Abstract
RNA sequencing (RNA-seq) is a genomic approach for the detection and quantitative analysis of messenger RNA molecules in a biological sample and is useful for studying cellular responses. RNA-seq has fueled much discovery and innovation in medicine over recent years. For practical reasons, the technique is usually conducted on samples comprising thousands to millions of cells. However, this has hindered direct assessment of the fundamental unit of biology—the cell. Since the first single-cell RNA-sequencing (scRNA-seq) study was published in 2009, many more have been conducted, mostly by specialist laboratories with unique skills in wet-lab single-cell genomics, bioinformatics, and computation. However, with the increasing commercial availability of scRNA-seq platforms, and the rapid ongoing maturation of bioinformatics approaches, a point has been reached where any biomedical researcher or clinician can use scRNA-seq to make exciting discoveries. In this review, we present a practical guide to help researchers design their first scRNA-seq studies, including introductory information on experimental hardware, protocol choice, quality control, data analysis and biological interpretation.
key: 在这篇综述中，我们提供了实用指南，以帮助研究人员设计其首次的scRNA-seq研究，包括有关实验硬件，方案选择，质量控制，数据分析和生物学解释的介绍性信息 drop-out web-browser-like interfaces CRISPR–Cas9 gene function in single cells
Abstract
Background
Genomic medicine has paved the way for identifying biomarkers and therapeutically actionable targets for complex diseases, but is complicated by the involvement of thousands of variably expressed genes across multiple cell types. Single-cell RNA-sequencing study (scRNA-seq) allows the characterization of such complex changes in whole organs.
Methods
The study is based on applying network tools to organize and analyze scRNA-seq data from a mouse model of arthritis and human rheumatoid arthritis, in order to find diagnostic biomarkers and therapeutic targets. Diagnostic validation studies were performed using expression profiling data and potential protein biomarkers from prospective clinical studies of 13 diseases. A candidate drug was examined by a treatment study of a mouse model of arthritis, using phenotypic, immunohistochemical, and cellular analyses as read-outs.
Results
We performed the first systematic analysis of pathways, potential biomarkers, and drug targets in scRNA-seq data from a complex disease, starting with inflamed joints and lymph nodes from a mouse model of arthritis. We found the involvement of hundreds of pathways, biomarkers, and drug targets that differed greatly between cell types. Analyses of scRNA-seq and GWAS data from human rheumatoid arthritis (RA) supported a similar dispersion of pathogenic mechanisms in different cell types. Thus, systems-level approaches to prioritize biomarkers and drugs are needed. Here, we present a prioritization strategy that is based on constructing network models of disease-associated cell types and interactions using scRNA-seq data from our mouse model of arthritis, as well as human RA, which we term multicellular disease models (MCDMs). We find that the network centrality of MCDM cell types correlates with the enrichment of genes harboring genetic variants associated with RA and thus could potentially be used to prioritize cell types and genes for diagnostics and therapeutics. We validated this hypothesis in a large-scale study of patients with 13 different autoimmune, allergic, infectious, malignant, endocrine, metabolic, and cardiovascular diseases, as well as a therapeutic study of the mouse arthritis model.
Conclusions
Overall, our results support that our strategy has the potential to help prioritize diagnostic and therapeutic targets in human disease.
数据：利用关节炎小鼠模型和人类RA的单细胞测序数据
方法：Ingenuity 寻找差异基因的分泌的或膜结合上游调控因子，接下来，我们在其他细胞类型的DEG中搜索了预测的上游调节子。如果发现了这种上游调节剂，则认为细胞类型之间存在相互作用。CellPhoneDB系统分析细胞相互作用
MCDM细胞类型的网络中心性与包含与RA相关的遗传变异的基因的丰富相关，因此可以潜在地用于区分细胞类型和基因以进行诊断和治疗。
我们对AIA和RA的分析使我们问到多细胞发病机制是否是人类疾病的普遍特征，以及是否可以在每种疾病中鉴定出最重要的细胞类型。13个疾病，每个疾病构建一个模块，每个模块具有高度相关表达模式的基因集。（把细胞换成了疾病）
Abstract
Background
Traditionally, the transcriptomic and proteomic characterisation of CD4+ T cells at the single-cell level has been performed by two largely exclusive types of technologies: single-cell RNA sequencing (scRNA-seq) and antibody-based cytometry. Here, we present a multi-omics approach allowing the simultaneous targeted quantification of mRNA and protein expression in single cells and investigate its performance to dissect the heterogeneity of human immune cell populations.
