Translational lung cancer research最新文献

Background: Primary tracheal lymphoepithelioma-like carcinoma (LELC) is extremely rare, with only a few cases reported so far, and few studies have focused on the radiological features. This study aimed to investigate contrast-enhanced computed tomography (CECT) and positron emission tomography-computed tomography (PET-CT) presentations of primary tracheal LELC to improve diagnosis.

Methods: A retrospective analysis was conducted on the clinical and imaging data of 13 patients with confirmed primary tracheal LELC between December 2013 and August 2022. We analyzed the radiological profiles of lesions on the CECT and PET-CT images.

Results: In 92.3% (12/13) of the cases, primary tracheal LELC lesions predominantly occurred in the thoracic segment. They manifested as singular, wide-based, eccentric, irregular nodules, or exhibited mass-like thickening of the tracheal wall with invasive growth both internally and externally along the wall. The thickest dimension of the lesion ranged from 9 to 28 mm, affecting a length of 30.8±13.5 mm. Luminal stenosis was evident in all patients, with the narrowest point reaching a stenosis rate of 85%. Lesion margins were clear in 69.2% (9/13), indistinct in 23.1% (3/13), and unclear in 7.7% (1/13) of all cases. Among the patients, 92.3% (12/13) exhibited a relatively uniform density on CT plain scans, with a CT value of 44.5±7.8 Hounsfield units (HU). Enhancement scans revealed moderate to marked enhancement in 75% (9/12) of cases. In 2 cases undergoing PET-CT examination, lesion standardized uptake values (SUVs) were 4.4 and 5.1, whereas enlarged lymph node SUVs were 7.7 and 6.3, respectively. Mediastinal lymph node enlargement was observed in 8 patients (61.5%, 8/13), with a maximum short axis of 11.1±5.5 mm. After treatment, 9 out of 12 patients (75%) showed no evidence of distant metastasis upon CT re-examination.

Conclusions: Early detection of primary tracheal LELC allows for curative resection and may lead to a favorable prognosis. It presents with characteristic CT findings, and the utilization of PET-CT improves diagnosis and staging.

Background: Severe radiation pneumonitis (RP), one of adverse events in patients with lung cancer receiving thoracic radiotherapy, is more likely to lead to more mortality and poor quality of life, which could be predicted by clinical information and treatment scheme. In this study, we aimed to explore the clinical predict model for severe RP.

Methods: We collected information on lung cancer patients who received radiotherapy from August 2020 to August 2022. Clinical features were obtained from 690 patients, including baseline and treatment data as well as radiation dose measurement parameters, including lung volume exceeding 5 Gy (V5), lung volume exceeding 20 Gy (V20), lung volume exceeding 30 Gy (V30), mean lung dose (MLD), etc. Among them, 621 patients were in the training cohort, and 69 patients were in the test cohort. Three models were built using different screening methods, including multivariate logistics regression (MLR), backward stepwise regression (BSR), and random forest regression (RFR), to evaluate their predictive power. Overoptimism in the training cohorts was evaluated by four validation methods, including hold-out, 10-fold, leave-one-out, and bootstrap methods, and test cohort was used to evaluate the predictive performance of the model. Model calibration, decision curve analysis (DCA), and evaluation of the nomograms for the three models were completed.

Results: Severe RP was up to 9.4%. The results of multivariate analysis of logistics regression in all patients showed that patients with subclinical (untreated and asymptomatic) interstitial lung disease (ILD) could increase the risk of severe RP, and patients with a better lung diffusion function and received standardized steroids treatment could decrease the risk of severe RP. The three models built by MLR, BSR, and RFR all had good accuracy (>0.850) and moderate κ value (>0.4), and the model 2 built by BSR had the highest area under the receiver operating characteristic (ROC) curve (AUC) in three models, which was 0.958 [95% confidence interval (CI): 0.932-0.985]. The calibration curve showed good agreement between the predicted and actual values, and the DCA showed a positive net benefit for the model 2 which drew the nomogram. The model 2 included subclinical ILD, diffusing capacity of the lung for carbon monoxide (DLCO), ipsilateral lung V20, and standardized steroid treatment, which could affect the incidence of severe RP.

