MET Exon 14 Skipping Mutations in Non–Small-Cell Lung Cancer: Testing Considerations and Clinical Outcomes a 3 years screening experience

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MET exon 14 mutations affect splice sites and are highly heterogeneous which makes them difficult to detect. Because of the approval of capmatinib for patients with MET exon 14 mutated tumors and the related poor response to immunotherapy (ICI) for a subset of patients with MET mutated tumors, MET screening has become mandatory for first line treatment decision. Methods Here we report our testing experience based on 1143 consecutive NSCLC addressed for molecular diagnosis. Two strategies using either DNA sequencing (NGS) and fragment analysis or DNA-RNA sequencing (NGS) were developed and validated to accurately detect MET exon 14 alterations including large deletions. For patients with MET tumors (n = 46), demographic characteristics, treatments and outcomes were obtained from medical records and discussed. Results 46 MET exon 14 alterations were identified, 4 were not called by DNA sequencing and rescued by fragment analysis or RNA sequencing. Sixty-seven percent tumors had a high PD-L1 expression > 50% and 42% of cases had co-occurring alterations, mainly TP53 mutations (24%) and PIK3CA mutations (9%). Response to MET inhibitors (Crizotinib and Capmatinib) was evaluated for 15 patients. The ORR (Objective Response Rate) and the median of PFS (Progression Free Survival) were 44% and 5.5 months [1.6–18.2 months] respectively. Thirteen patients were treated by immunotherapy, ORR and median PFS (Progression Free Survival) median were 30% and 4 months [0.7–55.5 months] respectively. The response to immunotherapy was not correlated with PD-L1 status but smokers seemed to better respond to ICIs. Conclusions This study highlights that a multimodal approach may be necessary to detect MET exon 14 mutations as large deletions may not be detected by DNA sequencing. Targeted DNA-ARN sequencing strategies broadly interrogate the diverse druggable genomic variations and permits direct detection of altered splicing or gene fusions. Because patients with MET exon 14 mutated tumors, demonstrate low response to immunotherapy despite high PDL1 and because MET exon 14 is druggable the detection of MET mutations is mandatory to optimize treatment. Biological sciences/Biological techniques Biological sciences/Cancer Health sciences/Biomarkers Health sciences/Biomarkers/Predictive markers Health sciences/Biomarkers/Prognostic markers MET exon 14 NSCLC PD-L1 NGS Capmatinib Crizotinib ICI Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Non-Small Cell Lung Cancer (NSCLC) is a frequent and aggressive tumor, and a leading cause of death by cancer [ 1 ]. Recent therapeutic advances came from the identification of molecular alterations associated with the effectiveness of targeted therapies. Recently, mutations in the MET receptor have been detected in lung cancer. These mutations, called MET exon 14 occur at intron-exon junctions and result in a loss of Exon 14. Loss of exon14 leads to a shorter transcript and to the expression of a protein lacking the negative regulatory region containing the Y1003 residue involved in c-Cbl E3 ubiquitin ligase binding. Decreased ubiquitination, increases MET signaling and drives oncogenesis [ 5 ], [ 6 ]. Met exon 14 mutations are observed in 3–4% of NSCLC cases [ 4 ]. The first targeted therapy demonstrating anti-tumor efficacy in METex14 mutated NSCLC was Crizotinib, [ 8 ],[ 9 ],[ 10 ]. More recently, selective MET inhibitors, such as Capmatinib and Tepotinib, have been developed [ 11 ],[ 12 ] and approved by the US Food and Drug Administration for patients with advanced MET exon 14 mutated NSCLC [ 13 ]. In patients with non- EGFR-ALK-ROS1 addicted metastatic NSCLC first line treatment relies on chemotherapy and immunotherapy. For MET exon 14 tumors, the efficacy of ICIs is controversial [ 14 , 15 ] and first line treatment is an issue [ 14 ]. MET testing is thus needed early to guide first and subsequent treatment lines. Screening for MET exon 14 mutations is challenging due to their diversity including large deletions, and their location within the introns flanking exon 14. Detection methods are based either on DNA or RNA screenings. RNA based methods include RT-PCR or RNA-sequencing. DNA based Next Generation sequencing (NGS) panels are designed to detect MET exon 14 splicing variants at exon boundaries but up to ¼ of MET exon 14 variants may be missed [ 16 ]. Indeed, ampliseq-based NGS may fail to detect large deletions when primers localize within the deleted region and large deletions may also impair fragment capture when capture-based libraries are used. Among other methods, large deletions can be analyzed using standard PCR and fragment analysis but it does not allow full characterization of the deletion and finally MET exon 14 skipping may be identify at the ARN level when RNAs are available [ 17 , 18 ]. In this study, we combined screening methods to optimize MET exon14 detection to propose an algorithm to screen MET exon14 alterations along with other alterations in NSCLC patients and provided clinical information and follow up in patients with MET exon14 mutated tumors treated by MET inhibitors and/or ICI. Materials and Methods Patients and Tumor samples A total of 1143 consecutive NSCLC were addressed to our laboratory for molecular diagnosis (following current guidelines, cIIIB or cIV NSCLC with treatment intent, patients not fit for surgical treatments or non-smokers) from December 2018 to November 2021. Molecular profiling was systematically performed on tumor tissues for patients with advanced NSCLC and for non-smoker patients with localized NSCLC as a standard of care using NGS technology. Molecular data were prospectively registered in the laboratory record informatics system that provides result reporting to clinicians [French National Commission for Informatics and Liberties (CNIL) declaration number 1922081 v 0, and the project was approved by the local ethic committee (REF2021-09-10 CERAPHP Centre)]. This research was performed in accordance with the relevant guidelines and regulations. It included informed consent from participants and has been performed in accordance with the Declaration of Helsinki. DNA extraction Formalin-fixed paraffin-embedded (FFPE) samples from non–small cell lung tumors were reviewed and qualified on hematoxylin-and-eosin–stained slides. For samples with < 20% macro-dissection was done. DNA was extracted from 10-mm-thick sections using the Maxwell® 16 FFPE Plus LEV DNA Purification Kit (Promega, France). Extracted DNA was quantified by fluorometric Quantitation on a Qubit™ using the Qubit dsDNA BR Assay Kit (Life Technologies–Thermo Fisher Scientific, Saint Aubin, France). Immunohistochemistry PD-L1 immunochemistry was performed on tumor samples using clone E1L3N (Cell Signaling Technology, Danvers, MA) on the automat Leica BOND-MAX (Leica Biosystems, Buffalo Grove, IL). The immunostaining was evaluated as the percentage of stained cells (TPS: tumoral proportion score) by an expert pathologist (AML, LG). NGS Panel Design Samples were characterized for molecular alterations by targeted NGS (Ion AmpliSeq™ Colon-Lung Cancer Research Panel v2, Life Technologies™, Carlsbad, CA) for analysis from December 2018 to May 2020 associated to a panel dedicated to ALK ROS1 and MET screening (Ion AmpliSeq™ ROSMETALK). From May 2020, tumors were analyzed using a single panel (Ion Ampliseq™ panel WG-IAD 196383_v2) that covers METex14 boundaries. Details of the panels are described in supplementary Table S1 . Briefly the multiplex barcoded libraries are generated from 10 to 30 ng of DNA following manufacturer’s recommendations (Ion ampliseq library kit V2) and are normalized using the Ion Library Equalizer™ Kit. The pooled libraries (max 96) are processed on Ion Chef™ System for template preparation and chip loading (Ion PI HI-Q Chef Kit, Ion PI Chip V3), and sequenced on the Ion Proton™ System (Life Technologies™). The FASTQs sequencing data are aligned to the human genome (hg19) and processed using IonTorrent Suite V5.0.4.0 This package included the Torrent Variant Caller V5.0.4.0 using the built-in “Somatic - low stringency” with optimized parameters to automatically call variants with allelic ratio > 2%. The FusionPlex® Lung v2 (Integrated DNA Technologies) panel was used for RNA sequencing on a MiSeq Illumina®. Analysis was done on the Archer analysis virtual machine and transcript NM_0011207500.1 was used for annotations. Fragment analysis Intron sequences flanking MET exon 14 were amplified with FAM-labeled primers in one tube. The primer pair for METexon14 5’boundary was: EX14_5F famCGTCGATTCTTGTGTGCTGT / EX14_5R CGGGCACTTACAAGCCTATC and the primer pair for METexon14 3’boundary was EX14_3F famGGCTTGTAAGTGCCCGAAGT / EX14_3R CAACAATGTCACAACCCACTG. PCR was performed in a final volume of 20 µl using HotStar Taq DNA polymerase and the following program: 95°C for 5 min; 35 cycles of 95°C for 30 sec, 58°C for 30 sec, and 72°C for 45 sec; and the final step of 72°C for 10 min. Fragments were analyzed on a sequencer 3730xl DNA Analyzer (Applied Biosystems – ThermoFisher®) in a formamide-size-standard mix (GeneScan™ 400HD Colorant ROX™ - Applied Biosystems). Runs were analyzed by GeneMapper (Applied Biosystems) software. Expected fragment sizes were 198 base pairs (bp) and 148 bp fragments at the 5’ and 3’ends respectively. Supplementary peaks at the 5’ or 3’ ends suggest the existence of a, MET exon 14 deletion or insertion that needs to be subsequently sequenced. Limit of detection of this assay is 10%. Sanger Sequencing Deletions at exon 14 boundaries detected by fragment analysis were characterized using Sanger sequencing. Amplification and sequencing primers were identical to those used for fragment analysis; for METexon14 5’boundary: EX14_5F CGTCGATTCTTGTGTGCTGT / EX14_5R CGGGCACTTACAAGCCTATC and for METexon14 3’boundary: EX14_3F GGCTTGTAAGTGCCCGAAGT / EX14_3R CAACAATGTCACAACCCACTG. PCR products were purified and sequenced on a 3730xl DNA Analyzer. Results were analyzed by Sequencher 5.0 (Applied Biosystems). Clinical data All consecutive patients with MET exon 14 mutated tumors diagnosed from December 2018 to November 2021 were included. Demographic characteristics: age at diagnosis, gender, patient's general condition assessed by the Performance Status Index (PS), smoking status (non-smoker defined as an individual who had smoked less than 100 cigarettes in his or her lifetime [ 8 ] versus former or active smoker), histological type, PD-L1 expression ( 50%), TNM classification at diagnosis were collected. Treatment lines, best response to treatment according to RECIST (Response Evaluation Criteria In Solid Tumors) and toxicity were obtained retrospectively from medical records in accordance with national guidelines. Statistical analysis PD-L1 expression (TPS) contingency were analyzed with Chi-Square test and P-values were one-sided (greater). P-values < 0.05 were considered to indicate significance. Statistical analyses were performed using GraphPad Prism version 8.0.0 for Mac, (GraphPad Software, San Diego, California USA, www.graphpad.com . ) Detailed data are available as supplementary information (table S2) Results Optimization of MET Exon 14 Splice Site Mutations detection During the period from December 2018 to May 2020 period (step 1), 518 patients had a NGS based DNA sequencing. 