Drug and disease signature integration identifies synergistic combinations in glioblastoma

Machine and deep learning approaches for cancer drug repurposing

Knowledge of the underpinnings of cancer initiation, progression and metastasis has increased exponentially in recent years. Advanced "omics" coupled with machine learning and artificial intelligence (deep learning) methods have helped elucidate targets and pathways critical to those processes that may be amenable to pharmacologic modulation. However, the current anti-cancer therapeutic armamentarium continues to lag behind. As the cost of developing a new drug remains prohibitively expensive, repurposing of existing approved and investigational drugs is sought after given known safety profiles and reduction in the cost barrier. Notably, successes in oncologic drug repurposing have been infrequent. Computational in-silico strategies have been developed to aid in modeling biological processes to find new disease-relevant targets and discovering novel drug-target and drug-phenotype associations. Machine and deep learning methods have especially enabled leaps in those successes. This review will discuss these methods as they pertain to cancer biology as well as immunomodulation for drug repurposing opportunities in oncologic diseases.

The Molecular and Microenvironmental Landscape of Glioblastomas: Implications for the Novel Treatment Choices

Glioblastoma (GBM) is the most frequent and aggressive primary central nervous system tumor. Surgery followed by radiotherapy and chemotherapy with alkylating agents constitutes standard first-line treatment of GBM. Complete resection of the GBM tumors is generally not possible given its high invasive features. Although this combination therapy can prolong survival, the prognosis is still poor due to several factors including chemoresistance. In recent years, a comprehensive characterization of the GBM-associated molecular signature has been performed. This has allowed the possibility to introduce a more personalized therapeutic approach for GBM, in which novel targeted therapies, including those employing tyrosine kinase inhibitors (TKIs), could be employed. The GBM tumor microenvironment (TME) exerts a key role in GBM tumor progression, in particular by providing an immunosuppressive state with low numbers of tumor-infiltrating lymphocytes (TILs) and other immune effector cell types that contributes to tumor proliferation and growth. The use of immune checkpoint inhibitors (ICIs) has been successfully introduced in numerous advanced cancers as well as promising results have been shown for the use of these antibodies in untreated brain metastases from melanoma and from non-small cell lung carcinoma (NSCLC). Consequently, the use of PD-1/PD-L1 inhibitors has also been proposed in several clinical trials for the treatment of GBM. In the present review, we will outline the main GBM molecular and TME aspects providing also the grounds for novel targeted therapies and immunotherapies using ICIs for GBM.

Depletion of glioma stem cells by synergistic inhibition of mTOR and c-Myc with a biological camouflaged cascade brain-targeting nanosystem

Glioma stem cells (GSCs), as a subpopulation of stem cell-like cells, have been proposed to play a crucial role in the progression of drug-resistance in glioblastoma (GBM). Therefore, the targeted eradication of GSCs can serve as a promising therapeutic strategy for the reversal of drug-resistance in GBM. Herein, the effects of silencing c-Myc and m-TOR on primary GBM cells extracted from patients were investigated. Results confirmed that dual inhibition treatment significantly (p < 0.05) and synergistically suppressed GSCs, and consequently reversed TMZ-resistance when compared with the single treatment group. Subsequently, to facilitate effective crossing of the BBB, a biological camouflaged cascade brain-targeting nanosystem (PMRT) was created. The PMRT significantly inhibited tumor growth and extended the lifespan of orthotopic transplantation TMZ-resistant GBM-grafted mice. Our data demonstrated that PMRT could precisely facilitate drug release at the tumor site across the BBB. Simultaneously, c-Myc and m-TOR could serve as synergistic targets to eradicate the GSCs and reverse GBM resistance to TMZ.

