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Systems Biology Approaches for Cancer Biology
Abstract
Cancers are complex heterogeneous diseases characterized by the unconditional and abnormal division of eukaryotic cells. When cell division remains unchecked within the body, they give rise to tissue masses known as tumors. This disease exhibits a high level of robustness to various therapeutic treatment inventions. The diagnosis and treatment of cancer is a key component of any overall cancer control plan. In the last few decades biological science has seen the emergence of new domain, aiming at solving the complexity of biological phenomena. The Systems-level understanding of biological science has become one of the essential biological analytical tools to detect powerful insights into human health and diseases. In the recent era of high scientific and medicinal research advancements, it’s becoming difficult for scientists to integrate the disease in the conventional way. So, they are trying to shift their views to genomics, proteomics specifically the ‘omics’ science, computational biology and Systems. The combined omics information is leading to the proper profiling of health and diseases at global level. The system biology is mainly focused on the integrative nature of the evolutionary developed biological systems and the fundamental principle in order to govern them. This technique focuses on engineering techniques to inscribe human diseases on biological perspective. The analysis of such analysis is illustrated to decipher complex pathological diseases such as prion disease, liver toxicity, diabetes and cancer.
This chapter is mainly focused on high-throughput technologies and approaches including diagnostic and therapeutics that help to understand the underlying biological processes involved in cancer biology. This study helps to improve in understanding the complex interaction that occurs between the normal cells and the onco-genes present in a human body.
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Today, cancer is the second leading cause of death in the world after cardiovascular disease. When the carcinogenesis formation processes are examined, we consider that many theories are trying to explain this. These theories vary widely in terms of both origin and predisposing causes in terms of genetic, epigenetic, immune system origin or monoclonal antibody theory. Starting from the DNA damage in the division stage of the cell, the mutated cell's escape from apoptosis, gaining unlimited proliferation capability with telomerase activity, angiogenesis ability, metastasis, invasion, escape from tumor suppressor genes, managing tumor microenvironment, tumor progression, and heterogeneity features is a multi-parameter complex equation. While trying to explain the mechanisms of carcinogenesis, many unknown pathways and biomarkers also emerge. Clustered regularly interspaced palindromic repeats/CRISPR-associated9 (CRISPR/CAS9) technology for cancer biology has the potential to respond to many unknowns at the cellular and genetic level. With this article, we aimed to predict the promise of CRISPR/CAS9 technology in the field of oncology.
Although PD-L1 expression on tumor is related to the prognosis of immune checkpoint blockade (ICB) therapy, a recent study also demonstrated clinical benefits even in patients without PD-L1 expression. To understand the relationship between innate resistance and antitumor cytotoxic T lymphocyte (CTL) responses especially against neoantigens, the interaction between PD-L1⁺ or genetically PD-L1-deleted colorectal tumors and CTLs was assessed under an ICB therapy, finding that robust CTLs activation in PD-L1-deleted tumor-bearing mice. Using antigen libraries based on immunogenomics, we identified three H2-Kb-restricted, somatic-mutated immunogenic neoantigens by utilizing enhanced CTLs responses due to PD-L1 deficiency. Furthermore, we identified three TCR repertoires relevant to the neoantigens, confirming the response of TCR gene-transduced CTLs to parental tumor cells. Notably, neoantigen-pulsed dendritic cell (DC) therapy reversed the tumor tolerance. Thus, innate resistance of tumors determines their responsiveness to neoantigens and mixed neoantigen peptides may be useful in DC therapy against innate resistance type tumor.
Due to rising costs and the difficulty to identify new targets, drug repurposing appears as a viable strategy for the development of new anti-cancer treatments. Although the interest of disulfiram (DSF), an anti-alcohol drug, to treat cancer was reported for many years, it is only very recently that one anticancer mechanism-of-action was highlighted. This would involve the inhibition of the p97 segregase adaptor NPL4, which is essential for the turnover of proteins involved in multiple regulatory and stress-response intracellular pathways. However, recently DSF was also reported as one of the first phosphoglycerate dehydrogenase (PHGDH) inhibitors, a tetrameric enzyme catalyzing the initial step of the serine synthetic pathway that is highly expressed in numerous cancer types. Here, we investigated the structure-activity relationships (SAR) of PHGDH inhibition by disulfiram analogues as well as the mechanism of action of DSF on PHGDH via enzymatic and cell-based evaluation, mass spectrometric and mutagenesis experiments.
Existing drugs have the potential to learn “new tricks” to fight cancer when working together in combinations. Researchers from The Ohio State University and Washington University in St. Louis developed a computational approach to predict synergistic drug combinations among thousands of potential pairs based upon how cancer cells alter certain genes and connections between genes. They found that drug predictions were more accurate when this method was applied to specific molecular subtypes of melanoma. The top drug combination predicted for melanoma with BRAF gene mutations included a known melanoma drug, vemurafenib, in combination with another agent not currently used for melanoma, tretinoin. This combination could kill melanoma cells and reduce tumor size in mice. Future applications include other examples of “precision medicine” to deliver the right drugs to the right patients at the right time.
Drug-target interaction (DTI) is the basis of drug discovery. However, it is time-consuming and costly to determine DTIs experimentally. Over the past decade, various computational methods were proposed to predict potential DTIs with high efficiency and low costs. These methods can be roughly divided into several categories, such as molecular docking-based, pharmacophore-based, similarity-based, machine learning-based, and network-based methods. Among them, network-based methods, which do not rely on three-dimensional structures of targets and negative samples, have shown great advantages over the others. In this article, we focused on network-based methods for DTI prediction, in particular our network-based inference (NBI) methods that were derived from recommendation algorithms. We first introduced the methodologies and evaluation of network-based methods, and then the emphasis was put on their applications in a wide range of fields, including target prediction and elucidation of molecular mechanisms of therapeutic effects or safety problems. Finally, limitations and perspectives of network-based methods were discussed. In a word, network-based methods provide alternative tools for studies in drug repurposing, new drug discovery, systems pharmacology and systems toxicology.
Cancer immunotherapy: Boosting anti-tumor immunity
New immunotherapies for cancer known as immune checkpoint blockers (ICBs) may help treat many tumor types. Homeostasis of the immune system is controlled by proteins known as immune checkpoints; cancer cells evade the anti-tumor immune response by displaying ligands of the immune checkpoint molecules, thereby incapacitating tumor-killing immune cells. ICBs disable the checkpoints, making cancer cells vulnerable to the immune response. Although ICBs are effective against many cancers, some patients show resistance or relapse. In a review of ICBs, Young-Jun Park and Yeonseok Chung at Seoul National University, South Korea, report that combining two ICBs can improve efficacy, and discuss new pre-, and post-treatment tests to determine whether a drug will work for a particular patient. They also report that manipulating patients’ intestinal microflora can improve response to ICBs. The findings reported may help to optimize these promising treatments.
