Evaluation and integration of cancer gene classifiers: identification and ranking of plausible drivers.
Ontology highlight
ABSTRACT:
SUBMITTER: Liu Y
PROVIDER: S-EPMC4650817 | biostudies-other | 2015 May
REPOSITORIES: biostudies-other
Publications
Evaluation and integration of cancer gene classifiers: identification and ranking of plausible drivers.
Liu Yang Y Tian Feng F Hu Zhenjun Z DeLisi Charles C
Scientific reports 20150511
The number of mutated genes in cancer cells is far larger than the number of mutations that drive cancer. The difficulty this creates for identifying relevant alterations has stimulated the development of various computational approaches to distinguishing drivers from bystanders. We develop and apply an ensemble classifier (EC) machine learning method, which integrates 10 classifiers that are publically available, and apply it to breast and ovarian cancer. In particular we find the following: (1 ...[more]
Similar Datasets
Project description:BackgroundRecent cancer genomic studies have generated detailed molecular data on a large number of cancer patients. A key remaining problem in cancer genomics is the identification of driver genes.ResultsWe propose BetweenNet, a computational approach that integrates genomic data with a protein-protein interaction network to identify cancer driver genes. BetweenNet utilizes a measure based on betweenness centrality on patient specific networks to identify the so-called outlier genes that correspond to dysregulated genes for each patient. Setting up the relationship between the mutated genes and the outliers through a bipartite graph, it employs a random-walk process on the graph, which provides the final prioritization of the mutated genes. We compare BetweenNet against state-of-the art cancer gene prioritization methods on lung, breast, and pan-cancer datasets.ConclusionsOur evaluations show that BetweenNet is better at recovering known cancer genes based on multiple reference databases. Additionally, we show that the GO terms and the reference pathways enriched in BetweenNet ranked genes and those that are enriched in known cancer genes overlap significantly when compared to the overlaps achieved by the rankings of the alternative methods.
| S-EPMC7877041 | biostudies-literature
Project description:BackgroundThe human immune system is responsible for protecting the host from infection. However, in immunocompromised individuals the risk of infection increases substantially with possible drastic consequences. In extreme, systemic infection can lead to sepsis which is responsible for innumerous deaths worldwide. Amongst its causes are infections by bacteria and fungi. To increase survival, it is mandatory to identify the type of infection rapidly. Discriminating between fungal and bacterial pathogens is key to determine if antifungals or antibiotics should be administered, respectively. For this, in situ experiments have been performed to determine regulation mechanisms of the human immune system to identify biomarkers. However, these studies led to heterogeneous results either due different laboratory settings, pathogen strains, cell types and tissues, as well as the time of sample extraction, to name a few.MethodsTo generate a gene signature capable of discriminating between fungal and bacterial infected samples, we employed Mixed Integer Linear Programming (MILP) based classifiers on several datasets comprised of the above mentioned pathogens.ResultsWhen combining the classifiers by a joint optimization we could increase the consistency of the biomarker gene list independently of the experimental setup. An increase in pairwise overlap (the number of genes that overlap in each cross-validation) of 43% was obtained by this approach when compared to that of single classifiers. The refined gene list was composed of 19 genes and ranked according to consistency in expression (up- or down-regulated) and most of them were linked either directly or indirectly to the ERK-MAPK signalling pathway, which has been shown to play a key role in the immune response to infection. Testing of the identified 12 genes on an unseen dataset yielded an average accuracy of 83%.ConclusionsIn conclusion, our method allowed the combination of independent classifiers and increased consistency and reliability of the generated gene signatures.
| S-EPMC5553868 | biostudies-literature
Project description:BACKGROUND:Immune escape is one of the hallmarks of cancer and several new treatment approaches attempt to modulate and restore the immune system's capability to target cancer cells. At the heart of the immune recognition process lies antigen presentation from somatic mutations. These neo-epitopes are emerging as attractive targets for cancer immunotherapy and new strategies for rapid identification of relevant candidates have become a priority. METHODS:We carefully screen TCGA data sets for recurrent somatic amino acid exchanges and apply MHC class I binding predictions. RESULTS:We propose a method for in silico selection and prioritization of candidates which have a high potential for neo-antigen generation and are likely to appear in multiple patients. While the percentage of patients carrying a specific neo-epitope and HLA-type combination is relatively small, the sheer number of new patients leads to surprisingly high reoccurence numbers. We identify 769 epitopes which are expected to occur in 77629 patients per year. CONCLUSION:While our candidate list will definitely contain false positives, the results provide an objective order for wet-lab testing of reusable neo-epitopes. Thus recurrent neo-epitopes may be suitable to supplement existing personalized T cell treatment approaches with precision treatment options.