Network-based approach highlighting interplay among anti-hypertensives: target coding-genes: diseases

Elucidating the relation between the medicines: targets, targets: diseases and diseases: diseases are of fundamental significance as-is for societal benefit. Hypertension is one of the dangerous health conditions prevalent in society, is a risk factor for several other diseases if left untreated and anti-hypertensives (AHs) are the approved drugs to treat it. The goal of the study is to decipher the connection between hypertension with other health conditions, however, is challenging due to the large interactome. To fulfill the aim, the strategy involves prior clustering of the AHs into groups as per our previous method, followed by the analyzing functional association of the target coding-genes (tc-genes) and health conditions for each group. Following our recently published work where the AHs are clustered into six groups such that molecules having similar patterns come together, here, the distribution of molecular functions and the cellular components adopted by the tc-genes of each group are analyzed. The analyses indicate that kidney, heart, brain or lung related ailments are commonly associated with the tc-genes. The association of selective tc-genes to health conditions suggests a preference for certain health conditions despite many possibilities. Analyses of experimentally validated drug–drug combinations indicate the trend in successful AHs combinations. Clinically validated combinations bind different targets. Our study provides a promising methodology in a network-based approach that considers the influence of structural diversity of AHs to the functional perspective of tc-genes concerning the health conditions. The method could be extended to explore disease–disease relationships.

The Clinical Kinase Index: A Method to Prioritize Understudied Kinases as Drug Targets for the Treatment of Cancer

The approval of the first kinase inhibitor, Gleevec, ushered in a paradigm shift for oncological treatment-the use of genomic data for targeted, efficacious therapies. Since then, over 48 additional small-molecule kinase inhibitors have been approved, solidifying the case for kinases as a highly druggable and attractive target class. Despite the role deregulated kinase activity plays in cancer, only 8% of the kinome has been effectively "drugged." Moreover, 24% of the 634 human kinases are understudied. We have developed a comprehensive scoring system that utilizes differential gene expression, pathological parameters, overall survival, and mutational hotspot analysis to rank and prioritize clinically relevant kinases across 17 solid tumor cancers from The Cancer Genome Atlas. We have developed the clinical kinase index (CKI) app (http://cki.ccs.miami.edu) to facilitate interactive analysis of all kinases in each cancer. Collectively, we report that understudied kinases have potential clinical value as biomarkers or drug targets that warrant further study.

Drug repositioning of antiretroviral ritonavir for combinatorial therapy in glioblastoma

BACKGROUND

The protease inhibitor ritonavir (RTV) is a clinical-stage inhibitor of the human immunodeficiency virus. In a drug repositioning approach, we here exhibit the additional potential of RTV to augment current treatment of glioblastoma, the most aggressive primary brain tumour of adulthood.

METHODS

We explored the antitumour activity of RTV and mechanisms of action in a broad spectrum of short-term expanded clinical cell samples from primary and recurrent glioblastoma and in a cohort of conventional cell lines and non-tumour human neural controls in vitro. To validate RTV efficacy in monotherapeutic and in combinatorial settings, we used patient-derived xenograft models in a series of in vivo studies.

RESULTS

RTV monotherapy induced a selective antineoplastic response and demonstrated cytostatic and anti-migratory activity at clinical plasma peak levels. Additional exposure to temozolomide or irradiation further enhanced the effects synergistically, fostered by mechanisms of autophagy and increased endoplasmic reticulum stress. In xenograft models, we consequently observed increasing overall survival under the combinatorial effect of RTV and temozolomide.

CONCLUSIONS

Our data establish RTV as a valuable repositioning candidate for further exploration as an adjunct therapeutic in the clinical care of glioblastoma.

Astrocytes in the regulation of cerebrovascular functions

Astrocytes are the most numerous type of neuroglia in the brain and have a predominant influence on the cerebrovascular system; they control perivascular homeostasis, the integrity of the blood-brain barrier, the dialogue with the peripheral immune system, the transfer of metabolites from the blood, and blood vessel contractility in response to neuronal activity. These regulatory processes occur in a specialized interface composed of perivascular astrocyte extensions that almost completely cover the cerebral blood vessels. Scientists have only recently started to study how this interface is formed and how it influences cerebrovascular functions. Here, we review the literature on the astrocytes' role in the regulation of the cerebrovascular system. We cover the anatomy and development of the gliovascular interface, the known gliovascular functions, and molecular factors, the latter's implication in certain pathophysiological situations, and recent cutting-edge experimental tools developed to examine the astrocytes' role at the vascular interface. Finally, we highlight some open questions in this field of research.

