Comprehensive assessment of nine target prediction web services: which should we choose for target fishing?

Identification of potential targets for known bioactive compounds and novel synthetic analogs is of considerable significance. In silico target fishing (TF) has become an alternative strategy because of the expensive and laborious wet-lab experiments, explosive growth of bioactivity data and rapid development of high-throughput technologies. However, these TF methods are based on different algorithms, molecular representations and training datasets, which may lead to different results when predicting the same query molecules. This can be confusing for practitioners in practical applications. Therefore, this study systematically evaluated nine popular ligand-based TF methods based on target and ligand-target pair statistical strategies, which will help practitioners make choices among multiple TF methods. The evaluation results showed that SwissTargetPrediction was the best method to produce the most reliable predictions while enriching more targets. High-recall similarity ensemble approach (SEA) was able to find real targets for more compounds compared with other TF methods. Therefore, SwissTargetPrediction and SEA can be considered as primary selection methods in future studies. In addition, the results showed that k = 5 was the optimal number of experimental candidate targets. Finally, a novel ensemble TF method based on consensus voting is proposed to improve the prediction performance. The precision of the ensemble TF method outperforms the individual TF method, indicating that the ensemble TF method can more effectively identify real targets within a given top-k threshold. The results of this study can be used as a reference to guide practitioners in selecting the most effective methods in computational drug discovery.

Reducing false positive rate of docking-based virtual screening by active learning.

Machine learning-based scoring functions (MLSFs) have become a very favorable alternative to classical scoring functions because of their potential superior screening performance. However, the information of negative data used to construct MLSFs was rarely reported in the literature, and meanwhile the putative inactive molecules recorded in existing databases usually have obvious bias from active molecules. Here we proposed an easy-to-use method named AMLSF that combines active learning using negative molecular selection strategies with MLSF, which can iteratively improve the quality of inactive sets and thus reduce the false positive rate of virtual screening. We chose energy auxiliary terms learning as the MLSF and validated our method on eight targets in the diverse subset of DUD-E. For each target, we screened the IterBioScreen database by AMLSF and compared the screening results with those of the four control models. The results illustrate that the number of active molecules in the top 1000 molecules identified by AMLSF was significantly higher than those identified by the control models. In addition, the free energy calculation results for the top 10 molecules screened out by the AMLSF, null model and control models based on DUD-E also proved that more active molecules can be identified, and the false positive rate can be reduced by AMLSF.

Association of RASGRP1 polymorphism with vascular complications in Chinese diabetic patients with glycemic control and antihypertensive treatment.

BACKGROUND: Studies have shown that RASGRP1 was potently associated with the onset of type 2 diabetes mellitus (T2DM), and RASGRP1 rs7403531 was significantly correlated with islet function in T2DM patients. However, the effect of RASGRP1 polymorphism on blood glucose and blood pressure in T2DM patients after continuous treatment has yet to be fully elucidated. OBJECTIVE: This study aimed to explore the association between RASGRP1 genetic polymorphism and cardiovascular complications in T2DM patients, so as to provide more evidence for the individualized treatment of T2DM patients. METHODS: We retrospectively analyzed a large-scale multicenter drug clinical study cohort that based on a 2 × 2 factorial (glucose control axis and blood pressure lowering axis) randomized controlled design, with follow-up for 5 years. The major vascular endpoint events included cardiovascular death, non-fatal stroke, coronary heart disease, new-onset or worsening renal disease, and diabetic retinopathy. RASGRP1 rs12593201, rs56254815 and rs7403531 were finally selected as candidate single nucleotide polymorphisms. Mixed linear model and Cox hazard ratio (HR) model were used for data analysis with IBM SPSS (version 20.0 for windows; Chicago, IL). RESULTS: Our study enrolled 1357 patients with high-risk diabetes, with a mean follow-up duration of 4.8 years. RASGRP1 rs7403531 was associated with vascular events in hypoglycemic and antihypertensive therapy. Specifically, compared with CC carriers, patients with CT/TT genotype had fewer major microvascular events (HR = 0.41, 95% confidence interval (CI) 0.21-0.80, P = 0.009), and reduced the risk of major eye disease events (HR = 0.44, 95% CI 0.20-0.94, P = 0.03). For glucose lowering axis, CT/TT carriers had a lower risk of secondary nephropathy (HR = 0.48, 95% CI 0.25-0.92, P = 0.03) in patients with standard glycemic control. For blood pressure lowering axis, all cerebrovascular events (HR = 2.24, 95% CI 1.11-4.51, P = 0.025) and stroke events (HR = 2.07, 95% CI 1.03-4.15, P = 0.04) were increased in patients with CC genotype compared to those with CT/TT genotype in the placebo group, respectively. Furthermore, patients with CC genotype showed a reduced risk of major cerebrovascular events in antihypertensive group (HR = 0.36, 95% CI 0.15-0.86, P = 0.021). For RASGRP1 rs56254815, compared with the AA genotype carriers, the systolic blood pressure of AG/GG carriers in the antihypertensive group decreased by 1.5mmhg on average (P = 0.04). In the placebo group, the blood pressure of AG/GG carriers was 1.7mmHg higher than that of AA carriers (P = 0.02). CONCLUSION: We found that patients with G allele of RASGRP1 (rs56254815) showed a better antihypertensive therapy efficacy in T2DM patients. The rs7403531 T allele could reduce the risk of major microvascular events and major eye diseases in T2DM patients receiving either hypoglycemic or antihypertensive therapy. Our findings suggest that RASGRP1 genetic polymorphism might predict the cardiovascular complications in T2DM patients.