Methods
We have quantified the single-cell expression of 397 genes at the mRNA level and up to 68 proteins using oligo-conjugated antibodies (AbSeq) in 43,656 primary CD4+ T cells isolated from the blood and 31,907 CD45+ cells isolated from the blood and matched duodenal biopsies. We explored the sensitivity of this targeted scRNA-seq approach to dissect the heterogeneity of human immune cell populations and identify trajectories of functional T cell differentiation.
Results
We provide a high-resolution map of human primary CD4+ T cells and identify precise trajectories of Th1, Th17 and regulatory T cell (Treg) differentiation in the blood and tissue. The sensitivity provided by this multi-omics approach identified the expression of the B7 molecules CD80 and CD86 on the surface of CD4+ Tregs, and we further demonstrated that B7 expression has the potential to identify recently activated T cells in circulation. Moreover, we identified a rare subset of CCR9+ T cells in the blood with tissue-homing properties and expression of several immune checkpoint molecules, suggestive of a regulatory function.
Conclusions
The transcriptomic and proteomic hybrid technology described in this study provides a cost-effective solution to dissect the heterogeneity of immune cell populations at extremely high resolution. Unexpectedly, CD80 and CD86, normally expressed on antigen-presenting cells, were detected on a subset of activated Tregs, indicating a role for these co-stimulatory molecules in regulating the dynamics of CD4+ T cell responses.
key: 通过蛋白质-RNA单细胞分析发现CD80和CD86作为调节性T细胞上的最新激活标记
同时对蛋白质，RNA单细胞定量分析，在活化的T调节细胞检测到了CD80，CD86
Abstract
Background
Tumor cell-intrinsic mechanisms and complex interactions with the tumor microenvironment contribute to therapeutic failure via tumor evolution. It may be possible to overcome treatment resistance by developing a personalized approach against relapsing cancers based on a comprehensive analysis of cell type-specific transcriptomic changes over the clinical course of the disease using single-cell RNA sequencing (scRNA-seq).
Methods
Here, we used scRNA-seq to depict the tumor landscape of a single case of chemo-resistant metastatic, muscle-invasive urothelial bladder cancer (MIUBC) addicted to an activating Harvey rat sarcoma viral oncogene homolog (HRAS) mutation. In order to analyze tumor evolution and microenvironmental changes upon treatment, we also applied scRNA-seq to the corresponding patient-derived xenograft (PDX) before and after treatment with tipifarnib, a HRAS-targeting agent under clinical evaluation.
Results
In the parallel analysis of the human MIUBC and the PDX, diverse stromal and immune cell populations recapitulated the cellular composition in the human and mouse tumor microenvironment. Treatment with tipifarnib showed dramatic anticancer effects but was unable to achieve a complete response. Importantly, the comparative scRNA-seq analysis between pre- and post-tipifarnib-treated PDX revealed the nature of tipifarnib-refractory tumor cells and the tumor-supporting microenvironment. Based on the upregulation of programmed death-ligand 1 (PD-L1) in surviving tumor cells, and the accumulation of multiple immune-suppressive subsets from post-tipifarnib-treated PDX, a PD-L1 inhibitor, atezolizumab, was clinically applied; this resulted in a favorable response from the patient with acquired resistance to tipifarnib.
Conclusion
We presented a single case report demonstrating the power of scRNA-seq for visualizing the tumor microenvironment and identifying molecular and cellular therapeutic targets in a treatment-refractory cancer patient.
key: 膀胱癌 患者源性肿瘤异种移植 tipifarnib PD-L1 inhibitor
Abstract
Background
Single-cell RNA sequencing (scRNA-Seq) is an increasingly popular platform to study heterogeneity at the single-cell level. Computational methods to process scRNA-Seq data are not very accessible to bench scientists as they require a significant amount of bioinformatic skills.
Results
We have developed Granatum, a web-based scRNA-Seq analysis pipeline to make analysis more broadly accessible to researchers. Without a single line of programming code, users can click through the pipeline, setting parameters and visualizing results via the interactive graphical interface. Granatum conveniently walks users through various steps of scRNA-Seq analysis. It has a comprehensive list of modules, including plate merging and batch-effect removal, outlier-sample removal, gene-expression normalization, imputation, gene filtering, cell clustering, differential gene expression analysis, pathway/ontology enrichment analysis, protein network interaction visualization, and pseudo-time cell series construction.