Combining kinase inhibitors for optimally co-targeting cancer and drug escape by exploitation of drug target promiscuities

Cancers resist targeted therapeutics by drug-escape signaling. Multitarget drugs co-targeting cancer and drug-escape mediators (DEMs) are clinically advantageous. DEM coverage may be expanded by drug combinations. This work evaluated to what extent the kinase DEMs (KDEMs) can be optimally co-targeted by drug combinations based on target promiscuities of individual drugs. We focused on 41 approved and 28 clinical trial small molecule kinase inhibitor drugs with available experimental kinome and clinical pharmacokinetic data. From the kinome inhibitory profiles of these drugs, drug combinations were assembled for optimally co-targeting an established cancer target (EGFR, HER2, ABL1, or MEK1) and 9-16 target-associated KDEMs at comparable potency levels as that against the cancer target. Each set of two-, three-, and four-drug combinations co-target 36-71%, 44-89%, 50-88%, and 27-55% KDEMs of EGFR, HER2, ABL1, and MEK1, respectively, compared with the 36, 33, 38, and 18% KDEMs maximally co-targeted by an existing drug or drug combination approved or clinically tested for the respective cancer. Some co-targeted KDEMs are not covered by any existing drug or drug combination. Our work suggested that novel drug combinations may be constructed for optimally co-targeting cancer and drug escape by the exploitation of drug target promiscuities.

Integrating Differential Gene Expression Analysis with Perturbagen-Response Signatures May Identify Novel Therapies for Thyroid-Associated Orbitopathy

Purpose

To evaluate the efficacy of Library of Integrated Network-based Cellular Signatures (LINCS) perturbagen prediction software to identify small molecules that revert pathologic gene signature and alter disease phenotype in orbital adipose stem cells (OASCs) derived from patients with thyroid-associated orbitopathy (TAO).

Methods

Differentially expressed genes identified via RNA sequencing were inputted into LINCS L1000 Characteristic Direction Signature Search Engine (L1000CDS²) to predict candidate small molecules to reverse pathologic gene expression. TAO OASC cell lines were treated in vitro with six identified small molecules (Torin-2, PX12, withaferin A, isoliquiritigenin, mitoxantrone, and MLN8054), and expression of key adipogenic and differentially expressed genes was measured with quantitative polymerase chain reaction after 7 days of treatment. OASCs were differentiated into adipocytes, treated for 15 days, and stained with Oil Red O (OD 490 nm) to evaluate adipogenic changes.

Results

The expression of key differentially expressed genes (IRX1, HOXB2, S100B, and KCNA4) and adipogenic genes (peroxisome proliferator activated receptor-γ, FABP4) was significantly decreased in TAO OASCs after treatment (P < .05). In treated TAO adipocytes (n = 3), all six tested small molecules yielded significant decrease (P < .05) in Oil Red O staining. In treated non-TAO adipocytes (n = 3), only three of the drugs yielded a significant decrease in Oil Red O staining.

Conclusions

Combining disease expression signatures with LINCS small molecule prediction software can identify promising preclinical drug candidates for TAO.

Translational Relevance

These findings may offer insight into future potential therapeutic options for TAO and demonstrate a streamlined model to predict drug candidates for other diseases.

A guide for bioinformaticians: 'omics-based drug discovery for precision oncology

Bioinformatics-centric drug development is inevitable in the era of precision medicine. Clinical 'omics information, including genomics, epigenomics, transcriptomics, and proteomics, provides the most comprehensive molecular landscape in which each patient's pathological history is delineated. Hence, the capability of bioinformaticians to manage integrative 'omics data is crucial to current drug development. Bioinformatics can accelerate drug development from initial time-consuming discoveries to the clinical stage by providing information-guided solutions. However, many bioinformaticians do not have opportunities to participate in drug discovery programs. As a starting point for bioinformaticians with no prior drug development experience, here we discuss bioinformatics applications during drug development with a focus on working-level omics-based methodologies.

SynergyFinder 2.0: visual analytics of multi-drug combination synergies

SynergyFinder (https://synergyfinder.fimm.fi) is a stand-alone web-application for interactive analysis and visualization of drug combination screening data. Since its first release in 2017, SynergyFinder has become a widely used web-tool both for the discovery of novel synergistic drug combinations in pre-clinical model systems (e.g. cell lines or primary patient-derived cells), and for better understanding of mechanisms of combination treatment efficacy or resistance. Here, we describe the latest version of SynergyFinder (release 2.0), which has extensively been upgraded through the addition of novel features supporting especially higher-order combination data analytics and exploratory visualization of multi-drug synergy patterns, along with automated outlier detection procedure, extended curve-fitting functionality and statistical analysis of replicate measurements. A number of additional improvements were also implemented based on the user requests, including new visualization and export options, updated user interface, as well as enhanced stability and performance of the web-tool. With these improvements, SynergyFinder 2.0 is expected to greatly extend its potential applications in various areas of multi-drug combinatorial screening and precision medicine.