137 samples EGFR, KRAS , ERBB2, BRAF, ALK WT, had second NGS testing ALK ROS and MET and fragment analysis (Fig. 1 ). MET alterations were identified in 29 samples by NGS testing, fragment analysis or both. One was classified benign, an insertion of 2 pb (NM_001127500.1 (MET): c.2942-52_2942-51insCT) as in silico analyses showed that it did not alter intron 13 3’ splicing site, one was classified as VUS, MET (NM_001127500.1) p.Gly983Val c.2948G > T). During the next period (step 2), our detection strategy changed with the implementation of a new DNA panel covering MET exon14 boundaries and RNA sequencing. Over 23 months of screening we found 46 MET exon14 alterations out of 1143 patients tested (4%). Four mutations were not detected by NGS and rescued by fragment analysis (patients 26 & 27) (Fig. 2 ) and by RNAseq (patients 45 & 46) over the first and second periods respectively. Deletions not called by NGS were visualized on Alamut Visual software and no deletion could be identified however coverage imbalances were noted between the 3’ and the 5’ amplicons. All MET mutations are summarized in Table 1. Fourteen mutations were consensus hotspot mutations (14/46, 30%) and 32 were deletions involving consensus splice (32/46, 70%). Alterations were distributed on both ends of exon 14, 20 were located at the intron13-exon14 boundary and 24 the exon14-intron14 boundary for the 2 detected by RNAseq the DNA mutated position cannot be specified. Concurrent Genetic Alterations In tumors with MET exon 14 mutations, co-occurring genetic alterations consisted mainly of TP53 mutations (11/46, 24%) and PIK3CA activating mutations (4/46, 9%). There were 2 cases with both MET exon 14 and MET amplification and one patient had 2 tumors one MET and one KRAS G12C ( Table 1 ). Clinicopathologic Characteristics of patients with METex14 mutated tumors The clinical and pathologic characteristics of patients with MET exon 14 mutated NSCLC are listed in Table 2 . Among these patients, the median age at disease onset was 79 years (range 58 to 95 years), with 30% age 85 or older. Forty-six percent were women and 46% were non-smokers. At diagnosis, 17% of patients had stage I/II NSCLC, 20% had stage III disease, and 63% had stage IV disease and 48% had a PS ≥ 2. The most prevalent site of metastasis was pleura (34%). Tumors were mainly adenocarcinomas but adenosquamous carcinomas (4/46, 8.6%) and sarcomatoid tumors (4/46, 8.6%) were identified. Samples were classified according to PD-L1 expression into three groups: < 1%, 1% ≤ % < 50 and ≥ 50%. Nearly ¾ of MET mutated tumors had a high PD-L1 expression of ≥ 50% ( Table 2 ) which is significantly higher as compared to WT samples ( p = 0,0012, one sided, Chi-square test) Treatments patterns of patients with MET exon 14 tumors Localized and locally advanced patients Eight patients with stage < IIIA tumors had local treatments with surgery for five and stereotaxic radiotherapy for three of them. Nine patients with locally advanced tumors received chemo-radiotherapy (n = 4), radiotherapy only (n = 1) or surgery followed by adjuvant chemotherapy (n = 4). Four of these patients relapse with metastatic disease during follow-up period. Metastatic patients Concerning the 33 patients with metastatic cancer (including relapses), 4 patients with PS > 2 had best supporting care and 29 had specific treatments (Fig. 3 ) . Half of the patients had chemotherapy first 15/29, 20% (6/29) immunotherapy, 17% (5/29) a MET inhibitor and 10% (3/29) had chemo-immunotherapy. Ten patients stopped specific treatments after L1. Altogether, 15 patients received a MET inhibitor in L1 (n = 5), L2 (n = 8) or L3 (n = 5), 3 patients had 2 lines of targeted therapy. Thirteen patients had immune checkpoints inhibitors (ICI) monotherapy based on PD-L1 expression. A subset of patients (n = 28) received targeted therapy, crizotinib (n = 11) or capmatinib (n = 7), table 3. In crizotinib treated patients, median PFS was 7.6 month [1.6–13.3] and overall response rate (ORR) was 45% (n = 5/11). All but one patients treated with MET TKIs Crizotinib experienced disease control (stable or partial response). The patient with concurrent MET amp (> 6 copies) experienced the deepest response (Best response RECIST − 75%). One patient that received prior immunotherapy with pembrolizumab had an early treatment discontinuation for hepatic toxicity and crizotinib response could not be assessed. In capmatinib treated patients (n = 7, 3 post crizotinib treatment), median PFS was 3 months [1.3–18.2] and ORR was 43% (3/7). Three patients experienced partial response and 2 patients experienced progression ( Table 3 , Fig. 4 ). Thirteen patients received immunotherapy as monotherapy with either Pembrolizumab (n = 7) or Nivolumab (n = 7). Median PFS was 4 months [0.7–55.5 months] and ORR was 30% (4/13) with significant patient disparity (Fig. 5 ). A more important response was noted among smokers: ORR and median PFS were 67% (4/6) and 10.3 [1.7–5.5 months] for smokers versus 0% (0/7) and 2.1 months [0.7-5 months] for non-smokers, respectively (p = 0.016). Two patients had a sustained response greater than 30 months. The characteristics of these two patients were presented in ( Table 4 ). Discussion In this study, MET exon 14 testing was analyzed in routine care settings. We showed that MET exon 14 mutations were a common event in the EGFR, KRAS, BRAF, ALK WT population justifying its systematic testing. Our routine pipeline uses ampliseq technology. When MET exon 14 testing became part of the molecular gene-set for lung cancer, we felt concerned by the issue of large deletion detection. The ampliseq technology uses primers designed to amplify small DNA fragments. This leads to intrinsic difficulties in calling large deletions either because amplicons are too small to be aligned or because primers match the deleted sequence. Therefore, we implemented a DNA-based algorithm to rescue the detection of large deletions using fragment analysis when NGS was negative. Two large deletions were identified (41bp and 65bp) by fragment analysis [ 16 ]. Fragment analysis is a fast, simple, and cost-efficient way to identify deletions. Although the sensitivity of this method was lower than that of NGS, the combination with amplicon-based NGS improved the detection of MET exon 14 alterations as previously described [ 19 ]. Other approaches exist to screen MET exon 14. One of the most powerful is to combine DNA and RNA-based detection as RNA sequencing is an efficient technic to detect splice-variant at transcript level. Moreover, as oncogenic transcripts are often highly expressed, it may more sensitive than DNA-based method [ 20 ]. Gene fusions represent promising targets in lung cancer and reliable detection of multiple gene fusions has become part of the routine screening [ 9 ]. This has impacted testing strategies. Indeed, as RNAseq becomes mandatory, MET exon14 skipping mutations that were missed by DNA sequencing would be rescued by RNA analysis. Moreover, RNAseq may also help classification of ambiguous splice mutations for which the functional impact on splicing is not clear. All together DNA/RNA based testing, seems to be the most accurate strategy to detect MET exon 14 mutation along with other targetable drivers. Here, 46 patients with MET exon 14 mutated tumors were identified. Most patients with MET exon 14 tumors are elderly patients with median age of 79 years [ 19 ]. This specificity underlines the importance of screening for MET exon 14 alterations in this population that is not always fit enough to receive chemotherapy. Half of the patients were smokers with a smoking history of 20 packs per years or more [ 21 – 23 ]. These clinical associations match to the current literature [ 24 – 25 ]. However, in a recent meta-analysis, based on 2661 NSCLC, patients with MET exon 14 mutations were less likely to associate with smoking history as compared to wild type patients (OR = 0.48, p = 0.008) [ 19 ]. These data highlight that MET exon 14 mutations affect both smokers and non-smokers, unlike most common oncogenic driver outside of KRAS . We collected real life data concerning patients’ treatments and response to MET inhibitors. In our cohort 11 patients received Crizotinib at different lines of treatment with a median PFS of 7.6 months. This was comparable to previous results. In PROFILE 1001 [ 26 ] ORR of 39% and median PFS of 7,3 months under Crizotinib with was the first MET TKI to receive FDA approval for MET exon14 NSCLC. More recently Capmatinib and Tepotinib have demonstrated ORR of 41% and 