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MET protooncogene (MET) alterations are known driver oncogenes in NSCLC. Since the identification of MET as a potential therapeutic target, extensive clinical trials have been performed. As a result, MET-targeted therapies, including MET tyrosine kinase inhibitors, monoclonal antibodies, and MET antibody–drug conjugates now play important roles in the standard treatment of MET-altered NSCLC; they have considerably improved the outcomes of patients with tumors that harbor MET oncogenic drivers. Although clinical agents are currently available and numerous other options are in development, particular challenges in the field require attention. For example, the therapeutic efficacy of each drug remains unsatisfactory, and concomitantly, the resistance mechanisms are not fully understood. Thus, there is an urgent need for optimal drug sequencing and combinations, along with a thorough understanding of treatment resistance. In this review, we describe the current landscape of pertinent clinical trials focusing on MET-targeted strategies and discuss future developmental directions in this rapidly expanding field.

Introduction: NSCLC transformation to SCLC has been best characterized with EGFR-mutant NSCLC, with emerging case reports seen in ALK, RET, and KRAS-altered NSCLC. Previous reports revealed transformed SCLC from EGFR-mutant NSCLC portends very poor prognosis and lack effective treatment. Genomic analyses revealed TP53 and RB1 loss of function increase the risk of SCLC transformation. Little has been reported on the detailed clinicogenomic characteristics and potential therapeutic targets for this patient population. Methods: In this study, we conducted a single-center retrospective analysis of clinical and genomic characteristics of patients with EGFR-mutant NSCLC transformed to SCLC. Demographic data, treatment course, and clinical molecular testing reports were extracted from electronic medical records. Kaplan-Meier analyses were used to estimate survival outcomes. Next generation sequencing-based assays was used to identify EGFR and co-occurring genetic alterations in tissue or plasma before and after SCLC transformation. Single-cell RNA sequencing (scRNA-seq) was performed on a patient-derived-xenograft model generated from a patient with EGFR-NSCLC transformed SCLC tumor. Results: A total of 34 patients were identified in our study. Median age at initial diagnosis was 58, and median time to SCLC transformation was 24.2 months. 68% were female and 82% were never smokers. 79% of patients were diagnosed as stage IV disease, and over half had brain metastases at baseline. Median overall survival of the entire cohort was 38.3 months from initial diagnoses and 12.4 months from time of SCLC transformation. Most patients harbored EGFR exon19 deletions as opposed to exon21 L858R alteration. Continuing EGFR tyrosine kinase inhibitor post-transformation did not improve overall survival compared with those patients where tyrosine kinase inhibitor was stopped in our cohort. In the 20 paired pretransformed and post-transformed patient samples, statistically significant enrichment was seen with PIK3CA alterations (p = 0.04) post-transformation. Profiling of longitudinal liquid biopsy samples suggest emergence of SCLC genetic alterations before biopsy-proven SCLC, as shown by increasing variant allele frequency of TP53, RB1, PIK3CA alterations. ScRNA-seq revealed potential therapeutic targets including DLL3, CD276 (B7-H3) and PTK7 were widely expressed in transformed SCLC. Conclusions: SCLC transformation is a potential treatment resistance mechanism in driver-mutant NSCLC. In our cohort of 34 EGFR-mutant NSCLC, poor prognosis was observed after SCLC transformation. Clinicogenomic analyses of paired and longitudinal samples identified genomic alterations emerging post-transformation and scRNA-seq reveal potential therapeutic targets in this population. Further studies are needed to rigorously validate biomarkers and therapeutic targets for this patient population.

Introduction: MET amplification is a known resistance mechanism to EGFR tyrosine kinase inhibitor (TKI) treatment in EGFR-mutant NSCLC. Dual EGFR-MET inhibition has been reported with success in overcoming such resistance and inducing clinical benefit. Resistance mechanisms to dual EGFR-MET inhibition require further investigation and characterization. Methods: Patients with NSCLC with both MET amplification and EGFR mutation who have received crizotinib, capmatinib, savolitinib, or tepotinib plus osimertinib (OSI) after progression on OSI at MD Anderson Cancer Center were included in this study. Molecular profiling was completed by means of fluorescence in situ hybridization (FISH) and next-generation sequencing (NGS). Radiological response was assessed on the basis of Response Evaluation Criteria in Solid Tumors version 1.1. Results: From March 2016 to March 2022, 23 treatments with dual MET inhibitor and osi were identified with a total of 20 patients included. Three patients received capmatinib plus OSI after progression on crizotinib plus OSI. Median age was 64 (38–89) years old and 75% were female. MET amplification was detected by FISH in 14 patients in the tissue, NGS in 10 patients, and circulating tumor DNA in three patients. Median MET gene copy number was 13.6 (6.4–20). Overall response rate was 34.8% (eight of 23). In assessable patients, tumor shrinkage was observed in 82.4% (14 of 17). Median time on treatment was 27 months. Two of three patients responded to capmatinib plus OSI after progression on crizotinib plus OSI. Dual EGFR-MET inhibition was overall well tolerated. Two patients on crizotinib plus OSI and one pt on capmatinib plus OSI discontinued therapy due to pneumonitis. One pt discontinued crizotinib plus OSI due to gastrointestinal toxicity. Six patients were still on double TKI treatment. At disease progression to dual EGFR-MET inhibition, FISH and NGS on tumor and plasma were completed in six patients. Notable resistance mechanisms observed include acquired MET D1246H (n = 1), acquired EGFR C797S (n = 2), FGFR2 fusion (n = 1, concurrent with C797S), and EGFR G796S (n = 1, concurrent with C797S). Four patients lost MET amplification. Conclusions: Dual EGFR and MET inhibition yielded high clinical response rate after progression on OSI. Resistance mechanisms to EGFR-MET double TKI inhibition include MET secondary mutation, EGFR secondary mutation, or loss of MET amplification.