Metformin has been the first-line drug treatment for hyperglycemia and insulin resistance for over 50 years. However, the molecular basis of its therapeutic role remained incompletely understood. Recent advances demonstrate that metformin could exert its glucose-lowering effect by multiple mechanisms, including activation of 5′-AMP-activated protein kinase, decreasing production of cyclic AMP, suppressing mitochondrial complex I of the electron transport chain, targeting glycerophosphate dehydrogenase, and altering the gut microbiome. In addition, epidemiological and clinical observation studies suggest that metformin reduced cancer risk in patients with type 2 diabetes and improved survival outcome of human cancers. Experimental studies have shown that this drug can inhibit cancer cell viability, growth, and proliferation through inhibiting mTORC1 signaling and mitochondrial complex I, suggesting that it may be a promising drug candidate for malignancy. Here, we summarize recent progress in studies of metformin in type 2 diabetes and tumorigenesis, which provides novel insight on the therapeutic development of human diseases.
In the era of precision medicine, cancer therapy can be tailored to an individual patient based on the genomic profile of a tumour. Despite the ever-increasing abundance of cancer genomic data, linking mutation profiles to drug efficacy remains a challenge. Herein, we report Cancer Drug Response profile scan (CDRscan) a novel deep learning model that predicts anticancer drug responsiveness based on a large-scale drug screening assay data encompassing genomic profiles of 787 human cancer cell lines and structural profiles of 244 drugs. CDRscan employs a two-step convolution architecture, where the genomic mutational fingerprints of cell lines and the molecular fingerprints of drugs are processed individually, then merged by 'virtual docking', an in silico modelling of drug treatment. Analysis of the goodness-of-fit between observed and predicted drug response revealed a high prediction accuracy of CDRscan (R2 > 0.84; AUROC > 0.98). We applied CDRscan to 1,487 approved drugs and identified 14 oncology and 23 non-oncology drugs having new potential cancer indications. This, to our knowledge, is the first-time application of a deep learning model in predicting the feasibility of drug repurposing. By further clinical validation, CDRscan is expected to allow selection of the most effective anticancer drugs for the genomic profile of the individual patient.
Proton pump inhibitors (PPIs), H+/K+-ATPase inhibitors, are the most commonly prescribed drugs for the treatment of gastroesophageal reflux and peptic ulcer diseases; they are highly safe and tolerable. Since PPIs are frequently used in cancer patients, studies investigating interactions between PPIs and anticancer agents are of particular importance to achieving effective and safe cancer chemotherapy. Several studies have revealed that PPIs inhibit not only the H+/K+-ATPase in gastric parietal cells, but also the vacuolar H+-ATPase (V-ATPase) overexpressed in tumor cells, as well as the renal basolateral organic cation transporter 2 (OCT2) associated with pharmacokinetics and/or renal accumulation of various drugs, including anticancer agents. In this mini-review, we summarize the current knowledge regarding the impact of PPIs on the efficacy and safety of cancer chemotherapeutics via inhibition of targets other than the H+/K+-ATPase. Co-administration of clinical doses of PPIs protected kidney function in patients receiving cisplatin and fluorouracil, presumably by decreasing accumulation of cisplatin in the kidney via OCT2 inhibition. In addition, co-administration or pretreatment with PPIs could inhibit H+ transport via the V-ATPase in tumor cells, resulting in lower extracellular acidification and intracellular acidic vesicles to enhance the sensitivity of the tumor cells to the anticancer agents. In the present mini-review, we suggest that PPIs enhance the efficacy and safety of anticancer agents via off-target inhibition (e.g., of OCT2 and V-ATPase), rather than on-target inhibition of the H+/K+-ATPase. The present findings should provide important information to establish novel supportive therapy with PPIs during cancer chemotherapy.
Methods that integrate molecular network information and tumor genome data could complement gene-based statistical tests to identify likely new cancer genes; but such approaches are challenging to validate at scale, and their predictive value remains unclear. We developed a robust statistic (NetSig) that integrates protein interaction networks with data from 4,742 tumor exomes. NetSig can accurately classify known driver genes in 60% of tested tumor types and predicts 62 new driver candidates. Using a quantitative experimental framework to determine in vivo tumorigenic potential in mice, we found that NetSig candidates induce tumors at rates that are comparable to those of known oncogenes and are ten-fold higher than those of random genes. By reanalyzing nine tumor-inducing NetSig candidates in 242 patients with oncogene-negative lung adenocarcinomas, we find that two (AKT2 and TFDP2) are significantly amplified. Our study presents a scalable integrated computational and experimental workflow to expand discovery from cancer genomes.
Background and purpose
Predicting the efficacy of anticancer therapy is the holy grail of drug development and treatment selection in the clinic. To achieve this goal, scientists require pre-clinical models that can reliably screen anticancer agents with robust clinical correlation. However, there is increasing challenge to develop models that can accurately capture the diversity of the tumor ecosystem, and therefore reliably predict how tumors respond or resistant to treatment. Indeed, tumors are made up of a heterogeneous landscape comprising malignant cells, normal and abnormal stroma, immune cells, and dynamic microenvironment containing chemokines, cytokines and growth factors. In this mini-review we present a focused, brief perspective on emerging preclinical models for anticancer therapy that attempt to address the challenge posed by tumor heterogeneity, highlighting biomarkers of response and resistance.
Recent findings
Starting from 2-dimensional and 3-dimensional in-vitro models, we discuss how organoid co-cultures have led to accelerated efforts in anti-cancer drug screening, and advanced our fundamental understanding for mechanisms of action using high-throughput platforms that interrogate various biomarkers of ‘clinical’ efficacy. Then, mentioning the limitations that exist, we focus on in-vivo and human explant technologies and models, which build-in intrinsic tumor heterogeneity using the native microenvironment as a scaffold. Importantly, we will address how these models can be harnessed to understand cancer immunotherapy, an emerging therapeutic strategy that seeks to recalibrate the body’s own immune system to fight cancer.
Conclusion
Over the past several decades, numerous model systems have emerged to address the exploding market of drug development for cancer. While all of the present models have contributed critical information about tumor biology, each one carries limitations. Harnessing pre-clinical models that incorporate cell heterogeneity is beginning to address some of the underlying challenges associated with predicting clinical efficacy of novel anticancer agents.
