MET alterations as resistance mechanisms of dabrafenib-trametinib in BRAF p.V600E mutated non-small cell lung cancer patient.

The combination of BRAF and MEK inhibitors demonstrated significant clinical benefit in patients with BRAF-mutant non-small cell lung cancer (NSCLC). However, the molecular mechanisms of acquired resistance to BRAF and MEK inhibition in NSCLC are still unknown. Herein, we report a case of a 76-year-old man with a history of smoking who was diagnosed with BRAF V600E-mutant lung adenocarcinoma (PD-L1 > 50%) and subsequently candidate to first-line therapy with pembrolizumab. After 18 months since the start of immunotherapy, computed tomography scan showed disease progression and a second-line therapy with dabrafenib and trametinib was initiated. Seven months later, due to a suspect disease progression, a left supraclavicular lymphadenectomy was performed and next-generation sequencing analysis revealed the appearance of MET exon 14 skipping mutation, while fluorescence in situ hybridization analysis showed MET amplification. The patient is still on BRAF and MEK inhibitor treatment. Our case highlights the relevance of performing tumor tissue rebiopsy at the time of progression during treatment with BRAF/MEK inhibition with the aim of identifying putative mechanisms of resistance.

Differential impact of lipid profile according to neutrophil-to-lymphocyte ratio status in patients with advanced cancer treated with immunotherapy.

Aim: To investigate the different impact of each component of lipid profile in advanced cancer patients treated with immune checkpoints inhibitors (ICIs) according to neutrophil-to-lymphocyte ratio (NLR) value. Methods: We retrospectively collected total cholesterol (TC), triglycerides (TGs), low-density lipoproteins (LDL), high-density lipoproteins (HDL). Results: 407 patients were enrolled. In NLR <4 subgroup, TGs <150 mg/dl led to longer PFS (p = 0.01) and OS (p = 0.02) compared with TGs ≥150 mg/dl; LDL <100 mg/dl led to longer PFS (p = 0.004) and OS (p = 0.007) compared with LDL ≥100 mg/dl. In NLR ≥4 subgroup, TC >200 mg/dl led to longer PFS (p = 0.008) and OS (p = 0.004) compared with TC <200 mg/dl. Conclusion: We showed a distinct prognostic impact of lipid profile according to NLR.

[Box: see text].

Intersecting Blood Cytokines With Cholesterol Parameters to Profile Patients With Advanced Solid Tumors Receiving Immune Checkpoint Inhibitors.

The study investigated the relationship between serum proinflammatory cytokine levels, cholesterol metabolism, and clinical outcome in cancer patients undergoing immune checkpoint inhibitors (ICIs). Peripheral blood was collected before therapy from ICI-treated advanced cancer patients. We retrospectively assessed plasma total cholesterol (TC), ABCA1- and ABCG1-mediated cholesterol efflux (CE), passive diffusion (PD), cholesterol loading capacity (CLC), and serum IL-6, IL-10, and TNF-α. The association between blood cholesterol parameters and inflammatory cytokines and their effect on overall survival (OS), progression-free survival (PFS), and clinical benefit (CB) from ICIs were statistically assessed. Among 70 consecutively enrolled patients (nonsmall cell lung cancer: 94%; renal cell carcinoma: 6%), TC, CLC, and cholesterol PD resulted significantly higher in IL-6low and IL-10low cases (P<0.05), whereas ABCA1-mediated CE was increased in IL-10high patients (P=0.018). Uni- and multivariable analysis revealed meaningfully longer OS and PFS in IL-6low (HR 2.13 and 2.97, respectively) and IL-10low (HR 3.17 and 2.62) groups. At univariate analysis all cholesterol-related indices significantly correlated with OS and PFS, whereas at multivariate only high PD was validated as a protection factor (OS, HR 0.75; PFS, HR 0.84). Finally, uni- and multivariable showed a statistically significant inverse association of CB with ABCG1-CE (OR 0.62), as with IL-6 (OR 0.13) and IL-10 (OR 0.10). In-depth characterization of the interplay between blood cholesterol metabolism and immune-inflammatory cytokines might provide novel insights into the complex relationship among cancer, inflammation, lipids profile, and response to immunotherapy.

Additional impact of genetic ancestry over race/ethnicity to prevalence of KRAS mutations and allele-specific subtypes in non-small cell lung cancer.

