Drug and disease signature integration identifies synergistic combinations in glioblastoma

A network-based trans-omics approach for predicting synergistic drug combinations

BACKGROUND

Combination therapy can offer greater efficacy on medical treatments. However, the discovery of synergistic drug combinations is challenging. We propose a novel computational method, SyndrumNET, to predict synergistic drug combinations by network propagation with trans-omics analyses.

METHODS

The prediction is based on the topological relationship, network-based proximity, and transcriptional correlation between diseases and drugs. SyndrumNET was applied to analyzing six diseases including asthma, diabetes, hypertension, colorectal cancer, acute myeloid leukemia (AML), and chronic myeloid leukemia (CML).

RESULTS

Here we show that SyndrumNET outperforms the previous methods in terms of high accuracy. We perform in vitro cell survival assays to validate our prediction for CML. Of the top 17 predicted drug pairs, 14 drug pairs successfully exhibits synergistic anticancer effects. Our mode-of-action analysis also reveals that the drug synergy of the top predicted combination of capsaicin and mitoxantrone is due to the complementary regulation of 12 pathways, including the Rap1 signaling pathway.

CONCLUSIONS

The proposed method is expected to be useful for discovering synergistic drug combinations for various complex diseases.

Dual p38MAPK and MEK inhibition disrupts adaptive chemoresistance in mesenchymal glioblastoma to temozolomide

BACKGROUND

Precision treatment of glioblastoma is increasingly focused on molecular subtyping, with the mesenchymal subtype particularly resistant to temozolomide. Here, we aim to develop a targeted therapy for temozolomide resensitization in the mesenchymal subtype.

METHODS

We integrated kinomic profiles and kinase inhibitor screens from patient-derived proneural and mesenchymal glioma-propagating cells and public clinical datasets to identify key protein kinases implicated in temozolomide resistance. RNAseq, apoptosis assays, and comet assays were used to examine the role of p38MAPK signaling and adaptive chemoresistance in mesenchymal cells. The efficacy of dual p38MAPK and MEK/ERK inhibition using ralimetinib (selective orally active p38MAPK inhibitor; phase I/II for glioblastoma) and binimetinib (approved MEK1/2 inhibitor for melanoma; phase II for high-grade glioma) in primary and recurrent mesenchymal tumors was evaluated using an intracranial patient-derived tumor xenograft model, focusing on survival analysis.

RESULTS

Our transcriptomic-kinomic integrative analysis revealed p38MAPK as the prime target whose gene signature enables patient stratification based on their molecular subtypes and provides prognostic value. Repurposed p38MAPK inhibitors synergize favorably with temozolomide to promote intracellular retention of temozolomide and exacerbate DNA damage. Mesenchymal cells exhibit adaptive chemoresistance to p38MAPK inhibition through a pH-/calcium-mediated MEK/ERK pathway. Dual p38MAPK and MEK inhibition effectively maintain temozolomide sensitivity in primary and recurrent intracranial mesenchymal glioblastoma xenografts.

CONCLUSIONS

Temozolomide resistance in mesenchymal glioblastoma is associated with p38MAPK activation. Adaptive chemoresistance in p38MAPK-resistant cells is mediated by MEK/ERK signaling. Adjuvant therapy with dual p38MAPK and MEK inhibition prolongs temozolomide sensitivity, which can be developed into a precision therapy for the mesenchymal subtype.

Computer-aided drug discovery strategies for novel therapeutics for prostate cancer leveraging next-generating sequencing data

INTRODUCTION

Prostate cancer (PC) is the most common malignancy and accounts for a significant proportion of cancer deaths among men. Although initial therapy success can often be observed in patients diagnosed with localized PC, many patients eventually develop disease recurrence and metastasis. Without effective treatments, patients with aggressive PC display very poor survival. To curb the current high mortality rate, many investigations have been carried out to identify efficacious therapeutics. Compared to de novo drug designs, computational methods have been widely employed to offer actionable drug predictions in a fast and cost-efficient way. Particularly, powered by an increasing availability of next-generation sequencing molecular profiles from PC patients, computer-aided approaches can be tailored to screen for candidate drugs.

AREAS COVERED

Herein, the authors review the recent advances in computational methods for drug discovery utilizing molecular profiles from PC patients. Given the uniqueness in PC therapeutic needs, they discuss in detail the drug discovery goals of these studies, highlighting their translational values for clinically impactful drug nomination.

EXPERT OPINION

Evolving molecular profiling techniques may enable new perspectives for computer-aided approaches to offer drug candidates for different tumor microenvironments. With ongoing efforts to incorporate new compounds into large-scale high-throughput screens, the authors envision continued expansion of drug candidate pools.

Antiretroviral Drug Repositioning for Glioblastoma

Outcomes for glioblastoma (GBM) remain poor despite standard-of-care treatments including surgical resection, radiation, and chemotherapy. Intratumoral heterogeneity contributes to treatment resistance and poor prognosis, thus demanding novel therapeutic approaches. Drug repositioning studies on antiretroviral therapy (ART) have shown promising potent antineoplastic effects in multiple cancers; however, its efficacy in GBM remains unclear. To better understand the pleiotropic anticancer effects of ART on GBM, we conducted a comprehensive drug repurposing analysis of ART in GBM to highlight its utility in translational neuro-oncology. To uncover the anticancer role of ART in GBM, we conducted a comprehensive bioinformatic and in vitro screen of antiretrovirals against glioblastoma. Using the DepMap repository and reversal of gene expression score, we conducted an unbiased screen of 16 antiretrovirals in 40 glioma cell lines to identify promising candidates for GBM drug repositioning. We utilized patient-derived neurospheres and glioma cell lines to assess neurosphere viability, proliferation, and stemness. Our in silico screen revealed that several ART drugs including reverse transcriptase inhibitors (RTIs) and protease inhibitors (PIs) demonstrated marked anti-glioma activity with the capability of reversing the GBM disease signature. RTIs effectively decreased cell viability, GBM stem cell markers, and proliferation. Our study provides mechanistic and functional insight into the utility of ART repurposing for malignant gliomas, which supports the current literature. Given their safety profile, preclinical efficacy, and neuropenetrance, ARTs may be a promising adjuvant treatment for GBM.

RAPID resistance to BET inhibitors is mediated by FGFR1 in glioblastoma

Bromodomain and extra-terminal domain (BET) proteins are therapeutic targets in several cancers including the most common malignant adult brain tumor glioblastoma (GBM). Multiple small molecule inhibitors of BET proteins have been utilized in preclinical and clinical studies. Unfortunately, BET inhibitors have not shown efficacy in clinical trials enrolling GBM patients. One possible reason for this may stem from resistance mechanisms that arise after prolonged treatment within a clinical setting. However, the mechanisms and timeframe of resistance to BET inhibitors in GBM is not known. To identify the temporal order of resistance mechanisms in GBM we performed quantitative proteomics using multiplex-inhibitor bead mass spectrometry and demonstrated that intrinsic resistance to BET inhibitors in GBM treatment occurs rapidly within hours and involves the fibroblast growth factor receptor 1 (FGFR1) protein. Additionally, small molecule inhibition of BET proteins and FGFR1 simultaneously induces synergy in reducing GBM tumor growth in vitro and in vivo. Further, FGFR1 knockdown synergizes with BET inhibitor mediated reduction of GBM cell proliferation. Collectively, our studies suggest that co-targeting BET and FGFR1 may dampen resistance mechanisms to yield a clinical response in GBM.

