second-site MET mutations and downstream signaling reactivation via acquired KRAS amplification have been reported at acquired resistance to MET TKI therapy, and may inform treatment decisions (7–10). By contrast, the mechanisms mediating both primary MET TKI resistance and tumor cell persistence during initial MET TKI treatment remain largely undefined, as does the full landscape of alterations promoting acquired resistance.

Recent studies show that advanced-stage NSCLCs often harbor multiple oncogenic alterations, which may impact response to targeted therapies (11–13). A prior 28-patient cohort describing co-occurring genomic alterations as measured by tissue biopsy demonstrated variability in frequency of co-occurring alterations between METex14-mutated NSCLC and NSCLC driven by other genomic alterations (14). Analysis of cell-free circulating tumor DNA (cfDNA) utilizing next-generation sequencing (NGS) provides another avenue to describe the genomic landscape within a cancer patient and offers the potential to capture genomic changes reflecting heterogeneity across distinct metastatic tumor sites (11,15). A more detailed understanding of the mutational landscape that co-exists with oncogenic MET alterations in NSCLC may facilitate an improved understanding of the determinants of MET TKI response and identify rational polytherapy strategies to improve clinical outcomes.

Here, we describe the spectrum of co-occurring genomic alterations observed within the cfDNA of patients with METex14-mutated, advanced-stage NSCLC and identify prominent co-alteration of RAS pathway genes as a contributing factor to disease progression.

Methods:

Patients:

This study and waiver of written consent were approved by the Institutional Review Board (IRB) at the University of California, San Francisco. The conduct of this research was in accordance with the U.S. Common Rule. The cfDNA analysis included 332 consecutive samples from 289 patients with advanced (stage IIIB/IV) non-small cell lung cancer with a METex14 mutation obtained between October 2015 and March 2018, and 1653 consecutively tested samples from 1489 patients with EGFR-mutated NSCLC (L858R and del19) obtained between April 2016 and May 2017, as well as previously published (11) cohorts of EGFR wild type and TKI-naïve EGFR-mutated NSCLC (Supplemental Table 1).

Cell lines and reagents:

Ba/F3 cells were purchased from ATCC (ATCC® HB-283™) and maintained in culture for a total of approximately 2–3 months in DMEM supplemented with 1 ng/mL IL-3 (Peprotech). Mycoplasma testing was not performed. Retrovirus was generated using TransIT-LT1 transfection reagent (Mirus). Cells were infected with filtered retrovirus, expressing either the ORF control mCherry, human wildtype MET, or human MET^ex14 in a pBABE-puro vector backbone as previously described (9) and selected in puromycin (2 μg/mL). Expression was confirmed by immunoblotting. KRAS-overexpressing cells were obtained by retroviral infection with a pBABE-hygro KRAS4B construct and selected in hygromycin (800 μg/mL). Knockdown of NF1 was achieved by lentiviral transduction of the following sequence: 5’-CCGGGCCAACCTTAACCTCTCTAATCTCGAGATTAGAGAGGTTAAGGTTGGCTTTTTG-3’, expressed from a pLKO.1-hygro plasmid backbone (Addgene #24150). Three days after lentiviral transduction, cells were selected via treatment with hygromycin B and knockdown was confirmed by immunoblotting. All drugs were purchased from Selleck Chemicals.

Transformation and cell proliferation assays:

Transformation assay was performed by removing IL-3 through centrifugation and adding 50 ng/mL human HGF (Peprotech 100–39H). For proliferation assays cells were seeded in 96-well plates at 5,000 cells/well and the following day were exposed to crizotinib (Selleck Chemicals, #S1068) at 0 to 10 μM and/or trametinib (Selleck Chemicals, #S2673) at 0.01 μM. After 72 hours of drug exposure, CellTiter-Glo (Promega) reagent was added and luminescence was measured on a Spectramax spectrophotometer (Molecular Devices, Sunnyvale, CA, USA) according the manufacturer’s instructions. Data are presented as percentage of viable cells compared with control cells (vehicle treatment).

