Prioritization of candidate cancer drugs based on a drug functional similarity network constructed by integrating pathway activities and drug activities

Integrated edge information and pathway topology for drug-disease associations

Drug repurposing is a promising approach to find new therapeutic indications for approved drugs. Many computational approaches have been proposed to prioritize candidate anticancer drugs by gene or pathway level. However, these methods neglect the changes in gene interactions at the edge level. To address the limitation, we develop a computational drug repurposing method (iEdgePathDDA) based on edge information and pathway topology. First, we identify drug-induced and disease-related edges (the changes in gene interactions) within pathways by using the Pearson correlation coefficient. Next, we calculate the inhibition score between drug-induced edges and disease-related edges. Finally, we prioritize drug candidates according to the inhibition score on all disease-related edges. Case studies show that our approach successfully identifies new drug-disease pairs based on CTD database. Compared to the state-of-the-art approaches, the results demonstrate our method has the superior performance in terms of five metrics across colorectal, breast, and lung cancer datasets.

DrugRepoBank: a comprehensive database and discovery platform for accelerating drug repositioning

In recent years, drug repositioning has emerged as a promising alternative to the time-consuming, expensive and risky process of developing new drugs for diseases. However, the current database for drug repositioning faces several issues, including insufficient data volume, restricted data types, algorithm inaccuracies resulting from the neglect of multidimensional or heterogeneous data, a lack of systematic organization of literature data associated with drug repositioning, limited analytical capabilities and user-unfriendly webpage interfaces. Hence, we have established the first all-encompassing database called DrugRepoBank, consisting of two main modules: the 'Literature' module and the 'Prediction' module. The 'Literature' module serves as the largest repository of literature-supported drug repositioning data with experimental evidence, encompassing 169 repositioned drugs from 134 articles from 1 January 2000 to 1 July 2023. The 'Prediction' module employs 18 efficient algorithms, including similarity-based, artificial-intelligence-based, signature-based and network-based methods to predict repositioned drug candidates. The DrugRepoBank features an interactive and user-friendly web interface and offers comprehensive functionalities such as bioinformatics analysis of disease signatures. When users provide information about a drug, target or disease of interest, DrugRepoBank offers new indications and targets for the drug, proposes new drugs that bind to the target or suggests potential drugs for the queried disease. Additionally, it provides basic information about drugs, targets or diseases, along with supporting literature. We utilize three case studies to demonstrate the feasibility and effectiveness of predictively repositioned drugs within DrugRepoBank. The establishment of the DrugRepoBank database will significantly accelerate the pace of drug repositioning. Database URL:  https://awi.cuhk.edu.cn/DrugRepoBank.

ssMutPA: single-sample mutation-based pathway analysis approach for cancer precision medicine

BACKGROUND

Single-sample pathway enrichment analysis is an effective approach for identifying cancer subtypes and pathway biomarkers, facilitating the development of precision medicine. However, the existing approaches focused on investigating the changes in gene expression levels but neglected somatic mutations, which play a crucial role in cancer development.

FINDINGS

In this study, we proposed a novel single-sample mutation-based pathway analysis approach (ssMutPA) to infer individualized pathway activities by integrating somatic mutation data and the protein-protein interaction network. For each sample, ssMutPA first uses local and global weighted strategies to evaluate the effects of genes from mutations according to the network topology and then calculates a single-sample mutation-based pathway enrichment score (ssMutPES) to reflect the accumulated effect of mutations of each pathway. To illustrate the performance of ssMutPA, we applied it to 33 cancer cohorts from The Cancer Genome Atlas database and revealed patient stratification with significantly different prognosis in each cancer type based on the ssMutPES profiles. We also found that the identified characteristic pathways with high overlap across different cancers could be used as potential prognosis biomarkers. Moreover, we applied ssMutPA to 2 melanoma cohorts with immunotherapy and identified a subgroup of patients who may benefit from therapy.

