Composite interaction tree for simultaneous learning of optimal individualized treatment rules and subgroups

Treatment response heterogeneity has long been observed in patients affected by chronic diseases. Administering an individualized treatment rule (ITR) offers an opportunity to tailor treatment strategies according to patient-specific characteristics. Overly complex machine learning methods for estimating ITRs may produce treatment rules that have higher benefit but lack transparency and interpretability. In clinical practices, it is desirable to derive a simple and interpretable ITR while maintaining certain optimality that leads to improved benefit in subgroups of patients, if not on the overall sample. In this work, we propose a tree-based robust learning method to estimate optimal piecewise linear ITRs and identify subgroups of patients with a large benefit. We achieve these goals by simultaneously identifying qualitative and quantitative interactions through a tree model, referred to as the composite interaction tree (CITree). We show that it has improved performance compared to existing methods on both overall sample and subgroups via extensive simulation studies. Lastly, we fit CITree to Research Evaluating the Value of Augmenting Medication with Psychotherapy trial for treating patients with major depressive disorders, where we identified both qualitative and quantitative interactions and subgroups of patients with a large benefit.     

Physiologically-based pharmacokinetic modeling for mirabegron: a multi-elimination pathway mediated by cytochrome P450 3A4, uridine 5'-diphosphate-glucuronosyltransferase 2B7, and butyrylcholinesterase

1. This was the first study to construct a physiologically-based pharmacokinetic (PBPK) model for mirabegron which incorporates the overall elimination pathways of metabolism by cytochrome P450 (CYP) 3A4, uridine 5'-diphosphate-glucuronosyltransferase (UGT) 2B7, and butyrylcholinesterase (BChE) and renal excretion. The objective was to assess the risk of drug-drug interactions (DDIs) by estimating the contribution of each elimination pathway and simulating the magnitude of the DDIs with UGT2B7 inhibitors.

2. A PBPK model for mirabegron was constructed to reproduce the plasma concentration-time curves from a phase 1 study and the magnitude of the DDI with ketoconazole taking into account the overall elimination pathways. The PBPK model was subsequently verified using data from other DDI studies.

3. The constructed PBPK model estimated the contribution for each elimination pathway: 44% and 29% for CYP3A4 and UGT2B7 in the liver, 1.6% for UGT2B7 in the kidney, 3.2% for BChE in plasma, and 22% for renal excretion.

4. Co-administration of probenecid (an UGT2B7 inhibitor) or fluconazole (an UGT2B7 and CYP3A4 inhibitor) was predicted to increase area under the curve for mirabegron to 115% or 174%, respectively.

5. In conclusion, PBPK modeling and simulation revealed a low DDI risk for mirabegron following co-administration with BChE or UGT2B7 inhibitors.

     

胶质母细胞瘤差异表达基因的生物信息学分析

目的:应用生物信息学方法挖掘胶质母细胞瘤(GBM)的相关基因，进而探讨发病机制，为GBM临床诊断和靶向治疗提供理论依据。方法:从GEO(Gene Expression Omnibus)数据库下载基因芯片数据集GSE4290和GSE15824，应用GEO2R筛选GBM的差异表达基因(DEGs)。采用DAVID数据库进行GO富集和KEGG通路富集分析，分别应用STRING数据库和Cytoscape软件构建蛋白质相互作用网络和关键基因模块，筛选GBM靶基因。进一步运用ONCOMINE数据库验证临床组织样本中靶基因与GBM的关系。结果:共筛选出76个DEGs，富集分析结果显示DEGs在血管生成的正调节、抗原的呈递和处理、信号转导、调节自噬等方面存在显著富集。共挖掘出POSTN、TAGLN、CALD1、EPCAM 4个GBM靶基因，经证实均在临床GBM组织样本中存在显著上调且靶基因的上调与患者的不良预后密切相关。结论:通过生物信息学共挖掘出4个与GBM显著相关的靶基因，可能是未来GBM发病机制、临床诊断、治疗的重要研究靶点。     