Conclusions
Granatum enables broad adoption of scRNA-Seq technology by empowering bench scientists with an easy-to-use graphical interface for scRNA-Seq data analysis. The package is freely available for research use at http://garmiregroup.org/granatum/app
key: 基于web 的scRNA-seq分析pipeline
包括后期合并和批量效应去除，离群样本去除，基因表达归一化，插补，基因过滤，细胞聚类，差异基因表达分析，途径/本体论富集分析，蛋白质网络相互作用可视化和伪时间细胞系列构建
Abstract
Background
Despite extensive molecular characterization, we lack a comprehensive understanding of lineage identity, differentiation, and proliferation in high-grade gliomas (HGGs).
Methods
We sampled the cellular milieu of HGGs by profiling dissociated human surgical specimens with a high-density microwell system for massively parallel single-cell RNA-Seq. We analyzed the resulting profiles to identify subpopulations of both HGG and microenvironmental cells and applied graph-based methods to infer structural features of the malignantly transformed populations.
Results
While HGG cells can resemble glia or even immature neurons and form branched lineage structures, mesenchymal transformation results in unstructured populations. Glioma cells in a subset of mesenchymal tumors lose their neural lineage identity, express inflammatory genes, and co-exist with marked myeloid infiltration, reminiscent of molecular interactions between glioma and immune cells established in animal models. Additionally, we discovered a tight coupling between lineage resemblance and proliferation among malignantly transformed cells. Glioma cells that resemble oligodendrocyte progenitors, which proliferate in the brain, are often found in the cell cycle. Conversely, glioma cells that resemble astrocytes, neuroblasts, and oligodendrocytes, which are non-proliferative in the brain, are generally non-cycling in tumors.
Conclusions
These studies reveal a relationship between cellular identity and proliferation in HGG and distinct population structures that reflects the extent of neural and non-neural lineage resemblance among malignantly transformed cells.
key: 高度恶性神经胶质瘤的谱系分析
Abstract
Only a small fraction of patients respond to the drug prescribed to treat their disease, which means that most are at risk of unnecessary exposure to side effects through ineffective drugs. This inter-individual variation in drug response is driven by differences in gene interactions caused by each patient’s genetic background, environmental exposures, and the proportions of specific cell types involved in disease. These gene interactions can now be captured by building gene regulatory networks, by taking advantage of RNA velocity (the time derivative of the gene expression state), the ability to study hundreds of thousands of cells simultaneously, and the falling price of single-cell sequencing. Here, we propose an integrative approach that leverages these recent advances in single-cell data with the sensitivity of bulk data to enable the reconstruction of personalized, cell-type- and context-specific gene regulatory networks. We expect this approach will allow the prioritization of key driver genes for specific diseases and will provide knowledge that opens new avenues towards improved personalized healthcare.
key: 从共表达网络创建个性化的基因调控网络 精准医疗 对特定疾病的关键驱动基因进行优先排序
Abstract
Background
T cells exhibit heterogeneous functional states in the tumor microenvironment. Immune checkpoint inhibitors (ICIs) can reinvigorate only the stem cell-like progenitor exhausted T cells, which suggests that inhibiting the exhaustion progress will improve the efficacy of immunotherapy. Thus, regulatory factors promoting T cell exhaustion could serve as potential targets for delaying the process and improving ICI efficacy.
ICIs抑制免疫细胞的功能
Methods
We analyzed the single-cell transcriptome data derived from human melanoma and non-small cell lung cancer (NSCLC) samples and classified the tumor-infiltrating (TI) CD8+ T cell population based on PDCD1 (PD-1) levels, i.e., PDCD1-high and PDCD1-low cells. Additionally, we identified differentially expressed genes as candidate factors regulating intra-tumoral T cell exhaustion. The co-expression of candidate genes with immune checkpoint (IC) molecules in the TI CD8+ T cells was confirmed by single-cell trajectory and flow cytometry analyses. The loss-of-function effect of the candidate regulator was examined by a cell-based knockdown assay. The clinical effect of the candidate regulator was evaluated based on the overall survival and anti-PD-1 responses.