The KRAS mutation is the most common oncogenic driver in patients with non-small cell lung cancer (NSCLC). However, a detailed understanding of how self-reported race and/or ethnicity (SIRE), genetically inferred ancestry (GIA), and their interaction affect KRAS mutation is largely unknown. Here, we investigated the associations between SIRE, quantitative GIA, and KRAS mutation and its allele-specific subtypes in a multi-ethnic cohort of 3,918 patients from the Boston Lung Cancer Survival cohort and the Chinese OrigiMed cohort with an independent validation cohort of 1,450 patients with NSCLC. This comprehensive analysis included detailed covariates such as age at diagnosis, sex, clinical stage, cancer histology, and smoking status. We report that SIRE is significantly associated with KRAS mutations, modified by sex, with SIRE-Asian patients showing lower rates of KRAS mutation, transversion substitution, and the allele-specific subtype KRASG12C compared to SIRE-White patients after adjusting for potential confounders. Moreover, GIA was found to correlate with KRAS mutations, where patients with a higher proportion of European ancestry had an increased risk of KRAS mutations, especially more transition substitutions and KRASG12D. Notably, among SIRE-White patients, an increase in European ancestry was linked to a higher likelihood of KRAS mutations, whereas an increase in admixed American ancestry was associated with a reduced likelihood, suggesting that quantitative GIA offers additional information beyond SIRE. The association of SIRE, GIA, and their interplay with KRAS driver mutations in NSCLC highlights the importance of incorporating both into population-based cancer research, aiming to refine clinical decision-making processes and mitigate health disparities.

Activating Point Mutations in the MET Kinase Domain Represent a Unique Molecular Subset of Lung Cancer and Other Malignancies Targetable with MET Inhibitors.

Activating point mutations in the MET tyrosine kinase domain (TKD) are oncogenic in a subset of papillary renal cell carcinomas. Here, using comprehensive genomic profiling among >600,000 patients, we identify activating MET TKD point mutations as putative oncogenic driver across diverse cancers, with a frequency of ∼0.5%. The most common mutations in the MET TKD defined as oncogenic or likely oncogenic according to OncoKB resulted in amino acid substitutions at positions H1094, L1195, F1200, D1228, Y1230, M1250, and others. Preclinical modeling of these alterations confirmed their oncogenic potential and also demonstrated differential patterns of sensitivity to type I and type II MET inhibitors. Two patients with metastatic lung adenocarcinoma harboring MET TKD mutations (H1094Y, F1200I) and no other known oncogenic drivers achieved confirmed partial responses to a type I MET inhibitor. Activating MET TKD mutations occur in multiple malignancies and may confer clinical sensitivity to currently available MET inhibitors. Significance: The identification of targetable genomic subsets of cancer has revolutionized precision oncology and offers patients treatments with more selective and effective agents. Here, we demonstrate that activating, oncogenic MET tyrosine kinase domain mutations are found across a diversity of cancer types and are responsive to MET tyrosine kinase inhibitors.

Association of Baseline Tumor-Specific Neoantigens and CD8+ T-Cell Infiltration With Immune-Related Adverse Events Secondary to Immune Checkpoint Inhibitors.

PURPOSE: Recent evidence has shown that higher tumor mutational burden strongly correlates with an increased risk of immune-related adverse events (irAEs). By using an integrated multiomics approach, we further studied the association between relevant tumor immune microenvironment (TIME) features and irAEs. METHODS: Leveraging the US Food and Drug Administration Adverse Event Reporting System, we extracted cases of suspected irAEs to calculate the reporting odds ratios (RORs) of irAEs for cancers treated with immune checkpoint inhibitors (ICIs). TIME features for 32 cancer types were calculated on the basis of the cancer genomic atlas cohorts and indirectly correlated with each cancer's ROR for irAEs. A separate ICI-treated cohort of non-small-cell lung cancer (NSCLC) was used to evaluate the correlation between tissue-based immune markers (CD8+, PD-1/L1+, FOXP3+, tumor-infiltrating lymphocytes [TILs]) and irAE occurrence. RESULTS: The analysis of 32 cancers and 33 TIME features demonstrated a significant association between irAE RORs and the median number of base insertions and deletions (INDEL), neoantigens (r = 0.72), single-nucleotide variant neoantigens (r = 0.67), and CD8+ T-cell fraction (r = 0.51). A bivariate model using the median number of INDEL neoantigens and CD8 T-cell fraction had the highest accuracy in predicting RORs (adjusted r2 = 0.52, P = .002). Immunoprofile assessment of 156 patients with NSCLC revealed a strong trend for higher baseline median CD8+ T cells within patients' tumors who experienced any grade irAEs. Using machine learning, an expanded ICI-treated NSCLC cohort (n = 378) further showed a treatment duration-independent association of an increased proportion of high TIL (>median) in patients with irAEs (59.7% v 44%, P = .005). This was confirmed by using the Fine-Gray competing risk approach, demonstrating higher baseline TIL density (>median) associated with a higher cumulative incidence of irAEs (P = .028). CONCLUSION: Our findings highlight a potential role for TIME features, specifically INDEL neoantigens and baseline-immune infiltration, in enabling optimal irAE risk stratification of patients.

