Computational purification of individual tumor gene expression profiles leads to significant improvements in prognostic prediction.
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ABSTRACT:
SUBMITTER: Quon G
PROVIDER: S-EPMC3706990 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundIdentifying transcriptional enhancers and other cis-regulatory modules (CRMs) is an important goal of post-sequencing genome annotation. Computational approaches provide a useful complement to empirical methods for CRM discovery, but it is critical that we develop effective means to evaluate their performance in terms of estimating their sensitivity and specificity.ResultsWe introduce here pCRMeval, a pipeline for in silico evaluation of any enhancer prediction tools that are flexible enough to be applied to the Drosophila melanogaster genome. pCRMeval compares the result of predictions with the extensive existing knowledge of experimentally-validated Drosophila CRMs in order to estimate the precision and relative sensitivity of the prediction method. In the case of supervised prediction methods-when training data composed of validated CRMs are used-pCRMeval can also assess the sensitivity of specific training sets. We demonstrate the utility of pCRMeval through evaluation of our SCRMshaw CRM prediction method and training data. By measuring the impact of different parameters on SCRMshaw performance, as assessed by pCRMeval, we develop a more robust version of SCRMshaw, SCRMshaw_HD, that improves the number of predictions while maintaining sensitivity and specificity. Our analysis also demonstrates that SCRMshaw_HD, when applied to increasingly less well-assembled genomes, maintains its strong predictive power with only a minor drop-off in performance.ConclusionOur pCRMeval pipeline provides a general framework for evaluation that can be applied to any CRM prediction method, particularly a supervised method. While we make use of it here primarily to test and improve a particular method for CRM prediction, SCRMshaw, pCRMeval should provide a valuable platform to the research community not only for evaluating individual methods, but also for comparing between competing methods.
| S-EPMC6451241 | biostudies-literature
Project description:BackgroundDeep brain stimulation (DBS) is an established therapy for patients with Parkinson's disease. In silico computer models for DBS hold the potential to inform a selection of stimulation parameters. In recent years, the focus has shifted towards DBS-induced firing in myelinated axons, deemed particularly relevant for the external modulation of neural activity.ObjectiveThe aim of this project was to investigate correlations between patient-specific pathway activation profiles and clinical motor improvement.MethodsWe used the concept of pathway activation modeling, which incorporates advanced volume conductor models and anatomically authentic fiber trajectories to estimate DBS-induced action potential initiation in anatomically plausible pathways that traverse in close proximity to targeted nuclei. We applied the method on two retrospective datasets of DBS patients, whose clinical improvement had been evaluated according to the motor part of the Unified Parkinson's Disease Rating Scale. Based on differences in outcome and activation levels for intrapatient DBS protocols in a training cohort, we derived a pathway activation profile that theoretically induces a complete alleviation of symptoms described by UPDRS-III. The profile was further enhanced by analyzing the importance of matching activation levels for individual pathways.ResultsThe obtained profile emphasized the importance of activation in pathways descending from the motor-relevant cortical regions as well as the pallidothalamic pathways. The degree of similarity of patient-specific profiles to the optimal profile significantly correlated with clinical motor improvement in a test cohort.ConclusionPathway activation modeling has a translational utility in the context of motor symptom alleviation in Parkinson's patients treated with DBS.
| S-EPMC9474565 | biostudies-literature
Project description:Tumor progression is typically accompanied by an accumulation of driver and passenger somatic mutations. A handful of those mutations occur in protein coding genes which introduce non-synonymous polymorphisms. Certain substitutions may give rise to novel, tumor-associated antigens or neoantigens, presentable by cancer cells to the host adaptive immune system. As antigen recognition is the core of an effective immune response, the identification of patient tumor specific antigens derived from transformed cells is of importance for immunotherapeutic approaches. Recent technological advances in DNA sequencing of tumor genomes, advances in gene expression analysis, algorithm development for antigen predictions and methods for T-cell receptor (TCR) repertoire sequencing have facilitated the selection of candidate immunogenic neoantigens. In this regard, multiple research groups have reported encouraging results of neoantigen-based cancer vaccines that generate tumor antigen specific immune responses, both in mouse models and clinical trials. Additionally, both the quantity and quality of neoantigens has been shown to have predictive value for clinical outcomes in checkpoint-blockade immunotherapy in certain tumor types. Neoantigen recognition by vaccination or through adoptive T cell therapy may have unprecedented potential to advance cancer immunotherapy in combination with other approaches. In our review we discuss three parameters regarding neoantigens: computational methods for epitope prediction, experimental methods for epitope immunogenicity validation and future directions for improvement of those methods. Within each section, we will describe the advantages and limitations of existing methods as well as highlight pressing fundamental problems to be addressed.
