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Programmed cell death protein-1/programmed cell death ligand-1 (PD-1/PD-L1) pathway blockade is a promising new cancer therapy. Although PD-1/PD-L1 treatment has yielded clinical benefits in several types of cancer, further studies are required to clarify predictive biomarkers for drug efficacy and to understand the fundamental mechanism of PD-1/PD-L1 interaction between host and tumor cells. Here, we show that exosomes derived from lung cancer cells express PD-L1 and play a role in immune escape by reducing T-cell activity and promoting tumor growth. The abundance of PD-L1 on exosomes represented the quantity of PD-L1 expression on cell surfaces. Exosomes containing PD-L1 inhibited interferon-gamma (IFN-γ) secretion by Jurkat T cells. IFN-γ secretion was restored by PD-L1 knockout or masking on the exosomes. Both forced expression of PD-L1 on cells without PD-L1 and treatment with exosomes containing PD-L1 enhanced tumor growth in vivo. PD-L1 was present on exosomes isolated from the plasma of patients with non-small cell lung cancer, and its abundance in exosomes was correlated with PD-L1 positivity in tumor tissues. Exosomes can impair immune functions by reducing cytokine production and inducing apoptosis in CD8 + T cells. Our findings indicate that tumor-derived exosomes expressing PD-L1 may be an important mediator of tumor immune escape. Lung cancer: Immune suppressant protein promotes tumor growth An immune suppressant protein expressed by non-small cell lung cancer cells (NSCLC) to facilitate tumor growth could be a valuable therapeutic target. NSCLC is often diagnosed at advanced stages, making treatment challenging. Therapies that inhibit an immune suppressant protein called programmed cell death ligand-1 (PD-L1) have shown promise for other cancers, but how PD-L1 interacts with host and tumor cells in NSCLC needs clarification. In experiments on human cell lines and mice, Jae Cheol Lee and Jin Kyung Rho at the University of Ulsan in Seoul, South Korea, and co-workers found that microvesicles (or ‘exosomes’) released by NSCLC cells carry PD-L1, which interacts with tumor-infiltrating immune cells, inhibiting their activity. The amount of PD-L1 in exosomes directly correlates with PD-L1 expression levels on tumor cell surfaces, providing a useful indication of disease activity.

The emergence of resistance to epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) in non-small cell lung cancer (NSCLC) with activating EGFR mutations is a major hindrance to treatment. We investigated the effects of p53 in primary sensitivity and acquired resistance to EGFR-TKIs in NSCLC cells. Changes in sensitivity to EGFR-TKIs were determined using p53 overexpression or knockdown in cells with activating EGFR mutations. We investigated EMT-related molecules, morphologic changes, and AXL induction to elucidate mechanisms of acquired resistance to EGFR-TKIs according to p53 status. Changes in p53 status affected primary sensitivity as well as acquired resistance to EGFR-TKIs according to cell type. Firstly, p53 silencing did not affect primary and acquired resistance to EGFR-TKIs in PC-9 cells, but it led to primary resistance to EGFR-TKIs through AXL induction in HCC827 cells. Secondly, p53 silencing in H1975 cells enhanced the sensitivity to osimertinib through the emergence of mesenchymal-to-epithelial transition, and the emergence of acquired resistance to osimertinib in p53 knockout cells was much slower than in H1975 cells. Furthermore, two cell lines (H1975 and H1975/p53 KO ) demonstrated the different mechanisms of acquired resistance to osimertinib. Lastly, the introduction of mutant p53-R273H induced the epithelial-to-mesenchymal transition and exerted resistance to EGFR-TKIs in cells with activating EGFR mutations. These findings indicate that p53 mutations can be associated with primary or acquired resistance to EGFR-TKIs. Thus, the status or mutations of p53 may be considered as routes to improving the therapeutic effects of EGFR-TKIs in NSCLC.

Tumor-derived exosomes (TEXs) contain enriched miRNAs, and exosomal miRNAs can affect tumor growth, including cell proliferation, metastasis, and drug resistance through cell-to-cell communication. We investigated the role of exosomal miR-1260b derived from non-small cell lung cancer (NSCLC) in tumor progression. Exosomal miR-1260b induced angiogenesis by targeting homeodomain-interacting protein kinase-2 (HIPK2) in human umbilical vein endothelial cells (HUVECs). Furthermore, exosomal miR-1260b or suppression of HIPK2 led to enhanced cellular mobility and cisplatin resistance in NSCLC cells. In patients with NSCLC, the level of HIPK2 was significantly lower in tumor tissues than in normal lung tissues, while that of miR-1260b was higher in tumor tissues. HIPK2 and miR-1260b expression showed an inverse correlation, and this correlation was strong in distant metastasis. Finally, the expression level of exosomal miR-1260b in plasma was higher in patients with NSCLC than in healthy individuals, and higher levels of exosomal miR-1260b were associated with high-grade disease, metastasis, and poor survival. In conclusion, exosomal miR-1260b can promote angiogenesis in HUVECs and metastasis of NSCLC by regulating HIPK2 and may serve as a prognostic marker for lung cancers.