48% respectively. The median PFS was 9.7 months for capmatinib naif MET exon 14 patients and 8,5 months for Tepotinib [ 11 , 12 ]. We noted a lower Capmatinib response than reported in GEOMETRY study (median PFS 3.0 versus 9.7 months respectively). However, in our study, 3/7 patients were previously treated with Crizotinib. Indeed, we cannot draw comparison because MET inhibitors pretreated patients were excluded from GEOMETRY. One patient had both MET amplification and MET exon 14 mutation. He almost achieved a complete response after 3 months of Crizotinib treatment. In the VISION trial, a better response of Tepotinib was reported in the 5 patients with concurrent MET exon 14 and MET amplification compared to MET exon 14 only (80% vs. 46% respectively). This finding suggests that this co-alteration may potentiate the activity of MET inhibitors. Regarding the response to ICIs in our patients, a median PFS of 4 months was noted. These results were comparable to those of the multicenter IMMUNOTARGET MET exon 14 cohort conducted by Mazières et al , [ 21 ]. We found a higher proportion of PD-L1 ≥ 50% compared to non-MET samples ( p = 0.0012) consistent with previous reports [ 10 ]. However, we found no correlation between response and PD-L1 expression in the 13 patients treated with immunotherapy, which was consistent with the findings of Sabari et al , suggesting that PD-L1 has no predictive value in MET exon 14 patients [ 13 ]. On the other hand, almost all of them have PD-L1 (TPS) > 50%. We also noted a longer response to ICIs in heavy smokers. A PFS greater than 20 months was observed in 2 patients having smoked more than 30 pack-year smokers. High levels of tobacco intoxication may be associated with high mutational burden and an immunogenic microenvironment potentiating immunotherapy [ 27 – 29 ]. Additionally, we reported one case of severe hepatic toxicity in a patient treated with immunotherapy followed by Crizotinib. Other study reported similarly an increase in Grade 3 and 4 hepatitis with Crizotinib in immunotherapy-experienced subjects [ 11 ] and there is growing concern that immunotherapy followed by targeted therapies induces potentially severe immune-mediated adverse effects [ 30 – 31 ]. This data highlighted the importance of an early MET screening in order to choose the appropriate therapeutic sequence. Not detecting MET mutations is deleterious because patients with MET mutated tumors may have access to MET-targeted therapies and because patients could receive sub-optimal first line treatment such as ICIs monotherapy based on PD-L1 expression. Our testing and clinical experience led to the validation of a new workflow, first step includes the use of taqman probes for EGFR and KRAS frequent mutations and subsequent DNA and RNA sequencing panels in parallel. Altogether, the use of taqman probes and hotspots DNA and RNA panels covers all drivers in lung cancer at reasonable costs in a turnaround time of 10 days for a complete characterization. Our study shows that the detection of MET mutations along with other potential drivers is feasible for all patients with advanced and metastatic NSCLC using optimized strategies at reasonable costs and rapid turnaround time. MET characterization is of major importance at diagnostic to optimize first line treatments as patients with MET mutations may be older, not fit for chemotherapy and low responders to immunotherapy despite high PDL-1. It has some limitations. First, the series is a real-life retrospective cohort with missing data for some patients and the number of patients with MET exon 14 tumors reflects reality. Patients were managed in different hospitals following recommended guidelines, but their treatments were different and MET inhibitors were not easily available in routine care at that time. Conclusions This work underlines the difficulties inherent to MET testing when using amplicon based NGS technologies, it shows that strategies are able to rescue false negative results and present an updated workflow that allows NSCLC testing in care settings. Our DNA/RNA-based algorithm (Fig. 6 ) is relevant to detect MET exon 14 skipping alterations and meets testing recommendations to inform clinical care for NSCLC patients. PD-L1 overexpression, low response to immunotherapy and availability of targeted therapy make the detection of MET exon 14 skipping mutations mandatory in routine to optimize lung cancer patient care. Abbreviations DNA deoxyribonucleic acid FFPE Formalin-fixed paraffin-embedded NGS Next Generation Sequencing ORR Objective Response Rate PFS Progression Free Survival RNA ribonucleic acid RNAseq RNA sequencing RECIST Response Evaluation Criteria In Solid Tumors TPS Tumor Proportion Score Declarations Credit authorship contribution statement Author Contributions: Conceptualization, R.B.D.; R.L.; S.G.; M.W.; H.B.; Methodology, R.B.D.; R.L.; S.G.; M.B.; M.W.; H.B.;; Formal Analysis, R.B.D.; R.L.; S.G.; M.B.; S.L-G; K..L.; M.W.; H.B.; Investigation, R.B.D.; R.L.; S.G.; M.B.; M.W.; H.B.; Resources, E.F.; A.M.L.; L.G.; S.J.; E.G-L.; Data Curation, , R.B.D.; R.L.; S.G.; E.F.; A.M.L.; L.G.; S.J.; E.G-L Writing – Original Draft Preparation, R.B.D.; R.L.; M.W.; H.B.; Writing – Review & Editing, R.B.D.; R.L.; M.W.; H.B.; Supervision, M.W.; H.B; Project Administration, M.W.; H.B; Funding: This research received no external funding Institutional Review Board Statement: The project was approved by the local ethic committee (REF2021-09-10 CERAPHP Centre). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study Data Availability Statement: Dataset available on request from the authors. Conflicts of Interest: M.W. consluting fees from MSD, BMS, Astra Zeneca, Janssen, Sanofi, H.B. consulting fees from MSD, Astra Zeneca, Janssen, Takeda, Amgen; the other authors state that there are no conflicts of interest to disclose Acknowledgments: We thank the technical staff of the ONSTeP unit for their active involvement in the project References Siegel, RL, Miller, KD, Wagle, NS, Jemal, A. Cancer statistics, 2023. CA Cancer J Clin. 2023; 73(1): 17–48. doi:(2) Jordan EJ, et al., Comprehensive Molecular Characterization of Lung Adenocarcinomas for Efficient Patient Matching to Approved and Emerging Therapies. Cancer Discov. 2017;7(6):596–609. doi: 10.1158/2159-8290.CD-16-1337 . Epub 2017 Mar 23. PMID: 28336552; PMCID: PMC5482929. G. M. 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Clinicopathological implications of MET exon 14 mutations in non-small cell lung cancer - A systematic review and meta-analysis. Lung Cancer. 2018;123:76–82. doi: 10.1016/j.lungcan.2018.07.006. Epub 2018 Jul 6. PMID: 30089599. Davies, Kurtis D., Anh T. Le, Jamie Sheren, Hala Nijmeh, Katherine Gowan, Kenneth L. Jones, Marileila Varella-Garcia, Dara L. Aisner, et Robert C. Doebele. « Comparison of Molecular Testing Modalities for Detection of ROS1 Rearrangements in a Cohort of Positive Patient Samples ». Journal of Thoracic Oncology: Official Publication of the International Association for the Study of Lung Cancer 13, no 10 (2018): 1474–82. https://doi.org/10.1016/j.jtho.2018.05.041 . Ota K, Azuma K, Kawahara A, Hattori S, Iwama E, Tanizaki J, et al. Induction of PD-L1 Expression by the EML4-ALK Onco- protein and Downstream Signaling Pathways in Non-Small Cell Lung Cancer. Clin Cancer Res Off J Am Assoc Cancer Res 2015;21:4014–21. Mazieres J, Drilon A, Lusque A, Mhanna L, Cortot AB, Mezquita L, et al . Immune checkpoint inhibitors for patients with advanced lung cancer and oncogenic driver alterations: results from the IMMUNOTARGET registry. Ann Oncol 2019;30:1321–8. Lee, J. K.; Madison, R.; Classon, A.; Gjoerup, O.; Rosenzweig, M.; Frampton, G. M.; Alexander, B. M.; Oxnard, G. R.; Venstrom, J. M.; Awad, M. M.; Schrock, A. B. Characterization of Non–Small-Cell Lung Cancers With MET Exon 14 Skipping Alterations Detected in Tissue or Liquid: Clinicogenomics and Real-World Treatment Patterns. JCO Precis Oncol 2021, No. 5, 1354–1376. Torre, Lindsey A., Freddie Bray, Rebecca L. Siegel, Jacques Ferlay, Joannie Lortet-Tieulent, et Ahmedin Jemal. « Global Cancer Statistics, 2012 ». CA: A Cancer Journal for Clinicians 65, no 2 (2015): 87–108. https://doi.org/10.3322/caac.21262 . Collisson, E. A.; Campbell, J. D.; Brooks, A. N. Comprehensive Molecular Profiling of Lung Adenocarcinoma. Nature 2014, 511 (7511), 543–550. Drilon A, Clark JW, Weiss J, Ou SI, Camidge DR, Solomon BJ, Otterson GA, Villaruz LC, Riely GJ, Heist RS, Awad MM, Shapiro GI, Satouchi M, Hida T, Hayashi H, Murphy DA, Wang SC, Li S, Usari T, Wilner KD, Paik PK. Antitumor activity of crizotinib in lung cancers harboring a MET exon 14 alteration. Nat Med. 2020;26(1):47–51. doi: 10.1038/s41591-019-0716-8 . Epub 2020 Jan 13. PMID: 31932802. Ng TL, Liu Y, Dimou A, Patil T, Aisner DL, Dong Z, et al. Predictive value of oncogenic driver subtype, programmed death-1 ligand (PD-L1) score, and smoking status on the efficacy of PD-1/PD-L1 inhibitors in patients with oncogene-driven non–small cell lung cancer. Cancer. 2019;125(7):1038–49. Guisier F, Dubos-Arvis C, Viñas F, Doubre H, Ricordel C, Ropert S, et al. Efficacy and Safety of Anti–PD-1 Immunotherapy in Patients With Advanced NSCLC With BRAF, HER2, or MET Mutations or RET Translocation: GFPC 01-2018. Journal of Thoracic Oncology. 