| S-EPMC6882202 | biostudies-literature
Project description:Identifying driver genes in cancer remains a crucial bottleneck in therapeutic development and basic understanding of the disease. We developed Helios, an algorithm that integrates genomic data from primary tumors with data from functional RNAi screens to pinpoint driver genes within large recurrently amplified regions of DNA. Applying Helios to breast cancer data identified a set of candidate drivers highly enriched with known drivers (p < 10(-14)). Nine of ten top-scoring Helios genes are known drivers of breast cancer, and in vitro validation of 12 candidates predicted by Helios found ten conferred enhanced anchorage-independent growth, demonstrating Helios's exquisite sensitivity and specificity. We extensively characterized RSF-1, a driver identified by Helios whose amplification correlates with poor prognosis, and found increased tumorigenesis and metastasis in mouse models. We have demonstrated a powerful approach for identifying driver genes and how it can yield important insights into cancer.
| S-EPMC4258423 | biostudies-literature
Project description:BACKGROUND: High throughput screening technologies enable biologists to generate candidate genes at a rate that, due to time and cost constraints, cannot be studied by experimental approaches in the laboratory. Thus, it has become increasingly important to prioritize candidate genes for experiments. To accomplish this, researchers need to apply selection requirements based on their knowledge, which necessitates qualitative integration of heterogeneous data sources and filtration using multiple criteria. A similar approach can also be applied to putative candidate gene relationships. While automation can assist in this routine and imperative procedure, flexibility of data sources and criteria must not be sacrificed. A tool that can optimize the trade-off between automation and flexibility to simultaneously filter and qualitatively integrate data is needed to prioritize candidate genes and generate composite networks from heterogeneous data sources. RESULTS: We developed the java application, EnRICH (Extraction and Ranking using Integration and Criteria Heuristics), in order to alleviate this need. Here we present a case study in which we used EnRICH to integrate and filter multiple candidate gene lists in order to identify potential retinal disease genes. As a result of this procedure, a candidate pool of several hundred genes was narrowed down to five candidate genes, of which four are confirmed retinal disease genes and one is associated with a retinal disease state. CONCLUSIONS: We developed a platform-independent tool that is able to qualitatively integrate multiple heterogeneous datasets and use different selection criteria to filter each of them, provided the datasets are tables that have distinct identifiers (required) and attributes (optional). With the flexibility to specify data sources and filtering criteria, EnRICH automatically prioritizes candidate genes or gene relationships for biologists based on their specific requirements. Here, we also demonstrate that this tool can be effectively and easily used to apply highly specific user-defined criteria and can efficiently identify high quality candidate genes from relatively sparse datasets.
| S-EPMC3564850 | biostudies-other
Project description:Automated protein annotation using the Gene Ontology (GO) plays an important role in the biosciences. Evaluation has always been considered central to developing novel annotation methods, but little attention has been paid to the evaluation metrics themselves. Evaluation metrics define how well an annotation method performs and allows for them to be ranked against one another. Unfortunately, most of these metrics were adopted from the machine learning literature without establishing whether they were appropriate for GO annotations. We propose a novel approach for comparing GO evaluation metrics called Artificial Dilution Series (ADS). Our approach uses existing annotation data to generate a series of annotation sets with different levels of correctness (referred to as their signal level). We calculate the evaluation metric being tested for each annotation set in the series, allowing us to identify whether it can separate different signal levels. Finally, we contrast these results with several false positive annotation sets, which are designed to expose systematic weaknesses in GO assessment. We compared 37 evaluation metrics for GO annotation using ADS and identified drastic differences between metrics. We show that some metrics struggle to differentiate between different signal levels, while others give erroneously high scores to the false positive data sets. Based on our findings, we provide guidelines on which evaluation metrics perform well with the Gene Ontology and propose improvements to several well-known evaluation metrics. In general, we argue that evaluation metrics should be tested for their performance and we provide software for this purpose (https://bitbucket.org/plyusnin/ads/). ADS is applicable to other areas of science where the evaluation of prediction results is non-trivial.
| S-EPMC6855565 | biostudies-literature
Project description:BackgroundImprovement of patient quality of life is the ultimate goal of biomedical research, particularly when dealing with complex, chronic and debilitating conditions such as inflammatory bowel disease (IBD). This is largely dependent on receiving an accurate and rapid diagnose, an effective treatment and in the prediction and prevention of side effects and complications. The low sensitivity and specificity of current markers burden their general use in the clinical practice. New biomarkers with accurate predictive ability are needed to achieve a personalized approach that take the inter-individual differences into consideration.MethodsWe performed a high throughput approach using microarray gene expression profiling of colon pinch biopsies from IBD patients to identify predictive transcriptional signatures associated with intestinal inflammation, differential diagnosis (Crohn's disease or ulcerative colitis), response to glucocorticoids (resistance and dependence) or prognosis (need for surgery). Class prediction was performed with self-validating Prophet software package.ResultsTranscriptional profiling divided patients in two subgroups that associated with degree of inflammation. Class predictors were identified with predictive accuracy ranging from 67 to 100%. The expression accuracy was confirmed by real time-PCR quantification. Functional analysis of the predictor genes showed that they play a role in immune responses to bacteria (PTN, OLFM4 and LILRA2), autophagy and endocytocis processes (ATG16L1, DNAJC6, VPS26B, RABGEF1, ITSN1 and TMEM127) and glucocorticoid receptor degradation (STS and MMD2).ConclusionsWe conclude that using analytical algorithms for class prediction discovery can be useful to uncover gene expression profiles and identify classifier genes with potential stratification utility of IBD patients, a major step towards personalized therapy.
