Investigating causal associations between pneumonia and lung cancer using a bidirectional mendelian randomization framework.

BACKGROUND: Pneumonia and lung cancer are both major respiratory diseases, and observational studies have explored the association between their susceptibility. However, due to the presence of potential confounders and reverse causality, the comprehensive causal relationships between pneumonia and lung cancer require further exploration. METHODS: Genome-wide association study (GWAS) summary-level data were obtained from the hitherto latest FinnGen database, COVID-19 Host Genetics Initiative resource, and International Lung Cancer Consortium. We implemented a bidirectional Mendelian randomization (MR) framework to evaluate the causal relationships between several specific types of pneumonia and lung cancer. The causal estimates were mainly calculated by inverse-variance weighted (IVW) approach. Additionally, sensitivity analyses were also conducted to validate the robustness of the causalty. RESULTS: In the MR analyses, overall pneumonia demonstrated a suggestive but modest association with overall lung cancer risk (Odds ratio [OR]: 1.21, 95% confidence interval [CI]: 1.01 - 1.44, P = 0.037). The correlations between specific pneumonia types and overall lung cancer were not as significant, including bacterial pneumonia (OR: 1.07, 95% CI: 0.91 - 1.26, P = 0.386), viral pneumonia (OR: 1.00, 95% CI: 0.95 - 1.06, P = 0.891), asthma-related pneumonia (OR: 1.18, 95% CI: 0.92 - 1.52, P = 0.181), and COVID-19 (OR: 1.01, 95% CI: 0.78 - 1.30, P = 0.952). Reversely, with lung cancer as the exposure, we observed that overall lung cancer had statistically crucial associations with bacterial pneumonia (OR: 1.08, 95% CI: 1.03 - 1.13, P = 0.001) and viral pneumonia (OR: 1.09, 95% CI: 1.01 - 1.19, P = 0.037). Sensitivity analysis also confirmed the robustness of these findings. CONCLUSION: This study has presented a systematic investigation into the causal relationships between pneumonia and lung cancer subtypes. Further prospective study is warranted to verify these findings.

The landscape of immune checkpoint inhibitor therapy in advanced lung cancer.

BACKGROUND: The advent of immune checkpoint inhibitors (ICIs) therapy has resulted in significant survival benefits in patients with non-small-cell lung cancer (NSCLC) without increasing toxicity. However, the utilisation of immunotherapy for small-cell lung cancer (SCLC) remains unclear, with a scarcity of systematic comparisons of therapeutic effects and safety of immunotherapy in these two major lung cancer subtypes. Herein, we aimed to provide a comprehensive landscape of immunotherapy and systematically review its specific efficacy and safety in advanced lung cancer, accounting for histological types. METHODS: We identified studies assessing immunotherapy for lung cancer with predefined endpoints, including overall survival (OS), progression-free survival (PFS), objective response rate (ORR), and treatment-related adverse events (TRAE), from PubMed, Embase, Medline, and Cochrane library. A random-effects or fixed-effect model was adopted according to different settings. RESULTS: Overall, 38 trials with 20,173 patients with lung cancer were included in this study. ICI therapy resulted in a significantly prolonged survival in both patients with NSCLC and SCLC when compared with chemotherapy (hazard ratio [HR] = 0.74; 95% confidence interval [CI], 0.70-0.79] and [HR = 0.82; 95% CI, 0.75-0.90], respectively). The magnitude of disease control and survival benefits appeared superior with ICI plus standard of care (SOC) when compared with SOC alone. OS and PFS advantages were observed only when immunotherapy was employed as the first-line treatment in patients with SCLC. CONCLUSION: ICI therapy is a promising therapeutic option in patients with NSCLC and SCLC. ICI plus SOC can be recommended as the optimal first-line treatment for patients with SCLC, and double-target ICIs combined with SOC are recommended in patients with NSCLC as both the first and subsequent lines of treatment. Additionally, non-first-line immunotherapy is not recommended in patients with SCLC.

Persistent increase and improved survival of stage I lung cancer based on a large-scale real-world sample of 26,226 cases.