1 avr 2020;15(4):628–36. Kauffmann-Guerrero D, Tufman A, Kahnert K, Bollmann BA, Reu S, Syunyaeva Z, et al. Response to Checkpoint Inhibition in Non-Small Cell Lung Cancer with Molecular Driver Alterations. ORT. 2020;43(6):289–98. El Husseini, Kinan, Nouha Chaabane, Audrey Mansuet-Lupo, Karen Leroy, Marie-Pierre Revel, et Marie Wislez. « Capmatinib-induced interstitial lung disease: A case report ». Current Problems in Cancer: Case Reports 2 (15 décembre 2020): 100024. https://doi.org/10.1016/j.cpccr.2020.100024 . El Husseini, K., et M. Wislez. « Sequential or combined immune checkpoint inhibitors and targeted therapy: Navigating uncharted waters ». Respiratory Medicine and Research 79 (1 mai 2021): 100820. https://doi.org/10.1016/j.resmer.2021.100820 Tables Tables 1 to 4 are available in the Supplementary Files section. Additional Declarations Competing interest reported. Conflicts of Interest: M.W. consluting fees from MSD, BMS, Astra Zeneca, Janssen, Sanofi, H.B. consulting fees from MSD, Astra Zeneca, Janssen, Takeda, Amgen; the other authors state that there are no conflicts of interest to disclose Supplementary Files table1.xlsx Table 1: Summary of molecular data in patients with MET mutation showing the type of MET exon14 mutation, method used to detect the alteration and the presence of co-mutations. table2.xlsx Table 2: Clinical and histological characteristics of patients with MET exon 14 mutated tumors Non-smoker: Patient has smoked less than 100 cigarettes over a lifetime ** Most common metastatic sites at diagnosis table3.xlsx Table 3. Characteristics at baseline of patients treated with Met inhibitors *Of the 8 patients who received ≥ 1 prior systemic therapy before Crizotinib, 2 patients (25%) received immunotherapy **Of the 5 patients who received ≥ 1 prior systemic therapy before Capmatinib, 1 (20%) patient received immunotherapy and 3 patients (60%) received Crizotinib. ECOG, Eastern Cooperative Oncology Group Table4.xlsx Table 4. Clinical, histological, and molecular characteristics of both patients with sustained response to immunotherapy. Tumor response: best percent change in target lesions from baseline TableS1v2.pptx TableS2dataVF.xlsx Cite Share Download PDF Status: Published Journal Publication published 02 Jun, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 31 Jan, 2025 Reviews received at journal 02 Jan, 2025 Reviews received at journal 30 Dec, 2024 Reviewers agreed at journal 18 Dec, 2024 Reviewers agreed at journal 16 Dec, 2024 Reviewers invited by journal 16 Jul, 2024 Editor assigned by journal 16 Jul, 2024 Editor invited by journal 15 Jul, 2024 Submission checks completed at journal 12 Jul, 2024 First submitted to journal 03 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-4520709","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":329684038,"identity":"23c2aab9-1206-4cb3-a124-ad159dd7f8b3","order_by":0,"name":"Romain Loyaux","email":"","orcid":"","institution":"Hôpital Européen Georges-Pompidou","correspondingAuthor":false,"prefix":"","firstName":"Romain","middleName":"","lastName":"Loyaux","suffix":""},{"id":329684039,"identity":"62c08f65-82db-4eed-847b-163ebb22667c","order_by":1,"name":"Rym Ben Dhiab","email":"","orcid":"","institution":"Hôpital Européen Georges-Pompidou","correspondingAuthor":false,"prefix":"","firstName":"Rym","middleName":"Ben","lastName":"Dhiab","suffix":""},{"id":329684040,"identity":"86bcbc01-d6c1-4561-b163-9791f9c0fb4f","order_by":2,"name":"Simon Garinet","email":"","orcid":"","institution":"Université Paris Cité","correspondingAuthor":false,"prefix":"","firstName":"Simon","middleName":"","lastName":"Garinet","suffix":""},{"id":329684041,"identity":"483e0bc0-e3aa-48cf-8283-5438e18a6ea3","order_by":3,"name":"Mathilda Bastide","email":"","orcid":"","institution":"Hôpital Européen Georges-Pompidou","correspondingAuthor":false,"prefix":"","firstName":"Mathilda","middleName":"","lastName":"Bastide","suffix":""},{"id":329684042,"identity":"7c6a1053-c978-469d-b20e-7074530dab17","order_by":4,"name":"Sophie Léonard-Goyet","email":"","orcid":"","institution":"Hôpital Européen 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Cité","correspondingAuthor":false,"prefix":"","firstName":"Karen","middleName":"","lastName":"Leroy","suffix":""},{"id":329684050,"identity":"860f8197-4516-46af-bddd-1d8a51ff9bcc","order_by":11,"name":"Marie Wislez","email":"","orcid":"","institution":"Université Paris Cité","correspondingAuthor":false,"prefix":"","firstName":"Marie","middleName":"","lastName":"Wislez","suffix":""},{"id":329684052,"identity":"be86c6f1-9eed-4b73-86ec-45ada2401253","order_by":12,"name":"Hélène Blons","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYBACAwbmBhCdwAciPwAZDAyMDwhoYYRoYQMSjDPAWpgNiNfCzEOMFnP2xsbHBRUMeWzsvYc/29TcyTNnYGb7gE+LZc/BZuMZZxiK2XjOpUnnHHtWbNnAzDwDr8NuJLZJ87YxJLZJ5Jgx57AdTtxwgP8wfr/cf9j+m/cfWIvxZ4t/IC3MzPi13GBsY+ZtAGsxkGZsI0bLmcRmaZ5jEkC/nDGT7O17lrizmZCW44cPfuapscnjZ+8x/vDj253E7ezN+LVAgQSMcYDBgCgNSACohUQdo2AUjIJRMPwBAMhYSIXJ+QtYAAAAAElFTkSuQmCC","orcid":"","institution":"Université Paris Cité","correspondingAuthor":true,"prefix":"","firstName":"Hélène","middleName":"","lastName":"Blons","suffix":""}],"badges":[],"createdAt":"2024-06-03 09:20:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4520709/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4520709/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-99541-4","type":"published","date":"2025-06-02T15:57:07+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":62220067,"identity":"5e05e5fb-df34-4121-9a50-026d460ad8f2","added_by":"auto","created_at":"2024-08-11 12:19:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":95328,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart showing the evolution of molecular testing strategies for \u003cem\u003eMET\u003c/em\u003e mutations identification in routine testing.From 2 steps strategies with NGS panels and fragment analysis for step1 and NGS DNA/RNA panels for step 2\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-4520709/v1/807b906b9a32b9c996ec34de.png"},{"id":62219135,"identity":"765e0835-299b-4c2a-9217-b5814d472dce","added_by":"auto","created_at":"2024-08-11 12:11:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":54281,"visible":true,"origin":"","legend":"\u003cp\u003eDetails of Fragment analysis. Amplicon coverage for MET exon 14 boundaries analysis using fragment analysis. Result of fragment analysis for tumor 27 showing the expected peak corresponding to full length amplicon and a 41 bp deleted amplicon. Deletions identified by fragment analysis were characterized by Sanger Sequencing.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-4520709/v1/e9c1a72b59138529d61b3329.png"},{"id":62220065,"identity":"44b82d89-ab01-4216-ae5d-cbdc24fca8e0","added_by":"auto","created_at":"2024-08-11 12:19:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":203952,"visible":true,"origin":"","legend":"\u003cp\u003eSankey diagram of treatment patterns grouped in 4 categories: chemotherapy, MET inhibitors, immune checkpoint inhibitors (ICI), and combination of ICI and chemotherapy, during the study time period for patients with metastatic disease (n=29).\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-4520709/v1/73842621876086fe9be8f848.png"},{"id":62219138,"identity":"5049142d-8d29-4b48-aafb-f8835f408aff","added_by":"auto","created_at":"2024-08-11 12:11:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":97100,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Waterfall plot of patients with non-small cell lung cancer treated by MET inhibitors. 3 patients had some tumor shrinkage (partial response) with capmatinib and 7 with crizotinib. Patients treated in first line had stable disease or response. (B) PFS of patients with non-small cell lung cancer treated by MET inhibitors.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTP53\u003c/em\u003emutation, \u003cem\u003eMET\u003c/em\u003e amplification, prior treatment with crizotinib are noted. Three patients could not be evaluated due to treatment discontinuation\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-4520709/v1/cddaac1a7dc74265588023f1.png"},{"id":62219141,"identity":"7ba097a2-c624-4a16-8320-dd129999f017","added_by":"auto","created_at":"2024-08-11 12:11:15","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":40011,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Waterfall plot of patients with MET mutated non-small cell lung cancer treated by ICIs. 4 patients had some tumor shrinkage (partial response) all were smokers. (B) PFS of patients with MET mutated non-small cell lung cancer treated by ICIs.\u003cem\u003e TP53\u003c/em\u003e mutations, \u003cem\u003eFGFR3\u003c/em\u003eamplifications and tobacco exposure are noted.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-4520709/v1/70bd3d88690ab0c49d3f919d.png"},{"id":62219144,"identity":"c5f5af32-b85e-486f-b48f-dc7c6141d65d","added_by":"auto","created_at":"2024-08-11 12:11:15","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":106176,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular screening strategy for NSCLC patients\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-4520709/v1/8d5d2ff0f59d82bb12450211.png"},{"id":84242500,"identity":"77929d52-80f6-4654-934b-64b9a3902753","added_by":"auto","created_at":"2025-06-09 16:08:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1479203,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4520709/v1/42427cf9-f9ef-41ea-b9eb-d41dce43119e.pdf"},{"id":62219137,"identity":"229af460-20b3-44b9-98b4-0c607311269c","added_by":"auto","created_at":"2024-08-11 12:11:15","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":11588,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable 1:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSummary of molecular data in patients with \u003cem\u003eMET\u003c/em\u003e mutation showing the type of \u003cem\u003eMET\u003c/em\u003e exon14 mutation, method used to detect the alteration and the presence of co-mutations.\u003c/p\u003e","description":"","filename":"table1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4520709/v1/2784ced68abc9faa22329744.xlsx"},{"id":62219146,"identity":"10a36449-794d-4e9c-a827-1b4b193a2f5f","added_by":"auto","created_at":"2024-08-11 12:11:16","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":10493,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable 2:\u003c/strong\u003e Clinical and histological characteristics of patients with \u003cem\u003eMET \u003c/em\u003eexon 14 mutated tumors\u003c/p\u003e\n\u003cp\u003e* Non-smoker: Patient has smoked less than 100 cigarettes over a lifetime\u003c/p\u003e\n\u003cp\u003e** Most common metastatic sites at diagnosis\u003c/p\u003e","description":"","filename":"table2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4520709/v1/aeda21d8479e00c2de1f7ee9.xlsx"},{"id":62219136,"identity":"a650a163-54f2-4e51-9b60-3b48cae5d7e6","added_by":"auto","created_at":"2024-08-11 12:11:15","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":10201,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Characteristics at baseline of patients treated with Met inhibitors\u003c/p\u003e\n\u003cp\u003e*Of the 8 patients who received ≥ 1 prior systemic therapy before Crizotinib, 2 patients (25%) received immunotherapy\u003c/p\u003e\n\u003cp\u003e**Of the 5 patients who received ≥ 1 prior systemic therapy before Capmatinib, 1 (20%) patient received immunotherapy and 3 patients (60%) received Crizotinib.