Preclinical PET Characterization of [68Ga]Ga-EMP-100 for Non-Invasive Assessment of c-Met in NSCLC

Preclinical PET Characterization of [68Ga]Ga-EMP-100 for Non-Invasive Assessment of c-Met in NSCLC | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Preclinical PET Characterization of [ 68 Ga]Ga-EMP-100 for Non-Invasive Assessment of c-Met in NSCLC Timofei Rusu, Anita Rodenas, Leo Capet, Alexandre Perrier, Jean-Pierre Pouget, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8970902/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background The c-Met receptor is a key therapeutic target in non-small cell lung cancer (NSCLC). Current methods for assessing c-Met level, are limited by biopsy sampling, which fails to account for spatio temporal and intra-tumour heterogeneity. This study aims to evaluate the use of PET/CT imaging for non-invasive, full-body quantification of c-Met expression in different subtypes of NSCLC, encompassing both adenocarcinoma and squamous cell carcinoma and compare it to MET gene alterations and IHC c-Met scoring. Results [ 68 Ga]Ga-EMP-100, a PET radiotracer targeting c-Met, was radiolabelled and characterized. Cell-Derived Xenograft (CDX) models of NSCLC with different characteristics were developed and validated for PET/CT imaging using [ 68 Ga]Ga-EMP-100. Tumour uptake and heterogeneity were quantified and compared to c-Met expression determined by IHC (H-score using SP44 and EP1454Y antibodies) and MET gene amplification detected by FISH and NGS. Automated radiolabelling of [ 68 Ga]Ga-EMP-100 demonstrated a high radiochemical yield and purity. Pharmacokinetics studies revealed rapid excretion predominantly by the renal pathway. PET/CT imaging resulted in high contrast and enabled non-invasive classification of CDX models regarding c-Met receptor levels. The highest tumour uptake was observed in H1648 and EBC-1 models. Although MET gene alterations were not correlated with c-Met protein expression at the cell surface, a good correlation was found between SUVmax and c-Met expression, when using the EP1454Y antibody. Conclusion PET/CT imaging using [ 68 Ga]Ga-EMP-100 successfully quantified c-Met expression in vivo , clearly adding up to conventional IHC and genetic methods. Our study adds novel comparative evidence across tumour histotypes, providing new insight into how tumour phenotype affects c-Met–targeted imaging. This radiotracer holds potential as a non-invasive tool for selecting patients for c-Met-targeted therapies and monitoring therapeutic response in NSCLC. Further clinical studies are warranted. c-Met PET/CT [68Ga]Ga-EMP-100 non-small cell lung cancer (NSCLC) Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Lung cancer remains the leading cause of cancer-related mortality worldwide, with over 1.8 million new cases each year. Non-small cell lung cancer (NSCLC) accounts for approximately 80% of all cases, and up to 70% of patients have a locally advanced or metastatic disease (bone, brain, or liver) at diagnosis. Among these, aberrant activation of the mesenchymal-epithelial transition factor (c-Met) receptor tyrosine kinase has been implicated in tumour progression, invasion, and resistance to therapy. Over recent years, this has driven intense therapeutic development targeting the MET pathway, from small-molecule tyrosine kinase inhibitors (TKIs) to antibody-based therapeutics. A major breakthrough in oncology has been the explosion of antibody-derived therapeutics, including antibody-drug conjugates (ADCs) 1 , 2 , bispecific antibodies 3 , and radiolabeled monoclonal antibodies (radioimmunoconjugates) 4 , many of which are currently in clinical development. These agents exploit c-Met receptor overexpression as a docking site for delivering cytotoxic payloads or radionuclides to tumour cells. However, their clinical benefit relies heavily on accurate patient selection, which remains challenging. Current biomolecular methods—such as MET gene amplification, exon 14 skipping detection, and immunohistochemistry (IHC) for c-Met expression—are limited by sampling bias, variability of staining interpretation, and lack of assay standardization. Furthermore, they depend on small biopsy specimens that may not represent the molecular heterogeneity of the entire disease. In addition, c-Met alterations can emerge under therapeutic pressure (e.g., TKI resistance in oncogene-driven NSCLC such as EGFR, ALK, ROS, or RET mutations), making repeated or multiple biopsies desirable but often impractical, invasive, or technically difficult—especially for lesions in hard-to-access locations such as the brain or bone 5 . In this context, molecular imaging represents an attractive complementary strategy, enabling whole-body, non-invasive, and repeatable assessment of c-Met expression. Such an approach could facilitate patient selection, early treatment monitoring, and longitudinal follow-up, overcoming many limitations of tissue-based assays. Several peptide- and antibody-based tracers for positron emission tomography (PET) imaging of c-Met have been developed in recent years 6 – 9 . The most advanced, [¹⁸F]AH113804, a cyclic 26-amino acid peptide conjugated to 4-[¹⁸F]fluorobenzaldehyde, demonstrated safety and favourable biodistribution in humans 10 . Its fluorescent analogue, EMI-137, has also shown promise in fluorescence-guided surgery 11 , 12 and early cancer detection 13 , 14 . A first-in-human study using [ 68 Ga]Ga-EMP-100 PET/CT, a DOTA-conjugated analogue of these ligands, reported promising results in metastatic renal cell carcinoma (mRCC) 15 . The study demonstrated the feasibility of visualizing c-Met expression in vivo and supported the concept that [ 68 Ga]Ga-EMP-100 allows clinicopathological staging and could serve as a predictive biomarker of response to targeted therapies, while also highlighting the need for further preclinical validation and molecular correlation. To address these unmet needs, and following the successful development of a robust and automated radiolabelling method for [⁶⁸Ga]Ga-EMP-100 16 our work aims to further characterize this tracer in a preclinical context in non-small cell lung cancer (NSCLC) models, including both adenocarcinoma and squamous cell carcinoma, where c-Met overexpression and dysregulation play a mechanistically and clinically distinct role. Specifically, we investigated it’s in vivo biodistribution, pharmacokinetics in multiple NSCLC models exhibiting variable levels of c-Met expression, and the quantitative relationship between in vivo tracer uptake, c-Met protein expression, and MET gene copy number within corresponding tumour tissues. Methods Competitive fluorescence polarization assay Synthesis of the EMP-100 and binding to recombinant human c-Met receptor (rhGFR/cMET Fc Chimera 358-MT/CF, R&D Systems.) was assessed by competition fluorescence polarization (FP) with EMI-137 (AH111972-Cy5** (EM Imaging Ltd)) are described in supplementary methods. Radiolabelling of [Ga]Ga-EMP-100 Materials required for radiolabelling in a GMP-compliant single-use kit were sourced from ABX (Advanced Biochemical Compounds, Radeberg, Germany) (ref RT-01-H, RT-101): 0.08 mol/L ammonium acetate buffer, 60% pure ethanol solution, ascorbic acid, 0.9% NaCl saline solution, water for injection (WFI, BBraun), eluent solution (5 mol/L NaCl, 0. 1 mol/L HCl), SCX (Bond Elut®, Agilent) and C18 cationic reversed-phase columns (Sep-Pack®, Waters), 0.22 µm filter (Millex-GV®, Merck Millipore LTd.) and sterile vials for the final product, with ultrapure gentisic acid supplied by Sigma-Merck. 68 Ga was obtained by elution from a commercial 68 Ge/ 68 Ga generator (GalliaPharm® 1850 MBq, Eckert & Ziegler radiopharma GmbH, Berlin, Germany) with a 0.1 M HCl solution (Eckert & Ziegler). The automated radiolabelling of [ 68 Ga]Ga-EMP-100 was performed using the Gaia/Luna commercial labelling synthesis module (Elysia-Raytest, GmbH, Straubenhardt, Germany), and radiochemical purity was determined by analytical thin layer chromatography (TLC) and high performance liquid chromatography (HPLC), as previously described by our team 16 . All chemicals were pure or analytical grade. The non-decay-corrected radiochemical yield (RCY) was calculated as the ratio of the final activity collected on C18 to the initial activity collected on SCX. Molar specific activity in MBq/nmol was calculated by dividing the activity of the final product by the total amount of EMP-100 ligand. Final activity and volumic activity were expressed in MBq and MBq/mL. Cell lines Human squamous non-small cell lung carcinoma EBC-1 cell line known as MET Amplified determined by qPCR 17 was provided by IRCM. Human pulmonary adenocarcinoma H1993 and H1648 known as MET Amplified determined by qPCR 17 , A549 and HCC827 known as MET low and EGFR mutation in exon deletion 19 for the latter 18 cell lines were purchased from ATCC. Cells were cultured at 37°C and 5% CO2 in RPMI Medium 1640-GlutaMAX containing 10% FBS, 100 U/mL penicillin and 100 µg/mL streptomycin. Determination of the c-Met expression by flow cytometry The c-Met expression was determined in EBC-1 using EMI-137 (AH111972-Cy5**). Cells were trypsined, washed in PBS, and fixed with 4% PFA for 10 minutes. After washing, cells were incubated overnight at 4°C with PBS-BSA 0.5%. The next day, cells were incubated with the EMI-137 (0.1 or 1) for 15 minutes. A part of the cells was previously incubated with 500 µg/mL of unlabelled EMP-100 for 15 min to evaluate the non-specific binding. c-Met expression was assessed by evaluation of the fluorescent intensity of cells using a Cytoflex® Cytometer (Beckman Coulter) and analysis was performed with FlowJo® software (BD). Cell Derived Xenograft models Athymic female NMRI nude mice or C57Bl6 mice (age: 4–6 weeks, weight: 17–20 g) were purchased from Janvier, France. Animal viability and behaviour were observed daily, and a clinical follow-up recorded if deemed necessary. Body weights were measured twice a week. For implantation, the tumour cells were harvested by trypsinization and 1 to 2x10 6 cells in 50% Matrigel (Corning, 354230) were inoculated subcutaneously into the right or left shoulders of the mice. Growth of the tumours was measured in two perpendicular directions twice per week using a calliper and the volumes of the tumours were calculated as 0.5 × L × W2 (L = longest axis and W = axis perpendicular to L in millimetres). Mice were submitted to imaging and biodistribution when tumour sizes reached volumes of 400–800 mm 3 . PET/CT imaging Isoflurane gas anaesthesia was used for IV injections, blood collection and PET/CT imaging. Anaesthesia was induced and maintained by the administration of a mixture of isoflurane (1.5–2.5%) and oxygen. Mice were injected intravenously (retro-orbitary sinus) with 1 to 5 MBq of [ 68 Ga]Ga-EMP-100. Whole body positron emission tomography (PET) coupled to computed tomography (CT) scans of the mice injected with [ 68 Ga]Ga-EMP-100 were performed under general anaesthesia in nanoScan PET/CT (Mediso Medical Imaging Systems Ltd., Budapest, Hungary) during 20 min scan, 40–60 min post injection (p.i.) and using multiple bed (3 mice simultaneously) for static PET images or during 90 min p.i. using list mode for dynamic PET. CT acquisition (35 kVp, 300 ms, 360 projections, binning 1:4) was immediately performed after PET acquisition using the same bed position. PET and CT files were fused and converted to standardized uptake value (SUV) images using Nucline 2.03 Software (Mediso Medical Imaging Systems, Hungary). Images were analysed and presented as maximum intensity projections (MIP) in RGB colour scale. Quantification was done using a volume of interest (VOI) technique and expressed as the maximum standardized uptake value (SUVmax) or %IA/cm 3 calculated in bladder, kidneys, tumour and background (close to the tumour) VOIs. Time Activity Curves (TAC) are represented by the accumulated radioactivity within the time interval up to 90 min after injection (in % of injected activity (IA)). Ex vivo biodistribution Ex vivo biodistribution studies were carried out on the same animals that underwent PET/CT imaging. Briefly, immediately after the PET/CT scan, 1h or 2h post injection (p.i.), mice were euthanized and dissected (n = 3–4) mice per time point. Blood, liver, spleen, kidneys, stomach, intestine, colon, tumour, were collected and weighed. Then, the organ radioactivity was measured using a gamma-counter (Packard Instrument) and uptake values of organs were calculated as percentage injected activity per gram tissue (% IA/g). c-Met Immunohistochemistry Tissue samples were formalin-fixed paraffin-embedded (FFPE) and sectioned at 3 µm-thick freshly cut. Immunostaining was performed using a pre-diluted at 9.75 µg/mL rabbit anti c-Met monoclonal primary antibody targeting C-Terminal region, clone SP44 (CONFIRM® anti-Total c-Met; Ventana Medical Systems, Tucson, ref. 790–4430, EU-IVD) or using a rabbit anti c-Met targeting N-Terminal region, clone EP1454Y (Abcam ab51067) used at 2.4 µg/mL, both using VENTANA BenchMark ULTRA instrument 19 . Immunostaining intensity was determined by two operators for each CDX and native cell line according to the H-score. The final H-score (range: 0-300) was calculated as (1 × [% cells 1+] + 2 × [% cells 2+] + 3 × [% cells 3+]). MET gene copy number (GCN) detection by FISH The MET FISH protocol was performed on 3 µm-thick freshly cut sections from FFPE NSCLC tumour blocks using a MET dual-color probe (ZytoLight® SPEC MET/CEN7 Dual Color Probe, Z-2087–50, ZytoVision GmbH, Bremerhaven, Germany) according to the manufacturers’ standard protocol (ZytoLight FISH-Tissue Implementation Kit, Z-2028-5, ZytoVision GmbH) and the routine in-house standards more described in supplementary methods. The MET FISH status was analysed by a pathologist with specific experience in this field (MA) using a Zeiss Imager. After identifying the tumour cells in DAPI, 50 non overlapping tumour nuclei were evaluated, and both green and orange signals per nucleus were counted to determine the mean GCN of MET and CEN7 , respectively. The MET gene copy number status was classified into four groups according to high, intermediate and low-level amplification or normal, non-amplified) 20 . Tumour cells harbouring CEN7 signals on average ≥ 3.6 were classified as polysomic 21 . MET gene copy number (GCN) and other molecular alterations by Next Generation Sequencing (NGS) NGS (AmpliSeq™ Kit for Illumina® Focus Panel, details in supplementary methods) was used to detect the MET gene alterations ( MET amplifications as well as MET exon 14 skipping mutation MET ex14) or others NSCLC panel gene alterations in the five types of CDX tumours. We annotated and described genetic changes like point mutations, deletions, and insertions using the standardized Human Genome Variation Society (HGVS) nomenclature. For gene amplification tests ( MET , CDK4 , EGFR , MYC , etc.), we have classified the level of amplification into 3 categories according to our laboratory's specific procedures and considering the cellularity of the tumour. Weak amplification (3–4 copies), moderate/intermediate (> 4–8 copies), strong (> 8 copies). Statistical Analysis Data for radiolabelling were presented as mean value standard deviation (SD). In vivo and ex vivo uptake data were presented as mean standard error (SEM). Statistical analyses were performed using GraphPad Prism v8.0. A one-way ANOVA (Tukey’s multiple comparison test) was used to assess significance. Significance levels are defined as ns (not significant, P > 0.05), *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001. The correlation between immunohistochemistry and SUVmax was determined using Pearson’s correlation coefficient. Results Radiolabelling and in vitro characterization of EMP-100 EMP-100 molecular weight (Mw) was confirmed by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry ( Fig. 1A ) using a Bruker MALDI TOF-TOF UltrafleXtreme with sinapic acid as a matrix. EMP-100 was shown to bind to c-Met with a dissociation constant (K d ) of 1.1 nmol/L, predetermined by competitive binding assay with EMI-137 whose affinity was previously measured as being 3.0 nmol/L ( Fig. 1B and supplementary Fig. S1 ). Structure of the fluorescent EMI-137 or the EMP-100 peptide conjugated with 68 Ga shown in Fig. 1C. Using fluorescent EMI-137 in flow cytometry we were able to identify c-Met expression on the EBC-1 cell line ( Fig. 1D ). The fluorescence intensity is consistently higher when the cells are treated with 1 µg/mL of EMI-137 compared to 0.1 µg/mL. Additionally, EMI-137 uptake was significantly reduced in cells co-incubated with unlabelled EMP-100, as shown by the downward shift in fluorescence intensity. This reduction in fluorescence indicates the competitive binding of EMP-100 to the c-Met receptors, demonstrating the in vitro specificity of the ligand. The fully automated radiolabelling of [ 68 Ga]Ga-EMP-100 was performed as previously reported on a Gaia/Luna system with an overall non-decay-corrected radiochemical yield (RCY) of 64 ± 0.8% with ≥ 99% radiochemical purity (RCP). The mean time of the entire labelling procedure was approximately 42 min. We obtained a high specific activity of 30.2 ± 6.8 MBq/nmol (n = 5). Radioactivity in final product was 606 ± 127 MBq. The formulated [ 68 Ga]Ga-EMP-100 was stable for 3 hours 16 . Figure 1 . Radiolabelling and in vitro characterization of EMP-100 (A) Molecular weight of EMP-100 peptide (DOTA-AH111972) measured by mass spectrometry analysis. (B) EMP-100 demonstrated high binding affinity to c-Met. Dissociation constant (Kd) was determined by competition with EMI-137 using fluorescence polarization. (C) Illustration of the AH111972 peptide conjugated with R= [ 68 Ga]Ga-DOTA ( EMP-100 ) or R= Cy5** (tetra SO 3 )-NH 2 ( EMI-137 for fluorescence guided surgery). (D) Number of cells as a function of the fluorescence intensity (c-Met expression) for EBC-1. Pharmacokinetics of [ 68 Ga]Ga-EMP-100 Dynamic PET imaging was performed on C57Bl6 mice for 80 min post-injection. The highest signal in the PET images was observed in the bladder, followed by the kidneys ( Fig. 2A ). Kinetic analysis of [ 68 Ga]Ga-EMP-100 showed rapid tissue distribution, where activity in the heart, and liver peaked after 2.5 min and diminished under 0.05% of the injected activity at the end of the dynamic acquisition (4800 sec). Rapid clearance through renal elimination was also observed, with the activity in the kidneys peaking at 450 sec and decreasing to 5.5% of injected activity, the bladder showing increasing uptake for the whole imaging period until 55% of injected activity ( Fig. 2B ). To better characterize biodistribution in a tumour model, static PET imaging was performed on a first model of cell derived xenograft (CDX) squamous (EBC-1) cell line. [ 68 Ga]Ga-EMP-100 showed a similar biodistribution profile to dynamic PET imaging in C57Bl6 mice with high-contrast PET images in tumour observed within 40–60 minutes after injection of 4.2 ± 1.2 MBq; 219 ± 6 pmol and still visible at 90–110 minutes, although with lower intensity ( Fig. 2C-D ). To confirm the PET results, ex vivo 2 h p.i. biodistribution studies were performed. The circulating activity was 0.17 ± 0.1% IA/g, uptake in kidneys was 6.73 ± 1.04% IA/g and the uptake in EBC-1 tumours was 1.83 ± 0.32% IA/g, corresponding to a tumour-to-blood ratio of 12.8 ± 3.6. The tumour-to-kidneys (R + L) ratio was 0.3 ± 0.1 ( Table S1 ). Very low tracer accumulation was observed in other organs, consistently with the visual observations on PET imaging. Therefore, we used the 40-60-minute time point for all further experiments, as it provided good contrast. (A) Dynamic PET/CT MIP of a C57Bl6 mouse injected (retroorbital injection point) with [ 68 Ga]Ga-EMP-100, time post injection, frame duration: 5min, 10min and 20 min. (B) Time Activity Curves of the accumulated radioactivity per organ (%IA, injected activity) in VOI (cm 3 ) within the time interval up to 80 min after injection in C57Bl6 mice (n = 3). (C) Representative maximum intensity projection (MIP) image of [ 68 Ga]Ga-EMP-100 in EBC-1 xenografted mice after injection of 4.2 ± 1.2 MBq; 219 ± 6 pmol. Left side, static PET/CT image at 40–60 min pi and right side 90–110 min pi. Respective axial slices on EBC-1 tumour. (D) Quantification in %IA/cm 3 of in vivo accumulation of radioactivity in key VOI at 40–60 min pi (black bar) and 90–110 min pi (grey bar). Data presented as mean ± SEM (n = 3). K kidneys; Bl. Bladder. Mean EBC-1 tumour Volume in VOI: 0.395 cm 3 (n = 3). [ 68 Ga]Ga-EMP-100 comparative PET imaging to quantify c-Met level expression in Cell Derived Xenograft (CDX) models of NSCLC Four NSCLC xenograft models other than EBC-1 were successfully developed using pulmonary adenocarcinoma (ADC) (H1993, H1648, A549 and HCC827) cell lines. Squamous EBC-1 CDX model was growing faster, with tumours reaching 469 ± 68 mm3 only 18 days post graft whereas more than double the time was needed to obtain approximately the same volume for the ADC CDX models (383 ± 111 mm 3 at day 56 for H1648; 458 ± 101 mm 3 at day 42 for H1993; 447 ± 164 mm 3 at day 39 for HCC827; 419 ± 14 mm 3 at day 48 for A549). Figure 3A. Due to technical constraint of production and availability of the radiotracer, [ 68 Ga]Ga-EMP-100 PET imaging was performed when tumours reach 800 mm 3 for EBC1, H1993 and HCC827 and when tumours reach 400 mm 3 for H1648 and A549 corresponding at the end of measurement of tumour growth. To avoid any in vivo competition of “cold” ligand against the “hot” radiopharmaceutical for the c-Met receptor, the same injected specific activity was maintained for each group of tumour types with no differences in pmol of EMP-100 injected (166 to 188 pmol) corresponding to 2.16 MBq to 3.70 MBq of [ 68 Ga]Ga-EMP-100 injected (Table 1 ). Representative maximum intensity projection (MIP) image of [ 68 Ga]Ga-EMP-100 in different CDX models of NSCLC provided, high-contrast PET images as early as 40–60 min p.i. with renal clearance and bladder accumulation, equivalent between mice, but with different level of [ 68 Ga]Ga-EMP-100 uptake in tumours. Mean %IA/cm³ provided an integrative measure of total tracer accumulation in tumours, ranging from 2.58 ± 0.87 for H1648 to 0.65 ± 0.22 for A549. However, mean %IA/cm³ can be affected by intra-tumoural heterogeneity and necrotic regions that dilute the apparent uptake. We therefore also performed an analysis based on SUVmax, focusing on the most metabolically active voxel, thus highlighting viable regions with the highest c-Met expression. Consequently, discrepancies observed between %IA/cm³ and SUVmax values across tumour models likely reflect biological heterogeneity within the lesions, such as necrosis, stromal content, or variable vascularization. SUVmax values within the tumour VOI corroborated these findings and therefore may allow non-invasive ranking of NSCLC CDX models according to c-Met expression. A549 was considered as negative for c-Met with a SUVmax of 1 ± 0.09, H1993 and HCC827 as moderate or low with a SUVmax of 2.11 ± 0.18 and 2.06 ± 0.2 respectively, whereas EBC-1 and particularly H1648 could be considered as high c-Met receptor expression and very different from A549 with a SUVmax of 2.67 ± 0.09 and 3.19 ± 0.61 respectively (*p < 0.05 and **p < 0.01, One way ANOVA with Tukey’s multiple comparisons post hoc test was performed). These data were confirmed by ex vivo gamma counting of animal tissues at 1h post tracer injection. The highest total tumour uptake was shown in the H1648 model, with 4.31 ± 0.45%IA/g and EBC-1 total tumour gamma counting revealed only 1.81 ± 0.07%IA/g ( n = 3) which could be explained by the low uptake in the necrotic tissue observed on PET imaging. Ex vivo gamma counting also indicated high radioactivity concentrations only in the kidneys (6.6 to 9.9%IA/g) and low blood concentrations (less than 0.84%IA/g). Best tumour-to-blood and tumour-to-kidneys ratios were 6.37 and 0.47 at 1 h p.i. in the H1648 CDX model (Table 2 ). Interestingly, there was no correlation between tumour growth and in vivo tumour c-Met expression regarding the different CDX models Fig. 3A and 3C . Figure 3: [ 68 Ga]Ga-EMP-100 comparative PET imaging (A) Tumour growth in squamous (EBC-1) and adenocarcinoma (H1993, H1648, A549 and HCC827) NSCLC cell lines. Mean ± SEM (n = 3–6 mice/cell line). (One-way ANOVA with a post hoc Tukey multiple comparison test; ns, non-significant , ****P < 0.0001 ). (B) Representative maximum intensity projection (MIP) image of [ 68 Ga]Ga-EMP-100 in different CDX models of NSCLC at 40–60 min pi. (C) Quantification in %IA/cm 3 and (D) in SUVmax of in vivo accumulation of radioactivity in tumour VOI at 40–60 min pi. Data presented as mean ± SEM. (*p < 0.05 and **p < 0.01, One way ANOVA with a post hoc Tukey multiple comparison test). (E) Following ex vivo gamma counting of tissues 1h after injection of [ 68 Ga]Ga-EMP-100 in CDX mice. Tissue radioactivity is expressed as the percentage of injected activity per gram (% IA/g, mean ± SEM). Table 1 Details of injections and tumour data extracted from PET imaging (n = 3) Tumour type H1648 EBC1 H1993 HCC827 A549 Ad SqCC Ad Ad Ad pmol injected 166 ± 17 159 ± 23 168 ± 9 182 ± 2 188 ± 10 MBq injected 3.1 ± 0.17 2.16 ± 0.31 3.39 ± 0.10 3.70 ± 0.42 2.92 ± 0.1 Tumour uptake (Mean %IA/cm 3 ) 2.58 ± 0.87 1.66 ± 0.59 1.48 ± 0.62 1.66 ± 0.60 0.65 ± 0.22 Tumour SUV Max (g/mL) 3.19 ± 1.05 2.67 ± 0.16 2.11 ± 0.32 2.06 ± 0.34 1 ± 0.15 Table 2 Ex vivo biodistribution of [ 68 Ga]Ga-EMP-100 at 1h p,i, in CDX mice. EBC1 CDX mice H1648 CDX mice H1993 CDX mice HCC827 CDX mice A549 CDX mice Mean SEM Mean SEM Mean SEM Mean SEM Mean SEM Blood 0.72 0.05 0.68 0.07 0.56 0.07 0.77 0.14 0.84 0.07 Liver 0.46 0.04 0.62 0.05 0.47 0.09 0.54 0.01 0.46 0.05 Spleen 0.31 0.07 0.37 0.06 0.26 0.05 0.33 0.03 0.34 0.07 Lung 0.70 0.10 0.59 0.06 0.56 0.08 0.68 0.00 0.70 0.02 Kidneys 7.15 0.70 9.88 0.57 6.38 1.12 7.45 0.57 8.51 0.17 Stomach 0.25 0.16 0.10 0.02 0.20 0.05 0.30 0.20 0.25 0.02 Intestin 0.33 0.08 0.28 0.04 0.39 0.08 0.27 0.05 0.31 0.17 Colon 0.17 0.03 0.14 0.03 0.14 0.05 0.48 0.24 0.23 0.02 Muscle 0.13 0.07 0.17 0.03 0.14 0.02 0.12 0.06 0.02 0.03 Brain 0.03 0.01 0.06 0.01 0.02 0.01 0.33 0.28 0.06 0.08 Tumour 1.81 0.07 4.31 0.45 2.33 0.08 2.28 0.19 1.25 0.07 Tumour / blood ratios 2.50 0.12 6.37 0.19 4.24 0.56 3.17 0.60 1.50 0.06 Tumour / kidneys ratios 0.23 0.03 0.47 0.07 0.38 0.08 0.30 0.00 0.13 0.03 Differential MET gene alterations (FISH and NGS) in CDX models, c-MET Expression in NSCLC Cell Derived Xenograft (CDX) models MET amplification FISH analysis on formalin-fixed, paraffin-embedded specimens of CDX tumours revealed strong MET amplification in EBC-1 and H1993 (GCN 16.5, ratio 6.9 and GCN 18.3, ratio 4.7, respectively) and intermediate MET amplification (GCN 6.9, ratio 3.5) in H1648, whereas HCC827 and A549 showed lower values, indicating no amplification (GCN 2.6 and GCN 2.1, respectively) ( Fig. 4A. and Table 3 ) NGS analyses were concordant with the above, demonstrating high MET amplification (gene copy number (GCN)) in EBC-1 and H1993 tumour explants, intermediate MET amplification in H1648 while HCC827 and A549 have no MET amplification. All CDX tumours were wild type for MET ex14. Other molecular alterations were observed in the A549 and HCC827 tumours explants as i.e., EGFR del19 previously described 18 , 22 (Table 3 ). c-Met expression c-Met protein levels were evaluated using two different c-Met targeting antibodies (SP44 clone, Ventana® and EP1454Y clone, Abcam®) in formalin-fixed paraffin-embedded tumours specimens and quantified using the H-Score 23 – 25 . Immunohistochemistry with SP44 revealed strong intensity expression in all CDX models, except A549. Expression was primarily membranous although variable cytoplasmic expression was also observed. Finally, H-Score was between 270 and 298 without distinction between all CDX models except the A549 (H-score: 82). There was not differential IHC expression between highly strong (EBC1, H1993), intermediate MET (H1648) and even with the no MET amplified HCC827 CDX models. Figure 4C and Table 3 . By contrast, IHC with EP1454Y clone was exclusively membranous and H-score was different between all CDX models ranging from 234 to 82 with the higher H-score for the EBC-1 (234) to the lower score for the A549 (82). Figure 4D and Table 3 . Although EBC1 and H1993 cell lines were both strongly MET amplified, H-Score was 234 for the former and 134 for the latter. Figure 4: Different Cell Derived Xenograft (CDX) models of NSCLC (A) FISH results. Orange dot: copy of centromere 7, green dot: copy of MET gene. (x60). (B) Immunohistochemistry with HES (x10) (C) SP44 antibody (x40) and with (D) EP1454Y (x40) in CDX tumours. Table 3 Recapitulative analysis of the MET gene copy number, c-Met expression by IHC in five different CDX model of NSCLC. Cell Derived Xenograft H1648 EBC-1 H1993 HCC827 A549 MET GCN (FISH) 6.9 16.5 18.3 2.6 2.1 Ratio MET/CEN7 3.5 6.9 4.7 NA (polysomic) 1 Level Intermediate High High Negative Negative MET GCN (NGS) 7 9 9 0 0 Level Intermediate High High Negative Negative Other alterations None None None EGFR del19 CDK4 ampli EGFR ampli KRAS G12S Histology Ad SqCC Ad Ad Ad H Score SP44 (0-300) 298 290 298 270 82 H Score EPY1454 (0-300) 207 234 128 143 82 [⁶⁸Ga]Ga-EMP-100 Uptake Reflects c-Met Expression Across NSCLC Xenografts A positive correlation was observed between c-Met expression levels determined by immunohistochemistry (IHC) and tumour uptake of [⁶⁸Ga]Ga-EMP-100 quantified by SUVmax ( Fig. 5 ). Quantification of c-Met expression using the H-score demonstrated that tumours with higher receptor expression exhibited markedly increased tracer accumulation. This correlation was statistically significant for the EP1454Y antibody (R² = 0.919, P < 0.05), supporting the specificity of [⁶⁸Ga]Ga-EMP-100 binding to extracellularly expressed c-Met-positive lesions. Figure 5: Differential c-Met Expression Relationship between c-Met expression detected by immunohistochemistry and quantified by H Scoring and tumour uptake (SUVmax) of [ 68 Ga]Ga-EMP-100. (R squared = 0.919 and **P < 0.05 for EP1454Y Antibody) Discussion In this study, our aim was to demonstrate the potential of PET imaging using [ 68 Ga]Ga-EMP-100 as a full body, non-invasive and quantitative imaging of the c-Met receptor in NSCLC. Results obtained from [ 68 Ga]Ga-EMP-100 PET imaging were compared with standard IHC analysis across different histological cell lines exhibiting various genetic alterations reported to be associated with variable c-Met expression. First, the EMP-100 peptide precursor was confirmed to bind to c-Met with high affinity (K d = 1.1 nmol/L) and its specificity for c-Met receptors was demonstrated in vitro by competition experiments with EMI-137, a known fluorescent binder. Using a robust and automated radiolabelling method 16 , we produced a highly pure (RCP ≥ 99%) and high-quantity (> 500 MBq) batches of [ 68 Ga]Ga-EMP-100 with a molar activity of 30.2 ± 6.8 MBq/nmol. Animal PET imaging with minimal peptide injection amounts (less than 200 pmol), yielded high-contrast images, demonstrating the high sensitivity of PET imaging to non-invasively detect very low levels of c-Met in the squamous EBC-1 CDX model within 40–60 minutes (1.37 ± 0.88%IA/cm 3 ). Subsequent in vivo and ex vivo pharmacokinetic analyses showed rapid accumulation of radioactivity in kidneys, bladder and urine, suggesting predominantly renal excretion. This trend was consistent with previous observations using analogues based on the same ligand, namely [ 18 F]AlF-EMP-105 and [ 18 F]AH113804, although both analogues displayed higher residual activity in kidneys (over 15%ID/g 7 and 4.9 ± 0.6%ID/g 26 respectively) with [ 18 F]AH113804 also showing liver uptake. This uptake resulted in mean absorbed doses of 0.052 mGy/MBq in human kidneys and 0.022 mGy/MBq in liver 10 . Given the shorter half-life of gallium-68 and higher hydrophilicity conferred by the DOTA chelator, [ 68 Ga]Ga-EMP-100 should represent a lower-radiation alternative with more favourable dosimetry for patients. With the above preliminary validation, we next conducted a comparative PET imaging study across various cell-derived xenograft mouse models. Distinguishable accumulation of [ 68 Ga]Ga-EMP-100 was observed in H1648 and EBC-1, both considered highly c-Met–overexpressing tumours, compared to models with low and moderate c-Met expression levels. These results highlight the potential usefulness of [ 68 Ga]Ga-EMP-100 PET for the non-invasive prediction of c-Met expression in NSCLC, consistent with recent clinical case evidence in NSCLC patients 27 . Finally, a comparison was made between the [ 68 Ga]Ga-EMP-100 PET findings and standard assays used for c-Met–targeted therapy selection (IHC and FISH/NGS). Several important trends emerged. First, MET gene alterations, as assessed by NGS and FISH did not correlate with c-Met protein expression at the cell surface. In the H1993 cell line, high MET gene amplification was associated with strong overall c-Met protein accumulation (as detected by SP44), but only moderate membrane expression (as detected by EP1454Y or reflected in SUVmax). In HCC827, high intracellular accumulation and moderate surface expression (EP1454Y or PET results) appeared to occur independently of MET mutations or amplification. Noteworthy is the fact that during this work we came to realise that the immunohistochemistry (IHC) tissue scoring is not a straightforward exercise: not only must a suitable antibody be chosen, but importantly the score obtained depends on the methods and the analysis from the pathologists. In addition, the IHC cut-off point for positivity was difficult to define and to date no consensus exists amongst the scientific community 24 , 28 – 32 . Notably, we found that IHC scoring is not a straightforward process: beyond the choice of a suitable antibody, results depend heavily on the analytical methods and pathologist interpretation. Moreover, the optimal IHC cut-off for c-Met positivity remains undefined, with no current consensus across studies 24 , 28 – 32 . While SP44 is clinically validated, our study intentionally compared both EP1454Y and SP44 using the same H-score method to capture potential discrepancies in antibody recognition epitopes. This difference may also explain the limited correlation observed between PET signal and IHC results obtained with the SP44 antibody. SP44 recognizes an intracellular epitope located in the C-terminal cytoplasmic domain of c-Met, thus reflecting total receptor content rather than the membrane-bound, functional fraction accessible to [ 68 Ga]Ga-EMP-100. In contrast, antibodies targeting extracellular domains, such as EP1454Y, are more representative of surface c-Met expression and therefore better