Purpose:

MET amplification is a frequent mechanism of resistance to EGFR tyrosine kinase inhibitors (TKI) in patients with EGFR-mutated non–small cell lung cancer (NSCLC), and combined treatment with EGFR TKIs and MET TKIs has been explored as a strategy to overcome resistance. However, durable response is invariably limited by the emergence of acquired resistance. Here, we investigated the preclinical activity of REGN5093-M114, a novel antibody–drug conjugate targeting MET in MET-driven patient-derived models.

Experimental Design:

Patient-derived organoids, patient-derived cells, or ATCC cell lines were used to investigate the in vitro/in vivo activity of REGN5093-M114.

Results:

REGN5093-M114 exhibited significant antitumor efficacy compared with MET TKI or unconjugated METxMET biparatopic antibody (REGN5093). Regardless of MET gene copy number, MET-overexpressed TKI-naïve EGFR-mutant NSCLC cells responded to REGN5093-M114 treatment. Cell surface MET expression had the most predictive power in determining the efficacy of REGN5093-M114. REGN5093-M114 potently reduced tumor growth of EGFR-mutant NSCLC with PTEN loss or MET Y1230C mutation after progression on prior osimertinib and savolitinib treatment.

Conclusions:

Altogether, REGN5093-M114 is a promising candidate to overcome the challenges facing functional MET pathway blockade.

Translational Relevance

MET amplification is one of the most common mechanisms of resistance to EGFR tyrosine kinase inhibitors in patients with EGFR-mutant non–small cell lung cancer. In this article, we investigated the preclinical activity of REGN5093-M114, a novel antibody–drug conjugate targeting MET in MET-driven patient-derived models. REGN5093-M114 potently inhibited MET-driven in vitro and in vivo models and the efficacy was consistently seen in patient-derived preclinical models. Altogether, REGN5093-M114 is a promising candidate to overcome the challenges facing functional MET pathway blockade in EGFR-mutant patients.

The use of EGFR tyrosine kinase inhibitors (TKI) has substantially improved the clinical outcomes of patients with EGFR-mutant non–small cell lung cancer (NSCLC; refs. 1, 2). However, the majority of these patients develop acquired resistance through various mechanisms, including secondary mutations in EGFR, phenotype transition, and activation of bypass signaling pathways. A secondary EGFRT790M mutation accounts for approximately 50% of acquired resistance to the first- and second-generation EGFR TKIs, which can be overcome by treatment with osimertinib, a third-generation EGFR TKI (3–5). Osimertinib is currently approved as the first-line treatment for metastatic NSCLC with EGFR-sensitive mutations, but tertiary resistance mutations such as EGFRC797S occur during treatment, eventually limiting osimertinib efficacy (4, 6–10).

The proto-oncogene MET encodes the tyrosine kinase receptor MET. Upon binding to its ligand, hepatocyte growth factor, MET dimerizes, autophosphorylates, and activates intracellular signaling pathways that promote proliferations and cell survival. MET amplification has been reported as a bypass resistance mechanism to all generations of EGFR TKIs (4, 11–17). In particular, it has been predominantly observed in patients who progressed on osimertinib, accounting for 15% of resistance to first-line therapy and 19% of resistance to second-line therapy (4, 7, 18). De novo MET amplification could also represent a potential mechanism of intrinsic resistance to EGFR TKI therapy and is typically associated with a poor prognosis (19–23). Therefore, a strategy to combine EGFR and MET TKIs is being explored in the clinic. However, there is no consensus on the optimal diagnostic cut-off value for MET copy-number alterations (24). Moreover, combination with selective MET TKIs may eventually be limited by tolerance, or the development of resistance due to secondary mutations in the MET kinase domain or alteration of bypass signals (25). In addition, MET overexpression has been reported in 20%–50% in NSCLC, which may cause abnormal activation of the MET pathway and limit clinical efficacy (26–28).

Antibody–drug conjugates (ADC) are a class of drugs designed to selectively deliver covalently bound cytotoxic agents into cancer cells that express target surface antigens (29–32). A recent phase II trial of telisotuzumab vedotin (Teliso-V), the leading MET ADC, showed limited activity in patients with EGFR mutations overexpressing MET. However, Teliso-V was granted a breakthrough therapy designation for patients with advanced or metastatic EGFR wild-type, nonsquamous NSCLC with high MET overexpression (33). REGN5093-M114, a biparatopic MET ADC is a novel therapeutic agent that promotes apoptosis by delivering a potent cytotoxin to MET-overexpressing tumor cells irrespective of pathway dependence. Previously, DaSilva and colleagues (34, 35) first produced a biparatopic METxMET antibody (REGN5093) in which each arm of antibody recognizes a distinct epitope of MET, and then developed the REGN5093-M114 by conjugating a novel maytansinoid M114 payload to REGN5093. REGN5093 promotes lysosomal trafficking and degradation of MET through inhibition of MET recycling, making it an ideal candidate for ADC approaches. In this study, we demonstrated potent antitumor activity of REGN5093-M114, which takes advantage of the unique trafficking properties of REGN5093, in patient-derived, MET-driven EGFR-TKI–resistant NSCLC models. Furthermore, REGN5093-M114 was effective in tumor models resistant to combination EGFR plus MET TKI therapy. Our findings provide evidence for REGN5093-M114 as an effective treatment option for patients with EGFR-TKI–resistant NSCLC whose tumors are MET driven as well as those that have progressed on prior MET TKI treatment.

Chemicals and antibodies

Osimertinib and capmatinib were purchased from Selleck Chemical. REGN1945 (IgG4 isotype control), REGN1945-M114 (IgG4 control-M114), REGN5093 (biparatopic METxMET antibody), and REGN5093-M114 (METxMET-M114) were provided from Regeneron Pharmaceuticals Inc. The M114 linker-payload has been described previously (35).

