Drug repurposing for glioblastoma based on molecular subtypes

Therapeutic Hypothermia in Treating Glioblastoma: A Review

Glioblastoma (GBM) is the most commonly occurring of all malignant central nervous system (CNS) tumors in adults. Considering the low median survival of only ∼15 months and poor prognosis in GBM patients, despite surgical resection with adjuvant radiation and chemotherapy, it is vital to seek brand new and innovative treatment in combination with already existing methods. Hypothermia participates in many metabolic pathways, inflammatory responses, and apoptotic processes, while also promoting the integrity of neurons. Following the successful application of therapeutic hypothermia across a spectrum of disorders such as traumatic CNS injury, cardiac arrest, and epilepsy, several clinical trials have set to evaluate the potency of hypothermia in treating a variety of cancers, including breast and ovaries cancer. In regard to primary neoplasms and more specifically, GBM, hypothermia has recently shown promising results as an auxiliary treatment, reinforcing chemotherapy's efficacy. In this review, we discuss the recent advances in utilizing hypothermia as treatment for GBM and other cancers.

Network-based drug repurposing for HPV-associated cervical cancer

In women, cervical cancer (CC) is the fourth most common cancer around the world with average cases of 604,000 and 342,000 deaths per year. Approximately 50% of high-grade CC are attributed to human papillomavirus (HPV) types 16 and 18. Chances of CC in HPV-positive patients are 6 times more than HPV-negative patients which demands timely and effective treatment. Repurposing of drugs is considered a viable approach to drug discovery which makes use of existing drugs, thus potentially reducing the time and costs associated with de-novo drug discovery. In this study, we present an integrative drug repurposing framework based on a systems biology-enabled network medicine platform. First, we built an HPV-induced CC protein interaction network named HPV2C following the CC signatures defined by the omics dataset, obtained from GEO database. Second, the drug target interaction (DTI) data obtained from DrugBank, and related databases was used to model the DTI network followed by drug target network proximity analysis of HPV-host associated key targets and DTIs in the human protein interactome. This analysis identified 142 potential anti-HPV repurposable drugs to target HPV induced CC pathways. Third, as per the literature survey 51 of the predicted drugs are already used for CC and 33 of the remaining drugs have anti-viral activity. Gene set enrichment analysis of potential drugs in drug-gene signatures and in HPV-induced CC-specific transcriptomic data in human cell lines additionally validated the predictions. Finally, 13 drug combinations were found using a network based on overlapping exposure. To summarize, the study provides effective network-based technique to quickly identify suitable repurposable drugs and drug combinations that target HPV-associated CC.

Gibson T, McCain S, Stone BC, Gill T, Pickett BE. Pathway2Targets: an open-source pathway-based approach to repurpose therapeutic drugs and prioritize human targets

Background

Recent efforts to repurpose existing drugs to different indications have been accompanied by a number of computational methods, which incorporate protein-protein interaction networks and signaling pathways, to aid with prioritizing existing targets and/or drugs. However, many of these existing methods are focused on integrating additional data that are only available for a small subset of diseases or conditions.

Methods

We have designed and implemented a new R-based open-source target prioritization and repurposing method that integrates both canonical intracellular signaling information from five public pathway databases and target information from public sources including OpenTargets.org. The Pathway2Targets algorithm takes a list of significant pathways as input, then retrieves and integrates public data for all targets within those pathways for a given condition. It also incorporates a weighting scheme that is customizable by the user to support a variety of use cases including target prioritization, drug repurposing, and identifying novel targets that are biologically relevant for a different indication.

