Translational lung cancer research最新文献

Background: A previous network meta-analysis (NMA) compared the efficacy of anaplastic lymphoma kinase (ALK) inhibitors in ALK-positive non-small cell lung cancer (NSCLC). The phase III INSPIRE study of iruplinalkib was published recently. The present study aimed to add the results related to iruplinalkib to the NMA.

Methods: A systematic literature search was performed in PubMed, Embase, Cochrane Library, Google, and Baidu. Randomized controlled trials (RCTs) reporting the independent review committee-assessed progression-free survival (PFS), objective response rate (ORR), or disease control rate (DCR) results of Asian patients with ALK inhibitor-naïve advanced ALK-positive NSCLC were eligible for inclusion in the NMA. Risk of bias was assessed using the Cochrane Risk of Bias 2 tool. Bayesian fixed-effect models were used for the direct and indirect pairwise comparisons. This study was registered with PROSPERO (CRD42024555299).

Results: Eight studies, involving 1,477 Asian patients and seven treatments (crizotinib, alectinib, brigatinib, ensartinib, envonalkib, iruplinalkib, and lorlatinib), were included in the NMA. In terms of the overall risks of bias, all of the studies had "some concerns". All the next-generation ALK inhibitors were statistically superior to crizotinib in terms of PFS. Iruplinalkib had the best surface under the cumulative ranking curve (74.0%), followed by brigatinib (69.1%) and ensartinib (63.7%). Most of the pairwise comparisons did not reveal significant differences in the ORR and DCR. In terms of both the ORR and DCR, alectinib ranked first, followed by lorlatinib.

Conclusions: Next-generation ALK inhibitors had better efficacy than crizotinib in the treatment of Asian patients with ALK inhibitor-naïve advanced ALK-positive NSCLC. Iruplinalkib may have more favorable PFS benefit than other ALK inhibitors for Asians.

Background: Patients with non-small cell lung cancer (NSCLC) carrying SMARCA4 mutations (SMARCA4-Mut) tend to have more advanced disease and a poor prognosis. However, due to the rarity of this mutation and the lack of related studies, the characteristics of SMARCA4-Mut NSCLC patients remains poorly determined. To clarify the clinical characteristics and prognostic factors of SMARCA4-Mut NSCLC, we initiated the present study to provide a clinical reference.

Methods: We used data from two cohorts of NSCLC-SMARCA4-mutated samples: The Cancer Genome Atlas (TCGA) database and our center's clinical data. The TCGA database was used to obtain 481 NSCLC-SMARCA4-Mut samples for clinical characterization. The center collected data on 224 consecutive NSCLC patients treated between December 2020 to July 2022. Among them, 26 harbored SMARCA4 mutations, and 20 were eligible for inclusion in the study. Clinical, pathological, and molecular features, as well as prognostic role of SMARCA4 mutations were analyzed. Additionally, we analyzed the prognostic impact of Napsin A expression in SMARCA4-Mut patients.

Results: The TCGA database included 480 patients with SMARCA4-Mut NSCLC, 311 males (64.8%) and 169 females (35.2%), with a median age of 67 years. Among the 20 SMARCA4-Mut patients in our center series, 12 (60%) were males and 8 (40%) females, with a median age of 63. The intergroup prognostic correlation analysis showed that SMARCA4-Mut patients had significantly worse prognosis than those the wild-type SMARCA4 (SMARCA4-WT) (P=0.04). Within the SMARCA4-Mut group, patients with Napsin A expression had longer overall survival (OS) (P=0.03) than those without expression. Median survival in the Napsin A-positive and negative groups was 32 and 15 months, respectively. According to time-dependent receiver operating curve analysis, patients with Napsin A expression had significantly longer first-line treatment progression-free survival (PFS1) [area under the curve (AUC) =0.748] and OS (AUC =0.586). No prognostic value of Napsin A was found in patients SMARCA4-WT patients.

Conclusions: SMARCA4-Mut is an adverse prognostic feature in NSCLC patients. Napsin A expression in SMARCA4-Mut patients is associated with prolonged OS.

Background: Sleeve lobectomy (SL) and extended SL (ESL), which aim to preserve pulmonary function and enhance the quality of life of patients while ensuring oncological outcomes, are valuable surgical options for the treatment of centrally located non-small cell lung cancer (NSCLC). This study aimed to compare perioperative adverse events and long-term survival between SL and ESL in NSCLC patients, providing a comprehensive review of surgical outcomes, complications, and survival to assess the roles of SL and ESL in thoracic oncology.

Methods: This single-center retrospective study assessed the outcomes of NSCLC patients who underwent SL or ESL from June 2014 to January 2022. The patients were selected based on specific inclusion criteria, and statistical analyses were conducted to examine the postoperative outcomes, overall survival (OS), and disease-free survival (DFS) of the patients.

Results: A total of 218 patients met the inclusion criteria. Among 218 patients, 33 underwent ESL and 185 underwent SL. Compared to SL, ESL was associated with longer operative times and higher R0 resection rates (93.9% vs. 78.8%, P=0.047). Despite the higher complexity of ESL compared to SL, there were no significant differences in the perioperative complications or mortality rates between the groups. Survival analysis was conducted on the propensity score matching (PSM) data, the results demonstrated superior OS and DFS in the ESL group compared to the SL group. Advanced age, more advanced nodal (N) status, and non-R0 resection were significant predictors of poorer prognosis.

