Analysis of bypass signaling in EGFR pathway and profiling of bypass genes for predicting response to anticancer EGFR tyrosine kinase inhibitors

Some drugs, such as anticancer EGFR tyrosine kinase inhibitors, elicit markedly different clinical response rates due to differences in drug bypass signaling as well as genetic variations of drug target and downstream drug-resistant genes. The profiles of these bypass signaling are expected to be useful for improved drug response prediction, which have not been systematically explored previously. In this work, we searched and analyzed 16 literature-reported EGFR tyrosine kinase inhibitor bypass signaling routes in the EGFR pathway, which include 5 compensatory routes of EGFR transactivation by another receptor, and 11 alternative routes activated by another receptor. These 16 routes are reportedly regulated by 11 bypass genes. Their expression profiles together with the mutational, amplification and expression profiles of EGFR and 4 downstream drug-resistant genes, were used as new sets of biomarkers for identifying 53 NSCLC cell-lines sensitive or resistant to EGFR tyrosine kinase inhibitors gefitinib, erlotinib and lapatinib. The collective profiles of all 16 genes distinguish sensitive and resistant cell-lines are better than those of individual genes and the combined EGFR and downstream drug resistant genes, and their derived cell-line response rates are consistent with the reported clinical response rates of the three drugs. The usefulness of cell-line data for drug response studies was further analyzed by comparing the expression profiles of EGFR and bypass genes in NSCLC cell-lines and patient samples, and by using a machine learning feature selection method for selecting drug response biomarkers. Our study suggested that the profiles of drug bypass signaling are highly useful for improved drug response prediction.     

The glucose‐deprivation network counteracts lapatinib‐induced toxicity in resistant ErbB2‐positive breast cancer cells

Dynamic interactions between intracellular networks regulate cellular homeostasis and responses to perturbations. Targeted therapy is aimed at perturbing oncogene addiction pathways in cancer, however, development of acquired resistance to these drugs is a significant clinical problem. A network‐based computational analysis of global gene expression data from matched sensitive and acquired drug‐resistant cells to lapatinib, an EGFR/ErbB2 inhibitor, revealed an increased expression of the glucose deprivation response network, including glucagon signaling, glucose uptake, gluconeogenesis and unfolded protein response in the resistant cells. Importantly, the glucose deprivation response markers correlated significantly with high clinical relapse rates in ErbB2‐positive breast cancer patients. Further, forcing drug‐sensitive cells into glucose deprivation rendered them more resistant to lapatinib. Using a chemical genomics bioinformatics mining of the CMAP database, we identified drugs that specifically target the glucose deprivation response networks to overcome the resistant phenotype and reduced survival of resistant cells. This study implicates the chronic activation of cellular compensatory networks in response to targeted therapy and suggests novel combinations targeting signaling and metabolic networks in tumors with acquired resistance.     

Effective similarity measures for expression profiles

It is commonly accepted that genes with similar expression profiles are functionally related. However, there are many ways one can measure the similarity of expression profiles, and it is not clear a priori what is the most effective one. Moreover, so far no clear distinction has been made as for the type of the functional link between genes as suggested by microarray data. Similarly expressed genes can be part of the same complex as interacting partners; they can participate in the same pathway without interacting directly; they can perform similar functions; or they can simply have similar regulatory sequences. Here we conduct a study of the notion of functional link as implied from expression data. We analyze different similarity measures of gene expression profiles and assess their usefulness and robustness in detecting biological relationships by comparing the similarity scores with results obtained from databases of interacting proteins, promoter signals and cellular pathways, as well as through sequence comparisons. We also introduce variations on similarity measures that are based on statistical analysis and better discriminate genes which are functionally nearby and faraway. Our tools can be used to assess other similarity measures for expression profiles, and are accessible at biozon.org/tools/expression/     

Computational exploration of the activated pathways associated with DNA damage response in breast cancer