Computation-based drug-repurposing/repositioning approaches can greatly speed up the traditional drug discovery process. To date, systematic and comprehensive computation-based approaches to identify and validate drug-repositioning candidates for epithelial ovarian cancer (EOC) have not been undertaken. Here, we present a novel drug discovery strategy that combines a computational drug-repositioning system (DrugPredict) with biological testing in cell lines in order to rapidly identify novel drug candidates for EOC. DrugPredict exploited unique repositioning opportunities rendered by a vast amount of disease genomics, phenomics, drug treatment, and genetic pathway and uniquely revealed that non-steroidal anti-inflammatories (NSAIDs) rank just as high as currently used ovarian cancer drugs. As epidemiological studies have reported decreased incidence of ovarian cancer associated with regular intake of NSAIDs, we assessed whether NSAIDs could have chemoadjuvant applications in EOC and found that (i) NSAID Indomethacin induces robust cell death in primary patient-derived platinum-sensitive and platinum- resistant ovarian cancer cells and ovarian cancer stem cells and (ii) downregulation of β-catenin is partially driving effects of Indomethacin in cisplatin-resistant cells. In summary, we demonstrate that DrugPredict represents an innovative computational drug- discovery strategy to uncover drugs that are routinely used for other indications that could be effective in treating various cancers, thus introducing a potentially rapid and cost-effective translational opportunity. As NSAIDs are already in routine use in gynecological treatment regimens and have acceptable safety profile, our results will provide with a rationale for testing NSAIDs as potential chemoadjuvants in EOC patient trials.
The emergence of large-scale genomic, chemical and pharmacological data provides new opportunities for drug discovery and repositioning. In this work, we develop a computational pipeline, called DTINet, to predict novel drug–target interactions from a constructed heterogeneous network, which integrates diverse drug-related information. DTINet focuses on learning a low-dimensional vector representation of features, which accurately explains the topological properties of individual nodes in the heterogeneous network, and then makes prediction based on these representations via a vector space projection scheme. DTINet achieves substantial performance improvement over other state-of-the-art methods for drug–target interaction prediction. Moreover, we experimentally validate the novel interactions between three drugs and the cyclooxygenase proteins predicted by DTINet, and demonstrate the new potential applications of these identified cyclooxygenase inhibitors in preventing inflammatory diseases. These results indicate that DTINet can provide a practically useful tool for integrating heterogeneous information to predict new drug–target interactions and repurpose existing drugs.
Cancers remain a major public health problem worldwide, which still require profound research in both the basic and preclinical fields. Patient-derived xenograft (PDX) models are created when cancerous cells or tissues from patients’ primary tumors are implanted into immunodeficient mice to simulate human tumor biology in vivo, which have been extensively used in cancer research. The routes of implantation appeared to affect the outcome of PDX research, and there has been increasing applications of patient-derived orthotopic xenograft (PDOX) models. In this review, we firstly summarize the methodology to establish PDX models and then go over recent application and function of PDX models in basic cancer research on the areas of cancer characterization, initiation, proliferation, metastasis, and tumor microenvironment and in preclinical explorations of anti-cancer targets, drugs, and therapeutic strategies and finally give our perspectives on the future prospects of PDX models.
With accumulating public omics data, great efforts have been made to characterize the genetic heterogeneity of breast cancer. However, identifying novel targets and selecting the best from the sizeable lists of candidate targets is still a key challenge for targeted therapy, largely owing to the lack of economical, efficient and systematic discovery and assessment to prioritize potential therapeutic targets. Here, we describe an approach that combines the computational evaluation and objective, multifaceted assessment to systematically identify and prioritize targets for biological validation and therapeutic exploration. We first establish the reference gene expression profiles from breast cancer cell line MCF7 upon genome-wide RNA interference (RNAi) of a total of 3689 genes, and the breast cancer query signatures using RNA-seq data generated from tissue samples of clinical breast cancer patients in the Cancer Genome Atlas (TCGA). Based on gene set enrichment analysis, we identified a set of 510 genes that when knocked down could significantly reverse the transcriptome of breast cancer state. We then perform multifaceted assessment to analyze the gene set to prioritize potential targets for gene therapy. We also propose drug repurposing opportunities and identify potentially druggable proteins that have been poorly explored with regard to the discovery of small-molecule modulators. Finally, we obtained a small list of candidate therapeutic targets for four major breast cancer subtypes, i.e., luminal A, luminal B, HER2+ and triple negative breast cancer. This RNAi transcriptome-based approach can be a helpful paradigm for relevant researches to identify and prioritize candidate targets for experimental validation.
Malignant melanoma is the deadliest form of all skin cancers. Itraconazole, a commonly used systemic antifungal drug, has been tested for its anti-tumor effects on basal cell carcinoma, prostate cancer, and non-small cell lung cancer. Whether itraconazole has any specific anti-tumor effect on melanoma remains unknown. However, the goal of this study is to investigate the effect of itraconazole on melanoma and to reveal some details of its underlying mechanism. In the in vivo xenograft mouse model, we find that itraconazole can inhibit melanoma growth and extend the survival of melanoma xenograft mice, compared to non-itraconazole-treated mice. Also, itraconazole can significantly inhibit cell proliferation, as demonstrated by Ki-67 staining in itraconazole-treated tumor tissues. In in vitro, we show that itraconazole inhibits the proliferation and colony formation of both SK-MEL-28 and A375 human melanoma cells. Moreover, we demonstrate that itraconazole significantly down-regulates Gli-1, Gli-2, Wnt3A, β-catenin and cyclin D1, while it up-regulates Gli-3 and Axin-1, indicating potent inhibitory effects of itraconazole on Hedgehog (Hh) and Wnt signaling pathways. Furthermore, itraconazole significantly suppresses the PI3K/mTOR signaling pathway - indicated by the down-regulated phosphorylation of p70S6K, 4E-BP1 and AKT - but has no effect on the phosphorylation of MEK or ERK. Our data suggest that itraconazole inhibits melanoma growth through an interacting regulatory network that includes Hh, Wnt, and PI3K/mTOR signaling pathways. These results suggest that this agent has several potent anti-melanoma features and may be useful in the synergesis of other anti-cancer drugs via blockage of the Hh, Wnt and PI3K/mTOR signaling pathways.
Cell culture is a widely used in vitro tool for improving our understanding of cell biology, tissue morphology, and mechanisms of diseases, drug action , protein production and the development of tissue engineering. Most research regarding cancer biology is based on experiments using two-dimensional (2D) cell cultures in vitro. However, 2D cultures have many limitations , such as the disturbance of interactions between the cellular and extracellular environments, changes in cell morphology, polarity, and method of division. These disadvantages led to the creation of models which are more closely able to mimic conditions in vivo. One such method is three-dimensional culture (3D). Optimisation of the culture conditions may allow for a better understanding of cancer biology and facilitate the study of biomark-ers and targeting therapies. In this review, we compare 2D and 3D cultures in vitro as well as different versions of 3D cultures.
Proton pump inhibitors (PPIs) were clinically introduced more than 25 years ago and have since proven to be invaluable, safe, and effective agents for the management of a variety of acid-related disorders. Although all members in this class act in a similar fashion, inhibiting active parietal cell acid secretion, there are slight differences among PPIs relating to their pharmacokinetic properties, metabolism, and Food and Drug Administration (FDA)-approved clinical indications. Nevertheless, each is effective in managing gastroesophageal reflux disease and uncomplicated or complicated peptic ulcer disease. Despite their overall efficacy, PPIs do have some limitations related to their short plasma half-lives and requirement for meal-associated dosing, which can lead to breakthrough symptoms in some individuals, especially at night. Longeracting PPIs and technology to prolong conventional PPI activity have been developed to specifically address these limitations and may improve clinical outcomes.