Results
We retrieved many known factors for regulating T cell exhaustion among the differentially expressed genes between PDCD1-high and PDCD1-low subsets of the TI CD8+ T cells in human melanoma and NSCLC. TOX was the only transcription factor (TF) predicted in both tumor types. TOX levels tend to increase as CD8+ T cells become more exhausted. Flow cytometry analysis revealed a correlation between TOX expression and severity of intra-tumoral T cell exhaustion. TOX knockdown in the human TI CD8+ T cells resulted in downregulation of PD-1, TIM-3, TIGIT, and CTLA-4, which suggests that TOX promotes intra-tumoral T cell exhaustion by upregulating IC proteins in cancer. Finally, the TOX level in the TI T cells was found to be highly predictive of overall survival and anti-PD-1 efficacy in melanoma and NSCLC.
Conclusions
We predicted the regulatory factors involved in T cell exhaustion using single-cell transcriptome profiles of human TI lymphocytes. TOX promoted intra-tumoral CD8+ T cell exhaustion via upregulation of IC molecules. This suggested that TOX inhibition can potentially impede T cell exhaustion and improve ICI efficacy. Additionally, TOX expression in the TI T cells can be used for patient stratification during anti-tumor treatments, including anti-PD-1 immunotherapy.
key: 人类癌症T细胞衰竭的促进因子和抗PD-1反应的预测因子 TOX通过上调IC分子促进肿瘤内CD8 + T细胞的衰竭。这表明，TOX抑制可能会阻止T细胞衰竭并改善ICI疗效
样本是什么？人类黑素瘤和非小细胞肺癌（NSCLC）样本的单细胞转录
如何找到TOX？系统地预测参与T细胞衰竭的调节因子，（TOX）和免疫检查点（IC）基因可以调节T细胞衰竭。对来自人类肿瘤的伪时间的CD8 + T细胞表达动态的分析表明，TOX的表达随CD8 + T细胞的耗尽而增加。另外，TOX阳性调节人TI CD8 + T细胞中PD-1，TIM-3，TIGIT和CTLA-4的表达。这表明TOX是通过诱导人类癌症中的IC分子促进T细胞衰竭的关键TF。最后，TI T细胞中TOX的表达水平可以预测人黑素瘤和NSCLC的总体存活率和对PD-1疗法的反应。这些结果表明，TOX水平可用于包括免疫疗法在内的抗癌治疗期间的患者分层，并且TOX可作为免疫检查点抑制剂（ICI）治疗的背景。
Abstract
Background
Human kidney organoids hold promise for studying development, disease modelling and drug screening. However, the utility of stem cell-derived kidney tissues will depend on how faithfully these replicate normal fetal development at the level of cellular identity and complexity.
Methods
Here, we present an integrated analysis of single cell datasets from human kidney organoids and human fetal kidney to assess similarities and differences between the component cell types.
Results
Clusters in the combined dataset contained cells from both organoid and fetal kidney with transcriptional congruence for key stromal, endothelial and nephron cell type-specific markers. Organoid enriched neural, glial and muscle progenitor populations were also evident. Major transcriptional differences between organoid and human tissue were likely related to technical artefacts. Cell type-specific comparisons revealed differences in stromal, endothelial and nephron progenitor cell types including expression of WNT2B in the human fetal kidney stroma.
Conclusions
This study supports the fidelity of kidney organoids as models of the developing kidney and affirms their potential in disease modelling and drug screening.
数据 肾脏类器官与胎儿肾
组合数据集中的聚类包含来自类器官和胎儿肾脏的细胞，其中关键基质，内皮和肾单位细胞类型特异性标记具有转录一致性。富含类器官的神经，神经胶质和肌肉祖细胞也很明显。类器官和人类组织之间的主要转录差异可能与技术伪像有关。细胞类型特异性比较揭示了人胎肾基质中基质，内皮和肾单位祖细胞类型的差异，包括WNT2B的表达。
这项研究支持肾脏类器官作为发育中的肾脏模型的保真度，并肯定了它们在疾病建模和药物筛选中的潜力。
Abstract
Personalized medicine requires the integration and processing of vast amounts of data. Here, we propose a solution to this challenge that is based on constructing Digital Twins. These are high-resolution models of individual patients that are computationally treated with thousands of drugs to find the drug that is optimal for the patient.
