NEXGB: A Network Embedding Framework for Anticancer Drug Combination Prediction

Compared to single-drug therapy, drug combinations have shown great potential in cancer treatment. Most of the current methods employ genomic data and chemical information to construct drug–cancer cell line features, but there is still a need to explore methods to combine topological information in the protein interaction network (PPI). Therefore, we propose a network-embedding-based prediction model, NEXGB, which integrates the corresponding protein modules of drug–cancer cell lines with PPI network information. NEXGB extracts the topological features of each protein node in a PPI network by struc2vec. Then, we combine the topological features with the target protein information of drug–cancer cell lines, to generate drug features and cancer cell line features, and utilize extreme gradient boosting (XGBoost) to predict the synergistic relationship between drug combinations and cancer cell lines. We apply our model on two recently developed datasets, the Oncology-Screen dataset (Oncology-Screen) and the large drug combination dataset (DrugCombDB). The experimental results show that NEXGB outperforms five current methods, and it effectively improves the predictive power in discovering relationships between drug combinations and cancer cell lines. This further demonstrates that the network information is valid for detecting combination therapies for cancer and other complex diseases.     

Performance Comparisons of AlexNet and GoogLeNet in Cell Growth Inhibition IC50 Prediction

Drug responses in cancer are diverse due to heterogenous genomic profiles. Drug responsiveness prediction is important in clinical response to specific cancer treatments. Recently, multi-class drug responsiveness models based on deep learning (DL) models using molecular fingerprints and mutation statuses have emerged. However, for multi-class models for drug responsiveness prediction, comparisons between convolution neural network (CNN) models (e.g., AlexNet and GoogLeNet) have not been performed. Therefore, in this study, we compared the two CNN models, GoogLeNet and AlexNet, along with the least absolute shrinkage and selection operator (LASSO) model as a baseline model. We constructed the models by taking drug molecular fingerprints of drugs and cell line mutation statuses, as input, to predict high-, intermediate-, and low-class for half-maximal inhibitory concentration (IC50) values of the drugs in the cancer cell lines. Additionally, we compared the models in breast cancer patients as well as in an independent gastric cancer cell line drug responsiveness data. We measured the model performance based on the area under receiver operating characteristic (ROC) curves (AUROC) value. In this study, we compared CNN models for multi-class drug responsiveness prediction. The AlexNet and GoogLeNet showed better performances in comparison to LASSO. Thus, DL models will be useful tools for precision oncology in terms of drug responsiveness prediction.     

Inferred Ancestral Origin of Cancer Cell Lines Associates with Differential Drug Response

Disparities between risk, treatment outcomes and survival rates in cancer patients across the world may be attributed to socioeconomic factors. In addition, the role of ancestry is frequently discussed. In preclinical studies, high-throughput drug screens in cancer cell lines have empowered the identification of clinically relevant molecular biomarkers of drug sensitivity; however, the genetic ancestry from tissue donors has been largely neglected in this setting. In order to address this, here, we show that the inferred ancestry of cancer cell lines is conserved and may impact drug response in patients as a predictive covariate in high-throughput drug screens. We found that there are differential drug responses between European and East Asian ancestries, especially when treated with PI3K/mTOR inhibitors. Our finding emphasizes a new angle in precision medicine, as cancer intervention strategies should consider the germline landscape, thereby reducing the failure rate of clinical trials.     

De Novo Prediction of Drug Targets and Candidates by Chemical Similarity-Guided Network-Based Inference

Identifying drug–target interactions is a crucial step in discovering novel drugs and for drug repositioning. Network-based methods have shown great potential thanks to the straightforward integration of information from different sources and the possibility of extracting novel information from the graph topology. However, despite recent advances, there is still an urgent need for efficient and robust prediction methods. Here, we present SimSpread, a novel method that combines network-based inference with chemical similarity. This method employs a tripartite drug–drug–target network constructed from protein–ligand interaction annotations and drug–drug chemical similarity on which a resource-spreading algorithm predicts potential biological targets for both known or failed drugs and novel compounds. We describe small molecules as vectors of similarity indices to other compounds, thereby providing a flexible means to explore diverse molecular representations. We show that our proposed method achieves high prediction performance through multiple cross-validation and time-split validation procedures over a series of datasets. In addition, we demonstrate that our method performed a balanced exploration of both chemical ligand space (scaffold hopping) and biological target space (target hopping). Our results suggest robust and balanced performance, and our method may be useful for predicting drug targets, virtual screening, and drug repositioning.     