As the recent development of high-throughput technologies in cancer pharmacogenomics, there is an urgent need to develop new computational approaches for comprehensive identification of new pharmacogenomic biomarkers, such as microRNAs (miRNAs). In this study, a network-based framework, namely the SMiR-NBI model, was developed to prioritize miRNAs as potential biomarkers characterizing treatment responses of anticancer drugs on the basis of a heterogeneous network connecting drugs, miRNAs and genes. A high area under the receiver operating characteristic curve of 0.820 ± 0.013 was yielded during 10-fold cross validation. In addition, high performance was further validated in identifying new anticancer mechanism-of-action for natural products and non-steroidal anti-inflammatory drugs. Finally, the newly predicted miRNAs for tamoxifen and metformin were experimentally validated in MCF-7 and MDA-MB-231 breast cancer cell lines via qRT-PCR assays. High success rates of 60% and 65% were yielded for tamoxifen and metformin, respectively. Specifically, 11 oncomiRNAs (e.g. miR-20a-5p, miR-27a-3p, miR-29a-3p, and miR-146a-5p) from the top 20 predicted miRNAs were experimentally verified as new pharmacogenomic biomarkers for metformin in MCF-7 or MDA-MB-231 cell lines. In summary, the SMiR-NBI model would provide a powerful tool to identify potential pharmacogenomic biomarkers characterized by miRNAs in the emerging field of precision cancer medicine, which is available at http://lmmd.ecust.edu.cn/database/smir-nbi/.
T-cell-originated protein kinase (TOPK) is highly expressed in several cancer cells and promotes tumorigenesis and progression, and therefore, it is an important target for drug treatment of tumor. Pantoprazole (PPZ) was identified to be a TOPK inhibitor from FDA-approved drug database by structure based virtual ligand screening. Herein, the data indicated that pantoprazole inhibited TOPK activities by directly binding with TOPK in vitro and in vivo. Ex vivo studies showed that pantoprazole inhibited TOPK activities in JB6 Cl41 cells and HCT 116 colorectal cancer cells. Moreover, knockdown of TOPK in HCT 116 cells decreased their sensitivities to pantoprazole. Results of an in vivo study demonstrated that i.p. injection of pantoprazole in HCT 116 colon tumor-bearing mice effectively suppressed cancer growth. The TOPK downstream signaling molecule phospho-histone H3 in tumor tissues was also decreased after pantoprazole treatment. In short, pantoprazole can suppress growth of colorectal cancer cells as a TOPK inhibitor both in vitro and in vivo.
Metformin, a drug prescribed to treat type-2 diabetes, exhibits anti-cancer effects in a portion of patients, but the direct molecular and genetic interactions leading to this pleiotropic effect have not yet been fully explored. To repurpose metformin as a precision anti-cancer therapy, we have developed a novel structural systems pharmacology approach to elucidate metformin’s molecular basis and genetic biomarkers of action. We integrated structural proteome-scale drug target identification with network biology analysis by combining structural genomic, functional genomic, and interactomic data. Through searching the human structural proteome, we identified twenty putative metformin binding targets and their interaction models. We experimentally verified the interactions between metformin and our top-ranked kinase targets. Notably, kinases, particularly SGK1 and EGFR were identified as key molecular targets of metformin. Subsequently, we linked these putative binding targets to genes that do not directly bind to metformin but whose expressions are altered by metformin through protein-protein interactions, and identified network biomarkers of phenotypic response of metformin. The molecular targets and the key nodes in genetic networks are largely consistent with the existing experimental evidence. Their interactions can be affected by the observed cancer mutations. This study will shed new light into repurposing metformin for safe, effective, personalized therapies.
Assembly of large biochemical networks can be achieved by confronting new cell-specific experimental data with an interaction
subspace constrained by prior literature evidence. The SIGnaling Network Open Resource, SIGNOR (available on line at http://signor.uniroma2.it), was developed to support such a strategy by providing a scaffold of prior experimental evidence of causal relationships
between biological entities. The core of SIGNOR is a collection of approximately 12 000 manually-annotated causal relationships
between over 2800 human proteins participating in signal transduction. Other entities annotated in SIGNOR are complexes, chemicals,
phenotypes and stimuli. The information captured in SIGNOR can be represented as a signed directed graph illustrating the
activation/inactivation relationships between signalling entities. Each entry is associated to the post-translational modifications
that cause the activation/inactivation of the target proteins. More than 4900 modified residues causing a change in protein
concentration or activity have been curated and linked to the modifying enzymes (about 351 human kinases and 94 phosphatases).
Additional modifications such as ubiquitinations, sumoylations, acetylations and their effect on the modified target proteins
are also annotated. This wealth of structured information can support experimental approaches based on multi-parametric analysis
of cell systems after physiological or pathological perturbations and to assemble large logic models.
Colorectal cancer is the third most common cancer worldwide, with 1.2 million patients diagnosed annually. In late-stage colorectal cancer, the most commonly used targeted therapies are the monoclonal antibodies cetuximab and panitumumab, which prevent epidermal growth factor receptor (EGFR) activation. Recent studies have identified alterations in KRAS and other genes as likely mechanisms of primary and secondary resistance to anti-EGFR antibody therapy. Despite these efforts, additional mechanisms of resistance to EGFR blockade are thought to be present in colorectal cancer and little is known about determinants of sensitivity to this therapy. To examine the effect of somatic genetic changes in colorectal cancer on response to anti-EGFR antibody therapy, here we perform complete exome sequence and copy number analyses of 129 patient-derived tumour grafts and targeted genomic analyses of 55 patient tumours, all of which were KRAS wild-type. We analysed the response of tumours to anti-EGFR antibody blockade in tumour graft models and in clinical settings and functionally linked therapeutic responses to mutational data. In addition to previously identified genes, we detected mutations in ERBB2, EGFR, FGFR1, PDGFRA, and MAP2K1 as potential mechanisms of primary resistance to this therapy. Novel alterations in the ectodomain of EGFR were identified in patients with acquired resistance to EGFR blockade. Amplifications and sequence changes in the tyrosine kinase receptor adaptor gene IRS2 were identified in tumours with increased sensitivity to anti-EGFR therapy. Therapeutic resistance to EGFR blockade could be overcome in tumour graft models through combinatorial therapies targeting actionable genes. These analyses provide a systematic approach to evaluating response to targeted therapies in human cancer, highlight new mechanisms of responsiveness to anti-EGFR therapies, and delineate new avenues for intervention in managing colorectal cancer.