Results

As a proof of concept, we applied this algorithm to a public colorectal cancer RNA-sequencing dataset with 144 case and control samples. Our analysis identified 430 targets and ~700 unique drugs based on differential gene expression and signaling pathway enrichment. We found that our highest-ranked predicted targets were significantly enriched in targets with FDA-approved therapeutics for colorectal cancer (p-value < 0.025) that included EGFR, VEGFA, and PTGS2. Interestingly, there was no statistically significant enrichment of targets for other cancers in this same list suggesting high specificity of the results. We also adjusted the weighting scheme to prioritize more novel targets for CRC. This second analysis revealed epidermal growth factor receptor (EGFR), phosphoinositide-3-kinase (PI3K), and two mitogen-activated protein kinases (MAPK14 and MAPK3). These observations suggest that our open-source method with a customizable weighting scheme can accurately prioritize targets that are specific and relevant to the disease or condition of interest, as well as targets that are at earlier stages of development. We anticipate that this method will complement other approaches to repurpose drugs for a variety of indications, which can contribute to the improvement of the quality of life and overall health of such patients.

Modeling glioblastoma complexity with organoids for personalized treatments

Glioblastoma (GBM) remains a fatal diagnosis despite the current standard of care of maximal surgical resection, radiation, and temozolomide (TMZ) therapy. One aspect that impedes drug development is the lack of an appropriate model representative of the complexity of patient tumors. Brain organoids derived from cell culture techniques provide a robust, easily manipulatable, and high-throughput model for GBM. In this review, we highlight recent progress in developing GBM organoids (GBOs) with a focus on generating the GBM microenvironment (i.e., stem cells, vasculature, and immune cells) recapitulating human disease. Finally, we also discuss the use of organoids as a screening tool in drug development for GBM.

A combination of metformin and epigallocatechin gallate potentiates glioma chemotherapy in vivo

Glioma is the most devastating high-grade tumor of the central nervous system, with dismal prognosis. Existing treatment modality does not provide substantial benefit to patients and demands novel strategies. One of the first-line treatments for glioma, temozolomide, provides marginal benefit to glioma patients. Repurposing of existing non-cancer drugs to treat oncology patients is gaining momentum in recent years. In this study, we investigated the therapeutic benefits of combining three repurposed drugs, namely, metformin (anti-diabetic) and epigallocatechin gallate (green tea-derived antioxidant) together with temozolomide in a glioma-induced xenograft rat model. Our triple-drug combination therapy significantly inhibited tumor growth in vivo and increased the survival rate (50%) of rats when compared with individual or dual treatments. Molecular and cellular analyses revealed that our triple-drug cocktail treatment inhibited glioma tumor growth in rat model through ROS-mediated inactivation of PI3K/AKT/mTOR pathway, arrest of the cell cycle at G1 phase and induction of molecular mechanisms of caspases-dependent apoptosis.In addition, the docking analysis and quantum mechanics studies performed here hypothesize that the effect of triple-drug combination could have been attributed by their difference in molecular interactions, that maybe due to varying electrostatic potential. Thus, repurposing metformin and epigallocatechin gallate and concurrent administration with temozolomide would serve as a prospective therapy in glioma patients.

Lessons learned from evolving frameworks in adult glioblastoma

Glioblastoma (GBM) is the most common and aggressive malignant adult brain tumor. Significant effort has been directed to achieve a molecular subtyping of GBM to impact treatment. The discovery of new unique molecular alterations has resulted in a more effective classification of tumors and has opened the door to subtype-specific therapeutic targets. Morphologically identical GBM may have different genetic, epigenetic, and transcriptomic alterations and therefore different progression trajectories and response to treatments. With a transition to molecularly guided diagnosis, there is now a potential to personalize and successfully manage this tumor type to improve outcomes. The steps to achieve subtype-specific molecular signatures can be extrapolated to other neuroproliferative as well as neurodegenerative disorders.

