Impact of coexisting type 2 diabetes mellitus on the urinary microbiota of kidney stone patients.

Objectives: Type 2 diabetes mellitus (T2DM) commonly complicates kidney stone disease (KSD). Our objective is to investigate the variations in the urinary microbiota between individuals with KSD alone and those with KSD plus T2DM. This exploration could have implications for disease diagnosis and treatment strategies. Methods: During lithotripsy, a ureterscope was employed, and 1 mL of urine was collected from the renal pelvis after bladder disinfection. Sequencing targeting the V3-V4 hypervariable region was performed using the 16S rRNA and Illumina Novaseq platform. Results: The Shannon index showed a significant decrease in the KSD plus T2DM group compared to the KSD-only group (false discovery rate = 0.041). Principal Coordinate Analysis (PCoA) demonstrated a distinct bacterial community in the KSD plus T2DM group compared to the KSD-only group (false discovery rate = 0.027). The abundance of Sphingomonas, Corynebacterium, and Lactobacillus was significantly higher in the KSD plus T2DM group than in the KSD-only group (false discovery rate < 0.05). Furthermore, Enhydrobacter, Chryseobacterium, and Allobaculum were positively correlated with fasting blood glucose and HbA1c values (P < 0.05). Conclusions: The urinary microbiota in the renal pelvis exhibits differences between patients with KSD plus T2DM and those with KSD alone. Further studies employing animal models are necessary to validate these distinctions, potentially paving the way for therapeutic developments based on the urinary microbiota.

A bidirectional interpretable compound-protein interaction prediction framework based on cross attention.

The identification of compound-protein interactions (CPIs) plays a vital role in drug discovery. However, the huge cost and labor-intensive nature in vitro and vivo experiments make it urgent for researchers to develop novel CPI prediction methods. Despite emerging deep learning methods have achieved promising performance in CPI prediction, they also face ongoing challenges: (i) providing bidirectional interpretability from both the chemical and biological perspective for the prediction results; (ii) comprehensively evaluating model generalization performance; (iii) demonstrating the practical applicability of these models. To overcome the challenges posed by current deep learning methods, we propose a cross multi-head attention oriented bidirectional interpretable CPI prediction model (CmhAttCPI). First, CmhAttCPI takes molecular graphs and protein sequences as inputs, utilizing the GCW module to learn atom features and the CNN module to learn residue features, respectively. Second, the model applies cross multi-head attention module to compute attention weights for atoms and residues. Finally, CmhAttCPI employs a fully connected neural network to predict scores for CPIs. We evaluated the performance of CmhAttCPI on balanced datasets and imbalanced datasets. The results consistently show that CmhAttCPI outperforms multiple state-of-the-art methods. We constructed three scenarios based on compound and protein clustering and comprehensively evaluated the model generalization ability within these scenarios. The results demonstrate that the generalization ability of CmhAttCPI surpasses that of other models. Besides, the visualizations of attention weights reveal that CmhAttCPI provides chemical and biological interpretation for CPI prediction. Moreover, case studies confirm the practical applicability of CmhAttCPI in discovering anticancer candidates.

A case of minimally invasive small-incision off-pump ligation of a coronary artery fistula monitored by transesophageal echocardiography.

This case demonstrated intraoperative real-time transesophageal echocardiographic monitoring in minimally invasive small-incision Off-pump ligation of a coronary artery fistula,demonstrating the importance of esophageal echocardiography in surgical monitoring.

Can antibiotics for enteritis or for urinary tract infection disrupt the urinary microbiota in rats?