Mechanistic insights and the clinical prospects of targeted therapies for glioblastoma: a comprehensive review

Glioblastoma (GBM) is a fatal brain tumour that is traditionally diagnosed based on histological features. Recent molecular profiling studies have reshaped the World Health Organization approach in the classification of central nervous system tumours to include more pathogenetic hallmarks. These studies have revealed that multiple oncogenic pathways are dysregulated, which contributes to the aggressiveness and resistance of GBM. Such findings have shed light on the molecular vulnerability of GBM and have shifted the disease management paradigm from chemotherapy to targeted therapies. Targeted drugs have been developed to inhibit oncogenic targets in GBM, including receptors involved in the angiogenic axis, the signal transducer and activator of transcription 3 (STAT3), the PI3K/AKT/mTOR signalling pathway, the ubiquitination-proteasome pathway, as well as IDH1/2 pathway. While certain targeted drugs showed promising results in vivo, the translatability of such preclinical achievements in GBM remains a barrier. We also discuss the recent developments and clinical assessments of targeted drugs, as well as the prospects of cell-based therapies and combinatorial therapy as novel ways to target GBM. Targeted treatments have demonstrated preclinical efficacy over chemotherapy as an alternative or adjuvant to the current standard of care for GBM, but their clinical efficacy remains hindered by challenges such as blood-brain barrier penetrance of the drugs. The development of combinatorial targeted therapies is expected to improve therapeutic efficacy and overcome drug resistance.

Metabolic models predict fotemustine and the combination of eflornithine/rifamycin and adapalene/cannabidiol for the treatment of gliomas

Gliomas are the most common type of malignant brain tumors, with glioblastoma multiforme (GBM) having a median survival of 15 months due to drug resistance and relapse. The treatment of gliomas relies on surgery, radiotherapy and chemotherapy. Only 12 anti-brain tumor chemotherapies (AntiBCs), mostly alkylating agents, have been approved so far. Glioma subtype-specific metabolic models were reconstructed to simulate metabolite exchanges, in silico knockouts and the prediction of drug and drug combinations for all three subtypes. The simulations were confronted with literature, high-throughput screenings (HTSs), xenograft and clinical trial data to validate the workflow and further prioritize the drug candidates. The three subtype models accurately displayed different degrees of dependencies toward glutamine and glutamate. Furthermore, 33 single drugs, mainly antimetabolites and TXNRD1-inhibitors, as well as 17 drug combinations were predicted as potential candidates for gliomas. Half of these drug candidates have been previously tested in HTSs. Half of the tested drug candidates reduce proliferation in cell lines and two-thirds in xenografts. Most combinations were predicted to be efficient for all three glioma types. However, eflornithine/rifamycin and cannabidiol/adapalene were predicted specifically for GBM and low-grade glioma, respectively. Most drug candidates had comparable efficiency in preclinical tests, cerebrospinal fluid bioavailability and mode-of-action to AntiBCs. However, fotemustine and valganciclovir alone and eflornithine and celecoxib in combination with AntiBCs improved the survival compared to AntiBCs in two-arms, phase I/II and higher glioma clinical trials. Our work highlights the potential of metabolic modeling in advancing glioma drug discovery, which accurately predicted metabolic vulnerabilities, repurposable drugs and combinations for the glioma subtypes.

Revisiting Aurora Kinase B: A promising therapeutic target for cancer therapy

Cancer continues to be a major health concern globally, although the advent of targeted therapy has revolutionized treatment options. Aurora Kinase B is a serine-threonine kinase that has been explored as an oncology therapeutic target for more than two decades. Aurora Kinase B inhibitors show promising biological results in in-vitro and in-vivo experiments. However, there are no inhibitors approved yet for clinical use, primarily because of the side effects associated with Aurora B inhibitors. Several studies demonstrate that Aurora B inhibitors show excellent synergy with various chemotherapeutic agents, radiation therapy, and targeted therapies. This makes it an excellent choice as an adjuvant therapy to first-line therapies, which greatly improves the therapeutic window and side effect profile. Recent studies indicate the role of Aurora B in some deadly cancers with limited therapeutic options, like triple-negative breast cancer and glioblastoma. Herein, we review the latest developments in Aurora Kinase B targeted research, with emphasis on its potential as an adjuvant therapy and its role in some of the most difficult-to-treat cancers.

PANCDR: precise medicine prediction using an adversarial network for cancer drug response

Pharmacogenomics aims to provide personalized therapy to patients based on their genetic variability. However, accurate prediction of cancer drug response (CDR) is challenging due to genetic heterogeneity. Since clinical data are limited, most studies predicting drug response use preclinical data to train models. However, such models might not be generalizable to external clinical data due to differences between the preclinical and clinical datasets. In this study, a Precision Medicine Prediction using an Adversarial Network for Cancer Drug Response (PANCDR) model is proposed. PANCDR consists of two sub-models, an adversarial model and a CDR prediction model. The adversarial model reduces the gap between the preclinical and clinical datasets, while the CDR prediction model extracts features and predicts responses. PANCDR was trained using both preclinical data and unlabeled clinical data. Subsequently, it was tested on external clinical data, including The Cancer Genome Atlas and brain tumor patients. PANCDR outperformed other machine learning models in predicting external test data. Our results demonstrate the robustness of PANCDR and its potential in precision medicine by recommending patient-specific drug candidates. The PANCDR codes and data are available at https://github.com/DMCB-GIST/PANCDR.

Genome-wide study reveals novel roles for formin-2 in axon regeneration as a microtubule dynamics regulator and therapeutic target for nerve repair

Peripheral nerves regenerate successfully; however, clinical outcome after injury is poor. We demonstrated that low-dose ionizing radiation (LDIR) promoted axon regeneration and function recovery after peripheral nerve injury (PNI). Genome-wide CpG methylation profiling identified LDIR-induced hypermethylation of the Fmn2 promoter, exhibiting injury-induced Fmn2 downregulation in dorsal root ganglia (DRGs). Constitutive knockout or neuronal Fmn2 knockdown accelerated nerve repair and function recovery. Mechanistically, increased microtubule dynamics at growth cones was observed in time-lapse imaging of Fmn2-deficient DRG neurons. Increased HDAC5 phosphorylation and rapid tubulin deacetylation were found in regenerating axons of neuronal Fmn2-knockdown mice after injury. Growth-promoting effect of neuronal Fmn2 knockdown was eliminated by pharmaceutical blockade of HDAC5 or neuronal Hdac5 knockdown, suggesting that Fmn2deletion promotes axon regeneration via microtubule post-translational modification. In silico screening of FDA-approved drugs identified metaxalone, administered either immediately or 24-h post-injury, accelerating function recovery. This work uncovers a novel axon regeneration function of Fmn2 and a small-molecule strategy for PNI.

Tumor-wide RNA splicing aberrations generate immunogenic public neoantigens

T-cell-mediated immunotherapies are limited by the extent to which cancer-specific antigens are homogenously expressed throughout a tumor. We reasoned that recurrent splicing aberrations in cancer represent a potential source of tumor-wide and public neoantigens, and to test this possibility, we developed a novel pipeline for identifying neojunctions expressed uniformly within a tumor across diverse cancer types. Our analyses revealed multiple neojunctions that recur across patients and either exhibited intratumor heterogeneity or, in some cases, were tumor-wide. We identified CD8+ T-cell clones specific for neoantigens derived from tumor-wide and conserved neojunctions in GNAS and RPL22 , respectively. TCR-engineered CD8 + T-cells targeting these mutations conferred neoantigen-specific tumor cell eradication. Furthermore, we revealed that cancer-specific dysregulation in splicing factor expression leads to recurrent neojunction expression. Together, these data reveal that a subset of neojunctions are both intratumorally conserved and public, providing the molecular basis for novel T-cell-based immunotherapies that address intratumoral heterogeneity.