BACKGROUND: Lung cancer prevails and induces high mortality around the world. This study provided real-world information on the evolution of clinicopathological profiles and survival outcomes of lung cancer, and provided survival information within stage I subtypes. METHODS: Patients pathologically confirmed with lung cancer between January 2009 and December 2018 were identified with complete clinicopathological information, molecular testing results, and follow-up data. Shifts in clinical characteristics were evaluated using χ2 tests. Overall survival (OS) was calculated through the Kaplan-Meier method. RESULTS: A total of 26,226 eligible lung cancer patients were included, among whom 62.55% were male and 52.89% were smokers. Non-smokers and elderly patients took increasingly larger proportions in the whole patient population. The proportion of adenocarcinoma increased from 51.63% to 71.80%, while that of squamous carcinoma decreased from 28.43% to 17.60%. Gene mutations including EGFR (52.14%), KRAS (12.14%), and ALK (8.12%) were observed. Female, younger, non-smoking, adenocarcinoma patients and those with mutated EGFR had better survival prognoses. Importantly, this study validated that early detection of early-stage lung cancer patients had contributed to pronounced survival benefits during the decade. Patients with stage I lung cancer, accounted for an increasingly considerable proportion, increasing from 15.28% to 40.25%, coinciding with the surgery rate increasing from 38.14% to 54.25%. Overall, period survival analyses found that 42.69% of patients survived 5 years, and stage I patients had a 5-year OS of 84.20%. Compared with that in 2009-2013, the prognosis of stage I patients in 2014-2018 was dramatically better, with 5-year OS increasing from 73.26% to 87.68%. Regarding the specific survival benefits among stage I patients, the 5-year survival rates were 95.28%, 93.25%, 82.08%, and 74.50% for stage IA1, IA2, IA3, and IB, respectively, far more promising than previous reports. CONCLUSIONS: Crucial clinical and pathological changes have been observed in the past decade. Notably, the increased incidence of stage I lung cancer coincided with an improved prognosis, indicating actual benefits of early detection and management of lung cancer.

Oxygen-carrying sequential preservation mitigates liver grafts ischemia-reperfusion injury.

Oxygen-dependent preservation has been proposed to protect liver grafts from ischemia-reperfusion injury (IRI), but its underlying mechanism remains elusive. Here, we proposed an oxygen-carrying sequential preservation (OCSP) method that combined oxygenated static cold storage (SCS) and normothermic mechanical perfusion. We demonstrated that OCSP, especially with high oxygen partial pressure level (500-650mmHg) during the oxygenated SCS phase, was associated with decreased IRI of liver grafts and improved rat survival after transplantation. A negative correlation between autophagy and endoplasmic reticulum stress response (ERSR) was found under OCSP and functional studies indicated OCSP suppressed ERSR-mediated cell apoptosis through autophagy activation. Further data showed that OCSP-induced autophagy activation and ERSR inhibition were oxygen-dependent. Finally, activated NFE2L2-HMOX1 signaling was found to induce autophagy under OCSP. Together, our findings indicate oxygen-dependent autophagy mitigates liver graft's IRI by ERSR suppression and modulates NFE2L2-HMOX1 signaling under OCSP, providing a theoretical basis for liver preservation using a composite-sequential mode.

The number of brain metastases predicts the survival of non-small cell lung cancer patients with EGFR mutation status.

BACKGROUND: Lung cancer is the common cause of cancer-related deaths throughout the world, and brain is a frequent metastatic site of lung cancer. AIM: This research sought to evaluate the impact of the number of brain metastases in prognosticating non-small cell lung cancer (NSCLC) patients accounting to the role of epidermal growth factor receptor (EGFR) mutations. METHODS AND RESULTS: NSCLC patients with brain metastases diagnosed/treated in West China Hospital, Sichuan University between 2009 and 2017 were identified retrospectively. Kaplan-Meier approach was adopted to estimate OS. And we performed univariate and multivariate Cox proportional hazards regression analyses of characteristics related to overall survival (OS) in both EGFR-mutated and wild-type cohorts. In total, this study included 611 eligible NSCLC patients with brain metastases. Extracranial metastases and chemotherapy were independent prognostic factors of OS in both cohorts. As the disease progressed, EGFR-mutated patients had brain metastasis significantly earlier (P < .0001), but they also had notably better survival outcomes than wild-type patients (P < .0001). And the number of brain metastases impacted the survival incidence in the progression significantly in both EGFR-mutated and wild-type groups (P = .0087/.037, respectively). CONCLUSION: The number of brain metastases was a prognostic factor for lung cancer patients either with EGFR mutations or with wild-type EGFR, with larger number indicating more unfavorble clinical outcomes. Patients with EGFR mutations had a better survival.

Development and validation of an abnormality-derived deep-learning diagnostic system for major respiratory diseases.

