Translational lung cancer research最新文献

Background: Epidermal growth factor receptor (EGFR) exon 20 insertion (ex20ins) mutations account for 5-12% of EGFR-mutated non-small cell lung cancers (NSCLCs) and are generally resistant to first- and second-generation tyrosine kinase inhibitors (TKIs). Among these, the A763_Y764insFQEA variant represents a rare, structurally distinct subtype that demonstrates sensitivity to multiple EGFR-TKIs. However, clinical evidence in Asian populations remains limited.

Case description: A 63-year-old Japanese woman with stage IVB lung adenocarcinoma [programmed death ligand-1 (PD-L1) tumor proportion score 70%] received multiple lines of systemic therapy, including pembrolizumab-based chemoimmunotherapy, docetaxel plus ramucirumab, and subsequent cytotoxic regimens. Initial polymerase chain reaction (PCR)-based EGFR testing was negative. After disease progression through fourth-line treatment, plasma-based comprehensive genomic profiling using FoundationOne^® Liquid identified the EGFR A763_Y764insFQEA variant. Osimertinib (80 mg daily) was initiated as fifth-line therapy, achieving a partial response by two months and disease control lasting approximately five months. Treatment continued beyond radiographic progression based on clinical benefit, with an overall survival of 10 months following osimertinib initiation.

Conclusions: This case highlights the diagnostic and therapeutic significance of liquid biopsy in detecting rare but actionable EGFR variants. The A763_Y764insFQEA mutation is uniquely sensitive to EGFR-TKIs, and comprehensive molecular testing can guide effective targeted therapy even in later treatment lines. Broader implementation of hybrid-capture-based assays may improve precision oncology outcomes for patients with rare EGFR alterations.

Background: Synchronous double primary lung adenocarcinoma (sDPLA) is a distinct subtype of lung adenocarcinoma characterized by the co-existence of two independent lesions in the same patient. We conducted the first comprehensive analysis of the immune microenvironment of sDPLA lesions with different degrees of invasiveness to examine immune evolution during early lung adenocarcinoma progression.

Methods: In total, 10 sDPLA patients undergoing synchronous surgical resection were enrolled in the study. The minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC) lesions were analyzed by next-generation sequencing (NGS) or single-cell RNA sequencing (scRNA-seq), focusing on T/natural killer (NK) cell subsets. The key cell subsets and signaling pathways were identified using Mfuzz clustering, CellChat, and Monocle, and validated by multiplex immunofluorescence and flow cytometry.

Results: We identified 11 T/NK cell subsets, among which the CD4⁺ exhausted T (Tex) cell CXCL13 population was significantly enriched in the IAC lesions. These cells exhibited high expression of PD-1 and TIM-3, enhanced interactions with epithelial cells, and enrichment of the JAK-STAT and PI3K-AKT pathways, suggesting a central role in immune suppression. Validation confirmed the preferential accumulation of this subset of cells in the IAC tissues.

Conclusions: This study found significant immune heterogeneity between sDPLA lesions with different degrees of invasiveness and identified CD4⁺ Tex CXCL13 cells as key drivers of tumor immune progression. Our findings provide new insights into early immune evolution and may inform precision immunotherapy strategies.

Background: The tumor-stroma ratio (TSR) has been validated as an independent prognostic factor across multiple cancer types, including lung squamous cell carcinoma (LUSC), in which a stroma-rich phenotype is generally linked to poor outcomes. However, traditional manual TSR assessment is subjective and has limited reproducibility. This study aimed to investigate the clinical utility of computer-aided (CA) quantitative stromal analysis in LUSC, elucidate the correlation between TSR and clinicopathological features, and provide a theoretical basis for precision diagnosis and treatment.

Methods: There were 189 patients with primary LUSC diagnosed between 2015 and 2020 included in a retrospective cohort. Using automated analysis techniques in conjunction with whole-slide imaging (WSI), a quantitative evaluation of the TSR was carried out automatically. Meanwhile, the TSR was also evaluated manually. Based on the median TSR value, patients were categorized into cohorts that were either stroma-poor (TSR-L) group or stroma-rich (TSR-H) group. Using Cox regression models and Kaplan-Meier survival analysis, prognostic significance was tested. In addition, the consistency between the results of manual evaluation and those of CA evaluation was analyzed.