数字孪生是工程学中的一个概念，已应用于飞机或城市等复杂系统[3]。目的是对这些系统进行计算建模，以便比实际环境中更快，更经济地开发和测试它们。理想情况下，数字孪生概念可以转化为患者，以改善诊断和治疗。
如何构建将与疾病相关的基因，映射到PPI ，还有mRNA,形成多层模块。例如，SNP导致蛋白质表达发生改变，阻断该蛋白的药物
目的：筛选药物
Abstract
Background
We have previously reported an antigen-specific protocol to induce transplant tolerance and linked suppression to human embryonic stem cell (hESC)-derived tissues in immunocompetent mice through coreceptor and costimulation blockade. However, the exact mechanisms of acquired immune tolerance in this model have remained unclear.
Methods
We utilize the NOD.Foxp3hCD2 reporter mouse line and an ablative anti-hCD2 antibody to ask if CD4+FOXP3+ regulatory T cells (Treg) are required for coreceptor and costimulation blockade-induced immune tolerance. We also perform genome-wide single-cell RNA-sequencing to interrogate Treg during immune rejection and tolerance and to indicate possible mechanisms involved in sustaining Treg function.
Results
We show that Treg are indispensable for tolerance induced by coreceptor and costimulation blockade as depletion of which with an anti-hCD2 antibody resulted in rejection of hESC-derived pancreatic islets in NOD.Foxp3hCD2 mice. Single-cell transcriptomic profiling of 12,964 intragraft CD4+ T cells derived from rejecting and tolerated grafts reveals that Treg are heterogeneous and functionally distinct in the two outcomes of transplant rejection and tolerance. Treg appear to mainly promote chemotactic and ubiquitin-dependent protein catabolism during transplant rejection while seeming to harness proliferative and immunosuppressive function during tolerance. We also demonstrate that this form of acquired transplant tolerance is associated with increased proliferation and PD-1 expression by Treg. Blocking PD-1 signaling with a neutralizing anti-PD-1 antibody leads to reduced Treg proliferation and graft rejection.
Conclusions
Our results suggest that short-term coreceptor and costimulation blockade mediates immune tolerance to hESC-derived pancreatic islets by promoting Treg proliferation through engagement of PD-1. Our findings could give new insights into clinical development of hESC-derived pancreatic tissues, combined with immunotherapies that expand intragraft Treg, as a potentially sustainable alternative treatment for T1D.
key: 移植的耐受和排斥 Treg似乎在移植排斥期间主要促进趋化性和泛素依赖性蛋白分解代谢，而在耐受过程中似乎具有增殖和免疫抑制功能。我们还证明，这种形式的获得性移植耐受性与Treg的增生和PD-1表达增加有关。用中和性抗PD-1抗体阻断PD-1信号传导会导致Treg增殖减少和移植物排斥。
key: 当我们在不同的发育和病理环境中收集来自不同组织的数据时，我们将能够定义与疾病相关并能预测疾病的组织中特定细胞类型和位置的分子变化
Abstract
Background
Acute myeloid leukemia (AML), caused by the abnormal proliferation of immature myeloid cells in the blood or bone marrow, is one of the most common hematologic malignancies. Currently, the interactions between malignant myeloid cells and the immune microenvironment, especially T cells and B cells, remain poorly characterized.
Methods
In this study, we systematically analyzed the T cell receptor and B cell receptor (TCR and BCR) repertoires from the RNA-seq data of 145 pediatric and 151 adult AML samples as well as 73 non-tumor peripheral blood samples.
Results
We inferred over 225,000 complementarity-determining region 3 (CDR3) sequences in TCR α, β, γ, and δ chains and 1,210,000 CDR3 sequences in B cell immunoglobulin (Ig) heavy and light chains. We found higher clonal expansion of both T cells and B cells in the AML microenvironment and observed many differences between pediatric and adult AML. Most notably, adult AML samples have significantly higher level of B cell activation and more secondary Ig class switch events than pediatric AML or non-tumor samples. Furthermore, adult AML with highly expanded IgA2 B cells, which might represent an immunosuppressive microenvironment, are associated with regulatory T cells and worse overall survival.
Conclusions
Our comprehensive characterization of the AML immune receptor repertoires improved our understanding of T cell and B cell immunity in AML, which may provide insights into immunotherapies in hematological malignancies.
