Focus on Molecules: interphotoreceptor retinoid-binding protein (IRBP).
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Animals↗
Biomedical subjects
Debashis Ghosh
Publications and source records attributed to Debashis Ghosh.
Identification of new genetic regions more prevalent in nontypeable Haemophilus influenzae otitis media strains than in throat strains.
Nontypeable (NT) Haemophilus influenzae strains cause significant respiratory illness and are isolated from up to half of middle ear aspirates from children with acute otitis media. Previous studies have identified two genes, lic2B and hmwA, that are associated with NT H. influenzae strains isolated from the middle ears of children with otitis media but that are not associated with NT H. influenzae strains isolated from the throats of healthy children, suggesting that they may play a role in virulence in otitis media. In this study, genomic subtraction was used to identify additional genetic regions unique to middle ear strains. The genome of NT H. influenzae middle ear strain G622 was subtracted from that of NT H. influenzae throat strain 23221, and the resultant gene regions unique to the middle ear strain were identified. Subsequently, the relative prevalence of the middle ear-specific gene regions among a large panel of otitis media and throat strains was determined by dot blot hybridization. By this approach, nine genetic regions were found to be significantly more prevalent in otitis media strains. Classification tree analysis of lic2B, hmwA, and the nine new potential otitis media virulence genes revealed two H. influenzae pathotypes associated with otitis media.
Bacterial Proteins↗
Peptide length-based prediction of peptide-MHC class II binding.
MOTIVATION: Algorithms for predicting peptide-MHC class II binding are typically similar, if not identical, to methods for predicting peptide-MHC class I binding despite known differences between the two scenarios. We investigate whether representing one of these differences, the greater range of peptide lengths binding MHC class II, improves the performance of these algorithms. RESULTS: A non-linear relationship between peptide length and peptide-MHC class II binding affinity was identified in the data available for several MHC class II alleles. Peptide length was incorporated into existing prediction algorithms using one of several modifications: using regression to pre-process the data, using peptide length as an additional variable within the algorithm, or representing register shifting in longer peptides. For several datasets and at least two algorithms these modifications consistently improved prediction accuracy. AVAILABILITY: http://malthus.micro.med.umich.edu/Bioinformatics
Algorithms↗
Epidermal growth factor receptor (ErbB1) expression in prostate cancer progression: correlation with androgen independence.
BACKGROUND: The role of the epidermal growth factor receptor (ErbB1) in the progression of prostate cancer is incompletely understood. METHODS: Tissue microarrays from hormone-naive and advanced androgen-independent tumors were used to investigate the role of ErbB1 in prostate cancer progression. RESULTS: ErbB1 expression in tumor tissues was strongly associated with hormone-refractory status (odds ratio = 6.67, 95% CI = (2.6, 17.4), P = 0.0001). However, ErbB1 overexpression was not a statistically significant covariate in a multivariate proportional hazards model for biochemical failure of hormone-naïve prostate cancer. Moreover, ErbB1 overexpression was not associated with tumor differentiation (P = 0.44), positive margins (P = 0.53), seminal vesicle invasion (P = 0.69), extraprostatic extension (P = 0.10), or preoperative PSA (P = 0.18) in the hormone-naïve group. CONCLUSIONS: These findings are consistent with a model in which ErbB1 expression increases during the development of the androgen-independent state, and suggest that drugs targeted toward ErbB signaling could be of therapeutic relevance in the management of advanced prostatic carcinoma.
Adult↗
Eigengene-based linear discriminant model for tumor classification using gene expression microarray data.
MOTIVATION: The nearest shrunken centroids classifier has become a popular algorithm in tumor classification problems using gene expression microarray data. Feature selection is an embedded part of the method to select top-ranking genes based on a univariate distance statistic calculated for each gene individually. The univariate statistics summarize gene expression profiles outside of the gene co-regulation network context, leading to redundant information being included in the selection procedure. RESULTS: We propose an Eigengene-based Linear Discriminant Analysis (ELDA) to address gene selection in a multivariate framework. The algorithm uses a modified rotated Spectral Decomposition (SpD) technique to select 'hub' genes that associate with the most important eigenvectors. Using three benchmark cancer microarray datasets, we show that ELDA selects the most characteristic genes, leading to substantially smaller classifiers than the univariate feature selection based analogues. The resulting de-correlated expression profiles make the gene-wise independence assumption more realistic and applicable for the shrunken centroids classifier and other diagonal linear discriminant type of models. Our algorithm further incorporates a misclassification cost matrix, allowing differential penalization of one type of error over another. In the breast cancer data, we show false negative prognosis can be controlled via a cost-adjusted discriminant function. AVAILABILITY: R code for the ELDA algorithm is available from author upon request.