Results: The median CA-TSR score was 0.34. Patients in the TSR-H group demonstrated significantly worse overall survival (OS) and disease-free survival (DFS) (both P<0.001), which corresponded with pleural invasion (P=0.03) and advanced pathological staging. The area under the curve (AUC) for OS demonstrated the superior predictive accuracy of CA-TSR over manual scoring (0.651-0.7 vs. 0.488-0.573), and multivariate analysis validated it as an independent prognostic factor [DFS: hazard ratio (HR) =2.790; OS: HR =2.633]. Furthermore, the manual and CA evaluations showed a moderate level of agreement (r=0.45, P<0.001).

Conclusions: CA-driven quantitative TSR analysis enables objective and efficient assessment of the stromal ratio in LUSC. The CA-TSR score serves as an independent prognostic predictor, significantly enhancing survival prediction accuracy compared to conventional methods.

Background: Endobronchial ultrasound-transbronchial needle aspiration (EBUS-TBNA) is widely used for mediastinal staging in non-small cell lung cancer (NSCLC). However, unexpected lymph node metastases leading to upstaging are often observed postoperatively. The characteristics of upstaged N2 lymph nodes and their impact on long-term survival remain unclear. This study verified whether postoperative N2 nodal upstaging in NSCLC has a greater impact on long-term survival than preoperatively diagnosed N2 disease.

Methods: This retrospective study analyzed 177 patients with NSCLC with pathologically confirmed N2 lymph nodes after surgery. Preoperative staging was performed using computed tomography (CT), positron emission tomography-computed tomography (PET-CT), and EBUS-TBNA. Based on nodal staging discrepancies, patients were classified as N2-upstaged or unchanged. The primary outcome was 5-year overall survival, and the secondary outcome was progression-free survival (PFS).

Results: Among 177 patients with postoperative N2-positive nodes, 53.1% (n=94) experienced nodal upstaging from preoperative N0 or N1. Upstaging was most commonly associated with subcarinal (38.3%) and aortic lymph nodes (21.3%), with 40% of the upstaged nodes measuring <5 mm. Five-year overall survival did not differ significantly between the upstaged and unchanged groups [hazard ratio (HR) 0.93, 95% confidence interval (CI): 0.60-1.45, P=0.76]. However, PFS was significantly higher in the upstaged group (HR 0.555, 95% CI: 0.336-0.915, P=0.02). Multivariate analysis identified nodal upstaging as an independent factor associated with improved PFS.

Conclusions: Among patients with N2 NSCLC, N2 nodal upstaging-often due to small or inaccessible lymph nodes-did not significantly impact 5-year overall survival but was associated with improved PFS. These findings reinforce the clinical value of EBUS-TBNA as an effective tool for preoperative mediastinal staging in NSCLC despite its limitations in detecting small or inaccessible lymph nodes.

Background: Lactylation, a recently identified post-translational modification, plays a critical role in tumor progression and immune regulation. However, its cellular heterogeneity and functional impact in lung adenocarcinoma (LUAD) remain poorly understood. This study was designed as exploratory biological research to characterize lactylation-associated patterns at the single-cell level and to propose a potential lactylation-related prognostic model.

Methods: Single-cell transcriptomic data from LUAD and normal lung tissues were analyzed to quantify lactylation activity using AUCell based on 332 lactylation-related genes. Cell-cell communication was inferred using CellChat to identify ligand-receptor interactions among subpopulations. Candidate genes were selected by integrating ligand-receptor pair genes, marker genes from highly lactylated subtypes, and previously reported lactylation-related genes. A total of 101 machine learning model combinations were evaluated to construct the prognostic model. Selected genes were further validated by quantitative reverse transcription polymerase chain reaction (qRT-PCR), and the potential relationship between ANGPTL4 expression and lactylation was examined. Immune characteristics were evaluated using multiple established computational approaches for estimating immune infiltration, dysfunction, and immunogenicity.

Results: Lactylation activity was higher in tumor epithelial and stromal cells, with particularly elevated levels in specific epithelial subpopulations. A 12-gene signature was identified, comprising nine risk genes (e.g., ANGPTL4, SOD1, and VEGFC) and three protective genes. The random survival forest (RSF) model demonstrated strong predictive performance [area under the curve (AUC) =0.99 for training, 0.68 for testing], and qRT-PCR validation largely confirmed the predicted gene expression patterns. High-risk patients exhibited poorer survival, reduced immune infiltration, increased immune exclusion, and higher enrichment of immunosuppressive cells. Moreover, ANGPTL4 expression positively correlated with lactylation-associated indicators. Cell-cell communication analysis further highlighted signaling pathways involving the identified genes.

Conclusions: This study presents a lactylation-based prognostic model for LUAD and uncovers potential immune-related mechanisms by which highly lactylated epithelial cells may contribute to immune evasion and tumor progression.