Development and therapeutic potential of DNA-dependent protein kinase inhibitors.

The deployment of DNA damage response (DDR) combats various forms of DNA damage, ensuring genomic stability. Cancer cells' propensity for genomic instability offers therapeutic opportunities to selectively kill cancer cells by suppressing the DDR pathway. DNA-dependent protein kinase (DNA-PK), a nuclear serine/threonine kinase, is crucial for the non-homologous end joining (NHEJ) pathway in the repair of DNA double-strand breaks (DSBs). Therefore, targeting DNA-PK is a promising cancer treatment strategy. This review elaborates on the structures of DNA-PK and its related large protein, as well as the development process of DNA-PK inhibitors, and recent advancements in their clinical application. We emphasize our analysis of the development process and structure-activity relationships (SARs) of DNA-PK inhibitors based on different scaffolds. We hope this review will provide practical information for researchers seeking to develop novel DNA-PK inhibitors in the future.

Association of CCND1 rs9344 polymorphism with lung cancer susceptibility and clinical outcomes: a case-control study.

BACKGROUND: Cyclin D1 (CCND1) plays a pivotal role in cancer susceptibility and the platinum-based chemotherapy response. This study aims to assess the relationship between a common polymorphism (rs9344 G > A) in CCND1 gene with cancer susceptibility, platinum-based chemotherapy response, toxicities and prognosis of patients with lung cancer. METHODS: This study involved 498 lung cancer patients and 213 healthy controls. Among them, 467 patients received at least two cycles of platinum-based chemotherapy. Unconditional logistical regression analysis and meta-analysis were performed to evaluate the associations. RESULTS: The lung adenocarcinoma risk was significantly higher in patients with AA than GG + GA genotype (adjusted OR = 1.755, 95%CI = 1.057-2.912, P = 0.030). CCND1 rs9344 was significantly correlated with platinum-based therapy response in patients receiving PP regimen (additive model: adjusted OR = 1.926, 95%CI = 1.029-3.605, P = 0.040; recessive model: adjusted OR = 11.340, 95%CI = 1.428-90.100, P = 0.022) and in the ADC subgroups (recessive model: adjusted OR = 3.345, 95%CI = 1.276-8.765, P = 0.014). Furthermore, an increased risk of overall toxicity was found in NSCLC patients (additive model: adjusted OR = 1.395, 95%CI = 1.025-1.897, P = 0.034; recessive model: adjusted OR = 1.852, 95%CI = 1.088-3.152, P = 0.023), especially ADC subgroups (additive model: adjusted OR = 1.547, 95%CI = 1.015-2.359, P = 0.043; recessive model: adjusted OR = 2.030, 95%CI = 1.017-4.052, P = 0.045). Additionally, CCND1 rs9344 was associated with an increased risk of gastrointestinal toxicity in non-smokers (recessive model: adjusted OR = 2.620, 95%CI = 1.083-6.336, P = 0.035). Non-significant differences were observed in the 5-year overall survival rate between CCND1 rs9344 genotypes. A meta-analysis of 5432 cases and 6452 control samples did not find a significant association between lung cancer risk and CCND1 rs9344 polymorphism. CONCLUSION: This study suggests that in the Chinese population, CCND1 rs9344 could potentially serve as a candidate biomarker for cancer susceptibility and treatment outcomes in specific subgroups of patients.

