Translational lung cancer research最新文献

Background: Lung adenocarcinoma (LUAD) is a sub-type of non-small cell lung cancer (NSCLC) that is often associated with genetic alterations, including the Kirsten rat sarcoma viral oncogene homolog (KRAS) mutation. The KRAS mutation is particularly challenging to treat due to resistance to targeted therapies. This study aims to develop a predictive model for the KRAS mutation in patients with LUAD by integrating clinical, dual-energy spectral computed tomography (DESCT), and radiomics features.

Methods: A total of 172 patients with LUAD were retrospectively enrolled and divided into a developing cohort (n=120) and a validation cohort (n=52). Clinical, DESCT and radiomics features were extracted and analyzed. Four predictive models were constructed: clinical, DESCT, radiomics, and combined clinical-DESCT-radiomics (C-S-R) model. The performance of these models was evaluated by the receiver operating characteristic curves. A nomogram incorporating clinical, DESCT, radiomics features with R-score was developed in the validation cohort.

Results: In this study, 8.7% (15/172) of the patients showed KRAS mutation. The C-S-R model demonstrated the best performance, with an area under the curve (AUC) of 0.92 in the developing cohort and 0.87 in the validation cohort. The C-S-R model was not superior to radiomics model (P=0.28), but it was significantly better than DESCT model (P=0.01).

Conclusions: This study suggests that integrating clinical, DESCT, and radiomics features can enhance the prediction of KRAS mutation in patients with LUAD.

Background: Thoracic tumors characterized by a deficiency in SMARCA4 are highly aggressive and linked to a poor prognosis. This retrospective study explores the efficacy and safety of immune checkpoint inhibitors (ICIs) in combination with chemotherapy for SMARCA4-deficient undifferentiated tumors (SMARCA4-dUT) and SMARCA4-deficient non-small cell lung cancer (SMARCA4-dNSCLC).

Methods: A cohort of 59 individuals was analyzed, including 35 patients with SMARCA4-dUT and 24 with SMARCA4-dNSCLC.

Results: Clinical characteristics as gender, age, smoking status, and metastatic sites did not significantly vary between SMARCA4-dUT and SMARCA4-dNSCLC. Nonsense and frameshift mutations in the SMARCA4 gene can result in the loss of its protein expression. Following a median follow-up of 7.6 months, the median progression-free survival (mPFS) notably increased with ICIs-based combination therapy compared to chemotherapy, the mPFS was 12.60 vs. 4.03 months in the SMARCA4-dUT subgroup (P=0.007) and not reached vs. 3.42 months in the SMARCA4-dNSCLC subgroup (P=0.03). In stage IV patients, the risk of disease progression and death decreased with ICIs-based combination therapy vs. chemotherapy [ICIs-based therapy vs. chemotherapy: hazard ratio (HR) =0.076; 95% confidence interval (CI): 0.009-0.624]. The most prevalent grade 3 or higher adverse events (AEs) in both groups were hematologic decreases, consistent with typical chemotherapy AEs. No treatment-related AEs led to patient fatalities.

Conclusions: The combination of ICIs and chemotherapy is more effective than chemotherapy for patients with advanced SMARCA4-deficient thoracic tumors (SMARCA4-dTT), and the safety is manageable.

Background: Small-cell lung cancer (SCLC) is highly malignant. Despite being highly sensitive to initial chemotherapy and radiotherapy, the recurrence rate is high. Atezolizumab is the first immune checkpoint inhibitor (ICI) that has been proven to provide an overall survival (OS) benefit for extensive-stage SCLC (ES-SCLC), making ICIs in combination with chemotherapy the standard first-line treatment for ES-SCLC. However, the real-world treatment of SCLC is more complex, and multimodal therapy may be needed to achieve long-term patient survival. Few reports on later-line chemotherapy combined with immunotherapy have been published thus far. Moreover, there is limited data on the efficacy and safety of thoracic radiotherapy and radiotherapy for metastatic lesions after multiple lines of treatment have failed in ES-SCLC, and the value of small-molecule antiangiogenesis combined with immunotherapy also needs further exploration.

