Translational lung cancer research最新文献

Background: Preliminary studies suggest that pan-immune-inflammation value (PIV) has the potential to serve as a prognostic tool for lung cancer. However, existing studies are limited by inconsistent findings regarding the impact of high PIV on patient outcomes. To provide a more comprehensive assessment, we conducted a meta-analysis to clarify the prognostic value of PIV in lung cancer.

Methods: Two researchers independently searched the PubMed, Cochrane, Embase, and Web of Science databases for studies evaluating the associations between PIV and prognoses in lung cancer patients (up to July 15, 2025). Studies were included if they reported high versus low PIV and provided hazard ratios (HRs) with 95% confidence intervals (CIs) for overall survival (OS), progression-free survival (PFS), etc. Study quality was assessed using the Newcastle-Ottawa Scale (NOS). Pooled HRs and 95% CIs were calculated to determine the associations between PIV and patient prognosis.

Results: A total of ten studies comprising 1,969 patients were included. Meta-analyses demonstrated that high PIV was significantly associated with OS (HR =2.86, 95% CI: 2.23-3.65, P<0.001) and PFS (HR =2.06, 95% CI: 1.65-2.59, P<0.001) in lung cancer patients. In non-small cell lung cancer (NSCLC) patients, high PIV was significantly associated with worse OS (HR =2.76, 95% CI: 2.14-3.56, P<0.001) and PFS (HR =1.94, 95% CI: 1.55-2.42, P<0.001). In small cell lung cancer (SCLC) patients, even stronger associations were observed for OS (HR =3.47, 95% CI: 2.21-5.44, P<0.001) and PFS (HR =2.33, 95% CI: 1.63-3.33, P<0.001). Subgroup analyses further confirmed that PIV served as a critical prognostic marker for both OS and PFS. All studies were of high quality according to the NOS.

Conclusions: PIV can serve as an independent prognostic biomarker for survival outcomes in lung cancer patients. Therefore, incorporating PIV into prognostic assessments may provide additional support for individualized treatment decision-making.

Background: Small-cell lung cancer (SCLC) is a highly aggressive disease with limited treatment options beyond first-line platinum-based chemotherapy. Although various second-line regimens such as topotecan and cyclophosphamide-adriamycin-vincristine have been evaluated, clinical outcomes remain poor, particularly in patients with platinum-resistant or refractory disease. The combination of paclitaxel and doxorubicin showed encouraging results in early-phase trials, but data on its use in real-world settings are lacking. This study aimed to assess the efficacy and safety of this chemotherapy regimen in patients with previously treated SCLC in a real-world clinical setting.

Methods: Data were retrospectively collected from all consecutive patients with previously treated SCLC at a single institution in France between 2000 and 2024.

Results: A total of 149 patients were included, of whom 79.9% had platinum-resistant or refractory disease at the time of paclitaxel-doxorubicin initiation. The median progression-free survival (PFS) was 2.4 months [95% confidence interval (CI): 2.1-3.8], and the median overall survival (OS) was 5.1 months (95% CI: 4.7-6.0). The objective response rate was 22.2% in the overall population and 27.7% when excluding patients not evaluable for response. The disease control rate was 41.6% including all patients and 52.1% when excluding non-evaluable cases. In multivariable analysis, an Eastern Cooperative Oncology Group performance status of 2-4 compared to 0-1 was associated with shorter PFS [hazard ratio (HR) =1.9, 95% CI: 1.22-2.8, P=0.004] and OS (HR =2.73, 95% CI: 1.76-4.2, P<0.001). Adverse events led to dose reductions in 54.9% of patients, primarily due to general deterioration, hematologic toxicity, or neuropathy. No treatment-related deaths were reported.

Conclusions: This real-world study suggests that the paclitaxel-doxorubicin combination provides some clinical activity in previously treated SCLC, including in platinum-resistant/refractory disease. Although toxicities were common, they were generally manageable. These findings support further investigation of this regimen with careful patient selection.

Background: Considerable differences exist in the prognosis of patients suffering from advanced non-small cell lung cancer. In fact, they undergo third-generation epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKI) single-agent targeted treatment. This study aimed to recognize the predictive factors influencing the prognosis of such patients, and identify patients at high risk of relapse to instruct clinical therapy and ameliorate prognosis.

Methods: Our research included 255 patients suffering from advanced lung adenocarcinoma (LUAD) treated with third-generation EGFR-TKI monotherapy alone, categorized into the training and validation cohorts. In univariate and multivariate analyses, clinicopathologic features and radiomic features were included. Moreover, statistically significant predictive factors were applied to formulate a nomogram. Area under the curve (AUC) of receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were utilized to verify the model.