\u003c/p\u003e\n\u003cp\u003eECOG, Eastern Cooperative Oncology Group\u003c/p\u003e","description":"","filename":"table3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4520709/v1/56a195b4a6e70972f92da1eb.xlsx"},{"id":62219140,"identity":"23ba19cd-936a-44f8-a4cc-10a020f44676","added_by":"auto","created_at":"2024-08-11 12:11:15","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":11357,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e Clinical, histological, and molecular characteristics of both patients with sustained response to immunotherapy. Tumor response: best percent change in target lesions from baseline\u003c/p\u003e","description":"","filename":"Table4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4520709/v1/ebf311359326d5f52670755b.xlsx"},{"id":62219145,"identity":"761a729e-52e7-4498-8985-39183ed7d643","added_by":"auto","created_at":"2024-08-11 12:11:16","extension":"pptx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":37198,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1v2.pptx","url":"https://assets-eu.researchsquare.com/files/rs-4520709/v1/af097c5c887264f371579f31.pptx"},{"id":62220068,"identity":"e392102e-0de3-464f-8e55-68710fa24272","added_by":"auto","created_at":"2024-08-11 12:19:15","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":14348,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2dataVF.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4520709/v1/458650c361d7701696950d3c.xlsx"}],"financialInterests":"Competing interest reported. Conflicts of Interest: M.W. consluting fees from MSD, BMS, Astra Zeneca, Janssen, Sanofi, H.B. consulting fees from MSD, Astra Zeneca, Janssen, Takeda, Amgen; the other authors state that there are no conflicts of interest to disclose","formattedTitle":"MET Exon 14 Skipping Mutations in Non–Small-Cell Lung Cancer: Testing Considerations and Clinical Outcomes a 3 years screening experience","fulltext":[{"header":"Background","content":"\u003cp\u003eNon-Small Cell Lung Cancer (NSCLC) is a frequent and aggressive tumor, and a leading cause of death by cancer [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Recent therapeutic advances came from the identification of molecular alterations associated with the effectiveness of targeted therapies. Recently, mutations in the \u003cem\u003eMET\u003c/em\u003e receptor have been detected in lung cancer. These mutations, called \u003cem\u003eMET\u003c/em\u003e exon 14 occur at intron-exon junctions and result in a loss of Exon 14. Loss of exon14 leads to a shorter transcript and to the expression of a protein lacking the negative regulatory region containing the Y1003 residue involved in c-Cbl E3 ubiquitin ligase binding. Decreased ubiquitination, increases MET signaling and drives oncogenesis [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Met exon 14 mutations are observed in 3\u0026ndash;4% of NSCLC cases [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The first targeted therapy demonstrating anti-tumor efficacy in \u003cem\u003eMETex14\u003c/em\u003e mutated NSCLC was Crizotinib, [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e],[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e],[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. More recently, selective MET inhibitors, such as Capmatinib and Tepotinib, have been developed [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e],[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] and approved by the US Food and Drug Administration for patients with advanced \u003cem\u003eMET exon 14\u003c/em\u003e mutated NSCLC [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In patients with non-\u003cem\u003eEGFR-ALK-ROS1\u003c/em\u003e addicted metastatic NSCLC first line treatment relies on chemotherapy and immunotherapy. For \u003cem\u003eMET exon 14\u003c/em\u003e tumors, the efficacy of ICIs is controversial [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] and first line treatment is an issue [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. \u003cem\u003eMET\u003c/em\u003e testing is thus needed early to guide first and subsequent treatment lines. Screening for \u003cem\u003eMET exon 14\u003c/em\u003e mutations is challenging due to their diversity including large deletions, and their location within the introns flanking exon 14. Detection methods are based either on DNA or RNA screenings. RNA based methods include RT-PCR or RNA-sequencing. DNA based Next Generation sequencing (NGS) panels are designed to detect \u003cem\u003eMET\u003c/em\u003e exon 14 splicing variants at exon boundaries but up to \u0026frac14; of \u003cem\u003eMET\u003c/em\u003e exon 14 variants may be missed [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Indeed, ampliseq-based NGS may fail to detect large deletions when primers localize within the deleted region and large deletions may also impair fragment capture when capture-based libraries are used. Among other methods, large deletions can be analyzed using standard PCR and fragment analysis but it does not allow full characterization of the deletion and finally \u003cem\u003eMET\u003c/em\u003e exon 14 skipping may be identify at the ARN level when RNAs are available [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In this study, we combined screening methods to optimize \u003cem\u003eMET\u003c/em\u003e exon14 detection to propose an algorithm to screen \u003cem\u003eMET\u003c/em\u003e exon14 alterations along with other alterations in NSCLC patients and provided clinical information and follow up in patients with \u003cem\u003eMET\u003c/em\u003e exon14 mutated tumors treated by MET inhibitors and/or ICI.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients and Tumor samples\u003c/h2\u003e \u003cp\u003e A total of 1143 consecutive NSCLC were addressed to our laboratory for molecular diagnosis (following current guidelines, cIIIB or cIV NSCLC with treatment intent, patients not fit for surgical treatments or non-smokers) from December 2018 to November 2021. Molecular profiling was systematically performed on tumor tissues for patients with advanced NSCLC and for non-smoker patients with localized NSCLC as a standard of care using NGS technology.\u003c/p\u003e \u003cp\u003e Molecular data were prospectively registered in the laboratory record informatics system that provides result reporting to clinicians [French National Commission for Informatics and Liberties (CNIL) declaration number 1922081 v 0, and the project was approved by the local ethic committee (REF2021-09-10 CERAPHP Centre)]. This research was performed in accordance with the relevant guidelines and regulations. It included informed consent from participants and has been performed in accordance with the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDNA extraction\u003c/h2\u003e \u003cp\u003eFormalin-fixed paraffin-embedded (FFPE) samples from non\u0026ndash;small cell lung tumors were reviewed and qualified on hematoxylin-and-eosin\u0026ndash;stained slides. For samples with \u0026lt;\u0026thinsp;20% macro-dissection was done. DNA was extracted from 10-mm-thick sections using the Maxwell\u0026reg; 16 FFPE Plus LEV DNA Purification Kit (Promega, France). Extracted DNA was quantified by fluorometric Quantitation on a Qubit\u0026trade; using the Qubit dsDNA BR Assay Kit (Life Technologies\u0026ndash;Thermo Fisher Scientific, Saint Aubin, France).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eImmunohistochemistry\u003c/h2\u003e \u003cp\u003ePD-L1 immunochemistry was performed on tumor samples using clone E1L3N (Cell Signaling Technology, Danvers, MA) on the automat Leica BOND-MAX (Leica Biosystems, Buffalo Grove, IL). The immunostaining was evaluated as the percentage of stained cells (TPS: tumoral proportion score) by an expert pathologist (AML, LG).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eNGS Panel Design\u003c/h2\u003e \u003cp\u003eSamples were characterized for molecular alterations by targeted NGS (Ion AmpliSeq\u0026trade; Colon-Lung Cancer Research Panel v2, Life Technologies\u0026trade;, Carlsbad, CA) for analysis from December 2018 to May 2020 associated to a panel dedicated to ALK ROS1 and MET screening (Ion AmpliSeq\u0026trade; ROSMETALK). From May 2020, tumors were analyzed using a single panel (Ion Ampliseq\u0026trade; panel WG-IAD 196383_v2) that covers \u003cem\u003eMETex14\u003c/em\u003e boundaries. Details of the panels are described in supplementary \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eTable S1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eBriefly the multiplex barcoded libraries are generated from 10 to 30 ng of DNA following manufacturer\u0026rsquo;s recommendations (Ion ampliseq library kit V2) and are normalized using the Ion Library Equalizer\u0026trade; Kit. The pooled libraries (max 96) are processed on Ion Chef\u0026trade; System for template preparation and chip loading (Ion PI HI-Q Chef Kit, Ion PI Chip V3), and sequenced on the Ion Proton\u0026trade; System (Life Technologies\u0026trade;). The FASTQs sequencing data are aligned to the human genome (hg19) and processed using IonTorrent Suite V5.0.4.0 This package included the Torrent Variant Caller V5.0.4.0 using the built-in \u0026ldquo;Somatic - low stringency\u0026rdquo; with optimized parameters to automatically call variants with allelic ratio\u0026thinsp;\u0026gt;\u0026thinsp;2%.