Case description: A patient was diagnosed with mediastinal limited-stage SCLC (LS-SCLC) and experienced local progression following standard chemoradiotherapy and prophylactic cranial irradiation. Subsequently, the patient underwent second-line irinotecan chemotherapy, which resulted in severe hematological toxicity. Upon initiation of third-line therapy with anlotinib, the disease remained stable for 9 months. Unfortunately, imaging revealed the presence of a new lesion at the right lung apex. Nevertheless, there was renewed hope for survival when atezolizumab was introduced as part of the treatment regimen. Despite the later development of brain metastases and metastasis adjacent to the aortic arch, long-term survival was achieved through combination therapy involving immunotherapy, antiangiogenic therapy, and radiotherapy targeting the metastatic lesions. By March 2024, the OS had reached 70 months, with a duration of treatment with atezolizumab of 48 months, and only grade II hypothyroidism occurred during treatment, with no other immunotherapy-related adverse events being observed.

Conclusions: This case report suggests the potential efficacy and safety of integrating chemotherapy, immunotherapy, radiotherapy, and antiangiogenic therapy for the treatment of SCLC. Further clinical trials are warranted to validate the value of combining chemotherapy, immunotherapy, radiotherapy, and antiangiogenic therapy.

Background: Despite the importance of early diagnosis of lung cancer and wide availability of chest radiography, the detection of operable stage lung cancer on chest radiographs (CXRs) remains challenging. This study aimed to investigate the effectiveness of artificial intelligence (AI)-based CXR analysis for detecting operable lung cancers.

Methods: Patients who underwent lung cancer surgery at two referral hospitals between March 2020 and February 2021 were retrospectively included in this study. Preoperative CXRs of the patients were analyzed using commercial AI-based lesion detection software, and the results of lesion location and types obtained using the software were reviewed by radiologists and pulmonologists, with computed tomography (CT) as a reference standard for determining nodule characteristics. Factors influencing AI detection of lung cancer on CXR were assessed using logistic regression analysis.

Results: Among the 594 patients who underwent surgery for lung cancer (median age: 65 years, 51.3% male), the sensitivity of AI for detecting lung cancer on CXR was 57.7%, and it identified 86% of CXR-visible lung cancers. Detection rates of lung cancer by AI increased according to the disease stage: 42.5% for stage IA, 86.3% for stage IB, and 90.9% for stages II-III. The detection rate increased to over 60% from stage IA2 onwards when tumor size exceeded 1 cm. Regarding lesion type on CT, 8.3%, 46.8%, and 77.3% of non-solid, part-solid, and solid nodules, respectively, were detected by AI. Multivariable analysis showed that nodule location in the upper zone [odds ratio (OR) 2.78, P<0.001], peripheral region (OR 4.59, P<0.001), and solid lesion diameter (OR 1.20, P<0.001) were significantly associated with AI detection of lung cancer.

Conclusions: AI could be an effective tool for detecting operable lung cancer on CXRs, particularly when lesions are larger and located in the upper and peripheral regions.

Background: Lung adenocarcinoma (LUAD) is the most common subtype of non-small cell lung cancer (NSCLC) and accounts for about 40% of all lung cancer cases. This research aims to investigate the effects of miR-9-3p on ferroptosis in LUAD cells and to elucidate its regulatory mechanisms. Studies have shown that LUAD is related to ferroptosis, and specific microRNAs (miRNA) are also related to ferroptosis. However, further research is needed to elucidate the mechanisms by which miR-9-3p induces ferroptosis in LUAD.