Immunoblotting:

Cells were washed in PBS and lysed with 25 mM Tris-HCL (pH 7.6), 150 mM NaCl, 1% NP-40, 1% sodium deoxycholate, 0.1% SDS supplemented with Halt Protease Inhibitor Cocktail (Thermo Fisher Scientific) and Halt Phosphatase Inhibitor Cocktail (Thermo Fisher Scientific). Lysates were separated in a 4%–15% SDS-PAGE gel and transferred onto a nitrocellulose membrane (Bio-Rad). Membranes were blocked with 5% fetal bovine serum (FBS) in Tris-buffered saline (TBS) containing 0.1% Tween and incubated with the appropriate antibodies. Detection was performed via ECL Prime (Amersham Biosciences). Antibodies against the following were obtained from Cell Signaling Technology (Danvers, MA, USA) and were used at a dilution of 1:1000: MET (#3148), p-MET Y1349 (#3121), pMEK S217/221 (#9121), ERK1/2 (#3493), p-ERK1/2 T202/Y204 (#9106), HSP90 (#4874), PARP (#9546), NF1 (#14623), horseradish peroxidase (HRP)-conjugated anti-mouse (#7076) and HRP-conjugated anti-rabbit (#7074). The following antibody was obtained from EMD Millipore (Burlington, MA, USA): RAS (05–516, 1:2000 dilution). Detection was performed via ECL Prime (Amersham Biosciences).

Cell-free DNA analysis:

Samples were shipped to a Clinical Laboratory Improvement Act (CLIA)-certified, College of American Pathologists–accredited laboratory (Guardant Health, Redwood City, CA). cfDNA was extracted from whole blood collected in 10-mL Streck tubes. After double centrifugation, 5–30 ng of cfDNA was isolated for digital sequencing of either a 70 or 73-gene panel (Supplementary Table 2) as previously described (16). Only those genes in common to these two panels were included in subsequent analysis. Nonsynonymous mutations were further processed with the R statistical computing program (version 3.3). Variants with unknown or neutral predicted functional significance were filtered prior to analysis as previously described (11) to include those with known impact on gene function within the Cosmic (17), GENIE (18,19), and ClinVar (20) databases. Those remaining with unknown functional impact after review of these databases were included if with predicted functional impact utilizing the Mutation Assessor (version 3) algorithm (21). Mutations previously reported as associated with clonal hematopoiesis were also excluded (22). Assignment as clonal or subclonal was performed by normalized mutational allele frequency to percentage detected using a cut off of 0.2 as previously described (11). Residue numbering was standardized to MET UniProtKB-P08581.

Next Generation Sequencing:

Tumor sample next generation sequencing (NGS) was performed in CLIA-approved laboratories. The Foundation One and Foundation ACT assays are commercially available assays which were used in the clinical standard-of-care setting. The UCSF500 assay sequences 479 cancer-associated genes to target 200X coverage, utilizing sequencing of a PBMC sample, target 100X coverage, as a control (23). The University of Florida GatorSeq NGS assay, utilized for both tumor tissue and PBMC analysis, performs sequencing of 76 cancer-associated genes with target 500X coverage (Supplemental Table 2). Germline mutations were subtracted utilizing sequencing of buccal swab samples to a target depth of 100X.

Droplet Digital PCR:

Isolated genomic DNA extracted from FFPE was amplified using ddPCR Supermix for Probes (Bio-Rad) using KRAS and MET assays (PrimePCR ddPCR Mutation Assay, Bio-Rad, and custom-designed). DNA template (8 μL) was added to 10 μL of ddPCR Supermix for Probes (Bio-Rad) and 2 μL of the primer/ probe mixture. This 20-μL reaction mix was added to a DG8 cartridge together with 70 μL of Droplet Generation Oil for Probes (Bio-Rad) and used for droplet generation. Droplets were then transferred to a 96-well plate (Eppendorf) and then thermal cycled with the following conditions: 5 minutes at 95°C, 40 cycles of 94°C for 30 seconds, 55°C for 1 minute followed by 98°C for 10 minutes (ramp rate 2°C/second). Droplets were analyzed with the QX200 Droplet Reader (Bio-Rad) for fluorescent measurement of FAM and HEX probes. Gating was performed based on positive and negative controls, and mutant populations were identified. The ddPCR data were analyzed with QuantaSoft analysis software (Bio-Rad) to obtain fractional abundance of the mutant DNA alleles in the wild-type (WT)/normal background. The quantification of the target molecule was presented as number of total copies (mutant plus WT) per sample in each reaction. Fractional abundance is calculated as follows: F.A. % = (Nmut/(Nmut + Nwt)) × 100), where Nmut is number of mutant events and Nwt is number of WT events per reaction. Multiple replicates were performed for each sample. ddPCR analysis of normal control genomic DNA (gene fragment obtained from IDT) and no DNA template (water) controls was performed in parallel with all samples, including multiple replicates as contamination-free controls.

