Cancer-linked targets modulated by curcumin

In spite of major advances in oncology, the World Health Organization predicts that cancer incidence will double within the next two decades. Although it is well understood that cancer is a hyperproliferative disorder mediated through dysregulation of multiple cell signaling pathways, most cancer drug development remains focused on modulation of specific targets, mostly one at a time, with agents referred to as “targeted therapies,” “smart drugs,” or “magic bullets.” How many cancer targets there are is not known, and how many targets must be attacked to control cancer growth is not well understood. Although more than 90% of cancer-linked deaths are due to metastasis of the tumor to vital organs, most drug targeting is focused on killing the primary tumor. Besides lacking specificity, the targeted drugs induce toxicity and side effects that sometimes are greater problems than the disease itself. Furthermore, the cost of some of these drugs is so high that most people cannot afford them. The present report describes the potential anticancer properties of curcumin, a component of the Indian spice turmeric (Curcuma longa), known for its safety and low cost. Curcumin can selectively modulate multiple cell signaling pathways linked to inflammation and to survival, growth, invasion, angiogenesis, and metastasis of cancer cells. More clinical trials of curcumin are needed to prove its usefulness in the cancer setting.     

MicroRNAs in cancer drug resistance: Basic evidence and clinical applications

Development of drug resistance has considerably limited the efficacy of cancer treatments, including chemotherapy and targeted therapies. Hence, understanding the molecular mechanisms underpinning the innate or the acquired resistance to these therapies is critical to improve drug efficiency and clinical outcomes. Several studies have implicated microRNAs (miRNA) in this process. MiRNAs repress gene expression by specific binding to complementary sequences in the 3' region of target messenger RNAs (mRNAs), followed by target mRNA degradation or blocked translation. By targeting molecules specific to a particular pathway within tumor cells, the new generation of cancer treatment strategies has shown significant advantages over conventional chemotherapy. However, the long-term efficacy of targeted therapies often remains poor, because tumor cells develop resistance to such therapeutics. Targeted therapies often involve monoclonal antibodies (mAbs), such as those blocking the ErB/HER tyrosine kinases, epidermal growth factor receptor (cetuximab) and HER2 (trastuzumab), and those inhibiting vascular endothelial growth factor receptor signaling (e.g., bevacizumab). Even though these are among the most used agents in tumor medicine, clinical response to these drugs is reduced due to the emergence of drug resistance as a result of toxic effects in the tumor microenvironment. Research on different types of human cancers has revealed that aberrant expression of miRNAs promotes resistance to the aforementioned drugs. In this study, we review the mechanisms of tumor cell resistance to mAb therapies and the role of miRNAs therein. Emerging treatment strategies combine therapies using innovative miRNA mimics or antagonizers with conventional approaches to maximize outcomes of patients with cancer.     

Molecular targeted therapies in breast cancer: Where are we now?

Targeted therapies, in cancer treatment, represent a new generation of drugs that interfere with specific molecular targets (typically proteins) having critical roles to play in tumour growth or progression. The principle of targeted therapy is certainly not new: tamoxifen, a hormonal agent targeted at the estrogen receptor, has been in use for more than 30 years. However, this principle has re-gained significant emphasis with the recent development of new biological agents, such as trastuzumab, which was first approved for the treatment of advanced breast cancer (BC) in 1998. Presently, there are at least three different targeted therapies with well documented activity in advanced BC and all three are now being studied in the adjuvant setting; trastuzumab and bevacizumab are monoclonal antibodies, and lapatinib is a dual inhibitor of HER-1 and HER-2. This paper will review the increasing role of molecular targeted therapies in BC, with a particular focus on those drugs currently being tested in early BC, as well as, on future perspectives.     

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.     

Validating cancer drug targets through chemical genetics

Targeted therapies for cancer promise to revolutionize treatment by specifically inactivating pathways needed for the growth of tumor cells. The most prominent example of such therapy is imatinib (Gleevec), which targets the BCR–ABL kinase and provides an effective low-toxicity treatment for chronic myelogenous leukemia. This success has spawned myriad efforts to develop similarly targeted drugs for other cancers. Unfortunately, the high expectations of these efforts have not yet been realized, likely due to the genetic diversity among and within tumors, as well as the complex and largely unpredictable interactions of drug-like compounds with innumerable targets that affect cellular and organismal metabolism. While improvements in sequencing technologies are beginning to address the first problem, solving the second problem requires methods for linking specific features of the cancer genome to their optimally targeted therapies. One approach, referred to as chemical genetics, accomplishes this by genetic control of chemical susceptibility. Chemical genetics is a crucial tool for the rational development of cancer drugs.     