Treatment of Status Epilepticus after Traumatic Brain Injury Using an Antiseizure Drug Combined with a Tissue Recovery Enhancer Revealed by Systems Biology

We tested a hypothesis that in silico-discovered compounds targeting traumatic brain injury (TBI)-induced transcriptomics dysregulations will mitigate TBI-induced molecular pathology and augment the effect of co-administered antiseizure treatment, thereby alleviating functional impairment. In silico bioinformatic analysis revealed five compounds substantially affecting TBI-induced transcriptomics regulation, including calpain inhibitor, chlorpromazine, geldanamycin, tranylcypromine, and trichostatin A (TSA). In vitro exposure of neuronal-BV2-microglial co-cultures to compounds revealed that TSA had the best overall neuroprotective, antioxidative, and anti-inflammatory effects. In vivo assessment in a rat TBI model revealed that TSA as a monotherapy (1 mg/kg/d) or in combination with the antiseizure drug levetiracetam (LEV 150 mg/kg/d) mildly mitigated the increase in plasma levels of the neurofilament subunit pNF-H and cortical lesion area. The percentage of rats with seizures during 0-72 h post-injury was reduced in the following order: TBI-vehicle 80%, TBI-TSA (1 mg/kg) 86%, TBI-LEV (54 mg/kg) 50%, TBI-LEV (150 mg/kg) 40% (p < 0.05 vs. TBI-vehicle), and TBI-LEV (150 mg/kg) combined with TSA (1 mg/kg) 30% (p < 0.05). Cumulative seizure duration was reduced in the following order: TBI-vehicle 727 ± 688 s, TBI-TSA 898 ± 937 s, TBI-LEV (54 mg/kg) 358 ± 715 s, TBI-LEV (150 mg/kg) 42 ± 64 (p < 0.05 vs. TBI-vehicle), and TBI-LEV (150 mg/kg) combined with TSA (1 mg/kg) 109 ± 282 s (p < 0.05). This first preclinical intervention study on post-TBI acute seizures shows that a combination therapy with the tissue recovery enhancer TSA and LEV was safe but exhibited no clear benefit over LEV monotherapy on antiseizure efficacy. A longer follow-up is needed to confirm the possible beneficial effects of LEV monotherapy and combination therapy with TSA on chronic post-TBI structural and functional outcomes, including epileptogenesis.

Combination drug screen targeting glioblastoma core vulnerabilities reveals pharmacological synergisms

BACKGROUND

Pharmacological synergisms are an attractive anticancer strategy. However, with more than 5000 approved-drugs and compounds in clinical development, identifying synergistic treatments represents a major challenge.

METHODS

High-throughput screening was combined with target deconvolution and functional genomics to reveal targetable vulnerabilities in glioblastoma. The role of the top gene hit was investigated by RNA interference, transcriptomics and immunohistochemistry in glioblastoma patient samples. Drug combination screen using a custom-made library of 88 compounds in association with six inhibitors of the identified glioblastoma vulnerabilities was performed to unveil pharmacological synergisms. Glioblastoma 3D spheroid, organotypic ex vivo and syngeneic orthotopic mouse models were used to validate synergistic treatments.

FINDINGS

Nine targetable vulnerabilities were identified in glioblastoma and the top gene hit RRM1 was validated as an independent prognostic factor. The associations of CHK1/MEK and AURKA/BET inhibitors were identified as the most potent amongst 528 tested pairwise drug combinations and their efficacy was validated in 3D spheroid models. The high synergism of AURKA/BET dual inhibition was confirmed in ex vivo and in vivo glioblastoma models, without detectable toxicity.

INTERPRETATION

Our work provides strong pre-clinical evidence of the efficacy of AURKA/BET inhibitor combination in glioblastoma and opens new therapeutic avenues for this unmet medical need. Besides, we established the proof-of-concept of a stepwise approach aiming at exploiting drug poly-pharmacology to unveil druggable cancer vulnerabilities and to fast-track the identification of synergistic combinations against refractory cancers.

FUNDING

This study was funded by institutional grants and charities.

Combination of antidepressants and antipsychotics as a novel treatment option for psychosis in Alzheimer's disease

Psychotic symptoms are reported as one of the most common complications of Alzheimer's disease (AD), in whom they are associated with more rapid deterioration and increased mortality. Empiric treatments, namely first and second-generation antipsychotics, confer modest efficacy in patients with AD and with psychosis (AD+P) and themselves increase mortality. Recent studies suggested the use and beneficial effects of antidepressants among patients with AD+P. This motivates our rationale for exploring their potential as a novel combination therapy option among these patients. We included electronic medical records of 10,260 patients with AD in our study. Survival analysis was performed to assess the effects of the combination of antipsychotics and antidepressants on the mortality of these patients. A protein-protein interaction network representing AD+P was built, and network analysis methods were used to quantify the efficacy of these drugs on AD+P. A combined score was developed to measure the potential synergetic effect against AD+P. Our survival analyses showed that the co-administration of antidepressants with antipsychotics have a significant beneficial effect in reducing mortality. Our network analysis showed that the targets of antipsychotics and antidepressants are well-separated, and antipsychotics and antidepressants have similar Signed Jaccard Index (SJI) scores to AD+P. Eight drug pairs, including some popular recommendations like aripiprazole/sertraline, showed higher than average scores which suggest their potential in treating AD+P via strong synergetic effects. Our proposed combinations of antipsychotic and antidepressant therapy showed a strong superiority over current antipsychotics treatment for AD+P. The observed beneficial effects can be further strengthened by optimizing drug-pair selection based on our systems pharmacology analysis.

Network neighborhood operates as a drug repositioning method for cancer treatment

Computational drug repositioning approaches are important, as they cost less compared to the traditional drug development processes. This study proposes a novel network-based drug repositioning approach, which computes similarities between disease-causing genes and drug-affected genes in a network topology to suggest candidate drugs with highest similarity scores. This new method aims to identify better treatment options by integrating systems biology approaches. It uses a protein-protein interaction network that is the main topology to compute a similarity score between candidate drugs and disease-causing genes. The disease-causing genes were mapped on this network structure. Transcriptome profiles of drug candidates were taken from the LINCS project and mapped individually on the network structure. The similarity of these two networks was calculated by different network neighborhood metrics, including Adamic-Adar, PageRank and neighborhood scoring. The proposed approach identifies the best candidates by choosing the drugs with significant similarity scores. The method was experimented on melanoma, colorectal, and prostate cancers. Several candidate drugs were predicted by applying AUC values of 0.6 or higher. Some of the predictions were approved by clinical phase trials or other in-vivo studies found in literature. The proposed drug repositioning approach would suggest better treatment options with integration of functional information between genes and transcriptome level effects of drug perturbations and diseases.

