Translational lung cancer research最新文献

Background: The benefits of spontaneous ventilation (SV)-video-assisted thoracoscopic surgery (VATS) in octogenarian patients with non-small-cell lung cancer (NSCLC) have rarely been reported. This retrospective study was conducted to evaluate the safety and feasibility of SV-VATS in octogenarian patients with NSCLC.

Methods: Patients with NSCLC aged >80 years who underwent SV-VATS or mechanical ventilation (MV)-VATS between 2017 and 2022 were included in this study. The baseline characteristics of the two groups were balanced by a 1:2 propensity score matching (PSM). Intraoperative and postoperative outcomes were compared. Overall survival (OS) and disease-free survival (DFS) were analyzed by Kaplan-Meier survival analysis and Cox regression.

Results: A total of 251 patients were initially included, and after applying selection criteria and PSM, 22 patients were in the SV-VATS group and 44 in the MV-VATS group. Baseline characteristics were well balanced between the two groups. Compared with the MV-VATS group, the SV-VATS group had shorter post-anesthesia care unit (PACU) stay (88.8±22.3 vs. 111±38.8, P=0.01) and shorter resuscitation time (88.8±22.7 vs. 112±40.4, P=0.02). No statistically significant differences were observed in the surgical and anaesthesia times, chest tube duration, total volume of chest drainage, intraoperative blood loss, postoperative hospital stay, or complications in the PACU. The OS and DFS of patients who underwent SV-VATS were comparable to those of patients who underwent MV-VATS.

Conclusions: SV-VATS appears to be a safe and feasible option for octogenarian patients with NSCLC, providing a new approach to surgical treatment. Large-scale prospective studies are required to further validate its feasibility.

Background: Lung adenocarcinoma (LUAD) is associated with high morbidity and mortality rates. Increasing evidence indicates that neutrophil extracellular traps (NETs) play a critical role in tumor progression, metastasis and immunosuppression in the LUAD tumor microenvironment (TME). Nevertheless, the use of NET formation-related genes (NFRGs) to predict LUAD patient survival and response to immunotherapy has not been explored. Therefore, this study aimed to construct a NFRGs-based prognostic signature for stratifying LUAD patients and informing individualized management strategies.

Methods: The cell composition of the LUAD TME was investigated using the single-cell sequencing data in Single-Cell Lung Cancer Atlas (LuCA). NFRGs were identified to construct a prognostic signature based on The Cancer Genome Atlas (TCGA) cohort which was validated in the Gene Expression Omnibus (GEO) dataset. The univariate Cox and least absolute shrinkage and selection operator (LASSO) Cox regression models, receiver operating characteristic (ROC) and Brier Score were applied to assess the prognostic model. A nomogram was established to facilitate the clinical application of the risk score. The Estimation of STromal and Immune cells in MAlignant Tumor tissues (ESTIMATE) and Tumor Immune Dysfunction and Exclusion (TIDE) algorithm were utilized to assess the TME and predict immunotherapy response. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was applied to quantify the expression levels of four NFRGs in LUAD paired tissue samples.

Results: Single‑cell RNA sequence analysis showed the importance of neutrophils in LUAD TME. We developed and validated a 4-NFRG (CAT, CTSG, ENO1, TLR2) prognostic signature based on TCGA and GEO cohorts, which stratified patients into high-risk and low-risk groups. Univariate and multivariate analyses showed that our risk model could independently predict the survival of LUAD patients. Patients in the low-risk group exhibited a more active immune microenvironment, lower TIDE scores, lower half-maximal inhibitory concentration (IC50) values and higher immune checkpoint molecule expression. Our risk signature could serve as a biomarker for predicting immunotherapeutic benefits.

Conclusions: We developed a novel prognostic signature for LUAD patients based on NFRGs and emphasized the critical role of this signature in predicting LUAD patient survival and immunotherapy response.

Background: Prognosis prediction is crucial for non-small cell lung cancer (NSCLC) treatment planning. While tumor hypoxia significantly impacts patient outcomes, identifying hypoxic genomic markers remains challenging. This study sought to identify hypoxic computed tomography (CT) radiomic features and create an artificial intelligence (AI) model for NSCLC through the integration of multi-modal data.