Background: To establish antibiotic preregimes and administration routes for studies on urinary microbiota. Methods and materials: Antibiotics for enteritis (Abx-enteritis) and UTIs (Abx-UTI) were administered via gavage and/or urinary catheterisation (UC) for 1 and/or 2 weeks. The effects of these Abx on the urinary microbiota of rats were examined via 16S rRNA sequencing and urine culture, including anaerobic and aerobic culture. Additionally, the safety of the Abx was examined. Results: Abx-enteritis/Abx-UTI (0.5 g/L and 1 g/L) administered via gavage did not alter the microbial community and bacterial diversity in the urine of rats (FDR > 0.05); however, Abx-UTI (1 g/L) administered via UC for 1 and 2 weeks altered the urinary microbial community (FDR < 0.05). Rats administered Abx-UTI (1 g/L) via UC for 1 week demonstrated a distinct urinary microbiota in culture. Abx-enteritis/Abx-UTI administered via gavage disrupted the microbial community and reduced bacterial diversity in the faeces of rats (FDR < 0.05), and Abx-UTI administered via UC for 2 weeks (FDR < 0.05) altered the fecal microbiota. Abx-UTI (1 g/L) administered via UC did not alter safety considerations. In addition, we noticed that UC did not induce infections and injuries to the bladder and kidney tissues. Conclusions: Administration of Abx-UTI via UC for 1 week can be considered a pre-treatment option while investigating the urinary microbiota.

Melatonin targets MoIcl1 and works synergistically with fungicide isoprothiolane in rice blast control.

Melatonina natural harmless molecule-displays versatile roles in human health and crop disease control such as for rice blast. Rice blast, caused by the filamentous fungus Magnaporthe oryzae, is one devastating disease of rice. Application of fungicides is one of the major measures in the control of various crop diseases. However, fungicide resistance in the pathogen and relevant environmental pollution are becoming serious problems. By screening for possible synergistic combinations, here, we discovered an eco-friendly combination for rice blast control, melatonin, and the fungicide isoprothiolane. These compounds together exhibited significant synergistic inhibitory effects on vegetative growth, conidial germination, appressorium formation, penetration, and plant infection by M. oryzae. The combination of melatonin and isoprothiolane reduced the effective concentration of isoprothiolane by over 10-fold as well as residual levels of isoprothiolane. Transcriptomics and lipidomics revealed that melatonin and isoprothiolane synergistically interfered with lipid metabolism by regulating many common targets, including the predicted isocitrate lyase-encoding gene MoICL1. Furthermore, using different techniques, we show that melatonin and isoprothiolane interact with MoIcl1. This study demonstrates that melatonin and isoprothiolane function synergistically and can be used to reduce the dosage and residual level of isoprothiolane, potentially contributing to the environment-friendly and sustainable control of crop diseases.

Self-Supervised Spatiotemporal Clustering of Vehicle Emissions With Graph Convolutional Network.

Spatiotemporal clustering of vehicle emissions, which reveals the evolution pattern of air pollution from road traffic, is a challenging representation learning task due to the lack of supervision. Some recent work building upon graph convolutional network (GCN) models the intrinsic spatiotemporal correlations among the nodes in road networks as graph representations for clustering. However, these existing methods ignore the interactions between spatial and temporal variations in vehicle emissions, resulting in incomplete descriptions and inaccurate detection of the evolution pattern of air pollution. To address this issue, this article proposes a two-way self-supervised spatiotemporal representation learning scheme, in which the temporal and spatial features are progressively learned in a mutually reinforced manner. Our proposed method is based on the observation that though the variation in vehicle emissions in the road network is consistent in the spatial and temporal domains, its expression is more distinct in temporal sequences. To this end, the input emission data are first projected into an initial temporal representation space spanned by the captured features from a pretrained BiLSTM network. Then the generated distribution of temporal features is used to construct an objective constraint for high-purity clustering through a two-way self-supervised mechanism, which is leveraged as a constraint for the feature clustering of a GCN. Furthermore, to eliminate the initial errors, a joint optimization scheme is presented to generate the decoupled clustering results through the progressive refinement of representation and clustering. Our proposed method is evaluated on the traffic emission dataset of Xian city in 2020, and the experimental results have demonstrated the superiority against the state-of-the-art.

Therapeutic effect of demethylated hydroxylated phillygenin derivative on Helicobacter pylori infection.

