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MET in Cancer Initiation and Driver Mutations
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The MET gene, known as MET proto-oncogene receptor tyrosine kinase, was first identified to induce tumor cell migration, invasion, and proliferation/survival through canonical RAS-CDC42-PAK-Rho kinase, RAS-MAPK, PI3K-AKT-mTOR, and β-catenin signaling pathways, and its driver mutations, such as MET gene amplification (METamp) and the exon 14 skipping alterations (METex14), activate cell transformation, cancer progression, and worse patient prognosis, principally in lung cancer through the overactivation of their own oncogenic and MET parallel signaling pathways. Because of this, MET driver alterations have become recognized as actionable alterations in lung adenocarcinomas since the FDA approval target therapies for METamp and METex14 in 2020. 

precision medicine NSCLC target therapies resistance mutations Driver and actionable mutations
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    1. Introduction

    The principal hallmark of tumorigenesis is cell transformation, involving the transition of normal cells into the tumorigenic state, which is characterized by the acquisition of enhanced cell proliferation and the activation of anchorage-independent growth until to form a mass, which could progress activating cell migration, the invasion of tumor cells, and finally, metastasis. However, depending on the signaling pathways altered by the driver mutations, the tumor mass would be highly proliferative, invasive, angiogenic, and or metastatic [1]. Until now, the driver mutations had demonstrated advantages favoring cell transformation, leading to tumor formation and progression. Therefore, driver statements would be evidence before testing their actionability with target drugs, which in precision medicine means that driver mutations would be recognized as predictors of therapy responses such as current actionable genes EGFR, ALK, ROS1, HER2, and MET (among others).
    The MET gene encodes a member of the receptor tyrosine kinase (RTK) family of proteins, and, since the early 1980s, different authors have studied the effect of MET on cancer development, starting by Cooper et al., who were the pioneers in described MET gene as a “driver gene” when this concept did not exist yet, identifying it as a transforming gene detected in chemically transformed cells [2]. Afterwards, Tward et al. found that the METamp would be able to induce cell transformation and hepatocellular carcinoma (HCC) in mice overexpressing a wild-type allele of human MET, although the carcinoma only arose in cooperation with the constitutively active β-catenin expression [3]. Perhaps this was possible through the crosstalk signaling Met/β-catenin since the inactivation of MET transgenes induced the regression of HCC [4]. In the same way, Mi et al., speculated whether MET could have initiated tumorigenesis in mice prostates, so they tested this idea designing a conditional Met transgenic mouse that mimicked human prostate cancer through an increased Met expression, which resulted in the oncogenic prostate transformation. Nevertheless, the presence of METamp and PTEN deletion leads to prostate neoplasia and prostatic adenocarcinomas, inducing an epithelial-mesenchymal transition and an increase of metastasis events [5]. As a result, METamp caused cell transformation; however, this had to take place in cooperation with another alteration in Met signaling pathways or MET parallel signaling co-activation, which could be needed to support the MET activities. All of these discoveries were the initial knowledge about MET in cancer; nevertheless, the development of target therapies against active MET was not initiated until the discovery of METex14, which is by far the most common driver and actionable MET mutation.

    2. MET in Cancer Initiation and Driver Mutations

    2.1. MET Amplification

    In 1996, Ichimura et al. revealed that MET protein and its specific ligand, hepatocyte growth factor (HGF), were highly expressed in lung cancer cell lines, as well as in non-small cell lung cancer (NSCLC) biopsies [6][7]. However, the protein levels did not reveal whether these results were associated with a specific MET mutation or with the gene copy number gain (GCN) [8], which could arise from polysomy or amplification. Still, the amplification represents a biologic selection process for MET as an oncogenic driver. As METamp has been recognized as a bad prognosis biomarker in NSCLC, HCC, gastric cancer, and triple-negative breast cancer [9][10][11][12], its identification in precision medicine through the GCN is currently calculated using the copy number variation (CNV) from next-generation sequencing (NGS) or using the standard assay fluorescence in situ hybridization (FISH) [13].