Molecular signaling events regulate cellular activity. Cancer stimulating signals trigger cellular responses that evade the regulatory control of cell development. To understand the mechanism of signaling regulation in cancer, it is necessary to identify the activated pathways in cancer. We have developed RepairPATH, a computational algorithm that explores the activated signaling pathways in cancer. The RepairPATH integrates RepairNET, an assembled protein interaction network associated with DNA damage response, with the gene expression profiles derived from the microarray data. Based on the observation that cofunctional proteins often exhibit correlated gene expression profiles, it identifies the activated signaling pathways in cancer by systematically searching the RepairNET for proteins with significantly correlated gene expression profiles. Analyzing the gene expression profiles of breast cancer, we found distinct similarities and differences in the activated signaling pathways between the samples from the patients who developed metastases and the samples from the patients who were disease free within 5 years. The cellular pathways associated with the various DNA repair mechanisms and the cell-cycle checkpoint controls are found to be activated in both sample groups. One of the most intriguing findings is that the pathways associated with different cellular processes are functionally coordinated through BRCA1 in the disease-free sample group, whereas such functional coordination is absent in the samples from patients who developed metastases. Our analysis revealed the potential cellular pathways that regulate the signaling events in breast cancer.     

Sequential application of anticancer drugs enhances cell death by rewiring apoptotic signaling networks

Crosstalk and complexity within signaling pathways and their perturbation by oncogenes limit component-by-component approaches to understanding human disease. Network analysis of how normal and oncogenic signaling can be rewired by drugs may provide opportunities to target tumors with high specificity and efficacy. Using targeted inhibition of oncogenic signaling pathways, combined with DNA-damaging chemotherapy, we report that time-staggered EGFR inhibition, but not simultaneous coadministration, dramatically sensitizes a subset of triple-negative breast cancer cells to genotoxic drugs. Systems-level analysis-using high-density time-dependent measurements of signaling networks, gene expression profiles, and cell phenotypic responses in combination with mathematical modeling-revealed an approach for altering the intrinsic state of the cell through dynamic rewiring of oncogenic signaling pathways. This process converts these cells to a less tumorigenic state that is more susceptible to DNA damage-induced cell death by reactivation of an extrinsic apoptotic pathway whose function is suppressed in the oncogene-addicted state.     

Proteome profiling reveals potential toxicity and detoxification pathways following exposure of BEAS-2B cells to engineered nanoparticle titanium dioxide

Oxidative stress is known to play important roles in engineered nanomaterial‐induced cellular toxicity. However, the proteins and signaling pathways associated with the engineered nanomaterial‐mediated oxidative stress and toxicity are largely unknown. To identify these toxicity pathways and networks that are associated with exposure to engineered nanomaterials, an integrated proteomic study was conducted using human bronchial epithelial cells, BEAS‐2B and nanoscale titanium dioxide. Utilizing 2‐DE and MS, we identified 46 proteins that were altered at protein expression levels. The protein changes detected by 2‐DE/MS were verified by functional protein assays. These identified proteins include some key proteins involved in cellular stress response, metabolism, adhesion, cytoskeletal dynamics, cell growth, cell death, and cell signaling. The differentially expressed proteins were mapped using Ingenuity Pathway Analyses^? canonical pathways and Ingenuity Pathway Analyses tox lists to create protein‐interacting networks and proteomic pathways. Twenty protein canonical pathways and tox lists were generated, and these pathways were compared to signaling pathways generated from genomic analyses of BEAS‐2B cells treated with titanium dioxide. There was a significant overlap in the specific pathways and lists generated from the proteomic and the genomic data. In addition, we also analyzed the phosphorylation profiles of protein kinases in titanium dioxide‐treated BEAS‐2B cells for a better understanding of upstream signaling pathways in response to the titanium dioxide treatment and the induced oxidative stress. In summary, the present study provides the first protein‐interacting network maps and novel insights into the biological responses and potential toxicity and detoxification pathways of titanium dioxide.     

Network-assisted investigation of antipsychotic drugs and their targets

Antipsychotic drugs are tranquilizing psychiatric medications primarily used in the treatment of schizophrenia and similar severe mental disorders. So far, most of these drugs have been discovered without knowing much on the molecular mechanisms of their actions. The available large amount of pharmacogenetics, pharmacometabolomics, and pharmacoproteomics data for many drugs makes it possible to systematically explore the molecular mechanisms underlying drug actions. In this study, we applied a unique network-based approach to investigate antipsychotic drugs and their targets. We first retrieved 43 antipsychotic drugs, 42 unique target genes, and 46 adverse drug interactions from the DrugBank database and then generated a drug-gene network and a drug-drug interaction network. Through drug-gene network analysis, we found that seven atypical antipsychotic drugs tended to form two clusters that could be defined by drugs with different target receptor profiles. In the drug-drug interaction network, we found that three drugs (zuclopenthixol, ziprasidone, and thiothixene) tended to have more adverse drug interactions than others, while clozapine had fewer adverse drug interactions. This investigation indicated that these antipsychotics might have different molecular mechanisms underlying the drug actions. This pilot network-assisted investigation of antipsychotics demonstrates that network-based analysis is useful for uncovering the molecular actions of antipsychotics.     

