Laryngeal cancer diagnosis via miRNA-based decision tree model

PURPOSE

Laryngeal cancer (LC) is the most common head and neck cancer, which often goes undiagnosed due to the inaccessible nature of current diagnosis methods in some parts of the world. Many recent studies have shown that microRNAs (miRNAs) are crucial biomarkers for a variety of cancers.

METHODS

In this study, we create a decision tree model for the diagnosis of laryngeal cancer using a created series of miRNA attributes, such as sequence-based characteristics, predicted miRNA target genes, and gene pathways. This series of attributes is extracted from both differentially expressed blood-based miRNAs in laryngeal cancer and random, non-associated with cancer miRNAs.

RESULTS

Several machine-learning (ML) algorithms were tested in the ML model, and the Hoeffding Tree classifier yields the highest accuracy (86.8%) in miRNAs-based recognition of laryngeal cancer. Furthermore, our model is validated with the independent laryngeal cancer datasets and can accurately diagnose laryngeal cancer with 86% accuracy. We also explored the biological relationships of the attributes used in our model to understand their relationship with cancer proliferation or suppression pathways.

CONCLUSION

Our study demonstrates that the proposed model and an inexpensive miRNA testing strategy have the potential to serve as an additional method for diagnosing laryngeal cancer.

Parkinson's Disease Diagnosis Using miRNA Biomarkers and Deep Learning

BACKGROUND

The current standard for Parkinson's disease (PD) diagnosis is often imprecise and expensive. However, the dysregulation patterns of microRNA (miRNA) hold potential as a reliable and effective non-invasive diagnosis of PD.

METHODS

We use data mining to elucidate new miRNA biomarkers and then develop a machine-learning (ML) model to diagnose PD based on these biomarkers.

RESULTS

The best-performing ML model, trained on filtered miRNA dysregulated in PD, was able to identify miRNA biomarkers with 95.65% accuracy. Through analysis of miRNA implicated in PD, thousands of descriptors reliant on gene targets were created that can be used to identify novel biomarkers and strengthen PD diagnosis.

CONCLUSIONS

The developed ML model based on miRNAs and their genomic pathway descriptors achieved high accuracies for the prediction of PD.

Diagnostics of Thyroid Cancer Using Machine Learning and Metabolomics

The objective of this research is, with the analysis of existing data of thyroid cancer (TC) metabolites, to develop a machine-learning model that can diagnose TC using metabolite biomarkers. Through data mining, pathway analysis, and machine learning (ML), the model was developed. We identified seven metabolic pathways related to TC: Pyrimidine metabolism, Tyrosine metabolism, Glycine, serine, and threonine metabolism, Pantothenate and CoA biosynthesis, Arginine biosynthesis, Phenylalanine metabolism, and Phenylalanine, tyrosine, and tryptophan biosynthesis. The ML classifications' accuracies were confirmed through 10-fold cross validation, and the most accurate classification was 87.30%. The metabolic pathways identified in relation to TC and the changes within such pathways can contribute to more pattern recognition for diagnostics of TC patients and assistance with TC screening. With independent testing, the model's accuracy for other unique TC metabolites was 92.31%. The results also point to a possibility for the development of using ML methods for TC diagnostics and further applications of ML in general cancer-related metabolite analysis.

Role of Senataxin in Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is a progressive, uncurable neurodegenerative disorder characterized by the degradation of motor neurons leading to muscle impairment, failure, and death. Senataxin, encoded by the SETX gene, is a human helicase protein whose mutations have been linked with ALS onset, particularly in its juvenile ALS4 form. Using senataxin's yeast homolog Sen1 as a model for study, it is suggested that senataxin's N-terminus interacts with RNA polymerase II, whilst its C-terminus engages in helicase activity. Senataxin is heavily involved in transcription regulation, termination, and R-loop resolution, enabled by recruitment and interactions with enzymes such as ubiquitin protein ligase SAN1 and ribonuclease H (RNase H). Senataxin also engages in DNA damage response (DDR), primarily interacting with the exosome subunit Rrp45. The Sen1 mutation E1597K, alongside the L389S and R2136H gain-of-function mutations to senataxin, is shown to cause negative structural and thus functional effects to the protein, thus contributing to a disruption in WT functions, motor neuron (MN) degeneration, and the manifestation of ALS clinical symptoms. This review corroborates and summarizes published papers concerning the structure and function of senataxin as well as the effects of their mutations in ALS pathology in order to compile current knowledge and provide a reference for future research. The findings compiled in this review are indicative of the experimental and therapeutic potential of senataxin and its mutations as a target in future ALS treatment/cure discovery, with some potential therapeutic routes also being discussed in the review.

Search of inhibitors of aldose reductase for treatment of diabetic cataracts using machine learning

Purpose

Patients with diabetes mellitus have an elevated chance of developing cataracts, a degenerative vision-impairing condition often needing surgery. The process of the reduction of glucose to sorbitol in the lens of the human eye that causes cataracts is managed by the Aldose Reductase Enzyme (AR), and it is been found that AR inhibitors may mitigate the onset of diabetic cataracts. There exists a large pool of natural and synthetic AR inhibitors that can prevent diabetic complications, and the development of a machine-learning (ML) prediction model may bring new AR inhibitors with better characteristics into clinical use.

Methods

Using known AR inhibitors and their chemical-physical descriptors we created the ML model for prediction of new AR inhibitors. The predicted inhibitors were tested by computational docking to the binding site of AR.

Results

Using cross-validation in order to find the most accurate ML model, we ended with final cross-validation accuracy of 90%. Computational docking testing of the predicted inhibitors gave a high level of correlation between the ML prediction score and binding free energy.

Conclusions

Currently known AR inhibitors are not used yet for patients for several reasons. We think that new predicted AR inhibitors have the potential to possess more favorable characteristics to be successfully implemented after clinical testing. Exploring new inhibitors can improve patient well-being and lower surgical complications all while decreasing long-term medical expenses.

Synergism in actions of HBV with aflatoxin in cancer development

Aflatoxin B1 (AFB1) is a fungal metabolite found in animal feeds and human foods. It is one of the most toxic and carcinogenic of aflatoxins and is classified as a Group 1 carcinogen. Dietary exposure to AFB1 and infection with chronic Hepatitis B Virus (HBV) make up two of the major risk factors for hepatocellular carcinoma (HCC). These two major risk factors raise the probability of synergism between the two agents. This review proposes some collaborative molecular mechanisms underlying the interaction between AFB1 and HBV in accelerating or magnifying the effects of HCC. The HBx viral protein is one of the main viral proteins of HBV and has many carcinogenic qualities that are involved with HCC. AFB1, when metabolized by CYP450, becomes AFB1-exo-8,9-epoxide (AFBO), an extremely toxic compound that can form adducts in DNA sequences and induce mutations. With possible synergisms that exist between HBV and AFB1 in mind, it is best to treat both agents simultaneously to reduce the risk by HCC.

Targeting of insulin receptor endocytosis as a treatment to insulin resistance

BACKGROUND

Insulin resistance is the decreased effectiveness of insulin receptor function during signaling of glucose uptake. Insulin receptors are regulated by endocytosis, a process that removes receptors from the cell surface to be marked for degradation or for re-use.

OBJECTIVES

Our goal was to discover insulin-resistance-related genes that play key roles in endocytosis which could serve as potential biological targets to enhance insulin sensitivity.

METHODS

The gene mutations related to insulin resistance were elucidated from ClinVar. These were used as the seed set. Using the GeneFriends program, the genes associated with this set were elucidated and used as an enriched set for the next step. The enriched gene set network was visualized by Cytoscape. After that, using the VisANT program, the most significant cluster of genes was identified. With the help of the DAVID program, the most important KEGG pathway corresponding to the gene cluster and insulin resistance was found. Eleven genes part of the KEGG endocytosis pathway were identified. Finally, using the ChEA3 program, seven transcription factors managing these genes were defined.