TMB in NSCLC: A Broken Dream?

Although immune checkpoint inhibitors have changed the treatment paradigm of a variety of cancers, including non-small-cell lung cancer, not all patients respond to immunotherapy in the same way. Predictive biomarkers for patient selection are thus needed. Tumor mutation burden (TMB), defined as the total number of somatic/acquired mutations per coding area of a tumor genome (Mut/Mb), has emerged as a potential predictive biomarker of response to immune checkpoint inhibitors. We found that the limited use of TMB in clinical practice is due to the difficulty in its detection and compounded by several different biological, methodological and economic issues. The incorporation of both TMB and PD-L1 expression or other biomarkers into multivariable predictive models could result in greater predictive power.     

Accurate Prediction of Epigenetic Multi-Targets with Graph Neural Network-Based Feature Extraction

Epigenetics, referring to genetic modifications that change gene expression, but which are not encoded in DNA, has been shown to be related to oncology, with the potential to influence associated treatments. As such, epigenetic drugs comprise an important new field in cancer therapy; however, drug development is a high-cost and time-consuming procedure. Different epigenetic modifications, such as mutations in DNA methyltransferase and somatic mutations in core histone genes that lead to a global loss of the histone modifications, have innumerable relationships. In this article, we propose a graph neural network-based model for the extraction of molecular features, thus reducing the computational requirements. Through integration with a popular and efficient supervised learner, our model achieves higher prediction accuracy in both single- and multi-target tasks and can determine the pleiotropy associated with drugs, providing theoretical support for drug combination and discovery research.     

Sample-Specific Perturbation of Gene Interactions Identifies Pancreatic Cancer Subtypes

Pancreatic cancer is a highly fatal disease and an increasing common cause of cancer mortality. Mounting evidence now indicates that molecular heterogeneity in pancreatic cancer significantly impacts its clinical features. However, the dynamic nature of gene expression pattern makes it difficult to rely solely on gene expression alterations to estimate disease status. By contrast, biological networks tend to be more stable over time under different situations. In this study, we used a gene interaction network from a new point of view to explore the subtypes of pancreatic cancer based on individual-specific edge perturbations calculated by relative gene expression value. Our study shows that pancreatic cancer patients from the TCGA database could be separated into four subtypes based on gene interaction perturbations at the individual level. The new network-based subtypes of pancreatic cancer exhibited substantial heterogeneity in many aspects, including prognosis, phenotypic traits, genetic mutations, the abundance of infiltrating immune cell, and predictive therapeutic efficacy (chemosensitivity and immunotherapy efficacy). The new network-based subtypes were closely related to previous reported molecular subtypes of pancreatic cancer. This work helps us to better understand the heterogeneity and mechanisms of pancreatic cancer from a network perspective.     

Comprehensive Evaluation of Biological Effects of Pentathiepins on Various Human Cancer Cell Lines and Insights into Their Mode of Action

Pentathiepins are polysulfur-containing compounds that exert antiproliferative and cytotoxic activity in cancer cells, induce oxidative stress and apoptosis, and inhibit glutathione peroxidase (GPx1). This renders them promising candidates for anticancer drug development. However, the biological effects and how they intertwine have not yet been systematically assessed in diverse cancer cell lines. In this study, six novel pentathiepins were synthesized to suit particular requirements such as fluorescent properties or improved water solubility. Structural elucidation by X-ray crystallography was successful for three derivatives. All six underwent extensive biological evaluation in 14 human cancer cell lines. These studies included investigating the inhibition of GPx1 and cell proliferation, cytotoxicity, and the induction of ROS and DNA strand breaks. Furthermore, selected hallmarks of apoptosis and the impact on cell cycle progression were studied. All six pentathiepins exerted high cytotoxic and antiproliferative activity, while five also strongly inhibited GPx1. There is a clear connection between the potential to provoke oxidative stress and damage to DNA in the form of single- and double-strand breaks. Additionally, these studies support apoptosis but not ferroptosis as the mechanism of cell death in some of the cell lines. As the various pentathiepins give rise to different biological responses, modulation of the biological effects depends on the distinct chemical structures fused to the sulfur ring. This may allow for an optimization of the anticancer activity of pentathiepins in the future.     