Statistical Analysis:

Pairwise sample group comparisons for cfDNA analysis were carried out using a two-tailed Fisher’s exact t-test, with Benjamini-Hochberg correction for multiple comparisons, using a false discovery rate of less than 0.2. For cell viability curves, comparisons were performed using the two-sided student t-test, with significance threshold of p-value < 0.05.

Results:

Co-occurring genomic alterations are common in advanced-stage METex14-mutated lung cancer

We analyzed a cohort of 289 patients with advanced-stage NSCLC who had a METex14 mutation identified upon cfDNA analysis of 68 cancer-relevant genes using a clinically validated assay (Guardant360). This is the largest reported cohort to-date describing the genomic landscape of advanced-stage METex14-mutated NSCLC. We evaluated the frequency with which METex14 mutations co-occur with other cancer-associated mutations (11,16). To focus our analysis on co-occurring mutations with potential functional impact, synonymous mutations and those with predicted neutral or unknown functional impact were excluded, as previously described (11), as were mutations previously associated with clonal hematopoiesis (22) (Supplemental Figure S1). 86.5% of samples contained co-occurring genomic alterations, with a mean of 2.74 alterations per sample (range 0–22), in addition to the METex14 mutation. We use the term genomic alterations here to include both gene mutations/rearrangements and copy number gain as detected by cfDNA. The most commonly altered genes, co-occurring with the METex14 mutation in at least 10% of patients, were TP53 (49.5% of patients), EGFR (16.3%), NF1 (neurofibromatosis-1) (15.6%), BRAF (10.7%), and CDK4 (10.4%). Additional MET gene alterations were also common in this patient population, in which 9.3% of patients had co-occurring MET copy number gain and 12.1% had a co-occurring MET mutation (Figure 1A).

Figure 1. Co-occurring genomic alterations are common in non-small cell lung cancer (NSCLC) with a MET exon 14 mutation.

A. Distribution of co-occurring genomic alterations in a targeted list of cancer-associated genes (Supplemental Table 2) as detected by cfDNA in 332 samples from 289 patients with advanced-stage METex14-mutated NSCLC. Results are filtered to exclude synonymous variants, variants predicted to result in an unknown or neutral function impact (via COSMIC, GENIE, ClinVar, and mutation assessor prediction algorithms) and mutations previously reported as associated with clonal hematopoiesis. Only genomic alterations occurring at a 2% or greater frequency are displayed. CNG, copy number gain.

Seventeen of 34 second-site MET mutations were located in the tyrosine kinase domain. All identified MET second-site mutations, regardless of predicted functional impact, were included in this analysis. Of these, fourteen (G1163R, L1195V/F, F1200I, D1228H/N/Y, and Y1230H/S) were located at residues previously associated with MET TKI resistance (7–9,25–27). Nine of these occurred in patients with known prior MET TKI exposure and five in patients with unknown prior treatment history. The remaining three mutations (H1094Y, R1336W, and I1084L) occurred in patients without available prior treatment history. While the MET H1094Y mutation is known to lead to MET activation (28), the effects of the other two MET mutations remain uncharacterized.

The co-occurrence of other established oncogenic driver alterations (KRAS, EGFR, ALK, ROS-1, BRAF) was uncommon except for the presence of activating KRAS mutations in 5.2% of patients (G12C/D/S/V 3.5%, G13C 1%, Q22K 0.3%, and Q61H 0.3%), canonical EGFR-activating mutations in 3.5% (del19 3.1%, L858R 0.7%, T790M 2%), and an ALK gene rearrangement in 0.7%. In addition, a HER2 exon 20 insertion was detected in one patient. In a patient with known clinical outcomes data, with both EGFR del19/EGFR T790M mutations and a METex14 mutation, there was partial response (RECIST 1.1) to treatment with the EGFR TKI osimertinib, which lasted 13.8 months. The remaining alterations detected in KRAS, EGFR, ALK, ROS-1 and BRAF reflected mutations which are not canonical-driver alterations as listed above and/or reflected copy number gain. While in the overall dataset the METex14 mutation detected was predominantly clonal (80.6% of samples, defined as normalized MAF >0.2), in samples with a detectable co-occurring oncogenic driver alteration the METex14 alteration was more likely to be subclonal (21.4% of samples clonal, p-value <0.0001). The converse was also true; co-occurring established oncogenic driver mutations that were detected were more likely to be clonal than the other detected co-occurring genomic alterations (68.6% versus 46.3%, p-value = 0.0144) (Supplemental Figure S2).

