Translational lung cancer research最新文献

Background: The Lung Cancer Staging Program of the International Association for the Study of Lung Cancer (IASLC) has proposed using solid component size, rather than overall tumor size, for T-staging. However, studies focusing on patients with ground-glass opacity (GGO) lesions with a diameter larger than 2 cm are limited. This study aims to validate the T stage classification strategy recommended by IASLC in this specific and less-studied patient group.

Methods: Patients diagnosed with primary non-small cell lung cancer (NSCLC) who underwent lobectomy between December 2009 and December 2018 were included in this study. Clinical, pathological, and prognostic data were prospectively collected and retrospectively reviewed. Patients were eligible if they were confirmed to have NSCLC, underwent lobectomy, had complete follow-up data, and were not diagnosed with any other malignancies. The propensity score matching (PSM) method was employed to ensure baseline characteristic balance. Two groups of patients matched with the GGO group at baseline were stratified based on overall tumor size (group matched by overall size) and solid component size (group matched by solid component size), respectively. Overall survival (OS) and recurrence-free survival (RFS) were analyzed using the Cox proportional model and Kaplan-Meier method. Follow-up was conducted regularly to assess these outcomes. The T-staging applied was based on the solid component size according to the 8th edition IASLC staging guidelines.

Results: A total of 4,472 NSCLC patients who underwent lobectomy were included in the study (including 4,083 cases of solid lesions and 389 cases of subsolid lesions). The median follow-up time was 75.4 months. Patients in the GGO group had significantly better OS and RFS than those in the solid group [OS: hazard ratio (HR) =0.55, 95% confidence interval (CI): 0.40-0.73, P<0.001; RFS: HR =0.53, 95% CI: 0.42-0.67, P<0.001]. Comparing patients' PSM by overall size, the GGO group still had better OS and RFS (OS: HR =0.60, 95% CI: 0.43-0.85, P=0.004; RFS: HR =0.59, 95% CI: 0.44-0.79, P<0.001). After PSM by solid component size, no significant difference was detected between the GGO group and the group matched by solid component size on OS and RFS (OS: HR =0.89, 95% CI: 0.61-1.28, P=0.52; RFS: HR =0.92, 95% CI: 0.67-1.26, P=0.61). In subgroup analysis, after PSM by solid component size, the results showed no difference in OS and RFS between the restaged patients (c-T1 and c-T2) and the corresponding patients in the solid group (for OS, HR =1.06, 95% CI: 0.61-1.83, P=0.83; HR =1.11, 95% CI: 0.60-2.07, P=0.73, respectively; and RFS, HR =1.17, 95% CI: 0.75-1.82, P=0.49; HR =0.80, 95% CI: 0.48-1.34, P=0.39, respectively).

Conclusions: The T stage classification strategy proposed by ISALC remains applicable in patients with GGOs larger than 2 cm.

Background: Dabrafenib plus trametinib (Dab + Tram) is an approved targeted therapy in patients with BRAF ^V600+ mutated metastatic non-small cell lung cancer (NSCLC). Here, we report the efficacy, safety, and quality of life (QoL) results of Dab + Tram treatment in Chinese patients with BRAF ^V600E mutation-positive metastatic NSCLC.

Methods: This is a single-arm, open-label, multicentre, phase II study (NCT04452877). Patients received dabrafenib 150 mg twice daily plus trametinib 2 mg once daily. The primary endpoint was overall response rate (ORR) by central independent review per Response Evaluation Criteria in Solid Tumours (RECIST) 1.1 criteria. Secondary endpoints included ORR by investigator assessment, progression-free survival (PFS), duration of response (DOR), overall survival (OS), safety, tolerability, and QoL.

Results: At the data cut-off (March 11, 2021), 18 of 20 enrolled patients were still receiving treatment. The median age was 64 years; majority were female (55%), non-smokers (55%), and had ≥3 metastatic sites (70%). Nine patients received prior anticancer therapy in a therapeutic or metastatic setting. The median duration of follow-up was 5 months. The ORR by both central and investigator assessment was 75% [95% confidence interval (CI): 50.9-91.3%]. The median DOR, PFS, and OS were not reached/estimable at the cut-off date. The most common treatment-related adverse events (AEs) (all grades, in ≥30% of patients) were pyrexia, increased aspartate aminotransferase (AST), decreased neutrophil count, and decreased white blood cell (WBC) count. The self-reported QoL was improved or maintained during the treatment period.

Conclusions: Dab + Tram treatment is safe, effective, and can preserve or improve QoL in majority of Chinese patients with BRAF ^V600E mutation-positive metastatic NSCLC. The results are consistent with the global phase II study.