RESULTS

Thirty-two genes of pathogenic significance in insulin resistance were elucidated, and then co-expression data for these genes were utilized. These genes were organized into clusters, one of which was singled out for its high node count of 58 genes and low p-value (p = 4.117 × 10-7). DAVID Pathways, a functional annotation tool, helped identify a set of 11 genes from a single cluster associated with the endocytosis pathway related to insulin resistance. These genes (AMPH, BIN1, CBL, DNM1, DNM2, DNM3, ITCH, SH3GL1, SH3GL2, SH3GL3, and SH3KBP1) are all involved in either clathrin-mediated endocytosis of the insulin receptor (IR) or clathrin-independent endocytosis of insulin-resistance-related G protein-coupled receptors (GPCR). They represent prime therapeutic targets to improve insulin sensitivity through modulation of transmembrane cell signaling. Using the ChEA3 database, we also found seven transcription factors (REST, MYPOP, CAMTA2, MYT1L, ZBTB18, NKX6-2, and CXXC5) that control the expression of these 11 genes. Inhibiting these key transcription factors would be another strategy to downregulate endocytosis.

CONCLUSION

We believe that delaying removal of insulin receptors from the cell surface would prolong signaling of glucose uptake and counteract the symptoms of insulin resistance.

Two-Stage Deep-Learning Classifier for Diagnostics of Lung Cancer Using Metabolites

We developed a machine-learning system for the selective diagnostics of adenocarcinoma (AD), squamous cell carcinoma (SQ), and small-cell carcinoma lung (SC) cancers based on their metabolomic profiles. The system is organized as two-stage binary classifiers. The best accuracy for classification is 92%. We used the biomarkers sets that contain mostly metabolites related to cancer development. Compared to traditional methods, which exclude hierarchical classification, our method splits a challenging multiclass task into smaller tasks. This allows a two-stage classifier, which is more accurate in the scenario of lung cancer classification. Compared to traditional methods, such a "divide and conquer strategy" gives much more accurate and explainable results. Such methods, including our algorithm, allow for the systematic tracking of each computational step.

Biological functions of the Olig gene family in brain cancer and therapeutic targeting

The Olig genes encode members of the basic helix-loop-helix (bHLH) family of transcription factors. Olig1, Olig2, and Olig3 are expressed in both the developing and mature central nervous system (CNS) and regulate cellular specification and differentiation. Over the past decade extensive studies have established functional roles of Olig1 and Olig2 in development as well as in cancer. Olig2 overexpression drives glioma proliferation and resistance to radiation and chemotherapy. In this review, we summarize the biological functions of the Olig family in brain cancer and how targeting Olig family genes may have therapeutic benefit.

Small Molecule Decoys of Aggregation for Elimination of Aβ-Peptide Toxicity

Several lines of evidence suggest that a characteristic of the neuropathology of Alzheimer's disease (AD) is the aggregation of the amyloid beta peptides (Aβ), fragments of the human amyloid precursor protein (hAPP). The dominating species are the Aβ40 and Aβ42 fragments with 40 and 42 amino acids, respectively. Aβ initially forms soluble oligomers that continue to expand to protofibrils, suggestively the neurotoxic intermediates, and thereafter turn into insoluble fibrils that are markers of the disease. Using the powerful tool of pharmacophore simulation, we selected small molecules not known to possess central nervous system (CNS) activity but that might interact with Aβ aggregation, from the NCI Chemotherapeutic Agents Repository, Bethesda, MD. We assessed the activity of these compounds on Aβ aggregation using the thioflavin T fluorescence correlation spectroscopy (ThT-FCS) assay. Förster resonance energy transfer-based fluorescence correlation spectroscopy (FRET-FCS) was used to characterize the dose-dependent activity of selected compounds at an early stage of Aβ aggregation. Transmission electron microscopy (TEM) confirmed that the interfering substances block fibril formation and identified the macrostructures of Aβ aggregates formed in their presence. We first found three compounds generating protofibrils with branching and budding never observed in the control. One compound generated a two-dimensional sheet structure and another generated a double-stranded filament. Importantly, these compounds generating protofibrils with altered macrostructure protected against Aβ-induced toxicity in a cell model while showing no toxicity in a model of cognition in normal mice. The data suggest that the active compounds act as decoys turning the aggregation into nontoxic trajectories and pointing toward novel approaches to therapy.

Diagnostics of ovarian cancer via metabolite analysis and machine learning

Ovarian cancer (OC) is the second most common cancer of the female reproductive system. Due to the asymptomatic nature of early stages of OC and an increasingly poor prognosis in later stages, methods of screening for OC are much desired. Furthermore, screening and diagnosis processes, in order to justify use on asymptomatic patients, must be convenient and non-invasive. Recent developments in machine-learning technologies have made this possible via techniques in the field of metabolomics. The objective of this research was to use existing metabolomics data on OC and various analytic methods to develop a machine-learning model for the classification of potentially OC-related metabolite biomarkers. Pathway analysis and metabolite-set enrichment analysis were performed on gathered metabolite sets. Quantitative molecular descriptors were then used with various machine-learning classifiers for the diagnostics of OC using related metabolites. We elucidated that the metabolites associated with OC used for machine-learning models are involved in five metabolic pathways linked to OC: Nicotinate and Nicotinamide Metabolism, Glycolysis/Gluconeogenesis, Aminoacyl-tRNA Biosynthesis, Valine, Leucine and Isoleucine Biosynthesis, and Alanine, Aspartate and Glutamate Metabolism. Several classification models for the identification of OC using related metabolites were created and their accuracies were confirmed through testing with 10-fold cross-validation. The most accurate model was able to achieve 85.29% accuracy. The elucidation of biological pathways specific to OC using metabolic data and the observation of changes in these pathways in patients have the potential to contribute to the development of screening techniques for OC. Our results demonstrate the possibility of development of the machine-learning models for OC diagnostics using metabolomics data.

Repurposing of drugs for combined treatment of COVID-19 cytokine storm using machine learning

Severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) induced cytokine storm is the major cause of COVID-19 related deaths. Patients have been treated with drugs that work by inhibiting a specific protein partly responsible for the cytokines production. This approach provided very limited success, since there are multiple proteins involved in the complex cell signaling disease mechanisms. We targeted five proteins: Angiotensin II receptor type 1 (AT1R), A disintegrin and metalloprotease 17 (ADAM17), Nuclear Factor‑Kappa B (NF‑κB), Janus kinase 1 (JAK1) and Signal Transducer and Activator of Transcription 3 (STAT3), which are involved in the SARS‑CoV‑2 induced cytokine storm pathway. We developed machine-learning (ML) models for these five proteins, using known active inhibitors. After developing the model for each of these proteins, FDA-approved drugs were screened to find novel therapeutics for COVID‑19. We identified twenty drugs that are active for four proteins with predicted scores greater than 0.8 and eight drugs active for all five proteins with predicted scores over 0.85. Mitomycin C is the most active drug across all five proteins with an average prediction score of 0.886. For further validation of these results, we used the PyRx software to conduct protein-ligand docking experiments and calculated the binding affinity. The docking results support findings by the ML model. This research study predicted that several drugs can target multiple proteins simultaneously in cytokine storm-related pathway. These may be useful drugs to treat patients because these therapies can fight cytokine storm caused by the virus at multiple points of inhibition, leading to synergistically effective treatments.