Conclusions: Subclinical ILD, DLCO, ipsilateral lung V20, and with or not standardized steroid treatment could affect the incidence of severe RP. Strict lung dose limitation and standardized steroid treatment could contribute to a decrease in severe RP.

Background: Vitamins, and their metabolic processes play essential regulatory roles in controlling proliferation, differentiation, and growth in carcinogenesis. However, the role of vitamin metabolism in lung adenocarcinoma (LUAD) has rarely been reported. Here, we established a novel prognostic model based on vitamin metabolism-related genes in LUAD.

Methods: In this research, we aimed to identify vitamin metabolism associated with differentially expressed genes (DEGs) in LUAD utilizing The Cancer Genome Atlas (TCGA)-LUAD, GSE68465 and GSE72094 data. Unsupervised clustering classified patients into distinct subgroups. By utilizing least absolute shrinkage and selection operator (LASSO)-Cox regression analysis, vitamin metabolism-related genes could be used to construct prognostic model. Then the vitamin metabolism gene-related risk score (VRS) was calculated based on best cut-off splitting. Kaplan-Meier analysis, time-dependent receiver operating characteristic (ROC) analysis, univariate and multivariate Cox analyses, chemotherapeutic drugs sensitivity analysis, immune infiltration analysis and nomogram were conducted to verify our models' accuracy. Finally, CPS1 was identified as a relevant diagnostic marker using Random Forests algorithms, single-cell RNA sequencing data was used to confirm its expression.

Results: We investigated the relationship between vitamin metabolism patterns, overall survival (OS), and immune infiltration levels of patients with LUAD. A prognostic signature consisting of 11 genes was developed, which was able to classify patients into high and low VRS groups. Through gene enrichment analysis, cell cycle was mainly enriched. Compared to the low VRS group, the high VRS group exhibited poorer OS, as demonstrated by the Kaplan-Meier survival analysis. Furthermore, VRS was identified as an independent predictor of poor prognosis and poor OS, as indicated by both univariate and multivariate Cox regression analyses. Additionally, a nomogram was constructed to improve the accuracy of survival predictions in LUAD patients. We also found that the two groups of patients might respond differently to immune targets and anti-tumor drugs. CPS1 was identified as a relevant diagnostic marker and the expression was also as confirmed by single-cell RNA sequencing data.

Conclusions: Overall, our findings suggest that vitamin metabolism can influence the prognosis of LUAD patients, and our prognostic signature represents a potentially helpful resource for predicting patient outcomes and informing clinical decision-making.

Background: Serum carbohydrate antigen 50 (CA50) is an auxiliary diagnostic marker for various solid tumors, but it remains unclear whether CA50 in pleural fluid can assist in the diagnosis of malignant pleural effusion (MPE). This study aimed to evaluate the diagnostic accuracy of pleural fluid CA50 for MPE in pleural effusion patients with undetermined causes.

Methods: This study prospectively recruited pleural effusion patients with undetermined causes who visited the Affiliated Hospital of Inner Mongolia Medical University between September 2018 and July 2021. We measured pleural fluid CA50 level with an electrochemiluminescence assay. We analyzed the diagnostic accuracy of CA50 and carcinoembryonic antigen (CEA) for MPE with the receiver operating characteristic (ROC) curve. The net benefits of CA50 and CEA were analyzed using the decision curve analysis (DCA).

Results: We enrolled 66 MPEs and 87 benign pleural effusions (BPEs). MPE patients had significantly higher CA50 and CEA than BPE patients. The area under the ROC curve (AUC) of CA50 was 0.72 (95% CI: 0.63-0.80). CA50 had a sensitivity of 0.30 (95% CI: 0.19-0.41) and a specificity of 1.00 (95% CI: 1.00-1.00) at the threshold of 15 IU/mL. The decision curve of CA50 was above the reference line at the calculated risk probability of between 0.30 and 1.00. Venn diagram indicated that some patients with low CEA (<50 or <150 ng/mL) and/or negative cytology can be identified by positive CA50 (>15 IU/mL).