    The current METamp frequency in different solid cancers was estimated thanks to the AACR Project Genomics Evidence Neoplasia Information Exchange (GENIE), which is an international pan-cancer registry [14]. According to GENIE, METamp represents a 2% in non-small cell lung cancer (NSCLC 10,451 patients), a 1.2% in renal (1,556 patients), and hepatobiliary (1,854 patients) cancers, followed by a 0.4% in colorectal (7,370 patients), 0.2% in ovarian (4,481 patients), breast cancer (8,365 patients), and prostate cancers (3,530 patients). NSCLC has the highest METamp and driver mutations frequency (Table 1), as the METex14 is the most common MET driver and actionable alteration [15][16]. Now, as much as METamp and METex14 are recognized as actionable because they account for different target therapies replacing the conventional chemotherapies; despite the success in METamp, the therapy response will depend on the GCN degree.

    2.2. MET Exon 14 Skipping Alterations

    The METex14 was first reported in small cell lung cancer and then in NSCLC patients. Furthermore, when METex14 was expressed in normal mouse NIH3T3 fibroblasts, cells were transformed and then became tumorigenic in vivo, which confirmed METex14 as a driver alteration [17], whereas Paik and colleagues were demonstrating METex14 tumor cells were sensitive to MET tyrosine kinase inhibitors (TKIs), demostrating the clinical benefit for NSCLC patients [18].
    Normally, in MET pre-mRNA, the introns flanking the exon 14 are spliced out, resulting in an mRNA containing exon 14, which encodes the juxtamembrane domain (JMD), which is key for MET protein degradation. The METex14 causes the loss of JMD when mutations at the splice donor or acceptor sites result in exon 14 loss, such as base substitutions, insertions, deletions, and intronic noncoding regions immediately adjacent to the splice acceptor site and the whole-exon deletion. When some of these alternatives derived in a truncated MET receptor, it shows a constitutive expression because the loss of the Tyr1003 residue located in the JMD prevents the binding of the E3 ubiquitin ligase Cbl and proteasomal degradation, which have shown overactive MET signaling pathways, triggering an exacerbate cell proliferation and invasion, contributing to the evolution of cancer and bad prognosis [19][20].
    METex14 occurs in 3 to 4% of NSCLC patients (Table 1), and it has been identified as a potent therapeutic target encouraging the approval of TKIs [21][22]. However, the actionability of different variants which originate the METex14 alteration have not been totally validated yet. Currently, several MET mutations are recognized as actionable METex14 by the approved test Foundation One (such as splice site 2888-10_2911del34, splice site 2888-37_2888-30delCGTCTTA, splice site 2888-18_2888-5del14, D1010N, splice site 3028+2T>C, splice site 2999_3028+4del34, splice site 3028+1G>A, and splice site 3028_3028+2delGGT), which were searched in the GENIE public database, and, as a result, only D1010N was found, representing 9.6% of 439 MET driver mutations (Figure 1 and Table 2). In addition, other frequent mutations recognized as a driver by GENIE impacting the exon 14 of MET were found, as the X1010_splice (29%), followed by the X963_splice (20.9%) and D1010H (9.7%) (Figure 1).
    Figure 1. Common MET driver mutations impact principally the juxtamembrane and kinase domains (exon 14). Lolliplot of MET protein domains (Sema, PSI, TIG, juxtamembrane, and Pkinase) showing common driver mutations identified in solid tumors from Table 1. Additionally, the protein structure shows the cancer hotspot (yellow circle), OncoKB prediction therapies (blue circles), exons numbered (blue and light blue rectangles), and protein topology. Yellow is the extracellular region, red, the cytoplasmic region, and, gray, the transmembrane region.
    After that, the researchers interrogate all these variants according to the bioinformatic driver predictors, and OncoKB as the actionability predictor, which is a precision oncology knowledge base developed at Memorial Sloan Kettering Cancer Center that contains biologic and oncogenic effects, prognostic, and predictive significance of somatic alterations [23]. As is shown in Table 2, sixteen alterations in exon 14 (affecting 374 patients) were recognized by different predictors, including OncoKB as oncogenic/likely oncogenic mutations with level 1 of evidence, which means these mutations concur with an FDA-approved drug. According to these results, many driver and actionable MET mutations had not been functionally or clinically validated yet (Table 2). Additionally, there is an extensive list of mutations in the MET hot spot between exons 14 and 19 with drugs predictions, which must be evaluated as actionable alterations (Figure 1 and Table 2).