RAS pathway alterations are more common in METex14-mutated NSCLC than in EGFR-mutated NSCLC

To understand how the genomic landscape in advanced-stage METex14-mutated NSCLC compares to NSCLCs with a different targetable oncogenic driver mutation associated with high upfront response rates to TKI therapy, we used identical cfDNA profiling to compare a cohort of patients with known METex14-mutated NSCLC to a previously unpublished, independent cohort of 1653 samples from 1489 patients with advanced-stage EGFR-mutant (del19, L858R) NSCLC (Supplemental Table 1). This comparison demonstrated differential frequency of co-occurring genomic alterations in 17 genes (Figure 2A). In the METex14-mutated cohort, co-occurring alterations in NF1, CDK4, STK11, ALK, KRAS, ATM, CDKN2A, NRAS, TSC1, and ESR1 were more commonly identified. In the EGFR-mutated cohort, co-occurring alterations in AR, ERBB2, CCNE1, PIK3CA, BRAF, CTNNB1, and MYC were more commonly identified, independently validating our prior findings identifying these as common co-occurring alterations in EGFR-mutated NSCLC (11). Among the 10 genes with a greater frequency of co-occurring genomic alteration in patients with a METex14 mutation, three (NF1, KRAS, and NRAS) are key components of the RAS-MAPK signaling pathway. When compared to a previously published, independent cohort of 918 patients with EGFR wildtype/METex14 negative NSCLC (29), alterations in NF1 remained significantly more common in METex14-mutated NSCLC. Genomic alterations in KRAS were significantly enriched in the EGFR wildtype/METex14 negative cohort compared to either the METex14- or EGFR-mutated cohorts, consistent with prior reported rates of KRAS gene alteration in NSCLC (12) (Figure 2A).

Figure 2. RAS-MAPK pathway alterations are common in METex14-mutated NSCLC.

A. Comparative frequency of genomic alterations as measured by cfDNA in a cohort of advanced-stage METex14 NSCLC patients (n=289) compared to an independent cohort of patients (n=1489) with a known canonical EGFR activating mutation (EGFR exon 19 deletions, EGFR L858R). A previously published independent cohort (11) of cfDNA from patients without either a canonical EGFR-activating mutation or METexon14 mutation (METex14 neg/EGFR wildtype, n = 918) is shown for comparison. Gene alterations with a statistically significant difference between METex14-mutated and EGFR-mutated NSCLC are displayed. q-values were calculated using Benjamini-Hochberg correction for false discovery rate < 0.2, with significant differences marked by an asterisk (*). B. Spectrum of gene alterations detectable by cfDNA in the subset of METex14-mutated NSCLC patients (n=61) without prior MET TKI treatment. Genomic alterations with a frequency of greater than 2% displayed. C. Genomic alterations detectable by cfDNA in the subset of patients with METex14-mutated NSCLC without prior MET TKI treatment (n=61) as compared to the subset of patients with EGFR-mutated NSCLC without prior EGFR TKI treatment (n = 58). q-values >0.95 not shown. CNG, copy number gain.

A propensity towards genomic alterations promoting downstream hyperactivation of the RAS pathway may favor primary resistance to TKI therapy and help explain the comparatively lower TKI response rates in METex14-mutated NSCLC. Examination of the subset of cfDNA samples obtained from patients with METex14-mutated NSCLC prior to known MET TKI treatment (n=61) demonstrated high rates of genomic alterations capable of promoting RAS-MAPK pathway activation (37.7% of patients, Figure 2B). Among those patients with a co-occurring RAS-MAPK pathway genomic alteration, 34.8% had more than one simultaneous alterations within this pathway (median 1, mean 1.53, range 1–5 RAS-MAPK genomic alterations). When compared to the subset of cfDNA samples from TKI-naïve patients with EGFR-mutated NSCLC (n=58) derived from the dataset discussed here and from a previously published patient cohort (11), there remained a trend towards more common detection of RAS-MAPK pathway alterations before treatment in METex14-mutated NSCLC (37.7% vs 19.0%, p-value 0.0297, q-value = 0.297 with correction for multiple hypothesis testing by Benjamini-Hochberg for FDR < 20%) which was not present for other categories of gene alterations (Figure 2C).

Both MET second-site mutations and RAS pathway alterations are newly detectable following MET TKI treatment.