| S-EPMC3796518 | biostudies-literature
Project description:The emergence of large-scale multi-omics data warrants method development for data integration. Genomic studies from cancer patients have identified epigenetic and genetic regulators - such as methylation marks, somatic mutations, and somatic copy number alterations (SCNAs), among others - as predictive features of cancer outcome. However, identification of "driver genes" associated with a given alteration remains a challenge. To this end, we developed a computational tool, iEDGE, to model cis and trans effects of (epi-)DNA alterations and identify potential cis driver genes, where cis and trans genes denote those genes falling within and outside the genomic boundaries of a given (epi-)genetic alteration, respectively. iEDGE first identifies the cis and trans gene expression signatures associated with the presence/absence of a particular epi-DNA alteration across samples. It then applies tests of statistical mediation to determine the cis genes predictive of the trans gene expression. Finally, cis and trans effects are annotated by pathway enrichment analysis to gain insights into the underlying regulatory networks. We used iEDGE to perform integrative analysis of SCNAs and gene expression data from breast cancer and 18 additional cancer types included in The Cancer Genome Atlas (TCGA). Notably, cis gene drivers identified by iEDGE were found to be significantly enriched for known driver genes from multiple compendia of validated oncogenes and tumor suppressors, suggesting that the remainder are of equal importance. Furthermore, predicted drivers were enriched for functionally relevant cancer genes with amplification-driven dependencies, which are of potential prognostic and therapeutic value. All the analyses results are accessible at https://montilab.bu.edu/iEDGE. In summary, integrative analysis of SCNAs and gene expression using iEDGE successfully identified known cancer driver genes and putative cancer therapeutic targets across 19 cancer types in the TCGA. The proposed method can easily be applied to the integration of gene expression profiles with other epi-DNA assays in a variety of disease contexts.
| S-EPMC6858347 | biostudies-literature
Project description:Predicting colorectal cancer (CRC) based on fecal microbiota presents a promising method for non-invasive screening of CRC, but the optimization of classification models remains an unaddressed question. The purpose of this study was to systematically evaluate the effectiveness of different supervised machine-learning models in predicting CRC in two independent eastern and western populations. The structures of intestinal microflora in feces in Chinese population (N = 141) were determined by 454 FLX pyrosequencing, and different supervised classifiers were employed to predict CRC based on fecal microbiota operational taxonomic unit (OTUs). As a result, Bayes Net and Random Forest displayed higher accuracies than other algorithms in both populations, although Bayes Net was found with a lower false negative rate than that of Random Forest. Gut microbiota-based prediction was more accurate than the standard fecal occult blood test (FOBT), and the combination of both approaches further improved the prediction accuracy. Moreover, when unclassified OTUs were used as input, the BayesDMNB text algorithm achieved higher accuracy in the Chinese population (AUC=0.994). Taken together, our results suggest that Bayes Net classification model combined with unclassified OTUs may present an accurate method for predicting CRC based on the compositions of gut microbiota.
| S-EPMC5354752 | biostudies-literature
Project description:A variety of species and experimental designs have been used to study genetic influences on alcohol dependence, ethanol response, and related traits. Integration of these heterogeneous data can be used to produce a ranked target gene list for additional investigation.In this study, we performed a unique multi-species evidence-based data integration using three microarray experiments in mice or humans that generated an initial alcohol dependence (AD) related genes list, human linkage and association results, and gene sets implicated in C. elegans and Drosophila. We then used permutation and false discovery rate (FDR) analyses on the genome-wide association studies (GWAS) dataset from the Collaborative Study on the Genetics of Alcoholism (COGA) to evaluate the ranking results and weighting matrices. We found one weighting score matrix could increase FDR based q-values for a list of 47 genes with a score greater than 2. Our follow up functional enrichment tests revealed these genes were primarily involved in brain responses to ethanol and neural adaptations occurring with alcoholism.These results, along with our experimental validation of specific genes in mice, C. elegans and Drosophila, suggest that a cross-species evidence-based approach is useful to identify candidate genes contributing to alcoholism.
| S-EPMC3535715 | biostudies-literature