aligned with tracer uptake patterns observed in vivo . The rapid expansion of antibody-based therapeutics, including antibody–drug conjugates (ADCs), bispecific antibodies, and radiopharmaceutical therapies, has revolutionized targeted oncology. However, the clinical success of these agents remains strongly dependent on accurate patient selection. In this context, we propose a complementary in vivo imaging-based approach using [ 68 Ga]Ga-EMP-100 PET to directly quantifies accessible c-Met in vivo , providing a whole-body, non-invasive assessment. This technology will enable visualization of target engagement in non-biopsiable lesions, allow early assessment of therapeutic response, and offer longitudinal repeatability during treatment monitoring. Ultimately, integrating c-Met PET imaging with conventional modalities like FDG-PET could provide a dual-assessment strategy to distinguish metabolically active recurrent lesions from those with sustained c-Met signaling, refining precision oncology paradigms. Further preclinical studies in our CDX models are warranted to confirm these findings and explore their translational relevance. Conclusions The potential of [ 68 Ga]Ga-EMP-100 PET imaging was successfully demonstrated through direct comparison with current methods for assessing c-Met pathway alteration, such as molecular testing and IHC analysis. Unlike conventional approaches relying on biopsied tissue, [ 68 Ga]Ga-EMP-100 offers a non-invasive, full body quantification of receptor expression. This innovative imaging technique may represent an added value in the clinicopathological staging of patients and their selection for c-Met targeted therapies such as antibody drug conjugates or radiopharmaceutical therapy. Furthermore, and pending further validation this technique could enhance therapeutic predictions and patient outcomes. Further studies are warranted to better establish the relationship between molecular imaging and treatment response, positioning it as a valuable complementary tool alongside current method. Abbreviations NSCLC non-small cell lung cancer c-Met mesenchymal-epithelial transition factor TKIs tyrosine kinase inhibitors FISH fluorescence in-situ hybridization NGS next-generation sequencing ADCs antibody drug conjugates RPT radiopharmaceutical therapy IHC immunohistochemistry PET positron emission tomography 68 Ga gallium-68 GCN gene copy number FP fluorescence polarization GMP Good Manufacturing Practices MBq mégabecquerel RCY radiochemical yield RCP radiochemical purity TLC thin layer chromatography HPLC high performance liquid chromatography PBS Phosphate Buffered Saline PFA Paraformaldehyde CT computed tomography kVp Peak kilovoltage MIP maximum intensity projections VOI volume of interest TAC Time Activity Curves SUVmax maximum standardized uptake value % IA/g percentage injected activity per gram tissue FFPE formalin-fixed paraffin-embedded CDX Cell Derived Xenograft Declarations All manuscripts must contain the following sections under the heading 'Declarations': Ethics approval and consent to participate All animal studies were approved by the Ethics Committee “Charles Darwin” for Animal Research in Paris (C2EA-05) with an APAFIS project (#25162). Consent for publication Not applicable Availability of data and material All data generated or analysed during this study are included in this published article and its supplementary information files. Competing interests The authors declares no potential conflict of interest. CP is a full time employee of Edinburgh Molecular Imaging Ltd. and is listed and an inventor on several patents related to EMP-100. Funding This work is part of the project “Meteoric” supported by the Association contre le Cancer Tous ensemble à Tenon (ACTT), Core resources (histology and imaging) were supported by Sorbonne University and the Region Ile de France through the CORDDIM for the use of PET/CT imaging system. Authors' contributions TR and AP conceptualized and designed the study. TR performed the radiochemistry experiments and optimized the radiolabelling protocol. JPP, AP, JC, MA conducted the in vitro experiments and data analysis. LC, TR and AP carried out the in vivo PET imaging studies and biodistribution experiments. TR and AP contributed to the interpretation of imaging data. JC and CP provided clinical expertise and contributed to the scientific discussion. AP supervised the project and SM secured funding. TR and AP drafted the manuscript. All authors critically reviewed the manuscript, contributed to revisions, and approved the final version. Acknowledgements We thank Dr. Martin Wear at Edinburgh University for performing FP experiments. Authors' information (optional) References Wang J, Anderson MG, Oleksijew A, Vaidya KS, Boghaert ER, Tucker L, Zhang Q, Han EK, Palma JP, Naumovski L, Reilly EB. ABBV-399, a c-Met Antibody-Drug Conjugate That Targets Both MET-Amplified and c-Met-Overexpressing Tumors, Irrespective of MET Pathway Dependence. Clin Cancer Res. 2017;23(4):992–1000. https://doi.org/10.1158/1078-0432.CCR-16-1568 . Waqar SN, Redman MW, Arnold SM, Hirsch FR, Mack PC, Schwartz LH, Gandara DR, Stinchcombe TE, Leighl NB, Ramalingam SS, Tanna SH, Raddin RS, Minichiello K, Bradley JD, Kelly K, Herbst RS, Papadimitrakopoulou V. A. A Phase II Study of Telisotuzumab Vedotin in Patients With c–MET-Positive Stage IV or Recurrent Squamous Cell Lung Cancer (LUNG-MAP Sub-Study S1400K, NCT03574753). Clin Lung Cancer. 2021;22(3):170–7. https://doi.org/10.1016/j.cllc.2020.09.013 . Ulaner G, Juloori A, Juneau D, Schindler J, Allan S, Jean-Louis L, Nawinne M, Mueller N, Littlewood G, Trigg W, Jacene H, Iagaru AA. Phase 1, First-in-Human, Multicentre, Open-Label, Dose-Escalation Study of [225Ac]-FPI-2068 in Adult Patients with Advanced Solid Tumors. Journal of Nuclear Medicine 2024, 65 (supplement 2), 242181–242181. Escorcia FE, Houghton JL, Abdel-Atti D, Pereira PR, Cho A, Gutsche NT, Baidoo KE, Lewis JS. ImmunoPET Predicts Response to Met-Targeted Radioligand Therapy in Models of Pancreatic Cancer Resistant to Met Kinase Inhibitors. Theranostics 2020, 10 (1), 151–165. https://doi.org/10.7150/thno.37098 Bonin S, Stanta G, Pre-Analytics, Heterogeneity T. New Biotechnol. 2020;55:30–5. https://doi.org/10.1016/j.nbt.2019.09.007 . Floresta G, Abbate V. Recent Progress in the Imaging of C-Met Aberrant Cancers with Positron Emission Tomography. Med Res Rev. 2022;42(4):1588–606. https://doi.org/10.1002/med.21885 . Teh JH, Amgheib A, Fu R, Barnes C, Abrahams J, Ashek A, Wang N, Yang Z, Mansoorudeen M, Long NJ, Aboagye EO. Evaluation of [18F]AlF-EMP-105 for Molecular Imaging of C-Met. Pharmaceutics 2023, 15 (7), 1915. https://doi.org/10.3390/pharmaceutics15071915 Liu X, Chen Y, Zhang F, Qiu F, Xu X, Zhang J, He S, Ding D, Tan W, Song S. Preclinical Evaluation of 68Ga-Labeled SL1 Aptamer for c-Met Targeted PET Imaging. Mol Pharm. 2025. https://doi.org/10.1021/acs.molpharmaceut.4c01344 . Minne RL, Luo NY, Mork CM, Wopat MR, Esbona K, Javeri S, Nickel KP, Hernandez R, LeBeau AM, Kimple RJ, Baschnagel AM. Evaluation of a Novel MET-Targeting Camelid-Derived Antibody in Head and Neck Cancer. Mol Pharm. 2024;21(12):6376–84. https://doi.org/10.1021/acs.molpharmaceut.4c00938 . Somer EJ, Owenius R, Wall A, Antoni G, Thibblin A, Sörensen J. The Clinical Safety, Biodistribution and Internal Radiation Dosimetry of [18F]AH113804 in Healthy Adult Volunteers. EJNMMI Res. 2016;6(1):87. https://doi.org/10.1186/s13550-016-0239-y . Metman MJH, Jonker PKC, Sondorp LHJ, van Hemel BM, Sywak MS, Gill AJ, Jansen L, van Diest PJ, van Ginhoven TM, Löwik CWGM, Nguyen AH, Robinson DJ, van Dam GM, Links TP, Coppes RP, Fehrmann RSN, Kruijff S. MET-Receptor Targeted Fluorescent Imaging and Spectroscopy to Detect Multifocal Papillary Thyroid Cancer. Eur J Nucl Med Mol Imaging. 2024;51(8):2384–94. https://doi.org/10.1007/s00259-023-06525-5 . Poel HG. C-MET Receptor-Targeted Fluorescence on the Road to Image-Guided Surgery in Penile Squamous Cell Carcinoma Patients. J Nucl Med. 2021. https://doi.org/10.2967/jnumed.120.261864 . BuckleT.Brouwer, O. R.van. Hardwick JCH, Robinson DJ, Burggraaf J, Kamerling IMC, Nagengast WB. The Optimal Imaging Window for Dysplastic Colorectal Polyp Detection Using C-Met–Targeted Fluorescence Molecular Endoscopy. J Nucl Med. 2020;61(10):1435–41. https://doi.org/10.2967/jnumed.119.238790 . KarrenbeldA.Voskuil, F. J.; Schmidt, I.; Westerhof, J.; Koornstra, J. J.; Kam, M. L. de. de Jongh SJ, Voskuil FJ, Schmidt I, Karrenbeld A, Kats-Ugurlu G, Meersma GJ, Westerhof J, Witjes MJH, van Dam GM, Robinson DJ, Nagengast WB. C-Met Targeted Fluorescence Molecular Endoscopy in Barrett’s Esophagus Patients and Identification of Outcome Parameters for Phase-I Studies. Theranostics. 2020;10(12):5357–67. https://doi.org/10.7150/thno.42224 . Mittlmeier LM, Todica A, Gildehaus F-J, Unterrainer M, Beyer L, Brendel M, Albert NL, Ledderose ST, Vettermann FJ, Schott M, Rodler S, Marcon J, Ilhan H, Cyran CC, Stief CG, Staehler M, Bartenstein P. Ga-EMP-100 PET/CT-a Novel Ligand for Visualizing c-MET Expression in Metastatic Renal Cell Carcinoma-First in-Human Biodistribution and Imaging Results. Eur J Nucl Med Mol Imaging. 2022;49(5):68. 1711–1720. https://doi.org/10.1007/s00259-021-05596-6 . Rusu T, Delion M, Pirot C, Blin A, Rodenas A, Talbot J-N, Veran N, Portal C, Montravers F, Cadranel J, Prignon A. Fully Automated Radiolabeling of [68Ga]Ga-EMP100 Targeting c-MET for PET-CT Clinical Imaging. EJNMMI Radiopharm Chem. 2023;8:30. https://doi.org/10.1186/s41181-023-00213-3 . Smith MA, Licata T, Lakhani A, Garcia MV, Schildhaus H-U, Vuaroqueaux V, Halmos B, Borczuk AC, Chen YA, Creelan BC, Boyle TA, Haura EB. MET-GRB2 Signaling-Associated Complexes Correlate with Oncogenic MET Signaling and Sensitivity to MET Kinase Inhibitors. Clin Cancer Res. 2017;23(22):7084–96. https://doi.org/10.1158/1078-0432.CCR-16-3006 . Kubo T, Yamamoto H, Lockwood WW, Valencia I, Soh J, Peyton M, Jida M, Otani H, Fujii T, Ouchida M, Takigawa N, Kiura K, Shimizu K, Date H, Minna JD, Varella-Garcia M, Lam WL, Gazdar AF, Toyooka S. MET Gene Amplification or EGFR Mutation Activate MET in Lung Cancers Untreated with EGFR Tyrosine Kinase Inhibitors. Int J Cancer. 2009;124(8):1778–84. https://doi.org/10.1002/ijc.24150 . Armstrong GR, Khot MI, Portal C, West NP, Perry SL, Maisey TI, Tiernan JP, Hughes TA, Tolan DJ, Jayne DG. A Novel Fluorescent C-Met Targeted Imaging Agent for Intra-Operative Colonic Tumour Mapping: Translation from the Laboratory into a Clinical Trial. Surg Oncol. 2022;40:101679. https://doi.org/10.1016/j.suronc.2021.101679 . Schildhaus H-U, Schultheis AM, Rüschoff J, Binot E, Merkelbach-Bruse S, Fassunke J, Schulte W, Ko Y-D, Schlesinger A, Bos M, Gardizi M, Engel-Riedel W, Brockmann M, Serke M, Gerigk U, Hekmat K, Frank KF, Reiser M, Schulz H, Krüger S, Stoelben E, Zander T, Wolf J, Buettner RMET. Amplification Status in Therapy-Naïve Adeno- and Squamous Cell Carcinomas of the Lung. Clin Cancer Res. 2015;21(4):907–15. https://doi.org/10.1158/1078-0432.CCR-14-0450 . Buckingham LE, Coon JS, Morrison LE, Jacobson KKB, Jewell SS, Kaiser KA, Mauer AM, Muzzafar T, Polowy C, Basu S, Gale M, Villaflor VM, Bonomi P. The Prognostic Value of Chromosome 7 Polysomy in Non-Small Cell Lung Cancer Patients Treated with Gefitinib. J Thorac Oncol. 2007;2(5):414–22. https://doi.org/10.1097/01.JTO.0000268675.02744.b0 . Engelman JA, Zejnullahu K, Mitsudomi T, Song Y, Hyland C, Park JO, Lindeman N, Gale C-M, Zhao X, Christensen J, Kosaka T, Holmes AJ, Rogers AM, Cappuzzo F, Mok T, Lee C, Johnson BE, Cantley LC, Jänne PA. MET Amplification Leads to Gefitinib Resistance in Lung Cancer by Activating ERBB3 Signaling. Science. 2007;316(5827):1039–43. https://doi.org/10.1126/science.1141478 . Camidge DR, Barlesi F, Goldman JW, Morgensztern D, Heist R, Vokes E, Angevin E, Hong DS, Rybkin II, Barve M, Bauer TM, Delmonte A, Dunbar M, Motwani M, Parikh A, Noon E, Wu J, Blot V, Kelly K. A Phase 1b Study of Telisotuzumab Vedotin in Combination With Nivolumab in Patients With NSCLC. JTO Clin Res Rep. 2021;3(1):100262. https://doi.org/10.1016/j.jtocrr.2021.100262 . Spigel DR, Ervin TJ, Ramlau RA, Daniel DB, Goldschmidt JH, Blumenschein GR, Krzakowski MJ, Robinet G, Godbert B, Barlesi F, Govindan R, Patel T, Orlov SV, Wertheim MS, Yu W, Zha J, Yauch RL, Patel PH, Phan S-C, Peterson AC. Randomized Phase II Trial of Onartuzumab in Combination With Erlotinib in Patients With Advanced Non–Small-Cell Lung Cancer. J Clin Oncol. 2013;31(32):4105–14. https://doi.org/10.1200/JCO.2012.47.4189 . Casadevall D, Gimeno J, Clavé S, Taus Á, Pijuan L, Arumí M, Lorenzo M, Menéndez S, Cañadas I, Albanell J, Serrano S, Espinet B, Salido M, Arriola EMET. Expression and Copy Number Heterogeneity in Nonsquamous Non-Small Cell Lung Cancer (nsNSCLC). Oncotarget. 2015;6(18):16215–26. Arulappu A, Battle M, Eisenblaetter M, McRobbie G, Khan I, Monypenny J, Weitsman G, Galazi M, Hoppmann S, Gazinska P, Wulaningsih W, Dalsgaard GT, Macholl S, Ng T. C-Met PET Imaging Detects Early-Stage Locoregional Recurrence of Basal-Like Breast Cancer. J Nucl Med. 2016;57(5):765–70. https://doi.org/10.2967/jnumed.115.164384 . Unterrainer LM, Todica A, Beyer L, Brendel M, Holzgreve A, Kauffmann-Guerrero D, Unterrainer M, Bartenstein P, Tufman A. Ga-EMP-100 PET/CT—a Novel Method for Non-Invasive Assessment of c-MET Expression in Non-Small Cell Lung Cancer. Eur J Nucl Med Mol Imaging. 2023;50(2):68. 628–629. https://doi.org/10.1007/s00259-022-05995-3 . Koeppen H, Yu W, Zha J, Pandita A, Penuel E, Rangell L, Raja R, Mohan S, Patel R, Desai R, Fu L, Do A, Parab V, Xia X, Januario T, Louie SG, Filvaroff E, Shames DS, Wistuba I, Lipkind M, Huang J, Lazarov M, Ramakrishnan V, Amler L, Phan S-C, Patel P, Peterson A, Yauch RL. Biomarker Analyses from a Placebo-Controlled Phase II Study Evaluating Erlotinib ± Onartuzumab in Advanced Non–Small Cell Lung Cancer: MET Expression Levels Are Predictive of Patient Benefit. Clin Cancer Res. 2014;20(17):4488–98. https://doi.org/10.1158/1078-0432.CCR-13-1836 . Spigel DR, Edelman MJ, Mok T, O’Byrne K, Paz-Ares L, Yu W, Rittweger K, Thurm H. Treatment Rationale Study Design for the MetLung Trial: A Randomized, Double-Blind Phase III Study of Onartuzumab (MetMAb) in Combination With Erlotinib Versus Erlotinib Alone in Patients Who Have Received Standard Chemotherapy for Stage IIIB or IV Met-Positive Non–Small-Cell Lung Cancer. Clin Lung Cancer. 2012;13(6):500–4. https://doi.org/10.1016/j.cllc.2012.05.009 . Neal JW, Dahlberg SE, Wakelee HA, Aisner SC, Bowden M, Huang Y, Carbone DP, Gerstner GJ, Lerner RE, Rubin JL, Owonikoko TK, Stella PJ, Steen PD, Khalid AA, Ramalingam SS. Erlotinib, Cabozantinib, or Erlotinib plus Cabozantinib as Second- or Third-Line Treatment of Patients with EGFR Wild-Type Advanced Non-Small Cell Lung Cancer (ECOG-ACRIN 1512): A Phase 2 Randomised Controlled Trial. Lancet Oncol. 2016;17(12):1661–71. https://doi.org/10.1016/S1470-2045(16)30561-7 . Bagchi A, Madaj Z, Engel KB, Guan P, Rohrer DC, Valley DR, Wolfrum E, Feenstra K, Roche N, Hostetter G, Moore HM, Jewell SD. Impact of Preanalytical Factors on the Measurement of Tumor Tissue Biomarkers Using Immunohistochemistry. J Histochem Cytochem. 2021;69(5):297–320. https://doi.org/10.1369/0022155421995600 . Masson E. L’immunohistochimie c-Met en oncologie thoracique, un nouvel enjeu pour le pathologiste. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8970902","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":601937188,"identity":"1095f44f-f076-484d-8a41-eadbb359a8b8","order_by":0,"name":"Timofei Rusu","email":"","orcid":"","institution":"AP-HP: Assistance Publique - Hopitaux de Paris","correspondingAuthor":false,"prefix":"","firstName":"Timofei","middleName":"","lastName":"Rusu","suffix":""},{"id":601937189,"identity":"62403452-01d6-463c-b78b-fe18aa2d4d59","order_by":1,"name":"Anita Rodenas","email":"","orcid":"","institution":"AP-HP: Assistance Publique - Hopitaux de Paris","correspondingAuthor":false,"prefix":"","firstName":"Anita","middleName":"","lastName":"Rodenas","suffix":""},{"id":601937190,"identity":"90660254-8305-4c6e-b981-9ccbb0f1bd90","order_by":2,"name":"Leo Capet","email":"","orcid":"","institution":"Sorbonne Université: Sorbonne Universite","correspondingAuthor":false,"prefix":"","firstName":"Leo","middleName":"","lastName":"Capet","suffix":""},{"id":601937191,"identity":"2a64a39d-9d5f-4b6b-8335-9eb63acf114e","order_by":3,"name":"Alexandre Perrier","email":"","orcid":"","institution":"AP-HP: Assistance Publique - 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Hopitaux de Paris","correspondingAuthor":false,"prefix":"","firstName":"Martine","middleName":"","lastName":"Antoine","suffix":""},{"id":601937196,"identity":"1abd68c8-2837-4f13-9332-0440c4283199","order_by":8,"name":"Serban Morosan","email":"","orcid":"","institution":"Sorbonne Universite","correspondingAuthor":false,"prefix":"","firstName":"Serban","middleName":"","lastName":"Morosan","suffix":""},{"id":601937197,"identity":"3b3612b8-d3c0-442c-afde-1517dba5b089","order_by":9,"name":"Christophe Portal","email":"","orcid":"","institution":"Edinburgh Molecular Imaging Ltd","correspondingAuthor":false,"prefix":"","firstName":"Christophe","middleName":"","lastName":"Portal","suffix":""},{"id":601937198,"identity":"f4b4feaa-8a96-4cad-b55f-b28236d73695","order_by":10,"name":"Jacques Cadranel","email":"","orcid":"","institution":"AP-HP: Assistance Publique - Hopitaux de Paris","correspondingAuthor":false,"prefix":"","firstName":"Jacques","middleName":"","lastName":"Cadranel","suffix":""},{"id":601937199,"identity":"ad023cbf-b62a-4ec6-9342-486ed910e3ac","order_by":11,"name":"Aurelie Prignon","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-3963-863X","institution":"Sorbonne Universite","correspondingAuthor":true,"prefix":"","firstName":"Aurelie","middleName":"","lastName":"Prignon","suffix":""}],"badges":[],"createdAt":"2026-02-25 19:52:01","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8970902/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8970902/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104412561,"identity":"afa56130-ebbe-42a4-96ec-2a363c008f37","added_by":"auto","created_at":"2026-03-11 12:59:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1290740,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRadiolabelling and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ein vitro\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e characterization of EMP-100\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(A) Molecular weight of EMP-100 peptide (DOTA-AH111972) measured by mass spectrometry analysis. (B) \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eEMP-100\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e demonstrated high binding affinity to c-Met. Dissociation constant (Kd) was determined by competition with EMI-137 using fluorescence polarization.