Modifying and transforming natural antibacterial products is a novel idea for developing new efficacious compounds. Phillygenin has an inhibitory effect on H. pylori. The aim of the present study was to prepare a phillygenin derivative (PHI-Der) through demethylation and hydroxylation. The minimum inhibitory concentration of 18 strains of H. pylori from different sources was 8-32 µg/mL in vitro, and the activity increased 2-8 times than that of phillygenin. PHI-Der could significantly inhibit the colonization of H. pylori in vivo, reduce the inflammatory response, and promote the repair of inflammatory damage. Further, we used SwissTargetPrediction to predict that its main targets are ALOX5, MCL1, and SLC6A4, and find that it can inhibit bacterial biofilm formation and reduce bacterial infection of cells. It can enhance the intracellular oxidative capacity of H. pylori to inhibit H. pylori growth. Further, it could prevent the oxidation of H. pylori-infected cells and reduce the inflammatory response, which plays a role in protection. In conclusion, compared to phillygenin, PHI-Der had better antibacterial activity and was more effective in treating H. pylori infection. It has characteristics of high safety, specificity, resistance to drug resistance and better antibacterial activity than phillygenin, it's a good antioxidant for host cells.

Outcomes and prognosis of non-small cell lung cancer patients who underwent curable surgery: a protocol for a real-world, retrospective, population-based and nationwide Chinese National Lung Cancer Cohort (CNLCC) study.

INTRODUCTION: Surgery is one of the main approaches for the comprehensive treatment of early and locally advanced non-small cell lung cancer (NSCLC). This study conducts a nationwide multicentre study to explore factors that could influence the outcomes of patients with I-IIIA NSCLC who underwent curable surgery in real-world scenarios. METHODS AND ANALYSIS: All patients diagnosed with NSCLC between January 2013 and December 2020 will be identified from 30 large public medical services centres in mainland China. The algorithm of natural language processing and artificial intelligence techniques were used to extract data from electronic health records of enrolled patients who fulfil the inclusion criteria. Six categories of parameters are collected and stored from the electronic records, then the parameters will be structured as a high-quality structured case report form. The code book will be compiled and each parameter will be classified and designated a code. In addition, the study retrieves the survival status and causes of death of patients from the Chinese Centre for Disease Control and Prevention. The primary endpoints are overall survival and the secondary endpoint is disease-free survival. Finally, an online platform is formed for data queries and the original records will be stored as secure electronic documents. ETHICS AND DISSEMINATION: The study has been approved by the Ethical Committee of the Chinese Academy of Medical Sciences. Study findings will be disseminated via presentations at conferences and publications in open-access journals. This study has been registered in the Chinese Trial Register (ChiCTR2100052773) on 11 May 2021, http://www.chictr.org.cn/showproj.aspx?proj=136659. TRIAL REGISTRATION NUMBER: ChiCTR2100052773.

Biologically Interpretable Deep Learning To Predict Response to Immunotherapy In Advanced Melanoma Using Mutations and Copy Number Variations.

Only 30-40% of advanced melanoma patients respond effectively to immunotherapy in clinical practice, so it is necessary to accurately identify the response of patients to immunotherapy pre-clinically. Here, we develop KP-NET, a deep learning model that is sparse on KEGG pathways, and combine it with transfer- learning to accurately predict the response of advanced melanomas to immunotherapy using KEGG pathway-level information enriched from gene mutation and copy number variation data. The KP-NET demonstrates best performance with AUROC of 0.886 on testing set and 0.803 on an unseen evaluation set when predicting responders (CR/PR/SD with PFS ≥6 mo) versus non-responders (PD/SD with PFS <6 mo) in anti-CTLA-4 treated melanoma patients. The model also achieves an AUROC of 0.917 and 0.833 in predicting CR/PR versus PD, respectively. Meanwhile, the AUROC is 0.913 when predicting responders versus non-responders in anti-PD-1/PD-L1 melanomas. Moreover, the KP-NET reveals some genes and pathways associated with response to anti-CTLA-4 treatment, such as genes PIK3CA, AOX1 and CBLB, and ErbB signaling pathway, T cell receptor signaling pathway, et al. In conclusion, the KP-NET can accurately predict the response of melanomas to immunotherapy and screen related biomarkers pre-clinically, which can contribute to precision medicine of melanoma.