\u003c/p\u003e \u003cp\u003eThe FusionPlex\u0026reg; Lung v2 (Integrated DNA Technologies) panel was used for RNA sequencing on a MiSeq Illumina\u0026reg;. Analysis was done on the Archer analysis virtual machine and transcript NM_0011207500.1 was used for annotations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eFragment analysis\u003c/h2\u003e \u003cp\u003eIntron sequences flanking MET exon 14 were amplified with FAM-labeled primers in one tube. The primer pair for METexon14 5\u0026rsquo;boundary was: EX14_5F famCGTCGATTCTTGTGTGCTGT / EX14_5R CGGGCACTTACAAGCCTATC and the primer pair for METexon14 3\u0026rsquo;boundary was EX14_3F famGGCTTGTAAGTGCCCGAAGT / EX14_3R CAACAATGTCACAACCCACTG. PCR was performed in a final volume of 20 \u0026micro;l using HotStar Taq DNA polymerase and the following program: 95\u0026deg;C for 5 min; 35 cycles of 95\u0026deg;C for 30 sec, 58\u0026deg;C for 30 sec, and 72\u0026deg;C for 45 sec; and the final step of 72\u0026deg;C for 10 min. Fragments were analyzed on a sequencer 3730xl DNA Analyzer (Applied Biosystems \u0026ndash; ThermoFisher\u0026reg;) in a formamide-size-standard mix (GeneScan\u0026trade; 400HD Colorant ROX\u0026trade; - Applied Biosystems). Runs were analyzed by GeneMapper (Applied Biosystems) software. Expected fragment sizes were 198 base pairs (bp) and 148 bp fragments at the 5\u0026rsquo; and 3\u0026rsquo;ends respectively. Supplementary peaks at the 5\u0026rsquo; or 3\u0026rsquo; ends suggest the existence of a, \u003cem\u003eMET\u003c/em\u003e exon 14 deletion or insertion that needs to be subsequently sequenced. Limit of detection of this assay is 10%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSanger Sequencing\u003c/h2\u003e \u003cp\u003eDeletions at exon 14 boundaries detected by fragment analysis were characterized using Sanger sequencing. Amplification and sequencing primers were identical to those used for fragment analysis; for METexon14 5\u0026rsquo;boundary: EX14_5F CGTCGATTCTTGTGTGCTGT / EX14_5R CGGGCACTTACAAGCCTATC and for METexon14 3\u0026rsquo;boundary: EX14_3F GGCTTGTAAGTGCCCGAAGT / EX14_3R CAACAATGTCACAACCCACTG. PCR products were purified and sequenced on a 3730xl DNA Analyzer. Results were analyzed by Sequencher 5.0 (Applied Biosystems).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eClinical data\u003c/h2\u003e \u003cp\u003eAll consecutive patients with \u003cem\u003eMET\u003c/em\u003e exon 14 mutated tumors diagnosed from December 2018 to November 2021 were included. Demographic characteristics: age at diagnosis, gender, patient's general condition assessed by the Performance Status Index (PS), smoking status (non-smoker defined as an individual who had smoked less than 100 cigarettes in his or her lifetime [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] versus former or active smoker), histological type, PD-L1 expression (\u0026lt;\u0026thinsp;1%, 1\u0026ndash;50% and \u0026gt;\u0026thinsp;50%), TNM classification at diagnosis were collected. Treatment lines, best response to treatment according to RECIST (Response Evaluation Criteria In Solid Tumors) and toxicity were obtained retrospectively from medical records in accordance with national guidelines.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003ePD-L1 expression (TPS) contingency were analyzed with Chi-Square test and \u003cem\u003eP-values\u003c/em\u003e were one-sided (greater). \u003cem\u003eP-values\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered to indicate significance. Statistical analyses were performed using GraphPad Prism version 8.0.0 for Mac, (GraphPad Software, San Diego, California USA, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.graphpad.com\" target=\"_blank\"\u003ewww.graphpad.com\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.graphpad.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDetailed data are available as supplementary information (table S2)\u003c/h2\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eOptimization of MET Exon 14 Splice Site Mutations detection\u003c/h2\u003e \u003cp\u003eDuring the period from December 2018 to May 2020 period (step 1), 518 patients had a NGS based DNA sequencing. 137 samples \u003cem\u003eEGFR, KRAS\u003c/em\u003e, \u003cem\u003eERBB2, BRAF, ALK\u003c/em\u003e WT, had second NGS testing ALK ROS and MET and fragment analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). \u003cem\u003eMET\u003c/em\u003e alterations were identified in 29 samples by NGS testing, fragment analysis or both. One was classified benign, an insertion of 2 pb (NM_001127500.1 (MET): c.2942-52_2942-51insCT) as in silico analyses showed that it did not alter intron 13 3\u0026rsquo; splicing site, one was classified as VUS, \u003cem\u003eMET\u003c/em\u003e (NM_001127500.1) p.Gly983Val c.2948G\u0026thinsp;\u0026gt;\u0026thinsp;T). During the next period (step 2), our detection strategy changed with the implementation of a new DNA panel covering MET exon14 boundaries and RNA sequencing. Over 23 months of screening we found 46 \u003cem\u003eMET\u003c/em\u003e exon14 alterations out of 1143 patients tested (4%). Four mutations were not detected by NGS and rescued by fragment analysis (patients 26 \u0026amp; 27) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) and by RNAseq (patients 45 \u0026amp; 46) over the first and second periods respectively. Deletions not called by NGS were visualized on Alamut Visual software and no deletion could be identified however coverage imbalances were noted between the 3\u0026rsquo; and the 5\u0026rsquo; amplicons. All \u003cem\u003eMET\u003c/em\u003e mutations are summarized in \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eTable\u0026nbsp;1.\u003c/span\u003e Fourteen mutations were consensus hotspot mutations (14/46, 30%) and 32 were deletions involving consensus splice (32/46, 70%). Alterations were distributed on both ends of exon 14, 20 were located at the intron13-exon14 boundary and 24 the exon14-intron14 boundary for the 2 detected by RNAseq the DNA mutated position cannot be specified.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eConcurrent Genetic Alterations\u003c/h2\u003e \u003cp\u003eIn tumors with \u003cem\u003eMET exon 14\u003c/em\u003e mutations, co-occurring genetic alterations consisted mainly of \u003cem\u003eTP53\u003c/em\u003e mutations (11/46, 24%) and \u003cem\u003ePIK3CA\u003c/em\u003e activating mutations (4/46, 9%). There were 2 cases with both \u003cem\u003eMET\u003c/em\u003e exon 14 and \u003cem\u003eMET\u003c/em\u003e amplification and one patient had 2 tumors one \u003cem\u003eMET\u003c/em\u003e and one \u003cem\u003eKRAS G12C\u003c/em\u003e (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eTable\u0026nbsp;1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eClinicopathologic Characteristics of patients with METex14 mutated tumors\u003c/h2\u003e \u003cp\u003eThe clinical and pathologic characteristics of patients with \u003cem\u003eMET\u003c/em\u003e exon 14 mutated NSCLC are listed in \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eTable\u0026nbsp;2\u003c/span\u003e. Among these patients, the median age at disease onset was 79 years (range 58 to 95 years), with 30% age 85 or older. Forty-six percent were women and 46% were non-smokers. At diagnosis, 17% of patients had stage I/II NSCLC, 20% had stage III disease, and 63% had stage IV disease and 48% had a PS\u0026thinsp;\u0026ge;\u0026thinsp;2. The most prevalent site of metastasis was pleura (34%).\u003c/p\u003e \u003cp\u003eTumors were mainly adenocarcinomas but adenosquamous carcinomas (4/46, 8.6%) and sarcomatoid tumors (4/46, 8.6%) were identified. Samples were classified according to PD-L1 expression into three groups: \u0026lt; 1%, 1% \u0026le; % \u0026lt; 50 and \u0026ge;\u0026thinsp;50%. Nearly \u0026frac34; of MET mutated tumors had a high PD-L1 expression of \u0026ge;\u0026thinsp;50% (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eTable\u0026nbsp;2\u003c/span\u003e) which is significantly higher as compared to WT samples (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0,0012, one sided, Chi-square test)\u003c/p\u003e \u003cp\u003e \u003cb\u003eTreatments patterns of patients with\u003c/b\u003e \u003cb\u003eMET\u003c/b\u003e \u003cb\u003eexon 14 tumors\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eLocalized and locally advanced patients\u003c/h2\u003e \u003cp\u003eEight patients with stage\u0026thinsp;\u0026lt;\u0026thinsp;IIIA tumors had local treatments with surgery for five and stereotaxic radiotherapy for three of them. Nine patients with locally advanced tumors received chemo-radiotherapy (n\u0026thinsp;=\u0026thinsp;4), radiotherapy only (n\u0026thinsp;=\u0026thinsp;1) or surgery followed by adjuvant chemotherapy (n\u0026thinsp;=\u0026thinsp;4). Four of these patients relapse with metastatic disease during follow-up period.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eMetastatic patients\u003c/h2\u003e \u003cp\u003eConcerning the 33 patients with metastatic cancer (including relapses), 4 patients with PS\u0026thinsp;\u0026gt;\u0026thinsp;2 had best supporting care and 29 had specific treatments (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e. Half of the patients had chemotherapy first 15/29, 20% (6/29) immunotherapy, 17% (5/29) a MET inhibitor and 10% (3/29) had chemo-immunotherapy. Ten patients stopped specific treatments after L1. Altogether, 15 patients received a MET inhibitor in L1 (n\u0026thinsp;=\u0026thinsp;5), L2 (n\u0026thinsp;=\u0026thinsp;8) or L3 (n\u0026thinsp;=\u0026thinsp;5), 3 patients had 2 lines of targeted therapy. Thirteen patients had immune checkpoints inhibitors (ICI) monotherapy based on PD-L1 expression.