\u003c/em\u003e\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003e\u003cem\u003e(C) Illustration of the AH111972 peptide conjugated with R= [\u003c/em\u003e\u003csup\u003e\u003cem\u003e68\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eGa]Ga-DOTA (\u003c/em\u003e\u003cem\u003e\u003cstrong\u003eEMP-100\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e) or R= Cy5** (tetra SO\u003c/em\u003e\u003csub\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e)-NH\u003c/em\u003e\u003csub\u003e\u003cem\u003e2 \u003c/em\u003e\u003c/sub\u003e\u003cem\u003e(\u003c/em\u003e\u003cem\u003e\u003cstrong\u003eEMI-137\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e for fluorescence guided surgery). (D) Number of cells as a function of the fluorescence intensity (c-Met expression) for EBC-1.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure1peptide.png","url":"https://assets-eu.researchsquare.com/files/rs-8970902/v1/17d43f17982bc7d121946532.png"},{"id":104412614,"identity":"24ece856-3550-4914-a18f-f1f1397a1741","added_by":"auto","created_at":"2026-03-11 13:00:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1809912,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePharmacokinetics of [\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e68\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eGa]Ga-EMP-100\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(A) Dynamic PET/CT MIP of a C57Bl6 mouse injected (retroorbital injection point) with [\u003c/em\u003e\u003csup\u003e\u003cem\u003e68\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eGa]Ga-EMP-100, time post injection, frame duration: 5min, 10min and 20 min. (B) Time Activity Curves of the accumulated radioactivity per organ (%IA, injected activity) in VOI (cm\u003c/em\u003e\u003csup\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e) within the time interval up to 80 min after injection in C57Bl6 mice (n=3).\u003c/em\u003e \u003cem\u003e(C) Representative maximum intensity projection (MIP) image of [\u003c/em\u003e\u003csup\u003e\u003cem\u003e68\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eGa]Ga-EMP-100\u003c/em\u003e \u003cem\u003ein EBC-1 xenografted mice after injection of 4.2 ± 1.2 MBq; 219 ± 6 pmol. Left side, static PET/CT image at 40-60 min pi and right side 90-110 min pi. Respective axial slices on EBC-1 tumour. (D) Quantification in %IA/cm\u003c/em\u003e\u003csup\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e\u0026nbsp;of in vivo accumulation of radioactivity in key VOI at 40-60 min pi (black bar) and 90-110 min pi (grey bar). Data presented as mean ± SEM (n=3). K kidneys; Bl. Bladder. Mean EBC-1 tumour Volume in VOI: 0.395 cm\u003c/em\u003e\u003csup\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e(n=3).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8970902/v1/837c20dc2b26fef621480791.png"},{"id":104412559,"identity":"83f76b9c-4b4b-48e8-9082-baba6de3430f","added_by":"auto","created_at":"2026-03-11 12:59:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3779066,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e[\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e68\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eGa]Ga-EMP-100\u003c/strong\u003e \u003cstrong\u003ecomparative PET imaging\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(A) Tumour growth in squamous (EBC-1) and adenocarcinoma (H1993, H1648, A549 and HCC827) NSCLC cell lines. Mean ± SEM (n=3-6 mice/cell line). (One-way ANOVA with a post hoc Tukey multiple comparison test; ns, non-significant, \u003c/em\u003e****P \u0026lt;0.0001\u003cem\u003e). (B) Representative maximum intensity projection (MIP) image of [\u003c/em\u003e\u003csup\u003e\u003cem\u003e68\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eGa]Ga-EMP-100\u003c/em\u003e \u003cem\u003ein different CDX models of NSCLC at 40-60 min pi. (C) Quantification in %IA/cm\u003c/em\u003e\u003csup\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e and (D) in SUVmax of in vivo accumulation of radioactivity in tumour VOI at 40-60 min pi. Data presented as mean ± SEM. (*p\u0026lt;0.05 and **p\u0026lt;0.01, One way ANOVA with a post hoc Tukey multiple comparison test). (E) Following ex vivo gamma counting of tissues 1h after injection of [\u003c/em\u003e\u003csup\u003e\u003cem\u003e68\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eGa]Ga-EMP-100\u003c/em\u003e \u003cem\u003ein CDX mice. Tissue radioactivity is expressed as the percentage of injected activity per gram (% IA/g, mean ± SEM).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure3PETimagingandexvivo.png","url":"https://assets-eu.researchsquare.com/files/rs-8970902/v1/1e86cfdd177fc6bd89c4a22a.png"},{"id":104414117,"identity":"6eadb009-0154-422d-a795-bbdfde988414","added_by":"auto","created_at":"2026-03-11 13:06:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":12179018,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferent Cell Derived Xenograft (CDX) models of NSCLC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(A) FISH results. Orange dot: copy of centromere 7, green dot: copy of MET gene. (x60). (B) Immunohistochemistry with HES (x10) (C) SP44 antibody (x40) and with (D) EP1454Y (x40) in CDX tumours.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure4FISHandIHC.png","url":"https://assets-eu.researchsquare.com/files/rs-8970902/v1/c8ae3a142d2fa191cfb3c665.png"},{"id":104413701,"identity":"dfcaf2d2-3564-4bbb-b612-41a49c5e97e4","added_by":"auto","created_at":"2026-03-11 13:05:19","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":273052,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential c-Met Expression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRelationship between c-Met expression detected by immunohistochemistry and quantified by H Scoring and tumour uptake (SUVmax) of [\u003c/em\u003e\u003csup\u003e\u003cem\u003e68\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eGa]Ga-EMP-100. (R squared = 0.919 and **P\u0026lt; 0.05 for EP1454Y Antibody)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure5correlation.png","url":"https://assets-eu.researchsquare.com/files/rs-8970902/v1/ce9292416c259a50024f7652.png"},{"id":104416656,"identity":"b020bade-a885-4041-b519-857841f09278","added_by":"auto","created_at":"2026-03-11 13:15:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":32259952,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8970902/v1/836f8d95-d194-4111-9011-58e2e96d37c2.pdf"},{"id":104412303,"identity":"9c292447-c19c-4765-a4a7-fd7f8af234ec","added_by":"auto","created_at":"2026-03-11 12:59:10","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":32047,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8970902/v1/eee06d6e3ab3af17e1b7a4d5.pdf"},{"id":104414115,"identity":"f55ba36d-2e98-4d08-9297-b1f995052674","added_by":"auto","created_at":"2026-03-11 13:06:46","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":70517,"visible":true,"origin":"","legend":"","description":"","filename":"EJNMMIResearchdatasuppV3.docx","url":"https://assets-eu.researchsquare.com/files/rs-8970902/v1/eb263ff7747aa12972c29e17.docx"}],"financialInterests":"","formattedTitle":"\u003cp\u003ePreclinical PET Characterization of [\u003csup\u003e68\u003c/sup\u003eGa]Ga-EMP-100 for Non-Invasive Assessment of c-Met in NSCLC\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eLung cancer remains the leading cause of cancer-related mortality worldwide, with over 1.8\u0026nbsp;million new cases each year. Non-small cell lung cancer (NSCLC) accounts for approximately 80% of all cases, and up to 70% of patients have a locally advanced or metastatic disease (bone, brain, or liver) at diagnosis. Among these, aberrant activation of the mesenchymal-epithelial transition factor (c-Met) receptor tyrosine kinase has been implicated in tumour progression, invasion, and resistance to therapy. Over recent years, this has driven intense therapeutic development targeting the MET pathway, from small-molecule tyrosine kinase inhibitors (TKIs) to antibody-based therapeutics.\u003c/p\u003e \u003cp\u003eA major breakthrough in oncology has been the explosion of antibody-derived therapeutics, including antibody-drug conjugates (ADCs) \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, bispecific antibodies \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, and radiolabeled monoclonal antibodies (radioimmunoconjugates) \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, many of which are currently in clinical development. These agents exploit c-Met receptor overexpression as a docking site for delivering cytotoxic payloads or radionuclides to tumour cells. However, their clinical benefit relies heavily on accurate patient selection, which remains challenging. Current biomolecular methods\u0026mdash;such as \u003cem\u003eMET\u003c/em\u003e gene amplification, exon 14 skipping detection, and immunohistochemistry (IHC) for c-Met expression\u0026mdash;are limited by sampling bias, variability of staining interpretation, and lack of assay standardization. Furthermore, they depend on small biopsy specimens that may not represent the molecular heterogeneity of the entire disease. In addition, c-Met alterations can emerge under therapeutic pressure (e.g., TKI resistance in oncogene-driven NSCLC such as EGFR, ALK, ROS, or RET mutations), making repeated or multiple biopsies desirable but often impractical, invasive, or technically difficult\u0026mdash;especially for lesions in hard-to-access locations such as the brain or bone \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this context, molecular imaging represents an attractive complementary strategy, enabling whole-body, non-invasive, and repeatable assessment of c-Met expression. Such an approach could facilitate patient selection, early treatment monitoring, and longitudinal follow-up, overcoming many limitations of tissue-based assays.\u003c/p\u003e \u003cp\u003eSeveral peptide- and antibody-based tracers for positron emission tomography (PET) imaging of c-Met have been developed in recent years \u003csup\u003e\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. The most advanced, [\u0026sup1;⁸F]AH113804, a cyclic 26-amino acid peptide conjugated to 4-[\u0026sup1;⁸F]fluorobenzaldehyde, demonstrated safety and favourable biodistribution in humans \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Its fluorescent analogue, EMI-137, has also shown promise in fluorescence-guided surgery\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e and early cancer detection \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eA first-in-human study using [\u003csup\u003e68\u003c/sup\u003eGa]Ga-EMP-100 PET/CT, a DOTA-conjugated analogue of these ligands, reported promising results in metastatic renal cell carcinoma (mRCC)\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. The study demonstrated the feasibility of visualizing c-Met expression \u003cem\u003ein vivo\u003c/em\u003e and supported the concept that [\u003csup\u003e68\u003c/sup\u003eGa]Ga-EMP-100 allows clinicopathological staging and could serve as a predictive biomarker of response to targeted therapies, while also highlighting the need for further preclinical validation and molecular correlation.\u003c/p\u003e \u003cp\u003eTo address these unmet needs, and following the successful development of a robust and automated radiolabelling method for [⁶⁸Ga]Ga-EMP-100 \u003csup\u003e16\u003c/sup\u003e our work aims to further characterize this tracer in a preclinical context in non-small cell lung cancer (NSCLC) models, including both adenocarcinoma and squamous cell carcinoma, where c-Met overexpression and dysregulation play a mechanistically and clinically distinct role.\u003c/p\u003e \u003cp\u003eSpecifically, we investigated it\u0026rsquo;s \u003cem\u003ein vivo\u003c/em\u003e biodistribution, pharmacokinetics in multiple NSCLC models exhibiting variable levels of c-Met expression, and the quantitative relationship between \u003cem\u003ein vivo\u003c/em\u003e tracer uptake, c-Met protein expression, and \u003cem\u003eMET\u003c/em\u003e gene copy number within corresponding tumour tissues.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCompetitive fluorescence polarization assay\u003c/h2\u003e \u003cp\u003eSynthesis of the EMP-100 and binding to recombinant human c-Met receptor (rhGFR/cMET Fc Chimera 358-MT/CF, R\u0026amp;D Systems.) was assessed by competition fluorescence polarization (FP) with EMI-137 (AH111972-Cy5** (EM Imaging Ltd)) are described in supplementary methods.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRadiolabelling of [Ga]Ga-EMP-100\u003c/h3\u003e\n\u003cp\u003eMaterials required for radiolabelling in a GMP-compliant single-use kit were sourced from ABX (Advanced Biochemical Compounds, Radeberg, Germany) (ref RT-01-H, RT-101): 0.08 mol/L ammonium acetate buffer, 60% pure ethanol solution, ascorbic acid, 0.9% NaCl saline solution, water for injection (WFI, BBraun), eluent solution (5 mol/L NaCl, 0. 1 mol/L HCl), SCX (Bond Elut\u0026reg;, Agilent) and C18 cationic reversed-phase columns (Sep-Pack\u0026reg;, Waters), 0.22 \u0026micro;m filter (Millex-GV\u0026reg;, Merck Millipore LTd.) and sterile vials for the final product, with ultrapure gentisic acid supplied by Sigma-Merck. \u003csup\u003e68\u003c/sup\u003eGa was obtained by elution from a commercial \u003csup\u003e68\u003c/sup\u003eGe/\u003csup\u003e68\u003c/sup\u003eGa generator (GalliaPharm\u0026reg; 1850 MBq, Eckert \u0026amp; Ziegler radiopharma GmbH, Berlin, Germany) with a 0.1 M HCl solution (Eckert \u0026amp; Ziegler).\u003c/p\u003e \u003cp\u003eThe automated radiolabelling of [\u003csup\u003e68\u003c/sup\u003eGa]Ga-EMP-100 was performed using the Gaia/Luna commercial labelling synthesis module (Elysia-Raytest, GmbH, Straubenhardt, Germany), and radiochemical purity was determined by analytical thin layer chromatography (TLC) and high performance liquid chromatography (HPLC), as previously described by our team \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. All chemicals were pure or analytical grade. The non-decay-corrected radiochemical yield (RCY) was calculated as the ratio of the final activity collected on C18 to the initial activity collected on SCX. Molar specific activity in MBq/nmol was calculated by dividing the activity of the final product by the total amount of EMP-100 ligand. Final activity and volumic activity were expressed in MBq and MBq/mL.\u003c/p\u003e\n\u003ch3\u003eCell lines\u003c/h3\u003e\n\u003cp\u003eHuman squamous non-small cell lung carcinoma EBC-1 cell line known as \u003cem\u003eMET\u003c/em\u003e Amplified determined by qPCR \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e was provided by IRCM. Human pulmonary adenocarcinoma H1993 and H1648 known as \u003cem\u003eMET\u003c/em\u003e Amplified determined by qPCR \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, A549 and HCC827 known as \u003cem\u003eMET\u003c/em\u003e low and \u003cem\u003eEGFR\u003c/em\u003e mutation in exon deletion 19 for the latter \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e cell lines were purchased from ATCC. Cells were cultured at 37\u0026deg;C and 5% CO2 in RPMI Medium 1640-GlutaMAX containing 10% FBS, 100 U/mL penicillin and 100 \u0026micro;g/mL streptomycin.\u003c/p\u003e\n\u003ch3\u003eDetermination of the c-Met expression by flow cytometry\u003c/h3\u003e\n\u003cp\u003eThe c-Met expression was determined in EBC-1 using EMI-137 (AH111972-Cy5**). Cells were trypsined, washed in PBS, and fixed with 4% PFA for 10 minutes. After washing, cells were incubated overnight at 4\u0026deg;C with PBS-BSA 0.5%. The next day, cells were incubated with the EMI-137 (0.1 or 1) for 15 minutes. A part of the cells was previously incubated with 500 \u0026micro;g/mL of unlabelled EMP-100 for 15 min to evaluate the non-specific binding. c-Met expression was assessed by evaluation of the fluorescent intensity of cells using a Cytoflex\u0026reg; Cytometer (Beckman Coulter) and analysis was performed with FlowJo\u0026reg; software (BD).