Adapt to Persist: Glioblastoma Microenvironment and Epigenetic Regulation on Cell Plasticity

Glioblastoma (GBM) is the most frequent and aggressive brain tumor, characterized by great resistance to treatments, as well as inter- and intra-tumoral heterogeneity. GBM exhibits infiltration, vascularization and hypoxia-associated necrosis, characteristics that shape a unique microenvironment in which diverse cell types are integrated. A subpopulation of cells denominated GBM stem-like cells (GSCs) exhibits multipotency and self-renewal capacity. GSCs are considered the conductors of tumor progression due to their high tumorigenic capacity, enhanced proliferation, invasion and therapeutic resistance compared to non-GSCs cells. GSCs have been classified into two molecular subtypes: proneural and mesenchymal, the latter showing a more aggressive phenotype. Tumor microenvironment and therapy can induce a proneural-to-mesenchymal transition, as a mechanism of adaptation and resistance to treatments. In addition, GSCs can transition between quiescent and proliferative substates, allowing them to persist in different niches and adapt to different stages of tumor progression. Three niches have been described for GSCs: hypoxic/necrotic, invasive and perivascular, enhancing metabolic changes and cellular interactions shaping GSCs phenotype through metabolic changes and cellular interactions that favor their stemness. The phenotypic flexibility of GSCs to adapt to each niche is modulated by dynamic epigenetic modifications. Methylases, demethylases and histone deacetylase are deregulated in GSCs, allowing them to unlock transcriptional programs that are necessary for cell survival and plasticity. In this review, we described the effects of GSCs plasticity on GBM progression, discussing the role of GSCs niches on modulating their phenotype. Finally, we described epigenetic alterations in GSCs that are important for stemness, cell fate and therapeutic resistance.

PBX2-Mediated circTLK1 Activates JAK/STAT Signaling to Promote Gliomagenesis via miR-452-5p/SSR1 Axis

Glioma is considered one of the most lethal brain tumors, as the aggressive blood vessel formation leads to high morbidity and mortality rates. However, the mechanisms underlying the initiation and progression of glioma remain unclear. Here, we aimed to reveal the role of circTLK1 in glioma development. Our results revealed that circTLK1 is highly expressed in glioma tumor tissues and glioma cell lines. We then conducted a series of experiments that showed that circTLK1 was involved in the progression of gliomas. Mechanistically, investigation of the factors downstream of circTLK1 revealed that circTLK1 activated JAK/STAT signaling in glioma cells. Furthermore, AGO2-RIP, RNA-pull down, and luciferase reporter gene assays led to the identification of the novel circTLK1/miR-452-5p/SSR1 axis. Moreover, we investigated the upstream regulator of circTLK1 and found that circTLK1 expression in glioma cells could be regulated by the transcriptional factor PBX2. Taken together, our findings show that circTLK1 mediated by PBX2 activates JAK/STAT signaling to promote glioma progression through the miR-452-5p/SSR1 pathway. These results provide new insights into glioma diagnosis and therapy.

Explainable artificial intelligence in high-throughput drug repositioning for subgroup stratifications with interventionable potential

Enabling precision medicine requires developing robust patient stratification methods as well as drugs tailored to homogeneous subgroups of patients from a heterogeneous population. Developing de novo drugs is expensive and time consuming with an ultimately low FDA approval rate. These limitations make developing new drugs for a small portion of a disease population unfeasible. Therefore, drug repositioning is an essential alternative for developing new drugs for a disease subpopulation. This shows the importance of developing data-driven approaches that find druggable homogeneous subgroups within the disease population and reposition the drugs for these subgroups. In this study, we developed an explainable AI approach for patient stratification and drug repositioning. Contrast pattern mining and network analysis were used to discover homogeneous subgroups within a disease population. For each subgroup, a biomedical network analysis was done to find the drugs that are most relevant to a given subgroup of patients. The set of candidate drugs for each subgroup was ranked using an aggregated drug score assigned to each drug. The proposed method represents a human-in-the-loop framework, where medical experts use the data-driven results to generate hypotheses and obtain insights into potential therapeutic candidates for patients who belong to a subgroup. Colorectal cancer (CRC) was used as a case study. Patients' phenotypic and genotypic data was utilized with a heterogeneous knowledge base because it gives a multi-view perspective for finding new indications for drugs outside of their original use. Our analysis of the top candidate drugs for the subgroups identified by medical experts showed that most of these drugs are cancer-related, and most of them have the potential to be a CRC regimen based on studies in the literature.