Artificial Intelligence↗
Incorporating monotonicity into the evaluation of a biomarker.
In the assessment of clinical utility of biomarkers, case-control studies are often undertaken based on existing serum samples. A common assumption made in these studies is that higher levels of the biomarker are associated with increased disease risk. In this article, we consider methods of analysis in which monotonicity is incorporated in associating the biomarker and the clinical outcome. We consider the roles of discrimination versus association and assess methods for both goals. In addition, we propose a semiparametric isotonic regression model for binary data and describe a simple estimation procedure as well as attendant inferential procedures. We apply the various methodologies to data from a prostate cancer study involving a serum biomarker.
Algorithms↗
COPA--cancer outlier profile analysis.
UNLABELLED: Chromosomal translocations are common in cancer, and in some cases may be causal in the progression of the disease. Using microarrays, in which the expression of thousands of genes are simultaneously measured, could potentially allow one to detect recurrent translocations for a particular cancer type. Standard statistical tests, such as the t-test are not suited for detecting these translocations, but a simple test based on robust centering and scaling of the data to help detect outlier samples, followed by a search for pairs of samples with mutually exclusive outliers, may be used to find genes involved in recurrent translocations. We have implemented this method, termed Cancer Outlier Profile Analysis (COPA) in an R package (that we call the copa package), and show its applicability on a publicly available dataset. AVAILABILITY: http://www.bioconductor.org
Algorithms↗
Semi-parametric inferences for association with semi-competing risks data.
In many biomedical studies, it is of interest to assess dependence between bivariate failure time data. We focus here on a special type of such data, referred to as semi-competing risks data. In this article, we develop methods for making inferences regarding dependence of semi-competing risks data across strata of a discrete covariate Z. A class of rank statistics for testing constancy of association across strata are proposed; its asymptotic properties are also derived. We develop a novel re-sampling-based technique for calculating the variances of the proposed test statistics. In addition, we develop methods for combining test statistics for assessing marginal effects of Z on the dependent censoring variable as well as its effects on association. The finite-sample properties of the proposed methodology are assessed using simulation studies, and they are applied to data from a leukaemia transplantation study.
Bone Marrow Transplantation↗
Modelling tumour biology-progression relationships in screening trials.
There has been some recent work in the statistical literature for modelling the relationship between tumour biology properties and tumour progression in screening trials. While non-parametric methods have been proposed for estimation of the tumour size distribution at which metastatic transition occurs, their asymptotic properties have not been studied. In addition, no testing or regression methods are available so that potential confounders and prognostic factors can be adjusted for. We develop a unified approach to non-parametric and semi-parametric analysis of modelling tumour size-metastasis data in this article. An association between the models considered by previous authors with survival data structures is discussed. Based on this relationship, we develop non-parametric testing procedures and semi-parametric regression methodology of modelling the effect of size of tumour on the probability at which metastatic transitions occur in two situations. Asymptotic properties of these estimators are provided. The proposed methodology is applied to data from a screening study in lung cancer.
Adenocarcinoma↗
Shrunken p-values for assessing differential expression with applications to genomic data analysis.
In many scientific problems involving high-throughput technology, inference must be made involving several hundreds or thousands of hypotheses. Recent attention has focused on how to address the multiple testing issue; much focus has been devoted toward the use of the false discovery rate. In this article, we consider an alternative estimation procedure titled shrunken p-values for assessing differential expression (SPADE). The estimators are motivated by risk considerations from decision theory and lead to a completely new method for adjustment in the multiple testing problem. In addition, the decision-theoretic framework can be used to derive a decision rule for controlling the number of false positive results. Some theoretical results are outlined. The proposed methodology is illustrated using simulation studies and with application to data from a prostate cancer gene expression profiling study.
Biometry↗
Diabetes reporting as a cause of death: results from the Translating Research Into Action for Diabetes (TRIAD) study.