The role of BAG3 in dilated cardiomyopathy and its association with Charcot-Marie-Tooth disease type 2

Bcl2-associated athanogene 3 (BAG3) is a multifunctional cochaperone responsible for protein quality control within cells. BAG3 interacts with chaperones HSPB8 and Hsp70 to transport misfolded proteins to the Microtubule Organizing Center (MTOC) and degrade them in autophagosomes in a process known as Chaperone Assisted Selective Autophagy (CASA). Mutations in the second conserved IPV motif of BAG3 are known to cause Dilated Cardiomyopathy (DCM) by inhibiting adequate removal of non-native proteins. The proline 209 to leucine (P209L) BAG3 mutant in particular causes the aggregation of BAG3 and misfolded proteins as well as the sequestration of essential chaperones. The exact mechanisms of protein aggregation in DCM are unknown. However, the similar presence of insoluble protein aggregates in Charcot-Marie-Tooth disease type 2 (CMT2) induced by the proline 182 to leucine (P182L) HSPB1 mutant points to a possible avenue for future research: IPV motif. In this review, we summarize the molecular mechanisms of CASA and the currently known pathological effects of mutated BAG3 in DCM. Additionally, we will provide insight on the importance of the IPV motif in protein aggregation by analyzing a potential association between DCM and CMT2.

Machine-Learning-Based Virtual Screening to Repurpose Drugs for Treatment of Candida albicans Infection

BACKGROUND

Approximately 30% of Candida genus isolates are resistant to all currently available antifungal drugs and it is highly important to develop new treatments. Additionally, many current drugs are toxic and cause unwanted side effects. 1,3-beta-glucan synthase is an essential enzyme that builds the cell walls of Candida.

OBJECTIVES

Targeting CaFKS1, a subunit of the synthase, could be used to fight Candida.

METHODS

In the present study, a machine-learning model based on chemical descriptors was trained to recognize drugs that inhibit CaFKS1. The model attained 96.72% accuracy for classifying between active and inactive drug compounds. Descriptors for FDA-approved and other drugs were calculated and the model was used to predict the potential activity of these drugs against CaFKS1.

RESULTS

Several drugs, including goserelin and icatibant, were detected as active with high confidence. Many of the drugs, interestingly, were gonadotrophin-releasing hormone (GnRH) antagonists or agonists. A literature search found that five of the predicted drugs inhibit Candida experimentally.

CONCLUSIONS

This study yields promising drugs to be repurposed to combat Candida albicans infection. Future steps include testing the drugs on fungal cells in vitro.

Alzheimer's Disease Diagnostics Using miRNA Biomarkers and Machine Learning

BACKGROUND

The current standard for Alzheimer's disease (AD) diagnosis is often imprecise, as with memory tests, and invasive or expensive, as with brain scans. However, the dysregulation patterns of miRNA in blood hold potential as useful biomarkers for the non-invasive diagnosis and even treatment of AD.

OBJECTIVE

The goal of this research is to elucidate new miRNA biomarkers and create a machine-learning (ML) model for the diagnosis of AD.

METHODS

We utilized pathways and target gene networks related to confirmed miRNA biomarkers in AD diagnosis and created multiple models to use for diagnostics based on the significant differences among miRNA expression between blood profiles (serum and plasma).

RESULTS

The best performing serum-based ML model, trained on filtered disease-specific miRNA datasets, was able to identify miRNA biomarkers with 92.0% accuracy and the best performing plasma-based ML model, trained on filtered disease-specific miRNA datasets, was able to identify miRNA biomarkers with 90.9% accuracy. Through analysis of AD implicated miRNA, thousands of descriptors reliant on target gene and pathways were created which can then be used to identify novel biomarkers and strengthen disease diagnosis.

CONCLUSION

Development of a ML model including miRNA and their genomic and pathway descriptors made it possible to achieve considerable accuracy for the prediction of AD.

Targeting Epigenetic Regulators Using Machine Learning: Potential Sirtuin 2 Inhibitors

Sirtuin 2 (SIRT2) is a nicotinamide adenine dinucleotide (NAD+)-dependent deacetylase that has been identified as a target for many diseases, including Parkinson’s disease (PD) and leukemia. Using 234 SIRT2 inhibitors from the ZINC15 database, we generated molecular descriptors with PaDEL and constructed a machine-learning (ML) model for the binary classification of SIRT2 inhibitors. To predict compounds with novel inhibitory mechanisms, we then applied the model on the ZINC15/FDA subset, yielding 107 potential SIRT2 inhibitors. For validation of these substances, we employed the binding analysis software AutoDock Vina to perform virtual screening, with which 43 compounds were considered best inhibitors at the [Formula: see text][Formula: see text]kcal/mol binding affinity threshold. Our results demonstrate the potential of ligand-based (LB) ML techniques in conjunction with receptor-based virtual screening (RBVS) to facilitate the drug discovery or repurposing.

Dynamic Cellular Cartography: Mapping the Local Determinants of Oligodendrocyte Transcription Factor 2 (OLIG2) Function in Live Cells Using Massively Parallel Fluorescence Correlation Spectroscopy Integrated with Fluorescence Lifetime Imaging Microscopy (mpFCS/FLIM)

Compartmentalization and integration of molecular processes through diffusion are basic mechanisms through which cells perform biological functions. To characterize these mechanisms in live cells, quantitative and ultrasensitive analytical methods with high spatial and temporal resolution are needed. Here, we present quantitative scanning-free confocal microscopy with single-molecule sensitivity, high temporal resolution (∼10 μs/frame), and fluorescence lifetime imaging capacity, developed by integrating massively parallel fluorescence correlation spectroscopy with fluorescence lifetime imaging microscopy (mpFCS/FLIM); we validate the method, use it to map in live cell location-specific variations in the concentration, diffusion, homodimerization, DNA binding, and local environment of the oligodendrocyte transcription factor 2 fused with the enhanced Green Fluorescent Protein (OLIG2-eGFP), and characterize the effects of an allosteric inhibitor of OLIG2 dimerization on these determinants of OLIG2 function. In particular, we show that cytoplasmic OLIG2-eGFP is largely monomeric and freely diffusing, with the fraction of freely diffusing OLIG2-eGFP molecules being f_D,free^cyt = (0.75 ± 0.10) and the diffusion time τ_D,free^cyt = (0.5 ± 0.3) ms. In contrast, OLIG2-eGFP homodimers are abundant in the cell nucleus, constituting ∼25% of the nuclear pool, some f_D,bound^nuc = (0.65 ± 0.10) of nuclear OLIG2-eGFP is bound to chromatin DNA, whereas freely moving OLIG2-eGFP molecules diffuse at the same rate as those in the cytoplasm, as evident from the lateral diffusion times τ_D,free^nuc = τ_D,free^cyt = (0.5 ± 0.3) ms. OLIG2-eGFP interactions with chromatin DNA, revealed through their influence on the apparent diffusion behavior of OLIG2-eGFP, τ_D,bound^nuc (850 ± 500) ms, are characterized by an apparent dissociation constant K_d,app^OLIG2-DNA = (45 ± 30) nM. The apparent dissociation constant of OLIG2-eGFP homodimers was estimated to be K_d,app^{(OLIG2-eGFP)2} ≈ 560 nM. The allosteric inhibitor of OLIG2 dimerization, compound NSC 50467, neither affects OLIG2-eGFP properties in the cytoplasm nor does it alter the overall cytoplasmic environment. In contrast, it significantly impedes OLIG2-eGFP homodimerization in the cell nucleus, increasing five-fold the apparent dissociation constant, K_{d,app,NSC50467}^{(OLIG2-eGFP)2} ≈ 3 μM, thus reducing homodimer levels to below 7% and effectively abolishing OLIG2-eGFP specific binding to chromatin DNA. The mpFCS/FLIM methodology has a myriad of applications in biomedical research and pharmaceutical industry. For example, it is indispensable for understanding how biological functions emerge through the dynamic integration of location-specific molecular processes and invaluable for drug development, as it allows us to quantitatively characterize the interactions of drugs with drug targets in live cells.