Targeting the post-synaptic proteome has therapeutic potential for psychosis in Alzheimer Disease

Individuals with Alzheimer Disease who develop psychotic symptoms (AD + P) experience more rapid cognitive decline and have reduced indices of synaptic integrity relative to those without psychosis (AD-P). We sought to determine whether the postsynaptic density (PSD) proteome is altered in AD + P relative to AD-P, analyzing PSDs from dorsolateral prefrontal cortex of AD + P, AD-P, and a reference group of cognitively normal elderly subjects. The PSD proteome of AD + P showed a global shift towards lower levels of all proteins relative to AD-P, enriched for kinases, proteins regulating Rho GTPases, and other regulators of the actin cytoskeleton. We computationally identified potential novel therapies predicted to reverse the PSD protein signature of AD + P. Five days of administration of one of these drugs, the C-C Motif Chemokine Receptor 5 inhibitor, maraviroc, led to a net reversal of the PSD protein signature in adult mice, nominating it as a novel potential treatment for AD + P.

A Transcriptome-Based Precision Oncology Platform for Patient-Therapy Alignment in a Diverse Set of Treatment-Resistant Malignancies

Predicting in vivo response to antineoplastics remains an elusive challenge. We performed a first-of-kind evaluation of two transcriptome-based precision cancer medicine methodologies to predict tumor sensitivity to a comprehensive repertoire of clinically relevant oncology drugs, whose mechanism of action we experimentally assessed in cognate cell lines. We enrolled patients with histologically distinct, poor-prognosis malignancies who had progressed on multiple therapies, and developed low-passage, patient-derived xenograft models that were used to validate 35 patient-specific drug predictions. Both OncoTarget, which identifies high-affinity inhibitors of individual master regulator (MR) proteins, and OncoTreat, which identifies drugs that invert the transcriptional activity of hyperconnected MR modules, produced highly significant 30-day disease control rates (68% and 91%, respectively). Moreover, of 18 OncoTreat-predicted drugs, 15 induced the predicted MR-module activity inversion in vivo. Predicted drugs significantly outperformed antineoplastic drugs selected as unpredicted controls, suggesting these methods may substantively complement existing precision cancer medicine approaches, as also illustrated by a case study.

SIGNIFICANCE

Complementary precision cancer medicine paradigms are needed to broaden the clinical benefit realized through genetic profiling and immunotherapy. In this first-in-class application, we introduce two transcriptome-based tumor-agnostic systems biology tools to predict drug response in vivo. OncoTarget and OncoTreat are scalable for the design of basket and umbrella clinical trials. This article is highlighted in the In This Issue feature, p. 1275.

Transcriptomics for Clinical and Experimental Biology Research: Hang on a Seq

Sequencing the human genome empowers translational medicine, facilitating transcriptome-wide molecular diagnosis, pathway biology, and drug repositioning. Initially, microarrays are used to study the bulk transcriptome; but now short-read RNA sequencing (RNA-seq) predominates. Positioned as a superior technology, that makes the discovery of novel transcripts routine, most RNA-seq analyses are in fact modeled on the known transcriptome. Limitations of the RNA-seq methodology have emerged, while the design of, and the analysis strategies applied to, arrays have matured. An equitable comparison between these technologies is provided, highlighting advantages that modern arrays hold over RNA-seq. Array protocols more accurately quantify constitutively expressed protein coding genes across tissue replicates, and are more reliable for studying lower expressed genes. Arrays reveal long noncoding RNAs (lncRNA) are neither sparsely nor lower expressed than protein coding genes. Heterogeneous coverage of constitutively expressed genes observed with RNA-seq, undermines the validity and reproducibility of pathway analyses. The factors driving these observations, many of which are relevant to long-read or single-cell sequencing are discussed. As proposed herein, a reappreciation of bulk transcriptomic methods is required, including wider use of the modern high-density array data-to urgently revise existing anatomical RNA reference atlases and assist with more accurate study of lncRNAs.

Identification of significant gene expression changes in multiple perturbation experiments using knockoffs

Large-scale multiple perturbation experiments have the potential to reveal a more detailed understanding of the molecular pathways that respond to genetic and environmental changes. A key question in these studies is which gene expression changes are important for the response to the perturbation. This problem is challenging because (i) the functional form of the nonlinear relationship between gene expression and the perturbation is unknown and (ii) identification of the most important genes is a high-dimensional variable selection problem. To deal with these challenges, we present here a method based on the model-X knockoffs framework and Deep Neural Networks to identify significant gene expression changes in multiple perturbation experiments. This approach makes no assumptions on the functional form of the dependence between the responses and the perturbations and it enjoys finite sample false discovery rate control for the selected set of important gene expression responses. We apply this approach to the Library of Integrated Network-Based Cellular Signature data sets which is a National Institutes of Health Common Fund program that catalogs how human cells globally respond to chemical, genetic and disease perturbations. We identified important genes whose expression is directly modulated in response to perturbation with anthracycline, vorinostat, trichostatin-a, geldanamycin and sirolimus. We compare the set of important genes that respond to these small molecules to identify co-responsive pathways. Identification of which genes respond to specific perturbation stressors can provide better understanding of the underlying mechanisms of disease and advance the identification of new drug targets.

Combination of Antidepressants and Antipsychotics as A Novel Treatment Option for Psychosis in Alzheimer's Disease

Background

Psychotic symptoms are reported as one of the most common complications of Alzheimer's disease (AD), affecting approximately half of AD patients, in whom they are associated with more rapid deterioration and increased mortality. Empiric treatments, namely first and second-generation antipsychotics, confer modest efficacy in AD patients with psychosis (AD+P) and themselves increase mortality. A recent genome-wide meta-analysis and early clinical trials suggest the use and beneficial effects of antidepressants among AD+P patients. This motivates our rationale for exploring their potential as a novel combination therapy option amongst these patients.

Methods

We included University of Pittsburgh Medical Center (UPMC) electronic medical records (EMRs) of 10,260 AD patients from January 2004 and October 2019 in our study. Survival analysis was performed to assess the effects of the combination of antipsychotics and antidepressants on the mortality of these patients. To provide more valuable insights on the hidden mechanisms of the combinatorial therapy, a protein-protein interaction (PPI) network representing AD+P was built, and network analysis methods were used to quantify the efficacy of these drugs on AD+P. An indicator score combining the measurements on the separation between drugs and the proximity between the drugs and AD+P was used to measure the effect of an antipsychotic-antidepressant drug pair against AD+P.

Results

Our survival analyses replicated that antipsychotic usage is strongly associated with increased mortality in AD patients while the co-administration of antidepressants with antipsychotics showed a significant beneficial effect in reducing mortality. Our network analysis showed that the targets of antipsychotics and antidepressants are well-separated, and antipsychotics and antidepressants have similar proximity scores to AD+P. Eight drug pairs, including some popular recommendations like Aripiprazole/Sertraline and other pairs not reported previously like Iloperidone/Maprotiline showed higher than average indicator scores which suggest their potential in treating AD+P via strong synergetic effects as seen in our study.

Conclusion

Our proposed combinations of antipsychotics and antidepressants therapy showed a strong superiority over current antipsychotics treatment for AD+P. The observed beneficial effects can be further strengthened by optimizing drug-pair selection based on our systems pharmacology analysis.

CCSynergy: an integrative deep-learning framework enabling context-aware prediction of anti-cancer drug synergy

Combination therapy is a promising strategy for confronting the complexity of cancer. However, experimental exploration of the vast space of potential drug combinations is costly and unfeasible. Therefore, computational methods for predicting drug synergy are much needed for narrowing down this space, especially when examining new cellular contexts. Here, we thus introduce CCSynergy, a flexible, context aware and integrative deep-learning framework that we have established to unleash the potential of the Chemical Checker extended drug bioactivity profiles for the purpose of drug synergy prediction. We have shown that CCSynergy enables predictions of superior accuracy, remarkable robustness and improved context generalizability as compared to the state-of-the-art methods in the field. Having established the potential of CCSynergy for generating experimentally validated predictions, we next exhaustively explored the untested drug combination space. This resulted in a compendium of potentially synergistic drug combinations on hundreds of cancer cell lines, which can guide future experimental screens.

