Immunohistochemical (IHC) determination of receptor status in breast cancer patients is frequently inaccurate. Since it directs the choice of systemic therapy, it is essential to increase its reliability. We increase the validity of IHC receptor expression by additionally considering gene expression (GE) measurements. Crisp therapeutic decisions are based on IHC estimates, even if they are borderline reliable. We further improve decision quality by a responsibility function, defining a critical domain for gene expression. Refined normalization is devised to file any newly diagnosed patient into existing data bases. Our approach renders receptor estimates more reliable by identifying patients with questionable receptor status. The approach is also more efficient since the rate of conclusive samples is increased. We have curated and evaluated gene expression data, together with clinical information, from 2880 breast cancer patients. Combining IHC with gene expression information yields a method more reliable and also more efficient as compared to common practice up to now. Several types of possibly suboptimal treatment allocations, based on IHC receptor status alone, are enumerated. A 'therapy allocation check' identifies patients possibly miss-classified. Estrogen: false negative 8%, false positive 6%. Progesterone: false negative 14%, false positive 11%. HER2: false negative 2%, false positive 50%. Possible implications are discussed. We propose an 'expression look-up-plot', allowing for a significant potential to improve the quality of precision medicine. Methods are developed and exemplified here for breast cancer patients, but they may readily be transferred to diagnostic data relevant for therapeutic decisions in other fields of oncology.

Self-interacting Proteins (SIPs) play an essential role in a wide range of biological processes, such as gene expression regulation, signal transduction, enzyme activation and immune response. Because of the limitations for experimental self-interaction proteins identification, developing an effective computational method based on protein sequence to detect SIPs is much important. In the study, we proposed a novel computational approach called RVMBIGP that combines the Relevance Vector Machine (RVM) model and Bi-gram probability (BIGP) to predict SIPs based on protein sequence. The proposed prediction model includes as following steps: (1) an effective feature extraction method named BIGP is used to represent protein sequences on Position Specific Scoring Matrix (PSSM); (2) Principal Component Analysis (PCA) method is employed for integrating the useful information and reducing the influence of noise; (3) the robust classifier Relevance Vector Machine (RVM) is used to carry out classification. When performed on yeast and human datasets, the proposed RVMBIGP model can achieve very high accuracies of 95.48% and 98.80%, respectively. The experimental results show that our proposed method is very promising and may provide a cost-effective alternative for SIPs identification. In addition, to facilitate extensive studies for future proteomics research, the RVMBIGP server is freely available for academic use at http://219.219.62.123:8888/RVMBIGP.

Traditional Chinese Medicine (TCM) has been widely used as a complementary medicine in Acute Myeloid Leukemia (AML) treatment. In this study, we proposed a new classification of Chinese Medicines (CMs) by integrating the latest discoveries in disease molecular mechanisms and traditional medicine theory. We screened out a set of chemical compounds on basis of AML differential expression genes and chemical-protein interactions and then mapped them to Traditional Chinese Medicine Integrated Database. 415 CMs contain those compounds and they were categorized into 8 groups according to the Traditional Chinese Pharmacology. Pathway analysis and synthetic lethality gene pairs were applied to analyze the dissimilarity, generality and intergroup relations of different groups. We defined hub CM pairs and alternative CM groups based on the analysis result and finally proposed a formula to form an effective anti-AML prescription which combined the hub CM pairs with alternative CMs according to patients' molecular features. Our method of formulating CMs based on patients' stratification provides novel insights into the new usage of conventional CMs and will promote TCM modernization.