LINCS Data Portal 2.0: next generation access point for perturbation-response signatures

The Library of Integrated Network-Based Cellular Signatures (LINCS) is an NIH Common Fund program with the goal of generating a large-scale and comprehensive catalogue of perturbation-response signatures by utilizing a diverse collection of perturbations across many model systems and assay types. The LINCS Data Portal (LDP) has been the primary access point for the compendium of LINCS data and has been widely utilized. Here, we report the first major update of LDP (http://lincsportal.ccs.miami.edu/signatures) with substantial changes in the data architecture and APIs, a completely redesigned user interface, and enhanced curated metadata annotations to support more advanced, intuitive and deeper querying, exploration and analysis capabilities. The cornerstone of this update has been the decision to reprocess all high-level LINCS datasets and make them accessible at the data point level enabling users to directly access and download any subset of signatures across the entire library independent from the originating source, project or assay. Access to the individual signatures also enables the newly implemented signature search functionality, which utilizes the iLINCS platform to identify conditions that mimic or reverse gene set queries. A newly designed query interface enables global metadata search with autosuggest across all annotations associated with perturbations, model systems, and signatures.

BET Inhibitors Synergize with Carfilzomib to Induce Cell Death in Cancer Cells via Impairing Nrf1 Transcriptional Activity and Exacerbating the Unfolded Protein Response

Currently, proteasome inhibitors bortezomib, carfilzomib, and ixazomib are successfully used in clinics to treat multiple myeloma. However, these agents show limited efficacy against solid tumors. Identification of drugs that can potentiate the action of proteasome inhibitors could help expand the use of this therapeutic modality to solid tumors. Here, we found that bromodomain extra-terminal (BET) family protein inhibitors such as JQ1, I-BET762, and I-BET151 synergize with carfilzomib in multiple solid tumor cell lines. Mechanistically, BET inhibitors attenuated the ability of the transcription factor Nrf1 to induce proteasome genes in response to proteasome inhibition, thus, impeding the bounce-back response of proteasome activity, a critical pathway by which cells cope with proteotoxic stress. Moreover, we found that treatment with BET inhibitors or depletion of Nrf1 exacerbated the unfolded protein response (UPR), signaling that was initiated by proteasome inhibition. Taken together, our work provides a mechanistic explanation behind the synergy between proteasome and BET inhibitors in cancer cell lines and could prompt future preclinical and clinical studies aimed at further investigating this combination.

Polymer Nanoformulation of Sorafenib and All-Trans Retinoic Acid for Synergistic Inhibition of Thyroid Cancer

Part of differentiated thyroid cancer will relapse or develop into dedifferentiated thyroid cancer after standard therapy, such as surgery or radionuclide therapy. Sorafenib (SOR) is recommended for the treatment of advanced or radioiodine-refractory thyroid cancer. The monotherapy using SOR is often hampered by its modest efficacy, serve systemic toxicity, and high occurrence of drug resistance. In order to enhance the antitumor effect of SOR and reduce its side effects, SOR and all-trans retinoic acid (ATRA), a differentiation-promoting drug, were loaded into poly(ethylene glycol)-poly(lactide-co-glycolide) (PEG-PLGA) polymer micelles in this study. The drug-loaded micelles, PM/(SOR+ATRA), exhibited relatively slow drug release and effective cell uptake. Compared with other treatment groups, the PM/(SOR+ATRA) treatment group showed the most significant antitumor effect and minimal systemic toxicity toward the FTC-133 thyroid cancer-bearing BALB/c nude mouse model. Immunofluorescence analysis confirmed that PM/(SOR+ATRA) could significantly promote apoptosis and re-differentiation of tumor cells. All the results demonstrated that polymer micelles loaded with SOR and ATRA could treat thyroid cancer more effectively and safely.