E2EFP-MIL: End-to-end and high-generalizability weakly supervised deep convolutional network for lung cancer classification from whole slide image.

Efficient and accurate distinction of histopathological subtype of lung cancer is quite critical for the individualized treatment. So far, artificial intelligence techniques have been developed, whose performance yet remained debatable on more heterogenous data, hindering their clinical deployment. Here, we propose an end-to-end, well-generalized and data-efficient weakly supervised deep learning-based method. The method, end-to-end feature pyramid deep multi-instance learning model (E2EFP-MIL), contains an iterative sampling module, a trainable feature pyramid module and a robust feature aggregation module. E2EFP-MIL uses end-to-end learning to extract generalized morphological features automatically and identify discriminative histomorphological patterns. This method is trained with 1007 whole slide images (WSIs) of lung cancer from TCGA, with AUCs of 0.95-0.97 in test sets. We validated E2EFP-MIL in 5 real-world external heterogenous cohorts including nearly 1600 WSIs from both United States and China with AUCs of 0.94-0.97, and found that 100-200 training images are enough to achieve an AUC of >0.9. E2EFP-MIL overperforms multiple state-of-the-art MIL-based methods with high accuracy and low hardware requirements. Excellent and robust results prove generalizability and effectiveness of E2EFP-MIL in clinical practice. Our code is available at https://github.com/raycaohmu/E2EFP-MIL.

HiRAND: A novel GCN semi-supervised deep learning-based framework for classification and feature selection in drug research and development.

The prediction of response to drugs before initiating therapy based on transcriptome data is a major challenge. However, identifying effective drug response label data costs time and resources. Methods available often predict poorly and fail to identify robust biomarkers due to the curse of dimensionality: high dimensionality and low sample size. Therefore, this necessitates the development of predictive models to effectively predict the response to drugs using limited labeled data while being interpretable. In this study, we report a novel Hierarchical Graph Random Neural Networks (HiRAND) framework to predict the drug response using transcriptome data of few labeled data and additional unlabeled data. HiRAND completes the information integration of the gene graph and sample graph by graph convolutional network (GCN). The innovation of our model is leveraging data augmentation strategy to solve the dilemma of limited labeled data and using consistency regularization to optimize the prediction consistency of unlabeled data across different data augmentations. The results showed that HiRAND achieved better performance than competitive methods in various prediction scenarios, including both simulation data and multiple drug response data. We found that the prediction ability of HiRAND in the drug vorinostat showed the best results across all 62 drugs. In addition, HiRAND was interpreted to identify the key genes most important to vorinostat response, highlighting critical roles for ribosomal protein-related genes in the response to histone deacetylase inhibition. Our HiRAND could be utilized as an efficient framework for improving the drug response prediction performance using few labeled data.

Compound Event-Triggered Distributed MPC for Coupled Nonlinear Systems.

This article investigates the event-triggered distributed model predictive control (DMPC) for perturbed coupled nonlinear systems subject to state and control input constraints. A novel compound event-triggered DMPC strategy, including a compound triggering condition and a new constraint tightening approach, is developed. In this event-triggered strategy, two stability-related conditions are checked in a parallel manner, which relaxes the requirement of the decrease of the Lyapunov function. An open-loop prediction scheme to avoid periodic transmission is designed for the states in the terminal set. As a result, the number of triggering and transmission instants can be reduced significantly. Furthermore, the proposed constraint tightening approach solves the problem of the state constraint satisfaction, which is quite challenging due to the external disturbances and the mutual influences caused by dynamical coupling. Simulations are conducted at last to validate the effectiveness of the proposed algorithm.

Preparation and Synergistic Effect of Biomimetic Poly(lactic acid)/Graphene Oxide Composite Scaffolds Loaded with Dual Drugs.