Type 2 diabetes mellitus is a major global public health burden. A complex metabolic disease, type 2 diabetes affects multiple different tissues, demanding a "systems medicine" approach to biomarker and novel diagnostic discovery, not to mention data integration across omics-es. In the present study, transcriptomics data from different tissues including beta-cells, pancreatic islets, arterial tissue, peripheral blood mononuclear cells, liver, and skeletal muscle of 228 samples were integrated with protein-protein interaction data and genome scale metabolic models to unravel the molecular and tissue-specific biomarker signatures of type 2 diabetes mellitus. Classifying differentially expressed genes, reconstruction and topological analysis of active protein-protein interaction subnetworks indicated that genomic reprogramming depends on the type of tissue, whereas there are common signatures at different levels. Among all tissue and cell types, Mannosidase Alpha Class 1A Member 2 was the common signature at genome level, and activation-ppara reaction, which stimulates a nuclear receptor protein, was found out as the mutual reporter at metabolic level. Moreover, miR-335 and miR-16-5p came into prominence in regulation of transcription at different tissues. On the other hand, distinct signatures were observed for different tissues at the metabolome level. Various coenzyme-A derivatives were significantly enriched metabolites in pancreatic islets, whereas skeletal muscle was enriched for cholesterol, malate, L-carnitine, and several amino acids. Results have showed utmost importance concerning relations between T2D and cancer, blood coagulation, neurodegenerative diseases, and specific metabolic and signaling pathways.
A drug exerts its effects typically through a signal transduction cascade, which is non-linear and involves intertwined networks of multiple signaling pathways. Construction of such a signaling pathway network (SPNetwork) can enable identification of novel drug targets and deep understanding of drug action. However, it is challenging to synopsize critical components of these interwoven pathways into one network. To tackle this issue, we developed a novel computational framework, the Drug-specific Signaling Pathway Network (DSPathNet). The DSPathNet amalgamates the prior drug knowledge and drug-induced gene expression via random walk algorithms. Using the drug metformin, we illustrated this framework and obtained one metformin-specific SPNetwork containing 477 nodes and 1,366 edges. To evaluate this network, we performed the gene set enrichment analysis using the disease genes of type 2 diabetes (T2D) and cancer, one T2D genome-wide association study (GWAS) dataset, three cancer GWAS datasets, and one GWAS dataset of cancer patients with T2D on metformin. The results showed that the metformin network was significantly enriched with disease genes for both T2D and cancer, and that the network also included genes that may be associated with metformin-associated cancer survival. Furthermore, from the metformin SPNetwork and common genes to T2D and cancer, we generated a subnetwork to highlight the molecule crosstalk between T2D and cancer. The follow-up network analyses and literature mining revealed that seven genes (CDKN1A, ESR1, MAX, MYC, PPARGC1A, SP1, and STK11) and one novel MYC-centered pathway with CDKN1A, SP1, and STK11 might play important roles in metformin's antidiabetic and anticancer effects. Some results are supported by previous studies. In summary, our study 1) develops a novel framework to construct drug-specific signal transduction networks; 2) provides insights into the molecular mode of metformin; 3) serves a model for exploring signaling pathways to facilitate understanding of drug action, disease pathogenesis, and identification of drug targets.
Non-small cell lung cancer is the predominant type of lung cancer, resulting in high mortality worldwide. Digoxin, a cardiac glycoside, has recently been suggested to be a novel chemotherapeutic agent. Src is an oncogene that plays an important role in cancer progression and is therefore a potential target for cancer therapy. Here, we investigated whether digoxin could suppress lung cancer progression through the inhibition of Src activity. The effects of digoxin on lung cancer cell functions were investigated using colony formation, migration and invasion assays. Western blotting and qPCR assays were used to analyze the mRNA and protein expression levels of Src and its downstream proteins, and a cell viability assay was used to measure cellular cytotoxicity effects. The results of the cell function assays revealed that digoxin inhibited the proliferation, invasion, migration, and colony formation of A549 lung cancer cells. Similar effects of digoxin were also observed in other lung cancer cell lines. Furthermore, we found that digoxin significantly suppressed Src activity and its protein expression in a dose- and time-dependent manner as well as reduced EGFR and STAT3 activity. Our data suggest that digoxin is a potential anticancer agent that may suppress lung cancer progression through inhibiting Src and the activity of related proteins.
Fatty acid synthase (FASN), the enzyme responsible for de novo synthesis of free fatty acids, is up-regulated in many cancers. FASN is essential for cancer cell survival and contributes to drug resistance and poor prognosis. However, it is not expressed in most nonlipogenic normal tissues. Thus, FASN is a desirable target for drug discovery. Although different FASN inhibitors have been identified, none has successfully moved into clinical use. In this study, using in silico screening of an FDA-approved drug database, we identified proton pump inhibitors (PPIs) as effective inhibitors of the thioesterase activity of human FASN. Further investigation showed that PPIs inhibited proliferation and induced apoptosis of cancer cells. Supplementation of palmitate, the end product of FASN catalysis, rescued cancer cells from PPI-induced cell death. These findings provide new evidence for the mechanism by which this FDA-approved class of compounds may be acting on cancer cells.
Unlabelled:
Recently, there has been an increasing interest in the development and characterization of patient-derived tumor xenograft (PDX) models for cancer research. PDX models mostly retain the principal histologic and genetic characteristics of their donor tumor and remain stable across passages. These models have been shown to be predictive of clinical outcomes and are being used for preclinical drug evaluation, biomarker identification, biologic studies, and personalized medicine strategies. This article summarizes the current state of the art in this field, including methodologic issues, available collections, practical applications, challenges and shortcomings, and future directions, and introduces a European consortium of PDX models.
Significance:
PDX models are increasingly used in translational cancer research. These models are useful for drug screening, biomarker development, and the preclinical evaluation of personalized medicine strategies. This review provides a timely overview of the key characteristics of PDX models and a detailed discussion of future directions in the field.
Drug repositioning (also referred to as drug repurposing), the process of finding new uses of existing drugs, has been gaining popularity in recent years. The availability of several established clinical drug libraries and rapid advances in disease biology, genomics and bioinformatics has accelerated the pace of both activity-based and in silico drug repositioning. Drug repositioning has attracted particular attention from the communities engaged in anticancer drug discovery due to the combination of great demand for new anticancer drugs and the availability of a wide variety of cell- and target-based screening assays. With the successful clinical introduction of a number of non-cancer drugs for cancer treatment, drug repositioning now became a powerful alternative strategy to discover and develop novel anticancer drug candidates from the existing drug space. In this review, recent successful examples of drug repositioning for anticancer drug discovery from non-cancer drugs will be discussed.