Background: Preoperative assessment of lymph node status is critical in managing lung cancer, as it directly impacts the surgical approach and treatment planning. However, in clinical stage I lung adenocarcinoma (LUAD), determining lymph node metastasis (LNM) is often challenging due to the limited sensitivity of conventional imaging modalities, such as computed tomography (CT) and positron emission tomography/CT (PET/CT). This study aimed to establish an effective radiomics prediction model using multicenter data for early assessment of LNM risk in patients with clinical stage I LUAD. The goal is to provide a basis for formulating lymph node dissection strategies before surgery in early-stage lung cancer patients.

Methods: A total of 578 patients with LUAD from three medical centers [Cancer Hospital, Chinese Academy of Medical Sciences (CCAM), the First Affiliated Hospital of Chongqing Medical University (1CMU), and Beijing Chao-Yang Hospital (BCYH)] who underwent preoperative chest CT were divided into three groups, the training group (n=336), the testing group (n=167), and the independent validation group (n=75). The records of 1,316 radiomics features of each primary tumor were extracted. The least absolute shrinkage and selection operator (LASSO) analysis and multivariable logistic regression were used to reduce the data dimensionality, select features, and construct the prediction models.

Results: In the training group, the area under the curve (AUC) for the clinical model, radiomics model, and composite model were 0.820, 0.871, and 0.883, respectively. In the testing group, the AUC for the clinical model, radiomics model, and composite model were 0.897, 0.915, and 0.934, respectively. In the validation set, the AUC of the radiomics model was the highest at 0.870, while the composite model and clinical model had AUCs of 0.841 and 0.710, respectively. The results of the Delong test showed that the AUCs of the radiomics model and composite model were significantly higher than those of the clinical model in both the training and validation groups. The decision curve analysis showed that the radiomics nomogram was clinically useful.

Conclusions: This study developed and validated a radiomics prediction model, which enables easy LNM prediction in stage I LUAD patients. This model provides a basis for formulating lymph node dissection strategies before surgery and helps to better determine the tumor node metastasis stage of the early-stage LUAD.

For over a century, we have appreciated that the biochemical processes through which micro- and macronutrients are anabolized and catabolized-collectively referred to as "cellular metabolism"-are reprogrammed in malignancies. Cancer cells in lung tumors rewire pathways of nutrient acquisition and metabolism to meet the bioenergetic demands for unchecked proliferation. Advances in precision medicine have ushered in routine genotyping of patient lung tumors, enabling a deeper understanding of the contribution of altered metabolism to tumor biology and patient outcomes. This paradigm shift in thoracic oncology has spawned a new enthusiasm for dissecting oncogenotype-specific metabolic phenotypes and creates opportunity for selective targeting of essential tumor metabolic pathways. In this review, we discuss metabolic states across histologic and molecular subtypes of lung cancers and the additional changes in tumor metabolic pathways that occur during acquired therapeutic resistance. We summarize the clinical investigation of metabolism-specific therapies, addressing successes and limitations to guide the evaluation of these novel strategies in the clinic. Beyond changes in tumor metabolism, we also highlight how non-cellular autonomous processes merit particular consideration when manipulating metabolic processes systemically, such as efforts to disentangle how lung tumor cells influence immunometabolism. As the future of metabolic therapeutics hinges on use of models that faithfully recapitulate metabolic rewiring in lung cancer, we also discuss best practices for harmonizing workflows to capture patient specimens for translational metabolic analyses.

Background: Stereotactic body radiation therapy (SBRT) is crucial for treating early-stage inoperable non-small cell lung cancer (NSCLC) due to its precision and high-dose delivery. This study aimed to investigate the dosimetric deviations in gated (GR) versus non-gated radiotherapy (NGR), analyzing the impact of tumor location, target volume, and tumor motion range on dose distribution accuracy.

Methods: Sixty patients treated with either gated (n=30) or non-gated (n=30) SBRT for early-stage NSCLC were retrospectively analyzed. The planned dose distributions were determined using four-dimensional computed tomography simulations to account for breathing motion, while the actual dose delivered was determined by accumulating each fractional dose with synthetic computed tomography (sCT) methods. The deviations between the planned and actual accumulated doses were statistically analyzed for both groups. The effects of tumor location and volume on dose distribution were also assessed.