Implications of viral infection in cancer development

Since the identification of the first human oncogenic virus in 1964, viruses have been studied for their potential role in aiding the development of cancer. Through the modulation of cellular pathways associated with proliferation, immortalization, and inflammation, viral proteins can mimic the effect of driver mutations and contribute to transformation. Aside from the modulation of signaling pathways, the insertion of viral DNA into the host genome and the deregulation of cellular miRNAs represent two additional mechanisms implicated in viral oncogenesis. In this review, we will discuss the role of twelve different viruses on cancer development and how these viruses utilize the abovementioned mechanisms to influence oncogenesis. The identification of specific mechanisms behind viral transformation of human cells could further elucidate the process behind cancer development.

Potential SARS-CoV-2 protease Mpro inhibitors: repurposing FDA-approved drugs

Using as a template the crystal structure of the SARS-CoV-2 main protease, we developed a pharmacophore model of functional centers of the protease inhibitor-binding pocket. With this model, we conducted data mining of the conformational database of FDA-approved drugs. This search brought 64 compounds that can be potential inhibitors of the SARS-CoV-2 protease. The conformations of these compounds undergone 3D fingerprint similarity clusterization. Then we conducted docking of possible conformers of these drugs to the binding pocket of the protease. We also conducted the same docking of random compounds. Free energies of the docking interaction for the selected compounds were clearly lower than random compounds. Three of the selected compounds were carfilzomib, cyclosporine A, and azithromycin-the drugs that already are tested for COVID-19 treatment. Among the selected compounds are two HIV protease inhibitors and two hepatitis C protease inhibitors. We recommend testing of the selected compounds for treatment of COVID-19.

Interactions of large T-Antigen (LT) protein of polyomaviruses with p53 unfold their cancerogenic potential

Polyomaviruses such as Simian Virus 40 (SV40) and John Cunningham Virus (JCV) have been extensively studied for their potential role in aiding oncogenic transformation. One of the mechanisms through which they do this is by inactivating p53, a known tumor suppressor, through one of their viral proteins, large T-antigen (LT). However, these two viruses represent only a fraction of existing polyomaviruses. Using Clustal Omega, we aligned the protein sequences of LT for 12 different polyomaviruses and found high similarity across polyomavirus LT. We then utilized Molecular Operating Environment (MOE) v2019.01 to compare the binding of SV40 LT to p53 and p53 to DNA to more precisely define the mechanism with which SV40 LT inactivates p53. By binding to p53 residues essential to DNA binding, SV40 LT prevents the proper interaction of p53 with DNA and consequently its fulfillment of transcription factor functions. To further explore the possibility for other polyomavirus LT to do the same, we either retrieved existing 3D structures from RCSB Protein Data Bank or generated 3D homology models of other polyomavirus LT and modeled their interactions with p53. These models interacted with p53 in a similar manner as SV40 LT and provide further evidence of the potential of other polyomavirus LT to inactivate p53. This work demonstrates the importance of investigating the oncogenic potential of polyomaviruses and elucidates future targets for cancer treatment.Communicated by Ramaswamy H. Sarma.

Potential COVID-19 papain-like protease PLpro inhibitors: repurposing FDA-approved drugs

Using the crystal structure of SARS-CoV-2 papain-like protease (PLpro) as a template, we developed a pharmacophore model of functional centers of the PLpro inhibitor-binding pocket. With this model, we conducted data mining of the conformational database of FDA-approved drugs. This search identified 147 compounds that can be potential inhibitors of SARS-CoV-2 PLpro. The conformations of these compounds underwent 3D fingerprint similarity clusterization, followed by docking of possible conformers to the binding pocket of PLpro. Docking of random compounds to the binding pocket of protease was also done for comparison. Free energies of the docking interaction for the selected compounds were lower than for random compounds. The drug list obtained includes inhibitors of HIV, hepatitis C, and cytomegalovirus (CMV), as well as a set of drugs that have demonstrated some activity in MERS, SARS-CoV, and SARS-CoV-2 therapy. We recommend testing of the selected compounds for treatment of COVID-19.

Finding distinctions between oral cancer and periodontitis using saliva metabolites and machine learning

OBJECTIVE

The aim of this research is the study of metabolic pathways related to oral cancer and periodontitis along with development of machine-learning model for elucidation of these diseases based on saliva metabolites of patients.

METHODS

Data mining, metabolomic pathways analysis, study of metabolite-gene networks related to these diseases. Machine-learning and deep-learning methods for development of the model for recognition of oral cancer versus periodontitis, using patients' saliva.

RESULTS

The most accurate classifications between oral cancer and periodontitis were performed using neural networks, logistic regression and stochastic gradient descent confirmed by the separate 10-fold cross-validations. The best results were achieved by the deep-learning neural network with the TensorFlow program. Accuracy of the resulting model was 79.54%. The other methods, which did not rely on deep learning, were able to achieve comparable, although slightly worse results with respect to accuracy.

CONCLUSION

Our results demonstrate a possibility to distinguish oral cancer from periodontal disease by analysis the saliva metabolites of a patient, using machine-learning methods. These findings may be useful in the development of a non-invasive method to aid care providers in determining between oral cancer and periodontitis quickly and effectively.

Prediction of Premature Termination Codon Suppressing Compounds for Treatment of Duchenne Muscular Dystrophy Using Machine Learning

A significant percentage of Duchenne muscular dystrophy (DMD) cases are caused by premature termination codon (PTC) mutations in the dystrophin gene, leading to the production of a truncated, non-functional dystrophin polypeptide. PTC-suppressing compounds (PTCSC) have been developed in order to restore protein translation by allowing the incorporation of an amino acid in place of a stop codon. However, limitations exist in terms of efficacy and toxicity. To identify new compounds that have PTC-suppressing ability, we selected and clustered existing PTCSC, allowing for the construction of a common pharmacophore model. Machine learning (ML) and deep learning (DL) models were developed for prediction of new PTCSC based on known compounds. We conducted a search of the NCI compounds database using the pharmacophore-based model and a search of the DrugBank database using pharmacophore-based, ML and DL models. Sixteen drug compounds were selected as a consensus of pharmacophore-based, ML, and DL searches. Our results suggest notable correspondence of the pharmacophore-based, ML, and DL models in prediction of new PTC-suppressing compounds.

Machine learning model to predict oncologic outcomes for drugs in randomized clinical trials

Predicting oncologic outcome is challenging due to the diversity of cancer histologies and the complex network of underlying biological factors. In this study, we determine whether machine learning (ML) can extract meaningful associations between oncologic outcome and clinical trial, drug-related biomarker and molecular profile information. We analyzed therapeutic clinical trials corresponding to 1102 oncologic outcomes from 104 758 cancer patients with advanced colorectal adenocarcinoma, pancreatic adenocarcinoma, melanoma and nonsmall-cell lung cancer. For each intervention arm, a dataset with the following attributes was curated: line of treatment, the number of cytotoxic chemotherapies, small-molecule inhibitors, or monoclonal antibody agents, drug class, molecular alteration status of the clinical arm's population, cancer type, probability of drug sensitivity (PDS) (integrating the status of genomic, transcriptomic and proteomic biomarkers in the population of interest) and outcome. A total of 467 progression-free survival (PFS) and 369 overall survival (OS) data points were used as training sets to build our ML (random forest) model. Cross-validation sets were used for PFS and OS, obtaining correlation coefficients (r) of 0.82 and 0.70, respectively (outcome vs model's parameters). A total of 156 PFS and 110 OS data points were used as test sets. The Spearman correlation (r_s ) between predicted and actual outcomes was statistically significant (PFS: r_s = 0.879, OS: r_s = 0.878, P < .0001). The better outcome arm was predicted in 81% (PFS: N = 59/73, z = 5.24, P < .0001) and 71% (OS: N = 37/52, z = 2.91, P = .004) of randomized trials. The success of our algorithm to predict clinical outcome may be exploitable as a model to optimize clinical trial design with pharmaceutical agents.