Methods: Our study comprehensively analyzed multiple databases to investigate miR-9-3p expression in LUAD tissues. Quantitative polymerase chain reaction (qPCR) was utilized to detect miR-9-3p levels in LUAD cells and tissues, examining its prognostic significance. Reactive oxygen species (ROS) and superoxide dismutase (SOD) assays assessed the impact of miR-9-3p on lipid peroxidation in LUAD cells. Dual-luciferase reporter assays were conducted to evaluate the binding affinity between miR-9-3p and target genes, while Western blotting and immunofluorescence were used to examine the regulation of miR-9-3p on downstream signaling pathways.

Results: We observed that miR-9-3p was upregulated in LUAD cells by qPCR, and the ferroptosis of LUAD cells increased upon treatment with erastin following the transfection of miR-9-3p inhibitor. Cell Counting Kit-8 (CCK-8), ROS, and SOD activity assays confirmed that inhibiting miR-9-3p enhanced lipid peroxidation in LUAD cells, contributing to higher rates of ferroptosis. Subsequent dual-luciferase reporter assays validated spermidine/spermine N1-acetyltransferase 1 (SAT1) as a target gene of miR-9-3p. Further Western blot confirmed that miR-9-3p regulated the expression of SAT1 and p53 proteins in p53 wild-type (WT) LUAD cells. Rescue experiments demonstrated that SAT1 was necessary for miR-9-3p to promote cell proliferation and suppress ferroptosis in p53 WT LUAD cells. Additionally, the effect of miR-9-3p on ferroptosis in LUAD cells was regulated by p53 signaling pathway.

Conclusions: Overall, these findings demonstrate that miR-9-3p negatively regulates ferroptosis in LUAD cells through SAT1 and p53 signaling pathway, suggesting that miR-9-3p plays a crucial role in LUAD pathogenesis and targeting this miRNA with an inhibitor exhibits promising potential for the treatment of LUAD.

Background: Patients diagnosed with non-small cell lung cancer (NSCLC) usually have a poor prognosis, so it is critical to identify effective biomarkers for prognosis prediction. The aim of this study is to establish a nomogram to evaluate the prognostic significance of blood markers in patients with NSCLC and provide reference for clinical work.

Methods: A total of 486 patients with NSCLC who were admitted to hospital from January 2009 to December 2019 were retrospectively analyzed. The cohort was divided into a training set (n=340) and a validation set (n=146). Eleven blood indicators were selected as prognostic parameters by the least absolute shrinkage and selection operator (LASSO) model to establish tumor marker inflammatory nutrition (TMIN) score. Univariate and multivariate regression analyses were performed to establish a TMIN-nomogram model for predicting overall survival (OS) and progression-free survival (PFS). Receiver operating characteristic (ROC) survival curve, calibration curve and clinical decision curve analysis (DCA) were used to evaluate the predictive performance of the TMIN-nomogram model.

Results: The TMIN score were constructed for 11 of the most valuable prognostic variables, including white blood cells (WBCs), neutrophils (N), platelets (PLT), albumin (ALB), globulin (GLB), prealbumin (PAB), carcinoembryonic antigen (CEA), cytokeratin fragment 21-1 (CYFRA21-1), fibrinogen (FIB), platelet/lymphocyte ratio (PLR), and lymphocyte/monocyte ratio (LMR), and patients were divided into low-risk and high-risk groups using optimal cutovers. The TMIN score showed good predictive value for both OS and PFS. In addition, The TMIN score and sex, smoke, pathological classification, American Joint Committee on Cancer stage (AJCC stage), tumor diameter and Eastern Cooperative Oncology Group-performance status (ECOG-PS) and other clinical indicators showed a strong correlation. Univariate and multivariate analyses confirmed that TMIN score was an independent risk factor for OS and PFS in NSCLC patients. It is worth noting that the TMIN nomogram model of OS and PFS based on multivariate analysis combined with TMIN score has very good prognostic value for NSCLC patients.

Conclusions: TMIN is a promising predictor for PFS and OS in NSCLC patients. The TMIN-nomogram prediction model can be used as an effective tool for the comprehensive prognosis evaluation of NSCLC patients.