Longitudinal drug synergy assessment using convolutional neural network image-decoding of glioblastoma single-spheroid cultures

Background

In recent years, drug combinations have become increasingly popular to improve therapeutic outcomes in various diseases, including difficult to cure cancers such as the brain cancer glioblastoma. Assessing the interaction between drugs over time is critical for predicting drug combination effectiveness and minimizing the risk of therapy resistance. However, as viability readouts of drug combination experiments are commonly performed as an endpoint where cells are lysed, longitudinal drug-interaction monitoring is currently only possible through combined endpoint assays.

Methods

We provide a method for massive parallel monitoring of drug interactions for 16 drug combinations in 3 glioblastoma models over a time frame of 18 days. In our assay, viabilities of single neurospheres are to be estimated based on image information taken at different time points. Neurosphere images taken on the final day (day 18) were matched to the respective viability measured by CellTiter-Glo 3D on the same day. This allowed to use of machine learning to decode image information to viability values on day 18 as well as for the earlier time points (on days 8, 11, and 15).

Results

Our study shows that neurosphere images allow us to predict cell viability from extrapolated viabilities. This enables to assess of the drug interactions in a time window of 18 days. Our results show a clear and persistent synergistic interaction for several drug combinations over time.

Conclusions

Our method facilitates longitudinal drug-interaction assessment, providing new insights into the temporal-dynamic effects of drug combinations in 3D neurospheres which can help to identify more effective therapies against glioblastoma.

Screening of predicted synergistic multi-target therapies in glioblastoma identifies new treatment strategies

Background

IDH-wildtype glioblastoma (GBM) is a highly malignant primary brain tumor with a median survival of 15 months after standard of care, which highlights the need for improved therapy. Personalized combination therapy has shown to be successful in many other tumor types and could be beneficial for GBM patients.

Methods

We performed the largest drug combination screen to date in GBM, using a high-throughput effort where we selected 90 drug combinations for their activity onto 25 patient-derived GBM cultures. 43 drug combinations were selected for interaction analysis based on their monotherapy efficacy and were tested in a short-term (3 days) as well as long-term (18 days) assay. Synergy was assessed using dose-equivalence and multiplicative survival metrics.

Results

We observed a consistent synergistic interaction for 15 out of 43 drug combinations on patient-derived GBM cultures. From these combinations, 11 out of 15 drug combinations showed a longitudinal synergistic effect on GBM cultures. The highest synergies were observed in the drug combinations Lapatinib with Thapsigargin and Lapatinib with Obatoclax Mesylate, both targeting epidermal growth factor receptor and affecting the apoptosis pathway. To further elaborate on the apoptosis cascade, we investigated other, more clinically relevant, apoptosis inducers and observed a strong synergistic effect while combining Venetoclax (BCL targeting) and AZD5991 (MCL1 targeting).

Conclusions

Overall, we have identified via a high-throughput drug screening several new treatment strategies for GBM. Moreover, an exceptionally strong synergistic interaction was discovered between kinase targeting and apoptosis induction which is suitable for further clinical evaluation as multi-targeted combination therapy.

A multi-omic analysis of MCF10A cells provides a resource for integrative assessment of ligand-mediated molecular and phenotypic responses

The phenotype of a cell and its underlying molecular state is strongly influenced by extracellular signals, including growth factors, hormones, and extracellular matrix proteins. While these signals are normally tightly controlled, their dysregulation leads to phenotypic and molecular states associated with diverse diseases. To develop a detailed understanding of the linkage between molecular and phenotypic changes, we generated a comprehensive dataset that catalogs the transcriptional, proteomic, epigenomic and phenotypic responses of MCF10A mammary epithelial cells after exposure to the ligands EGF, HGF, OSM, IFNG, TGFB and BMP2. Systematic assessment of the molecular and cellular phenotypes induced by these ligands comprise the LINCS Microenvironment (ME) perturbation dataset, which has been curated and made publicly available for community-wide analysis and development of novel computational methods ( synapse.org/LINCS_MCF10A ). In illustrative analyses, we demonstrate how this dataset can be used to discover functionally related molecular features linked to specific cellular phenotypes. Beyond these analyses, this dataset will serve as a resource for the broader scientific community to mine for biological insights, to compare signals carried across distinct molecular modalities, and to develop new computational methods for integrative data analysis.

Integrative genomic analysis facilitates precision strategies for glioblastoma treatment

Glioblastoma (GBM) is the most common form of malignant primary brain tumor with a dismal prognosis. Currently, the standard treatments for GBM rarely achieve satisfactory results, which means that current treatments are not individualized and precise enough. In this study, a multiomics-based GBM classification was established and three subclasses (GPA, GPB, and GPC) were identified, which have different molecular features both in bulk samples and at single-cell resolution. A robust GBM poor prognostic signature (GPS) score model was then developed using machine learning method, manifesting an excellent ability to predict the survival of GBM. NVP−BEZ235, GDC−0980, dasatinib and XL765 were ultimately identified to have subclass-specific efficacy targeting patients with a high risk of poor prognosis. Furthermore, the GBM classification and GPS score model could be considered as potential biomarkers for immunotherapy response. In summary, an integrative genomic analysis was conducted to advance individual-based therapies in GBM.

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A multiomics-based classification of GBM was established

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Single-cell transcriptomic profiling of GBM subclasses was revealed using Scissor

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A robust prognostic risk model was developed for GBM by machine learning method

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Prediction of potential agents based on molecular and prognostic risk stratification

In Vitro Brain Organoids and Computational Models to Study Cell Death in Brain Diseases

Understanding the mechanisms underlying the formation and progression of brain diseases is challenging due to the vast variety of involved genetic/epigenetic factors and the complexity of the environment of the brain. Current preclinical monolayer culture systems fail to faithfully recapitulate the in vivo complexities of the brain. Organoids are three-dimensional (3D) culture systems that mimic much of the complexities of the brain including cell-cell and cell-matrix interactions. Complemented with a theoretical framework to model the dynamic interactions between different components of the brain, organoids can be used as a potential tool for studying disease progression, transport of therapeutic agents in tissues, drug screening, and toxicity analysis. In this chapter, we first report on the fabrication and use of a novel self-filling microwell arrays (SFMWs) platform that is self-filling and enables the formation of organoids with uniform size distributions. Next, we will introduce a mathematical framework that predicts the organoid growth, cell death, and the therapeutic responses of the organoids to different therapeutic agents. Through systematic investigations, the computational model can identify shortcomings of in vitro assays and reduce the time and effort required to improve preclinical tumor models' design. Lastly, the mathematical model provides new testable hypotheses and encourages mathematically driven experiments.

Leveraging the neurosurgical operating room for therapeutic development in NeuroOncology

Glioblastoma (GBM) remains a disease with a dismal prognosis. For all the hope and promise immunotherapies and molecular targeted therapies have shown for systemic malignancies, these treatments have failed to show any promise in GBM. In this context, the paradigm of investigation of therapeutics for this disease itself must be examined and modifications considered. The unique challenge of the presence of blood-brain and blood-tumor barriers (BBB/BTB) raises questions about both the true levels of systemic drug delivery to the affected tissues. Window-of-opportunity (WoO) trials in neuro-oncology allow for proof-of-concept at the start of a classic phase I-II-III clinical trial progression. For therapeutics that do not have the ability to cross the BBB/BTB, direct delivery into tumor and/or tumor-infiltrated brain in the setting of a surgical procedure can provide a novel route of therapeutic access. These approaches permit neurosurgeons to play a greater role in therapeutic development for brain tumors.

