PCSK9 regulates the efficacy of immune checkpoint therapy in lung cancer

Proprotein convertase subtilisin/kexin type 9 (PCSK9) secreted by tumors was reported as a deleterious factor that led to the reduction of lymphocyte infiltration and the poorer efficacy of ICIs in vivo. This study aimed to explore whether PCSK9 expression in tumor tissue could predict the response of advanced non-small cell lung cancer (NSCLC) to anti-PD-1 immunotherapy and the synergistic antitumor effect of the combination of the PCSK9 inhibitor with the anti-CD137 agonist. One hundred fifteen advanced NSCLC patients who received anti-PD-1 immunotherapy were retrospectively studied with PCSK9 expression in baseline NSCLC tissues detected by immunohistochemistry (IHC). The mPFS of the PCSK9lo group was significantly longer than that of the PCSK9hi group [8.1 vs. 3.6 months, hazard ratio (HR): 3.450; 95% confidence interval (CI), 2.166-5.496]. A higher objective response rate (ORR) and a higher disease control rate (DCR) were observed in the PCSK9lo group than in the PCSK9hi group (54.4% vs. 34.5%, 94.7% vs. 65.5%). Reduction and marginal distribution of CD8+ T cells were observed in PCSK9hi NSCLC tissues. Tumor growth was retarded by the PCSK9 inhibitor and the anti-CD137 agonist alone in the Lewis lung carcinoma (LLC) mice model and further retarded by the PCSK9 inhibitor in combination with the CD137 agonist with long-term survival of the host mice with noticeable increases of CD8+ and GzmB+ CD8+ T cells and reduction of Tregs. Together, these results suggested that high PCSK9 expression in baseline tumor tissue was a deleterious factor for the efficacy of anti-PD-1 immunotherapy in advanced NSCLC patients. The PCSK9 inhibitor in combination with the anti-CD137 agonist could not only enhance the recruitment of CD8+ and GzmB+ CD8+ T cells but also deplete Tregs, which may be a novel therapeutic strategy for future research and clinical practice

Single-cell profiling reveals distinct immune response landscapes in tuberculous pleural effusion and non-TPE

BackgroundTuberculosis (TB) is caused by Mycobacterium tuberculosis (Mtb) and remains a major health threat worldwide. However, a detailed understanding of the immune cells and inflammatory mediators in Mtb-infected tissues is still lacking. Tuberculous pleural effusion (TPE), which is characterized by an influx of immune cells to the pleural space, is thus a suitable platform for dissecting complex tissue responses to Mtb infection.MethodsWe employed singe-cell RNA sequencing to 10 pleural fluid (PF) samples from 6 patients with TPE and 4 non-TPEs including 2 samples from patients with TSPE (transudative pleural effusion) and 2 samples with MPE (malignant pleural effusion).ResultCompared to TSPE and MPE, TPE displayed obvious difference in the abundance of major cell types (e.g., NK, CD4+T, Macrophages), which showed notable associations with disease type. Further analyses revealed that the CD4 lymphocyte population in TPE favored a Th1 and Th17 response. Tumor necrosis factors (TNF)-, and XIAP related factor 1 (XAF1)-pathways induced T cell apoptosis in patients with TPE. Immune exhaustion in NK cells was an important feature in TPE. Myeloid cells in TPE displayed stronger functional capacity for phagocytosis, antigen presentation and IFN-γ response, than TSPE and MPE. Systemic elevation of inflammatory response genes and pro-inflammatory cytokines were mainly driven by macrophages in patients with TPE.ConclusionWe provide a tissue immune landscape of PF immune cells, and revealed a distinct local immune response in TPE and non-TPE (TSPE and MPE). These findings will improve our understanding of local TB immunopathogenesis and provide potential targets for TB therapy

Biomarkers Correlated with Tuberculosis Preventive Treatment Response: A Systematic Review and Meta-Analysis