APOLLO 11 Project, Consortium in Advanced Lung Cancer Patients Treated With Innovative Therapies: Integration of Real-World Data and Translational Research.

INTRODUCTION: Despite several therapeutic efforts, lung cancer remains a highly lethal disease. Novel therapeutic approaches encompass immune-checkpoint inhibitors, targeted therapeutics and antibody-drug conjugates, with different results. Several studies have been aimed at identifying biomarkers able to predict benefit from these therapies and create a prediction model of response, despite this there is a lack of information to help clinicians in the choice of therapy for lung cancer patients with advanced disease. This is primarily due to the complexity of lung cancer biology, where a single or few biomarkers are not sufficient to provide enough predictive capability to explain biologic differences; other reasons include the paucity of data collected by single studies performed in heterogeneous unmatched cohorts and the methodology of analysis. In fact, classical statistical methods are unable to analyze and integrate the magnitude of information from multiple biological and clinical sources (eg, genomics, transcriptomics, and radiomics). METHODS AND OBJECTIVES: APOLLO11 is an Italian multicentre, observational study involving patients with a diagnosis of advanced lung cancer (NSCLC and SCLC) treated with innovative therapies. Retrospective and prospective collection of multiomic data, such as tissue- (eg, for genomic, transcriptomic analysis) and blood-based biologic material (eg, ctDNA, PBMC), in addition to clinical and radiological data (eg, for radiomic analysis) will be collected. The overall aim of the project is to build a consortium integrating different datasets and a virtual biobank from participating Italian lung cancer centers. To face with the large amount of data provided, AI and ML techniques will be applied will be applied to manage this large dataset in an effort to build an R-Model, integrating retrospective and prospective population-based data. The ultimate goal is to create a tool able to help physicians and patients to make treatment decisions. CONCLUSION: APOLLO11 aims to propose a breakthrough approach in lung cancer research, replacing the old, monocentric viewpoint towards a multicomprehensive, multiomic, multicenter model. Multicenter cancer datasets incorporating common virtual biobank and new methodologic approaches including artificial intelligence, machine learning up to deep learning is the road to the future in oncology launched by this project.

Genomic and Immunophenotypic Landscape of Acquired Resistance to PD-(L)1 Blockade in Non-Small-Cell Lung Cancer.

PURPOSE: Although immune checkpoint inhibitors (ICI) have extended survival in patients with non-small-cell lung cancer (NSCLC), acquired resistance (AR) to ICI frequently develops after an initial benefit. However, the mechanisms of AR to ICI in NSCLC are largely unknown. METHODS: Comprehensive tumor genomic profiling, machine learning-based assessment of tumor-infiltrating lymphocytes, multiplexed immunofluorescence, and/or HLA-I immunohistochemistry (IHC) were performed on matched pre- and post-ICI tumor biopsies from patients with NSCLC treated with ICI at the Dana-Farber Cancer Institute who developed AR to ICI. Two additional cohorts of patients with intervening chemotherapy or targeted therapies between biopsies were included as controls. RESULTS: We performed comprehensive genomic profiling and immunophenotypic characterization on samples from 82 patients with NSCLC and matched pre- and post-ICI biopsies and compared findings with a control cohort of patients with non-ICI intervening therapies between biopsies (chemotherapy, N = 32; targeted therapies, N = 89; both, N = 17). Putative resistance mutations were identified in 27.8% of immunotherapy-treated cases and included acquired loss-of-function mutations in STK11, B2M, APC, MTOR, KEAP1, and JAK1/2; these acquired alterations were not observed in the control groups. Immunophenotyping of matched pre- and post-ICI samples demonstrated significant decreases in intratumoral lymphocytes, CD3e+ and CD8a+ T cells, and PD-L1-PD1 engagement, as well as increased distance between tumor cells and CD8+PD-1+ T cells. There was a significant decrease in HLA class I expression in the immunotherapy cohort at the time of AR compared with the chemotherapy (P = .005) and the targeted therapy (P = .01) cohorts. CONCLUSION: These findings highlight the genomic and immunophenotypic heterogeneity of ICI resistance in NSCLC, which will need to be considered when developing novel therapeutic strategies aimed at overcoming resistance.