Results: Gated SBRT showed significantly higher dosimetric precision with median relative changes in the minimum dose within the ITV (ITV_D_min), mean dose received by the ITV (ITV_D_mean), and maximum dose within the ITV (ITV_D_max) of -0.44%, -0.33%, and -0.49%, respectively. Non-gated SBRT presented with larger median relative changes in these parameters (P<0.001 for the ITV_D_min). In gated SBRT, the PTV_D_min (minimum dose within the PTV) and PTV_D_mean (mean dose received over the entire PTV) differences were significantly lower favoring gated SBRT (P=0.01 and P=0.007, respectively), and for the prescribed dose volumes, the volume of PTV receiving 90% prescription dose (PTV_V_90%PD) and the volume of PTV receiving 100% prescription dose (PTV_V_100%PD) were more accurately delivered, also favoring gated SBRT (P=0.006 and P=0.03, respectively). The tumor location and volume analyses demonstrated that the dosimetric benefits of gated SBRT were particularly significant in the smaller internal target volumes (ITVs) and in the left lower central lung region (P<0.001 for the ITV_D_min in small volumes).

Conclusions: Gated SBRT affords dosimetric accuracy compared to non-gated SBRT, and thus could improve the therapeutic outcomes of NSCLC patients. These results should advocate for the preferential use of gated SBRT in cases requiring precise dose delivery due to large respiratory motion or small target volumes.

Background: Spread through air spaces (STAS) in lung adenocarcinoma (LUAD) is a distinct pattern of intrapulmonary metastasis where tumor cells disseminate within the pulmonary parenchyma beyond the primary tumor margins. This phenomenon was officially included in the World Health Organization (WHO)'s classification of lung tumors in 2015. STAS is characterized by the spread of tumor cells in three forms: single cells, micropapillary clusters, and solid nests. Clinical studies have linked STAS to a poorer prognosis, higher recurrence risk, and more advanced clinicopathological staging in LUAD patients. In this study, we constructed radiomics models and deep learning models based on computed tomography (CT) for predicting preoperative STAS status in LUAD.

Methods: A total of 395 (57.19±11.40 years old) patients with pathologically confirmed LUAD from two centers were enrolled in this retrospective study, in which STAS was detected in 146 patients (36.96%). The general clinical data, preoperative CT images, and the results of pathology reports of all patients were collected. Two experienced radiologists independently segmented the lesions by medical imaging interaction toolkit (MITK) software. The CT-based models only, the clinical-based models only, and the fusion model based on the two were constructed using radiomics and deep learning methods, respectively. The diagnostic performance of the different models was evaluated by comparing the area under the curve (AUC) of the subjects' receiver operating characteristics (ROCs).

Results: The deep learning model based on CT images achieved satisfactory discriminative performance in predicting STAS and outperformed the radiomics model and the clinical-radiomics model. The AUC of deep learning model was 0.918 for the internal test set and 0.766 for the external test set. The radiomics model had an AUC of 0.851 for the internal test set and an AUC of 0.699 for the external test set. The clinical-radiomics deep learning model was slightly less effective than the deep learning model (internal AUC =0.915, external AUC =0.773).

Conclusions: The constructed deep learning model based on preoperative chest CT can be used to determine the STAS status of LUAD patients with good diagnostic performance and is superior to radiomics models.

Background: Superior segmentectomies for clinical T1N0 non-small cell lung cancer (NSCLC) often suffer from inadequate surgical margins. Our study aimed to enhance the precision of superior segmentectomies by focusing on the anatomical features of the superior segmental vein (V⁶) branches, and assess the relevant outcomes.

Methods: The clinical data of 646 patients with cT1N0 NSCLC who underwent video-assisted thoracic surgery (VATS) from August 2020 to August 2021 were retrospectively analyzed. A total of 521 patients were enrolled for analyzing the prevalence and drainage patterns of V⁶b utilizing three-dimensional reconstruction images. Then, 162 patients who underwent segmentectomy were included to analyze the outcomes of superior segmentectomy. Disease-free survival (DFS) was estimated using the Kaplan-Meier method and compared across groups with the log-rank test.

Results: The prevalence of V⁶b2 (a type of intersegmental vein between S⁶ and S⁹) and V⁶b3 (between S⁶ and S⁸) were 91.2% (475/521) and 66.2% (345/521), respectively, both primarily converging with other branches of V⁶. The segmentectomy groups showed no significant differences in surgical margins, tumor size, or other malignancy-related factors, such as TNM stage. Correspondingly, during a median follow-up of 3.23 years [interquartile range (IQR), 2.99-3.61 years], the patients who underwent superior segment (S⁶) resection achieved an overall survival (OS) rate of 100% (68/68) and a DFS rate of 97.1% (66/68), demonstrating outcomes comparable to other segmentectomies (P>0.05).

Conclusions: High prevalence of V⁶b2 and V⁶b3 was observed with minimal variation in drainage patterns. Emphasizing these veins to ensure sufficient margins and potentially reducing aggressiveness through early detection, the outcomes of superior segmentectomies in this study are comparable to other segmentectomies and superior to those reported in previous studies.