Background: There is a need to identify alternative biomarkers to predict tuberculosis (TB) preventive treatment response because observing the incidence decline renders a long follow-up period. Methods: We searched PubMed, Embase and Web of Science up to 9 February 2023. The biomarker levels during preventive treatment were quantitatively summarized by means of meta-analysis using the random-effect model. Results: Eleven eligible studies, published during 2006–2022, were included in the meta-analysis, with frequently heterogeneous results. Twenty-six biomarkers or testing methods were identified regarding TB preventive treatment monitoring. The summarized standard mean differences of interferon-γ (INF-γ) were −1.44 (95% CI: −1.85, −1.03) among those who completed preventive treatment (τ2 = 0.21; I2 = 95.2%, p 2 = 0.13; I2 = 82.0%, p < 0.001), respectively. Subgroup analysis showed that the INF-γ level after treatment decreased significantly from baseline among studies with high TB burden (−0.98, 95% CI: −1.21, −0.75) and among those with a history of Bacillus Calmette–Guérin vaccination (−0.87, 95% CI: −1.10, −0.63). Conclusions: Our results suggested that decreased INF-γ was observed among those who completed preventive treatment but not in those without preventive treatment. Further studies are warranted to explore its value in preventive treatment monitoring due to limited available data and extensive between-study heterogeneity

Additional file 1 of Bacteriomes in lesions of pulmonary tuberculosis and its association with status of Mycobacterium tuberculosis excretion

Additional file 1: Supplemental Table 1. Values of bacterial MetaCyc pathways of all TB lesions (40 TB-E and 42 TB-NE)

DNA Methylation Analysis of the SHOX2 and RASSF1A Panel Using Cell-Free DNA in the Diagnosis of Malignant Pleural Effusion

Objectives. The differential diagnosis of pleural effusion (PE) is a common but major challenge in clinical practice. This study aimed to establish a strategy based on a PE-cell-free DNA (cfDNA) methylation detection system for the differential diagnosis of malignant pleural effusion (MPE) and benign pleural effusion (BPE). Methods. A total of 104 patients with PE were enrolled in this study, among which 50 patients had MPE, 9 malignant tumor patients had PE of indefinite causes, and the other 45 patients were classified as benign controls. The methylation status of short stature homeobox 2 (SHOX2) and RAS association domain family 1, isoform A (RASSF1A) was detected using PE-cfDNA specimens by real-time fluorescence quantitative PCR. Total methylation (TM) was defined as the combination of the methylation levels of SHOX2 and RASSF1A. The electrochemiluminescence immunoassay was applied to evaluate the levels of multiple serum tumor markers. Results. The PE-cfDNA methylation status of either SHOX2 or RASSF1A was much higher in MPE samples than in benign controls. The combination of SHOX2 and RASSF1A methylation in PE yielded a diagnostic sensitivity of 96% and a specificity of 100%, respectively. When compared with the corresponding serum tumor marker detection results, TM showed the highest diagnostic efficiency (AUC = 0.985). Furthermore, the combination of the SHOX2 and RASSF1A methylation panels using PE-cfDNA could apparently improve the differential diagnostic efficacy of BPE and MPE and could help compensate for the deficiency of cytology. Conclusions. Our results indicated that SHOX2 and RASSF1A methylation panel detection could accurately classify BPE and MPE diseases and showed better diagnostic performance than traditional serum parameters. The SHOX2 and RASSF1A methylation detection of PE-cfDNA could be a potentially effective complementary tool for cytology in the process of differential diagnosis. In summary, PE-cfDNA could be used as a promising non-invasive analyte for the auxiliary diagnosis of MPE

Deep learning-based diagnosis of histopathological patterns for invasive non-mucinous lung adenocarcinoma using semantic segmentation