\u003c/p\u003e\n\u003ch3\u003eCell Derived Xenograft models\u003c/h3\u003e\n\u003cp\u003eAthymic female NMRI nude mice or C57Bl6 mice (age: 4\u0026ndash;6 weeks, weight: 17\u0026ndash;20 g) were purchased from Janvier, France. Animal viability and behaviour were observed daily, and a clinical follow-up recorded if deemed necessary. Body weights were measured twice a week.\u003c/p\u003e \u003cp\u003eFor implantation, the tumour cells were harvested by trypsinization and 1 to 2x10\u003csup\u003e6\u003c/sup\u003e cells in 50% Matrigel (Corning, 354230) were inoculated subcutaneously into the right or left shoulders of the mice. Growth of the tumours was measured in two perpendicular directions twice per week using a calliper and the volumes of the tumours were calculated as 0.5 \u0026times; L \u0026times; W2 (L\u0026thinsp;=\u0026thinsp;longest axis and W\u0026thinsp;=\u0026thinsp;axis perpendicular to L in millimetres). Mice were submitted to imaging and biodistribution when tumour sizes reached volumes of 400\u0026ndash;800 mm\u003csup\u003e3\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePET/CT imaging\u003c/h2\u003e \u003cp\u003eIsoflurane gas anaesthesia was used for IV injections, blood collection and PET/CT imaging. Anaesthesia was induced and maintained by the administration of a mixture of isoflurane (1.5\u0026ndash;2.5%) and oxygen. Mice were injected intravenously (retro-orbitary sinus) with 1 to 5 MBq of [\u003csup\u003e68\u003c/sup\u003eGa]Ga-EMP-100.\u003c/p\u003e \u003cp\u003eWhole body positron emission tomography (PET) coupled to computed tomography (CT) scans of the mice injected with [\u003csup\u003e68\u003c/sup\u003eGa]Ga-EMP-100 were performed under general anaesthesia in nanoScan PET/CT (Mediso Medical Imaging Systems Ltd., Budapest, Hungary) during 20 min scan, 40\u0026ndash;60 min post injection (p.i.) and using multiple bed (3 mice simultaneously) for static PET images or during 90 min p.i. using list mode for dynamic PET. CT acquisition (35 kVp, 300 ms, 360 projections, binning 1:4) was immediately performed after PET acquisition using the same bed position.\u003c/p\u003e \u003cp\u003ePET and CT files were fused and converted to standardized uptake value (SUV) images using Nucline 2.03 Software (Mediso Medical Imaging Systems, Hungary). Images were analysed and presented as maximum intensity projections (MIP) in RGB colour scale. Quantification was done using a volume of interest (VOI) technique and expressed as the maximum standardized uptake value (SUVmax) or %IA/cm\u003csup\u003e3\u003c/sup\u003e calculated in bladder, kidneys, tumour and background (close to the tumour) VOIs. Time Activity Curves (TAC) are represented by the accumulated radioactivity within the time interval up to 90 min after injection (in % of injected activity (IA)).\u003c/p\u003e \u003cp\u003e \u003cb\u003eEx vivo\u003c/b\u003e \u003cb\u003ebiodistribution\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eEx vivo\u003c/em\u003e biodistribution studies were carried out on the same animals that underwent PET/CT imaging. Briefly, immediately after the PET/CT scan, 1h or 2h post injection (p.i.), mice were euthanized and dissected (n\u0026thinsp;=\u0026thinsp;3\u0026ndash;4) mice per time point. Blood, liver, spleen, kidneys, stomach, intestine, colon, tumour, were collected and weighed. Then, the organ radioactivity was measured using a gamma-counter (Packard Instrument) and uptake values of organs were calculated as percentage injected activity per gram tissue (% IA/g).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ec-Met Immunohistochemistry\u003c/h3\u003e\n\u003cp\u003eTissue samples were formalin-fixed paraffin-embedded (FFPE) and sectioned at 3 \u0026micro;m-thick freshly cut. Immunostaining was performed using a pre-diluted at 9.75 \u0026micro;g/mL rabbit anti c-Met monoclonal primary antibody targeting C-Terminal region, clone SP44 (CONFIRM\u0026reg; anti-Total c-Met; Ventana Medical Systems, Tucson, ref. 790\u0026ndash;4430, EU-IVD) or using a rabbit anti c-Met targeting N-Terminal region, clone EP1454Y (Abcam ab51067) used at 2.4 \u0026micro;g/mL, both using VENTANA BenchMark ULTRA instrument\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Immunostaining intensity was determined by two operators for each CDX and native cell line according to the H-score. The final H-score (range: 0-300) was calculated as (1 \u0026times; [% cells 1+]\u0026thinsp;+\u0026thinsp;2 \u0026times; [% cells 2+]\u0026thinsp;+\u0026thinsp;3 \u0026times; [% cells 3+]).\u003c/p\u003e \u003cp\u003e \u003cb\u003eMET\u003c/b\u003e \u003cb\u003egene copy number (GCN) detection by FISH\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eMET\u003c/em\u003e FISH protocol was performed on 3 \u0026micro;m-thick freshly cut sections from FFPE NSCLC tumour blocks using a \u003cem\u003eMET\u003c/em\u003e dual-color probe (ZytoLight\u0026reg; SPEC \u003cem\u003eMET/CEN7\u003c/em\u003e Dual Color Probe, Z-2087\u0026ndash;50, ZytoVision GmbH, Bremerhaven, Germany) according to the manufacturers\u0026rsquo; standard protocol (ZytoLight FISH-Tissue Implementation Kit, Z-2028-5, ZytoVision GmbH) and the routine in-house standards more described in supplementary methods.\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eMET\u003c/em\u003e FISH status was analysed by a pathologist with specific experience in this field (MA) using a Zeiss Imager. After identifying the tumour cells in DAPI, 50 non overlapping tumour nuclei were evaluated, and both green and orange signals per nucleus were counted to determine the mean GCN of \u003cem\u003eMET\u003c/em\u003e and \u003cem\u003eCEN7\u003c/em\u003e, respectively. The \u003cem\u003eMET\u003c/em\u003e gene copy number status was classified into four groups according to high, intermediate and low-level amplification or normal, non-amplified) \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Tumour cells harbouring \u003cem\u003eCEN7\u003c/em\u003e signals on average\u0026thinsp;\u0026ge;\u0026thinsp;3.6 were classified as polysomic \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMET\u003c/b\u003e \u003cb\u003egene copy number (GCN) and other molecular alterations by Next Generation Sequencing (NGS)\u003c/b\u003e\u003c/p\u003e \u003cp\u003eNGS (AmpliSeq\u0026trade; Kit for Illumina\u0026reg; Focus Panel, details in supplementary methods) was used to detect the \u003cem\u003eMET\u003c/em\u003e gene alterations (\u003cem\u003eMET\u003c/em\u003e amplifications as well as \u003cem\u003eMET\u003c/em\u003e exon 14 skipping mutation \u003cem\u003eMET\u003c/em\u003eex14) or others NSCLC panel gene alterations in the five types of CDX tumours. We annotated and described genetic changes like point mutations, deletions, and insertions using the standardized Human Genome Variation Society (HGVS) nomenclature. For gene amplification tests (\u003cem\u003eMET\u003c/em\u003e, \u003cem\u003eCDK4\u003c/em\u003e, \u003cem\u003eEGFR\u003c/em\u003e, \u003cem\u003eMYC\u003c/em\u003e, etc.), we have classified the level of amplification into 3 categories according to our laboratory's specific procedures and considering the cellularity of the tumour. Weak amplification (3\u0026ndash;4 copies), moderate/intermediate (\u0026gt;\u0026thinsp;4\u0026ndash;8 copies), strong (\u0026gt;\u0026thinsp;8 copies).\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eData for radiolabelling were presented as mean value standard deviation (SD). \u003cem\u003eIn vivo\u003c/em\u003e and \u003cem\u003eex vivo\u003c/em\u003e uptake data were presented as mean standard error (SEM). Statistical analyses were performed using GraphPad Prism v8.0. A one-way ANOVA (Tukey\u0026rsquo;s multiple comparison test) was used to assess significance. Significance levels are defined as ns (not significant, P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), *P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, and ****P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001. The correlation between immunohistochemistry and SUVmax was determined using Pearson\u0026rsquo;s correlation coefficient.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eRadiolabelling and\u003c/b\u003e \u003cb\u003ein vitro\u003c/b\u003e \u003cb\u003echaracterization of EMP-100\u003c/b\u003e\u003c/p\u003e \u003cp\u003eEMP-100 molecular weight (Mw) was confirmed by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (\u003cb\u003eFig.\u0026nbsp;1A\u003c/b\u003e) using a Bruker MALDI TOF-TOF UltrafleXtreme with sinapic acid as a matrix.\u003c/p\u003e \u003cp\u003eEMP-100 was shown to bind to c-Met with a dissociation constant (K\u003csub\u003ed\u003c/sub\u003e) of 1.1 nmol/L, predetermined by competitive binding assay with EMI-137 whose affinity was previously measured as being 3.0 nmol/L (\u003cb\u003eFig.\u0026nbsp;1B and supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). Structure of the fluorescent EMI-137 or the EMP-100 peptide conjugated with \u003csup\u003e68\u003c/sup\u003eGa shown in \u003cb\u003eFig.\u0026nbsp;1C.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eUsing fluorescent EMI-137 in flow cytometry we were able to identify c-Met expression on the EBC-1 cell line (\u003cb\u003eFig.\u0026nbsp;1D\u003c/b\u003e). The fluorescence intensity is consistently higher when the cells are treated with 1 \u0026micro;g/mL of EMI-137 compared to 0.1 \u0026micro;g/mL. Additionally, EMI-137 uptake was significantly reduced in cells co-incubated with unlabelled EMP-100, as shown by the downward shift in fluorescence intensity. This reduction in fluorescence indicates the competitive binding of EMP-100 to the c-Met receptors, demonstrating the \u003cem\u003ein vitro\u003c/em\u003e specificity of the ligand.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe fully automated radiolabelling of [\u003csup\u003e68\u003c/sup\u003eGa]Ga-EMP-100 was performed as previously reported on a Gaia/Luna system with an overall non-decay-corrected radiochemical yield (RCY) of 64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8% with \u0026ge;\u0026thinsp;99% radiochemical purity (RCP). The mean time of the entire labelling procedure was approximately 42 min. We obtained a high specific activity of 30.2\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8 MBq/nmol (n\u0026thinsp;=\u0026thinsp;5). Radioactivity in final product was 606\u0026thinsp;\u0026plusmn;\u0026thinsp;127 MBq. The formulated [\u003csup\u003e68\u003c/sup\u003eGa]Ga-EMP-100 was stable for 3 hours \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 1\u003c/b\u003e. \u003cb\u003eRadiolabelling and\u003c/b\u003e \u003cb\u003ein vitro\u003c/b\u003e \u003cb\u003echaracterization of EMP-100\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003e(A) Molecular weight of EMP-100 peptide (DOTA-AH111972) measured by mass spectrometry analysis. (B)\u003c/em\u003e \u003cb\u003eEMP-100\u003c/b\u003e \u003cem\u003edemonstrated high binding affinity to c-Met. Dissociation constant (Kd) was determined by competition with EMI-137 using fluorescence polarization. (C) Illustration of the AH111972 peptide conjugated with R= [\u003c/em\u003e\u003csup\u003e\u003cem\u003e68\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eGa]Ga-DOTA (\u003c/em\u003e\u003cb\u003eEMP-100\u003c/b\u003e\u003cem\u003e) or R= Cy5** (tetra SO\u003c/em\u003e\u003csub\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e)-NH\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e(\u003c/em\u003e\u003cb\u003eEMI-137\u003c/b\u003e \u003cem\u003efor fluorescence guided surgery). (D) Number of cells as a function of the fluorescence intensity (c-Met expression) for EBC-1.\u003c/em\u003e\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePharmacokinetics of [\u003csup\u003e68\u003c/sup\u003eGa]Ga-EMP-100\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eDynamic PET imaging was performed on C57Bl6 mice for 80 min post-injection. The highest signal in the PET images was observed in the bladder, followed by the kidneys (\u003cb\u003eFig.\u0026nbsp;2A\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eKinetic analysis of [\u003csup\u003e68\u003c/sup\u003eGa]Ga-EMP-100 showed rapid tissue distribution, where activity in the heart, and liver peaked after 2.5 min and diminished under 0.05% of the injected activity at the end of the dynamic acquisition (4800 sec). Rapid clearance through renal elimination was also observed, with the activity in the kidneys peaking at 450 sec and decreasing to 5.5% of injected activity, the bladder showing increasing uptake for the whole imaging period until 55% of injected activity (\u003cb\u003eFig.\u0026nbsp;2B\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eTo better characterize biodistribution in a tumour model, static PET imaging was performed on a first model of cell derived xenograft (CDX) squamous (EBC-1) cell line. [\u003csup\u003e68\u003c/sup\u003eGa]Ga-EMP-100 showed a similar biodistribution profile to dynamic PET imaging in C57Bl6 mice with high-contrast PET images in tumour observed within 40\u0026ndash;60 minutes after injection of 4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2 MBq; 219\u0026thinsp;\u0026plusmn;\u0026thinsp;6 pmol and still visible at 90\u0026ndash;110 minutes, although with lower intensity (\u003cb\u003eFig.\u0026nbsp;2C-D\u003c/b\u003e). To confirm the PET results, \u003cem\u003eex vivo\u003c/em\u003e 2 h p.i. biodistribution studies were performed. The circulating activity was 0.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1% IA/g, uptake in kidneys was 6.73\u0026thinsp;\u0026plusmn;\u0026thinsp;1.04% IA/g and the uptake in EBC-1 tumours was 1.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32% IA/g, corresponding to a tumour-to-blood ratio of 12.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6. The tumour-to-kidneys (R\u0026thinsp;+\u0026thinsp;L) ratio was 0.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1 (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eVery low tracer accumulation was observed in other organs, consistently with the visual observations on PET imaging. Therefore, we used the 40-60-minute time point for all further experiments, as it provided good contrast.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e(A) Dynamic PET/CT MIP of a C57Bl6 mouse injected (retroorbital injection point) with [\u003c/em\u003e \u003csup\u003e \u003cem\u003e68\u003c/em\u003e \u003c/sup\u003e \u003cem\u003eGa]Ga-EMP-100, time post injection, frame duration: 5min, 10min and 20 min. (B) Time Activity Curves of the accumulated radioactivity per organ (%IA, injected activity) in VOI (cm\u003c/em\u003e \u003csup\u003e \u003cem\u003e3\u003c/em\u003e \u003c/sup\u003e \u003cem\u003e) within the time interval up to 80 min after injection in C57Bl6 mice (n\u0026thinsp;=\u0026thinsp;3). (C) Representative maximum intensity projection (MIP) image of [\u003c/em\u003e \u003csup\u003e \u003cem\u003e68\u003c/em\u003e \u003c/sup\u003e \u003cem\u003eGa]Ga-EMP-100 in EBC-1 xenografted mice after injection of 4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2 MBq; 219\u0026thinsp;\u0026plusmn;\u0026thinsp;6 pmol. Left side, static PET/CT image at 40\u0026ndash;60 min pi and right side 90\u0026ndash;110 min pi. Respective axial slices on EBC-1 tumour. (D) Quantification in %IA/cm\u003c/em\u003e \u003csup\u003e \u003cem\u003e3\u003c/em\u003e \u003c/sup\u003e \u003cem\u003eof in vivo accumulation of radioactivity in key VOI at 40\u0026ndash;60 min pi (black bar) and 90\u0026ndash;110 min pi (grey bar). Data presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM (n\u0026thinsp;=\u0026thinsp;3). K kidneys; Bl. Bladder. Mean EBC-1 tumour Volume in VOI: 0.395 cm\u003c/em\u003e\u003csup\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e(n\u0026thinsp;=\u0026thinsp;3).\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003e[\u003c/b\u003e \u003csup\u003e \u003cb\u003e68\u003c/b\u003e \u003c/sup\u003e \u003cb\u003eGa]Ga-EMP-100 comparative PET imaging to quantify c-Met level expression in Cell Derived Xenograft (CDX) models of NSCLC\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFour NSCLC xenograft models other than EBC-1 were successfully developed using pulmonary adenocarcinoma (ADC) (H1993, H1648, A549 and HCC827) cell lines. Squamous EBC-1 CDX model was growing faster, with tumours reaching 469\u0026thinsp;\u0026plusmn;\u0026thinsp;68 mm3 only 18 days post graft whereas more than double the time was needed to obtain approximately the same volume for the ADC CDX models (383\u0026thinsp;\u0026plusmn;\u0026thinsp;111 mm\u003csup\u003e3\u003c/sup\u003e at day 56 for H1648; 458\u0026thinsp;\u0026plusmn;\u0026thinsp;101 mm\u003csup\u003e3\u003c/sup\u003e at day 42 for H1993; 447\u0026thinsp;\u0026plusmn;\u0026thinsp;164 mm\u003csup\u003e3\u003c/sup\u003e at day 39 for HCC827; 419\u0026thinsp;\u0026plusmn;\u0026thinsp;14 mm\u003csup\u003e3\u003c/sup\u003e at day 48 for A549). Figure\u0026nbsp;3A.\u003c/p\u003e \u003cp\u003eDue to technical constraint of production and availability of the radiotracer, [\u003csup\u003e68\u003c/sup\u003eGa]Ga-EMP-100 PET imaging was performed when tumours reach 800 mm\u003csup\u003e3\u003c/sup\u003e for EBC1, H1993 and HCC827 and when tumours reach 400 mm\u003csup\u003e3\u003c/sup\u003e for H1648 and A549 corresponding at the end of measurement of tumour growth.