Identification of protein-protein interactions (PPIs) is of critical importance for deciphering the underlying mechanisms of almost all biological processes of cell and providing great insight into the study of human disease. Although much effort has been devoted to identifying PPIs from various organisms, existing high-throughput biological techniques are time-consuming, expensive, and have high false positive and negative results. Thus it is highly urgent to develop in silico methods to predict PPIs efficiently and accurately in this post genomic era. In this article, we report a novel computational model combining our newly developed discriminative vector machine classifier (DVM) and an improved Weber local descriptor (IWLD) for the prediction of PPIs. Two components, differential excitation and orientation, are exploited to build evolutionary features for each protein sequence. The main characteristics of the proposed method lies in introducing an effective feature descriptor IWLD which can capture highly discriminative evolutionary information from position-specific scoring matrixes (PSSM) of protein data, and employing the powerful and robust DVM classifier. When applying the proposed method to Yeast and H. pylori data sets, we obtained excellent prediction accuracies as high as 96.52% and 91.80%, respectively, which are significantly better than the previous methods. Extensive experiments were then performed for predicting cross-species PPIs and the predictive results were also pretty promising. To further validate the performance of the proposed method, we compared it with the state-of-the-art support vector machine (SVM) classifier on Human data set. The experimental results obtained indicate that our method is highly effective for PPIs prediction and can be taken as a supplementary tool for future proteomics research.

Triple negative breast cancer (TNBC) is a group of cancers whose heterogeneity and shortage of effective drug therapies has prompted efforts to divide these cancers into molecular subtypes. Our computational platform, entitled GenEx-TNBC, applies concepts in systems biology and polypharmacology to prioritize thousands of approved and experimental drugs for therapeutic potential against each molecular subtype of TNBC. Using patient-based and cell line-based gene expression data, we constructed networks to describe the biological perturbation associated with each TNBC subtype at multiple levels of biological action. These networks were analyzed for statistical coincidence with drug action networks stemming from known drug-protein targets, while accounting for the direction of disease modulation for coinciding entities. GenEx-TNBC successfully designated drugs, and drug classes, that were previously shown to be broadly effective or subtype-specific against TNBC, as well as novel agents. We further performed biological validation of the platform by testing the relative sensitivities of three cell lines, representing three distinct TNBC subtypes, to several small molecules according to the degree of predicted biological coincidence with each subtype. GenEx-TNBC is the first computational platform to associate drugs to diseases based on inverse relationships with multi-scale disease mechanisms mapped from global gene expression of a disease. This method may be useful for directing current efforts in preclinical drug development surrounding TNBC, and may offer insights into the targetable mechanisms of each TNBC subtype.

Epilepsy is a common neurological disorder in domestic dogs. However, its complex mechanism involves multiple genetic and environmental factors that make it challenging to identify the real pathogenic factors contributing to epilepsy, particularly for idiopathic epilepsy. Conventional genome-wide association studies (GWASs) can detect various genes associated with epilepsy, although they primarily detect the effects of single-site mutations in epilepsy while ignoring their interactions. In this study, we used a systems genetics method combining both GWAS and gene interactions and obtained 26 significantly mutated subnetworks. Among these subnetworks, seven genes were reported to be involved in neurological disorders. Combined with gene ontology enrichment analysis, we focused on 4 subnetworks that included traditional GWAS-neglected genes. Moreover, we performed a drug enrichment analysis for each subnetwork and identified significantly enriched candidate anti-epilepsy drugs using a hypergeometric test. We discovered 22 potential drug combinations that induced possible synergistic effects for epilepsy treatment, and one of these drug combinations has been confirmed in the Drug Combination database (DCDB) to have beneficial anti-epileptic effects. The method proposed in this study provides deep insight into the pathogenesis of canine epilepsy and implications for anti-epilepsy drug discovery.

Meta-analysis was conducted to systematically assess the effectiveness and safety of programmed cell death protein-1 or ligand-1 (PD-1 or PD-L1) antibodies versus docetaxel alone in advanced non small cell lung cancer (NSCLC). In addition, the prognostic significance of PD-L1 expression in advanced NSCLC was also investigated. 5 eligible studies including 3579 patients were identified through comprehensive search of multiple databases. The results showed that pooled hazard ratios (HR) for overall survival (OS) and progression free survival (PFS) were 0.69 (95% CI: 0.63-0.75; p < 0.001) and 0.87 (95% CI: 0.80-0.94; p < 0.001), between PD-1/PD-L1 antibodies and docetaxel treatment arms, respectively. The pooled relative risk (RR) value for objective response rate (ORR) was 1.53, (95% CI: 1.16-2.01, p = 0.003). Further, subgroup analysis based on PD-L1 expression indicated that pooled HR for OS was significant with 0.66(95% CI: 0.59-0.74, p < 0.001) for PD-L1≥1%. However, PD-L1 < 1% had HR value of 0.79 (95% CI: 0.67-0.93, p = 0.006). Our study concluded that advanced NSCLC patients benefited more with PD-1/PD-L1 antibodies than docetaxel in the second line treatment. PD-L1≥10% in tumor tissues is sufficient to show significant improvement in patient's outcome with PD-1/PD-L1 antibodies compared to docetaxel. Moreover, PD-1/PD-L1 antibodies treatment showed significant decrease in conventional chemotherapy adverse events, but increased immune-associated adverse effects.