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA subset of patients (n\u0026thinsp;=\u0026thinsp;28) received targeted therapy, crizotinib (n\u0026thinsp;=\u0026thinsp;11) or capmatinib (n\u0026thinsp;=\u0026thinsp;7), table 3. In crizotinib treated patients, median PFS was 7.6 month [1.6\u0026ndash;13.3] and overall response rate (ORR) was 45% (n\u0026thinsp;=\u0026thinsp;5/11). All but one patients treated with MET TKIs Crizotinib experienced disease control (stable or partial response). The patient with concurrent \u003cem\u003eMET\u003c/em\u003eamp (\u0026gt;\u0026thinsp;6 copies) experienced the deepest response (Best response RECIST \u0026minus;\u0026thinsp;75%). One patient that received prior immunotherapy with pembrolizumab had an early treatment discontinuation for hepatic toxicity and crizotinib response could not be assessed. In capmatinib treated patients (n\u0026thinsp;=\u0026thinsp;7, 3 post crizotinib treatment), median PFS was 3 months [1.3\u0026ndash;18.2] and ORR was 43% (3/7). Three patients experienced partial response and 2 patients experienced progression (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eTable\u0026nbsp;3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThirteen patients received immunotherapy as monotherapy with either Pembrolizumab (n\u0026thinsp;=\u0026thinsp;7) or Nivolumab (n\u0026thinsp;=\u0026thinsp;7). Median PFS was 4 months [0.7\u0026ndash;55.5 months] and ORR was 30% (4/13) with significant patient disparity (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). A more important response was noted among smokers: ORR and median PFS were 67% (4/6) and 10.3 [1.7\u0026ndash;5.5 months] for smokers versus 0% (0/7) and 2.1 months [0.7-5 months] for non-smokers, respectively (p\u0026thinsp;=\u0026thinsp;0.016). Two patients had a sustained response greater than 30 months. The characteristics of these two patients were presented in (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eTable\u0026nbsp;4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, \u003cem\u003eMET\u003c/em\u003e exon 14 testing was analyzed in routine care settings. We showed that \u003cem\u003eMET\u003c/em\u003e exon 14 mutations were a common event in the \u003cem\u003eEGFR, KRAS, BRAF, ALK\u003c/em\u003e WT population justifying its systematic testing. Our routine pipeline uses ampliseq technology. When \u003cem\u003eMET\u003c/em\u003e exon 14 testing became part of the molecular gene-set for lung cancer, we felt concerned by the issue of large deletion detection. The ampliseq technology uses primers designed to amplify small DNA fragments. This leads to intrinsic difficulties in calling large deletions either because amplicons are too small to be aligned or because primers match the deleted sequence. Therefore, we implemented a DNA-based algorithm to rescue the detection of large deletions using fragment analysis when NGS was negative. Two large deletions were identified (41bp and 65bp) by fragment analysis [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Fragment analysis is a fast, simple, and cost-efficient way to identify deletions. Although the sensitivity of this method was lower than that of NGS, the combination with amplicon-based NGS improved the detection of \u003cem\u003eMET\u003c/em\u003e exon 14 alterations as previously described [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Other approaches exist to screen \u003cem\u003eMET\u003c/em\u003e exon 14. One of the most powerful is to combine DNA and RNA-based detection as RNA sequencing is an efficient technic to detect splice-variant at transcript level. Moreover, as oncogenic transcripts are often highly expressed, it may more sensitive than DNA-based method [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Gene fusions represent promising targets in lung cancer and reliable detection of multiple gene fusions has become part of the routine screening [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. This has impacted testing strategies. Indeed, as RNAseq becomes mandatory, \u003cem\u003eMET\u003c/em\u003e exon14 skipping mutations that were missed by DNA sequencing would be rescued by RNA analysis. Moreover, RNAseq may also help classification of ambiguous splice mutations for which the functional impact on splicing is not clear. All together DNA/RNA based testing, seems to be the most accurate strategy to detect \u003cem\u003eMET\u003c/em\u003e exon 14 mutation along with other targetable drivers. Here, 46 patients with MET exon 14 mutated tumors were identified. Most patients with \u003cem\u003eMET exon 14\u003c/em\u003e tumors are elderly patients with median age of 79 years [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. This specificity underlines the importance of screening for \u003cem\u003eMET\u003c/em\u003e exon 14 alterations in this population that is not always fit enough to receive chemotherapy. Half of the patients were smokers with a smoking history of 20 packs per years or more [\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. These clinical associations match to the current literature [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. However, in a recent meta-analysis, based on 2661 NSCLC, patients with \u003cem\u003eMET\u003c/em\u003e exon 14 mutations were less likely to associate with smoking history as compared to wild type patients (OR\u0026thinsp;=\u0026thinsp;0.48, p\u0026thinsp;=\u0026thinsp;0.008) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. These data highlight that \u003cem\u003eMET\u003c/em\u003e exon 14 mutations affect both smokers and non-smokers, unlike most common oncogenic driver outside of \u003cem\u003eKRAS\u003c/em\u003e. We collected real life data concerning patients\u0026rsquo; treatments and response to MET inhibitors. In our cohort 11 patients received Crizotinib at different lines of treatment with a median PFS of 7.6 months. This was comparable to previous results. In PROFILE 1001 [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] ORR of 39% and median PFS of 7,3 months under Crizotinib with was the first MET TKI to receive FDA approval for \u003cem\u003eMET\u003c/em\u003e exon14 NSCLC. More recently Capmatinib and Tepotinib have demonstrated ORR of 41% and 48% respectively. The median PFS was 9.7 months for capmatinib naif MET exon 14 patients and 8,5 months for Tepotinib [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. We noted a lower Capmatinib response than reported in GEOMETRY study (median PFS 3.0 versus 9.7 months respectively). However, in our study, 3/7 patients were previously treated with Crizotinib. Indeed, we cannot draw comparison because MET inhibitors pretreated patients were excluded from GEOMETRY. One patient had both \u003cem\u003eMET\u003c/em\u003e amplification and \u003cem\u003eMET\u003c/em\u003e exon 14 mutation. He almost achieved a complete response after 3 months of Crizotinib treatment. In the VISION trial, a better response of Tepotinib was reported in the 5 patients with concurrent \u003cem\u003eMET\u003c/em\u003e exon 14 and \u003cem\u003eMET\u003c/em\u003e amplification compared to \u003cem\u003eMET\u003c/em\u003e exon 14 only (80% vs. 46% respectively). This finding suggests that this co-alteration may potentiate the activity of MET inhibitors. Regarding the response to ICIs in our patients, a median PFS of 4 months was noted. These results were comparable to those of the multicenter IMMUNOTARGET \u003cem\u003eMET\u003c/em\u003e exon 14 cohort conducted by Mazi\u0026egrave;res \u003cem\u003eet al\u003c/em\u003e, [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. We found a higher proportion of PD-L1\u0026thinsp;\u0026ge;\u0026thinsp;50% compared to non-MET samples (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0012) consistent with previous reports [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, we found no correlation between response and PD-L1 expression in the 13 patients treated with immunotherapy, which was consistent with the findings of Sabari \u003cem\u003eet al\u003c/em\u003e, suggesting that PD-L1 has no predictive value in \u003cem\u003eMET\u003c/em\u003e exon 14 patients [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. On the other hand, almost all of them have PD-L1 (TPS)\u0026thinsp;\u0026gt;\u0026thinsp;50%. We also noted a longer response to ICIs in heavy smokers. A PFS greater than 20 months was observed in 2 patients having smoked more than 30 pack-year smokers. High levels of tobacco intoxication may be associated with high mutational burden and an immunogenic microenvironment potentiating immunotherapy [\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Additionally, we reported one case of severe hepatic toxicity in a patient treated with immunotherapy followed by Crizotinib. Other study reported similarly an increase in Grade 3 and 4 hepatitis with Crizotinib in immunotherapy-experienced subjects [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] and there is growing concern that immunotherapy followed by targeted therapies induces potentially severe immune-mediated adverse effects [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. This data highlighted the importance of an early MET screening in order to choose the appropriate therapeutic sequence. Not detecting \u003cem\u003eMET\u003c/em\u003e mutations is deleterious because patients with \u003cem\u003eMET\u003c/em\u003e mutated tumors may have access to MET-targeted therapies and because patients could receive sub-optimal first line treatment such as ICIs monotherapy based on PD-L1 expression. Our testing and clinical experience led to the validation of a new workflow, first step includes the use of taqman probes for \u003cem\u003eEGFR\u003c/em\u003e and \u003cem\u003eKRAS\u003c/em\u003e frequent mutations and subsequent DNA and RNA sequencing panels in parallel. Altogether, the use of taqman probes and hotspots DNA and RNA panels covers all drivers in lung cancer at reasonable costs in a turnaround time of 10 days for a complete characterization.