    Table 2. Driver and actionable statement prediction of known MET variants. All variants showed are predicted driver and actionable. D: Deletereous, T= Tolerated
    Protein Change Variant_ type exon CADD13 SIFT Mut. Taster fathm MKL LRT M-CAP MetaLR Polyphen2 driver_statement CGI ONCOGENIC STATEMENT ONCOKB´S EVIDENCE LEVELS
    128 X1010_splice Splice_Site 14 25.9 . D D . . . . predicted driver: tier 1 Likely Oncogenic Level 1
    92 X963_splice Splice_Site 14 42 . D D . . . . predicted driver: tier 1 Likely Oncogenic Level 1
    43 D1010H Missense 14 25 D D D D D T D predicted driver: tier 1 Likely Oncogenic Level 1
    42 D1010N Missense 14 25.9 T D D D D T D predicted driver: tier 1 Likely Oncogenic Level 1
    28 D1010Y Missense 14 28.1 D D D D D T D predicted driver: tier 1 Likely Oncogenic Level 1
    8 H1094Y Missense 16 29.0 D D D D D T D known in renal_carcinoma Oncogenic unknown
    8 X1006_splice Frame_Shift_Del 1415 27.7 . D D . . . . predicted driver: tier 1 Likely Oncogenic Level 1
    8 X1007_splice Frame_Shift_Del 1415 31 . D D . . . . predicted driver: tier 1 Likely Oncogenic Level 1
    7 Y1230H Missense 19 48 D D D D D D D known in renal_carcinoma Oncogenic Level R2
    7 X1008_splice Frame_Shift_Del 1415 26.7 . D D . . . . predicted driver: tier 1 Likely Oncogenic Level 1
    5 D1228N Missense 19 31 D A D D D D D predicted driver: tier 1 Likely Oncogenic Level R2
    5 X1009_splice In_Frame_ Del 1415 29.6 . D D . . . . predicted driver: tier 1 Likely Oncogenic Level 1
    4 D1228H Missense 19 27.7 D D D D D D D predicted driver: tier 1 likely oncogenic unknown
    4 T222M Missense 2 29 D D D D D T D predicted passenger Unknown unknown
    4 Y1230C Missense 19 28.7 D A D D D D D known in renal_carcinoma Likely Oncogenic Level 1
    3 Y1003N Missense 14 25.4 D D D D D T D predicted driver: tier 1 Likely oncogenic unknown
    3 V1070A Missense 15 32 D D D D D D D predicted driver: tier 1 Likely Oncogenic unknown
    3 M1250T Missense 19 31 D D D D D T D known in: renal_carcinoma Oncogenic Level 1
    3 Y1230N Missense 19 26.5 D D D D D D D predicted driver: tier 1 Likely Oncogenic Level 1
    3 Y1003F Missense 14 27.7 D D D D D T D predicted driver: tier 1 Oncogenic Level 1
    2 V1220L Missense 19 27.5 D D D D D T D predicted driver: tier 1 Unknown unknown
    2 V1092I Missense 16 31 D D D D D T D known in CANCER-PR;carcinoma Oncogenic level 1
    2 D1002G Missense 14 25.0 D D D D D T P predicted driver: tier 1 Likely Oncogenic level 1
    2 Y1003C Missense 14 28.5 D D D D D T D predicted driver: tier 1 Likely Oncogenic Level 1
    2 L1195F Missense 18 31 D D D D D D D predicted driver: tier 1 Unknown Level 1
    1 H1094R Missense 16 48 D A D D D T D known in renal_carcinoma Oncogenic unknown
    1 L1195V Missense 18 27.7 D D D D D D D predicted driver: tier 1 Oncogenic unknown
    1 V1220I Missense 19 28.2 D A D D D T D predicted driver: tier 1 Likely Oncogenic unknown
    1 Y1230S Missense 19 26.8 D D D D D D D predicted driver: tier 1 Likely Oncogenic unknown
    1 N998_Y1003delinsS In_Frame_ Del 14 48 . D D . . . . predicted driver: tier 1 Unknown unknown
    1 H1094D Missense 16 26.8 D D D D D D D predicted driver: tier 1 Unknown unknown
    1 V1188I Missense 18 29.1 D D D D D T D predicted driver: tier 1 Unknown unknown
    1 L1195I Missense 18 48 D D D D D D D predicted driver: tier 1 unknown unknown
    1 X972_splice Frame_Shift_Del 1415 24.9 . D D . . . . predicted passenger unknown unknown
    1 X979_splice Frame_Shift_Del 1415 29.3 . D D . . . . predicted passenger unknown unknown
    1 X1001_splice In_Frame_Del 1415 48 . D D . . . . predicted passenger unknown unknown
    1 X1003_splice Frame_Shift_Del 1415 48 . D D . . . . predicted passenger unknown unknown
    1 X1004_splice Frame_Shift_Del 1415 24.9 . D D . . . . predicted passenger unknown unknown