OBJECTIVE: To determine the frequency of reporting of diabetes on death certificates of decedents with known diabetes, define factors associated with reporting of diabetes, and describe trends in reporting over time. RESEARCH DESIGN AND METHODS: Data were obtained from 11,927 participants with diabetes who were enrolled in the Translating Research Into Action for Diabetes study, a multicenter prospective observational study of diabetes care in managed care. Data on decedents (n = 540) were obtained from the National Death Index. The primary dependent variable was the presence of ICD-10 codes for diabetes on the death certificate. Covariates included age at death, sex, race/ethnicity, education, income, duration of diabetes, type of diabetes, diabetes treatment, smoking status, and number of comorbidities. RESULTS: Diabetes was recorded on 39% of death certificates and as the underlying cause of death for 10% of decedents with diabetes. Diabetes was significantly less likely to be reported on the death certificates of decedents with diabetes dying of cancer. Predictors of recording diabetes anywhere on the death certificate included longer duration of diabetes and insulin treatment. Longer duration of diabetes, insulin treatment, and fewer comorbidities were associated with recording of diabetes as the underlying cause of death. CONCLUSIONS: Diabetes is much more likely to be reported on the death certificates of diabetic individuals who die of cardiovascular causes. Reporting of diabetes on death certificates has been stable over time. Death certificates underestimate the prevalence of diabetes among decedents and present a biased picture of the causes of death among people with diabetes.
Cause of Death↗
Identification of GATA3 as a breast cancer prognostic marker by global gene expression meta-analysis.
GATA binding protein 3 (GATA3) is a transcriptional activator highly expressed by the luminal epithelial cells in the breast. Here we did a meta-analysis of the available breast cancer cDNA data sets on a cohort of 305 patients and found that GATA3 was one of the top genes with low expression in invasive carcinomas with poor clinical outcome. To validate its prognostic utility, we did a tissue microarray analysis on a cohort of 139 consecutive invasive carcinomas (n = 417 tissue samples) immunostained with a monoclonal antibody against GATA3. Low GATA3 expression was associated with higher histologic grade (P < 0.001), positive nodes (P = 0.002), larger tumor size (P = 0.03), negative estrogen receptor and progesterone receptor (P < 0.001 for both), and HER2-neu overexpression (P = 0.03). Patients whose tumors expressed low GATA3 had significantly shorter overall and disease-free survival when compared with those whose tumors had high GATA3 levels. The hazard ratio of metastasis or recurrence according to the GATA3 status was 0.31 (95% confidence interval, 0.13-0.74; P = 0.009). Cox multivariate analysis showed that GATA3 had independent prognostic significance above and beyond conventional variables. Our data suggest that immunohistochemical analysis of GATA3 may be the basis for a new clinically applicable test to predict tumor recurrence early in the progression of breast cancer.
Biomarkers, Tumor↗
Structural basis of antigen mimicry in a clinically relevant melanoma antigen system.
Although mimics of human tumor antigens are effective immunogens to overcome host unresponsiveness to the nominal antigen, the structural basis of this mimicry remains poorly defined. Therefore, in this study we have characterized the structural basis of the human high molecular weight-melanoma-associated antigen (HMW-MAA) mimicry by the mouse anti-idiotypic (anti-id) monoclonal antibody (mAb) MK2-23. Using x-ray crystallography, we have characterized the three-dimensional structure of the anti-id mAb MK2-23 Fab' and shown that its heavy chain complementarity-determining region (CDR3) (H3) and its light chain CDR1 (L1) are closely associated. These moieties are the source of HMW-MAA mimicry, since they display partial amino acid sequence homology along with a similar structural fold with the HMW-MAA core protein. Furthermore, a 15-residue peptide comprising the H3 loop of anti-id mAb MK2-23 demonstrates HMW-MAA-like in vitro and in vivo reactivity. This peptide in conjunction with the structural data will facilitate the characterization of the effect of the degree of antigen mimicry on the induction of a self-antigen-specific immune response by a mimic.
Amino Acid Sequence↗
Autoantibody signatures in prostate cancer.