Bioinformatic identification of key candidate genes and pathways in axon regeneration after spinal cord injury in zebrafish

Zebrafish and human genomes are highly homologous;however,despite this genomic similarity,adult zebrafish can achieve neuronal proliferation,regeneration and functional restoration within 6–8 weeks after spinal cord injury,whereas humans cannot.To analyze differentially expressed zebrafish genes between axon-regenerated neurons and axon-non-regenerated neurons after spinal cord injury,and to explore the key genes and pathways of axonal regeneration after spinal cord injury,microarray GSE56842 was analyzed using the online tool,GEO2R,in the Gene Expression Omnibus database.Gene ontology and protein-protein interaction networks were used to analyze the identified differentially expressed genes.Finally,we screened for genes and pathways that may play a role in spinal cord injury repair in zebrafish and mammals.A total of 636 differentially expressed genes were obtained,including 255 up-regulated and 381 down-regulated differentially expressed genes in axon-regenerated neurons.Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment results were also obtained.A protein-protein interaction network contained 480 node genes and 1976 node connections.We also obtained the 10 hub genes with the highest correlation and the two modules with the highest score.The results showed that spectrin may promote axonal regeneration after spinal cord injury in zebrafish.Transforming growth factor beta signaling may inhibit repair after spinal cord injury in zebrafish.Focal adhesion or tight junctions may play an important role in the migration and proliferation of some cells,such as Schwann cells or neural progenitor cells,after spinal cord injury in zebrafish.Bioinformatic analysis identified key candidate genes and pathways in axonal regeneration after spinal cord injury in zebrafish,providing targets for treatment of spinal cord injury in mammals.     

A prognostic signature based on immune-related genes for cervical squamous cell carcinoma and endocervical adenocarcinoma

Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) is the fourth commonest female malignancy worldwide. CESC progresses in immune-microenvironment mainly composed of infiltrating immune and stromal cells. Here, we performed an integrated analysis incorporating the expression profiles from the Cancer Genome Atlas (TCGA) database and scores of immune and stromal cells calculated by Estimation of Stromal and Immune cells in Malignant Tumours using Expression data (ESTIMATE) algorithm. A two-gene signature (CD1C and CD6 genes) was established to predict the prognosis of CESC. Based on this signature, patients were divided into the high- and low-risk groups, and this signature showed good prognostic performance according to the results of Kaplan-Meier analysis and receiver operating characteristic (ROC) analysis in train set and two validation sets. A nomogram was built for evaluating the clinical applicability of this signature. In addition, based on Tumor Immune Estimation Resource (TIMER) database, 2 hub genes showed negative correlations with tumor purity and positive correlations with infiltrating levels of immune filtrating cells. What’s more, we propose new treatment strategies for the two prognostic subtypes. Low- risk patients were found presenting with a higher level of immune checkpoint molecules and showing higher immunogenicity in immunophenoscore (IPS) analysis, which indicated a better response for immunotherapy. Meanwhile, estimated by Genomics of Drug Sensitivity in Cancer (GDSC) database, the high-risk patients showed sensitive responses to five chemotherapy drugs. Finally, 10 candidate small-molecule drugs for CESC were defined. In summary, the CD1C-CD6 signature can accurately predict the prognosis of CESC.     

Research progress on magnetic resonance imaging-guided proton therapy for tumors

With the application of magnetic resonance imaging (MRI)-guided photon therapy, the concept of combining real-time MRI guidance with proton therapy, namely, MRI-guided proton therapy (MRPT), has attracted widespread attention. It is expected that MRPT canmitigate the uncertaintiesduring the treatment of proton therapy to make full use of the physical advantages of protons. However, multiple electromagnetic interactions between proton therapy and MRI-guided systems may lead to mutual interference between the two systems. This article review the research progress on the MRPT system, aiming to provide certain reference for the design of MRPT system.