Respiratory diseases impose a tremendous global health burden on large patient populations. In this study, we aimed to develop DeepMRDTR, a deep learning-based medical image interpretation system for the diagnosis of major respiratory diseases based on the automated identification of a wide range of radiological abnormalities through computed tomography (CT) and chest X-ray (CXR) from real-world, large-scale datasets. DeepMRDTR comprises four networks (two CT-Nets and two CXR-Nets) that exploit contrastive learning to generate pre-training parameters that are fine-tuned on the retrospective dataset collected from a single institution. The performance of DeepMRDTR was evaluated for abnormality identification and disease diagnosis on data from two different institutions: one was an internal testing dataset from the same institution as the training data and the second was collected from an external institution to evaluate the model generalizability and robustness to an unrelated population dataset. In such a difficult multi-class diagnosis task, our system achieved the average area under the receiver operating characteristic curve (AUC) of 0.856 (95% confidence interval (CI):0.843-0.868) and 0.841 (95%CI:0.832-0.887) for abnormality identification, and 0.900 (95%CI:0.872-0.958) and 0.866 (95%CI:0.832-0.887) for major respiratory diseases' diagnosis on CT and CXR datasets, respectively. Furthermore, to achieve a clinically actionable diagnosis, we deployed a preliminary version of DeepMRDTR into the clinical workflow, which was performed on par with senior experts in disease diagnosis, with an AUC of 0.890 and a Cohen's k of 0.746-0.877 at a reasonable timescale; these findings demonstrate the potential to accelerate the medical workflow to facilitate early diagnosis as a triage tool for respiratory diseases which supports improved clinical diagnoses and decision-making.

The heterogeneous immune landscape between lung adenocarcinoma and squamous carcinoma revealed by single-cell RNA sequencing.

A thorough interrogation of the immune landscape is crucial for immunotherapy strategy selection and prediction of clinical responses in non-small-cell lung cancer (NSCLC) patients. Single-cell RNA sequencing (scRNA-seq) techniques have prompted the opportunity to dissect the distinct immune signatures between lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), the two major subtypes of NSCLC. Here, we performed scRNA-seq on 72,475 immune cells from 40 samples of tumor and matched adjacent normal tissues spanning 19 NSCLC patients, and drew a systematic immune cell transcriptome atlas. Joint analyses of the distinct cellular compositions, differentially expressed genes (DEGs), cell-cell interactions, pseudotime trajectory, transcriptomic factors and prognostic factors based on The Cancer Genome Atlas (TCGA), revealed the central roles of cytotoxic and effector T and NK cells and the distinct functional macrophages (Mφ) subtypes in the immune microenvironment heterogeneity between LUAD and LUSC. The dominant subtype of Mφ was FABP4-Mφ in LUAD and SPP1-Mφ in LUSC. Importantly, we identified a novel lymphocyte-related Mφ cluster, which we named SELENOP-Mφ, and further established its antitumor role in both types, especially in LUAD. Our comprehensive depiction of the immune heterogeneity and definition of Mφ clusters could help design personalized treatment for lung cancer patients in clinical practice.

The application of artificial intelligence and radiomics in lung cancer.

Lung cancer is one of the most leading causes of death throughout the world, and there is an urgent requirement for the precision medical management of it. Artificial intelligence (AI) consisting of numerous advanced techniques has been widely applied in the field of medical care. Meanwhile, radiomics based on traditional machine learning also does a great job in mining information through medical images. With the integration of AI and radiomics, great progress has been made in the early diagnosis, specific characterization, and prognosis of lung cancer, which has aroused attention all over the world. In this study, we give a brief review of the current application of AI and radiomics for precision medical management in lung cancer.

A comprehensive algorithm to distinguish between MPLC and IPM in multiple lung tumors patients.

BACKGROUND: Diagnosis of multiple lung nodules has become convenient and frequent due to the improvement of computed tomography (CT) scans. However, to distinguish intrapulmonary metastasis (IPM) from multiple primary lung cancer (MPLC) remains challenging. Herein, for the accurate optimization of therapeutic options, we propose a comprehensive algorithm for multiple lung carcinomas based on a multidisciplinary approach, and investigate the prognosis of patients who underwent surgical resection. METHODS: Patients with multiple lung carcinomas who were treated at West China Hospital of Sichuan University from April, 2009 to December, 2017, were retrospectively identified. A comprehensive algorithm combining histologic assessment, molecular analysis, and imaging information was used to classify nodules as IPM or MPLC. The Kaplan-Meier method was used to estimate survival rates, and the relevant factors were evaluated using the log-rank test or Cox proportional hazards model. RESULTS: The study included 576 patients with 1,295 lung tumors in total. Significant differences were observed between the clinical features of 171 patients with IPM and 405 patients with MPLC. The final classification consistency was 0.65 and 0.72 compared with the criteria of Martini and Melamed (MM) and the American College of Chest Physicians (ACCP), respectively. Patients with independent primary tumors had better overall survival (OS) than patients with intra-pulmonary metastasis (HR =3.99, 95% CI: 2.86-5.57; P<0.001). Nodal involvement and radiotherapy were independent prognostic factors. CONCLUSIONS: The comprehensive algorithm was a relevant tool for classifying multifocal lung tumors as MPLC or IPM, and could help doctors with precise decision-making in routine clinical practice. Patients with multiple lesions without lymph node metastasis or without radiotherapy tended to have a better prognosis.