An Alternative Pipeline for Glioblastoma Therapeutics: A Systematic Review of Drug Repurposing in Glioblastoma

Simple Summary

Glioblastoma is a devastating malignancy that has continued to prove resistant to a variety of therapeutics. No new systemic therapy has been approved for use against glioblastoma in almost two decades. This observation is particularly disturbing given the amount of money invested in identifying novel therapies for this disease. A relatively rapid and economical pipeline for identification of novel agents is drug repurposing. Here, a comprehensive review detailing the state of drug repurposing in glioblastoma is provided. We reveal details on studies that have examined agents in vitro, in animal models and in patients. While most agents have not progressed beyond the initial stages, several drugs, from a variety of classes, have demonstrated promising results in early phase clinical trials.

Abstract

The treatment of glioblastoma (GBM) remains a significant challenge, with outcome for most pa-tients remaining poor. Although novel therapies have been developed, several obstacles restrict the incentive of drug developers to continue these efforts including the exorbitant cost, high failure rate and relatively small patient population. Repositioning drugs that have well-characterized mechanistic and safety profiles is an attractive alternative for drug development in GBM. In ad-dition, the relative ease with which repurposed agents can be transitioned to the clinic further supports their potential for examination in patients. Here, a systematic analysis of the literature and clinical trials provides a comprehensive review of primary articles and unpublished trials that use repurposed drugs for the treatment of GBM. The findings demonstrate that numerous drug classes that have a range of initial indications have efficacy against preclinical GBM models and that certain agents have shown significant potential for clinical benefit. With examination in randomized, placebo-controlled trials and the targeting of particular GBM subgroups, it is pos-sible that repurposing can be a cost-effective approach to identify agents for use in multimodal anti-GBM strategies.

Multifaceted microglia - key players in primary brain tumour heterogeneity

Microglia are the resident innate immune cells of the immune-privileged CNS and, as such, represent the first line of defence against tissue injury and infection. Given their location, microglia are undoubtedly the first immune cells to encounter a developing primary brain tumour. Our knowledge of these cells is therefore important to consider in the context of such neoplasms. As the heterogeneous nature of the most aggressive primary brain tumours is thought to underlie their poor prognosis, this Review places a special emphasis on the heterogeneity of the tumour-associated microglia and macrophage populations present in primary brain tumours. Where available, specific information on microglial heterogeneity in various types and subtypes of brain tumour is included. Emerging evidence that highlights the importance of considering the heterogeneity of both the tumour and of microglial populations in providing improved treatment outcomes for patients is also discussed.

Drug perturbation gene set enrichment analysis (dpGSEA): a new transcriptomic drug screening approach

Background

In this study, we demonstrate that our modified Gene Set Enrichment Analysis (GSEA) method, drug perturbation GSEA (dpGSEA), can detect phenotypically relevant drug targets through a unique transcriptomic enrichment that emphasizes biological directionality of drug-derived gene sets.

Results

We detail our dpGSEA method and show its effectiveness in detecting specific perturbation of drugs in independent public datasets by confirming fluvastatin, paclitaxel, and rosiglitazone perturbation in gastroenteropancreatic neuroendocrine tumor cells. In drug discovery experiments, we found that dpGSEA was able to detect phenotypically relevant drug targets in previously published differentially expressed genes of CD4+T regulatory cells from immune responders and non-responders to antiviral therapy in HIV-infected individuals, such as those involved with virion replication, cell cycle dysfunction, and mitochondrial dysfunction. dpGSEA is publicly available at https://github.com/sxf296/drug_targeting.

Conclusions

dpGSEA is an approach that uniquely enriches on drug-defined gene sets while considering directionality of gene modulation. We recommend dpGSEA as an exploratory tool to screen for possible drug targeting molecules.