Cell culture

H522, EBC-1, H1993, H2291, H460, H358, A549, PC9, HCC827, H4006, H1650, H1975, and H820 cells were purchased from ATCC. Cells were maintained in RPMI1640 medium with 10% FBS and 1% antibiotics at 37°C incubators with 5% CO2. To generate osimertinib (AZD9291, AR) acquired resistant cells, we followed previously described protocols (36). Resistant cells (HCC827-AR and H4006-AR) were derived after approximately 6 months of culture in the continuous presence of 1 μmol/L osimertinib. All cells were tested and confirmed to be negative in Mycoplasma contamination. Patient-derived cell lines (PDC) used passages within 40; patient-derived organoids (PDO) used passages within 20.

Patients

All patient samples were obtained from patients with EGFR-mutant NSCLC with acquired MET amplification after progression on EGFR-TKI treatment at Yonsei University Severance Hospital (Seoul, Republic of South Korea). The study protocol has been approved by the Institutional Review Board of Severance Hospital (IRB no. 4-2016-0001) and all patients have provided written informed consent. Patients treated with REGN5093 were enrolled in the clinical trial NCT04077099. This study conforms to the principles set out in the World Medical Association Declaration of Helsinki and the U.S. Department of Health and Human Services Belmont Report.

Establishment of patient-derived preclinical models

PDCs (YU-1089, YU-1093, and YU-1095) and PDOs (YUO-006, YUO-002, YUO-038, YUO-033, and YUO-010) were established from malignant effusions of patients as described previously (37, 38). The established PDCs were maintained and passaged by general cell culture methods. FACS staining of EpCAM confirmed PDCs with over 99% cancer purity. For PDOs, cells were suspended in cold Matrigel (Corning #256231) and then seeded in 24-well culture plates (Corning). After 15-minute incubation at 37°C, the complete organoid medium [DMEM/F12 medium (Invitrogen) supplemented with 20% conditioned R-spondin1 medium, 10 nmol/L HEPES (Invitrogen), 1× GlutaMax (Invitrogen), and 1× antibiotic–antimycotic (Invitrogen)] was added to the solidified Matrigel cells. Tumor purity of the organoids was confirmed by positive staining for thyroid transcription factor 1 (TTF-1, Clone EP1584Y; Abcam), Calretinin (Clone DAK-Calret-1; Agilent Technologies), and p53 (Leica Biosystems) using IHC. To determine whether patient-derived preclinical models maintained patient characteristics, Sanger sequencing and whole-exome sequencing (WES) were further performed.

Establishment of patient-derived xenografts

Patient-derived xenograft (PDX) models (YHIM-1035, YHIM-1042, and YHIM-1061) were established as described previously (39). To generate YU-1089 PDC-derived tumor xenograft models, cells (5 × 106 in 100 μL) were implanted subcutaneously into the flanks of 6-week-old female nu/nu mice. When tumor size reached 150–200 mm³, mice were randomly grouped and allocated to the following treatment groups: vehicle, osimertinib (25 mg/kg, oral daily), cabozantinib (30 mg/kg, oral daily), IgG4 control (10 mg/kg, subcutaneous injection twice per week), REGN5093 (10 mg/kg, subcutaneous twice per week), IgG4 control-M114 (10 mg/kg, subcutaneous only two times, separated by 1 week), REGN5093-M114 (10 mg/kg, subcutaneous only two times, separated by 1 week), and combinations of osimertinib with either REGN5093 or cabozantinib. Tumor volumes (0.532 × length × width) were measured with an electronic caliper. Tumor growth inhibition (TGI) was calculated with two formulas according to Drilon and colleagues (40). Body weight kinetics of tumor-bearing mice was monitored every other day throughout the study. All mice were euthanized via CO2 inhalation at the end of the experiment. Animal procedures were approved by the Institutional Animal Care and Use Committee and Animal Research Committee at Yonsei University College of Medicine (Seoul, Republic of South Korea).

WES

gDNA purity and concentration were tested by PicoGreen dsDNA assay (Invitrogen) and agarose gel electrophoresis method. Genomic fragment library was prepared using SureSelect v5 Kit (Agilent Technologies) and then sequenced on Illumina HiSeq 2500–based targeted sequencing capturing 171 cancer-related genes. Resultant reads were mapped to the human genome reference (hg19) using Burrows-Wheeler Alignment followed by analysis using Genome Analysis ToolKit. Somatic mutations were called using MuTect2 and annotated with Oncotator.

ISH

MET silver ISH (SISH) was performed on an automated Ventana BenchMark XT platform (Ventana Medical Systems), according to the manufacturer's protocols and using both MET-specific and centromere 7 (CEP7)-specific probes. The gene copy number (GCN) was independently assessed by a pathologist (Shim Hyo Sup) in a blinded manner.

Copy-number variation assay

To confirm the results of MET copy number obtained by SISH and WES, we additionally conducted a commercially available predesigned MET TaqMan Copy Number Assay (HS05018546) and a Reference RNase P Assay (PN4401631) according to the manufacturer's instructions (Applied Biosystems). The raw post-PCR data were used for MET copy-number calculation by the relative quantification method using the CopyCaller v.2.1. software (PN4412907) downloadable from www.appliedbiosystems.com.

Evaluation of MET-based prediction score for response of REGN5093-M114 treatment

To evaluate the predictive power of MET expression, amplification, or their combined effect for response to REGN5093-M114 treatment, we calculated AUC value. Cell lines with an IC50 of less than or greater than 3 μg/mL were defined as the responder and nonresponder groups, respectively. The combined predicted score with MET expression and amplification was generated by logistic regression model. All AUC values were calculated with R (Version: 4.1.2).