CONCLUSIONS

We provided evidence that ssMutPA could infer mutation-based individualized pathway activity profiles and complement the current individualized pathway analysis approaches focused on gene expression data, which may offer the potential for the development of precision medicine. ssMutPA is available at https://CRAN.R-project.org/package=ssMutPA.

PathwayTMB: A pathway-based tumor mutational burden analysis method for predicting the clinical outcome of cancer immunotherapy

Immunotherapy has become one of the most promising therapy methods for cancer, but only a small number of patients are responsive to it, indicating that more effective biomarkers are urgently needed. This study developed a pathway analysis method, named PathwayTMB, to identify genomic mutation pathways that serve as potential biomarkers for predicting the clinical outcome of immunotherapy. PathwayTMB first calculates the patient-specific pathway-based tumor mutational burden (PTMB) to reflect the cumulative extent of mutations for each pathway. It then screens mutated survival benefit-related pathways to construct an immune-related prognostic signature based on PTMB (IPSP). In a melanoma training set, IPSP-high patients presented a longer overall survival and a higher response rate than IPSP-low patients. Moreover, the IPSP showed a superior predictive effect compared with TMB. In addition, the prognostic and predictive value of the IPSP was consistently validated in two independent validation sets. Finally, in a multi-cancer dataset, PathwayTMB also exhibited good performance. Our results indicate that PathwayTMB could identify the mutation pathways for predicting immunotherapeutic survival, and their combination may serve as a potential predictive biomarker for immune checkpoint inhibitor therapy.

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

Background

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

Methods

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

Results

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

Informatics on Drug Repurposing for Breast Cancer

Moving a new drug from bench to bedside is a long and arduous process. The tactic of drug repurposing, which solves "new" diseases with "old" existing drugs, is more efficient and economical than conventional ab-initio way for drug development. Information technology has dramatically changed the paradigm of biomedical research in the new century, and drug repurposing studies have been significantly accelerated by implementing informatics techniques related to genomics, systems biology and biophysics during the past few years. A series of remarkable achievements in this field comes with the practical applications of in silico approaches including transcriptomic signature matching, gene-connection-based scanning, and simulated structure docking in repositioning drug therapies against breast cancer. In this review, we systematically curated these impressive accomplishments with summarization of the main findings on potentially repurposable drugs, and provide our insights into the current issues as well as future directions of the field. With the prospective improvement in reliability, the computer-assisted repurposing strategy will play a more critical role in drug research and development.

DTSEA: A network-based drug target set enrichment analysis method for drug repurposing against COVID-19

The Coronavirus Disease 2019 (COVID-19) pandemic is still wreaking havoc worldwide. Therefore, the urgent need for efficient treatments pushes researchers and clinicians into screening effective drugs. Drug repurposing may be a promising and time-saving strategy to identify potential drugs against this disease. Here, we developed a novel computational approach, named Drug Target Set Enrichment Analysis (DTSEA), to identify potent drugs against COVID-19. DTSEA first mapped the disease-related genes into a gene functional interaction network, and then it used a network propagation algorithm to rank all genes in the network by calculating the network proximity of genes to disease-related genes. Finally, an enrichment analysis was performed on drug target sets to prioritize disease-candidate drugs. It was shown that the top three drugs predicted by DTSEA, including Ataluren, Carfilzomib, and Aripiprazole, were significantly enriched in the immune response pathways indicating the potential for use as promising COVID-19 inhibitors. In addition to these drugs, DTSEA also identified several drugs (such as Remdesivir and Olumiant), which have obtained emergency use authorization (EUA) for COVID-19. These results indicated that DTSEA could effectively identify the candidate drugs for COVID-19, which will help to accelerate the development of drugs for COVID-19. We then performed several validations to ensure the reliability and validity of DTSEA, including topological analysis, robustness analysis, and prediction consistency. Collectively, DTSEA successfully predicted candidate drugs against COVID-19 with high accuracy and reliability, thus making it a formidable tool to identify potential drugs for a specific disease and facilitate further investigation.