The aim of this study was to discover a small molecule activator BL-AD008 targeting AMPK/ZIPK and inducing apoptosis in cervical cancer. In this study, we systematically constructed the global protein-protein interaction (PPI) network and predicted apoptosis-related protein connections by the Naïve Bayesian model. Then, we identified some classical apoptotic PPIs and other previously unrecognized PPIs between apoptotic kinases, such as AMPK and ZIPK. Subsequently, we screened a series of candidate compounds targeting AMPK/ZIPK, synthesized some compounds and eventually discovered a novel dual-target activator (BL-AD008). Moreover, we found BL-AD008 bear remarkable anti-proliferative activities toward cervical cancer cells and could induce apoptosis by death-receptor and mitochondrial pathways. Additionally, we found that BL-AD008-induced apoptosis was affected by the combination of AMPK and ZIPK. Then, we found that BL-AD008 bear its anti-tumor activities and induced apoptosis by targeting AMPK/ZIPK in vivo. In conclusion, these results demonstrate the ability of systems biology network to identify some key apoptotic kinase targets AMPK and ZIPK; thus providing a dual-target small molecule activator (BL-AD008) as a potential new apoptosis-modulating drug in future cervical cancer therapy.

Non-small-cell lung cancer (NSCLC) is the predominant type of lung cancer in the world. Lung adenocarcinoma (LADC) and lung squamous cell carcinoma (LSCC) are subtypes of NSCLC. We usually regard them as different disease due to their unique molecular characteristics, distinct cells of origin and dissimilar clinical response. However, the differences of genetic and epigenetic progression mechanism between LADC and LSCC are complicated to analyze. Therefore, we applied systems biology approaches and big databases mining to construct genetic and epigenetic networks (GENs) with next-generation sequencing data of LADC and LSCC. In order to obtain the real GENs, system identification and system order detection are conducted on gene regulatory networks (GRNs) and protein-protein interaction networks (PPINs) for each stage of LADC and LSCC. The core GENs were extracted via principal network projection (PNP). Based on the ranking of projection values, we got the core pathways in respect of KEGG pathway. Compared with the core pathways, we found significant differences between microenvironments, dysregulations of miRNAs, epigenetic modifications on certain signaling transduction proteins and target genes in each stage of LADC and LSCC. Finally, we proposed six genetic and epigenetic multiple-molecule drugs to target essential biomarkers in each progression stage of LADC and LSCC, respectively.

Around 3-7% of patients with non-small cell lung cancer (NSCLC), which represent 85% of diagnosed lung cancers, have a rearrangement in the ALK gene that produces an abnormal activity of the ALK protein cell signaling pathway. The developed ALK tyrosine kinase inhibitors (TKIs), such as crizotinib, ceritinib, alectinib, brigatinib and lorlatinb present good performance treating ALK+ NSCLC, although all patients invariably develop resistance due to ALK secondary mutations or bypass mechanisms. In the present study, we compare the potential differences between brigatinib and alectinib's mechanisms of action as first-line treatment for ALK+ NSCLC in a systems biology-based in silico setting. Therapeutic performance mapping system (TPMS) technology was used to characterize the mechanisms of action of brigatinib and alectinib and the impact of potential resistances and drug interferences with concomitant treatments. The analyses indicate that brigatinib and alectinib affect cell growth, apoptosis and immune evasion through ALK inhibition. However, brigatinib seems to achieve a more diverse downstream effect due to a broader cancer-related kinase target spectrum. Brigatinib also shows a robust effect over invasiveness and central nervous system metastasis-related mechanisms, whereas alectinib seems to have a greater impact on the immune evasion mechanism. Based on this in silico head to head study, we conclude that brigatinib shows a predicted efficacy similar to alectinib and could be a good candidate in a first-line setting against ALK+ NSCLC. Future investigation involving clinical studies will be needed to confirm these findings. These in silico systems biology-based models could be applied for exploring other unanswered questions.