    1 T1006_D1010delinsN In_Frame_ Del 14 48 . D D . . . . predicted passenger unknown unknown
    1 F1007_D1010delinsY In_Frame_ Del 14 48 . D D . . . . predicted driver: tier 1 likely oncogenic unknown
    1 N1100S Missense 16 48 T N N N T T B predicted passenger unknown unknown
    1 D1228V Missense 19 26.7 D D D D D D D known in LUAD Likely oncogenic unknown
    1 H1106D Missense 16 48 D D D D D T D predicted driver: tier 1 Likely Oncogenic Level R2
    1 F1200I Missense 18 32 D D D D D T D predicted driver: tier 1 Oncogenic Level R2
    1 V1001_F1007del In_Frame_Del 14 25.9 . D D . . . . predicted driver: tier 1 Likely Oncogenic Level 1
    1 D1002_F1007del In_Frame_Del 14 41 . D D . . . . predicted driver: tier 1 Likely Oncogenic Level 1
    Still, the validation and approval of additional MET driver alterations should be the next step to offer many potentially METex14 targetable mutations [24][25]. Indeed, a study identified five hundred genetic alterations that lead to METex14, and the analysis revealed that the most frequent regions impacted were the splice donor site (42%), followed by the polypyrimidine tract (15%), the splice acceptor site (~5%), and both the splice acceptor sites and the polypyrimidine tract (13%). All these alterations resulted in the elimination of exon 14 with an mRNA containing the exon 13 fused to exon 15 [26].
    Thereby, given the diversity of alterations leading to METex14 revealed in mRNA, the diagnosis sensitivity could challenge the identification of them in DNA assays. In contrast, RNA approaches directly identify 13–15 exons fusion in the transcript. For this reason, the amplicon-based approaches may fail to find METex14 alterations because it does not allow the detection of large deletions. However, hybrid capture is more amenable to detecting the alterations leading to METex14. Furthermore, this method generally isolates larger fragments of DNA, including sequences that flank the regions of interest, compared with amplicon-based methods when using DNA as the input material [27]. Additionally, 60% of positive results according to the RNA-based assay were negative using the DNA-based assay [28]. Likewise, the mRNA-based quantitative reverse transcriptase RT-PCR demonstrated 100% sensitivity in detecting METex14, compared to 61.5% sensitivity using conventional DNA-based Sanger sequencing [29], so RNA analysis seems to be best way to identify METex14 to follow with target drugs’ prescriptions.

    References

    1. Hanahan, D.; Weinberg, R.A. Hallmarks of cancer: The next generation. Cell 2011, 144, 646–674.
    2. Cooper, C.S.; Park, M.; Blair, D.G.; Tainsky, M.A.; Huebner, K.; Croce, C.M.; Vande Woude, G.F. Molecular cloning of a new transforming gene from a chemically transformed human cell line. Nature 1984, 311, 29–33.
    3. Wang, R.; Ferrell, L.D.; Faouzi, S.; Maher, J.J.; Bishop, J.M. Activation of the Met receptor by cell attachment induces and sustains hepatocellular carcinomas in transgenic mice. J. Cell Biol. 2001, 153, 1023–1034.
    4. Tward, A.D.; Jones, K.D.; Yant, S.; Cheung, S.T.; Fan, S.T.; Chen, X.; Kay, M.A.; Wang, R.; Bishop, J.M. Distinct pathways of genomic progression to benign and malignant tumors of the liver. Proc. Natl. Acad. Sci. USA 2007, 104, 14771–14776.
    5. Mi, J.; Hooker, E.; Balog, S.; Zeng, H.; Johnson, D.T.; He, Y.; Yu, E.J.; Wu, H.; Le, V.; Lee, D.H.; et al. Activation of hepatocyte growth factor/MET signaling initiates oncogenic transformation and enhances tumor aggressiveness in the murine prostate. J. Biol. Chem. 2018, 293, 20123–20136.
    6. Ichimura, E.; Maeshima, A.; Nakajima, T.; Nakamura, T. Expression of c-met/HGF receptor in human non-small cell lung carcinomas in vitro and in vivo and its prognostic significance. Jpn. J. Cancer Res. 1996, 87, 1063–1069.