To promote the bone repair ability of drug-loaded scaffolds, poly(lactic acid) (PLA)/graphene oxide (GO)/Salvianolic acid B (Sal-B)/aspirin (ASA) dual drug-loaded biomimetic composite scaffolds were prepared. The results showed that the addition of these two drugs delayed the gel formation of the composite system, but a biomimetic nanofiber structure could still be obtained by extending the gel time. The addition of Sal-B increased the hydrophilicity of the scaffold, while an increase in ASA reduced the porosity. Dual drug-loaded scaffolds had good haemocompatibility and synergically promoted the proliferation of MC3T3-E1 cells and enhanced alkaline phosphatase activity. Sustained-release experiments of the two drugs showed that the presence of ASA slowed the cumulative release of Sal-B, while Sal-B promoted the release of ASA. Kinetic modeling showed that the release of both drugs conforms to the Korsmeyer-Peppas model, but Sal-B conforms to the Fick diffusion mechanism and ASA follows Fick diffusion and carrier swelling/dissolution.

Preparation and sustained-release properties of poly(lactic acid)/graphene oxide porous biomimetic composite scaffolds loaded with salvianolic acid B.

Biomimetic scaffolds loaded with drugs can improve the osteogenesis and neovascularisation of scaffolds. A series of PLA/GO/Sal-B drug-loaded scaffolds was prepared by thermally induced phase separation. The addition of Sal-B increased the diameter of the fibres, but the scaffold showed a porous nanofibrous structure after drug release. X-ray diffraction results showed that the addition of Sal-B did not affect the formation of the nanofibre biomimetic structure of the scaffold. FTIR results indicated a certain interaction between Sal-B and PLA/GO. Water absorption and porosity test results revealed that the scaffolds had good hydrophilicity and appropriate porosity. The addition of Sal-B was also conducive to the formation of sediments possibly due to the good water solubility of Sal-B itself. The prepared scaffolds had good blood compatibility and cytocompatibility, and a small additional amount of Sal-B could significantly promote cell proliferation and alkaline phosphatase activity. Their sustained release performance indicated that the biomimetic scaffolds had controlled the release of Sal-B. The kinetic model showed that the PLA/GO/Sal-B drug-loaded biomimetic scaffolds followed the diffusion mechanism.

A novel subtype to predict prognosis and treatment response with DNA driver methylation-transcription in ovarian cancer.

Aims: To identify a novel subtype with DNA driver methylation-transcriptomic multiomics and predict prognosis and therapy response in serous ovarian cancer (SOC). Methods: SOC cohorts with both mRNA and methylation were collected, and DNA driver methylation (DNAme) was identified with the MithSig method. A novel prognostic subtype was developed by integrating the information on DNAme and prognosis-regulated DNAme-associated mRNA by similarity network fusion. Results: 43 overlapped DNAme were identified in three independent cohorts. SOC patients were categorized into three distinct subtypes by integrated multiomics. There were differences in prognosis, tumor microenvironment and response to therapy among the subtypes. Conclusion: This study identified 43 DNAmes and proposes a novel subtype toward personalized chemotherapy and immunotherapy for SOC patients based on multiomics.

Ovarian cancer is a highly malignant gynecological disease. The high heterogeneity of ovarian cancer may contribute to chemotherapy resistance and immunotherapy insensitivity. Gene alterations and aberrant methylation occur in the process of tumor initiation and progression, but not all alterations are drivers of tumor development. In this study, we aim to find the DNA driver methylation (DNAme) that plays a decisive role in ovarian cancer development and obtain a novel multiomics molecular subtype related to DNAme integrated by multiple omics information. We identified 43 overlapping DNAme in three cohorts. The multiomics subtype associated with DNAme could predict ovarian cancer prognosis and treatment response.

Characterization of the Immune Cell Infiltration Landscape Uncovers Prognostic and Immunogenic Characteristics in Lung Adenocarcinoma.