We identified twelve patients with cfDNA obtained following treatment with crizotinib and an available matched sample obtained prior to known crizotinib exposure (Figure 3A, B, Table 1). Details regarding one patient (patient #5) in this dataset have previously been published by other groups (7,9).

Figure 3. MET second-site and bypass pathway alterations are newly detectable after MET TKI treatment.

Oncoprints show detectable genomic alterations by in paired samples from 12 patients, obtained either (A) before known MET TKI exposure or (B) after known MET TKI exposure. All samples reflect the results of cfDNA analysis (Guardant360), with the exception of three patients for whom only tissue NGS was available pre-MET TKI. Genes not common to the cfDNA panel were excluded from analysis. Additional details included in Table 1. C. Spectrum of newly detectable genomic alterations following MET TKI exposure by functional category.

Table 1. Genomic alterations in the cfDNA of patients treated with a MET TKI.

Genomic alterations identified upon targeted sequencing for cancer-associated genes in cfDNA samples obtained following known MET TKI exposure compared to results of samples obtained prior to known MET TKI exposure, with newly detected alterations in bold font. Sequencing performed via the Guardant 360 assay, unless otherwise specified. Further details for patients one and two in Supplemental Figure S4. Additional details regarding patient five have previously been published (7,9). Abbreviations: CNG, copy number gain; NOS, not otherwise specified; SCC, squamous cell carcinoma.

Patient #	Histology	Sample Prior to Known MET TKI Exposure		Sample After MET TKI Exposure
		Treatment Status	Genomic Alterations	MET TKI Received	Genomic Alterations % cfDNA/CNG
1	Lung Adeno-carcinoma	Pre-treatment	METex14, PTEN E43Q, CCNE1 CNG	Crizotinib	METex14, BRAF R199G, CCNE1 CNG, PIK3CA R38H, MET Y1230S, MET F1200I, KRAS CNG
2a	Lung Adeno-carcinoma	Pre-treatment	METex14	Crizotinib	METex14, KRAS CNG
3	Lung Adeno-carcinoma	Carboplatin/ pemetrexed/ bevacizumab	METex14, BRCA1 R691G	Crizotinib	METex14, FBXW7 R689Q, AR W742C, TP53 Y163S, KIT CNG
4	Lung Adeno-carcinoma	Pre-treatment	METex14, NF1 I1499V, BRAF CNG, NF1 R1534Q, EGFR V851A, EGFR CNG, MET CNG, CDK6 CNG, MYC CNG	Crizotinibc	METex14, NF1 I1499V, CCND1 CNG, TP53 N239S, TP53 R248W, NF1 p.Gln2636fs, MET p.Ser244fs
5	Lung Adeno-carcinoma	Pre-treatment	METex14, MET CNG, CDK6 CNG, ATM splice site, MDM2 CNG, CDKN2A/B lossb	Crizotinib	METex14, MET CNG, CDK6 CNG, AR CNG, PIK3CA CNG, MET Y1230H, MET D1228N, EGFR CNG
				Glesatinib	METex14, MET CNG, CDK6 CNG, PIK3CA CNG, MET D1228N, MET L1195V
6	Lung SCC	Nivolumab	METex14	Crizotinib	METex14, EGFR CNG, TP53 p.Arg156del
7	Lung Adeno-carcinoma	Carboplatin/ Paclitaxel	TP53 P27L, TP53 c375+1G>C	Crizotinib	METex14
8	Lung Adeno-carcinoma	Carboplatin/ pemetrexed/ bevacizumab	METex14, BRAF S273G, MET R1170*	Crizotinib	BRAF S273G, TP53 V173M
9	Lung Adeno-carcinoma	Unknown	METex14, MET CNG, EGFR K80T, ERBB2 N68S, KRAS G12S	Crizotinib	METex14, MET CNG, EGFR K80T, ERBB2 N68S, MET L1195V, TP53 V216E, EGFR CNG
10	Lung Adeno-carcinoma	Unknown	METex14, MET CNG, CDKN2A p.Thr77fs	Crizotinib	TP53 R158H, EGFR R836H, PDGFRA R558H
11	NSCLC NOS	Pre-treatment	METex14, RECQL4 splice site, SMAD4 Q224X, CDK4 CNG, KMT2A CNG, MDM2 CNGb	Crizotinib	METex14, MET D1228H, TP53 F270L
12	Lung Adeno-carcinoma	Pre-treatment	METex14, ERBB4 E69K, CDK4 CNG, GLI1 CNG, MDM2 CNG, APC E1284Kb	Crizotinib	METex14, ATM N3003T

^acfDNA analysis prior to crizotinib via Foundation ACT assay also notable for KRAS G12D which was not detected upon sequencing (University of Florida in-house NGS assay) of a pre-treatment tumor biopsy sample. Sequencing of a tumor biopsy of a differing progressing site following crizotinib treatment (Foundation One) was notable for KRAS G12D, KRAS amplification.