Low expression or hypermethylation of PLK2 might predict favorable prognosis for patients with glioblastoma multiforme

Background

As the most aggressive brain tumor, patients with glioblastoma multiforme (GBM) have a poor prognosis. Our purpose was to explore prognostic value of Polo-like kinase 2 (PLK2) in GBM, a member of the PLKs family.

Methods

The expression profile of PLK2 in GBM was obtained from The Cancer Genome Atlas database. The PLK2 expression in GBM was tested. Kaplan–Meier curves were generated to assess the association between PLK2 expression and overall survival (OS) in patients with GBM. Furthermore, to assess its prognostic significance in patients with primary GBM, we constructed univariate and multivariate Cox regression models. The association between PLK2 expression and its methylation was then performed. Differentially expressed genes correlated with PLK2 were identified by Pearson test and functional enrichment analysis was performed.

Results

Overall survival results showed that low PLK2 expression had a favorable prognosis of patients with GBM (P-value = 0.0022). Furthermore, PLK2 (HR = 0.449, 95% CI [0.243–0.830], P-value = 0.011) was positively associated with OS by multivariate Cox regression analysis. In cluster 5, DNA methylated PLK2 had the lowest expression, which implied that PLK2 expression might be affected by its DNA methylation status in GBM. PLK2 in CpG island methylation phenotype (G-CIMP) had lower expression than non G-CIMP group (P = 0.0077). Regression analysis showed that PLK2 expression was negatively correlated with its DNA methylation (P = 0.0062, Pearson r = −0.3855). Among all differentially expressed genes of GBM, CYGB (r = 0.5551; P < 0.0001), ISLR2 (r = 0.5126; P < 0.0001), RPP25 (r = 0.5333; P < 0.0001) and SOX2 (r = −0.4838; P < 0.0001) were strongly correlated with PLK2. Functional enrichment analysis results showed that these genes were enriched several biological processes or pathways that were associated with GBM.

Conclusion

Polo-like kinase 2 expression is regulated by DNA methylation in GBM, and its low expression or hypermethylation could be considered to predict a favorable prognosis for patients with GBM.

A Serum Proteomic Signature Predicting Survival in Patients with Glioblastoma

Purpose

Glioblastoma (GBM) is the most common form of brain tumor and has a uniformly poor prognosis. Development of prognostic biomarkers in easily accessible serum samples have the potential to improve the outcomes of patients with GBM through personalized therapy planning.

Material/Methods

In this study pre-treatment serum samples from 30 patients newly diagnosed with GBM were evaluated using a 40-protein multiplex ELISA platform. Analysis of potentially relevant gene targets using The Cancer Genome Atlas database was done using the Glioblastoma Bio Discovery Portal (GBM-BioDP). A ten-biomarker subgroup of clinically relevant molecules was selected using a functional grouping analysis of the 40 plex genes with two genes selected from each group on the basis of degree of variance, lack of co-linearity with other biomarkers and clinical interest. A Multivariate Cox proportional hazard approach was used to analyze the relationship between overall survival (OS), gene expression, and resection status as covariates.

Results

Thirty of 40 of the MSD molecules mapped to known genes within TCGA and separated the patient cohort into two main clusters centered predominantly around a grouping of classical and proneural versus the mesenchymal subtype as classified by Verhaak. Using the values for the 30 proteins in a prognostic index (PI) demonstrated that patients in the entire cohort with a PI below the median lived longer than those patients with a PI above the median (HR 1.8, p=0.001) even when stratified by both age and MGMT status. This finding was also consistent within each Verhaak subclass and highly significant (range p=0.0001-0.011). Additionally, a subset of ten proteins including, CRP, SAA, VCAM1, VEGF, MDC, TNFA, IL7, IL8, IL10, IL16 were found to have prognostic value within the TCGA database and a positive correlation with overall survival in GBM patients who had received gross tumor resection followed by conventional radiation therapy and temozolomide treatment concurrent with the addition of valproic acid.

Conclusion

These findings demonstrate that proteomic approaches to the development of prognostic assays for treatment of GBM may hold potential clinical value.