The immune cell infiltration in TME has been reported to be associated with prognosis and immunotherapy efficiency of lung cancers. However, to date, the immune infiltrative landscape of lung adenocarcinoma (LUAD) has not been elucidated yet. Therefore, this study aimed to identify a new transcriptomic-based TME classification and develop a risk scoring system to predict the clinical outcomes of patients with LUAD. We applied "CIBERSORT" algorithm to analyze the transcriptomic data of LUAD samples and classified LUAD into four discrete subtypes according to the distinct immune cell infiltration patterns. Furthermore, we established a novel predictive tool (TMEscore) to quantify the immune infiltration patterns for each LUAD patient by principal component analysis. The TMEscore displayed as a reliable and independent prognostic biomarker for LUAD, with worse survival in TMEscrore-high patients and better survival in TMEscrore-low patients in both TCGA and other five GEO cohorts. In addition, enriched pathways and genomic alterations were also analyzed and compared in different TMEscore subgroups, and we observed that high TMEscore was significantly correlated with more aggressive molecular changes, while the low TMEscore subgroup enriched in immune active-related pathways. The TMEscore-low subtype showed overexpression of PD-1, CTLA4, and associations of other markers of sensitivity to immunotherapy, including TMB, immunophenoscore (IPS) analysis, and tumor immune dysfunction and exclusion (TIDE) algorithm. Conclusively, TMEscore is a promising and reliable biomarker to distinguish the prognosis, the molecular and immune characteristics, and the benefit from ICIs treatments in LUAD.

Improving ovarian cancer treatment decision using a novel risk predictive tool.

BACKGROUND: As a major component of the tumor tissue, the tumor microenvironment (TME) has been proven to associate with tumor progression and immunotherapy. Ovarian cancer accounts for the highest mortality rate among gynecologic malignancies. Its clinical treatment decision is highly correlated with the prognosis, underscoring the need to evaluate the prognosis and choose the proper clinical treatment through TME information. METHOD: This study constructs a score with TME information obtained by the CIBERSORT algorithm, which classifies the patients into high and low TMEscore groups with quantified TME infiltration patterns through the PCA algorithm. TMEscore was constructed by TCGA cohort and validated in GEO cohort. Univariate and multivariate Cox proportional hazards model analyses were used to demonstrate prognostic value of TMEscore in overall and stratified analysis. RESULT: TMEscore is highly correlated with survival and high TMEscore group has a better prognosis. In order to improve treatment decision, the expression of immune checkpoints, immunophenoscore (IPS) and ESTIMATE score showed a high TMEscore have a better immune microenvironment and respond better to immune checkpoint inhibitors (ICIs). Meanwhile, the mutation landscape between TMEscore groups was profiled, and 13 genes were found mutated differently between the two groups. Among them, BRCA1 has more mutations in the high TMEscore group and speculated that high TMEscore patients might be a beneficiary population of PARP inhibitors combined with immunotherapy. CONCLUSION: TMEscore based on TME with prognostic value and clinical value is proposed for the identification of targets treatment and immunotherapy strategies for ovarian cancer.

The Discovery of New Drug-Target Interactions for Breast Cancer Treatment.

Drug-target interaction (DTIs) prediction plays a vital role in probing new targets for breast cancer research. Considering the multifaceted challenges associated with experimental methods identifying DTIs, the in silico prediction of such interactions merits exploration. In this study, we develop a feature-based method to infer unknown DTIs, called PsePDC-DTIs, which fuses information regarding protein sequences extracted by pseudo-position specific scoring matrix (PsePSSM), detrended cross-correlation analysis coefficient (DCCA coefficient), and an FP2 format molecular fingerprint descriptor of drug compounds. In addition, the synthetic minority oversampling technique (SMOTE) is employed for dealing with the imbalanced data after Lasso dimensionality reduction. Then, the processed feature vectors are put into a random forest classifier to perform DTIs predictions on four gold standard datasets, including nuclear receptors (NR), G-protein-coupled receptors (GPCR), ion channels (IC), and enzymes (E). Furthermore, we explore new targets for breast cancer treatment using its risk genes identified from large-scale genome-wide genetic studies using PsePDC-DTIs. Through five-fold cross-validation, the average values of accuracy in NR, GPCR, IC, and E datasets are 95.28%, 96.19%, 96.74%, and 98.22%, respectively. The PsePDC-DTIs model provides us with 10 potential DTIs for breast cancer treatment, among which erlotinib (DB00530) and FGFR2 (hsa2263), caffeine (DB00201) and KCNN4 (hsa3783), as well as afatinib (DB08916) and FGFR2 (hsa2263) are found with direct or inferred evidence. The PsePDC-DTIs model has achieved good prediction results, establishing the validity and superiority of the proposed method.