^bSequencing prior to known MET TKI exposure performed on a tumor biopsy sample rather than via plasma cfDNA analysis, Foundation One (patients 5 and 12), or Cancer-Select assay (patient 11).

^cFollowed by pemetrexed prior to cfDNA testing.

Newly detectable MET second-site mutations (Y1230H/S, D1228H/N, F1200I, L1195V) were detected in four of twelve patients following MET TKI treatment (Figure 3C, Table 1). While some of these mutations have been reported at acquired MET TKI resistance (7–9,25–27), the MET F1200I has not yet been described in a patient sample. Identification of specific acquired second-site mutations at resistance to MET TKI therapy may inform treatment decisions. MET TKIs can be classified as type I TKIs (e.g. crizotinib, capmatinib) or type II TKIs (e.g. cabozantinib) based on the kinase domain conformation to which they bind and differ in their activity against second-site MET mutations. For example, the MET Y1230X and D1228X mutations develop at resistance to type I MET TKIs and may predict response to subsequent type II MET TKI treatment (7–9,25–27). Conversely, the MET L1195V mutation has been reported at resistance to type II MET TKI treatment (7).

The development of acquired MET F1200 mutations as a resistance mechanism to MET TKIs has previously been predicted in a preclinical drug resistance screen, in which MET F1200 mutations were the dominant resistance mechanism to a type II MET TKI and were observed, though less common, at resistance to a type I MET TKI (30). Molecular modeling studies suggest that MET F1200I alters the conformation of the kinase domain such that it interferes with both the binding of type II MET TKIs within the DFG-out binding pocket, and to a lesser extent, may promote type I MET TKI resistance through disruption of an autoinhibitory MET conformation (Supplemental Figure S3) (7). Moreover, the F1200 residue is conserved across multiple tyrosine kinases, including ALK, ROS1, NTRK, and ABL, in which mutations at the corresponding residue have been linked to TKI resistance (Supplemental Table 3) (30–36).

In contrast, parallel and downstream pathway alterations with the potential to provide alternative input for RAS-MAPK pathway signaling were newly detectable in eight of twelve patients following MET TKI treatment as compared to cfDNA samples obtained prior to MET TKI treatment (Figure 3C, Table 1). In addition to two patients with KRAS amplification, which was recently implicated in MET TKI resistance (10), an NF1 frameshift mutation, and copy number gain in EGFR and KIT were identified. In one patient with acquired KRAS amplification, addition of the MEK inhibitor trametinib to crizotinib treatment rapidly decreased detectable circulating tumor cfDNA. In the second patient with acquired KRAS amplification, an activating KRAS G12D mutation and amplification of the MET ligand HGF were also present within a progressing lesion on crizotinib. Further details regarding the two patients with acquired KRAS amplification are presented in Supplemental Figure S4. Potential bypass pathway alterations co-existed with second-site MET alterations in three of four patients with second-site MET alterations, while alterations specifically within RAS-pathway members co-occurred with only one of four patients with second-site MET alterations.

MEK inhibition overcomes crizotinib resistance induced by RAS-MAPK pathway alterations

The clinical data suggested that preexisting or acquired genomic changes leading to RAS-MAPK pathway activation (e.g. in KRAS, NF1) may limit response and induce resistance to MET TKI treatment in METex14-mutated NSCLC. In one patient with KRAS amplification detectable at acquired resistance to crizotinib treatment, combination treatment off-label with both crizotinib (250 mg po BID) and trametinib (2 mg po daily) resulted in rapid loss of both detectable METex14 and KRAS amplification by cfDNA suggestive of treatment response. However, despite molecular evidence of tumor response this combination therapy was poorly tolerated in the context of overall clinical decline, with fatigue, fluid retention, and diarrhea. Despite dose reduction to trametinib 2 mg every other day, the patient expired before radiographic response assessment (Figure S4).