Recently, experiments revealed the pupylation to be a signal for the selective regulation of proteins in several serious human diseases. As one of the most significant post translational modification in the field of biology and disease, pupylation has the ability to playing the key role in the regulation various diseases' biological processes. Meanwhile, effectively identification such type modification will be helpful for proteins to perform their biological functions and contribute to understanding the molecular mechanism, which is the foundation of drug design. The existing algorithms of identification such types of modified sites often have some defects, such as low accuracy and time-consuming. In this research, the pupylation sites' identification model, CIPPN, demonstrates better performance than other existing approaches in this field. The proposed predictor achieves Acc value of 89.12 and Mcc value of 0.7949 in 10-fold cross-validation tests in the Pupdb Database (http://cwtung.kmu.edu.tw/pupdb). Significantly, such algorithm not only investigates the sequential, structural and evolutionary hallmarks around pupylation sites but also compares the differences of pupylation from the environmental, conservative and functional characterization of substrates. Therefore, the proposed feature description approach and algorithm results prove to be useful for further experimental investigation of such modification's identification.

Tumor metastasis is a major cause leading to the deaths of cancer patients. Nordihydroguaiaretic acid (NDGA) is a natural product that has been demonstrated to show therapeutic values in multiple diseases. In this study, we report that NDGA can inhibit cell migration and tumor metastasis via a novel mechanism. NDGA suppresses NRP1 function by downregulating its expression, which leads to attenuated cell motility, cell adhesion to ECM and FAK signaling in cancer cells. Moreover, due to its cross-cell type activity on NRP1 suppression, NDGA also impairs angiogenesis function of endothelial cells and fibronectin assembly by fibroblasts, both of which are critical to promote metastasis. Based on these comprehensive effects, NDGA effectively suppresses tumor metastasis in nude mice model. Our findings reveal a novel mechanism underlying the anti-metastasis function of NDGA and indicate the potential value of NDGA in NRP1 targeting therapy for selected subtypes of cancer.

ChIP-seq has been commonly applied to identify genomic occupation of transcription factors (TFs) in a context-specific manner. It is generally assumed that a TF should have similar binding patterns in cells from the same or closely related tissues. Surprisingly, this assumption has not been carefully examined. To this end, we systematically compared the genomic binding of the cell cycle regulator FOXM1 in eight cell lines from seven different human tissues at binding signal, peaks and target genes levels. We found that FOXM1 binding in ER-positive breast cancer cell line MCF-7 are distinct comparing to those in not only other non-breast cell lines, but also MDA-MB-231, ER-negative breast cancer cell line. However, binding sites in MDA-MB-231 and non-breast cell lines were highly consistent. The recruitment of estrogen receptor alpha (ERα) caused the unique FOXM1 binding patterns in MCF-7. Moreover, the activity of FOXM1 in MCF-7 reflects the regulatory functions of ERα, while in MDA-MB-231 and non-breast cell lines, FOXM1 activities regulate cell proliferation. Our results suggest that tissue similarity, in some specific contexts, does not hold precedence over TF-cofactors interactions in determining transcriptional states and that the genomic binding of a TF can be dramatically affected by a particular co-factor under certain conditions.