Methods: In total, 452 NSCLC patients were enrolled in this study, including patients from The Second Affiliated Hospital of Soochow University (SC, n=112), The Cancer Genome Atlas (TCGA)-NSCLC dataset (n=74), the radiogenomics dataset (n=130), and the Gene Expression Omnibus (GEO) datasets (GSE19188: n=82, and GSE87340: n=54). Hypoxia status was classified using optimized cut-off values of hypoxia enrichment scores, which were calculated through single-sample gene set enrichment analysis (ssGSEA) of hypoxic genes. Radiomic features were extracted using three-dimensional (3D)-Slicer software. The least absolute shrinkage and selection operator (LASSO) algorithm was used to identify hypoxic CT radiomic features. A model named ssuBERT (semantic structured unit embedded in Bidirectional Encoder Representations from Transformers) was developed to analyze electronic health records (EHRs). An AI model for overall survival prediction was constructed by integrating CT radiomic features, ssuBERT features, and clinical data, and evaluated using five-fold cross-validation.

Results: Higher hypoxia levels were correlated with worse survival outcomes. Twenty-eight radiomic features showed significant discriminatory power in detecting hypoxia status with an area under the curve (AUC) of 0.8295. The ssuBERT model achieved a weighted accuracy of 0.945 in recognizing semantic structured units in EHRs. The EHR model exhibited superior predictive performance among the single-modal models with an AUC of 0.7662. However, the multi-modal AI model had the highest average AUC of 0.8449 and an F1 score of 0.7557.

Conclusions: The AI model demonstrated potential in predicting NSCLC patient prognosis through multi-modal data integration, warranting further validation.

Background and objective: Pulmonary carcinoids (PCs) represent a rare subset of neuroendocrine tumors (NETs) within the respiratory tract that exhibit unique characteristics and clinical behaviors. These tumors are currently staged according to the tumor-nodules-metastases (TNM) classification of non-small cell lung cancer (NSCLC), which brings their reliability into question. The aim of this study was to assess reliability of the current TNM staging of PCs and explore other relevant prognostic factors of patient outcomes.

Methods: From January 2023 to October 2023, the PubMed and Embase databases were searched according to predefined keywords. Studies focusing on PCs, TNM classification, surgical management, and lymph node involvement were included. The search included meta-analyses, retrospective studies, and case reports. Pediatric cases and articles written in languages other than English were excluded.

Key content and findings: This review identified several retrospective cohort studies investigating the correlation between TNM staging, lymph node involvement, and survival outcomes in PC patients. Inconsistencies in survival rates across TNM stages were observed, highlighting the limitations of the current TNM classification as a main predictor of patient outcomes. Lymph node involvement emerged as a significant predictor of survival, with higher nodal stages associated with a poorer prognosis, especially for patients with atypical carcinoid tumors.

Conclusions: Excluding PCs from TNM staging of NSCLC and implementing new staging methods based on histological subtype and lymph node involvement may provide a better classification of this type of tumor, which could lead to more effective care for patients in the future.

Background and objective: While bibliometric studies of single journals have been conducted, bibliometric mapping has not yet been used to analyze the literature published by the Translational Lung Cancer Research (TLCR). This study aimed to comprehensively review all publications of TLCR from its inception to 2024 and provide a detailed overview of its main publication characteristics.

Methods: This study analyzed publications from TLCR spanning 2012 to 2024 using CiteSpace, VOSviewer, and the 'Bibliometrix' package in R. Descriptive bibliometric methods were employed to examine the trends and dynamics in TLCR literature, identifying leading authors, institutions, and countries in terms of publication output. Furthermore, bibliometric maps were generated to visualize key research topics and terms, highlighting their evolution over time.

Key content and findings: The analysis included 2,032 publications in TLCR from 2012 to 2023 and 121 publications in 2024. The study revealed a positive trend in literature production, although there has been a slight recent decline in the number of articles published in the TLCR. China emerged as the most productive country (n=587), with Shanghai Jiao Tong University being the most productive institution (n=127). Jianxing He from the First Affiliated Hospital of Guangzhou Medical University was identified as the most prolific author (n=75). The top ten most cited articles primarily address treatment strategies, recurrence, immune-related toxicities, global trends in mortality, and mechanisms of resistance, reflecting the broad scope and critical importance of ongoing research in lung cancer. Research published in TLCR predominantly targeted old adults with non-small cell lung cancer (n=879), with significant emphasis on overall survival (n=507), cancer staging (n=406), and cancer immunotherapy.