Genes and Pathways Co-associated with the Exposure to Multiple Drugs of Abuse,Including Alcohol,Amphetamine/Methamphetamine,Cocaine, Marijuana,Morphine, and/or Nicotine: a Review of Proteomics Analyses

Drug addiction is a chronic neuronal disease. In recent years, proteomics technology has been widely used to assess the protein expression in the brain tissues of both animals and humans exposed to addictive drugs. Through this approach, a large number of proteins potentially involved in the etiology of drug addictions have been identified, which provide a valuable resource to study protein function, biochemical pathways, and networks related to the molecular mechanisms underlying drug dependence. In this article, we summarize the recent application of proteomics to profiling protein expression patterns in animal or human brain tissues after the administration of alcohol, amphetamine/methamphetamine, cocaine, marijuana, morphine/heroin/butorphanol, or nicotine. From available reports, we compiled a list of 497 proteins associated with exposure to one or more addictive drugs, with 160 being related to exposure to at least two abused drugs. A number of biochemical pathways and biological processes appear to be enriched among these proteins, including synaptic transmission and signaling pathways related to neuronal functions. The data included in this work provide a summary and extension of the proteomics studies on drug addiction. Furthermore, the proteins and biological processes highlighted here may provide valuable insight into the cellular activities and biological processes in neurons in the development of drug addiction.     

Gene expression‐based drug repurposing to target aging

Aging is the largest risk factor for a variety of noncommunicable diseases. Model organism studies have shown that genetic and chemical perturbations can extend both lifespan and healthspan. Aging is a complex process, with parallel and interacting mechanisms contributing to its aetiology, posing a challenge for the discovery of new pharmacological candidates to ameliorate its effects. In this study, instead of a target‐centric approach, we adopt a systems level drug repurposing methodology to discover drugs that could combat aging in human brain. Using multiple gene expression data sets from brain tissue, taken from patients of different ages, we first identified the expression changes that characterize aging. Then, we compared these changes in gene expression with drug‐perturbed expression profiles in the Connectivity Map. We thus identified 24 drugs with significantly associated changes. Some of these drugs may function as antiaging drugs by reversing the detrimental changes that occur during aging, others by mimicking the cellular defence mechanisms. The drugs that we identified included significant number of already identified prolongevity drugs, indicating that the method can discover de novo drugs that meliorate aging. The approach has the advantages that using data from human brain aging data, it focuses on processes relevant in human aging and that it is unbiased, making it possible to discover new targets for aging studies.     

Study of drug function based on similarity of pathway fingerprint

Drugs sharing similar therapeutic function may not bind to the same group of targets. However, their targets may be involved in similar pathway profiles which are associated with certain pathological process. In this study, pathway fingerprint was introduced to indicate the profile of significant pathways being influenced by the targets of drugs. Then drug-drug network was further constructed based on significant similarity of pathway fingerprints. In this way, the functions of a drug may be hinted by the enriched therapeutic functions of its neighboring drugs. In the test of 911 FDA approved drugs with more than one known target, 471 drugs could be connected into networks. 760 significant associations of drug-therapeutic function were generated, among which around 60% of them were supported by scientific literatures or ATC codes of drug functional classification. Therefore, pathway fingerprints may be useful to further study on the potential function of known drugs, or the unknown function of new drugs.     