非高斯建模方法的间歇过程的在线预测监控和预测模型

A new on-line predictive monitoring and prediction model for periodic biological processes is proposed using the multiway non-Gaussian modeling. The basic idea of this approach is to use multiway non-Gaussian modeling to extract some dominant key components from daily normal operation data in a periodic process, and subsequently combining these components with predictive statistical process monitoring techniques. The proposed predictive monitoring method has been applied to fault detection and diagnosis in the biological wastewater-treatment process, which is based on strong diurnal characteristics. The results show the power and advantages of the proposed predictive monitoring of a continuous process using the multiway predictive monitoring concept, which is thus able to give very useful conceptual results for a daily monitoring process and also enables a more rapid detection of the process fault than other traditional monitoring methods.     

Deconstructing the Potency and Cell-Line Selectivity of Membranolytic Anticancer Peptides**

Current cancer treatments damage healthy cells and tissues, causing short-term and long-term side effects. New treatments are desired that show greater selectivity toward cancer cells and evade the common mechanisms of multidrug resistance. Membranolytic anticancer peptides (mACPs) hold promise against cancer and multidrug resistance. Amphipathicity, hydrophobicity, and net charge of mACPs participate in their respective interactions with cell membranes and their overall inhibition of cancer cells. To support the design of cell-line selective mACPs, we investigated the relationships that amino acid composition, physicochemical properties, sequence motifs, and sequence homology could have with their potency and selectivity towards several healthy and cancer cell lines. Sequence length and net charge are known to affect the selectivity of mACPs between cancer and healthy cell lines. Our study reveals that increasing the net charge or flexibility (i. e., small and aliphatic residues) influences their selectivity between cancer cell lines with comparable lipid compositions.     

In-Silico Drug Toxicity and Interaction Prediction for Plant Complexes Based on Virtual Screening and Text Mining

Potential drug toxicities and drug interactions of redundant compounds of plant complexes may cause unexpected clinical responses or even severe adverse events. On the other hand, super-additivity of drug interactions between natural products and synthetic drugs may be utilized to gain better performance in disease management. Although without enough datasets for prediction model training, based on the SwissSimilarity and PubChem platforms, for the first time, a feasible workflow of prediction of both toxicity and drug interaction of plant complexes was built in this study. The optimal similarity score threshold for toxicity prediction of this system is 0.6171, based on an analysis of 20 different herbal medicines. From the PubChem database, 31 different sections of toxicity information such as “Acute Effects”, “NIOSH Toxicity Data”, “Interactions”, “Hepatotoxicity”, “Carcinogenicity”, “Symptoms”, and “Human Toxicity Values” sections have been retrieved, with dozens of active compounds predicted to exert potential toxicities. In Spatholobus suberectus Dunn (SSD), there are 9 out of 24 active compounds predicted to play synergistic effects on cancer management with various drugs or factors. The synergism between SSD, luteolin and docetaxel in the management of triple-negative breast cancer was proved by the combination index assay, synergy score detection assay, and xenograft model.     

Applications of Biomaterials in 3D Cell Culture and Contributions of 3D Cell Culture to Drug Development and Basic Biomedical Research

The process of evaluating the efficacy and toxicity of drugs is important in the production of new drugs to treat diseases. Testing in humans is the most accurate method, but there are technical and ethical limitations. To overcome these limitations, various models have been developed in which responses to various external stimuli can be observed to help guide future trials. In particular, three-dimensional (3D) cell culture has a great advantage in simulating the physical and biological functions of tissues in the human body. This article reviews the biomaterials currently used to improve cellular functions in 3D culture and the contributions of 3D culture to cancer research, stem cell culture and drug and toxicity screening.     