Identification and validation of cellular senescence patterns to predict clinical outcomes and immunotherapeutic responses in lung adenocarcinoma.

BACKGROUND: Aging and senescence can alter immune cell fitness and influence the efficacy of lung cancer treatments, especially immunotherapy. However, the correlations between cellular senescence and tumor microenvironment are still not clearly clarified and the value of cellular senescence-related genes in evaluating the immune infiltration and clinical outcomes of lung adenocarcinoma (LUAD) need further investigated. METHODS: We identified three cellular senescence clusters by NMF algorithm and correlated the cellular senescence clusters with the immune landscape in LUAD patients. A prognostic scoring system was established using random survival forest algorithm and validated in 4 external cohorts. Multivariate Cox regression analysis was performed to evaluate the prognostic value of the scoring system. Expression of LYPD3 was evaluated by immunohistochemistry in LUAD samples. RESULTS: Based on the mRNA expression profiles of 278 cellular senescence-related genes, three cellular senescence clusters with distinct prognosis were identified. We characterized three cellular senescence clusters by differences in biological processes, EMT score, expression of immunomodulatory genes, extent of intratumor heterogeneity and response to immunotherapy. Meanwhile, a cellular senescence-related scoring system (CSS) was established and validated as an independent prognostic factor and immunotherapy predictor of LUAD. Patients with low CSS was characterized by prolonged survival time. In response to anti-cancer drugs, patients with low CSS exhibited higher sensitivities to molecular drugs, such as Roscovitine (CDKs inhibitor), Lenaidornide (TNF-α inhibitor), MK2206 (Akt 1/2/3 inhibitor), and especially increased response to anti-PD-1/L1 immunotherapy. CONCLUSIONS: This study demonstrated the correlations between cellular senescence patterns and tumor immune landscape in LUAD, which enhanced our understanding of the tumor immune microenvironment and provided new insights for improving the outcome of immunotherapy for LUAD patients.

Preclinical evaluation of MRG002, a novel HER2-targeting antibody-drug conjugate with potent antitumor activity against HER2-positive solid tumors.

BACKGROUND: ERBB2 is a proto-oncogene of multiple cancers including breast and gastric cancers with HER2 protein overexpression or gene amplification and has been proven clinically as a valid target for these cancers. HER2-targeting agents such as Herceptin®, Kadcyla® and ENHERTU® have been approved by the FDA for the treatment of breast cancer, but these drugs still face the challenge of acquired resistance and/or severe adverse reactions in clinical use. Therefore, there is significant unmet medical need for developing new agents that are more effective and safer for patients with advanced HER2-positive solid tumors including breast and gastric cancers. METHODS: We report here the making of MRG002, a novel HER2-targeted antibody drug conjugate (ADC), and preclinical characterization including pharmacology, pharmacodynamics and toxicology and discuss its potential as a novel agent for treating patients with HER2-positive solid tumors. RESULTS: MRG002 exhibited similar antigen binding affinity but much reduced antibody-dependent cellular cytotoxicity (ADCC) activity compared to trastuzumab. In addition to potent in vitro cytotoxicity, MRG002 showed tumor regression in both high- and medium-to-low HER2 expressing in vivo xenograft models. Furthermore, MRG002 showed enhanced antitumor activity when used in combination with an anti-PD-1 antibody. Main findings from toxicology studies are related to the payload and are consistent with literature report of other ADCs with monomethyl auristatinE. CONCLUSION: MRG002 has demonstrated a favorable toxicity profile and potent antitumor activities in the breast and gastric PDX models with varying levels of HER2 expression, and/or resistance to trastuzumab or T-DM1. A phase I clinical study of MRG002 in patients with HER2-positive solid tumors is ongoing (CTR20181778).