| S-EPMC7025577 | biostudies-literature
Project description:Inflammation is an important feature of carcinogenesis. Tumor-associated macrophages (TAMs) can be associated with either poor or improved prognosis, depending on their properties and polarization. Current knowledge of the prognostic significance of TAMs in bladder cancer is limited and was investigated in this study. We analyzed 184 urothelial bladder cancer patients undergoing transurethral resection of a bladder tumor or radical cystectomy. CD68 (pan-macrophage marker), MAC387 (polarized towards type 1 macrophages), and CLEVER-1/Stabilin-1 (type 2 macrophages and lymphatic/blood vessels) were detected immunohistochemically. The median follow-up time was 6.0 years. High macrophage counts associated with a higher pT category and grade. Among patients undergoing transurethral resection, all studied markers apart from CLEVER-1/Stabilin-1 were associated with increased risk of progression and poorer disease-specific and overall survival in univariate analyses. High levels of two macrophage markers (CD68/MAC387+/+ or CD68/CLEVER-1+/+ groups) had an independent prognostic role after transurethral resection in multivariate analyses. In the cystectomy cohort, MAC387, alone and in combination with CD68, was associated with poorer survival in univariate analyses, but none of the markers were independent predictors of outcome in multivariate analyses. In conclusion, this study demonstrates that macrophage phenotypes provide significant independent prognostic information, particularly in bladder cancers undergoing transurethral resection.
| S-EPMC4511010 | biostudies-literature
Project description:To breed osmotic stress-tolerant rice, the mechanisms involved in maintaining root growth under osmotic stress is important to elucidate. In this study, two rice (Oryza sativa L.) cultivars, IR 58 (stress-tolerant cultivar) and Basilanon (stress-sensitive cultivar), were used. After 1, 3, and 7 days of -0.42 MPa osmotic stress treatment induced by polyethylene glycol (PEG) 6000, root metabolomes were analyzed, yielding 276 detected compounds. Among 276 metabolites, 102 metabolites increased with the duration of the stress treatment in IR 58 roots, and only nine metabolites decreased. In contrast, 51 metabolites increased, and 45 metabolites decreased in Basilanon roots. Principal component analysis (PCA) scores clearly indicated differences between the cultivars and the treatments. Pathway analysis showed that the metabolites exhibiting stress-induced increases in IR 58 were those involved in sugar metabolism (such as sucrose 6'-phosphate, glucose 1-phosphate), polyamine and phenylpropanoid metabolisms (such as spermine, spermidine, gamma-aminobutyric acid (GABA)), and glutathione metabolism (such as glutathione, cysteine, cadaverine). IR 58 roots showed an increase in the most proteinogenic amino acids such as proline, serine, glutamine and asparagine. It was also maintained or increased the tricarboxylic acid (TCA) cycle intermediates (citric acid, cis-Aconitic acid, isocitric acid, fumaric acid, malic acid) under osmotic stress compared with that under control. Therefore, IR 58 actively synthesized various metabolites, and the increase in these metabolites contributed to the maintenance of important biological functions such as energy production and antioxidant defense to promote root development under osmotic stress.
| S-EPMC7694650 | biostudies-literature
Project description:Cell lines are key tools for preclinical cancer research, but it remains unclear how well they represent patient tumor samples. Direct comparisons of tumor and cell line transcriptional profiles are complicated by several factors, including the variable presence of normal cells in tumor samples. We thus develop an unsupervised alignment method (Celligner) and apply it to integrate several large-scale cell line and tumor RNA-Seq datasets. Although our method aligns the majority of cell lines with tumor samples of the same cancer type, it also reveals large differences in tumor similarity across cell lines. Using this approach, we identify several hundred cell lines from diverse lineages that present a more mesenchymal and undifferentiated transcriptional state and that exhibit distinct chemical and genetic dependencies. Celligner could be used to guide the selection of cell lines that more closely resemble patient tumors and improve the clinical translation of insights gained from cell lines.
| S-EPMC7782593 | biostudies-literature
Project description:De novo experimental drug discovery is an expensive and time-consuming task. It requires the identification of drug-target interactions (DTIs) towards targets of biological interest, either to inhibit or enhance a specific molecular function. Dedicated computational models for protein simulation and DTI prediction are crucial for speed and to reduce the costs associated with DTI identification. In this paper we present a computational pipeline that enables the discovery of putative leads for drug repositioning that can be applied to any microbial proteome, as long as the interactome of interest is at least partially known. Network metrics calculated for the interactome of the bacterial organism of interest were used to identify putative drug-targets. Then, a random forest classification model for DTI prediction was constructed using known DTI data from publicly available databases, resulting in an area under the ROC curve of 0.91 for classification of out-of-sampling data. A drug-target network was created by combining 3,081 unique ligands and the expected ten best drug targets. This network was used to predict new DTIs and to calculate the probability of the positive class, allowing the scoring of the predicted instances. Molecular docking experiments were performed on the best scoring DTI pairs and the results were compared with those of the same ligands with their original targets. The results obtained suggest that the proposed pipeline can be used in the identification of new leads for drug repositioning. The proposed classification model is available at http://bioinformatics.ua.pt/software/dtipred/.