To assess the functional impact of RAS-MAPK pathway hyperactivation on sensitivity to MET TKI treatment, we engineered a new Ba/F3 cell-based system. The IL-3 dependent Ba/F3 cell line, while not of epithelial origin, is an established system to assess oncogenic capacity and putative drug resistance mechanisms (37,38). Stable expression of human METex14 in Ba/F3 cells in the presence of the MET ligand human HGF (Hepatocyte Growth Factor) induced IL-3-independent growth (Figure S5A, S5B) and increased downstream Erk phosphorylation compared to expression of wild type MET (Figure 4C, 4F). METex14-mutant expressing cells were sensitive to treatment with crizotinib, as measured by reduced cell growth in standard cell viability assays (Figure 4A, 4D).

Figure 4. KRAS overexpression or NF1 downregulation promotes MET TKI resistance that is overcome by combined crizotinib and trametinib polytherapy in METex14-mutated preclinical models.

A. Cell viability curves demonstrating a shift in the half maximal inhibitory concentration (IC50) to crizotinib with overexpression of wild type KRAS in MET exon 14-mutant expressing Ba/F3 cells. The cells were grown in culture with HGF supplementation (50 ng/mL). B. Cell viability curve for crizotinib treatment in Ba/F3 cells with both METex14 expression and wild-type KRAS overexpression (KRAS OE), in the setting of treatment with trametinib at 0.01 μM and supplementation with HGF 50 ng/mL. The KRAS OE, trametinib negative curves in panels A and B reflect the same experimental data, displayed on two graphs for clarity. C Ba/F3 cells with stable expression of wild type MET, MET with an exon 14 skipping mutation (METex14), or mCherry ORF control were treated with 50 ng/mL HGF with or without 24 hours treatment with crizotinib at 0.1 μM and/or trametinib (0.01μM). Treatment with crizotinib inhibits MET phosphorylation and inhibits downstream Erk phosphorylation, with associated increase in apoptosis as measured by cleaved PARP (cPARP). KRAS overexpression (KRAS OE) restored downstream Erk phosphorylation and reduced cleaved PARP, despite crizotinib treatment. Addition of trametinib inhibited Erk phosphorylation and increased cleaved PARP, consistent with induction of apoptosis, despite the presence of KRAS overexpression. D. Cell viability curves for crizotinib-treated MET exon 14-mutant expressing Ba/F3 cells with either NF1 knockdown (shNF1) or a negative control scrambled shRNA (shScr). E. Cell viability curves for crizotinib-treated MET exon-14 mutant expressing Ba/F3 cells with NF1 knockdown, with and without addition of 0.01 μM trametinib. The shNF1, trametinib negative curves in panels C and D reflect the same data displayed on two graphs for clarity. F. NF1 knockdown (NF1 KD) restores downstream Erk phosphorylation and decreases cleaved PARP in crizotinib-treated tumor cells. The addition of trametinib reduced Erk phosphorylation and restored PARP cleavage consistent with induction of apoptosis despite the presence of NF1. G. Summary graph of IC50 to crizotinib with trametinib with and without KRAS overexpression. H. IC50s of crizotinib with trametinib with or without NF1 downregulation. *** p-value < 0.001 by student’s t-test.

Overexpression of wild type KRAS or knockdown of wild type NF1 in METex14-expressing Ba/F3 cells induced resistance to crizotinib (Figure 4A, 4D). Overexpression of wild type KRAS increased the IC50 (inhibitory concentration of drug that decreases cell viability to 50%) to crizotinib from 0.16 μM to 1.68 μM (p-value < 0.001) (Figure 4A, 4G) and NF1 downregulation increased the IC50 to crizotinib from 0.16 μM to 0.75 μM (p-value < 0.001) (Figure 4D, 4H). Treatment of cells harboring both METex14 and KRAS overexpression with the combination of crizotinib and the MEK inhibitor trametinib in order to block both MET signaling and downstream MAPK pathway signaling restored sensitivity to treatment (crizotinib IC50 of 1.68 μM in the absence of trametinib versus 0.25 μM with trametinib co-treatment, p-value < 0.001) (Figure 4B, 4G). Similarly, co-treatment of cells harboring both METex14 and NF1 downregulation with both trametinib and crizotinib restored sensitivity to treatment (crizotinib monotherapy IC50 0.75 μM versus 0.18 μM upon addition of trametinib, p-value < 0.001 (Figure 4E, 4H). The selected trametinib dose modestly reduced but did not eliminate cell growth in the absence of crizotinib (Supplemental Figure S5). Immunoblotting demonstrated sustained Erk phosphorylation despite crizotinib treatment in samples with KRAS overexpression or NF1 downregulation, which was abrogated by the addition of trametinib (Figure 4C, 4F). In both genomic contexts, combination treatment was associated with increased levels of cleaved PARP, indicative of apoptosis, which was absent with monotherapy (Figure 4C, 4F).