Distinct genes related to drug response identified in ER positive and ER negative breast cancer cell lines

Breast cancer patients have different responses to chemotherapeutic treatments. Genes associated with drug response can provide insight to understand the mechanisms of drug resistance, identify promising therapeutic opportunities, and facilitate personalized treatment. Estrogen receptor (ER) positive and ER negative breast cancer have distinct clinical behavior and molecular properties. However, to date, few studies have rigorously assessed drug response genes in them. In this study, our goal was to systematically identify genes associated with multidrug response in ER positive and ER negative breast cancer cell lines. We tested 27 human breast cell lines for response to seven chemotherapeutic agents (cyclophosphamide, docetaxel, doxorubicin, epirubicin, fluorouracil, gemcitabine, and paclitaxel). We integrated publicly available gene expression profiles of these cell lines with their in vitro drug response patterns, then applied meta-analysis to identify genes related to multidrug response in ER positive and ER negative cells separately. One hundred eighty-eight genes were identified as related to multidrug response in ER positive and 32 genes in ER negative breast cell lines. Of these, only three genes (DBI, TOP2A, and PMVK) were common to both cell types. TOP2A was positively associated with drug response, and DBI was negatively associated with drug response. Interestingly, PMVK was positively associated with drug response in ER positive cells and negatively in ER negative cells. Functional analysis showed that while cell cycle affects drug response in both ER positive and negative cells, most biological processes that are involved in drug response are distinct. A number of signaling pathways that are uniquely enriched in ER positive cells have complex cross talk with ER signaling, while in ER negative cells, enriched pathways are related to metabolic functions. Taken together, our analysis indicates that distinct mechanisms are involved in multidrug response in ER positive and ER negative breast cells.     

Nonlinear Blending: A Useful General Concept for the Assessment of Combination Drug Synergy

Human diseases may involve cellular signaling networks that contain redundant pathways, so that blocking a single pathway in the system cannot achieve the desired effect. As such, the use of drugs in combination are particularly effective interventions in networked systems. However, common synergy measures are often inadequate to quantify the effect of two different drugs in complex cellular systems. This article proposes a general approach to quantifying the synergy of two drugs in combination. This approach is called strong nonlinear blending. Drugs with different relative potencies, different effect maxima, or situations of potentiation or coalism pose no problem for strong nonlinear blending as a way to assess the increased response benefit to be gained by combining two drugs. This is important as testing drug combinations in complex biological systems are likely to produce a wide variety of possible response surfaces. It is also shown that for monotone increasing (or decreasing) dose response surfaces that strong nonlinear blending is equivalent to improved potency along a ray of constant dose ratio. This is important because fixed dose ratios form the basis for many preclinical and clinical combination drug experiments. Two examples are given involving HIV and cancer chemotherapy combination drug experiments.     

PREDICT: a method for inferring novel drug indications with application to personalized medicine

Inferring potential drug indications, for either novel or approved drugs, is a key step in drug development. Previous computational methods in this domain have focused on either drug repositioning or matching drug and disease gene expression profiles. Here, we present a novel method for the large‐scale prediction of drug indications (PREDICT) that can handle both approved drugs and novel molecules. Our method is based on the observation that similar drugs are indicated for similar diseases, and utilizes multiple drug–drug and disease–disease similarity measures for the prediction task. On cross‐validation, it obtains high specificity and sensitivity (AUC=0.9) in predicting drug indications, surpassing existing methods. We validate our predictions by their overlap with drug indications that are currently under clinical trials, and by their agreement with tissue‐specific expression information on the drug targets. We further show that disease‐specific genetic signatures can be used to accurately predict drug indications for new diseases (AUC=0.92). This lays the computational foundation for future personalized drug treatments, where gene expression signatures from individual patients would replace the disease‐specific signatures.     

泛素链水解酶的选择性和产生机制

底物蛋白的多聚泛素链修饰参与调节多种生命运动过程(包括蛋白质降解、自噬、DNA损伤修复、细胞周期、信号转导、基因表达、转录调节、炎症免疫等).去泛素化酶通过水解底物蛋白的单泛素和泛素链修饰,对泛素相关过程进行反向调节.人类基因组中约含90余种去泛素化酶,它们通过对自身酶活性和底物识别特异性的调节,实现了对细胞内复杂泛素过程的精密且层次性的调控.本文针对去泛素化酶对不同泛素链的识别选择性,综述目前已知泛素链水解酶的选择性和产生机制.     

