Pulmonary Function Recovery and Displacement Patterns After Anatomic Segmentectomy vs Lobectomy.

BACKGROUND: The functional benefit of segmentectomy compared with lobectomy remains controversial. This ambispective study characterizes the changes in pulmonary function as correlated to displacement patterns of residual lung after segmentectomies vs lobectomies. METHODS: Patients with normal preoperative pulmonary function and undergoing segmentectomy or lobectomy between 2017 and 2021 were considered. Pulmonary function testing was scheduled preoperatively and at least 3 months postoperatively. Differences in the proportions of the median forced expiratory volume in 1 second (FEV1) reduction between segmentectomy and lobectomy were calculated. Covariance analysis was used to estimate the adjusted postoperative FEV1 (apoFEV1) and compare the difference value (DV) in apoFEV1 between segmentectomy and lobectomy. RESULTS: The study enrolled 634 patients (334 lobectomies and 300 segmentectomies). Median difference in the proportions of the FEV1 reduction between segmentectomy and lobectomy was 4.58%, with maximal difference observed in right S6 (9.08%) and minimal difference in left S1+2+3 (2.80%). For resections involving the upper lobe, apoFEV1 was significantly higher after segmentectomy than after lobectomy (DV, 0.15-0.22 L), except for left S3 and S1+2+3 segmentectomies (DV, 0.08 L and 0.06 L, respectively). Compared with a lower lobe lobectomy, S6 segmentectomy conferred a higher apoFEV1, whereas S7+8 and S9+10 had a similar apoFEV1 (DV, 0.16-0.18 L, 0.07 L, and 0.00-0.06 L, respectively). Functional recovery after segmentectomy was associated with the number of intersegment planes (P < .01) and the presence of an adjacent nonoperated on lobe (P = .03). CONCLUSIONS: Basilar and left S3 segmentectomies did not preserve more pulmonary function compared with their corresponding lobectomies, possibly due to the presence of multiple intersegmental resection planes.

Transformation to small cell lung cancer is irrespective of EGFR and accelerated by SMAD4-mediated ASCL1 transcription independently of RB1 in non-small cell lung cancer.

OBJECTIVES: Histological transformation to small cell lung cancer (SCLC) has been identified as a mechanism of TKIs resistance in EGFR-mutant non-small cell lung cancer (NSCLC). We aim to explore the prevalence of transformation in EGFR-wildtype NSCLC and the mechanism of SCLC transformation, which are rarely understood. METHODS: We reviewed 1474 NSCLC patients to investigate the NSCLC-to-SCLC transformed cases and the basic clinical characteristics, driver gene status and disease course of them. To explore the potential functional genes in SCLC transformation, we obtained pre- and post-transformation specimens and subjected them to a multigene NGS panel involving 416 cancer-related genes. To validate the putative gene function, we established knocked-out models by CRISPR-Cas 9 in HCC827 and A549-TP53-/- cells and investigated the effects on tumor growth, drug sensitivity and neuroendocrine phenotype in vitro and in vivo. We also detected the expression level of protein and mRNA to explore the molecular mechanism involved. RESULTS: We firstly reported an incidence rate of 9.73% (11/113) of SCLC transformation in EGFR-wildtype NSCLC and demonstrated that SCLC transformation is irrespective of EGFR mutation status (P = 0.16). We sequenced 8 paired tumors and identified a series of mutant genes specially in transformed SCLC such as SMAD4, RICTOR and RET. We firstly demonstrated that SMAD4 deficiency can accelerate SCLC transition by inducing neuroendocrine phenotype regardless of RB1 status in TP53-deficient NSCLC cells. Further mechanical experiments identified the SMAD4 can regulate ASCL1 transcription competitively with Myc in NSCLC cells and Myc inhibitor acts as a potential subsequent treatment agent. CONCLUSIONS: Transformation to SCLC is irrespective of EFGR status and can be accelerated by SMAD4 in non-small cell lung cancer. Myc inhibitor acts as a potential therapeutic drug for SMAD4-mediated resistant lung cancer. Video Abstract.

Cardiac magnetic resonance imaging-derived septum swing index detects pulmonary hypertension: A diagnostic study.

Background and Objectives: Because of pressure differences between the pulmonary artery and aorta, the ventricular septum moves in a swinging motion that is commonly observed on cardiac MR (CMR) cine sequences in patients with pulmonary hypertension (PH). We aimed to assess the use of septum swing index (SSI) derived by CMR for detecting PH. Methods: We retrospectively identified consecutive patients with suspected PH who underwent right heart catheterization (RHC) and CMR at a PH referral center between July 2019 and December 2020. The diagnostic accuracy of SSI for identifying PH (mean pulmonary artery pressure [mPAP] ≥ 25 mmHg) was assessed by receiver operating characteristic curves, sensitivity, specificity, and positive and negative predictive values. Results: A total of 105 patients (mean age: 47.8 ± 15.0 years; 68 females) were included in the final analysis. SSI and mPAP were negatively correlated in the total study population and patients with PH, but not in patients without PH. SSI was an independent predictor of PH (adjusted odds ratio: 12.9, 95% confidence interval: 3.6 to 45.5, P = 0.003). The area under the curve for SSI was 0.91, with a cut-off value of 0.9673 yielding the best balance of sensitivity (86.4%), specificity (88.2%), positive predictive value (97.4%), negative predictive value (55.6%), and accuracy (86.7%) for detecting PH. Conclusions: Septum swing index was lower in patients with PH and is a simple, reliable method for detecting PH.