Coronavirus disease 2019 (COVID-19) has strained healthcare systems worldwide. Predicting COVID-19 severity could optimize resource allocation, like oxygen devices and intensive care. If machine learning model could forecast the severity of COVID-19 patients, hospital resource allocation would be more comfortable. This study evaluated machine learning models using electronic records from 3,996 COVID-19 patients to forecast mild, moderate, or severe disease up to 2 days in advance. A deep neural network (DNN) model achieved 91.8% accuracy, 0.96 AUROC, and 0.90 AUPRC for 2-day predictions, regardless of disease phase. Tree-based models like random forest achieved slightly better metrics (random forest: 94.1% of accuracy, 0.98 AUROC, 0.95 AUPRC; Gradient boost: 94.1% of accuracy, 0.98 AUROC, 0.94 AUPRC), prioritizing treatment factors like steroid use. However, the DNN relied more on fixed patient factors like demographics and symptoms in aspect to SHAP value importance. Since treatment patterns vary between hospitals, the DNN may be more generalizable than tree-based models (random forest, gradient boost model). The results demonstrate accurate short-term forecasting of COVID-19 severity using routine clinical data. DNN models may balance predictive performance and generalizability better than other methods. Severity predictions by machine learning model could facilitate resource planning, like ICU arrangement and oxygen devices.

Detecting bacteremia among surgical in-patients is more obscure than other patients due to the inflammatory condition caused by the surgery. The previous criteria such as systemic inflammatory response syndrome or Sepsis-3 are not available for use in general wards, and thus, many clinicians usually rely on practical senses to diagnose postoperative infection. This study aims to evaluate the performance of continuous monitoring with a deep learning model for early detection of bacteremia for surgical in-patients in the general ward and the intensive care unit (ICU). In this retrospective cohort study, we included 36,023 consecutive patients who underwent general surgery between October and December 2017 at a tertiary referral hospital in South Korea. The primary outcome was the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC) for detecting bacteremia by the deep learning model, and the secondary outcome was the feature explainability of the model by occlusion analysis. Out of the 36,023 patients in the data set, 720 cases of bacteremia were included. Our deep learning-based model showed an AUROC of 0.97 (95% CI 0.974-0.981) and an AUPRC of 0.17 (95% CI 0.147-0.203) for detecting bacteremia in surgical in-patients. For predicting bacteremia within the previous 24-hour period, the AUROC and AUPRC values were 0.93 and 0.15, respectively. Occlusion analysis showed that vital signs and laboratory measurements (eg, kidney function test and white blood cell group) were the most important variables for detecting bacteremia. A deep learning model based on time series electronic health records data had a high detective ability for bacteremia for surgical in-patients in the general ward and the ICU. The model may be able to assist clinicians in evaluating infection among in-patients, ordering blood cultures, and prescribing antibiotics with real-time monitoring.

Trever Bivona and colleagues identify the upregulation of the AXL kinase in human non–small cell lung cancer with acquired resistance to erlotinib. Inhibition of AXL restores sensitivity to erlotinib in in vitro and in vivo tumor models. The authors suggest AXL as a potential therapeutic target that may prevent or overcome acquired resistance in patients with EGFR -mutant lung cancer. Human non–small cell lung cancers (NSCLCs) with activating mutations in EGFR frequently respond to treatment with EGFR-targeted tyrosine kinase inhibitors (TKIs), such as erlotinib, but responses are not durable, as tumors acquire resistance. Secondary mutations in EGFR (such as T790M) or upregulation of the MET kinase are found in over 50% of resistant tumors. Here, we report increased activation of AXL and evidence for epithelial-to-mesenchymal transition (EMT) in multiple in vitro and in vivo EGFR -mutant lung cancer models with acquired resistance to erlotinib in the absence of the EGFR p.Thr790Met alteration or MET activation. Genetic or pharmacological inhibition of AXL restored sensitivity to erlotinib in these tumor models. Increased expression of AXL and, in some cases, of its ligand GAS6 was found in EGFR -mutant lung cancers obtained from individuals with acquired resistance to TKIs. These data identify AXL as a promising therapeutic target whose inhibition could prevent or overcome acquired resistance to EGFR TKIs in individuals with EGFR -mutant lung cancer.

Non-small cell lung cancer (NSCLC) is histologically classified into lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LSCC). However, some tumors are histologically ambiguous and other pathophysiological features or microenvironmental factors may be more prominent. Here we report integrative multiomics analyses using data for 229 patients from a Korean NSCLC cohort and 462 patients from previous multiomics studies. Histological examination reveals five molecular subtypes, one of which is a NSCLC subtype with PI3K-Akt pathway upregulation, showing a high proportion of metastasis and poor survival outcomes regardless of any specific NSCLC histology. Proliferative subtypes are present in LUAD and LSCC, which show strong associations with whole genome doubling (WGD) events. Comprehensive characterization of the immune microenvironment reveals various immune cell compositions and neoantigen loads across molecular subtypes, which predicting different prognoses. Immunological subtypes exhibit a hot tumor-enriched state and a higher efficacy of adjuvant therapy. Subtyping of non-small cell lung cancer can be challenging based on pathology. Here, the authors utilise multi-omics analysis of 229 patients to identify further subtypes, and altered immune composition between subtypes.