Salicylates from plant sources have been used for centuries by different cultures to treat a variety of ailments such as inflammation, fever and pain. A chemical derivative of salicylic acid, aspirin, was synthesised and mass produced by the end of the 19th century and is one of the most widely used drugs in the world. Its cardioprotective properties are well established; however, recent evidence shows that it can also act as a chemopreventive agent. Its antithrombotic and anti-inflammatory actions occur through the inhibition of cyclooxygenases. The precise mechanisms leading to its anticancer effects are not clearly established, although multiple mechanisms affecting enzyme activity, transcription factors, cellular signalling and mitochondrial functions have been proposed. This review presents a brief account of the major COX-dependent and independent pathways described in connection with aspirin's anticancer effects. Aspirin's unique ability to acetylate biomolecules besides COX has not been thoroughly investigated nor have all the targets of its primary metabolite, salicylic acid been identified. Recent reports on the ability of aspirin to acetylate multiple cellular proteins warrant a comprehensive study to investigate the role of this posttranslational modification in its anticancer effects. In this review, we also raise the intriguing possibility that aspirin may interact and acetylate cellular molecules such as RNA, and metabolites such as CoA, leading to a change in their function. Research in this area will provide a greater understanding of the mechanisms of action of this drug.British Journal of Cancer advance online publication, 29 May 2014; doi:10.1038/bjc.2014.271 www.bjcancer.com.
Systems biology is in an exponential development stage in recent years and has been widely utilized in biomedicine to better understand the molecular basis of human disease and the mechanism of drug action. Here, we discuss the fundamental concept of systems biology and its two computational methods that have been commonly used, that is, network analysis and dynamical modeling. The applications of systems biology in elucidating human disease are highlighted, consisting of human disease networks, treatment response prediction, investigation of disease mechanisms, and disease-associated gene prediction. In addition, important advances in drug discovery, to which systems biology makes significant contributions, are discussed, including drug-target networks, prediction of drug-target interactions, investigation of drug adverse effects, drug repositioning, and drug combination prediction. The systems biology methods and applications covered in this review provide a framework for addressing disease mechanism and approaching drug discovery, which will facilitate the translation of research findings into clinical benefits such as novel biomarkers and promising therapies.
Colorectal cancer (CRC) is a major cause of cancer mortality. Whereas some patients respond well to therapy, others do not, and thus more precise, individualized treatment strategies are needed. To that end, we analyzed gene expression profiles from 1,290 CRC tumors using consensus-based unsupervised clustering. The resultant clusters were then associated with therapeutic response data to the epidermal growth factor receptor-targeted drug cetuximab in 80 patients. The results of these studies define six clinically relevant CRC subtypes. Each subtype shares similarities to distinct cell types within the normal colon crypt and shows differing degrees of 'stemness' and Wnt signaling. Subtype-specific gene signatures are proposed to identify these subtypes. Three subtypes have markedly better disease-free survival (DFS) after surgical resection, suggesting these patients might be spared from the adverse effects of chemotherapy when they have localized disease. One of these three subtypes, identified by filamin A expression, does not respond to cetuximab but may respond to cMET receptor tyrosine kinase inhibitors in the metastatic setting. Two other subtypes, with poor and intermediate DFS, associate with improved response to the chemotherapy regimen FOLFIRI in adjuvant or metastatic settings. Development of clinically deployable assays for these subtypes and of subtype-specific therapies may contribute to more effective management of this challenging disease.
Recent advances in computational biology suggest that any perturbation to the transcriptional program of the cell can be summarized by a proper ‘signature’: a set of genes combined with a pattern of expression. Therefore, it should be possible to generate proxies of clinicopathological phenotypes and drug effects through signatures acquired via DNA microarray technology.
Gene expression signatures have recently been assembled and compared through genome-wide metrics, unveiling unexpected drug-to-disease and drug-to-drug ‘connections’ by matching corresponding signatures. Consequently, novel applications for existing drugs have been predicted and experimentally validated.
Here, we describe related methods, case studies, and resources discussing challenges and benefits of exploiting existing repositories of microarray data that may serve as a search space for systematic drug repositioning.
Author Summary
Study of drug-target interaction is an important topic toward elucidation of protein functions and understanding of molecular mechanisms inside cells. Traditional methods to predict new targets for known drugs were based on small molecules, protein targets or phenotype features. Here, we proposed a network-based inference (NBI) method which only used drug-target bipartite network topology similarity to infer new targets for known drugs. The performance of NBI outperformed the drug-based similarity inference and target-based similarity inference methods as well as other published methods. Via the NBI method five old drugs, namely montelukast, diclofenac, simvastatin, ketoconazole, and itraconazole, were identified to have polypharmacological effects on human estrogen receptors or dipeptidyl peptidase-IV with half maximal inhibitory or effective concentration from submicromolar to micromolar by in vitro assays. Moreover, simvastatin and ketoconazole showed potent antiproliferative activities on human MDA-MB-231 breast cancer cell line in MTT assays. The results indicated that the drug-target bipartite network-based inference method could be a useful tool for fishing novel drug-target interactions in molecular polypharmacological space.
The tumor microenvironment (TME) consists of cells, soluble factors, signaling molecules, extracellular matrix, and mechanical cues that can promote neoplastic transformation, support tumor growth and invasion, protect the tumor from host immunity, foster therapeutic resistance, and provide niches for dormant metastases to thrive. An American Association for Cancer Research (AACR) special conference held on November 3-6, 2011, addressed five emerging concepts in our understanding of the TME: its dynamic evolution, how it is educated by tumor cells, pathways of communication between stromal and tumor cells, immunomodulatory roles of the lymphatic system, and contribution of the intestinal microbiota. These discussions raised critical questions on how to include the analysis of the TME in personalized cancer diagnosis and treatment.
Background
The RDF triple provides a simple linguistic means of describing limitless types of information. Triples can be flexibly combined into a unified data source we call a semantic model. Semantic models open new possibilities for the integration of variegated biological data. We use Semantic Web technology to explicate high throughput clinical data in the context of fundamental biological knowledge. We have extended Corvus, a data warehouse which provides a uniform interface to various forms of Omics data, by providing a SPARQL endpoint. With the querying and reasoning tools made possible by the Semantic Web, we were able to explore quantitative semantic models retrieved from Corvus in the light of systematic biological knowledge.
Results
For this paper, we merged semantic models containing genomic, transcriptomic and epigenomic data from melanoma samples with two semantic models of functional data - one containing Gene Ontology (GO) data, the other, regulatory networks constructed from transcription factor binding information. These two semantic models were created in an ad hoc manner but support a common interface for integration with the quantitative semantic models. Such combined semantic models allow us to pose significant translational medicine questions. Here, we study the interplay between a cell's molecular state and its response to anti-cancer therapy by exploring the resistance of cancer cells to Decitabine, a demethylating agent.
Conclusions
We were able to generate a testable hypothesis to explain how Decitabine fights cancer - namely, that it targets apoptosis-related gene promoters predominantly in Decitabine-sensitive cell lines, thus conveying its cytotoxic effect by activating the apoptosis pathway. Our research provides a framework whereby similar hypotheses can be developed easily.