    7. Siegfried, J.M.; Weissfeld, L.A.; Luketich, J.D.; Weyant, R.J.; Gubish, C.T.; Landreneau, R.J. The clinical significance of hepatocyte growth factor for non-small cell lung cancer. Ann. Thorac. Surg. 1998, 66, 1915–1918.
    8. Overbeck, T.R.; Cron, D.A.; Schmitz, K.; Rittmeyer, A.; Korber, W.; Hugo, S.; Schnalke, J.; Lukat, L.; Hugo, T.; Hinterthaner, M.; et al. Top-level MET gene copy number gain defines a subtype of poorly differentiated pulmonary adenocarcinomas with poor prognosis. Transl. Lung Cancer Res. 2020, 9, 603–616.
    9. Yin, W.; Cheng, J.; Tang, Z.; Toruner, G.; Hu, S.; Guo, M.; Robinson, M.; Medeiros, L.J.; Tang, G. MET Amplification (MET/CEP7 Ratio >/= 1.8) Is an Independent Poor Prognostic Marker in Patients With Treatment-naive Non-Small-cell Lung Cancer. Clin. Lung Cancer 2021, 22, e512–e518.
    10. Kim, J.H.; Kim, H.S.; Kim, B.J.; Jang, H.J.; Lee, J. Prognostic value of c-Met overexpression in hepatocellular carcinoma: A meta-analysis and review. Oncotarget 2017, 8, 90351–90357.
    11. Yang, Y.; Wang, C.; Dai, C.; Liu, X.; Li, W.; Huang, M.; Zhao, X.; Ji, D.; Li, J.; Guo, W. Amplification and expression of c-MET correlate with poor prognosis of patients with gastric cancer and upregulate the expression of PDL1. Acta Biochim. Biophys. Sin. (Shanghai) 2021, 53, 547–557.
    12. Zagouri, F.; Bago-Horvath, Z.; Rossler, F.; Brandstetter, A.; Bartsch, R.; Papadimitriou, C.A.; Dimitrakakis, C.; Tsigginou, A.; Papaspyrou, I.; Giannos, A.; et al. High MET expression is an adverse prognostic factor in patients with triple-negative breast cancer. Br. J. Cancer 2013, 108, 1100–1105.
    13. Noonan, S.A.; Berry, L.; Lu, X.; Gao, D.; Baron, A.E.; Chesnut, P.; Sheren, J.; Aisner, D.L.; Merrick, D.; Doebele, R.C.; et al. Identifying the Appropriate FISH Criteria for Defining MET Copy Number-Driven Lung Adenocarcinoma through Oncogene Overlap Analysis. J. Thorac. Oncol. 2016, 11, 1293–1304.
    14. Consortium, A.P.G. AACR Project GENIE: Powering Precision Medicine through an International Consortium. Cancer Discov. 2017, 7, 818–831.
    15. Cerami, E.; Gao, J.; Dogrusoz, U.; Gross, B.E.; Sumer, S.O.; Aksoy, B.A.; Jacobsen, A.; Byrne, C.J.; Heuer, M.L.; Larsson, E.; et al. The cBio cancer genomics portal: An open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2012, 2, 401–404.
    16. Gao, J.; Aksoy, B.A.; Dogrusoz, U.; Dresdner, G.; Gross, B.; Sumer, S.O.; Sun, Y.; Jacobsen, A.; Sinha, R.; Larsson, E.; et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci. Signal. 2013, 6, pl1.
    17. Frampton, G.M.; Ali, S.M.; Rosenzweig, M.; Chmielecki, J.; Lu, X.; Bauer, T.M.; Akimov, M.; Bufill, J.A.; Lee, C.; Jentz, D.; et al. Activation of MET via Diverse Exon 14 Splicing Alterations Occurs in Multiple Tumor Types and Confers Clinical Sensitivity to MET Inhibitors. Cancer Discov. 2015, 5, 850–859.
    18. Paik, P.K.; Drilon, A.; Fan, P.D.; Yu, H.; Rekhtman, N.; Ginsberg, M.S.; Borsu, L.; Schultz, N.; Berger, M.F.; Rudin, C.M.; et al. Response to MET inhibitors in patients with stage IV lung adenocarcinomas harboring MET mutations causing exon 14 skipping. Cancer Discov. 2015, 5, 842–849.
    19. Onozato, R.; Kosaka, T.; Kuwano, H.; Sekido, Y.; Yatabe, Y.; Mitsudomi, T. Activation of MET by gene amplification or by splice mutations deleting the juxtamembrane domain in primary resected lung cancers. J. Thorac. Oncol. 2009, 4, 5–11.