Blood and fluid analysis is extensively used for classifying the etiology of pleural effusion. However, most studies focused on determining the presence of a disease. This study classified pleural effusion etiology employing deep learning models by applying contrastive-loss. Patients with pleural effusion who underwent thoracentesis between 2009 and 2019 at the Asan Medical Center were analyzed. Five different models for categorizing the etiology of pleural effusion were compared. The performance metrics were top-1 accuracy, top-2 accuracy, and micro-and weighted-AUROC. UMAP and t-SNE were used to visualize the contrastive-loss model’s embedding space. Although the 5 models displayed similar performance in the validation set, the contrastive-loss model showed the highest accuracy in the extra-validation set. Additionally, the accuracy and micro-AUROC of the contrastive-loss model were 81.7% and 0.942 in the validation set, and 66.2% and 0.867 in the extra-validation set. Furthermore, the embedding space visualization in the contrastive-loss model exhibited typical and atypical effusion results by comparing the true and false positives of the rule-based criteria. Therefore, classifying the etiology of pleural effusion was achievable using the contrastive-loss model. Conclusively, visualization of the contrastive-loss model will provide clinicians with valuable insights for etiology diagnosis by differentiating between typical and atypical disease types.

Background Very few studies have compared the effects and side effects of vancomycin and teicoplanin in patients with methicillin-resistant Staphylococcus aureus pneumonia. This study aimed to compare the efficacy and safety of vancomycin and teicoplanin in patients with methicillin-resistant Staphylococcus aureus pneumonia. Methods This study examined 116 patients with methicillin-resistant Staphylococcus aureus pneumonia who met the inclusion criteria and were treated with either vancomycin ( n  = 54) or teicoplanin ( n  = 62). The primary (i.e., clinical failure during treatment) and secondary outcomes (i.e., mortality rates, discontinuation of study drugs due to treatment failure, side effects, and clinical cure) were evaluated. Results The vancomycin group presented lower clinical failure rates (25.9% vs. 61.3%, p  < 0.001), discontinuation due to treatment failure (22.2% vs. 41.9%, p  = 0.024), and mortality rates (3.7% vs 19.4%, p  = 0.010). The Cox proportional hazard model revealed that teicoplanin was a significant clinical failure predictor compared with vancomycin (adjusted odds ratio, 2.198; 95% confidence interval 1.163–4.154). The rates of drug change due to side effects were higher in the vancomycin group than in the teicoplanin group (24.1% vs. 1.6%, p  < 0.001). Conclusions Vancomycin presented favorable treatment outcomes and more side effects compared with teicoplanin, which suggests that clinicians would need to consider the efficacy and potential side effects of these drugs before prescription.

Background Small cell lung cancer (SCLC) has an exceptionally poor prognosis; as most of the cases are initially diagnosed as extensive disease with hematogenous metastasis. Therefore, the early diagnosis of SCLC is very important and may improve its prognosis. Methods To investigate the feasibility of early diagnosis of SCLC, we examined exosomal microRNAs (miRNAs) present in serum obtained from patients with SCLC. First, exosomes were isolated in serum from patients with SCLC and healthy individuals and were characterized using particle size and protein markers. Additionally, miRNA array was performed to define SCLC-specific exosomal miRNAs. Second, the obtained miRNAs were further validated employing a large cohort. Finally, the ability to diagnose SCLC was estimated by area under the curve (AUC), and intracellular mRNA change patterns were verified through validated miRNAs. Results From the miRNA array results, we selected 51-miRNAs based on p-values and top 10 differentially expressed genes, and 25-miRNAs were validated using quantitative reverse transcription-polymerase chain reaction. The 25-miRNAs were further validated employing a large cohort. Among them, 7-miRNAs showed significant differences. Furthermore, 6-miRNAs (miR-3565, miR-3124-5p, miR-200b-3p, miR-6515, miR-3126-3p and miR-9-5p) were up-regulated and 1-miRNA (miR-92b-5p) was down-regulated. The AUC value of each miRNA sets between 0.64 and 0.76, however the combined application of 3-miRNAs (miR-200b-3p, miR-3124-5p and miR-92b-5p) remarkably improved the diagnostic value (AUC = 0.93). Gene ontology analysis revealed that the 3-miRNA panel is linked to various oncogene pathways and nervous system development. When the 3-miRNAs were introduced to cells, the resulting changes in total mRNA expression strongly indicated the presence of lung diseases, including lung cancer. In addition, the 3-miRNA panel was significantly associated with a poorer prognosis, although individual miRNAs have not been validated as prognostic markers. Conclusion Our study identified SCLC-specific exosomal miRNAs, and the 3-miRNAs panel (miR-200b-3p, miR-3124-5p and miR-92b-5p) may serve as a diagnostic and prognostic marker for SCLC. Graphical abstract