Conclusions: Pleural fluid CA50 has moderate accuracy and net benefit for detecting MPE. CA50 >15 IU/mL can be used to diagnose MPE. The combination of CA50 and CEA improves the diagnostic sensitivity for MPE.

Background: MET rearrangements are infrequently observed in non-small cell lung cancer (NSCLC). Advanced genomic detection techniques have unveiled such infrequent genomic variations, particularly MET fusions in approximately 0.5% of NSCLC patients. Tyrosine kinase inhibitors (TKIs) have revolutionized the standard of care in lung cancer and more recently a second generation MET TKI tepotinib received Food and Drug Administration (FDA) approval for MET exon 14 alterations in metastatic NSCLC. Despite this, the therapeutic landscape for MET-rearranged NSCLC patients remains significantly unexplored. The aim of our report is to detail a unique case of a patient with metastatic lung adenocarcinoma with a novel HLA-DQB2::MET fusion detected by next-generation sequencing (NGS) following previous treatment resistance.

Case description: A 73-year-old female was initially started on carboplatin, pemetrexed and pembrolizumab with maintenance, but eventually had progression in the left upper lobe (LUL). Upon progression she was enrolled in a clinical trial of a monoclonal antibody with or without a PD-1 inhibitor, but brain metastasis progression was eventually detected by magnetic resonance imaging (MRI) requiring stereotactic radiosurgery (SRS) and a craniotomy. The trial drug was eventually discontinued due to progression and toxicity and NGS on bronchoscopy tissue revealed HLA-DQB2::MET fusion. The patient was initiated on tepotinib and continues with clinical and radiological stable disease for over 12 months. The patient's response to a MET inhibitor, tepotinib, underscores the potential efficacy of selective MET inhibitors for individuals with previously unexplored MET fusions.

Conclusions: The positive response to tepotinib of a patient with NSCLC harboring a novel MET-Fusion underscores the importance of the use of comprehensive next-generational sequencing-based panels and highlights the necessity for additional research and clinical exploration of selective MET inhibitors for managing NSCLC with MET rearrangements.

Background and objective: Neoadjuvant chemoimmunotherapy (NACI) is the standard of care for patients with resectable non-small cell lung cancer (NSCLC). Although the pathological complete response (pCR) after NACI reportedly exceeds 20%, an optimal predictor of pCR is yet to be established. This review aims to examine the possible predictors of pCR after NACI.

Methods: We identified research article published between 2018 and 2022 in English by the PubMed database. Fifty research studies were considered as relevant article, and were examined to edit information for this narrative review.

Key content and findings: Recently, several studies have explored potential biomarkers for the pathological response after NACI. For example, ¹⁸F-fluorodeoxyglucose positron emission tomography (¹⁸F-FDG-PET) imaging, tumor microenvironment (TME), genetic alternation such as circulating tumor DNA (ctDNA), and clinical markers such as neutrophil-to-lymphocyte ratio (NLR) and smoking signature were assessed in patients with resectable NSCLC to predict the pathological response after NACI. Based on the PET response criteria, the complete metabolic response (CMR) achieved a positive predictive value (PPV) of 71.4% for predicting pCR, and the decreasing rate of post-therapy maximum standardized uptake value (SUV_max) after NACI substantially correlated with the major pathological response (MPR). TME, as a significant marker for MPR in tumor specimens, was identified as an increase in CD8⁺ T cells and decrease in CD3⁺ T cells or Foxp3 T cells. Considering blood samples, TME comprised an increase in CD4⁺PD-1⁺ cells or natural killer cells and a decrease in CD3⁺CD56⁺CTLA4⁺ cells, total T cells, Th cells, myeloid-derived suppressor cells (MDSCs), or regulatory T cells. Although low pretreatment levels of ctDNA and undetectable ctDNA levels after NACI were markedly associated with survival, the relationship between ctDNA levels and pCR remains elusive. Moreover, the patients with a high baseline NLR had a low incidence of pCR. Heavy smoking (>40 pack-years) was favorable for predicting pathological response.