Cell viability assay

Cells were seeded onto 96-well plates (2 × 103/well), and after an attachment period, cells were treated with the various concentrations of drug for 120 hours. Cell viability was measured using Cell Titer Glo (Promega) according to protocol. Organoids were treated with Dispase for 30 minutes and then pellets were collected, washed with organoid medium without 20% conditioned R-spondin1 medium, and filtered using strainer (pluriSelect). A total of 20 to 70 μm organoids were seeded with 5% Matrigel onto 96-well ultra-low attachment plates (2 × 103/well). After an attachment period, cells were incubated with the various concentrations of drugs for 120 hours. Cell viability was analyzed using Cell Titer Glo-3D (Promega) according to manufacturer's protocol. The survival curves were estimated by Prism 8 software.

Immunoblot analysis

Primary antibodies for p-MET (3077), MET (8198), p-EGFR (2234), and EGFR (2232) were purchased from Cell Signaling Technology. β-Actin antibody was purchased from Sigma-Aldrich as an internal control. Cell lysates were centrifuged at 1,300 rpm for 20 minutes at 4°C and the resultant supernatants (cytosolic fraction) were moved to new tubes. Protein concentration was quantified using a Bradford assay (Bio-Rad). Equal amounts of protein were separated by SDS-PAGE and transferred to nitrocellulose membrane. The immunoblots were detected using SuperSignal West Pico Chemiluminescent Substrate (Thermo Fisher Scientific).

IHC

IHC was performed in 4-μm-thick formalin-fixed, paraffin-embedded tissue sections. The slides were baked, deparaffinized in xylene, passed through graded alcohol, and then the antigen was recovered in 1 mmol/L EDTA, pH 8.0 for 30 seconds at 125°C. The slides were pretreated with Peroxidase Block (Dako) for 5 minutes and then washed with 50 mmol/L Tris-Cl, pH 7.4. Slides were blocked using normal goat serum (Dako) and subsequently incubated for 1 hour with anti-phospho MET mAb (3077, Cell Signaling Technology)/anti-MET mAb (8198, Cell Signaling Technology)/anti-Ki67 antibody (9027, Cell Signaling Technology). Then the slides were washed with 50 mmol/L Tris-Cl, pH7.4 and the Signal stain boost IHC detection reagent (horseradish peroxidase, rabbit, #8114; Cell Signaling Technology) for 30 minutes. After further washing, immunoperoxidase staining was developed using a 3,3-diaminobenzidine chromogen (Dako) for 5 minutes. Slides were contrasted with hematoxylin, dehydrated with graded alcohol and xylene, mounted, and covered with slip. IHC of patients’ tumor was scored according to H-score, capturing both the intensity and the proportion of the MET membranous staining with values between 0 and 300. All pathologic analyses were conducted by an independent pathologist who was blinded to the clinical data (28).

Flow cytometry

Apoptosis analysis was measured in YU-1089 and HCC827-AR cells. Cells were treated with osimertinib, REGN1945, REGN5093, REGN1945-M114, or REGN5093-M114 for 48 hours. Apoptosis level was analyzed using FITC Annexin V Apoptosis Detection Kit I (#556547, BD) according to manufacturer's protocol.

MET and EGFR expression on the cell surface was measured using FITC anti-human c-MET (#11-8858-42, Invitrogen) fluorescence and APC anti-human EGFR (#352906, BioLegend) fluorescence. It was measured using the BD FACS verse equipment and analyzed using the Flow-Jo 10 program.

REGN5093 binding occupancy was measured in all cell lines. Surface MET was detected using FITC anti-human c-MET (#11-8858-42; Invitrogen), REGN5093, or REGN5093-M114 was confirmed using the IGg4PE (#9200-09, Southern Biotech) second antibody.

Statistical analysis

Statistical analyses were performed using GraphPad Prism software (version 7). Cell line experiments were independently repeated more than three times, with technical triplicate in each condition. Data are presented as the mean ± SEM. For animal studies, mice were randomly grouped when a tumor reached approximately 200 mm3 in size. The number of mice per group was 7–10. Data were expressed as the means ± SD. Between-group differences were evaluated using Kruskal–Wallis with Dunn post hoc test, or ANOVA with Tukey post hoc test, as appropriate. P < 0.05 was considered significant.

Data availability statement

The human sequence data generated in this study are not publicly available due to patient privacy requirements but are available upon reasonable request from the corresponding author.

REGN5093-M114 potently inhibits the growth of MET-amplified EGFR TKI resistance cells.

To evaluate the in vitro efficacy of REGN5093-M114, a MET-targeting ADC, in the context of MET-driven EGFR TKI resistance, two PDCs and five PDOs were tested. These cells were confirmed to have acquired MET amplification by WES and qRT-PCR–based GCN analysis. YUO-006 is a PDO in which MET amplification occurred after progression on first-generation EGFR TKI (gefitinib). YUO-010, YUO-033, and YUO-002 are PDOs that acquired MET amplification after failure of second-line osimertinib. YU-1089 is a PDC harboring MET amplification as acquired resistance to second-line olmutinib. YU-1095 (PDC) and YUO-038 (PDO) acquired MET amplification following resistance to first-line osimertinib and lazertinib, respectively. The detailed clinical annotations and genetic profiles of these models are summarized in Table 1, Supplementary Fig. S1, and Supplementary Table S1. We further performed qRT-PCR–based MET GCN analysis on 15 commercial NSCLC cell lines, including HCC827-AR, an osimertinib acquired resistant cell line generated from HCC827 cells, and assessed for response to REGN5093-M114 (Supplementary Table S2). The activity of MET-targeted therapy is closely related to the level of MET amplification (27, 39, 41), but clinically meaningful cut-off points for MET amplification vary with each technique and/or assay and are still not standardized (24). For this reason, we classified cells into three categories using qRT-PCR–based MET GCN results and then compared their responses with REGN5093-M114: (i) low: GCN ≥ 4 to < 8; (ii) intermediate: GCN ≥ 8 to < 15; and (iii) high: GCN ≥ 15. High MET-amplified EBC1 (17 copies) and H1993 (21 copies) NSCLC cell lines were used as positive controls (Supplementary Fig. S2). REGN5093-M114 potently decreased the viability of high MET-amplified HCC827-AR cells with an IC50 value of less than 0.03 μg/mL (similar to that observed in the positive control cell lines), whereas intermediate MET-amplified cells YU-1089 and YUO-010 exhibited IC50 values of 0.05 and 0.06 μg/mL, respectively, indicating that the activity of REGN5093-M114 increases with increasing MET copy number (Fig. 1A and B). Low MET-amplified cells, YUO-006, YU-1095, and H820 (de novo MET-amplified NSCLC) were sensitive to REGN5093-M114 with IC50 values of 0.1, 0.09, and <0.03 μg/mL, respectively, whereas YUO-002, YUO-033, and YUO-038 cells showed no response (Fig. 1C and D; Supplementary Fig. S3). We also found that REGN5093-M114 showed excellent antiproliferative effect as a single agent, but not synergistically in combination with osimertinib. In contrast, capmatinib and tepotinib, clinically available selective MET TKIs, exhibited antiproliferative effects at 3 nmol/L or less in positive control cell lines and were synergistic when combined with osimertinib in only HCC827-AR, which has the highest MET copy number (Supplementary Fig. S2 and S4; Fig. 1). Consistent with these results, REGN5093-M114 promoted apoptosis in both YU-1089 and HCC827-AR cells, whereas capmatinib induced apoptosis only in HCC827-AR cells (Supplementary Fig. S5). Collectively, these results suggest that REGN5093-M114 has broad antitumor activity in EGFR-TKI–resistant cells with varying levels of MET amplification, including those that were unresponsive to MET-TKI.