Application of MIA for a QSAR Study of Inhibitory Activity of DHP Derivatives and Design of New Compounds Using WT and GA for Pixel Processing

A quantitative structure-activity relationship (QSAR) modeling was carried out for the prediction of inhibitory activity of dihydropyridine (DHP) derivatives known as calcium channel blocker (CCB) drugs. Partial least squares (PLS) algorithm was used for prediction of inhibitory activity of calcium channel antagonists as a function of the bidimensional images. In the present study, it is investigated that the effect of pixel selection by application of genetic algorithms (GAs) for PLS model, because of the GAs is very useful in the variable selection in modeling. Pre-processing methods such as wavelet transform (WT) were also used to enhance the predictive power of multivariate calibration methods. The subset of pixels, which resulted in the low prediction error, was selected by GA. To evaluate the models applied in this study (PLS, GA-PLS and WT-GA-PLS), the inhibitory activities of several compounds, not included in the modeling procedure, were predicted. The results of models showed high prediction ability with root mean square error of prediction (RMSEP) of 0.51, 0.39 and 0.17 for PLS, GA-PLS and WT-GA-PLS, respectively. The WT-GA-PLS method was employed to predict the inhibitory activity of the new antagonists.     

A Gene Expression Signature to Predict Nucleotide Excision Repair Defects and Novel Therapeutic Approaches

Nucleotide excision repair (NER) resolves DNA adducts, such as those caused by ultraviolet light. Deficient NER (dNER) results in a higher mutation rate that can predispose to cancer development and premature ageing phenotypes. Here, we used isogenic dNER model cell lines to establish a gene expression signature that can accurately predict functional NER capacity in both cell lines and patient samples. Critically, none of the identified NER deficient cell lines harbored mutations in any NER genes, suggesting that the prevalence of NER defects may currently be underestimated. Identification of compounds that induce the dNER gene expression signature led to the discovery that NER can be functionally impaired by GSK3 inhibition, leading to synergy when combined with cisplatin treatment. Furthermore, we predicted and validated multiple novel drugs that are synthetically lethal with NER defects using the dNER gene signature as a drug discovery platform. Taken together, our work provides a dynamic predictor of NER function that may be applied for therapeutic stratification as well as development of novel biological insights in human tumors.     

Establishment and Characterization of Novel Human Intestinal In Vitro Models for Absorption and First-Pass Metabolism Studies

Commonly used intestinal in vitro models are limited in their potential to predict oral drug absorption. They either lack the capability to form a tight cellular monolayer mimicking the intestinal epithelial barrier or the expression of cytochrome P450 3A4 (CYP3A4). The aim of this study was to establish a platform of colorectal cancer patient-derived cell lines for evaluation of human intestinal drug absorption and metabolism. We characterized ten 2D cell lines out of our collection with confluent outgrowth and long-lasting barrier forming potential as well as suitability for high throughput applications with special emphasis on expression and inducibility of CYP3A4. By assessment of the transepithelial electrical resistance (TEER) the cells barrier function capacity can be quantified. Very high TEER levels were detected for HROC60. A high basal CYP3A4 expression and function was found for HROC32. Eight cell lines showed higher CYP3A4 induction by stimulation via the vitamin D receptor compared to Caco-2 cells (5.1- to 16.8-fold change). Stimulation of the pregnane X receptor led to higher CYP3A4 induction in two cell lines. In sum, we identified the two cell lines HROC183 T0 M2 and HROC217 T1 M2 as useful tools for in vitro drug absorption studies. Due to their high TEER values and inducibility by drug receptor ligands, they may be superior to Caco-2 cells to analyze oral drug absorption and intestinal drug–drug interactions. Significance statement: Selecting appropriate candidates is important in preclinical drug development. Therefore, cell models to predict absorption from the human intestine are of the utmost importance. This study revealed that the human cell lines HROC183 T0 M2 and HROC217 T1 M2 may be better suited models and possess higher predictive power of pregnane X receptor- and vitamin D-mediated drug metabolism than Caco-2 cells. Consequently, they represent useful tools for predicting intestinal absorption and simultaneously enable assessment of membrane permeability and first-pass metabolism.     