Bovine leukemia virus relation to human breast cancer: Meta-analysis

Bovine leukemia virus (BLV) is a virus that infects cattle around the world and is very similar to the human T-cell leukemia virus (HTLV), which causes adult T-cell leukemia/lymphoma (ATL). Recently, presence of BLV DNA and protein was demonstrated in commercial bovine products and in humans. BLV DNA is generally found at higher rates in humans who have or will develop breast cancer, according to research done with subjects from several countries. These findings have led to a hypothesis that BLV transmission plays a role in breast cancer oncogenesis in humans. Here we summarize the current knowledge in the field.

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DNA of BLV is found at higher rates in humans who have or will develop breast cancer.

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Global analysis links the frequency of breast cancer cases to consumption of milk and beef in the countries studied.

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These findings have led to a hypothesis that BLV transmission plays a role in breast cancer oncogenesis.

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There are contradicting results in majority of cases can be explained by different experimental methods used.

Artificial intelligence in drug combination therapy

Currently, the development of medicines for complex diseases requires the development of combination drug therapies. It is necessary because in many cases, one drug cannot target all necessary points of intervention. For example, in cancer therapy, a physician often meets a patient having a genomic profile including more than five molecular aberrations. Drug combination therapy has been an area of interest for a while, for example the classical work of Loewe devoted to the synergism of drugs was published in 1928-and it is still used in calculations for optimal drug combinations. More recently, over the past several years, there has been an explosion in the available information related to the properties of drugs and the biomedical parameters of patients. For the drugs, hundreds of 2D and 3D molecular descriptors for medicines are now available, while for patients, large data sets related to genetic/proteomic and metabolomics profiles of the patients are now available, as well as the more traditional data relating to the histology, history of treatments, pretreatment state of the organism, etc. Moreover, during disease progression, the genetic profile can change. Thus, the ability to optimize drug combinations for each patient is rapidly moving beyond the comprehension and capabilities of an individual physician. This is the reason, that biomedical informatics methods have been developed and one of the more promising directions in this field is the application of artificial intelligence (AI). In this review, we discuss several AI methods that have been successfully implemented in several instances of combination drug therapy from HIV, hypertension, infectious diseases to cancer. The data clearly show that the combination of rule-based expert systems with machine learning algorithms may be promising direction in this field.

Deep-learning- and pharmacophore-based prediction of RAGE inhibitors

The receptor for advanced glycation end products (RAGE) has been identified as a therapeutic target in a host of pathological diseases, including Alzheimer's disease. RAGE is a target with no crystallographic data on inhibitors in complex with RAGE, multiple hypothesized binding modes, and small amounts of activity data. The main objective of this study was to demonstrate the efficacy of deep-learning (DL) techniques on small bioactivity datasets, and to identify candidate inhibitors of RAGE. We applied transfer learning in the form of a semi-supervised molecular representation in order to address small dataset problems. To validate the candidate inhibitors, we examined them using more computationally expensive pharmacophore-modeling and docking techniques. We created a strong classifier of RAGE activity, producing 79 candidate inhibitors. These candidates agreed with docking models and were shown to have no significant statistical difference from pharmacophore-based results. The transfer-learning techniques used allow DL to generalize chemical features from small bioactivity datasets to a broader library of compounds with high accuracy. Furthermore, the DL model is able to handle multiple binding modes without explicit instructions. Our results demonstrate the potential of a broad family of DL techniques on bioactivity predictions.

Artificial Intelligence in Drug Treatment

The most common applications of artificial intelligence (AI) in drug treatment have to do with matching patients to their optimal drug or combination of drugs, predicting drug-target or drug-drug interactions, and optimizing treatment protocols. This review outlines some of the recently developed AI methods aiding the drug treatment and administration process. Selection of the best drug(s) for a patient typically requires the integration of patient data, such as genetics or proteomics, with drug data, like compound chemical descriptors, to score the therapeutic efficacy of drugs. The prediction of drug interactions often relies on similarity metrics, assuming that drugs with similar structures or targets will have comparable behavior or may interfere with each other. Optimizing the dosage schedule for administration of drugs is performed using mathematical models to interpret pharmacokinetic and pharmacodynamic data. The recently developed and powerful models for each of these tasks are addressed, explained, and analyzed here.

ANKRD9 is a metabolically-controlled regulator of IMPDH2 abundance and macro-assembly

Members of a large family of Ankyrin Repeat Domain (ANKRD) proteins regulate numerous cellular processes by binding to specific protein targets and modulating their activity, stability, and other properties. The same ANKRD protein may interact with different targets and regulate distinct cellular pathways. The mechanisms responsible for switches in the ANKRDs' behavior are often unknown. We show that cells' metabolic state can markedly alter interactions of an ANKRD protein with its target and the functional outcomes of this interaction. ANKRD9 facilitates degradation of inosine monophosphate dehydrogenase 2 (IMPDH2), the rate-limiting enzyme in GTP biosynthesis. Under basal conditions ANKRD9 is largely segregated from the cytosolic IMPDH2 in vesicle-like structures. Upon nutrient limitation, ANKRD9 loses its vesicular pattern and assembles with IMPDH2 into rodlike filaments, in which IMPDH2 is stable. Inhibition of IMPDH2 activity with ribavirin favors ANKRD9 binding to IMPDH2 rods. The formation of ANKRD9/IMPDH2 rods is reversed by guanosine, which restores ANKRD9 associations with the vesicle-like structures. The conserved Cys¹⁰⁹Cys¹¹⁰ motif in ANKRD9 is required for the vesicle-to-rods transition as well as binding and regulation of IMPDH2. Oppositely to overexpression, ANKRD9 knockdown increases IMPDH2 levels and prevents formation of IMPDH2 rods upon nutrient limitation. Taken together, the results suggest that a guanosine-dependent metabolic switch determines the mode of ANKRD9 action toward IMPDH2.

Recognition of early and late stages of bladder cancer using metabolites and machine learning

INTRODUCTION

Bladder cancer (BCa) is one of the most common and aggressive cancers. It is the sixth most frequently occurring cancer in men and its rate of occurrence increases with age. The current method of BCa diagnosis includes a cystoscopy and biopsy. This process is expensive, unpleasant, and may have severe side effects. Recent growth in the power and accessibility of machine-learning software has allowed for the development of new, non-invasive diagnostic methods whose accuracy and sensitivity are uncompromising to function.

OBJECTIVES

The goal of this research was to elucidate the biomarkers including metabolites and corresponding genes for different stages of BCa, show their distinguishing and common features, and create a machine-learning model for classification of stages of BCa.

METHODS

Sets of metabolites for early and late stages, as well as common for both stages were analyzed using MetaboAnalyst and Ingenuity^® Pathway Analysis (IPA^®) software. Machine-learning methods were utilized in the development of a binary classifier for early- and late-stage metabolites of BCa. Metabolites were quantitatively characterized using EDragon 1.0 software. The two modeling methods used are Multilayer Perceptron (MLP) and Stochastic Gradient Descent (SGD) with a logistic regression loss function.