Early non-response as a predictor of later non-response to antipsychotics in schizophrenia: a randomized trial.

BACKGROUND: It remains a challenge to predict the long-term response to antipsychotics in patients with schizophrenia who do not respond at an early stage. This study aimed to investigate the optimal predictive cut-off value for early non-response that would better predict later non-response to antipsychotics in patients with schizophrenia. METHODS: This multicenter, 8-week, open-label, randomized trial was conducted at 19 psychiatric centers throughout China. All enrolled participants were assigned to olanzapine, risperidone, amisulpride, or aripiprazole monotherapy for 8 weeks. The positive and negative syndrome scale (PANSS) was evaluated at baseline, week 2, week 4, and week 8. The main outcome was the prediction of nonresponse. Nonresponse is defined as a < 20% reduction in the total scores of PANSS from baseline to endpoint. Severity ratings of mild, moderate, and severe illness corresponded to baseline PANSS total scores of 58, 75, and 95, respectively. RESULTS: At week 2, a reduction of < 5% in the PANSS total score showed the highest total accuracy in the severe and mild schizophrenia patients (total accuracy, 75.0% and 80.8%, respectively), and patients who were treated with the risperidone and amisulpride groups (total accuracy, 82.4%, and 78.2%, respectively). A 10% decrease exhibited the best overall accuracy in the moderate schizophrenia patients (total accuracy, 84.0%), olanzapine (total accuracy, 79.2%), and aripiprazole group (total accuracy, 77.4%). At week 4, the best predictive cut-off value was < 20%, regardless of the antipsychotic or severity of illness (total accuracy ranging from 89.8 to 92.1%). CONCLUSIONS: Symptom reduction at week 2 has acceptable discrimination in predicting later non-response to antipsychotics in schizophrenia, and a more accurate predictive cut-off value should be determined according to the medication regimen and baseline illness severity. The response to treatment during the next 2 weeks after week 2 could be further assessed to determine whether there is a need to change antipsychotic medication during the first four weeks. TRIAL REGISTRATION: This study was registered on Clinicaltrials.gov (NCT03451734).

Disruption of Iron Homeostasis to Induce Ferroptosis with Albumin-Encapsulated Pt(IV) Nanodrug for the Treatment of Non-Small Cell Lung Cancer.

Non-small cell lung cancer (NSCLC) is the most common pathological type of lung cancer , accounting for approximately 85% of lung cancers. For more than 40 years, platinum (Pt)-based drugs are still one of the most widely used anticancer drugs even in the era of precision medicine and immunotherapy. However, the clinical limitations of Pt-based drugs, such as serious side effects and drug resistance, have not been well solved. This study constructs a new albumin-encapsulated Pt(IV) nanodrug (HSA@Pt(IV)) based on the Pt(IV) drug and nanodelivery system. The characterization of nanodrug and biological experiments demonstrate its excellent drug delivery and antitumor effects. The multi-omics analysis of the transcriptome and the ionome reveals that nanodrug can activate ferroptosis by affecting intracellular iron homeostasis in NSCLC. This study provides experimental evidence to suggest the potential of HSA@Pt(IV) as a nanodrug with clinical application.

Network analyses in microbiome based on high-throughput multi-omics data.

Together with various hosts and environments, ubiquitous microbes interact closely with each other forming an intertwined system or community. Of interest, shifts of the relationships between microbes and their hosts or environments are associated with critical diseases and ecological changes. While advances in high-throughput Omics technologies offer a great opportunity for understanding the structures and functions of microbiome, it is still challenging to analyse and interpret the omics data. Specifically, the heterogeneity and diversity of microbial communities, compounded with the large size of the datasets, impose a tremendous challenge to mechanistically elucidate the complex communities. Fortunately, network analyses provide an efficient way to tackle this problem, and several network approaches have been proposed to improve this understanding recently. Here, we systemically illustrate these network theories that have been used in biological and biomedical research. Then, we review existing network modelling methods of microbial studies at multiple layers from metagenomics to metabolomics and further to multi-omics. Lastly, we discuss the limitations of present studies and provide a perspective for further directions in support of the understanding of microbial communities.