Results: Neutrophil-lymphocyte ratio (NLR) ≥4.6, EGFRex21 L858R mutation, brain metastasis, and radiomic characteristics worked as independent risk factors for progression-free survival (PFS). Age ≥60 years, EGFRex21 L858R mutation, brain metastasis, monocyte-lymphocyte ratio (MLR) ≥0.3, and radiomic features acted as independent risk factors for overall survival (OS). Incorporating this into the nomogram, the AUC of the nomogram for forecasting 6-, 12-, and 24-month PFS and 1-, 2-, and 3-year OS were 0.810, 0.862, 0.873, and 0.886, 0.881, and 0.839, respectively, in the training cohort, and 0.885, 0.858, 0.847 in the validation cohort, and 0.804, 0.824, 0.806, all showing excellent discrimination. The calibration curves showed great consistency between predicted and actual observations. DCA exhibited a gratifying positive net benefit in most threshold probabilities, indicative of a beneficial clinical result. As claimed by PFS and OS survival analysis, the nomogram could distinguish low-risk group from high-risk group.

Conclusions: Our research formulated and corroborated a nomogram, according to clinicopathologic factors and radiomic features. Such a prediction model could be utilized as a strong tool for forecasting the prognosis of patients.

Background: Malignant pleural effusion (MPE) is a frequent complication of advanced lung cancer, and rapid and accurate diagnosis is critical for timely therapeutic decision-making. Although medical thoracoscopy (MT) provides direct visualization and targeted biopsy, resulting in high diagnostic yield, the clinical utility of its findings is contingent on operator experience and subsequent confirmation via pathological analysis. Recent advances in deep learning have enabled automated image classification in various fields, but its application in thoracoscopic images remains unexplored. The aim of our study was to develop a deep learning-based model to classify pleural malignancy and to evaluate its diagnostic performance.

Methods: We developed a deep learning-based classification model using thoracoscopic images obtained from 426 patients who underwent MT at Incheon St. Mary's Hospital between May 2015 and July 2024. After preprocessing and standardization of 4,932 images (2,093 benign, 2,839 malignant), we trained an InceptionV3-based convolutional neural network using transfer learning and online augmentation during training. Model performance was evaluated according to accuracy, precision, recall, the F1 score, the area under the receiver operating characteristic curve (ROC-AUC), and gradient-weighted class activation mapping (Grad-CAM) visualization.

Results: In total, 4,932 thoracoscopic images from 426 patients were used to train and evaluate the model. In the test set, the InceptionV3-based model achieved a classification accuracy of 81.7% [95% confidence interval (CI): ± 3.5%], with a precision of 82.4% (95% CI: ±4.3%), recall of 86.6% (95% CI: ±3.8%), and F1 score of 84.6% (95% CI: ±3.0%). The AUC was 0.90 (95% CI: ±2.6%), indicating excellent discriminative performance. A confusion matrix indicated 165 true positives, 25 false negatives, 29 false positives, and 262 true negatives. Grad-CAM visualizations confirmed that the model consistently focused on visually relevant pleural abnormalities such as nodularity, thickening, and neovascularization. Notably, the model maintained high accuracy even in cases without overt tumor nodules.

Conclusions: This study presents the first deep learning-based classification model of pleural malignancy using MT images. The model showed excellent diagnostic performance and has the potential to aid real-time clinical decision-making during thoracoscopy by suggesting malignant targets for biopsy or pleurodesis.

Background: Invasive mucinous lung adenocarcinoma (IMA) is challenging to diagnose via bronchoscopy due to its distinct histopathological characteristics, including poorly atypical tumor cells with abundant mucins. Given that cryobiopsy facilitates high-quality and quantity tissue sampling, we hypothesized that it could improve diagnostic success. This retrospective study aimed to determine whether cryobiopsy is a valid diagnostic tool for IMA.

Methods: We retrospectively reviewed consecutive patients who underwent surgical resection for lung cancer at our institution between March 2017 and July 2024. Among them, patients diagnosed with IMA were selected. Those who had undergone a diagnostic biopsy for peripheral pulmonary lesions (PPLs) were included in the study. These patients were divided into two groups: cryo and conventional, based on whether cryobiopsy was performed. The diagnostic yield and safety profiles of both groups were compared.

Results: Of 5,053 surgeries for lung cancer, 201 (4.0%) were diagnosed with IMA. Among them, 106 patients who had undergone bronchoscopy for PPLs were included in the analyses: 46 in the cryo group and 60 in the conventional group. The diagnostic yield was significantly higher in the cryo group than in the conventional group (93.5% vs. 73.3%, P=0.01). Multivariable analysis showed cryobiopsy was significantly associated with higher yield [adjusted odds ratio (OR), 5.07; 95% confidence interval (CI): 1.33-19.40; P=0.02]. Although not fatal, complications like bleeding and pneumonia were more frequent in the cryo group.

Conclusions: Cryobiopsy is a valid technique for making a diagnosis of IMA.

Background: Tracheal adenoid cystic carcinoma (TACC) is an extremely rare type of cancer, characterized by slow growth, but high rates of recurrence and metastasis. The treatment of TACC is greatly hampered by the limited understanding of its molecular characteristics. We sought to conduct an in-depth analysis of this type of cancer through single-cell RNA-sequencing (RNA-seq) to provide important evidence for the formulation of treatment strategies for TACC.