Guiding Drug Repositioning for Cancers Based on Drug Similarity Networks

Drug repositioning aims to discover novel clinical benefits of existing drugs, is an effective way to develop drugs for complex diseases such as cancer and may facilitate the process of traditional drug development. Meanwhile, network-based computational biology approaches, which allow the integration of information from different aspects to understand the relationships between biomolecules, has been successfully applied to drug repurposing. In this work, we developed a new strategy for network-based drug repositioning against cancer. Combining the mechanism of action and clinical efficacy of the drugs, a cancer-related drug similarity network was constructed, and the correlation score of each drug with a specific cancer was quantified. The top 5% of scoring drugs were reviewed for stability and druggable potential to identify potential repositionable drugs. Of the 11 potentially repurposable drugs for non-small cell lung cancer (NSCLC), 10 were confirmed by clinical trial articles and databases. The targets of these drugs were significantly enriched in cancer-related pathways and significantly associated with the prognosis of NSCLC. In light of the successful application of our approach to colorectal cancer as well, it provides an effective clue and valuable perspective for drug repurposing in cancer.

PANACEA: network-based methods for pharmacotherapy prioritization in personalized oncology

MOTIVATION

Identifying appropriate pharmacotherapy options from genomics results is a significant challenge in personalized oncology. However, computational methods for prioritizing drugs are underdeveloped. With the hypothesis that network-based approaches can improve the performance by extending the use of potential drug targets beyond direct interactions, we devised two network-based methods for personalized pharmacotherapy prioritization in cancer.

RESULTS

We developed novel personalized drug prioritization approaches, PANACEA: PersonAlized Network-based Anti-Cancer therapy EvaluAtion. In PANACEA, initially, the protein interaction network is extended with drugs, and a driverness score is assigned to each altered gene. For scoring drugs, either (i) the 'distance-based' method, incorporating the shortest distance between drugs and altered genes, and driverness scores, or (ii) the 'propagation' method involving the propagation of driverness scores via a random walk with restart framework is performed. We evaluated PANACEA using multiple datasets, and demonstrated that (i) the top-ranking drugs are relevant for cancer pharmacotherapy using TCGA data; (ii) drugs that cancer cell lines are sensitive to are identified using GDSC data; and (iii) PANACEA can perform adequately in the clinical setting using cases with known drug responses. We also illustrate that the proposed methods outperform iCAGES and PanDrugs, two previous personalized drug prioritization approaches.

AVAILABILITY AND IMPLEMENTATION

The corresponding R package is available on GitHub. (https://github.com/egeulgen/PANACEA.git).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

DrugSim2DR: systematic prediction of drug functional similarities in the context of specific disease for drug repurposing

BACKGROUND

Traditional approaches to drug development are costly and involve high risks. The drug repurposing approach can be a valuable alternative to traditional approaches and has therefore received considerable attention in recent years.

FINDINGS

Herein, we develop a previously undescribed computational approach, called DrugSim2DR, which uses a network diffusion algorithm to identify candidate anticancer drugs based on a drug functional similarity network. The innovation of the approach lies in the drug-drug functional similarity network constructed in a manner that implicitly links drugs through their common biological functions in the context of a specific disease state, as the similarity relationships based on general states (e.g., network proximity or Jaccard index of drug targets) ignore disease-specific molecular characteristics. The drug functional similarity network may provide a reference for prediction of drug combinations. We describe and validate the DrugSim2DR approach through analysis of data on breast cancer and lung cancer. DrugSim2DR identified some US Food and Drug Administration-approved anticancer drugs, as well as some candidate drugs validated by previous studies in the literature. Moreover, DrugSim2DR showed excellent predictive performance, as evidenced by receiver operating characteristic analysis and multiapproach comparisons in various cancer datasets.