Expression profiling provides new insights into regulatory and metabolic processes and in particular into pathogenic mechanisms associated with diseases. Besides genes, non-coding transcripts as microRNAs (miRNAs) gained increasing relevance in the last decade. To understand the regulatory processes of miRNAs on genes, integrative computer-aided approaches are essential, especially in the light of complex human diseases as cancer.
Here, we present miRTrail, an integrative tool that allows for performing comprehensive analyses of interactions of genes and miRNAs based on expression profiles. The integrated analysis of mRNA and miRNA data should generate more robust and reliable results on deregulated pathogenic processes and may also offer novel insights into the regulatory interactions between miRNAs and genes. Our web-server excels in carrying out gene sets analysis, analysis of miRNA sets as well as the combination of both in a systems biology approach. To this end, miRTrail integrates information on 20.000 genes, almost 1.000 miRNAs, and roughly 280.000 putative interactions, for Homo sapiens and accordingly for Mus musculus and Danio rerio. The well-established, classical Chi-squared test is one of the central techniques of our tool for the joint consideration of miRNAs and their targets. For interactively visualizing obtained results, it relies on the network analyzers and viewers BiNA or Cytoscape-web, also enabling direct access to relevant literature. We demonstrated the potential of miRTrail by applying our tool to mRNA and miRNA data of malignant melanoma. MiRTrail identified several deregulated miRNAs that target deregulated mRNAs including miRNAs hsa-miR-23b and hsa-miR-223, which target the highest numbers of deregulated mRNAs and regulate the pathway "basal cell carcinoma". In addition, both miRNAs target genes like PTCH1 and RASA1 that are involved in many oncogenic processes.
The application on melanoma samples demonstrates that the miRTrail platform may open avenues for investigating the regulatory interactions between genes and miRNAs for a wide range of human diseases. Moreover, miRTrail cannot only be applied to microarray based expression profiles, but also to NGS-based transcriptomic data. The program is freely available as web-server at mirtrail.bioinf.uni-sb.de.
Research on the immune system and cancer has led to the development of new medicines that enable the former to attack cancer cells. Drugs that specifically target and destroy cancer cells are on the horizon; there are also drugs that use specific signals to stop cancer cells multiplying. Machine learning algorithms can significantly support and increase the rate of research on complicated diseases to help find new remedies. One area of medical study that could greatly benefit from machine learning algorithms is the exploration of cancer genomes and the discovery of the best treatment protocols for different subtypes of the disease. However, developing a new drug is time-consuming, complicated, dangerous, and costly. Traditional drug production can take up to 15 years, costing over USD 1 billion. Therefore, computer-aided drug design (CADD) has emerged as a powerful and promising technology to develop quicker, cheaper, and more efficient designs. Many new technologies and methods have been introduced to enhance drug development productivity and analytical methodologies, and they have become a crucial part of many drug discovery programs; many scanning programs, for example, use ligand screening and structural virtual screening techniques from hit detection to optimization. In this review, we examined various types of computational methods focusing on anticancer drugs. Machine-based learning in basic and translational cancer research that could reach new levels of personalized medicine marked by speedy and advanced data analysis is still beyond reach. Ending cancer as we know it means ensuring that every patient has access to safe and effective therapies. Recent developments in computational drug discovery technologies have had a large and remarkable impact on the design of anticancer drugs and have also yielded useful insights into the field of cancer therapy. With an emphasis on anticancer medications, we covered the various components of computer-aided drug development in this paper. Transcriptomics, toxicogenomics, functional genomics, and biological networks are only a few examples of the bioinformatics techniques used to forecast anticancer medications and treatment combinations based on multi-omics data. We believe that a general review of the databases that are now available and the computational techniques used today will be beneficial for the creation of new cancer treatment approaches.
For the past three decades, the use of genomics to inform drug discovery and development pipelines has generated both excitement and scepticism. Although earlier efforts successfully identified some new drug targets, the overall clinical efficacy of developed drugs has remained unimpressive, owing in large part to the heterogeneous causes of disease. Recent technological and analytical advances in genomics, however, have now made it possible to rapidly identify and interpret the genetic variation underlying a single patient's disease, thereby providing a window into patient-specific mechanisms that cause or contribute to disease, which could ultimately enable the 'precise' targeting of these mechanisms. Here, we first examine and highlight the successes and limitations of the earlier phases of genomics in drug discovery and development. We then review the current major efforts in precision medicine and discuss the potential broader utility of mechanistically guided treatments going forward.
Pharmacology has evolved over the years. Describing the explicit effects of biologically active chemicals originally, this scientific discipline now explores the biological effects of drugs at the molecular level. In the last decade, functional genomics data and systems biology outlook did lead this evolvement. On the other hand, the role of systems outlook gradually increases through improving existent approaches and developing novel concepts in order to provide possible solutions to the grand challenge in pharmacology, i.e. the development of efficient treatment strategies for tackling the complexities of the diseases.
Digoxin is a member of cardiac glycosides and recent studies show that digoxin plays anticancer role in several types of cancer. However, the anticancer effects and mechanism of digoxin in leukemia is largely unknown. Her, our data show that digoxin treatment significantly inhibits leukemia cell viability. In addition, digoxin treatment significantly induced apoptosis and G2/M cell cycle arrest in leukemia cells. Furthermore, we demonstrated that digoxin treatment inactivate that oncogenic pathway Akt/mTOR signaling in leukemia cells. In addition, our data show that digoxin treatment induces activation of unfolded protein response (UPR) signaling in leukemia cells. Interestingly, our in vitro and in vivo experiments show that combination treatment of digoxin and UPR inhibitor can synergistically suppress leukemia growth and induces apoptosis and cell cycle arrest compared to single drug treatment. In summary, our findings indicate that digoxin has potential anticancer effects on leukemia. The combination of digoxin and UPR signaling inhibitor can exerts synergistic anticancer activity against leukemia. © 2017 BioFactors, 2017.
Invasive fungal infections continue to appear in record numbers as the immunocompromised population of the world increases, owing partially to the increased number of individuals who are infected with HIV and partially to the successful treatment of serious underlying diseases. The effectiveness of current antifungal therapies - polyenes, flucytosine, azoles and echinocandins (as monotherapies or in combinations for prophylaxis, or as empiric, pre-emptive or specific therapies) - in the management of these infections has plateaued. Although these drugs are clinically useful, they have several limitations, such as off-target toxicity, and drug-resistant fungi are now emerging. New antifungals are therefore needed. In this Review, I discuss the robust and dynamic antifungal pipeline, including results from preclinical academic efforts through to pharmaceutical industry products, and describe the targets, strategies, compounds and potential outcomes.