数据： 145例儿科和151例成人AML样本以及73例非肿瘤性外周血样本的RNA-seq数据中系统地分析了T细胞受体和B细胞受体（TCR和BCR）库。
我们在AML微环境中发现了T细胞和B细胞的更高的克隆扩增。 此外，成人AML样本比儿童AML样本或非肿瘤样本具有更高的B细胞活化水平和更多的继发性Ig类转换事件。 此外，我们发现具有高度扩展的IgA1 B细胞的儿科AML和具有高度扩展的IgA2 B细胞的成人AML与较差的总体生存率相关。 鉴定的TCR / BCR资料库以及从这项工作中观察到的关联为血液恶性肿瘤新型免疫疗法的未来发展提供了有用的资源和见识。
Abstract
Background
Alzheimer’s disease (AD) is characterized by neuronal loss and astrocytosis in the cerebral cortex. However, the specific effects that pathological mutations and coding variants associated with AD have on the cellular composition of the brain are often ignored.
Methods
We developed and optimized a cell-type-specific expression reference panel and employed digital deconvolution methods to determine brain cellular distribution in three independent transcriptomic studies.
Results
We found that neuronal and astrocyte relative proportions differ between healthy and diseased brains and also among AD cases that carry specific genetic risk variants. Brain carriers of pathogenic mutations in APP, PSEN1, or PSEN2 presented lower neuron and higher astrocyte relative proportions compared to sporadic AD. Similarly, the APOE ε4 allele also showed decreased neuronal and increased astrocyte relative proportions compared to AD non-carriers. In contrast, carriers of variants in TREM2 risk showed a lower degree of neuronal loss compared to matched AD cases in multiple independent studies.
Conclusions
These findings suggest that genetic risk factors associated with AD etiology have a specific imprinting in the cellular composition of AD brains. Our digital deconvolution reference panel provides an enhanced understanding of the fundamental molecular mechanisms underlying neurodegeneration, enabling the analysis of large bulk RNA-sequencing studies for cell composition and suggests that correcting for the cellular structure when performing transcriptomic analysis will lead to novel insights of AD.
key: 不同的基因突变导致星形胶质细胞和神经细胞比例的不同
digital deconvolution reference panel？科学界对使用脑表达研究来尝试确定AD中新的致病机制并鉴定新的治疗靶标非常感兴趣。这些努力正在产生大量的大量RNA-seq数据，因为来自人脑组织的大样本量的单细胞RNA（scRNA-seq）不可行。单细胞分选需要用新鲜组织进行[74]，这限制了AD研究中心收集的高度特征化的新鲜冷冻大脑的分析。我们的结果表明，数字反卷积方法可以从脑大量RNA-seq数据中准确推断出相对细胞分布
Abstract
Side-effects are the unintended consequence of therapeutic treatments, but they can also be seen as valuable read-outs of drug effects in humans; these effects are difficult to infer or predict from pre-clinical models. Indeed, some studies suggest that drugs with similar side-effect profiles may also share therapeutic properties through related mechanisms of action. A recent publication exploits this concept to systematically investigate new indications for already marketed drugs, and presents a strategy to get the most out of the tiny portion of chemicals that have proved to be effective and safe.
For almost a century, drug discovery was driven by the quest for magic bullets, which act by targeting one critical step in a disease process and elicit a cure with few other consequences. However, this concept is far from biological reality, and even the most successful rationally designed drugs (such as Gleevec®) show a quite promiscuous binding behavior, which has opened novel therapeutic possibilities [1]. Today, the emerging picture is that drugs rarely bind specifically to a single target, and this challenges the concept of a magic bullet. Indeed, recent analyses of drug and drug-target networks show a rich pattern of interactions among drugs and their targets, where drugs acting on a single target seem to be the exception. Likewise, many proteins are targeted by several drugs with quite distinct chemical structures [2].
Drug-repositioning strategies seek to exploit the notion of polypharmacology [3], together with the high connectivity among apparently unrelated cellular processes, to identify new therapeutic uses for already approved drugs. The main advantage of this approach is that, since it starts from approved compounds with well-characterized pharmacology and safety profiles, it should drastically reduce the risk of attrition in clinical phases. There are several successful examples of drug repositioning (for example, thalidomide to treat leprosy or finasteride for the prevention of baldness), although they were all found by serendipity and are not the result of well-thought strategies.