Conclusions: ESL is a feasible and effective alternative for treating centrally located NSCLC, with better R0 resection rates and comparable survival outcomes to SL, without increasing the risk of grade III-IV complications. Further studies with larger cohorts need to be conducted to validate these findings and refine the surgical techniques.

Background: Radiomics has shown promise in improving malignancy risk stratification of indeterminate pulmonary nodules (IPNs) with many platforms available, but with no head-to-head comparisons. This study aimed to evaluate transportability of radiomic models across platforms by comparing performances of a commercial radiomic feature extractor (HealthMyne) with an open-source extractor (PyRadiomics) on diagnosis of lung cancer in IPNs.

Methods: A commercial radiomic feature extractor was used to segment IPNs from computed tomography (CT) scans, and a previously validated radiomic model based on commercial features was used as baseline (ComRad). Using same segmentation masks, PyRadiomics, an open-source feature extractor was used to build three open-source radiomic models (OpenRad) using different methods: de novo open-source model derived using least absolute shrinkage and selection operator (LASSO) for feature selection, selecting open-source features matched to ComRad features based upon Imaging Biomarker Standardization Initiative (IBSI) nomenclature, and selecting open-source features most highly correlated to ComRad features. Radiomic models were trained on an internal cohort (n=161) and externally validated on 3 cohorts (n=278). We added Mayo clinical risk score to OpenRad and ComRad models, creating integrated clinical radiomic (ClinRad) models. All models were compared using area under the curve (AUC) and evaluated for clinical improvement using bias-corrected clinical net reclassification indices (cNRIs).

Results: ComRad AUC was 0.76 [95% confidence interval (CI): 0.71-0.82], and OpenRad AUC was 0.75 (95% CI: 0.69-0.81) for LASSO model, 0.74 (95% CI: 0.68-0.79) for Spearman's correlation, and 0.71 (95% CI: 0.65-0.77) for IBSI. Mayo scores were added to OpenRad LASSO model, which performed best, forming open-source ClinRad model with AUC of 0.80 (95% CI: 0.74-0.86), identical to commercial ClinRad's AUC. Both ClinRad models showed clinical improvement compared to Mayo alone, with commercial ClinRad achieving cNRI of 0.09 (95% CI: 0.02-0.15) for benign and 0.07 (95% CI: 0.00-0.13) for malignant, and open-source ClinRad achieving cNRI of 0.09 (95% CI: 0.02-0.15) for benign and 0.06 (95% CI: 0.00-0.12) for malignant.

Conclusions: Transportability of radiomic models across platforms directly does not conserve performance, but radiomic platforms can provide equivalent results when building de novo models allowing for flexibility in feature selection to maximize prediction accuracy.

Background: Definitive chemoradiotherapy (dCRT) is the cornerstone for locally advanced non-small cell lung cancer (LA-NSCLC). The study aimed to construct a multi-omics model integrating baseline clinical data, computed tomography (CT) images and genetic information to predict the prognosis of dCRT in LA-NSCLC patients.

Methods: The study retrospectively enrolled 105 stage III LA-NSCLC patients who had undergone dCRT. The pre-treatment CT images were collected, and the primary tumor was delineated as a region of interest (ROI) on the image using 3D-Slicer, and the radiomics features were extracted. The least absolute shrinkage and selection operator (LASSO) was employed for dimensionality reduction and selection of features. Genomic information was obtained from the baseline tumor tissue samples. We then constructed a multi-omics model by combining baseline clinical data, radiomics and genomics features. The predictive performance of the model was evaluated by the area under the curve (AUC) of the receiver operating characteristic (ROC) and the concordance index (C-index).

Results: The median follow-up time was 30.1 months, and the median progression-free survival (PFS) was 10.60 months. Four features were applied to construct the radiomics model. Multivariable analysis demonstrated the Rad-score, KEAP1 and MET mutations were independent prognostic factors for PFS. The C-index of radiomics model, genomics model and radiogenomics model all performed well in the training group (0.590 vs. 0.606 vs. 0.663) and the validation group (0.599 vs. 0.594 vs. 0.650).

Conclusions: The radiomics model, genomics model and radiogenomics model can all predict the prognosis of dCRT for LA-NSCLC, and the radiogenomics model is superior to the single type model.

Background: Molecular and transcription factor subtyping were recently introduced to identify patients with unique clinical features in small cell lung cancer (SCLC). However, its prognostic relevance is yet to be established. This study aims to investigate the clinical implications and prognostic significance of transcription factor subtyping in SCLC using immunohistochemistry.

Methods: One hundred and ninety consecutive SCLC patients treated with platinum-based chemotherapy at a single institution were retrospectively reviewed. Expression of ASCL1, NeuroD1, POU2F3, and YAP1 was assessed by immunohistochemical staining and applied to determine the transcription factor subtype of each case.

Results: The association among transcription factors was not entirely mutually exclusive. YAP1 expression was the most significant prognostic indicator compared with other transcription factors or their related subtypes. Among patients with limited-stage disease (LD), complete response (CR) rates were 46.2% and 22.4% in the YAP1-positive and YAP1-negative groups, respectively. The median duration of response among patients who achieved CR was 64.8 and 36.4 months in the YAP1-positive and YAP1-negative groups, respectively (P=0.06). Median overall survival (OS) in LD was 35.6 and 16.9 months in the YAP1-positive and YAP1-negative groups, respectively (P=0.03). In extensive-stage disease (ED), the median OS was 11.3 months for the YAP1-positive group and 11 months for the YAP1-negative group (P=0.03).

Conclusions: Positive expression of YAP1 can be associated with durable CR and favorable survival outcomes in patients with SCLC, especially in LD.