Table 1.

The detailed clinical annotations and genetic profiles of patient-derived models.

Sample IDTKI therapySample genotypeMET alteration
YHIM-1061 (post TATTON) Gefitinib/Lazertinib E19Del Secondary mutation Patient MET SISH (+) (MET:CEP7=3.92) (TATTON) 
    PDX MET SISH (−), MET (Y1248C) 
YHIM-1042 (post TATTON) Afatinib L858R Amplification Patient MET SISH (+) (MET:CEP7=2.27) (TATTON) 
    PDX 17 copy (qPCR) 
YHIM-1035 (post TATTON) Gefitinib/Lazertinib/Osimertinib E19Del Amplification Patient MET SISH (+) (MET:CEP7=12.55) (TATTON) 
    PDX MET SISH (+) (17.26 copy) 33 copy (WES), 21 copy (qPCR) 
YU-1089 (third EGFR TKI PD) Gefitinib/Olmutinib E19Del Amplification (high) Patient Not done 
    PDX qPCR:14 copy 
YU-1095 (first-line Osi PD) Osimertinib E19Del Amplification (low) Patient MET SISH (−) 
    PDX 4 copy (WES, qPCR) 
YUO-010 (second-line Osi PD) Erlotinib/Osimertinib E19Del/T790M loss Amplification (intermediate) Patient MET SISH (−) 
    PDX 8 copy (WES, qPCR) 
YUO-006 (first EGFR TKI PD) Erlotinib/Gefitinib E19Del Amplification (low) Patient Not done 
    PDX 4 copy (WES, qPCR) 
YUO-033 (second-line Osi PD) Gefitinib/Osimertinib E19Del/T790M loss Amplification (low) Patient MET SISH (+) (MET:CEP7=3.44) 
    PDX 4 copy (WES, qPCR) 
YUO-002 (second-line Osi PD) Gefitinib/Erlotinib/Osimertinib E19Del/T790M loss Amplification (low) Patient MET SISH (-) 
    PDO 4 copy (WES, qPCR) 
YUO-038 (first line/third EGFR TKI PD) Lazertinib L858R Amplification (low) Patient Not done 
    PDX 4 copy (WES, qPCR) 
YU-1093 (third EGFR TKI PD) Gefitinib/Olmutinib/Erlotinib E19Del High expression Patient MET SISH (−) 
    PDX 2 copy (qPCR) 
Sample IDTKI therapySample genotypeMET alteration
YHIM-1061 (post TATTON) Gefitinib/Lazertinib E19Del Secondary mutation Patient MET SISH (+) (MET:CEP7=3.92) (TATTON) 
    PDX MET SISH (−), MET (Y1248C) 
YHIM-1042 (post TATTON) Afatinib L858R Amplification Patient MET SISH (+) (MET:CEP7=2.27) (TATTON) 
    PDX 17 copy (qPCR) 
YHIM-1035 (post TATTON) Gefitinib/Lazertinib/Osimertinib E19Del Amplification Patient MET SISH (+) (MET:CEP7=12.55) (TATTON) 
    PDX MET SISH (+) (17.26 copy) 33 copy (WES), 21 copy (qPCR) 
YU-1089 (third EGFR TKI PD) Gefitinib/Olmutinib E19Del Amplification (high) Patient Not done 
    PDX qPCR:14 copy 
YU-1095 (first-line Osi PD) Osimertinib E19Del Amplification (low) Patient MET SISH (−) 
    PDX 4 copy (WES, qPCR) 
YUO-010 (second-line Osi PD) Erlotinib/Osimertinib E19Del/T790M loss Amplification (intermediate) Patient MET SISH (−) 
    PDX 8 copy (WES, qPCR) 
YUO-006 (first EGFR TKI PD) Erlotinib/Gefitinib E19Del Amplification (low) Patient Not done 
    PDX 4 copy (WES, qPCR) 
YUO-033 (second-line Osi PD) Gefitinib/Osimertinib E19Del/T790M loss Amplification (low) Patient MET SISH (+) (MET:CEP7=3.44) 
    PDX 4 copy (WES, qPCR) 
YUO-002 (second-line Osi PD) Gefitinib/Erlotinib/Osimertinib E19Del/T790M loss Amplification (low) Patient MET SISH (-) 
    PDO 4 copy (WES, qPCR) 
YUO-038 (first line/third EGFR TKI PD) Lazertinib L858R Amplification (low) Patient Not done 
    PDX 4 copy (WES, qPCR) 
YU-1093 (third EGFR TKI PD) Gefitinib/Olmutinib/Erlotinib E19Del High expression Patient MET SISH (−) 
    PDX 2 copy (qPCR) 
Figure 1.