Discussion:

The challenge of therapeutic resistance in patients harboring METex14 mutations is of increasing clinical relevance given the emergence of MET-targeted therapies into the clinic. While off-label use of MET TKI therapy has demonstrated clinical activity, objective reported response rates of approximately 30–70% as reported in early studies (4–6) are generally lower than those seen in response to TKI treatment in NSCLC driven by other canonical oncogenes (EGFR, ALK), where response rates greater than 80% have been reported (39,40).

RAS-MAPK pathway hyperactivation has an established role in promoting resistance to EGFR, ALK, BRAF, and ROS1 targeted therapies via diverse mechanisms including KRAS amplification and KRAS, BRAF, and NF1 mutations (41–49). While KRAS amplification has also recently been reported at acquired MET TKI resistance in METex14-mutated NSCLC (10), the broader role of compensatory genomic events providing signaling pathway re-activation remains less well-understood in METex14-mutated NSCLC. Here, we describe frequent co-occurring RAS-MAPK pathway alterations in METex14-mutated NSCLC as compared to EGFR-mutated NSCLC. RAS-MAPK pathway alterations were detected even in TKI-naïve patients; our preclinical and clinical data suggest these co-occurring alterations may promote resistance to MET TKI therapy. More specifically, RAS-MAPK pathway alterations when present as co-occurring genomic events in METex14-mutated NSCLC prior to MET TKI treatment may induce not only primary resistance but also contribute to tumor cell persistence, thus limiting response magnitude and potentially duration of response to initial treatment. This notion is supported by our findings in the Ba/F3 preclinical system we engineered. We also describe the spectrum and relative frequencies of newly detectable genomic alterations following MET TKI treatment as measured by cfDNA, which included alterations within the RAS-MAPK pathway or within RTKs upstream of the RAS-MAPK pathway (50) in two thirds of patients.

While our reported dataset is limited by lack of complete clinical outcomes data it highlights the importance of developing future patient cohorts incorporating outcomes data to link the understanding of the genomic landscape to treatment response and prognosis, particularly for those patients with less common or emerging driver mutations. The recently reported association between MET protein expression and response to MET TKI therapy in METex14-mutated NSCLC (51) additionally raises the question of future need for assays incorporating information regarding protein expression to assist with therapeutic decision-making in this patient population and warrants prospective study.

Clinically, while many MET second-site mutations acquired during type I MET TKI (e.g. crizotinib) treatment may be overcome by use of type II MET TKIs (e.g. cabozantinib) (7–9), the genomic alterations favoring RAS-MAPK pathway activation described here will likely require a combination therapy strategy. In our METex14-mutated preclinical model system, RAS-MAPK pathway hyperactivation via KRAS overexpression or NF1 downregulation induced MET TKI resistance that was overcome by the addition of the MEK inhibitor trametinib. Changes in the cfDNA profile suggested early evidence of molecular response to treatment with a crizotinib and trametinib combination therapy in a patient with acquired KRAS amplification, but treatment was poorly tolerated. Future efforts to develop combination therapies against these targets will require attention to agent selection, dosing, and scheduling to achieve both tolerability and efficacy.

This study enhances the understanding of the role of co-occurring genomic alterations in METex14-mutated NSCLC, with implications for the development of personalized therapeutic strategies to enhance the initial response magnitude and duration to MET TKI and delay or overcome acquired resistance. Given the prominence of genomic alterations favoring RAS-MAPK pathway hyperactivation, the addition of a MEK or potentially an ERK inhibitor to MET TKI therapy is a promising combination therapy strategy which warrants further prospective study.

Statement of Translational Relevance:

This report describes targeted sequencing of the largest reported cohort of advanced-stage non-small cell lung cancers (NSCLCs) with a MET exon 14 skipping (METex14) mutation. While MET tyrosine kinase inhibitors (TKIs) are active in METex14-mutated NSCLC, response rates are lower than those seen to TKI therapy in other forms of oncogene-driven NSCLC. The data presented here uncover enrichment for both primary and acquired RAS-MAPK pathway alterations in METex14-mutated NSCLC, events that may limit initial response magnitude or duration to MET TKI treatment. Combined MET plus MEK inhibitor treatment can overcome RAS-MAPK pathway-mediated resistance, suggesting a novel polytherapy strategy for evaluation in prospective clinical trials.