RESULTS

We explored metabolic pathways related to early-stage BCa (Galactose metabolism and Starch and sucrose metabolism) and to late-stage BCa (Glycine, serine, and threonine metabolism, Arginine and proline metabolism, Glycerophospholipid metabolism, and Galactose metabolism) as well as those common to both stages pathways. The central metabolite impacting the most cancerogenic genes (AKT, EGFR, MAPK3) in early stage is D-glucose, while late-stage BCa is characterized by significant fold changes in several metabolites: glycerol, choline, 13(S)-hydroxyoctadecadienoic acid, 2'-fucosyllactose. Insulin was also seen to play an important role in late stages of BCa. The best performing model was able to predict metabolite class with an accuracy of 82.54% and the area under precision-recall curve (PRC) of 0.84 on the training set. The same model was applied to three separate sets of metabolites obtained from public sources, one set of the late-stage metabolites and two sets of the early-stage metabolites. The model was better at predicting early-stage metabolites with accuracies of 72% (18/25) and 95% (19/20) on the early sets, and an accuracy of 65.45% (36/55) on the late-stage metabolite set.

CONCLUSION

By examining the biomarkers present in the urine samples of BCa patients as compared with normal patients, the biomarkers associated with this cancer can be pinpointed and lead to the elucidation of affected metabolic pathways that are specific to different stages of cancer. Development of machine-learning model including metabolites and their chemical descriptors made it possible to achieve considerable accuracy of prediction of stages of BCa.

Polycomb repressive 2 complex-Molecular mechanisms of function

Numerous molecular processes conduct epigenetic regulation of protein transcription to maintain cell specification. In this review, we discuss molecular mechanisms of the Polycomb group of proteins and its enzymatic role in epigenetics. More specifically, we focus on the Polycomb repressive complex 2 (PRC2) and the effects of its repressive marker. We have compiled information regarding the biological structure and how that impacts the stability of the complex. In addition, we examined functions of the individual core proteins of PRC2 in relation to the accessory proteins that interact with the complex. Lastly, we discuss the implications of unregulated and downregulated PRC2 activity in Alzheimer's disease and cancer and possible methods of treatment related to PRC2 regulation.

APOBEC-related mutagenesis and neo-peptide hydrophobicity: implications for response to immunotherapy

Tumor-associated neo-antigens are mutated peptides that allow the immune system to recognize the affected cell as foreign. Cells carrying excessive mutation load often develop mechanisms of tolerance. PD-L1/PD-1 checkpoint immunotherapy is a highly promising approach to overcome these protective signals and induce tumor shrinkage. Yet, the nature of the neo-antigens driving those beneficial responses remains unclear. Here, we show that APOBEC-related mutagenesis - a mechanism at the crossroads between anti-viral immunity and endogenous nucleic acid editing - increases neo-peptide hydrophobicity (a feature of immunogenicity), as demonstrated by in silico computation and in the TCGA pan-cancer cohort, where APOBEC-related mutagenesis was also strongly associated with immune marker expression. Moreover, APOBEC-related mutagenesis correlated with immunotherapy response in a cohort of 99 patients with diverse cancers, and this correlation was independent of the tumor mutation burden (TMB). Combining APOBEC-related mutagenesis estimate and TMB resulted in greater predictive ability than either parameter alone. Based on these results, further investigation of APOBEC-related mutagenesis as a marker of response to anti-cancer checkpoint blockade is warranted.

In Vitro Elucidation of Drug Combination Synergy in Treatment of Pancreatic Ductal Adenocarcinoma

BACKGROUND/AIM

Advances in therapies targeting proteins and pathways affected by genetic alterations has raised the possibility of personalized cancer treatments.

MATERIALS AND METHODS

The efficacy of targeting molecular aberrations was determined in the pancreatic ductal adenocarcinoma (PDAC) cell line, CAPAN2. Two mutations were targeted, KRAS (p.G12V) and ABL1 (p.G1060D), and cells were treated with regorafenib and trametinib, individually and in combination.

RESULTS

Exposure to either drug significantly increased cell death compared to the current standard of care, gemcitabine. Treatment with combinations of the drugs led to significant increases in cell death compared to either monotherapy. Strong additive/synergistic interactions were observed across a range of dosages and ratios, reducing dose requirements with potential clinical relevance.

CONCLUSION

The data obtained in this PDAC cell model: i) support the use of matched monotherapies; ii) indicate the effectiveness of matched combination therapies; and iii) provide potential proof-of-concept for precision medicine approach to cancer treatment.

Next-Generation Sequencing of Circulating Tumor DNA Reveals Frequent Alterations in Advanced Hepatocellular Carcinoma

This article reports unique aspects of the management of hepatocellular carcinoma. The study aimed to determine if next‐generation sequencing of blood‐derived circulating tumor DNA from patients with hepatocellular carcinoma could identify actionable somatic molecular alterations. Illustrative examples of treated patients and of in silico molecular dynamic simulation to reveal genomic variant function are included.

Background.

Because imaging has a high sensitivity to diagnose hepatocellular carcinoma (HCC) and tissue biopsies carry risks such as bleeding, the latter are often not performed in HCC. Blood‐derived circulating tumor DNA (ctDNA) analysis can identify somatic alterations, but its utility has not been characterized in HCC.

Materials and Methods.

We evaluated 14 patients with advanced HCC (digital ctDNA sequencing [68 genes]). Mutant relative to wild‐type allele fraction was calculated.

Results.

All patients (100%) had somatic alterations (median = 3 alterations/patient [range, 1–8]); median mutant allele fraction, 0.29% (range, 0.1%–37.77%). Mutations were identified in several genes: TP53 (57% of patients), CTNNB1 (29%), PTEN (7%), CDKN2A (7%), ARID1A (7%), and MET (7%); amplifications, in CDK6 (14%), EGFR (14%), MYC (14%), BRAF (7%), RAF1 (7%), FGFR1 (7%), CCNE1 (7%), PIK3CA (7%), and ERBB2/HER2 (7%). Eleven patients (79%) had ≥1 theoretically actionable alteration. No two patients had identical genomic portfolios, suggesting the need for customized treatment. A patient with a CDKN2A‐inactivating and a CTNNB1‐activating mutation received matched treatment: palbociclib (CDK4/6 inhibitor) and celecoxib (COX‐2/Wnt inhibitor); des‐gamma‐carboxy prothrombin level decreased by 84% at 2 months (1,410 to 242 ng/mL [normal: ≤7.4 ng/mL]; alpha fetoprotein [AFP] low at baseline). A patient with a PTEN‐inactivating and a MET‐activating mutation (an effect suggested by in silico molecular dynamic simulations) received sirolimus (mechanistic target of rapamycin inhibitor) and cabozantinib (MET inhibitor); AFP declined by 63% (8,320 to 3,045 ng/mL [normal: 0–15 ng/mL]).

Conclusion.

ctDNA derived from noninvasive blood tests can provide exploitable genomic profiles in patients with HCC.

Implications for Practice.

This study reports that blood‐derived circulating tumor DNA can provide therapeutically exploitable genomic profiles in hepatocellular cancer, a malignancy that is known to be difficult to biopsy.