\u003c/p\u003e \u003cp\u003eOur study shows that the detection of \u003cem\u003eMET\u003c/em\u003e mutations along with other potential drivers is feasible for all patients with advanced and metastatic NSCLC using optimized strategies at reasonable costs and rapid turnaround time. \u003cem\u003eMET\u003c/em\u003e characterization is of major importance at diagnostic to optimize first line treatments as patients with \u003cem\u003eMET\u003c/em\u003e mutations may be older, not fit for chemotherapy and low responders to immunotherapy despite high PDL-1. It has some limitations. First, the series is a real-life retrospective cohort with missing data for some patients and the number of patients with \u003cem\u003eMET\u003c/em\u003e exon 14 tumors reflects reality. Patients were managed in different hospitals following recommended guidelines, but their treatments were different and MET inhibitors were not easily available in routine care at that time.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis work underlines the difficulties inherent to MET testing when using amplicon based NGS technologies, it shows that strategies are able to rescue false negative results and present an updated workflow that allows NSCLC testing in care settings. Our DNA/RNA-based algorithm (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) is relevant to detect \u003cem\u003eMET\u003c/em\u003e exon 14 skipping alterations and meets testing recommendations to inform clinical care for NSCLC patients. PD-L1 overexpression, low response to immunotherapy and availability of targeted therapy make the detection of \u003cem\u003eMET\u003c/em\u003e exon 14 skipping mutations mandatory in routine to optimize lung cancer patient care.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edeoxyribonucleic acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFFPE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFormalin-fixed paraffin-embedded\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNGS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNext Generation Sequencing\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eORR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eObjective Response Rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePFS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProgression Free Survival\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eribonucleic acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRNAseq\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRNA sequencing\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRECIST\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eResponse Evaluation Criteria In Solid Tumors\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTPS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTumor Proportion Score\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCredit authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e Conceptualization, R.B.D.; R.L.; S.G.; M.W.; H.B.; Methodology, R.B.D.; R.L.; S.G.; M.B.; M.W.; H.B.;; Formal Analysis, R.B.D.; R.L.; S.G.; M.B.; S.L-G; K..L.; M.W.; H.B.; Investigation, R.B.D.; R.L.; S.G.; M.B.; M.W.; H.B.;\u0026nbsp;Resources, E.F.; A.M.L.; L.G.; S.J.; E.G-L.; Data Curation,\u0026nbsp;, R.B.D.; R.L.; S.G.; E.F.; A.M.L.; L.G.; S.J.; E.G-L\u0026nbsp;Writing \u0026ndash; Original Draft Preparation, R.B.D.; R.L.; M.W.; H.B.;\u0026nbsp;Writing \u0026ndash; Review \u0026amp; Editing,\u0026nbsp;R.B.D.; R.L.; M.W.; H.B.; Supervision, M.W.; H.B;\u0026nbsp;Project Administration,\u0026nbsp;M.W.; H.B;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This research received no external funding\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement:\u003c/strong\u003e The project was approved by the local ethic committee (REF2021-09-10 CERAPHP Centre).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement:\u003c/strong\u003e Informed consent was obtained from all subjects involved in the study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e Dataset available on request from the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e M.W. consluting fees from MSD, BMS, Astra Zeneca, Janssen, Sanofi, H.B. consulting fees from MSD, Astra Zeneca, Janssen, Takeda, Amgen; the other authors state that there are no conflicts of interest to disclose\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e We thank the technical staff of the ONSTeP unit for their active involvement in the project\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel, RL, Miller, KD, Wagle, NS, Jemal, A. 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Wislez. \u0026laquo; Sequential or combined immune checkpoint inhibitors and targeted therapy: Navigating uncharted waters \u0026raquo;. \u003cem\u003eRespiratory Medicine and Research\u003c/em\u003e 79 (1 mai 2021): 100820. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.resmer.2021.100820\u003c/span\u003e\u003cspan address=\"10.1016/j.resmer.2021.100820\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 4 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"MET exon 14, NSCLC, PD-L1, NGS, Capmatinib, Crizotinib, ICI","lastPublishedDoi":"10.21203/rs.3.rs-4520709/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4520709/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003e \u003cem\u003eMET\u003c/em\u003e exon 14 skipping is an oncogenic driver observed in 1 to 4% of non-small cell lung cancer (NSCLC). \u003cem\u003eMET\u003c/em\u003e exon 14 mutations affect splice sites and are highly heterogeneous which makes them difficult to detect. Because of the approval of capmatinib for patients with \u003cem\u003eMET\u003c/em\u003e exon 14 mutated tumors and the related poor response to immunotherapy (ICI) for a subset of patients with \u003cem\u003eMET\u003c/em\u003e mutated tumors, \u003cem\u003eMET\u003c/em\u003e screening has become mandatory for first line treatment decision.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eHere we report our testing experience based on 1143 consecutive NSCLC addressed for molecular diagnosis. Two strategies using either DNA sequencing (NGS) and fragment analysis or DNA-RNA sequencing (NGS) were developed and validated to accurately detect \u003cem\u003eMET\u003c/em\u003e exon 14 alterations including large deletions. For patients with \u003cem\u003eMET\u003c/em\u003e tumors (n\u0026thinsp;=\u0026thinsp;46), demographic characteristics, treatments and outcomes were obtained from medical records and discussed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003e46 \u003cem\u003eMET\u003c/em\u003e exon 14 alterations were identified, 4 were not called by DNA sequencing and rescued by fragment analysis or RNA sequencing. Sixty-seven percent tumors had a high PD-L1 expression\u0026thinsp;\u0026gt;\u0026thinsp;50% and 42% of cases had co-occurring alterations, mainly \u003cem\u003eTP53\u003c/em\u003e mutations (24%) and \u003cem\u003ePIK3CA\u003c/em\u003e mutations (9%). Response to MET inhibitors (Crizotinib and Capmatinib) was evaluated for 15 patients. The ORR (Objective Response Rate) and the median of PFS (Progression Free Survival) were 44% and 5.5 months [1.6\u0026ndash;18.2 months] respectively. Thirteen patients were treated by immunotherapy, ORR and median PFS (Progression Free Survival) median were 30% and 4 months [0.7\u0026ndash;55.5 months] respectively. The response to immunotherapy was not correlated with PD-L1 status but smokers seemed to better respond to ICIs.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study highlights that a multimodal approach may be necessary to detect \u003cem\u003eMET\u003c/em\u003e exon 14 mutations as large deletions may not be detected by DNA sequencing. Targeted DNA-ARN sequencing strategies broadly interrogate the diverse druggable genomic variations and permits direct detection of altered splicing or gene fusions. Because patients with \u003cem\u003eMET\u003c/em\u003e exon 14 mutated tumors, demonstrate low response to immunotherapy despite high PDL1 and because \u003cem\u003eMET\u003c/em\u003e exon 14 is druggable the detection of \u003cem\u003eMET\u003c/em\u003e mutations is mandatory to optimize treatment.\u003c/p\u003e","manuscriptTitle":"MET Exon 14 Skipping Mutations in Non–Small-Cell Lung Cancer: Testing Considerations and Clinical Outcomes a 3 years screening experience","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-11 12:11:10","doi":"10.21203/rs.3.rs-4520709/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-01-31T07:28:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-03T04:55:58+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-30T09:41:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"255386472894648825219415400815167430129","date":"2024-12-18T09:13:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"207966000612447387448576361331321934895","date":"2024-12-17T02:29:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-16T08:41:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-16T08:39:24+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-07-15T08:02:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-12T12:18:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-06-03T09:16:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"de26c9e6-61a1-4cc6-87e2-a61ae0d97e70","owner":[],"postedDate":"August 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":34898932,"name":"Biological sciences/Biological techniques"},{"id":34898933,"name":"Biological sciences/Cancer"},{"id":34898934,"name":"Health sciences/Biomarkers"},{"id":34898935,"name":"Health sciences/Biomarkers/Predictive markers"},{"id":34898936,"name":"Health sciences/Biomarkers/Prognostic markers"}],"tags":[],"updatedAt":"2025-06-09T15:59:54+00:00","versionOfRecord":{"articleIdentity":"rs-4520709","link":"https://doi.org/10.1038/s41598-025-99541-4","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-06-02 15:57:07","publishedOnDateReadable":"June 2nd, 2025"},"versionCreatedAt":"2024-08-11 12:11:10","video":"","vorDoi":"10.1038/s41598-025-99541-4","vorDoiUrl":"https://doi.org/10.1038/s41598-025-99541-4","workflowStages":[]},"version":"v1","identity":"rs-4520709","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4520709","identity":"rs-4520709","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}