REGN5093-M114 potently inhibits the growth of MET-amplified EGFR TKI resistance cells. A–D, PDOs (YUO-006 and YUO-010), patient-derived cells (YU-1089 and YU-1095), or ATCC cell lines (HCC827-AR and H820) were seeded on a 96-well plate and incubated with REGN5093-M114, control Ab-M114, and EGFR TKIs (osimertinib or gefitinib). REGN 5093-M114 was treated with concentration of 0, 0.03, 0.1, 0.3, 1, 3 μg/mL. MET TKIs, capmatinib were treated with serial concentrations from 0 to 100 nmol/L. The curved graphs show the cell viability results for a single drug, and the bar graphs show the cell viability for the combination of two drugs. Cell viability was measured after 5 days of REGN5093-M114, 3 days of capmatinib treatment via CellTiter-Glo. (Kruskal–Wallis with Dunn post hoc test: n.s., P < 0.001; ***, vs. negative control and con Ab).

Figure 1.

REGN5093-M114 potently inhibits the growth of MET-amplified EGFR TKI resistance cells. A–D, PDOs (YUO-006 and YUO-010), patient-derived cells (YU-1089 and YU-1095), or ATCC cell lines (HCC827-AR and H820) were seeded on a 96-well plate and incubated with REGN5093-M114, control Ab-M114, and EGFR TKIs (osimertinib or gefitinib). REGN 5093-M114 was treated with concentration of 0, 0.03, 0.1, 0.3, 1, 3 μg/mL. MET TKIs, capmatinib were treated with serial concentrations from 0 to 100 nmol/L. The curved graphs show the cell viability results for a single drug, and the bar graphs show the cell viability for the combination of two drugs. Cell viability was measured after 5 days of REGN5093-M114, 3 days of capmatinib treatment via CellTiter-Glo. (Kruskal–Wallis with Dunn post hoc test: n.s., P < 0.001; ***, vs. negative control and con Ab).

Close modal

REGN5093-M114 inhibits the growth of MET-overexpressed NSCLC cells

To correlate REGN5093-M114 efficacy with MET cell surface expression, FACS analysis was used. As expected, cells insensitive to REGN5093-M114 displayed lower levels of MET compared with sensitive cells. We also found that REGN5093-M114 decreased the viability of YU-1093, HCC827, and H4006 EGFR-mutated NSCLC cell lines overexpressing MET without MET amplification, with IC50 values of 0.54, 0.22, and 0.27 μg/mL, respectively. Conversely, H4006-AR cells that acquired resistance to osimertinib due to MET gene loss, exhibited reduced sensitivity to REGN5093-M114 (Supplementary Fig. S6A, S6B, and S7A). FACS analysis further demonstrated that higher expression of MET on the cell surface correlates with higher REGN5093-M114 binding (Supplementary Fig. S7B). Consistent with previous work (35), our findings indicate that the antitumor activity of REGN5093-M114 is closely related to the degree of MET expression. Interestingly, YUO-010 cells, which were highly sensitive to REGN5093-M114, had very low MET expression on the cell surface compared with other sensitive cells.

Predictive cut-off values for responsiveness to REGN5093-M114

To evaluate the usefulness of MET surface expression and GCN as predictive biomarkers for REGN5093-M114 response, AUC scores were calculated and the cell lines were grouped into responders (IC50 < 3) and nonresponders (IC50 > 3) based on the IC50 value of REGN5093-M114 (Fig. 2A; Supplementary Fig. S8). MET copy number, expression mean fluorescence intensity (MFI), and percentage of cells with surface MET expression were all higher within the responder group (P < 0.01; Fig. 2A). Given the possibility of functional cross-talk between EGFR and MET, expression of EGFR on the cell surface was also evaluated, but there was no correlation between surface EGFR expression level and REGN5093-M114 response (Fig. 2A; Supplementary Fig. 7A). Figure 2B shows the ROC curves in the top panel and summarizes the AUC values with the 95% confidence intervals (CI) and statistical summary in the bottom panel. The MFI value (AUC 0.985, P = 0.002) and the percentage of cells with surface MET expression (AUC 0.97, P = 1.70E-06) exhibited higher AUC values and a greater difference between responder and nonresponder groups than MET amplification (AUC 0.852, P = 0.014). Combining MET expression and amplification status showed higher discriminant power for response to REGN5093-M114 treatment (AUC: 1.0) than when MET expression or amplification alone were used. The optimal threshold value determining the sensitivity to REGN5093-M114 was 136.5 MFI or higher.

Figure 2.

Predictive cut-off values for responsivensess to REGN5093-M114. A, Evaluation of MET-based predictive potential for response to REGN5093-M114 treatment. Bar plot of IC50 scores of each cell line with REGN5093-M114 treatment and heatmap of MET expression, MET amplification, and EGFR expression according to IC50 score. B, ROC curve of MET expression, amplification, their combined status, and EGFR expression to response to REGN5093-M114 treatment (top) and performance information (bottom).

Figure 2.

Predictive cut-off values for responsivensess to REGN5093-M114. A, Evaluation of MET-based predictive potential for response to REGN5093-M114 treatment. Bar plot of IC50 scores of each cell line with REGN5093-M114 treatment and heatmap of MET expression, MET amplification, and EGFR expression according to IC50 score. B, ROC curve of MET expression, amplification, their combined status, and EGFR expression to response to REGN5093-M114 treatment (top) and performance information (bottom).