CONCLUSIONS

DrugSim2DR could accurately assess drug-drug functional similarity within a specific disease context and may more effectively prioritize disease candidate drugs. To increase the usability of our approach, we have developed an R-based software package, DrugSim2DR, which is freely available on CRAN (https://CRAN.R-project.org/package=DrugSim2DR).

Tumor Microenvironment Characterization in Breast Cancer Identifies Prognostic Pathway Signatures

Breast cancer is one of the most common female malignancies worldwide. Due to its early metastases formation and a high degree of malignancy, the 10 year-survival rate of metastatic breast cancer does not exceed 30%. Thus, more precise biomarkers are urgently needed. In our study, we first estimated the tumor microenvironment (TME) infiltration using the xCell algorithm. Based on TME infiltration, the three main TME clusters were identified using consensus clustering. Our results showed that the three main TME clusters cause significant differences in survival rates and TME infiltration patterns (log-rank test, p = 0.006). Then, multiple machine learning algorithms were used to develop a nine-pathway-based TME-related risk model to predict the prognosis of breast cancer (BRCA) patients (the immune-related pathway-based risk score, defined as IPRS). Based on the IPRS, BRCA patients were divided into two subgroups, and patients in the IPRS-low group presented significantly better overall survival (OS) rates than the IPRS-high group (log-rank test, p < 0.0001). Correlation analysis revealed that the IPRS-low group was characterized by increases in immune-related scores (cytolytic activity (CYT), major histocompatibility complex (MHC), T cell-inflamed immune gene expression profile (GEP), ESTIMATE, immune, and stromal scores) while exhibiting decreases in tumor purity, suggesting IPRS-low patients may have a strong immune response. Additionally, the gene-set enrichment analysis (GSEA) result confirmed that the IPRS-low patients were significantly enriched in several immune-associated signaling pathways. Furthermore, multivariate Cox analysis revealed that the IPRS was an independent prognostic biomarker after adjustment by clinicopathologic characteristics. The prognostic value of the IPRS model was further validated in three external validation cohorts. Altogether, our findings demonstrated that the IPRS was a powerful predictor to screen out certain populations with better prognosis in breast cancer and may serve as a potential biomarker guiding clinical treatment decisions.

A novel pathway mutation perturbation score predicts the clinical outcomes of immunotherapy

The link between tumor genetic variations and immunotherapy benefits has been widely recognized. Recent studies suggested that the key biological pathways activated by accumulated genetic mutations may act as an effective biomarker for predicting the efficacy of immune checkpoint inhibitor (ICI) therapy. Here, we developed a novel individual Pathway Mutation Perturbation (iPMP) method that measures the pathway mutation perturbation level by combining evidence of the cumulative effect of mutated genes with the position of mutated genes in the pathways. In iPMP, somatic mutations on a single sample were first mapped to genes in a single pathway to infer the pathway mutation perturbation score (PMPscore), and then, an integrated PMPscore profile was produced, which can be used in place of the original mutation dataset to identify associations with clinical outcomes. To illustrate the effect of iPMP, we applied it to a melanoma cohort treated with ICIs and identified seven significant perturbation pathways, which jointly constructed a pathway-based signature. With the signature, patients were classified into two subgroups with significant distinctive overall survival and objective response rate to immunotherapy. Moreover, the pathway-based signature was consistently validated in two independent melanoma cohorts. We further applied iPMP to two non-small cell lung cancer cohorts and also obtained good performance. Altogether, the iPMP method could be used to identify the significant mutation perturbation pathways for constructing the pathway-based biomarker to predict the clinical outcomes of immunotherapy. The iPMP method has been implemented as a freely available R-based package (https://CRAN.R-project.org/package=PMAPscore).