Analysis and Prediction of the Metabolic Stability of Proteins Based on Their Sequential Features,Subcellular Locations and Interaction Networks

The metabolic stability is a very important idiosyncracy of proteins that is related to their global flexibility, intramolecular fluctuations, various internal dynamic processes, as well as many marvelous biological functions. Determination of protein''s metabolic stability would provide us with useful information for in-depth understanding of the dynamic action mechanisms of proteins. Although several experimental methods have been developed to measure protein''s metabolic stability, they are time-consuming and more expensive. Reported in this paper is a computational method, which is featured by (1) integrating various properties of proteins, such as biochemical and physicochemical properties, subcellular locations, network properties and protein complex property, (2) using the mRMR (Maximum Relevance & Minimum Redundancy) principle and the IFS (Incremental Feature Selection) procedure to optimize the prediction engine, and (3) being able to identify proteins among the four types: “short”, “medium”, “long”, and “extra-long” half-life spans. It was revealed through our analysis that the following seven characters played major roles in determining the stability of proteins: (1) KEGG enrichment scores of the protein and its neighbors in network, (2) subcellular locations, (3) polarity, (4) amino acids composition, (5) hydrophobicity, (6) secondary structure propensity, and (7) the number of protein complexes the protein involved. It was observed that there was an intriguing correlation between the predicted metabolic stability of some proteins and the real half-life of the drugs designed to target them. These findings might provide useful insights for designing protein-stability-relevant drugs. The computational method can also be used as a large-scale tool for annotating the metabolic stability for the avalanche of protein sequences generated in the post-genomic age.     

靶向蛋白质降解技术研究进展

靶向蛋白质降解技术可有效克服DNA敲除、RNA干扰等传统药物靶点确认及干扰策略的局限性。近年来,一系列新型靶向蛋白质降解技术不断涌现,在药物研发领域展现出极好的应用前景。本文综述了靶向蛋白质降解技术的最新研究进展,重点介绍各种技术的作用机制、应用情况、技术优势及目前存在问题,以期为药物靶点确认及新药开发提供有力理论及技术支持。     

Predicting selective drug targets in cancer through metabolic networks

The interest in studying metabolic alterations in cancer and their potential role as novel targets for therapy has been rejuvenated in recent years. Here, we report the development of the first genome‐scale network model of cancer metabolism, validated by correctly identifying genes essential for cellular proliferation in cancer cell lines. The model predicts 52 cytostatic drug targets, of which 40% are targeted by known, approved or experimental anticancer drugs, and the rest are new. It further predicts combinations of synthetic lethal drug targets, whose synergy is validated using available drug efficacy and gene expression measurements across the NCI‐60 cancer cell line collection. Finally, potential selective treatments for specific cancers that depend on cancer type‐specific downregulation of gene expression and somatic mutations are compiled.     

Various strategies for obtaining artificial hemoproteins: From “hemoabzymes” to “hemozymes”

The design of artificial hemoproteins that could lead to new biocatalysts for selective oxidation reactions of organic compounds presents a huge interest especially in pharmacology, both for a better understanding of the metabolic profile of drugs and for the synthesis of enantiomerically pure molecules that could be involved in the design of drugs.The present results show that the so-called “host-guest strategy” that involves the non-covalent incorporation of anionic water-soluble iron-porphyrins into xylanase A from Streptomyces lividans, a low cost protein, leads to such an artificial hemoprotein that is able to perform the stereoselective oxidation of sulfides.     

Novel biotechnology approaches in colorectal cancer diagnosis and therapy

With ever-increasing molecular information about colorectal cancer (CRC), there is an expectation to detect more sensitive and specific molecular markers for new advanced diagnostic methods that can surpass the limitations of current screening tests. Moreover, enhanced molecular pathology knowledge about cancer has led to the development of targeted therapies, designed to interfere with specific aberrant biological pathways in cancer. Furthermore, biotechnology has opened a new window in CRC diagnosis and treatment by introducing different application of antibodies, antibody fragments, non-Ig scaffold proteins, and aptamers in targeted therapy and drug delivery. This review summarizes the molecular diagnostic and therapeutic approaches in CRC with a focus on genetic and epigenetic alterations, protein and metabolite markers as well as targeted therapy and drug delivery by Ig-scaffold proteins, non-Ig scaffold proteins, nanobodies, and aptamers.     