Close modal

In vivo activity of REGN5093-M114 and expectations for clinical potency

The in vivo efficacy of REGN5093-M114 was evaluated in the YU-1089 xenograft model. Consistent with in vitro assays, REGN5093-M114 induced pronounced tumor regression at a dose of 10 mg/kg (Fig. 3A). As a comparison, unconjugated REGN5093 at the same dose significantly inhibited tumor growth when combined with osimertinib. The IHC staining of YU1089 tumor sections using p-MET, MET, and Ki67 antibodies showed marked reduction in the staining of p-MET and t-MET and Ki67 with the treatment with REGN5093-M114 (Fig. 3B).

Figure 3.

In vivo activity of REGN5093-M114 and expectations for clinical potency. A, Nude mice bearing established tumors were randomized (n = 10/group) and treated with osimertinib, IgG4 control, REGN5093, REGN5093 plus osimertinib, IgG4 control-M114, and REGN5093-M114. Osimertinib was daily administered orally; IgG4 control and REGN5093 were administered by subcutaneous injection twice per week for 30 days. IgG4 control-M114 and REGN5093-M114 were administered by subcutaneous injection once when the tumor size reached 150–200 mm³, and was administered again 1 week later. Body weight and tumor volumes were measured once every 3 days. (Kruskal–Wallis with Dunn post hoc test: n.s., P > 0.05; *, P < 0.01; **, P < 0.001 ***, vs. vehicle; P > 0.05; #, P <0.01; ##, P < 0.001; ###, vs. osimertnib; P > 0.05; §, P < 0.01; §§, P < 0.001; §§§, vs. con Ab; P > 0.05; †, P < 0.01; ††, P <0.001; †††, vs. REGN5093 N = 10). B, IHC evaluation of YU-1089 tumor sections using p-MET (3077), MET (8198), and Ki67 (9027) antibody.

Figure 3.

In vivo activity of REGN5093-M114 and expectations for clinical potency. A, Nude mice bearing established tumors were randomized (n = 10/group) and treated with osimertinib, IgG4 control, REGN5093, REGN5093 plus osimertinib, IgG4 control-M114, and REGN5093-M114. Osimertinib was daily administered orally; IgG4 control and REGN5093 were administered by subcutaneous injection twice per week for 30 days. IgG4 control-M114 and REGN5093-M114 were administered by subcutaneous injection once when the tumor size reached 150–200 mm³, and was administered again 1 week later. Body weight and tumor volumes were measured once every 3 days. (Kruskal–Wallis with Dunn post hoc test: n.s., P > 0.05; *, P < 0.01; **, P < 0.001 ***, vs. vehicle; P > 0.05; #, P <0.01; ##, P < 0.001; ###, vs. osimertnib; P > 0.05; §, P < 0.01; §§, P < 0.001; §§§, vs. con Ab; P > 0.05; †, P < 0.01; ††, P <0.001; †††, vs. REGN5093 N = 10). B, IHC evaluation of YU-1089 tumor sections using p-MET (3077), MET (8198), and Ki67 (9027) antibody.

Close modal

REGN5093-M114 overcomes acquired resistance to combined EGFR and MET inhibition

Several combination trials, including those testing tepotinib with gefitinib (NCT01982955; ref. 27), osimertinib, savolitinib (NCT02143466; ref. 42), and INC280 and gefitinib (NCT01610336; ref. 41) have shown the clinical benefits of EGFR plus MET TKI therapy in patients with EGFR-mutated, MET-dysregulated NSCLC. Nevertheless, these therapeutic strategies eventually face acquired resistance. Thus, we determined whether REGN5093-M114 could overcome resistance to combination therapy. YHIM-1035, YHIM-1042, and YHIM-1061 are PDXs generated from patients that progressed upon osimertinib and savolitinib treatment, all of whom participated in a global TATTON trial. The H-scores for MET IHC staining of YHIM-1035, YHIM-1042, and YHIM-1061 PDXs were 200, 280, and 240, respectively (refs. 43–45; Supplementary Fig. S9). WES analysis revealed that YHIM-1035 acquired loss of PTEN and RB1 as well as amplification of EGFR, ERBB2, and MET. In YHIM-1042, only alterations to MET and EGFR were confirmed by targeted sequencing and qRT-PCR. REGN5093-M114 at a dose of 10 mg/kg promoted durable tumor regression as a single agent in both models (Supplementary Fig. S1; Fig. 4A and B). We also identified acquired MET p.Y1230C mutation (MAF = 0.165) and PIK3CA p.H1047R mutation (MAF = 0.488) in YHIM-1061. On the basis of recent studies examining the efficacy of osimertinib plus cabozantinib in tumors harboring MET p.Y1230C mutation (46), we compared the efficacy of this combination therapy with REGN5093-M114 in YHIM-1061. REGN5093-M114 monotherapy promoted tumor regression while the combination of cabozantinib and osimertinib significantly suppressed tumor growth (97.6% TGI; Fig. 4C). On the other hand, unconjugated REGN5093 significantly delayed tumor growth with 71.4% TGI in combination with osimertinib in the YHIM-1035 model, but had no effect in the YHIM-1061 and YHIM-1042 models. IHC analysis further demonstrated that REGN5093-M114 treatment resulted in a significant reduction of cells staining positive for Ki67, phosphorylated MET, and MET compared with vehicle or IgG4 control treatment in all PDXs (Supplementary Fig. S9). Taken together, these results suggest that REGN5093-M114 could be a promising option to overcome the acquired resistance of EGFR and MET TKI combination therapy caused by MET secondary mutations or other acquired genetic alterations. Therefore, we suggest the potential treatment algorithm for MET-dysregulated NSCLC depicted in Supplementary Fig. S10. When MET amplification occurs after failure on EGFR TKI, upfront use of REGN5093-M114 ± EGFR TKI is feasible. Alternatively, sequential treatment after EGFR and MET TKI combination is a potential option for clinical studies. Switching to another type of MET TKI (e.g., type I to type II) is an option when MET secondary mutation develops after EGFR TKI and MET TKI therapy. In addition, chemotherapy ± immunotherapy is possible for unknown mechanisms of resistance. Clinical application and their treatment outcomes will need to be further evaluated in an ongoing trial (NCT04982224).