Discussion of tumor mutation burden as an indicator to predict efficacy of immune checkpoint inhibitors: A case report

There are many treatment options for advanced lung cancer, among which immunotherapy has developed rapidly and benefited a lot of patients. However, immunotherapy can only benefit a subgroup of patients, and how to select patients suitable for this therapy is critical. Tumor mutation burden (TMB) is one of the important reference indicators for immune checkpoint inhibitors (ICIs). However, there are many factors influencing the usage of this indicator, which will lead to considerable consequences if not treated well. In this study, we performed a case study on a male advanced lung squamous cell carcinoma patient of age 83. The patient suffered from “cough and sputum”, and did chest CT scans on 24 October 2018, which showed “a mass-like mass in the anterior segment of the right lung upper lobe, about 38mm×28mm”. He was treated with systemic chemotherapy; however, the tumor was still under progression. Although PD-L1 was not tested in gene testing, he had a TMB value of 10.26 mutations/Mb with a quantile value 88.63%. Thus, “toripalimab injection” was added as immunotherapy and the size of the lesion decreased. In summary, we adopted a clinical case as the basis to explore the value and significance of TMB in immunotherapy in this study. We hope that more predictive molecular markers will be discovered, which will bring more treatment methods for advanced lung cancer.

Lung Cancer Stage Prediction Using Multi-Omics Data

Lung cancer is one of the leading causes of cancer death. Patients with early-stage lung cancer can be treated by surgery, while patients in the middle and late stages need chemotherapy or radiotherapy. Therefore, accurate staging of lung cancer is crucial for doctors to formulate accurate treatment plans for patients. In this paper, the random forest algorithm is used as the lung cancer stage prediction model, and the accuracy of lung cancer stage prediction is discussed in the microbiome, transcriptome, microbe, and transcriptome fusion groups, and the accuracy of the model is measured by indicators such as ACC, recall, and precision. The results showed that the prediction accuracy of microbial combinatorial transcriptome fusion analysis was the highest, reaching 0.809. The study reveals the role of multimodal data and fusion algorithm in accurately diagnosing lung cancer stage, which could aid doctors in clinics.

Development and Validation of a Three-Gene Prognostic Signature Based on Tumor Microenvironment for Gastric Cancer

Gastric cancer (GC), which has high morbidity and low survival rate, is one of the most common malignant tumors in the world. The increasing evidences show that the tumor microenvironment (TME) is related to the occurrence and progression of tumors and the prognosis of patients. In this study, we aimed to develop a TME-based prognostic signature for GC. We first identified the differentially expressed genes (DEGs) related to the TME using the Wilcoxon rank-sum test in a training set of GC. Univariate Cox regression analysis was used to identify prognostic-related DEGs. To decrease the overfitting, we performed the least absolute shrinkage and selection operator (LASSO) regression to reduce the number of signature genes and obtained three genes (LPPR4, ADAM12, NOX4). Next, the multivariate Cox regression was performed to construct the risk score model, and a three-gene prognostic signature was developed. According to the signature, patients were classified into high-risk and low-risk groups with significantly different survival. The signature was then applied to three independent validated sets and obtained the same results. We conducted the time-dependent Receiver Operating Characteristic (ROC) curve analysis to evaluate our signature. We further evaluated the differential immune characters between high-risk and low-risk patients to reveal the potential immune mechanism of the impact on the prognosis of the model. Overall, we identified a three-gene prognostic signature based on TME to predict the prognosis of patients with GC and facilitate the development of a precise treatment strategy.