The functional determinants in the organization of bacterial genomes.

Bacterial genomes are now recognized as interacting intimately with cellular processes. Uncovering organizational mechanisms of bacterial genomes has been a primary focus of researchers to reveal the potential cellular activities. The advances in both experimental techniques and computational models provide a tremendous opportunity for understanding these mechanisms, and various studies have been proposed to explore the organization rules of bacterial genomes associated with functions recently. This review focuses mainly on the principles that shape the organization of bacterial genomes, both locally and globally. We first illustrate local structures as operons/transcription units for facilitating co-transcription and horizontal transfer of genes. We then clarify the constraints that globally shape bacterial genomes, such as metabolism, transcription and replication. Finally, we highlight challenges and opportunities to advance bacterial genomic studies and provide application perspectives of genome organization, including pathway hole assignment and genome assembly and understanding disease mechanisms.

Deep forest ensemble learning for classification of alignments of non-coding RNA sequences based on multi-view structure representations.

Non-coding RNAs (ncRNAs) play crucial roles in multiple biological processes. However, only a few ncRNAs' functions have been well studied. Given the significance of ncRNAs classification for understanding ncRNAs' functions, more and more computational methods have been introduced to improve the classification automatically and accurately. In this paper, based on a convolutional neural network and a deep forest algorithm, multi-grained cascade forest (GcForest), we propose a novel deep fusion learning framework, GcForest fusion method (GCFM), to classify alignments of ncRNA sequences for accurate clustering of ncRNAs. GCFM integrates a multi-view structure feature representation including sequence-structure alignment encoding, structure image representation and shape alignment encoding of structural subunits, enabling us to capture the potential specificity between ncRNAs. For the classification of pairwise alignment of two ncRNA sequences, the F-value of GCFM improves 6% than an existing alignment-based method. Furthermore, the clustering of ncRNA families is carried out based on the classification matrix generated from GCFM. Results suggest better performance (with 20% accuracy improved) than existing ncRNA clustering methods (RNAclust, Ensembleclust and CNNclust). Additionally, we apply GCFM to construct a phylogenetic tree of ncRNA and predict the probability of interactions between RNAs. Most ncRNAs are located correctly in the phylogenetic tree, and the prediction accuracy of RNA interaction is 90.63%. A web server (http://bmbl.sdstate.edu/gcfm/) is developed to maximize its availability, and the source code and related data are available at the same URL.

A novel computational framework for genome-scale alternative transcription units prediction.

Alternative transcription units (ATUs) are dynamically encoded under different conditions and display overlapping patterns (sharing one or more genes) under a specific condition in bacterial genomes. Genome-scale identification of ATUs is essential for studying the emergence of human diseases caused by bacterial organisms. However, it is unrealistic to identify all ATUs using experimental techniques because of the complexity and dynamic nature of ATUs. Here, we present the first-of-its-kind computational framework, named SeqATU, for genome-scale ATU prediction based on next-generation RNA-Seq data. The framework utilizes a convex quadratic programming model to seek an optimum expression combination of all of the to-be-identified ATUs. The predicted ATUs in Escherichia coli reached a precision of 0.77/0.74 and a recall of 0.75/0.76 in the two RNA-Sequencing datasets compared with the benchmarked ATUs from third-generation RNA-Seq data. In addition, the proportion of 5'- or 3'-end genes of the predicted ATUs, having documented transcription factor binding sites and transcription termination sites, was three times greater than that of no 5'- or 3'-end genes. We further evaluated the predicted ATUs by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional enrichment analyses. The results suggested that gene pairs frequently encoded in the same ATUs are more functionally related than those that can belong to two distinct ATUs. Overall, these results demonstrated the high reliability of predicted ATUs. We expect that the new insights derived by SeqATU will not only improve the understanding of the transcription mechanism of bacteria but also guide the reconstruction of a genome-scale transcriptional regulatory network.