Nebulization of risedronate alleviates airway obstruction and inflammation of chronic obstructive pulmonary diseases via suppressing prenylation-dependent RAS/ERK/NF-κB and RhoA/ROCK1/MLCP signaling.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a progressive disorder that causes airway obstruction and lung inflammation. The first-line treatment of COPD is the bronchodilators of ß2-agonists and antimuscarinic drugs, which can help control the airway obstruction, but the long-term use might render the drug tolerance. Bisphosphonates are widely used in osteoclast-mediated bone diseases treatment for decades. For drug repurposing, can delivery of a third generation of nitrogen-containing bisphosphonate, risedronate (RIS) ameliorate the progression of COPD? METHODS: COPD rats or mice models have been established through cigarette-smoking and elastase injection, and then the animals are received RIS treatment via nebulization. Lung deposition of RIS was primarily assessed by high-performance liquid chromatography (HPLC). The respiratory parameters of airway obstruction in COPD rats and mice were documented using plethysmography method and resistance-compliance system. RESULTS: High lung deposition and bioavailability of RIS was monitored with 88.8% of RIS input dose. We found that RIS could rescue the lung function decline of airspace enlargement and mean linear intercept in the COPD lung. RIS could curb the airway obstruction by suppressing 60% of the respiratory resistance and elevating the airway's dynamic compliance, tidal volume and mid-expiratory flow. As an inhibitor of farnesyl diphosphate synthase (FDPS), RIS suppresses FDPS-mediated RAS and RhoA prenylation to obstruct its membrane localization in airway smooth muscle cells (ASMCs), leading to the inhibition of downstream ERK-MLCK and ROCK1-MLCP pathway to cause ASMCs relaxation. Additionally, RIS nebulization impeded pro-inflammatory cell accumulation, particularly macrophages infiltration in alveolar parenchyma. The NF-κB, tumor necrosis factor-alpha, IL-1ß, IL-8, and IL-6 declined in microphages following RIS nebulization. Surprisingly, nebulization of RIS could overcome the tolerance of ß2-agonists in COPD-rats by increasing the expression of ß2 receptors. CONCLUSIONS: Nebulization of RIS could alleviate airway obstruction and lung inflammation in COPD, providing a novel strategy for treating COPD patients, even those with ß2-agonists tolerance.

Mining whole-lung information by artificial intelligence for predicting EGFR genotype and targeted therapy response in lung cancer: a multicohort study.

BACKGROUND: Epidermal growth factor receptor (EGFR) genotype is crucial for treatment decision making in lung cancer, but it can be affected by tumour heterogeneity and invasive biopsy during gene sequencing. Importantly, not all patients with an EGFR mutation have good prognosis with EGFR-tyrosine kinase inhibitors (TKIs), indicating the necessity of stratifying for EGFR-mutant genotype. In this study, we proposed a fully automated artificial intelligence system (FAIS) that mines whole-lung information from CT images to predict EGFR genotype and prognosis with EGFR-TKI treatment. METHODS: We included 18 232 patients with lung cancer with CT imaging and EGFR gene sequencing from nine cohorts in China and the USA, including a prospective cohort in an Asian population (n=891) and The Cancer Imaging Archive cohort in a White population. These cohorts were divided into thick CT group and thin CT group. The FAIS was built for predicting EGFR genotype and progression-free survival of patients receiving EGFR-TKIs, and it was evaluated by area under the curve (AUC) and Kaplan-Meier analysis. We further built two tumour-based deep learning models as comparison with the FAIS, and we explored the value of combining FAIS and clinical factors (the FAIS-C model). Additionally, we included 891 patients with 56-panel next-generation sequencing and 87 patients with RNA sequencing data to explore the biological mechanisms of FAIS. FINDINGS: FAIS achieved AUCs ranging from 0·748 to 0·813 in the six retrospective and prospective testing cohorts, outperforming the commonly used tumour-based deep learning model. Genotype predicted by the FAIS-C model was significantly associated with prognosis to EGFR-TKIs treatment (log-rank p<0·05), an important complement to gene sequencing. Moreover, we found 29 prognostic deep learning features in FAIS that were able to identify patients with an EGFR mutation at high risk of TKI resistance. These features showed strong associations with multiple genotypes (p<0·05, t test or Wilcoxon test) and gene pathways linked to drug resistance and cancer progression mechanisms. INTERPRETATION: FAIS provides a non-invasive method to detect EGFR genotype and identify patients with an EGFR mutation at high risk of TKI resistance. The superior performance of FAIS over tumour-based deep learning methods suggests that genotype and prognostic information could be obtained from the whole lung instead of only tumour tissues. FUNDING: National Natural Science Foundation of China.