Computational Drug Repurposing Based on a Recommendation System and Drug-Drug Functional Pathway Similarity

Drug repurposing identifies new clinical indications for existing drugs. It can be used to overcome common problems associated with cancers, such as heterogeneity and resistance to established therapies, by rapidly adapting known drugs for new treatment. In this study, we utilized a recommendation system learning model to prioritize candidate cancer drugs. We designed a drug-drug pathway functional similarity by integrating multiple genetic and epigenetic alterations such as gene expression, copy number variation (CNV), and DNA methylation. When compared with other similarities, such as SMILES chemical structures and drug targets based on the protein-protein interaction network, our approach provided better interpretable models capturing drug response mechanisms. Furthermore, our approach can achieve comparable accuracy when evaluated with other learning models based on large public datasets (CCLE and GDSC). A case study about the Erlotinib and OSI-906 (Linsitinib) indicated that they have a synergistic effect to reduce the growth rate of tumors, which is an alternative targeted therapy option for patients. Taken together, our computational method characterized drug response from the viewpoint of a multi-omics pathway and systematically predicted candidate cancer drugs with similar therapeutic effects.

SynPathy: Predicting Drug Synergy through Drug-Associated Pathways Using Deep Learning

Drug combination therapy has become a promising therapeutic strategy for cancer treatment. While high-throughput drug combination screening is effective for identifying synergistic drug combinations, measuring all possible combinations is impractical due to the vast space of therapeutic agents and cell lines. In this study, we propose a biologically-motivated deep learning approach to identify pathway-level features from drug and cell lines' molecular data for predicting drug synergy and quantifying the interactions in synergistic drug pairs. This method obtained an MSE of 70.6{plus minus}6.4, significantly surpassing previous approaches while providing potential candidate pathways to explain the prediction. We further demonstrate that drug combinations tend to be more synergistic when their top contributing pathways are closer to each other on a protein interaction network, suggesting a potential strategy for combination therapy with topologically interacting pathways. Our computational approach can thus be utilized both for pre-screening of potential drug combinations and for designing new combinations based on proximity of pathways associated with drug targets and cell lines. Implications: Our computational framework may be translated in the future to clinical scenarios where synergistic drugs are tailored to the patient and additionally, drug development could benefit from designing drugs that target topologically close pathways.

A Random Walk with Restart Model Based on Common Neighbors for Predicting the Clinical Drug Combinations on Coronary Heart Disease

As the approaching of the clinical big data era, the prediction of whether drugs can be used in combination in clinical practice is a fundamental problem in the analysis of medical data. Compared with high-throughput screening, it is more cost-effective to treat this problem as a link prediction problem and predict by algorithms. Inspired by the rule of combined clinical medication, a new computational model is proposed. The drug-drug combination was predicted by combining the number of adjacent complete subgraphs shared by the two points with the restart random walk algorithm. The model is based on the semisupervised random walk algorithm, and the same neighborhood is used to improve the random walk with restart (CN-RWR). The algorithm can effectively improve the prediction performance and assign a score to any combination of drugs. To fairly compare the predictive performance of the improved model with that of the random walk with restart model (RWR), a cross-validation of the two models on the same drug data was performed. The AUROC of CN-RWR and RWR under the LOOCV validation framework is 0.9741 and 0.9586, respectively, and the improved model results are more reliable. In addition, the top 3 predictive drug combinations have been approved by the public. The new model is expected that this model can be extended to predict the use of combination drugs for other diseases to find combinations of drugs with potential clinical benefits.

Identification of Somatic Mutation-Driven Immune Cells by Integrating Genomic and Transcriptome Data

Tumor somatic mutations in protein-coding regions may generate neoantigens which may trigger antitumor immune cell response. Increasing evidence supports that immune cell response may profoundly influence tumor progression. However, there are no calculated tools to systematically identify immune cells driven by specific somatic mutations. It is urgent to develop a calculated method to comprehensively detect tumor-infiltrating immune cells driven by the specific somatic mutations in cancer. We developed a novel software package (SMDIC) that enables the automated identification of somatic mutation-driven immune cell. SMDIC provides a novel pipeline to discover mutation-specific immune cells by integrating genomic and transcriptome data. The operation modes include inference of the relative abundance matrix of tumor-infiltrating immune cells, detection of differential abundance immune cells with respect to the gene mutation status, conversion of the abundance matrix of significantly dysregulated cells into two binary matrices (one for upregulated and one for downregulated cells), identification of somatic mutation-driven immune cells by comparing the gene mutation status with each immune cell in the binary matrices across all samples, and visualization of immune cell abundance of samples in different mutation status for each gene. SMDIC provides a user-friendly tool to identify somatic mutation-specific immune cell response. SMDIC may contribute to understand the mechanisms underlying anticancer immune response and find targets for cancer immunotherapy. The SMDIC was implemented as an R-based tool which was freely available from the CRAN website https://CRAN.R-project.org/package=SMDIC.