A Computational Framework to Characterize the Cancer Drug Induced Effect on Aging Using Transcriptomic Data

Cancer treatments such as chemotherapies may change or accelerate aging trajectories in cancer patients. Emerging evidence has shown that “omics” data can be used to study molecular changes of the aging process. Here, we integrated the drug-induced and normal aging transcriptomic data to computationally characterize the potential cancer drug-induced aging process in patients. Our analyses demonstrated that the aging-associated gene expression in the GTEx dataset can recapitulate the well-established aging hallmarks. We next characterized the drug-induced transcriptomic changes of 28 FDA approved cancer drugs in brain, kidney, muscle, and adipose tissues. Further drug-aging interaction analysis identified 34 potential drug regulated aging events. Those events include aging accelerating effects of vandetanib (Caprelsa®) and dasatinib (Sprycel®) in brain and muscle, respectively. Our result also demonstrated aging protective effect of vorinostat (Zolinza®), everolimus (Afinitor®), and bosutinib (Bosulif®) in brain.

CBX3 accelerates the malignant progression of glioblastoma multiforme by stabilizing EGFR expression

CBX3, also known as HP1γ, is a major isoform of heterochromatin protein 1, whose deregulation has been reported to promote the development of human cancers. However, the molecular mechanism of CBX3 in glioblastoma multiforme (GBM) are unclear. Our study reported the identification of CBX3 as a potential therapeutic target for GBM. Briefly, we found that, CBX3 is significantly upregulated in GBM and reduces patient survival. In addition, functional assays demonstrated that CBX3 significantly promote the proliferation, invasion and tumorigenesis of GBM cells in vitro and in vivo. Mechanistically, Erlotinib, a small molecule targeting epidermal growth factor receptor (EGFR) tyrosine kinase, was used to demonstrate that CBX3 direct the malignant progression of GBM are EGFR dependent. Previous studies have shown that PARK2(Parkin) and STUB1(Carboxy Terminus of Hsp70-Interacting Protein) are EGFR-specific E3 ligases. Notably, we verified that CBX3 directly suppressed PARK2 and STUB1 at the transcriptional level through its CD domain to reduce the ubiquitination of EGFR. Moreover, the CSD domain of CBX3 interacted with PARK2 and regulated its ubiquitination to further reduce its protein level. Collectively, these results revealed an unknown mechanism underlying the pathogenesis of GBM and confirmed that CBX3 is a promising therapeutic target.

Computational drug repurposing against SARS-CoV-2 reveals plasma membrane cholesterol depletion as key factor of antiviral drug activity

Comparing SARS-CoV-2 infection-induced gene expression signatures to drug treatment-induced gene expression signatures is a promising bioinformatic tool to repurpose existing drugs against SARS-CoV-2. The general hypothesis of signature-based drug repurposing is that drugs with inverse similarity to a disease signature can reverse disease phenotype and thus be effective against it. However, in the case of viral infection diseases, like SARS-CoV-2, infected cells also activate adaptive, antiviral pathways, so that the relationship between effective drug and disease signature can be more ambiguous. To address this question, we analysed gene expression data from in vitro SARS-CoV-2 infected cell lines, and gene expression signatures of drugs showing anti-SARS-CoV-2 activity. Our extensive functional genomic analysis showed that both infection and treatment with in vitro effective drugs leads to activation of antiviral pathways like NFkB and JAK-STAT. Based on the similarity-and not inverse similarity-between drug and infection-induced gene expression signatures, we were able to predict the in vitro antiviral activity of drugs. We also identified SREBF1/2, key regulators of lipid metabolising enzymes, as the most activated transcription factors by several in vitro effective antiviral drugs. Using a fluorescently labeled cholesterol sensor, we showed that these drugs decrease the cholesterol levels of plasma-membrane. Supplementing drug-treated cells with cholesterol reversed the in vitro antiviral effect, suggesting the depleting plasma-membrane cholesterol plays a key role in virus inhibitory mechanism. Our results can help to more effectively repurpose approved drugs against SARS-CoV-2, and also highlights key mechanisms behind their antiviral effect.

A survey of optimal strategy for signature-based drug repositioning and an application to liver cancer

Pharmacologic perturbation projects, such as Connectivity Map (CMap) and Library of Integrated Network-based Cellular Signatures (LINCS), have produced many perturbed expression data, providing enormous opportunities for computational therapeutic discovery. However, there is no consensus on which methodologies and parameters are the most optimal to conduct such analysis. Aiming to fill this gap, new benchmarking standards were developed to quantitatively evaluate drug retrieval performance. Investigations of potential factors influencing drug retrieval were conducted based on these standards. As a result, we determined an optimal approach for LINCS data-based therapeutic discovery. With this approach, homoharringtonine (HHT) was identified to be a candidate agent with potential therapeutic and preventive effects on liver cancer. The antitumor and antifibrotic activity of HHT was validated experimentally using subcutaneous xenograft tumor model and carbon tetrachloride (CCL₄)-induced liver fibrosis model, demonstrating the reliability of the prediction results. In summary, our findings will not only impact the future applications of LINCS data but also offer new opportunities for therapeutic intervention of liver cancer.

Development of a T-cell activation-related module with predictive value for the prognosis and immune checkpoint blockade therapy response in glioblastoma

Background

Despite the rise in the use of immune checkpoint blockade drugs (ICBs) in recent years, there are no ICB drugs that are currently approved or under large-scale clinical trials for glioblastoma (GBM). T-cells, which mainly mediate adaptive immunity, are an important part of the tumor immune microenvironment. The activation of T-cells in tumors plays a key role in evaluating the sensitivity of patients to immunotherapy. Therefore, we applied bioinformatics approaches to construct a T-cell activation related risk score to study the effect of the activation of T-cells on the prognosis and ICB response of patients with GBM.

Materials and Methods

This study collected TCGA, CGGA, and GSE16011 glioma cohorts, as well as the IMvigor210 immunotherapy dataset, with complete mRNA expression profiles and clinical information. GraphPad Prism 8 and R 3.6.3 were used for bioinformatics analysis and plotting.

Results

The activation of T-cells in patients with GBM is characterized by obvious heterogeneity. We established a T-cell activation-related risk score based on five univariate Cox regression prognostic genes (CD276, IL15, SLC11A1, TNFSF4, and TREML2) in GBM. The risk score was an independent risk factor for poor prognosis. The overall survival time of patients in the high-risk group was significantly lower than in the low-risk group. Moreover, the high-risk score was accompanied by a stronger immune response and a more complex tumor immune microenvironment. “Hot tumors” were mainly enriched in the high-risk group, and high-risk group patients highly expressed inhibitory immune checkpoints (PD1, PD-L1, TIM3 etc.). By combining the risk and priming scores we obtained the immunotherapy score, which was shown to be a good evaluation index for sensitivity to GBM immunotherapy.

Conclusions

As an independent risk factor for poor prognosis, the T-cell activation-related risk score, combined with other clinical characteristics, could efficiently evaluate the survival of patients with GBM. The immunotherapy score obtained by combining the risk and priming scores could evaluate the ICB response of patients with GBM, providing treatment opportunities.