Structural Analysis and Prediction of Hematotoxicity Using Deep Learning Approaches.

Hematotoxicity has been becoming a serious but overlooked toxicity in drug discovery. However, only a few in silico models have been reported for the prediction of hematotoxicity. In this study, we constructed a high-quality dataset comprising 759 hematotoxic compounds and 1623 nonhematotoxic compounds and then established a series of classification models based on a combination of seven machine learning (ML) algorithms and nine molecular representations. The results based on two data partitioning strategies and applicability domain (AD) analysis illustrate that the best prediction model based on Attentive FP yielded a balanced accuracy (BA) of 72.6%, an area under the receiver operating characteristic curve (AUC) value of 76.8% for the validation set, and a BA of 69.2%, an AUC of 75.9% for the test set. In addition, compared with existing filtering rules and models, our model achieved the highest BA value of 67.5% for the external validation set. Additionally, the shapley additive explanation (SHAP) and atom heatmap approaches were utilized to discover the important features and structural fragments related to hematotoxicity, which could offer helpful tips to detect undesired positive substances. Furthermore, matched molecular pair analysis (MMPA) and representative substructure derivation technique were employed to further characterize and investigate the transformation principles and distinctive structural features of hematotoxic chemicals. We believe that the novel graph-based deep learning algorithms and insightful interpretation presented in this study can be used as a trustworthy and effective tool to assess hematotoxicity in the development of new drugs.

Improved GNNs for Log D7.4 Prediction by Transferring Knowledge from Low-Fidelity Data.

The n-octanol/buffer solution distribution coefficient at pH = 7.4 (log D7.4) is an indicator of lipophilicity, and it influences a wide variety of absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties and druggability of compounds. In log D7.4 prediction, graph neural networks (GNNs) can uncover subtle structure-property relationships (SPRs) by automatically extracting features from molecular graphs that facilitate the learning of SPRs, but their performances are often limited by the small size of available datasets. Herein, we present a transfer learning strategy called pretraining on computational data and then fine-tuning on experimental data (PCFE) to fully exploit the predictive potential of GNNs. PCFE works by pretraining a GNN model on 1.71 million computational log D data (low-fidelity data) and then fine-tuning it on 19,155 experimental log D7.4 data (high-fidelity data). The experiments for three GNN architectures (graph convolutional network (GCN), graph attention network (GAT), and Attentive FP) demonstrated the effectiveness of PCFE in improving GNNs for log D7.4 predictions. Moreover, the optimal PCFE-trained GNN model (cx-Attentive FP, Rtest2 = 0.909) outperformed four excellent descriptor-based models (random forest (RF), gradient boosting (GB), support vector machine (SVM), and extreme gradient boosting (XGBoost)). The robustness of the cx-Attentive FP model was also confirmed by evaluating the models with different training data sizes and dataset splitting strategies. Therefore, we developed a webserver and defined the applicability domain for this model. The webserver (http://tools.scbdd.com/chemlogd/) provides free log D7.4 prediction services. In addition, the important descriptors for log D7.4 were detected by the Shapley additive explanations (SHAP) method, and the most relevant substructures of log D7.4 were identified by the attention mechanism. Finally, the matched molecular pair analysis (MMPA) was performed to summarize the contributions of common chemical substituents to log D7.4, including a variety of hydrocarbon groups, halogen groups, heteroatoms, and polar groups. In conclusion, we believe that the cx-Attentive FP model can serve as a reliable tool to predict log D7.4 and hope that pretraining on low-fidelity data can help GNNs make accurate predictions of other endpoints in drug discovery.

Integrative analysis of expression profile indicates the ECM receptor and LTP dysfunction in the glioma-related epilepsy.