Conclusions: A reduced rate of ¹⁸F-FDG uptake post-NACI and TME-related surface markers on lymphocytes could be optimal predictors for pCR. However, the role of these pCR predictors for NACI remains poorly validated, warranting further investigations. This review focuses on predictive biomarkers for pathological response after NACI in patients with resectable NSCLC.

Background: Tumor metastasis commonly affects pleura in advanced lung cancer and results in malignant pleural effusion (MPE). MPE is related to poor prognosis, but without systematic investigation on different cell types and their crosstalk at single cell resolution.

Methods: We conducted single-cell RNA-sequencing (scRNA-seq) of lung cancer patients with pleural effusion. Next, our data were integrated with 5 datasets derived from individuals under normal, non-malignant disease and lung carcinomatous conditions. Mesothelial cells were re-clustered and their interactions with epithelial cells were comprehensively analyzed. Taking advantage of inferred ligand-receptor pairs, a prediction model of prognosis was constructed. The co-culture of mesothelial cells and malignant epithelial cells in vitro and RNA-seq was performed. Epidermal growth factor receptor (EGFR) antagonist cetuximab was utilized to prevent the lung cancer cells' invasiveness. Spatial distribution of cells in lung adenocarcinoma patients' samples were also analyzed to validate our findings.

Results: The most distinctive transcriptome profiles between tumor and control were revealed in mesothelial cells, which is the predominate cell type of pleura. Five subtypes were divided, including one predominately identified in MPE which was characterized by enriched cancer-related pathways (e.g., cell migration) along evolutionary trajectory from normal mesothelial cells. Cancer-associated mesothelial cells (CAMCs) exhibited varied interactions with different subtypes of malignant epithelial cells, and multiple ligands/receptors exhibited significant correlation with poor prognosis. Experimentally, mesothelial cells can increase the migration ability of lung cancer cells through co-culturing. EGFR was the only affected gene in cancer cells that exhibited interaction with mesothelial cells and was associated with poor prognosis. Using EGFR antagonist cetuximab prevented the lung cancer cells' increased invasiveness caused by mesothelial cells. Moreover, epithelial mitogen (EPGN)-EGFR interaction was supported through spatial distribution analysis, revealing the significant proximity between EPGN⁺ mesothelial cells and EGFR⁺ epithelial cells.

Conclusions: Our findings highlighted the important role of mesothelial cells and their interactions with cancer cells in pleural metastasis of lung cancer, providing potential targets for treatment.

Background: Bone is a common metastatic site in postoperative metastasis, but related risk factors for early-stage non-small cell lung cancer (NSCLC) remain insufficiently investigated. Thus, the study aimed to identify risk factors for postoperative bone metastasis in early-stage NSCLC and construct a nomogram to identify high-risk individuals.

Methods: Between January 2015 and January 2021, we included patients with resected stage I-II NSCLC at the Department of Thoracic Surgery, West China Hospital. Univariable and multivariable Cox regression analyses were used to identify related risk factors. Additionally, we developed a visual nomogram to forecast the likelihood of bone metastasis. Evaluation of the model involved metrics such as the area under the curve (AUC), C-index, and calibration curves. To ensure reliability, internal validation was performed through bootstrap resampling.

Results: Our analyses included 2,106 eligible patients, with 54 (2.56%) developing bone metastasis. Multivariable Cox analyses showed that tumor nodules with solid component, higher pT stage, higher pN stage, and histologic subtypes especially solid/micropapillary predominant types were considered as independent risk factors of bone metastasis. In the training set, the developed model demonstrated AUCs of 0.807, 0.769, and 0.761 for 1-, 3-, and 5-year follow-ups, respectively. The C-index, derived from 1,000 bootstrap resampling, showed values of 0.820, 0.793, and 0.777 for 1-, 3-, and 5-year follow-ups. The calibration curve showed that the model was well calibrated.

Conclusions: The predictive model is proven to be valuable in estimating the probability of bone metastasis in early-stage NSCLC following surgery. Leveraging four easy-to-acquire clinical parameters, this model effectively identifies high-risk patients and enables individualized surveillance strategies for better patient care.