A Multiparametric Pharmacogenomic Strategy for Drug Repositioning predicts Therapeutic Efficacy for Glioblastoma Cell Lines

Background

Poor prognosis of glioblastoma patients and the extensive heterogeneity of glioblastoma at both the molecular and cellular level necessitates developing novel individualized treatment modalities via genomics-driven approaches.

Methods

This study leverages numerous pharmacogenomic and tissue databases to examine drug repositioning for glioblastoma. RNAseq of glioblastoma tumor samples from The Cancer Genome Atlas (TCGA, n=117) were compared to “normal” frontal lobe samples from Genotype-Tissue Expression Portal (GTEX, n=120) to find differentially expressed genes (DEGs). Using compound-gene expression data and drug activity data from the Library of Integrated Network-Based Cellular Signatures (LINCS, n=66,512 compounds) CCLE (71 glioma cell lines), and Chemical European Molecular Biology Laboratory (ChEMBL) platforms, we employed a summarized reversal gene expression metric (sRGES) to “reverse” the resultant disease signature for GBM and its subtypes. A multi-parametric strategy was employed to stratify compounds capable of blood brain barrier penetrance with a favorable pharmacokinetic profile (CNS-MPO).

Results

Significant correlations were identified between sRGES and drug efficacy in GBM cell lines in both ChEMBL(r=0.37,p&lt;.001) and Cancer Therapeutic Response Portal (CTRP) databases (r=0.35, p&lt;0.001). Our multiparametric algorithm identified two classes of drugs with highest sRGES and CNS-MPO: HDAC inhibitors (vorinostat and entinostat) and topoisomerase inhibitors suitable for drug repurposing.

Conclusions

Our studies suggest that reversal of glioblastoma disease signature correlates with drug potency for various GBM subtypes. This multiparametric approach may set the foundation for an early-phase personalized -omics clinical trial for glioblastoma by effectively identifying drugs that are capable of reversing the disease signature and have favorable pharmacokinetic and safety profiles.

The novel BET inhibitor UM-002 reduces glioblastoma cell proliferation and invasion

Bromodomain and extraterminal domain (BET) proteins have emerged as therapeutic targets in multiple cancers, including the most common primary adult brain tumor glioblastoma (GBM). Although several BET inhibitors have entered clinical trials, few are brain penetrant. We have generated UM-002, a novel brain penetrant BET inhibitor that reduces GBM cell proliferation in vitro and in a human cerebral brain organoid model. Since UM-002 is more potent than other BET inhibitors, it could potentially be developed for GBM treatment. Furthermore, UM-002 treatment reduces the expression of cell-cycle related genes in vivo and reduces the expression of invasion related genes within the non-proliferative cells present in tumors as measured by single cell RNA-sequencing. These studies suggest that BET inhibition alters the transcriptional landscape of GBM tumors, which has implications for designing combination therapies. Importantly, they also provide an integrated dataset that combines in vitro and ex vivo studies with in vivo single-cell RNA-sequencing to characterize a novel BET inhibitor in GBM.

Anti-cancer impact of Hypericin in B-CPAP cells: Extrinsic caspase dependent apoptosis induction and metastasis obstruction

Thyroid cancer is the most common type of endocrine-related cancer. According to the literature, its incidence is not very high, but its rate increasing especially in developed countries. With this regard, finding approaches to prevent, and exert anti-tumor activity with the least side effects on the normal cells at the next step after diagnosis is demanded. Herbal medicine is a branch of integrative oncology that seems to be a practically beneficial goddess for cancer treatment in many cases. Here we utilized Hypericin (HYP) to investigate its anti-tumor (apoptotic and anti-metastatic) activity on B-CPAP (a thyroid cancer cell line) and cytotoxicity on TPC-1 (thyroid cancer cell line with wild type TP53) cell lines. To assess whether HYP may exert preventive and anti-tumor effects and does not have a potential side effect, we dubbed the experiments on the fibroblast cells (as a normal cell line). Cytotoxicity and kind of cellular death were examined by MTT and AnnexinV/PI respectively. Extrinsic/intrinsic apoptosis pathway induction was clarified by western blotting on pro/cleaved caspases 9, 8, and 3. According to our data HYP induces an extrinsic apoptosis pathway and no other types (necroptosis, necrosis, etc.) in B-CPAP cells. Moreover, CDH1 mRNA expression calculated to be up-regulated, and that of LGALS3 down-regulated in the B-CPAP cell line after treatment. Besides tumor cytotoxic activity, we suggest that HYP impedes with invasion and/or metastasis process.

A novel strategy for combination of clofarabine and pictilisib is synergistic in gastric cancer

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Drug sensitivity testing identified novel drugs like clofarabine effective in treating gastric cancer.

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mRNA sequencing can be used to identify agents with synergistic activity to a reference compound.

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Pictilisib sensitizes gastric cancer to clofarabine treatment through AKT inhibition.

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The combination of clofarabine and pictilisib inhibits tumor growth in cell lines and PDX models.

Gastric cancer (GC) is frequently characterized by resistance to standard chemotherapeutic regimens and poor clinical outcomes. We aimed to identify a novel therapeutic approach using drug sensitivity testing (DST) and our computational SynerySeq pipeline. DST of GC cell lines was performed with a library of 215 Federal Drug Administration (FDA) approved compounds and identified clofarabine as a potential therapeutic agent. RNA-sequencing (RNAseq) of clofarabine treated GC cells was analyzed according to our SynergySeq pipeline and identified pictilisib as a potential synergistic agent. Clonogenic survival and Annexin V assays demonstrated increased cell death with clofarabine and pictilisib combination treatment (P<0.01). The combination induced double strand breaks (DSB) as indicated by phosphorylated H2A histone family member X (γH2AX) immunofluorescence and western blot analysis (P<0.01). Pictilisib treatment inhibited the protein kinase B (AKT) cell survival pathway and promoted a pro-apoptotic phenotype as evidenced by quantitative real time polymerase chain reaction (qRT-PCR) analysis of the B-cell lymphoma 2 (BCL2) protein family members (P<0.01). Patient derived xenograft (PDX) data confirmed that the combination is more effective in abrogating tumor growth with prolonged survival than single-agent treatment (P<0.01). The novel combination of clofarabine and pictilisib in GC promotes DNA damage and inhibits key cell survival pathways to induce cell death beyond single-agent treatment.

Targeted Therapies in Cancer: To Be or Not to Be, Selective

Development of targeted therapies in recent years revealed several nonchemotherapeutic options for patients. Chief among targeted therapies is small molecule kinase inhibitors targeting key oncogenic signaling proteins. Through competitive and noncompetitive inhibition of these kinases, and therefore the pathways they activate, cancers can be slowed or completely eradicated, leading to partial or complete remissions for many cancer types. Unfortunately, for many patients, resistance to targeted therapies, such as kinase inhibitors, ultimately develops and can necessitate multiple lines of treatment. Drug resistance can either be de novo or acquired after months or years of drug exposure. Since resistance can be due to several unique mechanisms, there is no one-size-fits-all solution to this problem. However, combinations that target complimentary pathways or potential escape mechanisms appear to be more effective than sequential therapy. Combinations of single kinase inhibitors or alternately multikinase inhibitor drugs could be used to achieve this goal. Understanding how to efficiently target cancer cells and overcome resistance to prior lines of therapy became imperative to the success of cancer treatment. Due to the complexity of cancer, effective treatment options in the future will likely require mixing and matching these approaches in different cancer types and different disease stages.