Objectives The application of artificial intelligence (AI) to the field of pathology has facilitated the development of digital pathology, hence, making AI-assisted diagnosis possible. Due to the variety of lung cancers and the subjectivity of manual evaluation, invasive non-mucinous lung adenocarcinoma (ADC) is difficult to diagnose. We aim to offer a deep learning solution that automatically classifies invasive non-mucinous lung ADC histological subtypes.Design For this investigation, 523 whole-slide images (WSIs) were obtained. We divided 376 of the WSIs at random for model training. According to WHO diagnostic criteria, six histological components of invasive non-mucinous lung ADC, comprising lepidic, papillary, acinar, solid, micropapillary and cribriform arrangements, were annotated at the pixel level and employed as the predicting target. We constructed the deep learning model using DeepLab v3, and used 27 WSIs for model validation and the remaining 120 WSIs for testing. The predictions were analysed by senior pathologists.Results The model could accurately predict the predominant subtype and the majority of minor subtypes and has achieved good performance. Except for acinar, the area under the curve of the model was larger than 0.8 for all the subtypes. Meanwhile, the model was able to generate pathological reports. The NDCG scores were greater than 75%. Through the analysis of feature maps and incidents of model misdiagnosis, we discovered that the deep learning model was consistent with the thought process of pathologists and revealed better performance in recognising minor lesions.Conclusions The findings of the deep learning model for predicting the major and minor subtypes of invasive non-mucinous lung ADC are favourable. Its appearance and sensitivity to tiny lesions can be of great assistance to pathologists

High accuracy epidermal growth factor receptor mutation prediction via histopathological deep learning

Abstract Background The detection of epidermal growth factor receptor (EGFR) mutations in patients with non-small cell lung cancer is critical for tyrosine kinase inhibitor therapy. EGFR detection requires tissue samples, which are difficult to obtain in some patients, costing them the opportunity for further treatment. To realize EGFR mutation prediction without molecular detection, we aimed to build a high-accuracy deep learning model with only haematoxylin and eosin (H&E)-stained slides. Methods We collected 326 H&E-stained non-small cell lung cancer slides from Beijing Chest Hospital, China, and used 226 slides (88 with EGFR mutations) for model training. The remaining 100 images (50 with EGFR mutations) were used for testing. We trained a convolutional neural network based on ResNet-50 to classify EGFR mutation status on the slide level. Results The sensitivity and specificity of the model were 76% and 74%, respectively, with an area under the curve of 0.82. When applying the double-threshold approach, 33% of the patients could be predicted by the deep learning model as EGFR positive or negative with a sensitivity and specificity of 100.0% and 87.5%. The remaining 67% of the patients got an uncertain result and will be recommenced to perform further examination. By incorporating adenocarcinoma subtype information, we achieved 100% sensitivity in predicting EGFR mutations in 37.3% of adenocarcinoma patients. Conclusions Our study demonstrates the potential of a deep learning-based EGFR mutation prediction model for rapid and cost-effective pre-screening. It could serve as a high-accuracy complement to current molecular detection methods and provide treatment opportunities for non-small cell lung cancer patients from whom limited samples are available

Simultaneous diagnosis of tuberculous pleurisy and malignant pleural effusion using metagenomic next-generation sequencing (mNGS)

Abstract Background Metagenomic next-generation sequencing (mNGS) has become a powerful tool for pathogen detection, but the value of human sequencing reads generated from it is underestimated. Methods A total of 138 patients with pleural effusion (PE) were diagnosed with tuberculous pleurisy (TBP, N = 82), malignant pleural effusion (MPE, N = 35), or non-TB infection (N = 21), whose PE samples all underwent mNGS analysis. Clinical TB tests including culture, Acid-Fast Bacillus (AFB) test, Xpert, and T-SPOT, were performed. To utilize mNGS for MPE identification, 25 non-MPE samples (20 TBP and 5 non-TB infection) were randomly selected to set human chromosome copy number baseline and generalized linear modeling was performed using copy number variant (CNV) features of the rest 113 samples (35 MPE and 78 non-MPE). Results The performance of TB detection was compared among five methods. T-SPOT demonstrated the highest sensitivity (61% vs. culture 32%, AFB 12%, Xpert 35%, and mNGS 49%) but with the highest false-positive rate (10%) as well. In contrast, mNGS was able to detect TB-genome in nearly half (40/82) of the PE samples from TBP subgroup, with 100% specificity. To evaluate the performance of using CNV features of the human genome for MPE prediction, we performed the leave-one-out cross-validation (LOOCV) in the subcohort excluding the 25 non-MPE samples for setting copy number standards, which demonstrated 54.1% sensitivity, 80.8% specificity, 71.7% accuracy, and an AUC of 0.851. Conclusion In summary, we exploited the value of human and non-human sequencing reads generated from mNGS, which showed promising ability in simultaneously detecting TBP and MPE