Genomic Landscape of Cell-Free DNA in Patients with Colorectal Cancer

"Liquid biopsy" approaches analyzing cell-free DNA (cfDNA) from the blood of patients with cancer are increasingly utilized in clinical practice. However, it is not yet known whether cfDNA sequencing from large cohorts of patients with cancer can detect genomic alterations at frequencies similar to those observed by direct tumor sequencing, and whether this approach can generate novel insights. Here, we report next-generation sequencing data from cfDNA of 1,397 patients with colorectal cancer. Overall, frequencies of genomic alterations detected in cfDNA were comparable to those observed in three independent tissue-based colorectal cancer sequencing compendia. Our analysis also identified a novel cluster of extracellular domain (ECD) mutations in EGFR, mediating resistance by blocking binding of anti-EGFR antibodies. Patients with EGFR ECD mutations displayed striking tumor heterogeneity, with 91% harboring multiple distinct resistance alterations (range, 1-13; median, 4). These results suggest that cfDNA profiling can effectively define the genomic landscape of cancer and yield important biological insights.Significance: This study provides one of the first examples of how large-scale genomic profiling of cfDNA from patients with colorectal cancer can detect genomic alterations at frequencies comparable to those observed by direct tumor sequencing. Sequencing of cfDNA also generated insights into tumor heterogeneity and therapeutic resistance and identified novel EGFR ectodomain mutations. Cancer Discov; 8(2); 164-73. ©2017 AACR.This article is highlighted in the In This Issue feature, p. 127.

Cripto stabilizes GRP78 on the cell membrane

The ER resident chaperone molecule GRP78 has been shown to translocate to the cell surface where it associates with Cripto and signals cell growth, playing a still partially understood role in tumorigenesis. Consequently, a better understanding of GRP78 topology and structure at the surface of cancer cells represents an important step in the development of a new class of therapeutics. Here, we used a set of programs for creation of a complex containing GRP78 and Cripto proteins. We elucidated possible interactions of GRP78, Cripto, and their complex with the membrane. Using molecular dynamics simulations, we demonstrated that Cripto binding to GRP78 completely changes the dynamics of its behavior on the membrane, not allowing GRP78 to disconnect from it, thus enabling GRP78 tumorigenic functions.

Multiple spatially related pharmacophores define small molecule inhibitors of OLIG2 in glioblastoma

Transcription factors (TFs) are a major class of protein signaling molecules that play key cellular roles in cancers such as the highly lethal brain cancer—glioblastoma (GBM). However, the development of specific TF inhibitors has proved difficult owing to expansive protein-protein interfaces and the absence of hydrophobic pockets. We uniquely defined the dimerization surface as an expansive parental pharmacophore comprised of several regional daughter pharmacophores. We targeted the OLIG2 TF which is essential for GBM survival and growth, we hypothesized that small molecules able to fit each subpharmacophore would inhibit OLIG2 activation. The most active compound was OLIG2 selective, it entered the brain, and it exhibited potent anti-GBM activity in cell-based assays and in pre-clinical mouse orthotopic models. These data suggest that (1) our multiple pharmacophore approach warrants further investigation, and (2) our most potent compounds merit detailed pharmacodynamic, biophysical, and mechanistic characterization for potential preclinical development as GBM therapeutics.

BRAF mutation as a novel driver of eosinophilic cystitis

Eosinophilic cystitis is a rare manifestation of hypereosinophilia and a cause of morbidity, including dysuria and hematuria. Although some cases can be attributed to infection or allergy, most cases are assessed to be idiopathic and treated with corticosteroids. However, hypereosinophilia can also be due to actionable clonal molecular alterations in the haematopoietic cells, similar to other myeloproliferative neoplasms. Common mutations associated with eosonophilic syndromes are of platelet-derived growth factor receptor α or β or c-kit, though other pathogenic mutations have been found by next generation sequencing. Determination of a specific mutation may therefore identify clonality and refine treatment of some cases. Here we review the molecular features of eosinophilic disorders. We also describe the use of a liquid biopsy of circulating cell-free DNA in the workup of a case of eosinophilic cystitis in which next generation sequencing of cell-free DNA showed a BRAF I463T mutation. In silico modeling supports the functional impact and potential clinical relevance of BRAF I463T.

High expression of PD-1 ligands is associated with kataegis mutational signature and APOBEC3 alterations

Immunotherapy with checkpoint inhibitors, such as antibodies blocking the programmed cell-death receptor-1 (PD-1), has resulted in remarkable responses in patients having traditionally refractory cancers. Although response to PD-1 inhibitors correlates with PD-1 ligand (PD-L1 or PD-L2) expression, PD-1 ligand positivity represents only a part of the predictive model necessary for selecting patients predisposed to respond to immunotherapy. We used all genomic, transcriptomic, proteomic and phenotypic data related to 8,475 pan-cancer samples available in The Cancer Genome Atlas (TCGA) and conducted a logistic regression analysis based on a large set of variables, such as microsatellite instability (MSI-H), mismatch repair (MMR) alterations, polymerase δ (POLD1) and polymerase ε (POLE) mutations, activation-induced/apolipoprotein-B editing cytidine deaminases (AID/APOBEC) alterations, lymphocyte markers and mutation burden estimates to determine independent factors that associate with PD-1 ligand overexpression. PD-1 ligand overexpression was independently and significantly correlated with overexpression of and mutations in APOBEC3 paralogs. Additionally, while high tumor mutation burden and overexpression of PD-L1 have been previously correlated with each other, we demonstrate that the specific mutation pattern caused by APOBEC enzymes and called kataegis—rather than overall mutation burden, MSI-H or MMR alterations—correlates independently with PD-L1/PD-L2 expression. These observations suggest that APOBEC3 alterations, APOBEC3 overexpression and kataegis play an important role in the regulation of PD-1 ligand overexpression, and thus, their relationship with immune checkpoint inhibitor response warrants exploration.

A de novo compound targeting α-synuclein improves deficits in models of Parkinson's disease

Abnormal accumulation and propagation of the neuronal protein α-synuclein has been hypothesized to underlie the pathogenesis of Parkinson's disease, dementia with Lewy bodies and multiple system atrophy. Here we report a de novo-developed compound (NPT100-18A) that reduces α-synuclein toxicity through a novel mechanism that involves displacing α-synuclein from the membrane. This compound interacts with a domain in the C-terminus of α-synuclein. The E83R mutation reduces the compound interaction with the 80-90 amino acid region of α-synuclein and prevents the effects of NPT100-18A. In vitro studies showed that NPT100-18A reduced the formation of wild-type α-synuclein oligomers in membranes, reduced the neuronal accumulation of α-synuclein, and decreased markers of cell toxicity. In vivo studies were conducted in three different α-synuclein transgenic rodent models. Treatment with NPT100-18A ameliorated motor deficits in mThy1 wild-type α-synuclein transgenic mice in a dose-dependent manner at two independent institutions. Neuropathological examination showed that NPT100-18A decreased the accumulation of proteinase K-resistant α-synuclein aggregates in the CNS and was accompanied by the normalization of neuronal and inflammatory markers. These results were confirmed in a mutant line of α-synuclein transgenic mice that is prone to generate oligomers. In vivo imaging studies of α-synuclein-GFP transgenic mice using two-photon microscopy showed that NPT100-18A reduced the cortical synaptic accumulation of α-synuclein within 1 h post-administration. Taken together, these studies support the notion that altering the interaction of α-synuclein with the membrane might be a feasible therapeutic approach for developing new disease-modifying treatments of Parkinson's disease and other synucleinopathies.