A STAT3-based gene signature stratifies glioma patients for targeted therapy

Intratumoral heterogeneity is a hallmark of glioblastoma (GBM) tumors, thought to negatively influence therapeutic outcome. Previous studies showed that mesenchymal tumors have a worse outcome than the proneural subtype. Here we focus on STAT3 as its activation precedes the proneural-mesenchymal transition. We first establish a STAT3 gene signature that stratifies GBM patients into STAT3-high and -low cohorts. STAT3 inhibitor treatment selectively mitigates STAT3-high cell viability and tumorigenicity in orthotopic mouse xenograft models. We show the mechanism underlying resistance in STAT3-low cells by combining STAT3 signature analysis with kinome screen data on STAT3 inhibitor-treated cells. This allows us to draw connections between kinases affected by STAT3 inhibitors, their associated transcription factors and target genes. We demonstrate that dual inhibition of IGF-1R and STAT3 sensitizes STAT3-low cells and improves survival in mice. Our study underscores the importance of serially profiling tumors so as to accurately target individuals who may demonstrate molecular subtype switching.

Chemical-genetic interaction landscape of mono-(2-ethylhexyl)-phthalate using chemogenomic profiling in yeast

Integration of chemical-genetic interaction data with biological functions provides a mechanistic understanding of how toxic compounds affect cells. Mono-(2-ethylhexyl)-phthalate (MEHP) is an active metabolite of di-(2-ethylhexyl)-phthalate (DEHP), a commonly used plasticizer. MEHP adversely affects human health causing hepatotoxicity and reproductive toxicity. How MEHP affects cellular physiology is not fully understood. We utilized a genome-wide competitive fitness-based assay called 'chemogenomic profiling' to determine the genetic interaction map of MEHP in Saccharomyces cerevisiae. Gene Ontology enrichment analysis of 218 genes that provide resistance to MEHP indicated that MEHP affects seven cellular processes namely: (1) cellular amino acid biosynthetic process, (2) sterol biosynthetic process, (3) cellular transport, (4) transcriptional and translational regulation, (5) protein glycosylation, (6) cytokinesis and cell morphogenesis and (7) ionic homeostasis. We show that MEHP protects yeast cells from membrane perturbing agents such as amphotericin B, dihydrosphingosine and phytosphingosine. Moreover, we also demonstrate that MEHP compromises the integrity of the yeast plasma membrane and cell wall. Our work provides a basis for further investigation of MEHP toxicity in humans.

Novel Curcumin Inspired Bis-Chalcone Promotes Endoplasmic Reticulum Stress and Glioblastoma Neurosphere Cell Death

Glioblastoma (GBM) has a dismal prognosis and successful elimination of GBM stem cells (GSCs) is a high-priority as these cells are responsible for tumor regrowth following therapy and ultimately patient relapse. Natural products and their derivatives continue to be a source for the development of effective anticancer drugs and have been shown to effectively target pathways necessary for cancer stem cell self-renewal and proliferation. We generated a series of curcumin inspired bis-chalcones and examined their effect in multiple patient-derived GSC lines. Of the 19 compounds synthesized, four analogs robustly induced GSC death in six separate GSC lines, with a half maximal inhibitory concentration (IC50) ranging from 2.7–5.8 μM and significantly reduced GSC neurosphere formation at sub-cytotoxic levels. Structural analysis indicated that the presence of a methoxy group at position 3 of the lateral phenylic appendages was important for activity. Pathway and drug connectivity analysis of gene expression changes in response to treatment with the most active bis-chalcone 4j (the 3,4,5 trimethoxy substituted analog) suggested that the mechanism of action was the induction of endoplasmic reticulum (ER) stress and unfolded protein response (UPR) mediated cell death. This was confirmed by Western blot analysis in which 4j induced robust increases in CHOP, p-jun and caspase 12. The UPR is believed to play a significant role in GBM pathogenesis and resistance to therapy and as such represents a promising therapeutic target.

Therapy for glioblastoma: is it working?

Glioblastoma (GBM) remains one of the most intransigent of cancers, with a median overall survival of only 15 months after diagnosis. Drug treatments have largely proven ineffective; it is thought that this is related to the heterogeneous nature and plasticity of GBM-initiating stem cell lineages. Although many combination drug therapies are being positioned to address tumour heterogeneity, the most promising therapeutic approaches for GBM to date appear to be those targeting GBM by vaccination or antibody- and cell-based immunotherapy. We review the most recent clinical trials for GBM and discuss the role of adaptive clinical trials in developing personalised treatment strategies to address intra- and inter-tumoral heterogeneity.