Methods: The peripheral blood, tumor tissue, and adjacent tissue of the same TACC patient was obtained and tested by single-cell RNA-seq. A bioinformatics analysis was conducted to identify the cell clones with key biological functions and determine their molecular characteristics. Fluorescent multiplex immunohistochemistry (mIHC) was used to confirm the markers with abnormal expression.

Results: RNA-seq and mIHC verification were successfully performed in one and four TACC patients, respectively. All the sequenced cells were divided into three main categories of epithelial, immune, and stromal cells. In relation to the epithelial cells in the tumor tissue, ciliated cells expressing commonly observed stem-cell markers were found at the beginning of the pseudo-time curve, indicating that they might be related to the origin of the TACC. Further analysis revealed eight genes in the ciliated cells may warrant further investigation. Additionally, the function of immune cells like natural killer (NK) cells and cytotoxic T cells were found to be impaired, which might be the reason for the expansion of TACC. Notably, a distinct subgroup of mesothelial cells disguised as stromal cells was identified. These cells possessed a malignant phenotype and probably interact with macrophages through the MIF-(CD74 + CXCR4 or CD44) pathway. The high density of the CD68⁺CD74⁺ cells and their close distance to the Vimentin⁺PANCK⁺ cells was confirmed by mIHC. These findings suggest that these markers could be used in the development of drugs targeting TACC.

Conclusions: Our research showed that TACC might originate from ciliated epithelial cells. Abnormal gene expression triggered by external factors, along with the dysfunction of NK and T cells, are critical factors in the genesis and progression of TACC. Developing drugs that target MIF-CD74 signaling pathways may lead to breakthroughs in the treatment of TACC.

Background: The heterogeneity of locally advanced non-small cell lung cancer (LA-NSCLC) has led to varied treatment strategies. Before the immunotherapy era, definitive chemoradiotherapy (CRT) was the standard for inoperable patients. This real-world study evaluates the role of thoracic radiotherapy (RT) in patients receiving first-line immunotherapy-based treatment.

Methods: This retrospective analysis included stage III NSCLC patients from January 2018 to December 2022 who were inoperable or declined surgery and received immunotherapy. Treatment patterns, survival outcomes, and failure modes were assessed. Real-world overall survival (OS), progression-free survival (PFS), and local recurrence-free survival (LRFS) were analyzed using Kaplan-Meier methods.

Results: Among 410 eligible patients, 173 received RT (RT group), while 237 underwent chemoimmunotherapy (CIT) alone (non-RT group). The RT group demonstrated significantly longer median OS (55.5 vs. 26.6 months) and PFS (21.3 vs. 14.1 months) compared to the non-RT group (P<0.001). Patients receiving ≤4 CIT induction cycles before RT had improved outcomes versus >4 cycles (P=0.04). No survival difference was observed between RT doses of 50 Gy and 60 Gy.

Conclusions: This study confirms that thoracic RT remains essential for inoperable LA-NSCLC in the immunotherapy era. An exploratory analysis suggests that a potential strategy could involve ≤4 cycles of CIT induction followed by concurrent CRT (cCRT) and immune checkpoint inhibitor maintenance. These findings encourage further investigation into RT integration within multimodal treatment, with 50-60 Gy appearing potentially comparable in selected patients.

Background: Patients with advanced non-small cell lung cancer (NSCLC) harboring EGFR L858R mutations have a poorer prognosis than those with exon 19 deletions and exhibit variable responses to first-generation EGFR tyrosine kinase inhibitors (TKIs). Evidence on the use of gefitinib plus bevacizumab in this subgroup remains limited. This study aimed to evaluate the efficacy and safety of gefitinib combined with bevacizumab and to investigate plasma circulating tumor DNA (ctDNA) biomarkers in this patient population.

Methods: In this randomized trial, 81 stage III/IV L858R-mutant NSCLC patients received gefitinib alone or gefitinib plus bevacizumab. The primary endpoint was progression-free survival (PFS), with secondary endpoints including objective response rate (ORR) and safety. Plasma ctDNA was evaluated for prognostic value and detection of treatment-related resistance.

Results: Combination therapy significantly improved median PFS (15.1 vs. 8.2 months, P<0.01) and ORR (90.2% vs. 59.0%, P<0.01) compared to gefitinib alone. Adverse effects were similar, with hypertension occurring exclusively in the combination group. Positive ctDNA at 6 weeks post-treatment correlated with shorter PFS in both groups. A decline in maximal somatic variant allelic frequency (maxVAF) exceeding 98% was associated with improved PFS in the combination group (17.0 vs. 5.4 months, P<0.01). The combination group showed a trend towards lower EGFR T790M mutation frequency (30.8% vs. 58.8%, P=0.16).

Conclusions: Gefitinib plus bevacizumab shows promising efficacy and tolerability in EGFR-mutated NSCLC patients. Plasma ctDNA assessment facilitates the evaluation of treatment efficacy, monitoring disease progression, and informing second-line treatment options.

Trial registration: ClinicalTrials.gov NCT04425187.