SubtypeDrug: a software package for prioritization of candidate cancer subtype-specific drugs

SUMMARY

Cancer can be classified into various subtypes by its molecular, histological, or clinical characteristics. Discovering cancer-subtype-specific drugs is a crucial step in personalized medicine. SubtypeDrug is a system biology R-based software package that enables the prioritization of subtype-specific drugs based on cancer expression data from samples of many subtypes. This provides a novel approach to identify the subtype-specific drug by considering biological functions regulated by drugs at the subpathway level. The operation modes include extraction of subpathways from biological pathways, identification of dysregulated subpathways induced by each drug, inference of sample-specific subpathway activity profiles, evaluation of drug-disease reverse association at the subpathways level, identification of cancer-subtype-specific drugs through subtype sample set enrichment analysis, and visualization of the results. Its capabilities enable SubtypeDrug to find subtype-specific drugs, which will fill the gaps in the recent tools which only identify the drugs for a particular cancer type. SubtypeDrug may help to facilitate the development of tailored treatment for patients with cancer.

AVAILABILITY AND IMPLEMENTATION

The package is implemented in R and available under GPL-2 license from the CRAN website (https://CRAN.R-project.org/package=SubtypeDrug).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Inference of Subpathway Activity Profiles Reveals Metabolism Abnormal Subpathway Regions in Glioblastoma Multiforme

Glioblastoma, also known as glioblastoma multiforme (GBM), is the most malignant form of glioma and represents 81% of malignant brain and central nervous system (CNS) tumors. Like most cancers, GBM causes metabolic recombination to promote cell survival, proliferation, and invasion of cancer cells. In this study, we propose a method for constructing the metabolic subpathway activity score matrix to accurately identify abnormal targets of GBM metabolism. By integrating gene expression data from different sequencing methods, our method identified 25 metabolic subpathways that were significantly abnormal in the GBM patient population, and most of these subpathways have been reported to have an effect on GBM. Through the analysis of 25 GBM-related metabolic subpathways, we found that (S)-2,3-Epoxysqualene, which was at the central region of the sterol biosynthesis subpathway, may have a greater impact on the entire pathway, suggesting a potential high association with GBM. Analysis of CCK8 cell activity indicated that (S)-2,3-Epoxysqualene can indeed inhibit the activity of U87-MG cells. By flow cytometry, we demonstrated that (S)-2,3-Epoxysqualene not only arrested the U87-MG cell cycle in the G0/G1 phase but also induced cell apoptosis. These results confirm the reliability of our proposed metabolic subpathway identification method and suggest that (S)-2,3-Epoxysqualene has potential therapeutic value for GBM. In order to make the method more broadly applicable, we have developed an R system package crmSubpathway to perform disease-related metabolic subpathway identification and it is freely available on the GitHub (https://github.com/hanjunwei-lab/crmSubpathway).

Silybin Prevents Prostate Cancer by Inhibited the ALDH1A1 Expression in the Retinol Metabolism Pathway

Background

Silybin was known to exert inhibition in prostate cancer, but the underlying mechanism remained largely unknown. This study was designed to find out the potential target of Silybin on prostate cancer and explore the relative mechanisms.