BACKGROUND: New biomarkers, such as autoantibody signatures, may improve the early detection of prostate cancer. METHODS: With a phage-display library derived from prostate-cancer tissue, we developed and used phage protein microarrays to analyze serum samples from 119 patients with prostate cancer and 138 controls, with the samples equally divided into training and validation sets. A phage-peptide detector that was constructed from the training set was evaluated on an independent validation set of 128 serum samples (60 from patients with prostate cancer and 68 from controls). RESULTS: A 22-phage-peptide detector had 88.2 percent specificity (95 percent confidence interval, 0.78 to 0.95) and 81.6 percent sensitivity (95 percent confidence interval, 0.70 to 0.90) in discriminating between the group with prostate cancer and the control group. This panel of peptides performed better than did prostate-specific antigen (PSA) in distinguishing between the group with prostate cancer and the control group (area under the curve for the autoantibody signature, 0.93; 95 percent confidence interval, 0.88 to 0.97; area under the curve for PSA, 0.80; 95 percent confidence interval, 0.71 to 0.88). Logistic-regression analysis revealed that the phage-peptide panel provided additional discriminative power over PSA (P<0.001). Among the 22 phage peptides used as a detector, 4 were derived from in-frame, named coding sequences. The remaining phage peptides were generated from untranslated sequences. CONCLUSIONS: Autoantibodies against peptides derived from prostate-cancer tissue could be used as the basis for a screening test for prostate cancer.
Aged↗
Classification and selection of biomarkers in genomic data using LASSO.
High-throughput gene expression technologies such as microarrays have been utilized in a variety of scientific applications. Most of the work has been done on assessing univariate associations between gene expression profiles with clinical outcome (variable selection) or on developing classification procedures with gene expression data (supervised learning). We consider a hybrid variable selection/classification approach that is based on linear combinations of the gene expression profiles that maximize an accuracy measure summarized using the receiver operating characteristic curve. Under a specific probability model, this leads to the consideration of linear discriminant functions. We incorporate an automated variable selection approach using LASSO. An equivalence between LASSO estimation with support vector machines allows for model fitting using standard software. We apply the proposed method to simulated data as well as data from a recently published prostate cancer study.
Journal Article↗
Analysis of the 2.0 A crystal structure of the protein-DNA complex of the human PDEF Ets domain bound to the prostate specific antigen regulatory site.
PDEF, a prostate epithelial specific transcription factor, is a member of the Ets family of DNA binding proteins. Here we report a 2.0 A crystal structure of the PDEF Ets domain in complex with a natural, high-affinity DNA binding site in the promoter/enhancer region of the human prostate specific antigen gene. Comparison of the PDEF-DNA complex with other Ets complexes revealed key features that are shared among Ets members, as well as important differences in substrate specification at both the "GGA" core and the flanking regions of the DNA site. The combination of the serine residue at position 308 and the glutamine at position 311 explains the previous observation that the PDEF binds preferentially to a thymine at the +4 position of its binding site. Despite the common essential features that are shared among Ets members, PDEF demonstrates distinct patterns of interactions at different positions of DNA in achieving sequence specific recognition. Collectively, the common and unique interactions with both the DNA bases and the backbone phosphates lead to substrate specificity and individual preference for certain DNA sites.
3' Flanking Region↗
Linkage disequilibrium: ancient history drives the new genetics.
This brief review provides a summary of the biological causes of genetic association between tightly linked markers--termed linkage disequilibrium--and unlinked markers--termed population structure. We also review the utility of linkage disequilibrium data in gene mapping in isolated populations, in the estimation of recombination rates and in studying the history of particular alleles, including the detection of natural selection. We discuss current understanding of the extent and patterns of linkage disequilibrium in the genome, and its promise for genetic association studies in complex disease. Finally, we highlight the importance of using appropriate statistical procedures, such as the false discovery rate, to maximize the chances of success in large scale association studies.
Chromosome Mapping↗
A model-based scan statistic for identifying extreme chromosomal regions of gene expression in human tumors.
MOTIVATION: The analysis of gene expression data in its chromosomal context has been a recent development in cancer research. However, currently available methods fail to account for variation in the distance between genes, gene density and genomic features (e.g. GC content) in identifying increased or decreased chromosomal regions of gene expression. RESULTS: We have developed a model-based scan statistic that accounts for these aspects of the complex landscape of the human genome in the identification of extreme chromosomal regions of gene expression. This method may be applied to gene expression data regardless of the microarray platform used to generate it. To demonstrate the accuracy and utility of this method, we applied it to a breast cancer gene expression dataset and tested its ability to predict regions containing medium-to-high level DNA amplification (DNA ratio values >2). A classifier was developed from the scan statistic results that had a 10-fold cross-validated classification rate of 93% and a positive predictive value of 88%. This result strongly suggests that the model-based scan statistic and the expression characteristics of an increased chromosomal region of gene expression can be used to accurately predict chromosomal regions containing amplified genes. AVAILABILITY: Functions in the R-language are available from the author upon request. CONTACT: fcouples@umich.edu.
Base Composition↗