Metformin is the most frequently prescribed drug for type 2 diabetes. In addition to its hypoglycemic effects, metformin also lowers cancer incidence. This anti-cancer activity is incompletely understood. Here, we profiled the metformin-dependent changes in the proteome and phosphoproteome of breast cancer cells using high-resolution mass spectrometry. In total, we quantified changes of 7,875 proteins and 15,813 phosphosites after metformin changes. To interpret these datasets, we developed a generally applicable strategy that overlays metformin-dependent changes in the proteome and phosphoproteome onto a literature-derived network. This approach suggested that metformin treatment makes cancer cells more sensitive to apoptotic stimuli and less sensitive to pro-growth stimuli. These hypotheses were tested in vivo; as a proof-of-principle, we demonstrated that metformin inhibits the p70S6K-rpS6 axis in a PP2A-phosphatase dependent manner. In conclusion, analysis of deep proteomics reveals both detailed and global mechanisms that contribute to the anti-cancer activity of metformin.
Results from large-scale phenome-wide association studies (PheWAS) allow association of genetic variants with a wide spectrum of human disorders and have provided considerable insight into disease etiologies. The PheWAS strategy relies on electronically available phenotypic data collected from patient cohorts. PheWAS is similar to a genome-wide association study…
TOPK (T-lymphokine-activated killer cell-originated protein kinase) is highly and frequently transactivated in various cancer tissues, including lung and triple-negative breast cancers, and plays an indispensable role in the mitosis of cancer cells. We report the development of a potent TOPK inhibitor, OTS964 {(R)-9-(4-(1-(dimethylamino)propan-2-yl)phenyl)-8-hydroxy-6-methylthieno[2,3-c]quinolin-4(5H)-one}, which inhibits TOPK kinase activity with high affinity and selectivity. Similar to the knockdown effect of TOPK small interfering RNAs (siRNAs), this inhibitor causes a cytokinesis defect and the subsequent apoptosis of cancer cells in vitro as well as in xenograft models of human lung cancer. Although administration of the free compound induced hematopoietic adverse reactions (leukocytopenia associated with thrombocytosis), the drug delivered in a liposomal formulation effectively caused complete regression of transplanted tumors without showing any adverse reactions in mice. Our results suggest that the inhibition of TOPK activity may be a viable therapeutic option for the treatment of various human cancers.
Metformin, a biguanide derived from the French lilac, has become the preferred first-line therapy for the treatment of type 2 diabetes (1). This drug is inexpensive, has an excellent safety profile, and can be safely combined with other antidiabetes agents (2). As a result, it has become the most widely prescribed antidiabetes drug worldwide.
In addition to metformin’s well-established antidiabetes effects, there has been considerable interest in its antitumor properties. Such interest started from a short report of an observational study published in 2005 that suggested that the use of metformin was associated with a 23% decreased risk of any cancer (3). Since then, a large number of observational studies have been published with several “corroborating” a possible decreased incidence of cancer with this drug (4). In parallel, several laboratory studies have also suggested that metformin has antineoplastic activity, although doses used in such experiments were typically higher than the conventional doses used in the treatment of type 2 diabetes (5). Nonetheless, this apparent convergence of evidence from both observational and laboratory studies has led some to call for large randomized clinical trials (RCTs) of metformin in cancer prevention and treatment (6–9). However, a careful assessment of the observational studies conducted to date point to some important time-related biases that systematically exaggerated the reported antitumor effects of metformin (10).
Time-related biases, such as immortal time bias, time window bias, and time lag bias, have been previously described in studies of diabetes treatment (10) and in other therapeutic areas (11–14). These biases result from not properly classifying exposure during the follow-up of a cohort study or from …
Preclinical models of cancer are essential for a basic understanding of cancer biology and its translation into efficient treatment options for affected patients. Cancer cell lines and xenografts derived directly from primary human tumors have proven very valuable in fundamental oncology research and anticancer drug discovery. Both models inherently comprise advantages and caveats that have to be accounted for. We will outline in these and discuss primary patient derived organoids as third preclinical cancer model. We propose that cancer organoids could potentially fill the gap between simple cancer cell lines suitable for high-throughput screens and complicated, but physiologically relevant xenografts. The resulting applications for cancer organoids range from basic research to drug screens and patient stratification.
This article introduces fundamental concepts to guide the analysis and interpretation of drug-target interaction networks. An overview of the generation and integration of interaction networks is followed by key strategies for extracting biologically meaningful information. The article highlights how this information can enable novel translational and clinically motivated applications. Important advances for the discovery of new treatments and for the detection of adverse drug effects are discussed. Examples of applications and findings originating from cardiovascular research are presented. The review ends with a discussion of crucial challenges and opportunities.
The serine-threonine mitogen-activated protein kinase kinase family member T-LAK cell-originated protein kinase (TOPK/PBK) is heavily involved in tumor development, cancer growth, apoptosis, and inflammation. Despite the identification of TOPK as a promising novel therapeutic target, no inhibitor of TOPK has yet been reported. In this study, we screened 36 drug candidates using an in vitro kinase assay and identified the novel TOPK inhibitor HI-TOPK-032. In vitro, HI-TOPK-032 strongly suppressed TOPK kinase activity but had little effect on extracellular signal-regulated kinase 1 (ERK1), c-jun-NH2-kinase 1, or p38 kinase activities. HI-TOPK-032 also inhibited anchorage-dependent and -independent colon cancer cell growth by reducing ERK-RSK phosphorylation as well as increasing colon cancer cell apoptosis through regulation of the abundance of p53, cleaved caspase-7, and cleaved PARP. In vivo, administration of HI-TOPK-032 suppressed tumor growth in a colon cancer xenograft model. Our findings therefore show that HI-TOPK-032 is a specific inhibitor of TOPK both in vitro and in vivo that may be further developed as a potential therapeutic against colorectal cancer.
Clinically relevant in vitro methods are needed to identify new cancer drugs for solid tumors. We report on a new 3-D spheroid cell culture system aimed to mimic the properties of solid tumors in vivo. The colon cancer cell lines HCT-116 wt and HCT-116 wt/GFP were grown as monolayers and for 3 or 6 days on 96-well NanoCulture® plates to form spheroids. Expression of surface markers, genes and hypoxia were assessed to characterize the spheroids and drug induced cytotoxicity was evaluated based on fluorescein diacetate (FDA) conversion by viable cells to fluorescent fluorescein or by direct measurement of fluorescence of GFP marked cells after a 72 h drug incubation. The cells reproducibly formed spheroids in the NanoCulture® plates with tight cell-attachment after 6 days. Cells in spheroids showed geno- and phenotypical properties reminiscent of hypoxic stem cells. Monolayer cultured cells were sensitive to standard and investigational drugs, whereas the spheroids gradually turned resistant. Similar results for cytotoxicity were observed using simplified direct measurement of fluorescence of GFP marked cells compared with FDA incubation. In conclusion, this new 3-D spheroid cell culture system provides a convenient and clinically relevant model for the identification and characterization of cancer drugs for solid tumors.