Image_2_PCSK9 regulates the efficacy of immune checkpoint therapy in lung cancer.tif

Proprotein convertase subtilisin/kexin type 9 (PCSK9) secreted by tumors was reported as a deleterious factor that led to the reduction of lymphocyte infiltration and the poorer efficacy of ICIs in vivo. This study aimed to explore whether PCSK9 expression in tumor tissue could predict the response of advanced non-small cell lung cancer (NSCLC) to anti-PD-1 immunotherapy and the synergistic antitumor effect of the combination of the PCSK9 inhibitor with the anti-CD137 agonist. One hundred fifteen advanced NSCLC patients who received anti-PD-1 immunotherapy were retrospectively studied with PCSK9 expression in baseline NSCLC tissues detected by immunohistochemistry (IHC). The mPFS of the PCSK9lo group was significantly longer than that of the PCSK9hi group [8.1 vs. 3.6 months, hazard ratio (HR): 3.450; 95% confidence interval (CI), 2.166-5.496]. A higher objective response rate (ORR) and a higher disease control rate (DCR) were observed in the PCSK9lo group than in the PCSK9hi group (54.4% vs. 34.5%, 94.7% vs. 65.5%). Reduction and marginal distribution of CD8+ T cells were observed in PCSK9hi NSCLC tissues. Tumor growth was retarded by the PCSK9 inhibitor and the anti-CD137 agonist alone in the Lewis lung carcinoma (LLC) mice model and further retarded by the PCSK9 inhibitor in combination with the CD137 agonist with long-term survival of the host mice with noticeable increases of CD8+ and GzmB+ CD8+ T cells and reduction of Tregs. Together, these results suggested that high PCSK9 expression in baseline tumor tissue was a deleterious factor for the efficacy of anti-PD-1 immunotherapy in advanced NSCLC patients. The PCSK9 inhibitor in combination with the anti-CD137 agonist could not only enhance the recruitment of CD8+ and GzmB+ CD8+ T cells but also deplete Tregs, which may be a novel therapeutic strategy for future research and clinical practice.</p

Image_3_PCSK9 regulates the efficacy of immune checkpoint therapy in lung cancer.tif

Proprotein convertase subtilisin/kexin type 9 (PCSK9) secreted by tumors was reported as a deleterious factor that led to the reduction of lymphocyte infiltration and the poorer efficacy of ICIs in vivo. This study aimed to explore whether PCSK9 expression in tumor tissue could predict the response of advanced non-small cell lung cancer (NSCLC) to anti-PD-1 immunotherapy and the synergistic antitumor effect of the combination of the PCSK9 inhibitor with the anti-CD137 agonist. One hundred fifteen advanced NSCLC patients who received anti-PD-1 immunotherapy were retrospectively studied with PCSK9 expression in baseline NSCLC tissues detected by immunohistochemistry (IHC). The mPFS of the PCSK9lo group was significantly longer than that of the PCSK9hi group [8.1 vs. 3.6 months, hazard ratio (HR): 3.450; 95% confidence interval (CI), 2.166-5.496]. A higher objective response rate (ORR) and a higher disease control rate (DCR) were observed in the PCSK9lo group than in the PCSK9hi group (54.4% vs. 34.5%, 94.7% vs. 65.5%). Reduction and marginal distribution of CD8+ T cells were observed in PCSK9hi NSCLC tissues. Tumor growth was retarded by the PCSK9 inhibitor and the anti-CD137 agonist alone in the Lewis lung carcinoma (LLC) mice model and further retarded by the PCSK9 inhibitor in combination with the CD137 agonist with long-term survival of the host mice with noticeable increases of CD8+ and GzmB+ CD8+ T cells and reduction of Tregs. Together, these results suggested that high PCSK9 expression in baseline tumor tissue was a deleterious factor for the efficacy of anti-PD-1 immunotherapy in advanced NSCLC patients. The PCSK9 inhibitor in combination with the anti-CD137 agonist could not only enhance the recruitment of CD8+ and GzmB+ CD8+ T cells but also deplete Tregs, which may be a novel therapeutic strategy for future research and clinical practice.</p