Figure 4.

REGN5093-M114 demonstrates efficient antitumor activity in PDX models. A and B, Nude mice bearing established tumors were randomized (n = 10/group) and treated with osimertinib, IgG4 control, REGN, REGN5093 plus osimertinib, IgG4 control-M114, and REGN5093-M114. Osimertinib was administered orally every day; IgG4 control and REGN5093 were administered by subcutaneous injection twice per week for 30 days. IgG4 control-M114 (10 mg/kg) and REGN5093-M114 (10 mg/kg) were administered by subcutaneous injection once when the tumor size reached 150–200 mm³, and was administered again 1 week later. (Kruskal–Wallis with Dunn post hoc test: n.s., P > 0.05; *, P < 0.01; **, P < 0.001; ***, vs. vehicle; P > 0.05; #, P < 0.01; ##, P < 0.001; ###, vs. osimertnib; P > 0.05; §, P < 0.01; §§, P < 0.001; §§§, vs. con Ab N = 10). C, Nude mice bearing established tumors were randomized (n = 10/group) and treated with osimertinib, IgG4 control, REGN5093, REGN5093 plus osimertinib, cabozantinib, cabozantinib plus osimertinib, IgG4 control-M114, and REGN5093-M114. Osimertinib and cabozantinib were administered orally daily for 30 days. (Kruskal–Wallis with Dunn post hoc test: n.s., P > 0.05; *, P < 0.01; **, P < 0.001; ***, vs. vehicle; P > 0.05; #, P < 0.01; ##, P < 0.001; ###, vs. osimertinib; P > 0.05; †, P < 0.01; ††, P < 0.001; †††, vs. REGN5093; P > 0.05; ξ, P < 0.01; ξξ, P < 0.001; ξξξ, vs. REGN5093 + osimertinib; P > 0.05; §, P < 0.01; §§, P < 0.001; §§§, vs. con Ab N = 10).

Figure 4.

REGN5093-M114 demonstrates efficient antitumor activity in PDX models. A and B, Nude mice bearing established tumors were randomized (n = 10/group) and treated with osimertinib, IgG4 control, REGN, REGN5093 plus osimertinib, IgG4 control-M114, and REGN5093-M114. Osimertinib was administered orally every day; IgG4 control and REGN5093 were administered by subcutaneous injection twice per week for 30 days. IgG4 control-M114 (10 mg/kg) and REGN5093-M114 (10 mg/kg) were administered by subcutaneous injection once when the tumor size reached 150–200 mm³, and was administered again 1 week later. (Kruskal–Wallis with Dunn post hoc test: n.s., P > 0.05; *, P < 0.01; **, P < 0.001; ***, vs. vehicle; P > 0.05; #, P < 0.01; ##, P < 0.001; ###, vs. osimertnib; P > 0.05; §, P < 0.01; §§, P < 0.001; §§§, vs. con Ab N = 10). C, Nude mice bearing established tumors were randomized (n = 10/group) and treated with osimertinib, IgG4 control, REGN5093, REGN5093 plus osimertinib, cabozantinib, cabozantinib plus osimertinib, IgG4 control-M114, and REGN5093-M114. Osimertinib and cabozantinib were administered orally daily for 30 days. (Kruskal–Wallis with Dunn post hoc test: n.s., P > 0.05; *, P < 0.01; **, P < 0.001; ***, vs. vehicle; P > 0.05; #, P < 0.01; ##, P < 0.001; ###, vs. osimertinib; P > 0.05; †, P < 0.01; ††, P < 0.001; †††, vs. REGN5093; P > 0.05; ξ, P < 0.01; ξξ, P < 0.001; ξξξ, vs. REGN5093 + osimertinib; P > 0.05; §, P < 0.01; §§, P < 0.001; §§§, vs. con Ab N = 10).

Close modal

We have demonstrated for the first time the potent in vitro and in vivo activity of a biparatopic MET ADC, REGN5093-M114, in patient-derived, EGFR-mutated NSCLC with acquired MET amplification following progression on prior EGFR TKI.

MET amplification is a major bypass resistance mechanism, occurring in 10% to 25% of patients with EGFR-TKI–resistant NSCLC. Although clinical trials evaluating the activity of MET plus EGFR TKI combinations in the setting of MET-driven EGFR TKI resistance are ongoing, most preliminary data have shown benefit only in a small subset of MET-amplified patients (27, 41, 42). For this reason, antibody-based therapies targeting the extracellular domain of MET (34, 47–49) and more recently ADCs (35, 48, 50, 51) have been explored as alternatives to TKI treatment. REGN5093-M114 exhibited therapeutic potential in EGFR-TKI–resistant models with a broader range of MET amplification levels than did MET TKIs. In addition, both surface expression and GCN of MET demonstrated good performance as predictive biomarkers for the REGN5093-M114 response (Fig. 2). Of note, among the low MET-amplified cells, MET expression above 136.5 MFI correlated with potent REGN5093-M114 efficacy (Figs.1 and 2; Supplementary Fig. S3). Although our study is limited by diagnostic methods and small sample size, our findings suggest the need for complementary diagnostics to determine amplification and expression of MET for more reliable prediction of responsiveness to REGN5093-M114 treatment.

Patients who initially show benefit to the combination of EGFR and MET TKI eventually develop resistance. Although resistance mechanisms such as MET point mutations (Y1230C, D1246H), MET overexpression, and ERBB2 amplification have been detected in several case reports (25), there are currently no sequential treatment options available after failure of this combination therapy. In our study, we found acquired resistance to osimertinib plus savolitinib treatment resulted from various mechanisms, including MET overexpression, MET Y1248C secondary mutation, PIK3CA-H1047R point mutation, PTEN gene loss, or other unknown genomic mechanisms (Supplementary Fig. S1 and S9; Table 1; Supplementary Table S1). We found that REGN5093-M114 monotherapy resulted in remarkable tumor shrinkage in PDX models harboring these alterations (Fig. 4). Taken together, REGN5093-M114 may provide a viable treatment strategy for MET-amplified EGFR-TKI resistance.