Ciprofloxacin and Levofloxacin as Potential Drugs in Genitourinary Cancer Treatment—The Effect of Dose–Response on 2D and 3D Cell Cultures

Introduction: Introducing new drugs for clinical application is a very difficult, long, drawn-out, and costly process, which is why drug repositioning is increasingly gaining in importance. The aim of this study was to analyze the cytotoxic properties of ciprofloxacin and levofloxacin on bladder and prostate cell lines in vitro. Methods: Bladder and prostate cancer cell lines together with their non-malignant counterparts were used in this study. In order to evaluate the cytotoxic effect of both drugs on tested cell lines, MTT assay, real-time cell growth analysis, apoptosis detection, cell cycle changes, molecular analysis, and 3D cultures were examined. Results: Both fluoroquinolones exhibited a toxic effect on all of the tested cell lines. In the case of non-malignant cell lines, the cytotoxic effect was weaker, which was especially pronounced in the bladder cell line. A comparison of both fluoroquinolones showed the advantage of ciprofloxacin (lower doses of drug caused a stronger cytotoxic effect). Both fluoroquinolones led to an increase in late apoptotic cells and an inhibition of cell cycle mainly in the S phase. Molecular analysis showed changes in BAX, BCL2, TP53, and CDKN1 expression in tested cell lines following incubation with ciprofloxacin and levofloxacin. The downregulation of topoisomerase II genes (TOP2A and TOP2B) was noticed. Three-dimensional (3D) cell culture analysis confirmed the higher cytotoxic effect of tested fluoroquinolone against cancer cell lines. Conclusions: Our results suggest that both ciprofloxacin and levofloxacin may have great potential, especially in the supportive therapy of bladder cancer treatment. Taking into account the low costs of such therapy, fluoroquinolones seem to be ideal candidates for repositioning into bladder cancer therapeutics.     

The Preliminary Study on the Proapoptotic Effect of Reduced Graphene Oxide in Breast Cancer Cell Lines

Breast cancer is the most common cancer diagnosed in women, however traditional therapies have several side effects. This has led to an urgent need to explore novel drug approaches to treatment strategies such as graphene-based nanomaterials such as reduced graphene oxide (rGO). It was noticed as a potential drug due to its target selectivity, easy functionalisation, chemisensitisation, and high drug-loading capacity. rGO is widely used in many fields, including biological and biomedical, due to its unique physicochemical properties. However, the possible mechanisms of rGO toxicity remain unclear. In this paper, we present findings on the cytotoxic and antiproliferative effects of rGO and its ability to induce oxidative stress and apoptosis of breast cancer cell lines. We indicate that rGO induced time- and dose-dependent cytotoxicity in MDA-MB-231 and ZR-75-1 cell lines, but not in T-47D, MCF-7, Hs 578T cell lines. In rGO-treated MDA-MB-231 and ZR-75-1 cell lines, we noticed increased induction of apoptosis and necrosis. In addition, rGO has been found to cause oxidative stress, reduce proliferation, and induce structural changes in breast cancer cells. Taken together, these studies provide new insight into the mechanism of oxidative stress and apoptosis in breast cancer cells.     

CRPC Membrane-Camouflaged,Biomimetic Nanosystem for Overcoming Castration-Resistant Prostate Cancer by Cellular Vehicle-Aided Tumor Targeting

Castration-resistant prostate cancer (CRPC) is the most common malignant tumor of the male urinary system. Nanodrug delivery systems (NDDS) have been widely applied in drug delivery for tumor therapy; however, nanotherapeutics encounter various biological barriers that prevent successful accumulation of drugs, specifically at diseased sites. Therefore, there is an urgent need to develop a CRPC-targeting nanocomposite with fine biocompatibility for penetrating various biological barriers, delivering sufficient drugs to the targeting site and improving therapeutic efficiency. In this work, CRPC cell membranes were firstly adapted as biomimetic vectors for the encapsulating PEG−PLGA polymer containing the chemotherapy drug docetaxel (DTX). The CRPC membrane-camouflaged nanoparticles can easily escape early recognition by the immune system, penetrate the extracellular barrier, and evade clearance by the circulatory system. In addition to the characteristics of traditional nanoparticles, the CRPC cell membrane contains an arsenal of highly specific homotypic moieties that can be used to recognize the same cancer cell types and increase the targeted drug delivery of DTX. In vivo fluorescence and radionuclide dual-model imaging were fulfilled by decorating the biomimetic nanosystem with near-infrared dye and isotope, which validated the homotypic targeting property offered by the CRPC cell membrane coating. Importantly, remarkably improved therapeutic efficacy was achieved in a mice model bearing CRPC tumors. This homologous cell membrane enabled an efficient drug delivery strategy and enlightened a new pathway for the clinical application of tumor chemotherapy drugs in the future.