\u003c/p\u003e \u003cp\u003eTo avoid any \u003cem\u003ein vivo\u003c/em\u003e competition of \u0026ldquo;cold\u0026rdquo; ligand against the \u0026ldquo;hot\u0026rdquo; radiopharmaceutical for the c-Met receptor, the same injected specific activity was maintained for each group of tumour types with no differences in pmol of EMP-100 injected (166 to 188 pmol) corresponding to 2.16 MBq to 3.70 MBq of [\u003csup\u003e68\u003c/sup\u003eGa]Ga-EMP-100 injected (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Representative maximum intensity projection (MIP) image of [\u003csup\u003e68\u003c/sup\u003eGa]Ga-EMP-100 in different CDX models of NSCLC provided, high-contrast PET images as early as 40\u0026ndash;60 min p.i. with renal clearance and bladder accumulation, equivalent between mice, but with different level of [\u003csup\u003e68\u003c/sup\u003eGa]Ga-EMP-100 uptake in tumours.\u003c/p\u003e \u003cp\u003eMean %IA/cm\u0026sup3; provided an integrative measure of total tracer accumulation in tumours, ranging from 2.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87 for H1648 to 0.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22 for A549. However, mean %IA/cm\u0026sup3; can be affected by intra-tumoural heterogeneity and necrotic regions that dilute the apparent uptake. We therefore also performed an analysis based on SUVmax, focusing on the most metabolically active voxel, thus highlighting viable regions with the highest c-Met expression. Consequently, discrepancies observed between %IA/cm\u0026sup3; and SUVmax values across tumour models likely reflect biological heterogeneity within the lesions, such as necrosis, stromal content, or variable vascularization. SUVmax values within the tumour VOI corroborated these findings and therefore may allow non-invasive ranking of NSCLC CDX models according to c-Met expression.\u003c/p\u003e \u003cp\u003eA549 was considered as negative for c-Met with a SUVmax of 1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09, H1993 and HCC827 as moderate or low with a SUVmax of 2.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18 and 2.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2 respectively, whereas EBC-1 and particularly H1648 could be considered as high c-Met receptor expression and very different from A549 with a SUVmax of 2.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09 and 3.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61 respectively (*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, One way ANOVA with Tukey\u0026rsquo;s multiple comparisons \u003cem\u003epost hoc\u003c/em\u003e test was performed).\u003c/p\u003e \u003cp\u003eThese data were confirmed by \u003cem\u003eex vivo\u003c/em\u003e gamma counting of animal tissues at 1h post tracer injection. The highest total tumour uptake was shown in the H1648 model, with 4.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45%IA/g and EBC-1 total tumour gamma counting revealed only 1.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07%IA/g (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3) which could be explained by the low uptake in the necrotic tissue observed on PET imaging. \u003cem\u003eEx vivo\u003c/em\u003e gamma counting also indicated high radioactivity concentrations only in the kidneys (6.6 to 9.9%IA/g) and low blood concentrations (less than 0.84%IA/g). Best tumour-to-blood and tumour-to-kidneys ratios were 6.37 and 0.47 at 1 h p.i. in the H1648 CDX model (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Interestingly, there was no correlation between tumour growth and \u003cem\u003ein vivo\u003c/em\u003e tumour c-Met expression regarding the different CDX models \u003cb\u003eFig.\u0026nbsp;3A and 3C\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 3: [\u003c/b\u003e \u003csup\u003e \u003cb\u003e68\u003c/b\u003e \u003c/sup\u003e \u003cb\u003eGa]Ga-EMP-100 comparative PET imaging\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e(A) Tumour growth in squamous (EBC-1) and adenocarcinoma (H1993, H1648, A549 and HCC827) NSCLC cell lines. Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM (n\u0026thinsp;=\u0026thinsp;3\u0026ndash;6 mice/cell line). (One-way ANOVA with a post hoc Tukey multiple comparison test; ns, non-significant\u003c/em\u003e, ****P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003cem\u003e). (B) Representative maximum intensity projection (MIP) image of [\u003c/em\u003e\u003csup\u003e\u003cem\u003e68\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eGa]Ga-EMP-100 in different CDX models of NSCLC at 40\u0026ndash;60 min pi. (C) Quantification in %IA/cm\u003c/em\u003e\u003csup\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sup\u003e \u003cem\u003eand (D) in SUVmax of in vivo accumulation of radioactivity in tumour VOI at 40\u0026ndash;60 min pi. Data presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM. (*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, One way ANOVA with a post hoc Tukey multiple comparison test). (E) Following ex vivo gamma counting of tissues 1h after injection of [\u003c/em\u003e\u003csup\u003e\u003cem\u003e68\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eGa]Ga-EMP-100 in CDX mice. Tissue radioactivity is expressed as the percentage of injected activity per gram (% IA/g, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM).\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDetails of injections and tumour data extracted from PET imaging (n\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eTumour type\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eH1648\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEBC1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH1993\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHCC827\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eA549\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAd\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eSqCC\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eAd\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eAd\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eAd\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003epmol injected\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e166\u0026thinsp;\u0026plusmn;\u0026thinsp;17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e159\u0026thinsp;\u0026plusmn;\u0026thinsp;23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e168\u0026thinsp;\u0026plusmn;\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e182\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e188\u0026thinsp;\u0026plusmn;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMBq injected\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e3.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e3.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e2.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTumour uptake (Mean %IA/cm\u003c/b\u003e\u003csup\u003e\u003cb\u003e3\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e0.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTumour SUV Max (g/mL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.19\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e2.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e2.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eEx vivo biodistribution of [\u003c/em\u003e\u003csup\u003e\u003cem\u003e68\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eGa]Ga-EMP-100 at 1h p,i, in CDX mice.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eEBC1 CDX mice\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eH1648 CDX mice\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eH1993 CDX mice\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eHCC827 CDX mice\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eA549 CDX mice\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSEM\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eSEM\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eSEM\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eSEM\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003eSEM\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.05\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.07\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e0.07\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003e0.14\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003e0.07\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.04\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.05\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e0.09\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003e0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003e0.05\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpleen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.07\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.06\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e0.05\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003e0.03\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003e0.07\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.10\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.06\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e0.08\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003e0.00\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003e0.02\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKidneys\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.70\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.57\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e1.12\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003e0.57\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e8.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003e0.17\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStomach\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.16\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.02\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e0.05\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003e0.20\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003e0.02\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntestin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.08\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.04\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e0.08\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003e0.05\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003e0.17\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eColon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.03\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.03\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e0.05\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003e0.24\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003e0.02\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMuscle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.07\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.03\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e0.02\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003e0.06\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003e0.03\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e0.01\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003e0.28\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003e0.08\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTumour\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.81\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.07\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e4.31\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.45\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e2.33\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.08\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e2.28\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.19\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e1.25\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.07\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTumour / blood ratios\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.12\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.19\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e0.56\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003e0.60\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003e0.06\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTumour / kidneys ratios\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.03\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.07\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e0.08\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003e0.00\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003e0.03\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eDifferential\u003c/b\u003e \u003cb\u003eMET\u003c/b\u003e \u003cb\u003egene alterations (FISH and NGS) in CDX models, c-MET Expression in NSCLC Cell Derived Xenograft (CDX) models\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMET amplification\u003c/h2\u003e \u003cp\u003eFISH analysis on formalin-fixed, paraffin-embedded specimens of CDX tumours revealed strong \u003cem\u003eMET\u003c/em\u003e amplification in EBC-1 and H1993 (GCN 16.5, ratio 6.9 and GCN 18.3, ratio 4.7, respectively) and intermediate \u003cem\u003eMET\u003c/em\u003e amplification (GCN 6.9, ratio 3.5) in H1648, whereas HCC827 and A549 showed lower values, indicating no amplification (GCN 2.6 and GCN 2.1, respectively) (\u003cb\u003eFig.\u0026nbsp;4A. and\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eNGS analyses were concordant with the above, demonstrating high \u003cem\u003eMET\u003c/em\u003e amplification (gene copy number (GCN)) in EBC-1 and H1993 tumour explants, intermediate \u003cem\u003eMET\u003c/em\u003e amplification in H1648 while HCC827 and A549 have no \u003cem\u003eMET\u003c/em\u003e amplification. All CDX tumours were wild type for \u003cem\u003eMET\u003c/em\u003eex14. Other molecular alterations were observed in the A549 and HCC827 tumours explants as i.e., \u003cem\u003eEGFR\u003c/em\u003e del19 previously described \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ec-Met expression\u003c/h2\u003e \u003cp\u003ec-Met protein levels were evaluated using two different c-Met targeting antibodies (SP44 clone, Ventana\u0026reg; and EP1454Y clone, Abcam\u0026reg;) in formalin-fixed paraffin-embedded tumours specimens and quantified using the H-Score \u003csup\u003e\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eImmunohistochemistry with SP44 revealed strong intensity expression in all CDX models, except A549. Expression was primarily membranous although variable cytoplasmic expression was also observed. Finally, H-Score was between 270 and 298 without distinction between all CDX models except the A549 (H-score: 82). There was not differential IHC expression between highly strong (EBC1, H1993), intermediate \u003cem\u003eMET\u003c/em\u003e (H1648) and even with the no \u003cem\u003eMET\u003c/em\u003e amplified HCC827 CDX models. Figure\u0026nbsp;4C and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eBy contrast, IHC with EP1454Y clone was exclusively membranous and H-score was different between all CDX models ranging from 234 to 82 with the higher H-score for the EBC-1 (234) to the lower score for the A549 (82). Figure\u0026nbsp;4D and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Although EBC1 and H1993 cell lines were both strongly \u003cem\u003eMET\u003c/em\u003e amplified, H-Score was 234 for the former and 134 for the latter.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 4: Different Cell Derived Xenograft (CDX) models of NSCLC\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e(A) FISH results. Orange dot: copy of centromere 7, green dot: copy of MET gene. (x60). (B) Immunohistochemistry with HES (x10) (C) SP44 antibody (x40) and with (D) EP1454Y (x40) in CDX tumours.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRecapitulative analysis of the MET gene copy number, c-Met expression by IHC in five different CDX model of NSCLC.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCell Derived Xenograft\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eH1648\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEBC-1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH1993\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHCC827\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eA549\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMET\u003c/em\u003e GCN (FISH)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRatio \u003cem\u003eMET/CEN7\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA (polysomic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntermediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMET\u003c/em\u003e GCN (NGS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntermediate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther alterations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEGFR del19\u003c/p\u003e \u003cp\u003eCDK4 ampli\u003c/p\u003e \u003cp\u003eEGFR ampli\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eKRAS G12S\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSqCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAd\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH Score SP44\u003c/p\u003e \u003cp\u003e(0-300)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH Score EPY1454\u003c/p\u003e \u003cp\u003e(0-300)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e[⁶⁸Ga]Ga-EMP-100 Uptake Reflects c-Met Expression Across NSCLC Xenografts\u003c/h2\u003e \u003cp\u003eA positive correlation was observed between c-Met expression levels determined by immunohistochemistry (IHC) and tumour uptake of [⁶⁸Ga]Ga-EMP-100 quantified by SUVmax (\u003cb\u003eFig.