Previous trials exploring MET-targeted therapies have shown limited clinical activity due to omission or difficulty in patient stratification (52–55). Most of these trials have been restricted to either MET amplification or MET overexpression, and neither has a standardized definition. In particular, the usefulness of MET overexpression as an independent prognostic factor is still controversial (29, 56, 57). In addition, MET overexpression determined using IHC has a weak correlation with MET amplification (43, 44). Therefore, an important area of future investigation is to define clearer biomarkers and thresholds to identify patients with a high likelihood of clinical benefit from REGN5093-M114 therapy. Importantly, a phase I, dose-escalation and dose-expansion study has been initiated to evaluate REGN5093-M114 in adult patients with MET-overexpressing advanced cancer (NCT04982224). Our findings may help guide future clinical trials and expand the scope of ADCs as cancer therapy. Another MET ADC, Teliso-V, was previously investigated for its efficacy in EGFR-mutant, c-MET–overexpressed patients in a phase II LUMINOSITY trial (NCT03539536). This trial included both EGFR wild-type and EGFR-mutant patients; however, the activity of Teliso-V was only modest in EGFR-mutant patients. While the ORR was 36.5% in the EGFR wild-type cohort (52.2% in c-MET high group and 24.1% in c-MET intermediate group), it was only 11.6% in the EGFR-mutant cohort, leading to the early closure of the EGFR-mutant cohort (58). These findings suggest that unmet needs still exist for the EGFR-mutant, c-MET–overexpressing patients.

In summary, this study demonstrates the potency of REGN5093-M114 in tumor models that are refractory and/or have acquired resistance to MET TKI treatment of MET-amplified EGFR-TKI–resistant NSCLC. Our findings suggest that REGN5093-M114 may be a feasible therapeutic option to address the current challenges faced in targeting the MET pathway after EGFR TKI failure. This provides a rationale for continued evaluation of REGN5093-M114 in a variety of clinically relevant scenarios.

J. DaSilva reports other support from Regeneron Pharmaceuticals during the conduct of the study; in addition, J. DaSilva has a patent for US2018/0134794A issued. C. Daly reports a patent for MET antibodies pending to Regeneron Pharmaceuticals, Inc. B.C. Cho reports non-financial support from ASCO, AstraZeneca, Guardant, Roche, ESMO, IASLC, Korean Cancer Association, Korean Society of Medical Oncology, Korean Society of Thyroid-Head and Neck Surgery, Korean Cancer Study Group, Novartis, MSD, The Chinese Thoracic Oncology Society, and Pfizer; personal fees from Kanaph Therapeutic Inc, Bridgebio Therapeutics, Cyrus Therapeutics, Guardant Health, Oscotec Inc, Interpark Bio Convergence Corp., J INTS BIO, TheraCanVac Inc, Gencurix Inc, Champions Oncology, Abion, BeiGene, Novartis, AstraZeneca, Boehringer Ingelheim, Roche, BMS, CJ, CureLogen, Ono, Onegene Biotechnology, Yuhan, Pfizer, Eli Lilly, GI-Cell, HK Inno-N, Imnewrun Biosciences Inc., Janssen, Takeda, MSD, Janssen, Medpacto, Blueprint Medicines, RandBio, and Hanmi; and other support from DAAN Biotherapeutics; grants from MOGAM Institute, LG Chem, Oscotec, Interpark Bio Convergence Corp, GI Innovation, GI-Cell, Abion, AbbVie, AstraZeneca, Bayer, Blueprint Medicines, Boehringer Ingelheim, Champions Oncology, CJ Bioscience, CJ Blossom Park, Cyrus, Dizal Pharma, Genexine, Janssen, Lilly, MSD, Novartis, Nuvalent, Oncternal, Ono, Regeneron, Dong-A ST, Bridgebio Therapeutics, Yuhan, ImmuneOncia, Illumina, Kanaph Therapeutics, Therapex, JINTSbio, and Hanmi outside the submitted work. M.R. Yun reports grants from National Research Foundation of Korea, as well as other support from Regeneron Pharmaceuticals Inc during the conduct of the study. No disclosures were reported by the other authors.

S.Y. Oh: Conceptualization, resources, data curation, software, formal analysis, supervision, validation, investigation, visualization, methodology, writing–original draft. Y.W. Lee: Investigation, visualization, methodology, project administration, writing–review and editing. E.J. Lee: Data curation, software, formal analysis, supervision, investigation, writing–original draft, writing–review and editing. J.H. Kim: Formal analysis, supervision, investigation, methodology, writing–review and editing. Y. Park: Validation, investigation, visualization, methodology, writing–review and editing. S.G. Heo: Investigation, visualization, methodology, writing–review and editing. M.R. Yu: Supervision, validation, investigation, visualization, methodology. M.H. Hong: Supervision, investigation. J. DaSilva: Supervision, project administration, writing–review and editing. C. Daly: Project administration, writing–review and editing. B.C. Cho: Conceptualization, resources, data curation, software, project administration, writing–review and editing. S.M. Lim: Conceptualization, resources, data curation, software, formal analysis, supervision, validation, investigation, methodology, writing–original draft, project administration, writing–review and editing. M.R. Yun: Conceptualization, resources, data curation, software, visualization, methodology, writing–original draft, project administration.

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) no. 2022R1A2C3005817, to B.C. Cho; no. 2022R1A2B5B02001403, to S.M. Lim; and no. 2018R1D1A1B07050233, to M.R. Yun.

The publication costs of this article were defrayed in part by the payment of publication fees. Therefore, and solely to indicate this fact, this article is hereby marked “advertisement” in accordance with 18 USC section 1734.

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

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Supplementary data