Current Opinion on Molecular Characterization for GBM Classification in Guiding Clinical Diagnosis, Prognosis, and Therapy

Glioblastoma (GBM) is highly invasive and the deadliest brain tumor in adults. It is characterized by inter-tumor and intra-tumor heterogeneity, short patient survival, and lack of effective treatment. Prognosis and therapy selection is driven by molecular data from gene transcription, genetic alterations and DNA methylation. The four GBM molecular subtypes are proneural, neural, classical, and mesenchymal. More effective personalized therapy heavily depends on higher resolution molecular subtype signatures, combined with gene therapy, immunotherapy and organoid technology. In this review, we summarize the principal GBM molecular classifications that guide diagnosis, prognosis, and therapeutic recommendations.

Mechanistic models of signaling pathways deconvolute the glioblastoma single-cell functional landscape

Single-cell RNA sequencing is revealing an unexpectedly large degree of heterogeneity in gene expression levels across cell populations. However, little is known on the functional consequences of this heterogeneity and the contribution of individual cell fate decisions to the collective behavior of the tissues these cells are part of. Here, we use mechanistic modeling of signaling circuits, which reveals a complex functional landscape at single-cell level. Different clusters of neoplastic glioblastoma cells have been defined according to their differences in signaling circuit activity profiles triggering specific cancer hallmarks, which suggest different functional strategies with distinct degrees of aggressiveness. Moreover, mechanistic modeling of effects of targeted drug inhibitions at single-cell level revealed, how in some cells, the substitution of VEGFA, the target of bevacizumab, by other expressed proteins, like PDGFD, KITLG and FGF2, keeps the VEGF pathway active, insensitive to the VEGFA inhibition by the drug. Here, we describe for the first time mechanisms that individual cells use to avoid the effect of a targeted therapy, providing an explanation for the innate resistance to the treatment displayed by some cells. Our results suggest that mechanistic modeling could become an important asset for the definition of personalized therapeutic interventions.

ST1926 inhibits glioma progression through regulating mitochondrial complex II

The antitumor activity of atypical adamantyl retinoid ST1926 has been frequently reported in cancer studies; nevertheless, its effect on glioma has not been fully understood. Mitochondria are critical in regulating tumorigenesis and are defined as a promising target for anti-tumor therapy. In the present study, we found that ST1926 might be a mitochondria-targeting anti-glioma drug. ST1926 showed significantly inhibitory role in the viability of glioma cells mainly through inducing apoptosis and autophagy. The results showed that ST1926 alleviated mitochondria-regulated bioenergetics in glioma cells via reducing ATP production and promoting reactive oxygen species production. Importantly, ST1926 significantly impaired complex II (CII) function, which was associated with the inhibition of succinate dehydrogenase (SDH) activity. In addition, the effects of ST1926 on the induction of apoptosis and ROS were further promoted by the treatment of CII inhibitors, including TTFA and 3-NPA. Furthermore, the in vivo experiments confirmed the role of ST1926 in suppressing xenograft tumor growth with few toxicity. Therefore, ST1926 might be an effective anti-glioma drug through targeting CII.

Glioma and Neurokinin-1 Receptor Antagonists: A New Therapeutic Approach

BACKGROUND

In adults, the most lethal and frequent primary brain tumor is glioblastoma. Despite multimodal aggressive therapies, the median survival time after diagnosis is around 15 months. In part, this is due to the blood-brain barrier that restricts common treatments (e.g., chemotherapy). Unfortunately, glioma recurs in 90% of patients. New therapeutic strategies against glioma are urgently required. Substance P (SP), through the neurokinin (NK)-1 receptor, controls cancer cell proliferation by activating c-myc, mitogenactivated protein kinases, activator protein 1 and extracellular signal-regulated kinases 1 and 2. Glioma cells overexpress NK-1 receptors when compared with normal cells. The NK-1 receptor/SP system regulates the proliferation/migration of glioma cells and stimulates angiogenesis, triggering inflammation which contributes to glioma progression. In glioma cells, SP favors glycogen breakdown, essential for glycolysis. By contrast, in glioma, NK-1 receptor antagonists block the proliferation of tumor cells and the breakdown of glycogen and also promote the death (apoptosis) of these cells. These antagonists also inhibit angiogenesis and exert antimetastatic and anti-inflammatory actions.