[This corrects the article DOI: 10.21037/tlcr-24-641.].

Background: Accurate comprehensive prediction of occult lymph node metastasis (OLNM) is crucial for optimizing treatment strategy in early-stage lung adenocarcinoma (LUAD). This study aimed to develop and validate a machine learning (ML) model integrating multimodal data for the individualized prediction of OLNM.

Methods: A retrospective cohort of 12,679 patients with clinical T1N0 LUAD (≤3 cm) was identified from a single institution. After propensity score matching (PSM) to address class imbalance, a balanced cohort of 614 patients (307 with OLNM, 307 without) was used for model development. Univariable and multivariable logistic regression identified independent predictors. Eight ML models were trained on 80% of the data using these predictors and evaluated on a held-out 20% validation set. The optimal model was selected based on the area under the receiver operating characteristic (ROC) curve (AUC), accuracy, calibration, and decision curve analysis (DCA). SHapley Additive exPlanations (SHAP) were used for model interpretation.

Results: Multivariable analysis identified consolidation-to-tumor ratio (CTR), tumor stage (T stage), histologic type, grade, spread through air spaces (STAS) status, and epidermal growth factor receptor (EGFR) mutation as independent predictors. Among all algorithms, the random forest model achieved superior performance, with an AUC of 0.981 on the training set and 0.934 on the validation set. It also demonstrated excellent calibration and provided the highest net benefit across a wide range of threshold probabilities on DCA. SHAP analysis confirmed the dominant role of grade, T stage, and CTR in predicting OLNM and unveiled clinically relevant feature interactions.

Conclusions: We developed an interpretable ML model that accurately predicts the risk of OLNM using readily available data. This tool facilitates personalized surgical decision-making, potentially guiding the extent of lymph node dissection (LND) to avoid overtreatment in low-risk patients while ensuring adequate staging and resection in high-risk individuals.

Background: Ultrathin cryobiopsy (CB) using a 1.1-mm cryoprobe has been shown to improve the diagnostic yield of bronchoscopy for peripheral pulmonary lesions (PPLs). However, CB may not be actively adopted in some institutions because of concerns about bleeding and pneumothorax. Each facility needs to develop its own strategies to perform CB selectively and safely. Rapid on-site cytologic evaluation of touch imprint cytology (ROSE-TIC) of small (1.5 mm) forceps biopsy (FB) may help guide the selective application of CB. This study aimed to evaluate the safety and efficacy of adding CB in patients with ROSE-TIC-negative FB.

Methods: Patients with PPLs <30 mm were enrolled in this single-center prospective study. First, they underwent bronchoscopic biopsy with a 1.5-mm FB and ROSE-TIC. CB using a 1.1-mm cryoprobe was added only when the ROSE-TIC of the FB was negative. Safety outcomes (especially bleeding) and diagnostic yields were compared solely between ROSE-TIC-positive and ROSE-TIC-negative groups. In addition, the diagnostic yield of FB results, regardless of ROSE-TIC positivity or negativity, was evaluated. The specimen sizes and ROSE-TIC accuracies were also assessed.

Results: From November 2023 to March 2025, 50 patients underwent bronchoscopic examinations according to the intended protocol. Grade ≥3 bleeding events did not occur, and Grade 1-2 bleeding was not significantly different between the ROSE-TIC-positive and ROSE-TIC-negative groups (P=0.35). The specific diagnostic yield of the ROSE-TIC-positive group was 87.5%, and that of the ROSE-TIC-negative group was 73.l%. In the ROSE-TIC-negative group, the FB-only results made a specific diagnosis in 34.6% [95% confidence interval (CI): 17.2-55.7%] compared with combined CB in 73.1% (95% CI: 44.3-82.8%). The overall diagnostic yield of specific findings was significantly higher in the combined CB group than that in the FB group (80% vs. 60%; P≤0.01). Additional CB provided diagnostic benefits, particularly for small lesions of ≤20 mm (73.6% vs. 52.6%, P=0.01), and partial solid nodules on computed tomography were more beneficial for pathological findings with additional CB (76% vs. 41%, P=0.04). CB samples were significantly larger than FB samples (median 2.70 vs. 1.35 mm²; P<0.001). ROSE-TIC demonstrated a sensitivity of 79.3% and specificity of 95.2%.

Conclusions: The selective addition of a 1.1-mm CB based on ROSE-TIC results of FB is a safe, feasible, and effective strategy for improving the specific diagnostic yield. This approach may be particularly beneficial for small PPLs of ≤20 mm.