Immunocell-array for molecular dissection of multiple signaling pathways in mammalian cells

The knowledge of signaling pathways that are triggered by physiological and pathological conditions or drug treatment is essential for the comprehension of the biological events that regulate cellular responses. Recently novel platforms based on "reverse-phase protein arrays" have proven to be useful in the study of different pathways, but they still lack the possibility to detect events in the complexity of a cellular context. We developed an "immunocell-array" of cells on chip where, upon cell plating, growing, drug treatment, and fixation, by spotting specific antibodies we can detect the localization and state of hundreds of proteins involved in specific signaling pathways. By applying this technology to mammalian cells we analyzed signaling proteins involved in the response to DNA damage and identified a chromatin remodeling pathway following bleomycin treatment. We propose our technology as a new tool for the array-based multiplexed analysis of signaling pathways in drug response screening, for the proteomics of profiling patient cells, and ultimately for the high throughput screening of antibodies for immunofluorescence applications.     

The lysosomal TRPML1 channel regulates triple negative breast cancer development by promoting mTORC1 and purinergic signaling pathways

The triple-negative breast cancer (TNBC) that comprises approximately 10%–20% of breast cancers is an aggressive subtype lacking effective therapeutics. Among various signaling pathways, mTORC1 and purinergic signals have emerged as potentially fruitful targets for clinical therapy of TNBC. Unfortunately, drugs targeting these signaling pathways do not successfully inhibit the progression of TNBC, partially due to the fact that these signaling pathways are essential for the function of all types of cells. In this study, we report that TRPML1 is specifically upregulated in TNBCs and that its genetic downregulation and pharmacological inhibition suppress the growth of TNBC. Mechanistically, we demonstrate that TRPML1 regulates TNBC development, at least partially, through controlling mTORC1 activity and the release of lysosomal ATP. Because TRPML1 is specifically activated by cellular stresses found in tumor microenvironments, antagonists of TRPML1 could represent anticancer drugs with enhanced specificity and potency. Our findings are expected to have a major impact on drug targeting of TNBCs.     

Nonlinear blending: a useful general concept for the assessment of combination drug synergy

Human diseases may involve cellular signaling networks that contain redundant pathways, so that blocking a single pathway in the system cannot achieve the desired effect. As such, the use of drugs in combination are particularly effective interventions in networked systems. However, common synergy measures are often inadequate to quantify the effect of two different drugs in complex cellular systems. This article proposes a general approach to quantifying the synergy of two drugs in combination. This approach is called strong nonlinear blending. Drugs with different relative potencies, different effect maxima, or situations of potentiation or coalism pose no problem for strong nonlinear blending as a way to assess the increased response benefit to be gained by combining two drugs. This is important as testing drug combinations in complex biological systems are likely to produce a wide variety of possible response surfaces. It is also shown that for monotone increasing (or decreasing) dose response surfaces that strong nonlinear blending is equivalent to improved potency along a ray of constant dose ratio. This is important because fixed dose ratios form the basis for many preclinical and clinical combination drug experiments. Two examples are given involving HIV and cancer chemotherapy combination drug experiments.     

Therapeutic resistance resulting from mutations in Raf/MEK/ERK and PI3K/PTEN/Akt/mTOR signaling pathways

Chemotherapy remains a commonly used therapeutic approach for many cancers. Indeed chemotherapy is relatively effective for treatment of certain cancers and it may be the only therapy (besides radiotherapy) that is appropriate for certain cancers. However, a common problem with chemotherapy is the development of drug resistance. Many studies on the mechanisms of drug resistance concentrated on the expression of membrane transporters and how they could be aberrantly regulated in drug resistant cells. Attempts were made to isolate specific inhibitors which could be used to treat drug resistant patients. Unfortunately most of these drug transporter inhibitors have not proven effective for therapy. Recently the possibilities of more specific, targeted therapies have sparked the interest of clinical and basic researchers as approaches to kill cancer cells. However, there are also problems associated with these targeted therapies. Two key signaling pathways involved in the regulation of cell growth are the Ras/Raf/MEK/ERK and PI3K/PTEN/Akt/mTOR pathways. Dysregulated signaling through these pathways is often the result of genetic alterations in critical components in these pathways as well as mutations in upstream growth factor receptors. Furthermore, these pathways may be activated by chemotherapeutic drugs and ionizing radiation. This review documents how their abnormal expression can contribute to drug resistance as well as resistance to targeted therapy. This review will discuss in detail PTEN regulation as this is a critical tumor suppressor gene frequently dysregulated in human cancer which contributes to therapy resistance. Controlling the expression of these pathways could improve cancer therapy and ameliorate human health.