Molecular mechanisms of OLIG2 transcription factor in brain cancer

Oligodendrocyte lineage transcription factor 2 (OLIG2) plays a pivotal role in glioma development. Here we conducted a comprehensive study of the critical gene regulatory networks involving OLIG2. These include the networks responsible for OLIG2 expression, its translocation to nucleus, cell cycle, epigenetic regulation, and Rho-pathway interactions. We described positive feedback loops including OLIG2: loops of epigenetic regulation and loops involving receptor tyrosine kinases. These loops may be responsible for the prolonged oncogenic activity of OLIG2. The proposed schemes for epigenetic regulation of the gene networks involving OLIG2 are confirmed by patient survival (Kaplan-Meier) curves based on the cancer genome atlas (TCGA) datasets. Finally, we elucidate the Coherent-Gene Modules (CGMs) networks-framework of OLIG2 involvement in cancer. We showed that genes interacting with OLIG2 formed eight CGMs having a set of intermodular connections. We showed also that among the genes involved in these modules the most connected hub is EGFR, then, on lower level, HSP90 and CALM1, followed by three lower levels including epigenetic genes KDM1A and NCOR1. The genes on the six upper levels of the hierarchy are involved in interconnections of all eight CGMs and organize functionally defined gene-signaling subnetworks having specific functions. For example, CGM1 is involved in epigenetic control. CGM2 is significantly related to cell proliferation and differentiation. CGM3 includes a number of interconnected helix-loop-helix transcription factors (bHLH) including OLIG2. Many of these TFs are partially controlled by OLIG2. The CGM4 is involved in PDGF-related: angiogenesis, tumor cell proliferation and differentiation. These analyses provide testable hypotheses and approaches to inhibit OLIG2 pathway and relevant feed-forward and feedback loops to be interrogated. This broad approach can be applied to other TFs.

Cisplatin inhibits MEK1/2

Cisplatin (cDDP) is known to bind to the CXXC motif of proteins containing a ferrodoxin-like fold but little is known about its ability to interact with other Cu-binding proteins. MEK1/2 has recently been identified as a Cu-dependent enzyme that does not contain a CXXC motif. We found that cDDP bound to and inhibited the activity of recombinant MEK1 with an IC₅₀ of 0.28 μM and MEK1/2 in whole cells with an IC₅₀ of 37.4 μM. The inhibition of MEK1/2 was relieved by both Cu⁺¹ and Cu⁺² in a concentration-dependent manner. cDDP did not inhibit the upstream pathways responsible for activating MEK1/2, and did not cause an acute depletion of cellular Cu that could account for the reduction in MEK1/2 activity. cDDP was found to bind MEK1/2 in whole cells and the extent of binding was augmented by supplementary Cu and reduced by Cu chelation. Molecular modeling predicts 3 Cu and cDDP binding sites and quantum chemistry calculations indicate that cDDP would be expected to displace Cu from each of these sites. We conclude that, at clinically relevant concentrations, cDDP binds to and inhibits MEK1/2 and that both the binding and inhibitory activity are related to its interaction with Cu bound to MEK1/2. This may provide the basis for useful interactions of cDDP with other drugs that inhibit MAPK pathway signaling.

Molecular determinants of drug-specific sensitivity for epidermal growth factor receptor (EGFR) exon 19 and 20 mutants in non-small cell lung cancer

We hypothesized that aberrations activating epidermal growth factor receptor (EGFR) via dimerization would be more sensitive to anti-dimerization agents (e.g., cetuximab). EGFR exon 19 abnormalities (L747_A750del; deletes amino acids LREA) respond to reversible EGFR kinase inhibitors (TKIs). Exon 20 in-frame insertions and/or duplications (codons 767 to 774) and T790M mutations are clinically resistant to reversible/some irreversible TKIs. Their impact on protein function/therapeutic actionability are not fully elucidated.In our study, the index patient with non-small cell lung cancer (NSCLC) harbored EGFR D770_P772del_insKG (exon 20). A twenty patient trial (NSCLC cohort) (cetuximab-based regimen) included two participants with EGFR TKI-resistant mutations ((i) exon 20 D770>GY; and (ii) exon 19 LREA plus exon 20 T790M mutations). Structural modeling predicted that EGFR exon 20 anomalies (D770_P772del_insKG and D770>GY), but not T790M mutations, stabilize the active dimer configuration by increasing the interaction between the kinase domains, hence sensitizing to an agent preventing dimerization. Consistent with predictions, the two patients harboring D770_P772del_insKG and D770>GY, respectively, responded to an EGFR antibody (cetuximab)-based regimen; the T790M-bearing patient showed no response to cetuximab combined with erlotinib. In silico modeling merits investigation of its ability to optimize therapeutic selection based on structural/functional implications of different aberrations within the same gene.

Neuroprotective effects of the anti-cancer drug sunitinib in models of HIV neurotoxicity suggests potential for the treatment of neurodegenerative disorders

BACKGROUND AND PURPOSE

Anti-retrovirals have improved and extended the life expectancy of patients with HIV. However, as this population ages, the prevalence of cognitive changes is increasing. Aberrant activation of kinases, such as receptor tyrosine kinases (RTKs) and cyclin-dependent kinase 5 (CDK5), play a role in the mechanisms of HIV neurotoxicity. Inhibitors of CDK5, such as roscovitine, have neuroprotective effects; however, CNS penetration is low. Interestingly, tyrosine kinase inhibitors (TKIs) display some CDK inhibitory activity and ability to cross the blood-brain barrier.

EXPERIMENTAL APPROACH

We screened a small group of known TKIs for a candidate with additional CDK5 inhibitory activity and tested the efficacy of the candidate in in vitro and in vivo models of HIV-gp120 neurotoxicity.

KEY RESULTS

Among 12 different compounds, sunitinib inhibited CDK5 with an IC50 of 4.2 μM. In silico analysis revealed that, similarly to roscovitine, sunitinib fitted 6 of 10 features of the CDK5 pharmacophore. In a cell-based model, sunitinib reduced CDK5 phosphorylation (pCDK5), calpain-dependent p35/p25 conversion and protected neuronal cells from the toxic effects of gp120. In glial fibrillary acidic protein-gp120 transgenic (tg) mice, sunitinib reduced levels of pCDK5, p35/p25 and phosphorylated tau protein, along with amelioration of the neurodegenerative pathology.

CONCLUSIONS AND IMPLICATIONS

Compounds such as sunitinib with dual kinase inhibitory activity could ameliorate the cognitive impairment associated with chronic HIV infection of the CNS. Moreover, repositioning existing low MW compounds holds promise for the treatment of patients with neurodegenerative disorders.

The copper transporter 1 (CTR1) is required to maintain the stability of copper transporter 2 (CTR2)

Mammalian cells have two influx Cu transporters that form trimers in membranes. CTR1 is the high affinity transporter that resides largely in the plasma membrane, and CTR2 is the low affinity transporter that is primarily associated with vesicular structures inside the cell. The major differences between CTR1 and CTR2 are that CTR1 contains a HIS/MET-rich domain N-terminal of the METS that participate in the first two stacked rings that form the pore, and a longer C-terminal tail that includes a Cu binding HIS-CYS-HIS (HCH) motif right at the end. It has been reported that CTR1 and CTR2 are physically associated with each other in the cell. We used the CRISPR-Cas9 technology to knock out either CTR1 or CTR2 in fully malignant HEK293T and OVCAR8 human ovarian cancer cells to investigate the interaction of CTR1 and CTR2. We report here that the level of CTR2 protein is markedly decreased in CTR1 knockout clones while the CTR2 transcript level remains unchanged. CTR2 was found to be highly ubiquitinated in the CTR1 knock out cells, and inhibition of the proteasome prevented the degradation of CTR2 when CTR1 was not present while inhibition of autophagy had no effect. Re-expression of CTR1 rescued CTR2 from degradation in the CTR1 knockout cells. We conclude that CTR1 is essential to maintain the stability of CTR2 and that in the absence of CTR1 CTR2 is degraded by the proteasome. This reinforces the concept that the functions of CTR1 and CTR2 are inter-dependent within the Cu homeostasis system.