\u0026nbsp;5\u003c/b\u003e). Quantification of c-Met expression using the H-score demonstrated that tumours with higher receptor expression exhibited markedly increased tracer accumulation. This correlation was statistically significant for the EP1454Y antibody (R\u0026sup2; = 0.919, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), supporting the specificity of [⁶⁸Ga]Ga-EMP-100 binding to extracellularly expressed c-Met-positive lesions.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 5: Differential c-Met Expression\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eRelationship between c-Met expression detected by immunohistochemistry and quantified by H Scoring and tumour uptake (SUVmax) of [\u003c/em\u003e \u003csup\u003e \u003cem\u003e68\u003c/em\u003e \u003c/sup\u003e \u003cem\u003eGa]Ga-EMP-100. (R squared\u0026thinsp;=\u0026thinsp;0.919 and **P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for EP1454Y Antibody)\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, our aim was to demonstrate the potential of PET imaging using [\u003csup\u003e68\u003c/sup\u003eGa]Ga-EMP-100 as a full body, non-invasive and quantitative imaging of the c-Met receptor in NSCLC. Results obtained from [\u003csup\u003e68\u003c/sup\u003eGa]Ga-EMP-100 PET imaging were compared with standard IHC analysis across different histological cell lines exhibiting various genetic alterations reported to be associated with variable c-Met expression.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eFirst, the EMP-100 peptide precursor was confirmed to bind to c-Met with high affinity (K\u003csub\u003ed\u003c/sub\u003e = 1.1 nmol/L) and its specificity for c-Met receptors was demonstrated \u003cem\u003ein vitro\u003c/em\u003e by competition experiments with EMI-137, a known fluorescent binder. Using a robust and automated radiolabelling method \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, we produced a highly pure (RCP\u0026thinsp;\u0026ge;\u0026thinsp;99%) and high-quantity (\u0026gt;\u0026thinsp;500 MBq) batches of [\u003csup\u003e68\u003c/sup\u003eGa]Ga-EMP-100 with a molar activity of 30.2\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8 MBq/nmol. Animal PET imaging with minimal peptide injection amounts (less than 200 pmol), yielded high-contrast images, demonstrating the high sensitivity of PET imaging to non-invasively detect very low levels of c-Met in the squamous EBC-1 CDX model within 40\u0026ndash;60 minutes (1.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88%IA/cm\u003csup\u003e3\u003c/sup\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eSubsequent \u003cem\u003ein vivo\u003c/em\u003e and \u003cem\u003eex vivo\u003c/em\u003e pharmacokinetic analyses showed rapid accumulation of radioactivity in kidneys, bladder and urine, suggesting predominantly renal excretion. This trend was consistent with previous observations using analogues based on the same ligand, namely [\u003csup\u003e18\u003c/sup\u003eF]AlF-EMP-105 and [\u003csup\u003e18\u003c/sup\u003eF]AH113804, although both analogues displayed higher residual activity in kidneys (over 15%ID/g \u003csup\u003e7\u003c/sup\u003e and 4.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6%ID/g \u003csup\u003e26\u003c/sup\u003e respectively) with [\u003csup\u003e18\u003c/sup\u003eF]AH113804 also showing liver uptake. This uptake resulted in mean absorbed doses of 0.052 mGy/MBq in human kidneys and 0.022 mGy/MBq in liver \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Given the shorter half-life of gallium-68 and higher hydrophilicity conferred by the DOTA chelator, [\u003csup\u003e68\u003c/sup\u003eGa]Ga-EMP-100 should represent a lower-radiation alternative with more favourable dosimetry for patients.\u003c/p\u003e \u003cp\u003eWith the above preliminary validation, we next conducted a comparative PET imaging study across various cell-derived xenograft mouse models. Distinguishable accumulation of [\u003csup\u003e68\u003c/sup\u003eGa]Ga-EMP-100 was observed in H1648 and EBC-1, both considered highly c-Met\u0026ndash;overexpressing tumours, compared to models with low and moderate c-Met expression levels. These results highlight the potential usefulness of [\u003csup\u003e68\u003c/sup\u003eGa]Ga-EMP-100 PET for the non-invasive prediction of c-Met expression in NSCLC, consistent with recent clinical case evidence in NSCLC patients \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFinally, a comparison was made between the [\u003csup\u003e68\u003c/sup\u003eGa]Ga-EMP-100 PET findings and standard assays used for c-Met\u0026ndash;targeted therapy selection (IHC and FISH/NGS). Several important trends emerged. First, \u003cem\u003eMET\u003c/em\u003e gene alterations, as assessed by NGS and FISH did not correlate with c-Met protein expression at the cell surface. In the H1993 cell line, high \u003cem\u003eMET\u003c/em\u003e gene amplification was associated with strong overall c-Met protein accumulation (as detected by SP44), but only moderate membrane expression (as detected by EP1454Y or reflected in SUVmax). In HCC827, high intracellular accumulation and moderate surface expression (EP1454Y or PET results) appeared to occur independently of \u003cem\u003eMET\u003c/em\u003e mutations or amplification. Noteworthy is the fact that during this work we came to realise that the immunohistochemistry (IHC) tissue scoring is not a straightforward exercise: not only must a suitable antibody be chosen, but importantly the score obtained depends on the methods and the analysis from the pathologists. In addition, the IHC cut-off point for positivity was difficult to define and to date no consensus exists amongst the scientific community \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan additionalcitationids=\"CR29 CR30 CR31\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNotably, we found that IHC scoring is not a straightforward process: beyond the choice of a suitable antibody, results depend heavily on the analytical methods and pathologist interpretation. Moreover, the optimal IHC cut-off for c-Met positivity remains undefined, with no current consensus across studies \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan additionalcitationids=\"CR29 CR30 CR31\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWhile SP44 is clinically validated, our study intentionally compared both EP1454Y and SP44 using the same H-score method to capture potential discrepancies in antibody recognition epitopes. This difference may also explain the limited correlation observed between PET signal and IHC results obtained with the SP44 antibody. SP44 recognizes an intracellular epitope located in the C-terminal cytoplasmic domain of c-Met, thus reflecting total receptor content rather than the membrane-bound, functional fraction accessible to [\u003csup\u003e68\u003c/sup\u003eGa]Ga-EMP-100. In contrast, antibodies targeting extracellular domains, such as EP1454Y, are more representative of surface c-Met expression and therefore better aligned with tracer uptake patterns observed \u003cem\u003ein vivo\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eThe rapid expansion of antibody-based therapeutics, including antibody\u0026ndash;drug conjugates (ADCs), bispecific antibodies, and radiopharmaceutical therapies, has revolutionized targeted oncology. However, the clinical success of these agents remains strongly dependent on accurate patient selection.\u003c/p\u003e \u003cp\u003eIn this context, we propose a complementary \u003cem\u003ein vivo\u003c/em\u003e imaging-based approach using [\u003csup\u003e68\u003c/sup\u003eGa]Ga-EMP-100 PET to directly quantifies accessible c-Met \u003cem\u003ein vivo\u003c/em\u003e, providing a whole-body, non-invasive assessment. This technology will enable visualization of target engagement in non-biopsiable lesions, allow early assessment of therapeutic response, and offer longitudinal repeatability during treatment monitoring. Ultimately, integrating c-Met PET imaging with conventional modalities like FDG-PET could provide a dual-assessment strategy to distinguish metabolically active recurrent lesions from those with sustained c-Met signaling, refining precision oncology paradigms. Further preclinical studies in our CDX models are warranted to confirm these findings and explore their translational relevance.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe potential of [\u003csup\u003e68\u003c/sup\u003eGa]Ga-EMP-100 PET imaging was successfully demonstrated through direct comparison with current methods for assessing c-Met pathway alteration, such as molecular testing and IHC analysis. Unlike conventional approaches relying on biopsied tissue, [\u003csup\u003e68\u003c/sup\u003eGa]Ga-EMP-100 offers a non-invasive, full body quantification of receptor expression. This innovative imaging technique may represent an added value in the clinicopathological staging of patients and their selection for c-Met targeted therapies such as antibody drug conjugates or radiopharmaceutical therapy. Furthermore, and pending further validation this technique could enhance therapeutic predictions and patient outcomes. Further studies are warranted to better establish the relationship between molecular imaging and treatment response, positioning it as a valuable complementary tool alongside current method.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eNSCLC non-small cell lung cancer\u003c/p\u003e\n\u003cp\u003ec-Met mesenchymal-epithelial transition factor\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTKIs tyrosine kinase inhibitors\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFISH fluorescence \u003cem\u003ein-situ\u003c/em\u003e hybridization\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNGS next-generation sequencing\u003c/p\u003e\n\u003cp\u003eADCs antibody drug conjugates\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRPT radiopharmaceutical therapy\u003c/p\u003e\n\u003cp\u003eIHC immunohistochemistry\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePET positron emission tomography\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e68\u003c/sup\u003eGa gallium-68\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGCN gene copy number\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFP fluorescence polarization\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGMP Good Manufacturing Practices\u003c/p\u003e\n\u003cp\u003eMBq m\u0026eacute;gabecquerel\u003c/p\u003e\n\u003cp\u003eRCY radiochemical yield\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRCP radiochemical purity\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTLC thin layer chromatography\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHPLC high performance liquid chromatography\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePBS Phosphate Buffered Saline\u003c/p\u003e\n\u003cp\u003ePFA Paraformaldehyde\u003c/p\u003e\n\u003cp\u003eCT computed tomography\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ekVp Peak kilovoltage\u003c/p\u003e\n\u003cp\u003eMIP maximum intensity projections\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eVOI volume of interest\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTAC Time Activity Curves\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSUVmax maximum standardized uptake value\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e% IA/g percentage injected activity per gram tissue\u003c/p\u003e\n\u003cp\u003eFFPE formalin-fixed paraffin-embedded\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCDX Cell Derived Xenograft\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAll manuscripts must contain the following sections under the heading \u0026apos;Declarations\u0026apos;:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll animal studies were approved by the Ethics Committee \u0026ldquo;Charles Darwin\u0026rdquo; for Animal Research in Paris (C2EA-05) with an APAFIS project (#25162).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article and its supplementary information files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors\u0026nbsp;declares no potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; CP is a full time employee of Edinburgh Molecular Imaging Ltd. and is listed and an inventor on several patents related to EMP-100.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work is part of the project \u0026ldquo;Meteoric\u0026rdquo; supported by the Association contre le Cancer Tous ensemble \u0026agrave; Tenon (ACTT),\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCore resources (histology and imaging) were supported by Sorbonne University and the Region Ile de France through the CORDDIM for the use of PET/CT imaging system.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTR and AP conceptualized and designed the study. TR performed the radiochemistry experiments and optimized the radiolabelling protocol. JPP, AP, JC, MA conducted the in vitro experiments and data analysis. LC, TR and AP carried out the in vivo PET imaging studies and biodistribution experiments. TR and AP contributed to the interpretation of imaging data. JC and CP provided clinical expertise and contributed to the scientific discussion. AP supervised the project and SM secured funding. TR and AP drafted the manuscript. All authors critically reviewed the manuscript, contributed to revisions, and approved the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Dr. Martin Wear at Edinburgh University for performing FP experiments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; information (optional)\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWang J, Anderson MG, Oleksijew A, Vaidya KS, Boghaert ER, Tucker L, Zhang Q, Han EK, Palma JP, Naumovski L, Reilly EB. ABBV-399, a c-Met Antibody-Drug Conjugate That Targets Both MET-Amplified and c-Met-Overexpressing Tumors, Irrespective of MET Pathway Dependence. Clin Cancer Res. 2017;23(4):992\u0026ndash;1000. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1158/1078-0432.CCR-16-1568\u003c/span\u003e\u003cspan address=\"10.1158/1078-0432.CCR-16-1568\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWaqar SN, Redman MW, Arnold SM, Hirsch FR, Mack PC, Schwartz LH, Gandara DR, Stinchcombe TE, Leighl NB, Ramalingam SS, Tanna SH, Raddin RS, Minichiello K, Bradley JD, Kelly K, Herbst RS, Papadimitrakopoulou V. A. A Phase II Study of Telisotuzumab Vedotin in Patients With c\u0026ndash;MET-Positive Stage IV or Recurrent Squamous Cell Lung Cancer (LUNG-MAP Sub-Study S1400K, NCT03574753). Clin Lung Cancer. 2021;22(3):170\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cllc.2020.09.013\u003c/span\u003e\u003cspan address=\"10.1016/j.cllc.2020.09.013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUlaner G, Juloori A, Juneau D, Schindler J, Allan S, Jean-Louis L, Nawinne M, Mueller N, Littlewood G, Trigg W, Jacene H, Iagaru AA. Phase 1, First-in-Human, Multicentre, Open-Label, Dose-Escalation Study of [225Ac]-FPI-2068 in Adult Patients with Advanced Solid Tumors. \u003cem\u003eJournal of Nuclear Medicine\u003c/em\u003e 2024, \u003cem\u003e65\u003c/em\u003e (supplement 2), 242181\u0026ndash;242181.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEscorcia FE, Houghton JL, Abdel-Atti D, Pereira PR, Cho A, Gutsche NT, Baidoo KE, Lewis JS. ImmunoPET Predicts Response to Met-Targeted Radioligand Therapy in Models of Pancreatic Cancer Resistant to Met Kinase Inhibitors. \u003cem\u003eTheranostics\u003c/em\u003e 2020, \u003cem\u003e10\u003c/em\u003e (1), 151\u0026ndash;165. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7150/thno.37098\u003c/span\u003e\u003cspan address=\"10.7150/thno.37098\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBonin S, Stanta G, Pre-Analytics, Heterogeneity T. New Biotechnol. 2020;55:30\u0026ndash;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.nbt.2019.09.007\u003c/span\u003e\u003cspan address=\"10.1016/j.nbt.2019.09.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFloresta G, Abbate V. Recent Progress in the Imaging of C-Met Aberrant Cancers with Positron Emission Tomography. Med Res Rev. 2022;42(4):1588\u0026ndash;606. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/med.21885\u003c/span\u003e\u003cspan address=\"10.1002/med.21885\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTeh JH, Amgheib A, Fu R, Barnes C, Abrahams J, Ashek A, Wang N, Yang Z, Mansoorudeen M, Long NJ, Aboagye EO. Evaluation of [18F]AlF-EMP-105 for Molecular Imaging of C-Met. \u003cem\u003ePharmaceutics\u003c/em\u003e 2023, \u003cem\u003e15\u003c/em\u003e (7), 1915. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/pharmaceutics15071915\u003c/span\u003e\u003cspan address=\"10.3390/pharmaceutics15071915\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu X, Chen Y, Zhang F, Qiu F, Xu X, Zhang J, He S, Ding D, Tan W, Song S. Preclinical Evaluation of 68Ga-Labeled SL1 Aptamer for c-Met Targeted PET Imaging. Mol Pharm. 2025. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/acs.molpharmaceut.4c01344\u003c/span\u003e\u003cspan address=\"10.1021/acs.molpharmaceut.4c01344\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMinne RL, Luo NY, Mork CM, Wopat MR, Esbona K, Javeri S, Nickel KP, Hernandez R, LeBeau AM, Kimple RJ, Baschnagel AM. Evaluation of a Novel MET-Targeting Camelid-Derived Antibody in Head and Neck Cancer. Mol Pharm. 2024;21(12):6376\u0026ndash;84. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/acs.molpharmaceut.4c00938\u003c/span\u003e\u003cspan address=\"10.1021/acs.molpharmaceut.4c00938\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSomer EJ, Owenius R, Wall A, Antoni G, Thibblin A, S\u0026ouml;rensen J. The Clinical Safety, Biodistribution and Internal Radiation Dosimetry of [18F]AH113804 in Healthy Adult Volunteers. EJNMMI Res. 2016;6(1):87. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13550-016-0239-y\u003c/span\u003e\u003cspan address=\"10.1186/s13550-016-0239-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMetman MJH, Jonker PKC, Sondorp LHJ, van Hemel BM, Sywak MS, Gill AJ, Jansen L, van Diest PJ, van Ginhoven TM, L\u0026ouml;wik CWGM, Nguyen AH, Robinson DJ, van Dam GM, Links TP, Coppes RP, Fehrmann RSN, Kruijff S. MET-Receptor Targeted Fluorescent Imaging and Spectroscopy to Detect Multifocal Papillary Thyroid Cancer. Eur J Nucl Med Mol Imaging. 2024;51(8):2384\u0026ndash;94. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00259-023-06525-5\u003c/span\u003e\u003cspan address=\"10.1007/s00259-023-06525-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePoel HG. C-MET Receptor-Targeted Fluorescence on the Road to Image-Guided Surgery in Penile Squamous Cell Carcinoma Patients. J Nucl Med. 2021. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2967/jnumed.120.261864\u003c/span\u003e\u003cspan address=\"10.2967/jnumed.120.261864\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. BuckleT.Brouwer, O. R.van.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHardwick JCH, Robinson DJ, Burggraaf J, Kamerling IMC, Nagengast WB. The Optimal Imaging Window for Dysplastic Colorectal Polyp Detection Using C-Met\u0026ndash;Targeted Fluorescence Molecular Endoscopy. J Nucl Med. 2020;61(10):1435\u0026ndash;41. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2967/jnumed.119.238790\u003c/span\u003e\u003cspan address=\"10.2967/jnumed.119.238790\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. KarrenbeldA.Voskuil, F. J.; Schmidt, I.; Westerhof, J.; Koornstra, J. J.; Kam, M. L. de.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Jongh SJ, Voskuil FJ, Schmidt I, Karrenbeld A, Kats-Ugurlu G, Meersma GJ, Westerhof J, Witjes MJH, van Dam GM, Robinson DJ, Nagengast WB. C-Met Targeted Fluorescence Molecular Endoscopy in Barrett\u0026rsquo;s Esophagus Patients and Identification of Outcome Parameters for Phase-I Studies. Theranostics. 2020;10(12):5357\u0026ndash;67. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7150/thno.42224\u003c/span\u003e\u003cspan address=\"10.7150/thno.42224\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMittlmeier LM, Todica A, Gildehaus F-J, Unterrainer M, Beyer L, Brendel M, Albert NL, Ledderose ST, Vettermann FJ, Schott M, Rodler S, Marcon J, Ilhan H, Cyran CC, Stief CG, Staehler M, Bartenstein P. Ga-EMP-100 PET/CT-a Novel Ligand for Visualizing c-MET Expression in Metastatic Renal Cell Carcinoma-First in-Human Biodistribution and Imaging Results. Eur J Nucl Med Mol Imaging. 2022;49(5):68. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e1711\u0026ndash;1720. https://doi.org/10.1007/s00259-021-05596-6\u003c/span\u003e\u003cspan address=\"1711\u0026ndash;1720. 10.1007/s00259-021-05596-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRusu T, Delion M, Pirot C, Blin A, Rodenas A, Talbot J-N, Veran N, Portal C, Montravers F, Cadranel J, Prignon A. Fully Automated Radiolabeling of [68Ga]Ga-EMP100 Targeting c-MET for PET-CT Clinical Imaging. EJNMMI Radiopharm Chem. 2023;8:30. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s41181-023-00213-3\u003c/span\u003e\u003cspan address=\"10.1186/s41181-023-00213-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmith MA, Licata T, Lakhani A, Garcia MV, Schildhaus H-U, Vuaroqueaux V, Halmos B, Borczuk AC, Chen YA, Creelan BC, Boyle TA, Haura EB. MET-GRB2 Signaling-Associated Complexes Correlate with Oncogenic MET Signaling and Sensitivity to MET Kinase Inhibitors. Clin Cancer Res. 2017;23(22):7084\u0026ndash;96. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1158/1078-0432.CCR-16-3006\u003c/span\u003e\u003cspan address=\"10.1158/1078-0432.CCR-16-3006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKubo T, Yamamoto H, Lockwood WW, Valencia I, Soh J, Peyton M, Jida M, Otani H, Fujii T, Ouchida M, Takigawa N, Kiura K, Shimizu K, Date H, Minna JD, Varella-Garcia M, Lam WL, Gazdar AF, Toyooka S. MET Gene Amplification or EGFR Mutation Activate MET in Lung Cancers Untreated with EGFR Tyrosine Kinase Inhibitors. Int J Cancer. 2009;124(8):1778\u0026ndash;84. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ijc.24150\u003c/span\u003e\u003cspan address=\"10.1002/ijc.24150\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArmstrong GR, Khot MI, Portal C, West NP, Perry SL, Maisey TI, Tiernan JP, Hughes TA, Tolan DJ, Jayne DG. A Novel Fluorescent C-Met Targeted Imaging Agent for Intra-Operative Colonic Tumour Mapping: Translation from the Laboratory into a Clinical Trial. Surg Oncol. 2022;40:101679. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.suronc.2021.101679\u003c/span\u003e\u003cspan address=\"10.1016/j.suronc.2021.101679\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchildhaus H-U, Schultheis AM, R\u0026uuml;schoff J, Binot E, Merkelbach-Bruse S, Fassunke J, Schulte W, Ko Y-D, Schlesinger A, Bos M, Gardizi M, Engel-Riedel W, Brockmann M, Serke M, Gerigk U, Hekmat K, Frank KF, Reiser M, Schulz H, Kr\u0026uuml;ger S, Stoelben E, Zander T, Wolf J, Buettner RMET. Amplification Status in Therapy-Na\u0026iuml;ve Adeno- and Squamous Cell Carcinomas of the Lung. Clin Cancer Res. 2015;21(4):907\u0026ndash;15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1158/1078-0432.CCR-14-0450\u003c/span\u003e\u003cspan address=\"10.1158/1078-0432.CCR-14-0450\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBuckingham LE, Coon JS, Morrison LE, Jacobson KKB, Jewell SS, Kaiser KA, Mauer AM, Muzzafar T, Polowy C, Basu S, Gale M, Villaflor VM, Bonomi P. The Prognostic Value of Chromosome 7 Polysomy in Non-Small Cell Lung Cancer Patients Treated with Gefitinib. J Thorac Oncol. 2007;2(5):414\u0026ndash;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/01.JTO.0000268675.02744.b0\u003c/span\u003e\u003cspan address=\"10.1097/01.JTO.0000268675.02744.b0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEngelman JA, Zejnullahu K, Mitsudomi T, Song Y, Hyland C, Park JO, Lindeman N, Gale C-M, Zhao X, Christensen J, Kosaka T, Holmes AJ, Rogers AM, Cappuzzo F, Mok T, Lee C, Johnson BE, Cantley LC, J\u0026auml;nne PA. MET Amplification Leads to Gefitinib Resistance in Lung Cancer by Activating ERBB3 Signaling. Science. 2007;316(5827):1039\u0026ndash;43. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/science.1141478\u003c/span\u003e\u003cspan address=\"10.1126/science.1141478\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCamidge DR, Barlesi F, Goldman JW, Morgensztern D, Heist R, Vokes E, Angevin E, Hong DS, Rybkin II, Barve M, Bauer TM, Delmonte A, Dunbar M, Motwani M, Parikh A, Noon E, Wu J, Blot V, Kelly K. A Phase 1b Study of Telisotuzumab Vedotin in Combination With Nivolumab in Patients With NSCLC. JTO Clin Res Rep. 2021;3(1):100262. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jtocrr.2021.100262\u003c/span\u003e\u003cspan address=\"10.1016/j.jtocrr.2021.100262\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpigel DR, Ervin TJ, Ramlau RA, Daniel DB, Goldschmidt JH, Blumenschein GR, Krzakowski MJ, Robinet G, Godbert B, Barlesi F, Govindan R, Patel T, Orlov SV, Wertheim MS, Yu W, Zha J, Yauch RL, Patel PH, Phan S-C, Peterson AC. Randomized Phase II Trial of Onartuzumab in Combination With Erlotinib in Patients With Advanced Non\u0026ndash;Small-Cell Lung Cancer. J Clin Oncol. 2013;31(32):4105\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1200/JCO.2012.47.4189\u003c/span\u003e\u003cspan address=\"10.1200/JCO.2012.47.4189\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCasadevall D, Gimeno J, Clav\u0026eacute; S, Taus \u0026Aacute;, Pijuan L, Arum\u0026iacute; M, Lorenzo M, Men\u0026eacute;ndez S, Ca\u0026ntilde;adas I, Albanell J, Serrano S, Espinet B, Salido M, Arriola EMET. Expression and Copy Number Heterogeneity in Nonsquamous Non-Small Cell Lung Cancer (nsNSCLC). Oncotarget. 2015;6(18):16215\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArulappu A, Battle M, Eisenblaetter M, McRobbie G, Khan I, Monypenny J, Weitsman G, Galazi M, Hoppmann S, Gazinska P, Wulaningsih W, Dalsgaard GT, Macholl S, Ng T. C-Met PET Imaging Detects Early-Stage Locoregional Recurrence of Basal-Like Breast Cancer. J Nucl Med. 2016;57(5):765\u0026ndash;70. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2967/jnumed.115.164384\u003c/span\u003e\u003cspan address=\"10.2967/jnumed.115.164384\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUnterrainer LM, Todica A, Beyer L, Brendel M, Holzgreve A, Kauffmann-Guerrero D, Unterrainer M, Bartenstein P, Tufman A. Ga-EMP-100 PET/CT\u0026mdash;a Novel Method for Non-Invasive Assessment of c-MET Expression in Non-Small Cell Lung Cancer. Eur J Nucl Med Mol Imaging. 2023;50(2):68. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e628\u0026ndash;629. https://doi.org/10.1007/s00259-022-05995-3\u003c/span\u003e\u003cspan address=\"628\u0026ndash;629. 10.1007/s00259-022-05995-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoeppen H, Yu W, Zha J, Pandita A, Penuel E, Rangell L, Raja R, Mohan S, Patel R, Desai R, Fu L, Do A, Parab V, Xia X, Januario T, Louie SG, Filvaroff E, Shames DS, Wistuba I, Lipkind M, Huang J, Lazarov M, Ramakrishnan V, Amler L, Phan S-C, Patel P, Peterson A, Yauch RL. Biomarker Analyses from a Placebo-Controlled Phase II Study Evaluating Erlotinib\u0026thinsp;\u0026plusmn;\u0026thinsp;Onartuzumab in Advanced Non\u0026ndash;Small Cell Lung Cancer: MET Expression Levels Are Predictive of Patient Benefit. Clin Cancer Res. 2014;20(17):4488\u0026ndash;98. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1158/1078-0432.CCR-13-1836\u003c/span\u003e\u003cspan address=\"10.1158/1078-0432.CCR-13-1836\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpigel DR, Edelman MJ, Mok T, O\u0026rsquo;Byrne K, Paz-Ares L, Yu W, Rittweger K, Thurm H. Treatment Rationale Study Design for the MetLung Trial: A Randomized, Double-Blind Phase III Study of Onartuzumab (MetMAb) in Combination With Erlotinib Versus Erlotinib Alone in Patients Who Have Received Standard Chemotherapy for Stage IIIB or IV Met-Positive Non\u0026ndash;Small-Cell Lung Cancer. Clin Lung Cancer. 2012;13(6):500\u0026ndash;4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cllc.2012.05.009\u003c/span\u003e\u003cspan address=\"10.1016/j.cllc.2012.05.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNeal JW, Dahlberg SE, Wakelee HA, Aisner SC, Bowden M, Huang Y, Carbone DP, Gerstner GJ, Lerner RE, Rubin JL, Owonikoko TK, Stella PJ, Steen PD, Khalid AA, Ramalingam SS. Erlotinib, Cabozantinib, or Erlotinib plus Cabozantinib as Second- or Third-Line Treatment of Patients with EGFR Wild-Type Advanced Non-Small Cell Lung Cancer (ECOG-ACRIN 1512): A Phase 2 Randomised Controlled Trial. Lancet Oncol. 2016;17(12):1661\u0026ndash;71. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S1470-2045(16)30561-7\u003c/span\u003e\u003cspan address=\"10.1016/S1470-2045(16)30561-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBagchi A, Madaj Z, Engel KB, Guan P, Rohrer DC, Valley DR, Wolfrum E, Feenstra K, Roche N, Hostetter G, Moore HM, Jewell SD. Impact of Preanalytical Factors on the Measurement of Tumor Tissue Biomarkers Using Immunohistochemistry. J Histochem Cytochem. 2021;69(5):297\u0026ndash;320. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1369/0022155421995600\u003c/span\u003e\u003cspan address=\"10.1369/0022155421995600\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMasson E. L\u0026rsquo;immunohistochimie c-Met en oncologie thoracique, un nouvel enjeu pour le pathologiste. EM-Consulte. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.em-consulte.com/article/1641925/l-immunohistochimie-c-met-en-oncologie-thoracique-\u003c/span\u003e\u003cspan address=\"https://www.em-consulte.com/article/1641925/l-immunohistochimie-c-met-en-oncologie-thoracique-\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (accessed 2024-09-07).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"ejnmmi-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejre","sideBox":"Learn more about [EJNMMI Research](http://ejnmmires.springeropen.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ejre/default.aspx","title":"EJNMMI Research","twitterHandle":"@officialEANM","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"c-Met, PET/CT, [68Ga]Ga-EMP-100, non-small cell lung cancer (NSCLC)","lastPublishedDoi":"10.21203/rs.3.rs-8970902/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8970902/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground \u003c/strong\u003eThe c-Met receptor is a key therapeutic target in non-small cell lung cancer (NSCLC). Current methods for assessing c-Met level, are limited by biopsy sampling, which fails to account for spatio temporal and intra-tumour heterogeneity. This study aims to evaluate the use of PET/CT imaging for non-invasive, full-body quantification of c-Met expression in different subtypes of NSCLC, encompassing both adenocarcinoma and squamous cell carcinoma and compare it to \u003cem\u003eMET\u003c/em\u003e\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003egene alterations\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003eand IHC c-Met scoring.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003e[\u003csup\u003e68\u003c/sup\u003eGa]Ga-EMP-100, a PET radiotracer targeting c-Met, was radiolabelled and characterized. Cell-Derived Xenograft (CDX) models of NSCLC with different characteristics were developed and validated for PET/CT imaging using [\u003csup\u003e68\u003c/sup\u003eGa]Ga-EMP-100. Tumour uptake and heterogeneity were quantified and compared to c-Met expression determined by IHC (H-score using SP44 and EP1454Y antibodies) and \u003cem\u003eMET\u003c/em\u003e gene amplification detected by FISH and NGS.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAutomated radiolabelling of [\u003csup\u003e68\u003c/sup\u003eGa]Ga-EMP-100 demonstrated a high radiochemical yield and purity. Pharmacokinetics studies revealed rapid excretion predominantly by the renal pathway. PET/CT imaging resulted in high contrast and enabled non-invasive classification of CDX models regarding c-Met receptor levels. The highest tumour uptake was observed in H1648 and EBC-1 models.\u003cem\u003e \u003c/em\u003eAlthough\u003cem\u003e MET\u003c/em\u003e gene alterations were not correlated with c-Met protein expression at the cell surface, a good correlation was found between SUVmax and c-Met expression, when using the EP1454Y antibody.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion \u003c/strong\u003ePET/CT imaging using [\u003csup\u003e68\u003c/sup\u003eGa]Ga-EMP-100 successfully quantified c-Met expression \u003cem\u003ein vivo\u003c/em\u003e, clearly adding up to conventional IHC and genetic methods. Our study adds novel comparative evidence across tumour histotypes, providing new insight into how tumour phenotype affects c-Met–targeted imaging.\u003c/p\u003e\n\u003cp\u003eThis radiotracer holds potential as a non-invasive tool for selecting patients for c-Met-targeted therapies and monitoring therapeutic response in NSCLC. Further clinical studies are warranted.\u0026nbsp;\u003c/p\u003e","manuscriptTitle":"Preclinical PET Characterization of [68Ga]Ga-EMP-100 for Non-Invasive Assessment of c-Met in NSCLC","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-11 12:04:42","doi":"10.21203/rs.3.rs-8970902/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2026-04-14T07:43:09+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-06T14:15:07+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"EJNMMI Research","date":"2026-03-06T06:46:27+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-05T01:40:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"EJNMMI Research","date":"2026-03-04T05:23:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"ejnmmi-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejre","sideBox":"Learn more about [EJNMMI Research](http://ejnmmires.springeropen.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ejre/default.aspx","title":"EJNMMI Research","twitterHandle":"@officialEANM","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c516c605-4622-4dae-8758-909a3366bcc4","owner":[],"postedDate":"March 11th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-11T12:04:43+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-11 12:04:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8970902","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8970902","identity":"rs-8970902","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}