OBJECTIVE

This review updates the involvement of the NK-1 receptor/SP system in the development of glioma and the potential clinical application of NK-1 receptor antagonists as antiglioma agents.

CONCLUSION

The NK-1 receptor plays a crucial role in glioma and NK-1 receptor antagonists could be used as anti-glioma drugs.

MUDENG Expression Profiling in Cohorts and Brain Tumor Biospecimens to Evaluate Its Role in Cancer

Mu-2-related death-inducing gene (MUDENG, MuD) has been reported to be involved in the tumor necrosis factor-related apoptosis-inducing ligand (TRAIL)-associated apoptotic pathway of glioblastoma multiforme (GBM) cells; however, its expression level, interactors, and role in tumors are yet to be discovered. To investigate whether MuD expression correlates with cancer progression, we analyzed The Cancer Genome Atlas (TCGA) database using UALCAN and Gene Expression Profiling Interactive Analysis (GEPIA). Differential expression of MuD was detected in 6 and 10 cancer types, respectively. Validation performed using data from the Gene Expression Omnibus database showed that MuD expression is downregulated in KIRC tumor and correlate with higher chance of survival. Upregulation of MuD expression in GBM tumors was detected through GEPIA and high MuD expression correlated with higher survival in proneural GBM, whereas the opposite was observed in classical GBM subtype. GBM biospecimens analysis shows that MuD protein level was upregulated in three of six specimens, whereas mRNA level remained relatively unaltered. Therefore, MuD may exert differential effects according to subtypes, and/or be subjected to post-translational regulation in GBM. Correlation analysis between GBM cohort database and experiments using GBM cell lines revealed its positive effect on regulation of protein phosphatase 2 regulatory subunit B’Epsilon (PPP2R5E) and son of sevenless homolog 2 (SOS2). STRING database analysis indicated that the components of adaptor protein complexes putatively interacted with MuD but showed no correlation in terms of survival of patients with different GBM subtypes. In summary, we analyzed the expression of MuD in publicly available cancer patient data sets, GBM cell lines, and biospecimens to demonstrate its potential role as a biomarker for cancer prognosis and identified its candidate interacting molecules.

Context-sensitive network analysis identifies food metabolites associated with Alzheimer's disease: an exploratory study

BACKGROUND

Diet plays an important role in Alzheimer's disease (AD) initiation, progression and outcomes. Previous studies have shown individual food-derived substances may have neuroprotective or neurotoxic effects. However, few works systematically investigate the role of food and food-derived metabolites on the development and progression of AD.

METHODS

In this study, we systematically investigated 7569 metabolites and identified AD-associated food metabolites using a novel network-based approach. We constructed a context-sensitive network to integrate heterogeneous chemical and genetic data, and to model context-specific inter-relationships among foods, metabolites, human genes and AD.

RESULTS

Our metabolite prioritization algorithm ranked 59 known AD-associated food metabolites within top 4.9%, which is significantly higher than random expectation. Interestingly, a few top-ranked food metabolites were specifically enriched in herbs and spices. Pathway enrichment analysis shows that these top-ranked herb-and-spice metabolites share many common pathways with AD, including the amyloid processing pathway, which is considered as a hallmark in AD-affected brains and has pathological roles in AD development.

CONCLUSIONS

Our study represents the first unbiased systems approach to characterizing the effects of food and food-derived metabolites in AD pathogenesis. Our ranking approach prioritizes the known AD-associated food metabolites, and identifies interesting relationships between AD and the food group "herbs and spices". Overall, our study provides intriguing evidence for the role of diet, as an important environmental factor, in AD etiology.