Conclusions: This study reviewed TLCR publications from 2012 to 2024, identifying key trends, top contributors, and research focuses. Future research directions in TLCR might focus on first-line treatment, ensartinib, and advanced data analysis methods such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) to revolutionize lung cancer research and practice. In conclusion, this study underscores TLCR's significant contributions to lung cancer research and provides valuable insights into its evolution and future directions.

Background: Non-small cell lung cancers (NSCLCs) with ALK fusions are effectively treated with ALK tyrosine kinase inhibitors (TKIs). The widespread use of next-generation sequencing (NGS) assays to study the molecular profile of NSCLCs, can identify rare fusion partners of ALK. Therapy decisions are made without considering which fusion partner is present and its potential oncogenic properties. However clinical and experimental studies have shown that the 5' partner of kinase fusion variants could have a biological role in the response to targeted therapies. The objective of this report was to study the impact of a rare fusion partner of ALK on the specific TKI treatment with an in silico molecular modelling evaluating the efficiency of the protein-ligand site.

Case description: Here we describe a case of a stage IV lung adenocarcinoma with a rare striatin STRN-ALK fusion with a Partial Response of short duration to alectinib and no response to lorlatinib at progression. We investigated a computational molecular model of the protein translated from the translocated gene to suggest a mechanistic explanation for the clinical findings.

Conclusions: Our model calculations suggested that the effect of the translocation was to induce the dimerization of ALK into a complex that distorted the binding pocket, which is the same for alectinib, lorlatinib and crizotinib. The distortion of the binding pocket observed in the simulations also provides a rationale to explain the different variations of efficacy of alectinib, lorlatinib and crizotinib caused by the translocation. Our observations suggest that molecular modelling based on artificial intelligence (AI) tools may offer potential predictive information in fusions with rare partner genes. Further retrospective and prospective studies are warranted to demonstrate the predictive robustness of these tools.

Lung cancer is a malignant tumor with high incidence and mortality rates in both men and women worldwide. Although anticancer drugs are prescribed to treat lung cancer patients, individual responses to these drugs vary, making it crucial to identify the most suitable treatment for each patient. Therefore, it is necessary to develop an anticancer drug efficacy prediction model that can analyze drug efficacy before patient treatment and establish personalized treatment strategies. Unlike two-dimensional (2D) cultured lung cancer cells, lung cancer organoid (LCO) models have a three-dimensional (3D) structure that effectively mimics the characteristics and heterogeneity of lung cancer cells. Lung cancer patient-derived organoids (PDOs) also have the advantage of recapitulating histological and genetic characteristics similar to those of patient tissues under in vitro conditions. Due to these advantages, LCO models are utilized in various fields, including cancer research, and precision medicine, and are especially employed in various new drug development processes, such as targeted therapies and immunotherapy. LCO models demonstrate potential applications in precision medicine and new drug development research. This review discusses the various methods for implementing LCO models, LCO-based anticancer drug efficacy analysis models, and new trends in lung cancer-targeted drug development.

Background: Osimertinib, a third-generation tyrosine kinase inhibitor (TKI), has been authorized for use in patients with epidermal growth factor receptor (EGFR)-mutant non-small cell lung cancer (NSCLC). This study aimed to evaluate the effectiveness and safety of neoadjuvant osimertinib in individuals with resectable locally advanced NSCLC harboring EGFR mutation.

Methods: Ten centers located in mainland China took part in a single-arm, real-world, multicenter retrospective study (registration number: ChiCTR2100049954). Enrollment included individuals with lung adenocarcinoma who had EGFR mutations. Following the administration of osimertinib, the patients underwent a surgical procedure for resection. The main endpoint was the objective response rate (ORR). The subsequent endpoint analyzed was the joint assessment of overall survival (OS) and disease-free survival (DFS).

Results: From July 31, 2018 to April 28, 2023, a total of 38 individuals were involved and received neoadjuvant osimertinib treatment. The ORR was 60.5% (23/38). Thirty-eight patients underwent surgery, and 36 (94.7%) underwent successful R0 resection. Out of 38 patients, sixteen (42.1%) experienced adverse events (AEs) due to treatment in the neoadjuvant phase, with none of them reaching grade 3. Skin irritation [14 (36.8%)], stomach upset [5 (13.2%)], mouth sores [1 (2.6%)] and increased liver enzyme levels [1 (2.6%)] were the common AEs of treatment. The follow-up period lasted an average of 24.9 months. The 1-year OS rate is 94.2%, while the 2-year OS rate is 89.2%. The 1-year DFS rate is 87.9%, and the 2-year DFS rate remains at 87.9%.