High-Throughput Strategies for the Discovery of Anticancer Drugs by Targeting Transcriptional Reprogramming

Transcriptional reprogramming contributes to the progression and recurrence of cancer. However, the poorly elucidated mechanisms of transcriptional reprogramming in tumors make the development of effective drugs difficult, and gene expression signature is helpful for connecting genetic information and pharmacologic treatment. So far, there are two gene-expression signature-based high-throughput drug discovery approaches: L1000, which measures the mRNA transcript abundance of 978 "landmark" genes, and high-throughput sequencing-based high-throughput screening (HTS²); they are suitable for anticancer drug discovery by targeting transcriptional reprogramming. L1000 uses ligation-mediated amplification and hybridization to Luminex beads and highlights gene expression changes by detecting bead colors and fluorescence intensity of phycoerythrin signal. HTS² takes advantage of RNA-mediated oligonucleotide annealing, selection, and ligation, high throughput sequencing, to quantify gene expression changes by directly measuring gene sequences. This article summarizes technological principles and applications of L1000 and HTS², and discusses their advantages and limitations in anticancer drug discovery.

Prediction and identification of synergistic compound combinations against pancreatic cancer cells

Resistance to current therapies is common for pancreatic cancer and hence novel treatment options are urgently needed. In this work, we developed and validated a computational method to select synergistic compound combinations based on transcriptomic profiles from both the disease and compound side, combined with a pathway scoring system, which was then validated prospectively by testing 30 compounds (and their combinations) on PANC-1 cells. Some compounds selected as single agents showed lower GI50 values than the standard of care, gemcitabine. Compounds suggested as combination agents with standard therapy gemcitabine based on the best performing scoring system showed on average 2.82-5.18 times higher synergies compared to compounds that were predicted to be active as single agents. Examples of highly synergistic in vitro validated compound pairs include gemcitabine combined with Entinostat, thioridazine, loperamide, scriptaid and Saracatinib. Hence, the computational approach presented here was able to identify synergistic compound combinations against pancreatic cancer cells.

Artificial intelligence in early drug discovery enabling precision medicine

Introduction: Precision medicine is the concept of treating diseases based on environmental factors, lifestyles, and molecular profiles of patients. This approach has been found to increase success rates of clinical trials and accelerate drug approvals. However, current precision medicine applications in early drug discovery use only a handful of molecular biomarkers to make decisions, whilst clinics gear up to capture the full molecular landscape of patients in the near future. This deep multi-omics characterization demands new analysis strategies to identify appropriate treatment regimens, which we envision will be pioneered by artificial intelligence.Areas covered: In this review, the authors discuss the current state of drug discovery in precision medicine and present our vision of how artificial intelligence will impact biomarker discovery and drug design.Expert opinion: Precision medicine is expected to revolutionize modern medicine; however, its traditional form is focusing on a few biomarkers, thus not equipped to leverage the full power of molecular landscapes. For learning how the development of drugs can be tailored to the heterogeneity of patients across their molecular profiles, artificial intelligence algorithms are the next frontier in precision medicine and will enable a fully personalized approach in drug design, and thus ultimately impacting clinical practice.

Reconciling multiple connectivity scores for drug repurposing

The basis of several recent methods for drug repurposing is the key principle that an efficacious drug will reverse the disease molecular ‘signature’ with minimal side effects. This principle was defined and popularized by the influential ‘connectivity map’ study in 2006 regarding reversal relationships between disease- and drug-induced gene expression profiles, quantified by a disease-drug ‘connectivity score.’ Over the past 15 years, several studies have proposed variations in calculating connectivity scores toward improving accuracy and robustness in light of massive growth in reference drug profiles. However, these variations have been formulated inconsistently using various notations and terminologies even though they are based on a common set of conceptual and statistical ideas. 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In addition to providing clarity and deeper insights, this coherent definition of connectivity scores and their relationships provides a unified scheme that newer methods can adopt, enabling the computational drug-development community to compare and investigate different approaches easily. To facilitate the continuous and transparent integration of newer methods, this article will be available as a live document (https://jravilab.github.io/connectivity_scores) coupled with a GitHub repository (https://github.com/jravilab/connectivity_scores) that any researcher can build on and push changes to.

Prediction of Synergistic Drug Combinations for Prostate Cancer by Transcriptomic and Network Characteristics

Prostate cancer (PRAD) is a major cause of cancer-related deaths. Current monotherapies show limited efficacy due to often rapidly emerging resistance. Combination therapies could provide an alternative solution to address this problem with enhanced therapeutic effect, reduced cytotoxicity, and delayed the appearance of drug resistance. However, it is prohibitively cost and labor-intensive for the experimental approaches to pick out synergistic combinations from the millions of possibilities. Thus, it is highly desired to explore other efficient strategies to assist experimental researches. Inspired by the challenge, we construct the transcriptomics-based and network-based prediction models to quickly screen the potential drug combination for Prostate cancer, and further assess their performance by in vitro assays. The transcriptomics-based method screens nine possible combinations. However, the network-based method gives discrepancies for at least three drug pairs. Further experimental results indicate the dose-dependent effects of the three docetaxel-containing combinations, and confirm the synergistic effects of the other six combinations predicted by the transcriptomics-based model. For the network-based predictions, in vitro tests give opposite results to the two combinations (i.e. mitoxantrone-cyproheptadine and cabazitaxel-cyproheptadine). Namely, the transcriptomics-based method outperforms the network-based one for the specific disease like Prostate cancer, which provide guideline for selection of the computational methods in the drug combination screening. More importantly, six combinations (the three mitoxantrone-containing and the three cabazitaxel-containing combinations) are found to be promising candidates to synergistically conquer Prostate cancer.

Network-based screen in iPSC-derived cells reveals therapeutic candidate for heart valve disease

Mapping the gene-regulatory networks dysregulated in human disease would allow the design of network-correcting therapies that treat the core disease mechanism. However, small molecules are traditionally screened for their effects on one to several outputs at most, biasing discovery and limiting the likelihood of true disease-modifying drug candidates. Here, we developed a machine-learning approach to identify small molecules that broadly correct gene networks dysregulated in a human induced pluripotent stem cell (iPSC) disease model of a common form of heart disease involving the aortic valve (AV). Gene network correction by the most efficacious therapeutic candidate, XCT790, generalized to patient-derived primary AV cells and was sufficient to prevent and treat AV disease in vivo in a mouse model. This strategy, made feasible by human iPSC technology, network analysis, and machine learning, may represent an effective path for drug discovery.

Organoid Models of Glioblastoma and Their Role in Drug Discovery

Glioblastoma (GBM) is a devastating adult brain cancer with high rates of recurrence and treatment resistance. Cellular heterogeneity and extensive invasion of surrounding brain tissues are characteristic features of GBM that contribute to its intractability. Current GBM model systems do not recapitulate some of the complex features of GBM and have not produced sufficiently-effective treatments. This has cast doubt on the effectiveness of current GBM models and drug discovery paradigms. In search of alternative pre-clinical GBM models, various 3D organoid-based GBM model systems have been developed using human cells. The scalability of these systems and potential to more accurately model characteristic features of GBM, provide promising new avenues for pre-clinical GBM research and drug discovery efforts. Here, we review the current suite of organoid-GBM models, their individual strengths and weaknesses, and discuss their future applications with an emphasis on compound screening.

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.