Methods

Firstly, we screened the possible targets of Silybin through the PubChem database and Subpathway – GM. Then DU145 cells were transferred to investigate the correction about related targets, magnetic bead sorting and flow cytometry were used to sort and identify the cells. Proliferation, migration and invasion ability of DU145 cells were detected by MTT assay, Transwell assay, plate clonality and sphere formation assay. BALB/c nude mice were constructed models with implanted sarcoma and measured the tumor volume every 5 days as wells tumor weight. The levels of proteins were detected by Western blot and immunocytochemistry. RT-PCR was selected to test the expression of protein’s mRNA.

Results

It was screened out the ALDH1A1 was highly correlated with subpathways of the Silybin risk metabolic pathway. And ALDH1A1 expression was positively correlated RARα with Ets1 by interfering with the ALDH1A1 gene. Importantly, ALDH1A1(+) cells showed proliferation, migration and invasion ability. In addition, it showed that Silybin exerted the inhibition on prostate cells by suppressed the proliferation, migration and invasion ability of cells in vitro experiment. Silybin also reduced the tumor volume and weight. And Silybin displayed obviously reduced the proteins and mRNA of ALDH1A1, RARα, Ets1 and MMP9 expressions.

Conclusion

Our results indicated that Silybin showed inhibition of prostate cancer and the mechanism was involving with downregulating ALDH1A1 expression, thereby inhibiting the activation of RARα and preventing the activation of Ets1 to inhibit the growth and invasion of prostate cancer.

Ibrutinib in Gynecological Malignancies and Breast Cancer: A Systematic Review

Ibrutinib is an orally available, small-molecule tyrosine kinase inhibitor. Its main purpose is to inhibit Bruton's tyrosine kinase (BTK), an enzyme that is crucial in B cell development. It is FDA approved for the treatment of certain hematological malignancies. Several promising off-target drug effects have led to multiple, mostly preclinical investigations regarding its use in solid tumors. Unfortunately, data on its effectiveness in gynecological malignancies are limited, and (systematic) reviews are missing. The objective of this review was to summarize the existing literature and to analyze the evidence of ibrutinib as a treatment option in gynecological malignancies, including breast cancer. Studies were identified in MEDLINE and EMBASE using a defined search strategy, and preclinical or clinical research projects investigating ibrutinib in connection with these malignancies were considered eligible for inclusion. Our findings showed that preclinical studies generally confirm ibrutinib's efficacy in cell lines and animal models of ovarian, breast, and endometrial cancer. Ibrutinib exerts multiple antineoplastic effects, such as on-target BTK inhibition, off-target kinase inhibition, and immunomodulation by interference with myeloid-derived suppressor cells (MDSCs), programmed death-ligand 1 (PD-L1), and T cell response. These mechanisms were elaborated and discussed in the context of the evidence available. Further research is needed in order to transfer the preclinical results to a broader clinical appliance.

Its2vec: Fungal Species Identification Using Sequence Embedding and Random Forest Classification

Fungi play essential roles in many ecological processes, and taxonomic classification is fundamental for microbial community characterization and vital for the study and preservation of fungal biodiversity. To cope with massive fungal barcode data, tools that can implement extensive volumes of barcode sequences, especially the internal transcribed spacer (ITS) region, are necessary. However, high variation in the ITS region and computational requirements for processing high-dimensional features remain challenging for existing predictors. In this study, we developed Its2vec, a bioinformatics tool for the classification of fungal ITS barcodes to the species level. An ITS database covering more than 25,000 species in a broad range of fungal taxa was assembled. For dimensionality reduction, a word embedding algorithm was used to represent an ITS sequence as a dense low-dimensional vector. A random forest-based classifier was built for species identification. Benchmarking results showed that our model achieved an accuracy comparable to that of several state-of-the-art predictors, and more importantly, it could implement large datasets and greatly reduce dimensionality. We expect the Its2vec model to be helpful for fungal species identification and, thus, for revealing microbial community structures and in deepening our understanding of their functional mechanisms.