Takehito Shukuya

Background: Immune checkpoint inhibitors (ICIs) have transformed treatment for non-small cell lung cancer (NSCLC), but their efficacy varies across oncogenic driver alterations. The benefit of adding ICIs to platinum-based chemotherapy in rare molecular subgroups remains uncertain.Patients and Methods: In this retrospective multicenter study, we analyzed 902 patients with NSCLC harboring ALK, ROS1, or RET fusions; MET exon 14 skipping; BRAF V600E; KRAS; or HER2 mutations across 45 institutions in Japan. Objective response rate (ORR), progression-free survival (PFS), overall survival, and immune-related adverse events were evaluated. Survival outcomes were analyzed using Kaplan-Meier estimates and Cox proportional hazards models.Results: MET exon 14 skipping tumors had the highest prevalence of PD-L1 tumor proportion score >=50% (62.3%), whereas HER2-mutated tumors had the lowest (13.9%). With ICI monotherapy, MET tumors had a higher ORR than ALK-rearranged tumors. With ICI plus platinum chemotherapy, KRAS G12C tumors had a higher ORR than ALK-rearranged tumors. ICI plus platinum-doublet chemotherapy significantly prolonged PFS versus chemotherapy alone in BRAF V600E (12.7 vs 6.6 months; HR, 0.34; P=.02) and KRAS (8.8 vs 5.4 months; HR, 0.60; P=.001) cohorts. No new safety signals were observed.Conclusion: ICI efficacy differed by driver genotype. BRAF V600E- and KRAS-mutated tumors derived greater benefit from immunochemotherapy, whereas fusion-driven tumors showed limited responsiveness.

Background: Lung cancer is the leading cause of cancer‐related deaths worldwide. Although osimertinib is more effective than previous‐generation drugs, its efficacy is often limited by both primary and acquired resistance. Cycling cancer persister cells (CPCs) have recently emerged as a transient, drug‐tolerant subpopulation that may contribute to early resistance to targeted therapies. However, their biological characteristics and role in osimertinib resistance remain poorly understood. Methods: We established osimertinib‐resistant CPCs derived from epidermal growth factor receptor ( EGFR) ‐mutant non‐small‐cell lung cancer (NSCLC) cell lines PC9 and HCC827. RNA sequencing (RNA‐seq) was performed to identify CPC‐specific transcriptional programs. Results: RNA‐seq analyses demonstrated that the tumor necrosis factor alpha (TNF‐α)/nuclear factor‐κB (NF‐κB) signaling pathway was upregulated in osimertinib‐resistant PC9 cells (PC9‐CPCs), with interleukin‐1 alpha (IL‐1α) being the most significantly overexpressed gene. Treatment with IL‐1α increased TNF‐α and NF‐κB p65 expression in PC9 parental cells, whereas IL‐1α inhibition decreased TNF‐α expression in PC9‐CPCs. Consequently, the inhibition of either IL‐1α or TNF‐α restored the sensitivity of EGFR ‐mutant NSCLC cells to osimertinib. Immunohistochemical analysis confirmed that NF‐κB expression was higher in tumor specimens from patients who had developed osimertinib resistance than in specimens collected before treatment. Conclusion: CPCs exhibit a distinct inflammatory phenotype driven by IL‐1α–driven activation of the TNF‐α/NF‐κB axis, representing an alternative mechanism underlying osimertinib resistance. Targeting IL‐1α or TNF‐α may provide a promising strategy to overcome resistance in patients with EGFR ‐mutant NSCLC.

Lung cancer remains the leading cause of cancer-related death worldwide, with over two million new cases annually. Advances in molecular biology and immuno-oncology have fundamentally transformed treatment strategies. Comprehensive genomic profiling has enabled precision medicine approaches, allowing the selection of targeted therapies based on driver alterations such as EGFR, ALK, and HER2. For EGFR-mutant NSCLC, sequential development of tyrosine kinase inhibitors (TKIs) from first- to third-generation agents―culminating in osimertinib―has markedly improved survival. Similarly, successive generations of ALK inhibitors, including alectinib, brigatinib, and lorlatinib, have extended disease control, particularly within the central nervous system. The introduction of antibody-drug conjugates (ADCs), such as trastuzumab deruxtecan for HER2-mutant NSCLC, and emerging TKIs like zongertinib, represent new therapeutic milestones. Immunotherapy has become central to the management of both NSCLC and SCLC, with immune checkpoint inhibitors (ICIs) demonstrating unprecedented survival benefits. Beyond lung cancer, our group, in collaboration with Juntendo University ARO (academic research organization) and fifteen institutions in Japan, conducted the MARBLE phase II trial of atezolizumab plus chemotherapy for thymic carcinoma, achieving a 56% objective response rate and 9.6-month median progression-free survival, supporting potential ICI approval in Japan. Furthermore, novel immune strategies such as bispecific T-cell engagers (BiTEs) have shown promise. The DLL3-targeted BiTE tarlatamab significantly improved overall survival to 13.6 months in the phase III DeLLphi-304 trial for relapsed SCLC, with manageable cytokine release syndrome. Collectively, these advances signify a shift toward biologically driven, molecular-targeted or immune-integrated therapy, aiming to transform lung cancer into a chronic, manageable disease in the future, hopefully.

Background: Drugs for systemic therapy to advanced or recurrent non-small cell lung cancer (NSCLC) are determined by the type of driver oncogenes and programmed death-ligand 1 (PD-L1) immunostaining. In this study, we investigated the potential for artificial intelligence to assist in detecting epidermal growth factor receptor (EGFR) mutation, which is one of the most frequent and important driver oncogenes, and predicting the efficacy of EGFR-tyrosine kinase inhibitors (TKIs) in patients with NSCLC. Methods: We built an ensemble model that combined machine learning models (logistic regression, Bernoulli naive Bayes and Gaussian naive Bayes) for clinical information and tumor characteristics analysis, and a deep convolutional neural network for analyzing computed tomography (CT) images. The convolutional neural network was trained from scratch on our imaging dataset. Clinical and imaging features were analyzed separately within their respective models and subsequently integrated in the ensemble framework. The models were trained to predict EGFR mutation and the response of EGFR-TKI using cross-entropy loss. The performance of the proposed model was evaluated by 5-fold cross-validation. Results: One hundred and fifty consecutive evaluable patients were included in this study. Among them, 61 patients had EGFR mutation, including 59 patients with common EGFR mutation and 2 patients with uncommon EGFR mutation. The ensemble model predicted EGFR mutation with an area under the curve (AUC) of 0.88 [95% confidence interval (CI): 0.79–0.97]. Each sub-model integrated into the ensemble model predicted EGFR mutation with AUC of 0.83 (95% CI: 0.71–0.95), 0.88 (95% CI: 0.79–0.97), 0.87 (95% CI: 0.79–0.94), and 0.84 (95% CI: 0.72–0.96) for logistic regression, Bernoulli naive Bayes, Gaussian naive Bayes and deep neural network respectively. The predictive accuracy of the ensemble model was 0.56. Specifically, the ensemble model reported that the predictive accuracy of the Del19 EGFR mutation was higher than that of the L858R EGFR mutation, with overall response rate predictive accuracy (AUC) of 0.67 versus 0.52, and disease control rate AUC of 0.88 versus 0.53. Conclusions: The ensemble model demonstrated strong performance in predicting EGFR mutations and showed higher predictive treatment response for EGFR exon 19 deletions than for L858R mutations. These findings suggest that ensemble learning may enhance the non-invasive prediction of EGFR mutation status and EGFR genotype-specific therapeutic outcomes in NSCLC, although further validation in larger cohorts is warranted.