Recently, researchers have been increasingly focusing on microRNAs (miRNAs) with accumulating evidence indicating that miRNAs serve as a vital role in various biological processes and dysfunctions of miRNAs are closely related with human complex diseases. Predicting potential associations between miRNAs and diseases is attached considerable significance in the domains of biology, medicine, and bioinformatics. In this study, we developed a computational model of Personalized Recommendation-based MiRNA-Disease Association prediction (PRMDA) to predict potential related miRNA for all diseases by implementing personalized recommendation-based algorithm based on integrated similarity for diseases and miRNAs. PRMDA is a global method capable of prioritizing candidate miRNAs for all diseases simultaneously. Moreover, the model could be applied to diseases without any known associated miRNAs. PRMDA obtained AUC of 0.8315 based on leave-one-out cross validation, which demonstrated that PRMDA could be regarded as a reliable tool for miRNA-disease association prediction. Besides, we implemented PRMDA on the HMDD V1.0 and HMDD V2.0 databases for three kinds of case studies about five important human cancers in order to test the performance of the model from different perspectives. As a result, 92%, 94%, 88%, 96% and 88% out of the top 50 candidate miRNAs predicted by PRMDA for Colon Neoplasms, Esophageal Neoplasms, Lymphoma, Lung Neoplasms and Breast Neoplasms, respectively, were confirmed by experimental reports.

Erythropoietin (EPO) is a frequently prescribed anti-anemic drug for patients with advanced renal carcinoma. However, recent evidence from clinical studies suggested that EPO accelerated tumor progression and jeopardized the 5-year survival. Herein, we show, starting from the in silico microarray bioinformatics analysis, that activation of Erythropoietin signaling pathway enhanced renal clear carcinoma (RCC) progression. EPO accelerated the proliferative and migratory ability in 786-O and Caki-2 cells. Moreover, comparative proteomics expression profiling suggested that exogenous EPO stimulated RCC progression via up-regulation of KIAA0101 expression. Loss of KIAA0101 impeded the undesirable propensity of EPO in RCC. Finally, low expression of KIAA0101 was associated with the excellent prognosis and prognosticated a higher 5-year survival in human patients with renal carcinoma. Overall, KIAA0101 appears to be a key promoter of RCC malignancy induced by EPO, which provide mechanistic insights into KIAA0101 functions, and pave the road to develop new therapeutics for treatment of cancer-related and chemotherapy-induced anemia in patients with RCC.

Cytoreductive surgery combined with intraperitoneal chemotherapy (IPC) is currently the standard treatment for selected patients with peritoneal carcinomatosis of colorectal cancer. However, especially after incomplete cytoreduction, disease progression is common and this is likely due to limited tissue penetration and efficacy of intraperitoneal cytotoxic drugs. Tumor microenvironment-targeting drugs, such as VEGF(R) and PDGFR inhibitors, can lower the heightened interstitial fluid pressure in tumors, a barrier to drug delivery. Here, we investigated whether tumor microenvironment-targeting drugs enhance the effectiveness of intraperitoneal chemotherapy. A mouse xenograft model with two large peritoneal implants of colorectal cancer cells was developed to study drug distribution and tumor physiology during intraperitoneal Oxaliplatin perfusion. Mice were treated for six days with either Placebo, Imatinib (anti-PDGFR, daily), Bevacizumab (anti-VEGF, twice) or Pazopanib (anti-PDGFR, -VEGFR; daily) followed by intraperitoneal oxaliplatin chemotherapy. Bevacizumab and Pazopanib significantly lowered interstitial fluid pressure, increased Oxaliplatin penetration (assessed by laser ablation inductively coupled plasma mass spectrometry) and delayed tumor growth of peritoneal implants (assessed by MRI). Our findings suggest that VEGF(R)-inhibition may improve the efficacy of IPC, particularly for patients for whom a complete cytoreduction might not be feasible.

Recently, microRNAs (miRNAs) have drawn more and more attentions because accumulating experimental studies have indicated miRNA could play critical roles in multiple biological processes as well as the development and progression of human complex diseases. Using the huge number of known heterogeneous biological datasets to predict potential associations between miRNAs and diseases is an important topic in the field of biology, medicine, and bioinformatics. In this study, considering the limitations in the previous computational methods, we developed the computational model of Heterogeneous Graph Inference for MiRNA-Disease Association prediction (HGIMDA) to uncover potential miRNA-disease associations by integrating miRNA functional similarity, disease semantic similarity, Gaussian interaction profile kernel similarity, and experimentally verified miRNA-disease associations into a heterogeneous graph. HGIMDA obtained AUCs of 0.8781 and 0.8077 based on global and local leave-one-out cross validation, respectively. Furthermore, HGIMDA was applied to three important human cancers for performance evaluation. As a result, 90% (Colon Neoplasms), 88% (Esophageal Neoplasms) and 88% (Kidney Neoplasms) of top 50 predicted miRNAs are confirmed by recent experiment reports. Furthermore, HGIMDA could be effectively applied to new diseases and new miRNAs without any known associations, which overcome the important limitations of many previous computational models.