Disruption of cell wall fatty acid biosynthesis in <Emphasis Type="Italic">Mycobacterium tuberculosis</Emphasis> using a graph theoretic approach

Fatty acid biosynthesis of Mycobacterium tuberculosis was analyzed using graph theory and influential (impacting) proteins were identified. The graphs (digraphs) representing this biological network provide information concerning the connectivity of each protein or metabolite in a given pathway, providing an insight into the importance of various components in the pathway, and this can be quantitatively analyzed. Using a graph theoretic algorithm, the most influential set of proteins (sets of {1, 2, 3}, etc.), which when eliminated could cause a significant impact on the biosynthetic pathway, were identified. This set of proteins could serve as drug targets. In the present study, the metabolic network of Mycobacterium tuberculosis was constructed and the fatty acid biosynthesis pathway was analyzed for potential drug targeting. The metabolic network was constructed using the KEGG LIGAND database and subjected to graph theoretical analysis. The nearness index of a protein was used to determine the influence of the said protein on other components in the network, allowing the proteins in a pathway to be ordered according to their nearness indices. A method for identifying the most strategic nodes to target for disrupting the metabolic networks is proposed, aiding the development of new drugs to combat this deadly disease.     

Identifying Unexpected Therapeutic Targets via Chemical-Protein Interactome

Drug medications inevitably affect not only their intended protein targets but also other proteins as well. In this study we examined the hypothesis that drugs that share the same therapeutic effect also share a common therapeutic mechanism by targeting not only known drug targets, but also by interacting unexpectedly on the same cryptic targets. By constructing and mining an Alzheimer''s disease (AD) drug-oriented chemical-protein interactome (CPI) using a matrix of 10 drug molecules known to treat AD towards 401 human protein pockets, we found that such cryptic targets exist. We recovered from CPI the only validated therapeutic target of AD, acetylcholinesterase (ACHE), and highlighted several other putative targets. For example, we discovered that estrogen receptor (ER) and histone deacetylase (HDAC), which have recently been identified as two new therapeutic targets of AD, might already have been targeted by the marketed AD drugs. We further established that the CPI profile of a drug can reflect its interacting character towards multi-protein sets, and that drugs with the same therapeutic attribute will share a similar interacting profile. These findings indicate that the CPI could represent the landscape of chemical-protein interactions and uncover “behind-the-scenes” aspects of the therapeutic mechanisms of existing drugs, providing testable hypotheses of the key nodes for network pharmacology or brand new drug targets for one-target pharmacology paradigm.     

Combined Drug Action of 2-Phenylimidazo[2,1-b]Benzothiazole Derivatives on Cancer Cells According to Their Oncogenic Molecular Signatures

The development of targeted molecular therapies has provided remarkable advances into the treatment of human cancers. However, in most tumors the selective pressure triggered by anticancer agents encourages cancer cells to acquire resistance mechanisms. The generation of new rationally designed targeting agents acting on the oncogenic path(s) at multiple levels is a promising approach for molecular therapies. 2-phenylimidazo[2,1-b]benzothiazole derivatives have been highlighted for their properties of targeting oncogenic Met receptor tyrosine kinase (RTK) signaling. In this study, we evaluated the mechanism of action of one of the most active imidazo[2,1-b]benzothiazol-2-ylphenyl moiety-based agents, Triflorcas, on a panel of cancer cells with distinct features. We show that Triflorcas impairs in vitro and in vivo tumorigenesis of cancer cells carrying Met mutations. Moreover, Triflorcas hampers survival and anchorage-independent growth of cancer cells characterized by “RTK swapping” by interfering with PDGFRβ phosphorylation. A restrained effect of Triflorcas on metabolic genes correlates with the absence of major side effects in vivo. Mechanistically, in addition to targeting Met, Triflorcas alters phosphorylation levels of the PI3K-Akt pathway, mediating oncogenic dependency to Met, in addition to Retinoblastoma and nucleophosmin/B23, resulting in altered cell cycle progression and mitotic failure. Our findings show how the unusual binding plasticity of the Met active site towards structurally different inhibitors can be exploited to generate drugs able to target Met oncogenic dependency at distinct levels. Moreover, the disease-oriented NCI Anticancer Drug Screen revealed that Triflorcas elicits a unique profile of growth inhibitory-responses on cancer cell lines, indicating a novel mechanism of drug action. The anti-tumor activity elicited by 2-phenylimidazo[2,1-b]benzothiazole derivatives through combined inhibition of distinct effectors in cancer cells reveal them to be promising anticancer agents for further investigation.     

Dynamics of targeted cancer therapy

Targeted cancer therapies offer renewed hope for an eventual 'cure for cancer'. At present, however, their success is often compromised by the emergence of resistant tumor cells. In many cancers, patients initially respond to single therapy treatment but relapse within months. Mathematical models of somatic evolution can predict and explain patterns in the success or failure of anticancer drugs. These models take into account the rate of cell division and death, the mutation rate, the size of the tumor, and the dynamics of tumor growth including density limitations caused by geometric and metabolic constraints. As more targeted therapies become available, mathematical modeling will provide an essential tool to inform the design of combination therapies that minimize the evolution of resistance.     