| S-EPMC5125559 | biostudies-literature
Project description:Background:Ubiquitination, as a post-translational modification, is a crucial biological process in cell signaling, apoptosis, and localization. Identification of ubiquitination proteins is of fundamental importance for understanding the molecular mechanisms in biological systems and diseases. Although high-throughput experimental studies using mass spectrometry have identified many ubiquitination proteins and ubiquitination sites, the vast majority of ubiquitination proteins remain undiscovered, even in well-studied model organisms. Objective:To reduce experimental costs, computational methods have been introduced to predict ubiquitination sites, but the accuracy is unsatisfactory. If it can be predicted whether a protein can be ubiquitinated or not, it will help in predicting ubiquitination sites. However, all the computational methods so far can only predict ubiquitination sites. Methods:In this study, the first computational method for predicting ubiquitination proteins without relying on ubiquitination site prediction has been developed. The method extracts features from sequence conservation information through a grey system model, as well as functional domain annotation and subcellular localization. Results:Together with the feature analysis and application of the relief feature selection algorithm, the results of 5-fold cross-validation on three datasets achieved a high accuracy of 90.13%, with Matthew's correlation coefficient of 80.34%. The predicted results on an independent test data achieved 87.71% as accuracy and 75.43% of Matthew's correlation coefficient, better than the prediction from the best ubiquitination site prediction tool available. Conclusion:Our study may guide experimental design and provide useful insights for studying the mechanisms and modulation of ubiquitination pathways. The code is available at: https://github.com/Chunhuixu/UBIPredic_QWRCHX.
| S-EPMC7235393 | biostudies-literature
Project description:MotivationRecent evidence shows significant involvement of microRNAs (miRNAs) in the initiation and progression of numerous cancers; however, the role of these in tumor drug resistance remains unknown.ResultsBy comparing global miRNA and mRNA expression patterns, we examined the role of miRNAs in resistance to the 'pure antiestrogen' fulvestrant, using fulvestrant-resistant MCF7-FR cells and their drug-sensitive parental estrogen receptor (ER)-positive MCF7 cells. We identified 14 miRNAs downregulated in MCF7-FR cells and then used both TargetScan and PITA to predict potential target genes. We found a negative correlation between expression of these miRNAs and their predicted target mRNA transcripts. In genes regulated by multiple miRNAs or having multiple miRNA-targeting sites, an even stronger negative correlation was found. Pathway analyses predicted these miRNAs to regulate specific cancer-associated signal cascades. These results suggest a significant role for miRNA-regulated gene expression in the onset of breast cancer antiestrogen resistance, and an improved understanding of this phenomenon could lead to better therapies for this often fatal condition.
| S-EPMC2642642 | biostudies-literature
Project description:Saccharomyces cerevisiae strains from diverse natural habitats harbour a vast amount of phenotypic diversity, driven by interactions between yeast and the respective environment. In grape juice fermentations, strains are exposed to a wide array of biotic and abiotic stressors, which may lead to strain selection and generate naturally arising strain diversity. Certain phenotypes are of particular interest for the winemaking industry and could be identified by screening of large number of different strains. The objective of the present work was to use data mining approaches to identify those phenotypic tests that are most useful to predict a strain's potential for winemaking. We have constituted a S. cerevisiae collection comprising 172 strains of worldwide geographical origins or technological applications. Their phenotype was screened by considering 30 physiological traits that are important from an oenological point of view. Growth in the presence of potassium bisulphite, growth at 40 °C, and resistance to ethanol were mostly contributing to strain variability, as shown by the principal component analysis. In the hierarchical clustering of phenotypic profiles the strains isolated from the same wines and vineyards were scattered throughout all clusters, whereas commercial winemaking strains tended to co-cluster. Mann-Whitney test revealed significant associations between phenotypic results and strain's technological application or origin. Naïve Bayesian classifier identified 3 of the 30 phenotypic tests of growth in iprodion (0.05 mg/mL), cycloheximide (0.1 µg/mL) and potassium bisulphite (150 mg/mL) that provided most information for the assignment of a strain to the group of commercial strains. The probability of a strain to be assigned to this group was 27% using the entire phenotypic profile and increased to 95%, when only results from the three tests were considered. Results show the usefulness of computational approaches to simplify strain selection procedures.
| S-EPMC3713011 | biostudies-literature