More recently, and following the observation that most novel entities are found by phenotypic profiling techniques [4], systematic initiatives to find new indications for old drugs have flourished. These approaches rely mostly on genome-wide transcriptional expression data from cultured human cells treated with small molecules, and pattern-matching algorithms to discover functional connections between drugs, genes and diseases through concerted gene-expression changes [5]. However, unfortunately, pre-clinical outcomes often do not correlate with therapeutic efficacy; only approximately 30% of the compounds that work well in cell assays work in animal models and, of these, only 5% work in humans [6].
key: 副作用是治疗的意想不到的结果,但对治疗可能有作用，sildenafil西地那非 thalidomide沙利度胺
Abstract
We present an integrated approach that predicts and validates novel anti-cancer drug targets. We first built a classifier that integrates a variety of genomic and systematic datasets to prioritize drug targets specific for breast, pancreatic and ovarian cancer. We then devised strategies to inhibit these anti-cancer drug targets and selected a set of targets that are amenable to inhibition by small molecules, antibodies and synthetic peptides. We validated the predicted drug targets by showing strong anti-proliferative effects of both synthetic peptide and small molecule inhibitors against our predicted targets.
key:新型抗癌药物靶点
方法：如何寻找靶点
小分子，抗体，多肽抑制的靶点
首先，我们收集了代表人类癌症的大量信息的基因组水平数据集，包括基因必需性，mRNA表达，DNA拷贝数改变，体细胞突变模式以及PPI网络数据。 然后，我们系统地分析了这些基因组和系统特性在多大程度上可以将药物靶标与其他蛋白质区分开。 接下来，我们生成了三种特定于癌症类型的分类器，以表征在特定癌症中具有功能相关性的靶标。
已知癌症药物靶标的独特基因组和网络拓扑特性可将新型癌症药物靶标与其他蛋白质区分开
SVM 我们发现潜在的癌症药物靶标可能在给定的癌症类型中是必不可少的，过表达的，扩增的和经常突变的，并且在维持PPI网络方面起着至关重要的作用
Abstract
The current therapeutic arsenal against viral infections remains limited, with often poor efficacy and incomplete coverage, and appears inadequate to face the emergence of drug resistance. Our understanding of viral biology and pathophysiology and our ability to develop a more effective antiviral arsenal would greatly benefit from a more comprehensive picture of the events that lead to viral replication and associated symptoms. Towards this goal, the construction of virus-host interactomes is instrumental, mainly relying on the assumption that a viral infection at the cellular level can be viewed as a number of perturbations introduced into the host protein network when viral proteins make new connections and disrupt existing ones. Here, we review advances in interactomic approaches for viral infections, focusing on high-throughput screening (HTS) technologies and on the generation of high-quality datasets. We show how these are already beginning to offer intriguing perspectives in terms of virus-host cell biology and the control of cellular functions, and we conclude by offering a summary of the current situation regarding the potential development of host-oriented antiviral therapeutics.
key: 当病毒蛋白建立新的连接并破坏现有蛋白时，可以将细胞水平的病毒感染视为引入到宿主蛋白网络中的许多干扰
如何发现抗病毒新药 病毒-宿主相互作用组 寻找人类蛋白的相关抑制剂
key: 如何应对不断变化的基因组变异？
如何选择治疗方案？
由全基因组测序，发现突变列表
对基因-癌症相关性进行评分，对药物可行性进行评分
提供抗癌治疗方案
Abstract
Progress in genomics has raised expectations in many fields, and particularly in personalized cancer research. The new technologies available make it possible to combine information about potential disease markers, altered function and accessible drug targets, which, coupled with pathological and medical information, will help produce more appropriate clinical decisions. The accessibility of such experimental techniques makes it all the more necessary to improve and adapt computational strategies to the new challenges. This review focuses on the critical issues associated with the standard pipeline, which includes: DNA sequencing analysis; analysis of mutations in coding regions; the study of genome rearrangements; extrapolating information on mutations to the functional and signaling level; and predicting the effects of therapies using mouse tumor models. We describe the possibilities, limitations and future challenges of current bioinformatics strategies for each of these issues. Furthermore, we emphasize the need for the collaboration between the bioinformaticians who implement the software and use the data resources, the computational biologists who develop the analytical methods, and the clinicians, the systems' end users and those ultimately responsible for taking medical decisions. Finally, the different steps in cancer genome analysis are illustrated through examples of applications in cancer genome analysis.
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