Summary: Background: Only around 20–30% of patients with non-small-cell lung cancer (NCSLC) have durable benefit from immune-checkpoint inhibitors. Although tissue-based biomarkers (eg, PD-L1) are limited by suboptimal performance, tissue availability, and tumour heterogeneity, radiographic images might holistically capture the underlying cancer biology. We aimed to investigate the application of deep learning on chest CT scans to derive an imaging signature of response to immune checkpoint inhibitors and evaluate its added value in the clinical context. Methods: In this retrospective modelling study, 976 patients with metastatic, EGFR/ALK negative NSCLC treated with immune checkpoint inhibitors at MD Anderson and Stanford were enrolled from Jan 1, 2014, to Feb 29, 2020. We built and tested an ensemble deep learning model on pretreatment CTs (Deep-CT) to predict overall survival and progression-free survival after treatment with immune checkpoint inhibitors. We also evaluated the added predictive value of the Deep-CT model in the context of existing clinicopathological and radiological metrics. Findings: Our Deep-CT model demonstrated robust stratification of patient survival of the MD Anderson testing set, which was validated in the external Stanford set. The performance of the Deep-CT model remained significant on subgroup analyses stratified by PD-L1, histology, age, sex, and race. In univariate analysis, Deep-CT outperformed the conventional risk factors, including histology, smoking status, and PD-L1 expression, and remained an independent predictor after multivariate adjustment. Integrating the Deep-CT model with conventional risk factors demonstrated significantly improved prediction performance, with overall survival C-index increases from 0·70 (clinical model) to 0·75 (composite model) during testing. On the other hand, the deep learning risk scores correlated with some radiomics features, but radiomics alone could not reach the performance level of deep learning, indicating that the deep learning model effectively captured additional imaging patterns beyond known radiomics features. Interpretation: This proof-of-concept study shows that automated profiling of radiographic scans through deep learning can provide orthogonal information independent of existing clinicopathological biomarkers, bringing the goal of precision immunotherapy for patients with NSCLC closer. Funding: National Institutes of Health, Mark Foundation Damon Runyon Foundation Physician Scientist Award, MD Anderson Strategic Initiative Development Program, MD Anderson Lung Moon Shot Program, Andrea Mugnaini, and Edward L C Smith.

Introduction: The efficacy of targeted therapies in oncogene-driven lung adenocarcinomas (LUADs) has been well established; however, the benefit for EGFR-mutant or ALK-rearranged lung squamous cell carcinomas (LUSCs) is less known, partially owing to the rarity of the incidence. Methods: We reviewed the database of the MD Anderson Cancer Center and identified metastatic LUSC with classic EGFR or ALK alterations. Results: There were eight patients with EGFR-mutant LUSC (median age = 58 y) and six patients with EML4-ALK LUSC (median age = 50 y) who received tyrosine kinase inhibitors (TKIs) that were identified. Of the 14 patients, 11 (79%) were females and 12 (86%) were never smokers, similar to the demographics of EGFR or ALK LUAD. With TKI treatment, seven of eight cases of EGFR LUSC and four of six cases of ALK LUSC achieved partial response or stable disease, but the progression-free survival was 4.9 months and 2.9 months for EGFR-mutant and ALK-rearranged LUSC, respectively. In addition, we compared comutation profile of EGFR-mutant LUAD (The Cancer Genome Atlas, n = 46) versus LUSC (n = 19) and found that the comutation patterns are more consistent with squamous disease with a higher incidence of PIK3CA (p = 0.02) and KRAS or BRAF (p = 0.04) alterations. Conclusions: EGFR or ALK alterations occur in patients with LUSC, especially never-smoker females. TKI treatments render clinical benefit in disease control, but the duration was considerably truncated compared with those historically observed in LUAD.