    20. Kong-Beltran, M.; Seshagiri, S.; Zha, J.; Zhu, W.; Bhawe, K.; Mendoza, N.; Holcomb, T.; Pujara, K.; Stinson, J.; Fu, L.; et al. Somatic mutations lead to an oncogenic deletion of met in lung cancer. Cancer Res. 2006, 66, 283–289.
    21. Kron, A.; Scheffler, M.; Heydt, C.; Ruge, L.; Schaepers, C.; Eisert, A.K.; Merkelbach-Bruse, S.; Riedel, R.; Nogova, L.; Fischer, R.N.; et al. Genetic Heterogeneity of MET-Aberrant NSCLC and Its Impact on the Outcome of Immunotherapy. J. Thorac. Oncol. 2021, 16, 572–582.
    22. Sattler, M.; Salgia, R. MET in the Driver’s Seat: Exon 14 Skipping Mutations as Actionable Targets in Lung Cancer. J. Thorac. Oncol. 2016, 11, 1381–1383.
    23. Chakravarty, D.; Gao, J.; Phillips, S.M.; Kundra, R.; Zhang, H.; Wang, J.; Rudolph, J.E.; Yaeger, R.; Soumerai, T.; Nissan, M.H.; et al. OncoKB: A Precision Oncology Knowledge Base. JCO Precis. Oncol. 2017, 2017.
    24. Subramanian, J.; Tawfik, O. Detection of MET exon 14 skipping mutations in non-small cell lung cancer: Overview and community perspective. Expert Rev. Anticancer Ther. 2021, 21, 877–886.
    25. Socinski, M.A.; Pennell, N.A.; Davies, K.D. MET Exon 14 Skipping Mutations in Non-Small-Cell Lung Cancer: An Overview of Biology, Clinical Outcomes, and Testing Considerations. JCO Precis. Oncol. 2021, 5.
    26. Awad, M.M.; Lee, J.K.; Madison, R.; Classon, A.; Kmak, J.; Frampton, G.M.; Alexander, B.M.; Venstrom, J.; Schrock, A.B. Characterization of 1,387 NSCLCs with MET exon 14 (METex14) skipping alterations (SA) and potential acquired resistance (AR) mechanisms. J. Clin. Oncol. 2020, 38, 9511.
    27. Poirot, B.; Doucet, L.; Benhenda, S.; Champ, J.; Meignin, V.; Lehmann-Che, J. MET Exon 14 Alterations and New Resistance Mutations to Tyrosine Kinase Inhibitors: Risk of Inadequate Detection with Current Amplicon-Based NGS Panels. J. Thorac. Oncol. 2017, 12, 1582–1587.
    28. Davies, K.D.; Lomboy, A.; Lawrence, C.A.; Yourshaw, M.; Bocsi, G.T.; Camidge, D.R.; Aisner, D.L. DNA-Based versus RNA-Based Detection of MET Exon 14 Skipping Events in Lung Cancer. J. Thorac. Oncol. 2019, 14, 737–741.
    29. Kim, E.K.; Kim, K.A.; Lee, C.Y.; Kim, S.; Chang, S.; Cho, B.C.; Shim, H.S. Molecular Diagnostic Assays and Clinicopathologic Implications of MET Exon 14 Skipping Mutation in Non-small-cell Lung Cancer. Clin. Lung Cancer 2019, 20, e123–e132.
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      Rivas, S.; Marín, A.; Samtani, S.; González-Feliú, E.; Armisén, R. MET in Cancer Initiation and Driver Mutations. Encyclopedia. Available online: https://encyclopedia.pub/entry/36582 (accessed on 27 January 2023).
      Rivas S, Marín A, Samtani S, González-Feliú E, Armisén R. MET in Cancer Initiation and Driver Mutations. Encyclopedia. Available at: https://encyclopedia.pub/entry/36582. Accessed January 27, 2023.
      Rivas, Solange, Arnaldo Marín, Suraj Samtani, Evelin González-Feliú, Ricardo Armisén. "MET in Cancer Initiation and Driver Mutations," Encyclopedia, https://encyclopedia.pub/entry/36582 (accessed January 27, 2023).
      Rivas, S., Marín, A., Samtani, S., González-Feliú, E., & Armisén, R. (2022, November 25). MET in Cancer Initiation and Driver Mutations. In Encyclopedia. https://encyclopedia.pub/entry/36582
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