BACKGROUND: Seizures are a common symptom in glioma patients, and they can cause brain dysfunction. However, the mechanism by which glioma-related epilepsy (GRE) causes alterations in brain networks remains elusive. OBJECTIVE: To investigate the potential pathogenic mechanism of GRE by analyzing the dynamic expression profiles of microRNA/ mRNA/ lncRNA in brain tissues of glioma patients. METHODS: Brain tissues of 16 patients with GRE and 9 patients with glioma without epilepsy (GNE) were collected. The total RNA was dephosphorylated, labeled, and hybridized to the Agilent Human miRNA Microarray, Release 19.0, 8 × 60 K. The cDNA was labeled and hybridized to the Agilent LncRNA + mRNA Human Gene Expression Microarray V3.0, 4 × 180 K. The raw data was extracted from hybridized images using Agilent Feature Extraction, and quantile normalization was performed using the Agilent GeneSpring. P-value < 0.05 and absolute fold change > 2 were considered the threshold of differential expression data. Data analyses were performed using R and Bioconductor. RESULTS: We found that 3 differentially expressed miRNAs (miR-10a-5p, miR-10b-5p, miR-629-3p), 6 differentially expressed lncRNAs (TTN-AS1, LINC00641, SNHG14, LINC00894, SNHG1, OIP5-AS1), and 49 differentially expressed mRNAs play a vitally critical role in developing GRE. The expression of GABARAPL1, GRAMD1B, and IQSEC3 were validated more than twofold higher in the GRE group than in the GNE group in the validation cohort. Pathways including ECM receptor interaction and long-term potentiation (LTP) may contribute to the disease's progression. Meanwhile, We built a lncRNA-microRNA-Gene regulatory network with structural and functional significance. CONCLUSION: These findings can offer a fresh perspective on GRE-induced brain network changes.

METTL3 inhibition reduces N6 -methyladenosine levels and prevents allogeneic CD4+ T-cell responses.

Alloreactive CD4+ T cells play a central role in allograft rejection. However, the post-transcriptional regulation of the effector program in alloreactive CD4+ T cells remains unclear. N6 -methyladenosine (m6 A) RNA modification is involved in various physiological and pathological processes. Herein, we investigated whether m6 A methylation plays a role in the allogeneic T-cell effector program. m6 A levels of CD4+ T cells from spleens, draining lymph nodes and skin allografts were determined in a skin transplantation model. The effects of a METTL3 inhibitor (STM2457) on CD4+ T-cell characteristics including proliferation, cell cycle, cell apoptosis and effector differentiation were determined after stimulation of polyclonal and alloantigen-specific (TEa; CD4+ T cells specific for I-Eα52-68 ) CD4+ T cells with α-CD3/α-CD28 monoclonal antibodies and cognate CB6F1 alloantigen, respectively. We found that graft-infiltrating CD4+ T cells expressed high m6 A levels. Administration of STM2457 reduced m6 A levels, inhibited T-cell proliferation and suppressed effector differentiation of polyclonal CD4+ T cells. Alloreactive TEa cells challenged with 40 µm STM2457 exhibited deficits in T-cell proliferation and T helper type 1 cell differentiation, a cell cycle arrest in the G0 phase and elevated cell apoptosis. Moreover, these impaired T-cell responses were associated with the diminished expression levels of transcription factors Ki-67, c-Myc and T-bet. Therefore, METTL3 inhibition reduces the expression of several key transcriptional factors for the T-cell effector program and suppresses alloreactive CD4+ T-cell effector function and differentiation. Targeting m6 A-related enzymes and molecular machinery in CD4+ T cells represents an attractive therapeutic approach to prevent allograft rejection.

Targeting translation regulators improves cancer therapy.

Deregulation of protein synthesis may be involved in multiple aspects of cancer, such as gene expression, signal transduction and drive specific cell biological responses, resulting in promoting cancer growth, invasion and metastasis. Study the molecular mechanisms about translational control may help us to find more effective anti-cancer drugs and develop novel therapeutic opportunities. Recently, the researchers had focused on targeting translational machinery to overcome cancer, and various small molecular inhibitors targeting translation factors or pathways have been tested in clinical trials and exhibited improving outcomes in several cancer types. There is no doubt that an insight into the class of translation regulation protein would provide new target for pharmacologic intervention and further provide opportunities to develop novel anti-tumor therapeutic interventions. In this review, we summarized the developments of translational control in cancer survival and progression et al, and highlighted the therapeutic approach targeted translation regulation to overcome the cancer.

Restoration of the Immunogenicity of Tumor Cells for Enhanced Cancer Therapy via Nanoparticle-Mediated Copper Chaperone Inhibition.