Retrospective analysis of health claims to evaluate pharmacotherapies with potential for repurposing: Association of bupropion and stimulant use disorder remission

Drug repurposing is the identification of novel indication(s) for existing medications. Health claims data provide a burgeoning resource to evaluate pharmacotherapies with repurposing potential. To demonstrate a workflow for drug repurposing using claims data, we assessed the association between prescription of bupropion and stimulant use disorder (StUD) remission. Using the Truven Marketscan database, 96,156 individuals with a StUD were identified. Logistic regression was used to model the association between new bupropion prescriptions and remission while controlling for age, sex, region, StUD severity, antidepressant co-prescriptions, and comorbid mood and attention disorders. Prescription of bupropion within 30 days offirst documented StUD diagnosis increased odds of a subsequent remission diagnosis by 2.1 times (99% confidence interval: 1.09-3.89) in individuals with an amphetamine use disorder, but not those with a cocaine use disorder. This work provides a framework for reverse-translational drug repurposing, which may be applied to many other medical conditions.

A systems biology approach to predict and characterize human gut microbial metabolites in colorectal cancer

Colorectal cancer (CRC) is the second leading cause of cancer-related deaths. It is estimated that about half the cases of CRC occurring today are preventable. Recent studies showed that human gut microbiota and their collective metabolic outputs play important roles in CRC. However, the mechanisms by which human gut microbial metabolites interact with host genetics in contributing CRC remain largely unknown. We hypothesize that computational approaches that integrate and analyze vast amounts of publicly available biomedical data have great potential in better understanding how human gut microbial metabolites are mechanistically involved in CRC. Leveraging vast amount of publicly available data, we developed a computational algorithm to predict human gut microbial metabolites for CRC. We validated the prediction algorithm by showing that previously known CRC-associated gut microbial metabolites ranked highly (mean ranking: top 10.52%; median ranking: 6.29%; p-value: 3.85E-16). Moreover, we identified new gut microbial metabolites likely associated with CRC. Through computational analysis, we propose potential roles for tartaric acid, the top one ranked metabolite, in CRC etiology. In summary, our data-driven computation-based study generated a large amount of associations that could serve as a starting point for further experiments to refute or validate these microbial metabolite associations in CRC cancer.

Using a novel computational drug-repositioning approach (DrugPredict) to rapidly identify potent drug candidates for cancer treatment

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.

Interrogating Patient-level Genomics and Mouse Phenomics towards Understanding Cytokines in Colorectal Cancer Metastasis

Background: Colorectal cancer is the second leading cancer-related death worldwide and a majority of patients die from metastasis. Chronic intestinal inflammation plays an important role in tumor progression of colorectal cancer. However, few study works on systematically predicting colorectal cancer metastasis using inflammatory cytokine genes. Results: We developed a supervised machine learning approach to predict colorectal cancer tumor progression using patient level genomic features. To better understand the role of cytokines, we integrated the metastatic-related genes from mouse phenotypic data. In addition, pathway analysis and network visualization were also applied to top significant genes ranked by feature weights of the final prediction model. The combined model of cytokines and mouse phenotypes achieved a predictive accuracy of 75.54%, higher than the model based on mouse phenotypes independently (70.42%, p-value<0.05). In additional, the combined model outperformed the model based on the existing metastatic-related epithelial-to-mesenchymal transition (EMT) genes (75.54% vs. 71.61%, p-value<0.05). We also observed that the most important cytokine gene features of the our model interact with the cancer driver genes and are highly associated with the colorectal cancer metastasis signaling pathway. Conclusion: We developed a combined model using both cytokine and mouse phenotype information to predict colorectal cancer metastasis. The results suggested that the inflammatory cytokines increase the power of predicting metastasis. We also systematically demonstrated the critical role of cytokines in progression of colorectal tumor.