Conclusions: In the actual clinical setting, osimertinib displays encouraging possibilities as a neoadjuvant therapy for individuals with operable EGFR-mutated NSCLC, exhibiting adequate efficacy and an acceptable safety record. The phase III clinical trial of NeoADAURA is expected to provide further efficacy and safety results.

Background: Sublobar resection is suitable for peripheral stage I lung adenocarcinoma (LUAD). However, if tumor spread through air spaces (STAS) present, the lobectomy will be considered for a survival benefit. Therefore, STAS status guide peripheral stage I LUAD surgical approach. This study aimed to identify radiological features associated with STAS in peripheral stage I LUAD and to develop a predictive machine learning (ML) model using radiomics to improve surgical decision-making for thoracic surgeons.

Methods: We conducted a retrospective analysis of patients who underwent surgical treatment for lung tumors from January 2022 to December 2023, focusing on clinical peripheral stage I LUAD. High-resolution computed tomography (CT) scans were used to extract 1,581 radiomics features. Least absolute shrinkage and selection operator (LASSO) regression was applied to select the most relevant features for predicting STAS, reducing model overfitting and enhancing predictability. Ten ML algorithms were evaluated using performance metrics such as area under the receiver operating characteristic curve (AUROC), accuracy, recall, F1-score, and Matthews Correlation Coefficient (MCC) after a 10-fold cross-validation process. SHapley Additive exPlanations (SHAP) values were calculated to provide interpretability and illustrate the contribution of individual features to the model's predictions. Additionally, a user-friendly web application was developed to enable clinicians to use these predictive models in real-time for assessing the risk of STAS.

Results: The study identified significant associations between STAS and radiological features, including the longest diameter, consolidation-to-tumor ratio (CTR), and the presence of spiculation. The Random Forest (RF) model for 3-mm peritumoral extensions demonstrated strong predictive performance, with a Recall_Mean of 0.717, Accuracy_Mean of 0.891, F1-Score_Mean of 0.758, MCC_Mean of 0.708, and an AUROC_Mean of 0.944. SHAP analyses highlighted the influential radiomics features, enhancing our understanding of the model's decision-making process.

Conclusions: The RF model, employing specific intratumoral and 3-mm peritumoral radiomics features, was highly effective in predicting STAS in peripheral stage I LUAD. This model is recommended for clinical use to optimize surgical strategies for LUAD patients, supported by a real-time web application for STAS risk assessment.

Background: Oligoprogression (OP) is common in patients with metastatic non-small cell lung cancer (mNSCLC) treated with immune checkpoint inhibitors (ICIs). This study aims to assess the benefit and the safety profile of ablative radiotherapy (RT) for OP in mNSCLC treated with pembrolizumab in first-line setting.

Methods: We retrospectively analyzed records of all consecutive mNSCLC patients who underwent treatment with pembrolizumab (+/- chemotherapy) in first-line setting and developed an OP treated with ablative RT while continuing pembrolizumab, in a French Hospital from 2019 to 2022. Primary endpoint was time to next systemic treatment (TTNT). Secondary endpoints included progression-free survival (PFS), overall survival (OS), objective response rate (ORR), and safety profile. Furthermore, we investigated features associated with clinical outcomes.

Results: Thirty-six patients were included and 47 OPs were reported (27 patients experienced one OP, 7 two OP, and 2 three OP). The median TTNT (mTTNT) after the first OP was 19.6 months [95% confidence interval (CI): 12.4-not reached (NR)]. The median PFS (mPFS) after the first OP was 12 months (95% CI: 6.1-NR) and 10.4 months (95% CI: 3.9-NR) after the second or third OP. The median OS (mOS) from the first OP and from pembrolizumab initiation were NR. In multivariable analysis, the presence of adrenal gland was associated with shorter TTNT and OS, while OP involving bone metastasis was associated with shorter PFS. The ORR of the lesions treated with RT was 70.2%. No RT-induced severe adverse event was reported. Three patients experienced severe pembrolizumab-induced adverse events.

Conclusions: In this study, RT alongside the maintenance of pembrolizumab for patients experiencing OP during first-line pembrolizumab-based therapy for mNSCLC demonstrated an acceptable safety profile and favorable outcomes. Data from phase 3 randomized trials are needed to clearly establish the benefits of this strategy in treating oligoprogressive mNSCLC.