A novel binding pocket in the D2 domain of protein tyrosine phosphatase mu (PTPmu) guides AI screen to identify small molecules that modulate tumour cell adhesion,growth and migration

Approximately 40% of people will get cancer in their lifetime in the US, and 20% are predicted to die from the condition when it is invasive and metastatic. Targeted screening for drugs that interact with proteins that drive cancer cell growth and migration can lead to new therapies. We screened molecular libraries with the AtomNet® AI-based drug design tool to identify compounds predicted to interact with the cytoplasmic domain of protein tyrosine phosphatase mu. Protein tyrosine phosphatase mu (PTPmu) is proteolytically downregulated in cancers such as glioblastoma generating fragments that stimulate cell survival and migration. Aberrant nuclear localization of PTPmu intracellular fragments drives cancer progression, so we targeted a predicted drug-binding site between the two cytoplasmic phosphatase domains we termed a D2 binding pocket. The function of the D2 domain is controversial with various proposed regulatory functions, making the D2 domain an attractive target for the development of allosteric drugs. Seventy-five of the best-scoring and chemically diverse computational hits predicted to interact with the D2 binding pocket were screened for effects on tumour cell motility and growth in 3D culture as well as in a direct assay for PTPmu-dependent adhesion. We identified two high-priority hits that inhibited the migration and glioma cell sphere formation of multiple glioma tumour cell lines as well as aggregation. We also identified one activator of PTPmu-dependent aggregation, which was able to stimulate cell migration. We propose that the PTPmu D2 binding pocket represents a novel regulatory site and that inhibitors targeting this region may have therapeutic potential for treating cancer.     

Targeted molecular trait stacking in cotton through targeted double‐strand break induction

Recent developments of tools for targeted genome modification have led to new concepts in how multiple traits can be combined. Targeted genome modification is based on the use of nucleases with tailor‐made specificities to introduce a DNA double‐strand break (DSB) at specific target loci. A re‐engineered meganuclease was designed for specific cleavage of an endogenous target sequence adjacent to a transgenic insect control locus in cotton. The combination of targeted DNA cleavage and homologous recombination–mediated repair made precise targeted insertion of additional trait genes (hppd, epsps) feasible in cotton. Targeted insertion events were recovered at a frequency of about 2% of the independently transformed embryogenic callus lines. We further demonstrated that all trait genes were inherited as a single genetic unit, which will simplify future multiple‐trait introgression.     

Perinatal adaptation in mammals: the impact of metabolic rate

Mammalian birth is accompanied by profound changes in metabolic rate that can be described in terms of body size relationship (Kleiber's rule). Whereas the fetus, probably as an adaptation to the low intrauterine pO₂, exhibits an “inappropriately” low, adult-like specific metabolic rate, the term neonate undergoes a rapid metabolic increase up to the level to be expected from body size. A similar, albeit slowed, “switching-on” of metabolic size allometry is found in human preterm neonates whereas animals that are normally born in a very immature state are able to retard or even suppress the postnatal metabolic increase in favor of weight gain and O₂ supply. Moreover, small immature mammalian neonates exhibit a temporary oxyconforming behavior which enhances their hypoxia tolerance, yet is lost to the extent by which the size-adjusted metabolic rate is “locked” by increasing mitochondrial density. Beyond the perinatal period, there are no other deviations from metabolic size allometry among mammals except in hibernation where the temporary “switching-off” of Kleiber's rule is accompanied by a deep reduction in tissue pO₂. This gives support to the hypothesis that the postnatal metabolic increase represents an “escape from oxygen” similar to the evolutionary roots of mitochondrial respiration, and that the overall increase in specific metabolic rate with decreasing size might contribute to prevent tissues from O₂ toxicity.     

Autophagy and chemotherapy resistance: a promising therapeutic target for cancer treatment

Induction of cell death and inhibition of cell survival are the main principles of cancer therapy. Resistance to chemotherapeutic agents is a major problem in oncology, which limits the effectiveness of anticancer drugs. A variety of factors contribute to drug resistance, including host factors, specific genetic or epigenetic alterations in the cancer cells and so on. Although various mechanisms by which cancer cells become resistant to anticancer drugs in the microenvironment have been well elucidated, how to circumvent this resistance to improve anticancer efficacy remains to be defined. Autophagy, an important homeostatic cellular recycling mechanism, is now emerging as a crucial player in response to metabolic and therapeutic stresses, which attempts to maintain/restore metabolic homeostasis through the catabolic lysis of excessive or unnecessary proteins and injured or aged organelles. Recently, several studies have shown that autophagy constitutes a potential target for cancer therapy and the induction of autophagy in response to therapeutics can be viewed as having a prodeath or a prosurvival role, which contributes to the anticancer efficacy of these drugs as well as drug resistance. Thus, understanding the novel function of autophagy may allow us to develop a promising therapeutic strategy to enhance the effects of chemotherapy and improve clinical outcomes in the treatment of cancer patients.