Recent progress in studying copper-dependent targets and pathways in the context of tumor treatment has provided new insights into therapeutic strategies of leveraging copper-dependent disease vulnerabilities and pharmacological manipulation of intratumor copper transportation to improve chemotherapy. Here, we developed reactive oxygen species (ROS)-sensitive nanoparticles loaded with copper chaperone inhibitor DC_AC50 and cisplatin(IV) prodrug. The released DC_AC50 can promote a remarkable accumulation of intracellular cisplatin and copper through inhibition of the Atox1-ATPase pathways, thereby enhancing the chemotherapeutic effect of cisplatin and inducing significant ROS generation. Excessive ROS then elicits intense endoplasmic reticulin (ER) stress which facilitates the immunogenic cell death (ICD) spurring a sustained immune response. Our study suggests that nanoparticle-mediated copper chaperone inhibition via DC_AC50 can restore the immunogenicity of tumor cells for enhanced chemotherapy and cancer immunotherapy.

eIF3a R803K mutation mediates chemotherapy resistance by inducing cellular senescence in small cell lung cancer.

Drug resistance in small cell lung cancer (SCLC) significantly affects the efficacy of chemotherapy treatment. However, due to the lack of tumor tissue samples, especially serial tumor samples during chemotherapy, the mechanism of chemotherapy resistance has not been fully studied. Circulating tumor DNA, which can be obtained in a noninvasive manner, can complement tumor sampling approaches for research in this field. We identified an SCLC patient with acquired drug resistance from 52 SCLC patients for whom follow-up data were available. By comparing somatic mutations in circulating tumor DNA before and after chemotherapy, for the first time, we found that the somatic mutation eIF3A R803K may be related to acquired chemotherapy resistance. Then, the association between the eIF3A R803K mutation and chemotherapy resistance was confirmed by samples from 254 lung cancer patients receiving chemotherapy. We found that the eIF3a R803K mutation weakened the proliferation ability of tumor cells but increased their resistance to chemotherapy. Further studies revealed that the eIF3A R803K mutation promotes cellular senescence. In addition, fisetin showed a synergistic effect with chemotherapy in eIF3A R803K mutant cells. These results suggest that the eIF3A R803K somatic mutation has the potential to predict chemotherapy resistance in SCLC. Moreover, the eIF3A R803K mutation promotes chemotherapy resistance by inducing senescence. Furthermore, a senolytic drug, fisetin, can reverse chemotherapy resistance mediated by the eIF3A R803K mutation.

Response prediction biomarkers and drug combinations of PARP inhibitors in prostate cancer.

PARP inhibitors are a group of inhibitors targeting poly(ADP-ribose) polymerases (PARP1 or PARP2) involved in DNA repair and transcriptional regulation, which may induce synthetic lethality in BRCAness tumors. Systematic analyzes of genomic sequencing in prostate cancer show that ~10%-19% of patients with primary prostate cancer have inactivated DNA repair genes, with a notably higher proportion of 23%-27% in patients with metastatic castration-resistant prostate cancer (mCRPC). These characteristic genomic alterations confer possible vulnerability to PARP inhibitors in patients with mCRPC who benefit only modestly from other therapies. However, only a small proportion of patients with mCRPC shows sensitivity to PARP inhibitors, and these sensitive patients cannot be fully identified by existing response prediction biomarkers. In this review, we provide an overview of the potential response prediction biomarkers and synergistic combinations studied in the preclinical and clinical stages, which may expand the population of patients with prostate cancer who may benefit from PARP inhibitors.

Research progress of omics technology in the field of tumor resistance: From single-omics to multi-omics combination application. / ç»„å­¦æŠ€æœ¯åœ¨è‚¿ç˜¤è€è¯é¢†åŸŸçš„ç ”ç©¶è¿›å±•ï¼šä»Žå•ç»„å­¦åˆ°å¤šç»„å­¦è”åˆåº”ç”¨.

Drug resistance is the main obstacle in the treatment of many cancers. It is of great clinical significance to study the mechanism of drug resistance and find new targets. Multi-omics mainly includes genomics, epigenomics, transcriptomics, proteomics, metabolomics, and radiomics. In recent years, the research of tumor resistance has made rapid development, which has significantly accelerated the discovery of new targets.