In recent years, accumulating evidences have shown that the dysregulations of lncRNAs are associated with a wide range of human diseases. It is necessary and feasible to analyze known lncRNA-disease associations, predict potential lncRNA-disease associations, and provide the most possible lncRNA-disease pairs for experimental validation. Considering the limitations of traditional Random Walk with Restart (RWR), the model of Improved Random Walk with Restart for LncRNA-Disease Association prediction (IRWRLDA) was developed to predict novel lncRNA-disease associations by integrating known lncRNA-disease associations, disease semantic similarity, and various lncRNA similarity measures. The novelty of IRWRLDA lies in the incorporation of lncRNA expression similarity and disease semantic similarity to set the initial probability vector of the RWR. Therefore, IRWRLDA could be applied to diseases without any known related lncRNAs. IRWRLDA significantly improved previous classical models with reliable AUCs of 0.7242 and 0.7872 in two known lncRNA-disease association datasets downloaded from the lncRNADisease database, respectively. Further case studies of colon cancer and leukemia were implemented for IRWRLDA and 60% of lncRNAs in the top 10 prediction lists have been confirmed by recent experimental reports.

The rising incidence and mortality of endometrial cancer (EC) in the United States calls for an improved understanding of the disease's progression. Current methodologies for diagnosis and treatment rely on the use of cell lines as models for tumor biology. However, due to inherent heterogeneity and differential growing environments between cell lines and tumors, these comparative studies have found little parallels in molecular signatures. As a consequence, the development and discovery of preclinical models and reliable drug targets are delayed. In this study, we established transcriptome parallels between cell lines and tumors from The Cancer Genome Atlas (TCGA) with the use of optimized normalization methods. We identified genes and signaling pathways associated with regulating the transformation and progression of EC. Specifically, the LXR/RXR activation, neuroprotective role for THOP1 in Alzheimer's disease, and glutamate receptor signaling pathways were observed to be mostly downregulated in advanced cancer stage. While some of these highlighted markers and signaling pathways are commonly found in the central nervous system (CNS), our results suggest a novel function of these genes in the periphery. Finally, our study underscores the value of implementing appropriate normalization methods in comparative studies to improve the identification of accurate and reliable markers.

Effective choice of anticancer drugs is important problem of modern medicine. We developed a method termed OncoFinder for the analysis of new type of biomarkers reflecting activation of intracellular signaling and metabolic molecular pathways. These biomarkers may be linked with the sensitivity to anticancer drugs. In this study, we compared the experimental data obtained in our laboratory and in the Genomics of Drug Sensitivity in Cancer (GDS) project for testing response to anticancer drugs and transcriptomes of various human cell lines. The microarray-based profiling of transcriptomes was performed for the cell lines before the addition of drugs to the medium, and experimental growth inhibition curves were built for each drug, featuring characteristic IC50 values. We assayed here four target drugs - Pazopanib, Sorafenib, Sunitinib and Temsirolimus, and 238 different cell lines, of which 11 were profiled in our laboratory and 227 - in GDS project. Using the OncoFinder-processed transcriptomic data on ~600 molecular pathways, we identified pathways showing significant correlation between pathway activation strength (PAS) and IC50 values for these drugs. Correlations reflect relationships between response to drug and pathway activation features. We intersected the results and found molecular pathways significantly correlated in both our assay and GDS project. For most of these pathways, we generated molecular models of their interaction with known molecular target(s) of the respective drugs. For the first time, our study uncovered mechanisms underlying cancer cell response to drugs at the high-throughput molecular interactomic level.