The identification of phosphoglycerate kinase-1 and histone H4 autoantibodies in pancreatic cancer patient serum using a natural protein microarray.

Protein microarrays have been used to explore whether a humoral response to pancreatic cancer-specific tumor antigens has utility as a biomarker of pancreatic cancer. To determine if such arrays can be used to identify novel autoantibodies in the sera from pancreatic cancer patients, proteins from a pancreatic adenocarcinoma cell line (MIAPACA) were resolved by 2-D liquid-based separations, and then arrayed on nitrocellulose slides. The slides were probed with serum from a set of patients diagnosed with pancreatic cancer and compared with age- and sex-matched normal subjects.

Global gene expression analysis reveals evidence for decreased lipid biosynthesis and increased innate immunity in uninvolved psoriatic skin.

Psoriasis is a genetically determined inflammatory skin disease. Although the transition from uninvolved into lesional skin is accompanied by changes in the expression of multiple genes, much less is known about the difference between uninvolved skin from psoriatic patients as opposed to skin from normal individuals. Multiple biochemical and morphological changes were reported decades ago in uninvolved psoriatic skin but remain poorly understood.

Hierarchical hidden Markov model with application to joint analysis of ChIP-chip and ChIP-seq data.

Motivation: Chromatin immunoprecipitation (ChIP) experiments followed by array hybridization, or ChIP-chip, is a powerful approach for identifying transcription factor binding sites (TFBS) and has been widely used. Recently, massively parallel sequencing coupled with ChIP experiments (ChIP-seq) has been increasingly used as an alternative to ChIP-chip, offering cost-effective genome-wide coverage and resolution up to a single base pair. For many well-studied TFs, both ChIP-seq and ChIP-chip experiments have been applied and their data are publicly available.

Genetics, gene expression and bioinformatics of the pituitary gland.

Genetic cases of congenital pituitary hormone deficiency are common and many are caused by transcription factor defects. Mouse models with orthologous mutations are invaluable for uncovering the molecular mechanisms that lead to problems in organ development and typical patient characteristics. We are using mutant mice defective in the transcription factors PROP1 and POU1F1 for gene expression profiling to identify target genes for these critical transcription factors and candidates for cases of pituitary hormone deficiency of unknown aetiology.

Are health-related quality-of-life and self-rated health associated with mortality? Insights from Translating Research Into Action for Diabetes (TRIAD).

Aims: To determine if health-related quality-of-life and self-rated health are associated with mortality in persons with diabetes.

Methods: Survey and medical record data were obtained from 7892 patients with diabetes in Translating Research Into Action for Diabetes (TRIAD), a multicenter prospective observational study of diabetes care in managed care. Vital status at follow-up was determined from the National Death Index.

Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression.

Multiple, complex molecular events characterize cancer development and progression. Deciphering the molecular networks that distinguish organ-confined disease from metastatic disease may lead to the identification of critical biomarkers for cancer invasion and disease aggressiveness. Although gene and protein expression have been extensively profiled in human tumours, little is known about the global metabolomic alterations that characterize neoplastic progression.

Bayesian variable selection with joint modeling of categorical and survival outcomes: an application to individualizing chemotherapy treatment in advanced colorectal cancer.

Colorectal cancer is the second leading cause of cancer related deaths in the United States, with more than 130,000 new cases of colorectal cancer diagnosed each year. Clinical studies have shown that genetic alterations lead to different responses to the same treatment, despite the morphologic similarities of tumors. A molecular test prior to treatment could help in determining an optimal treatment for a patient with regard to both toxicity and efficacy.

Structural basis for androgen specificity and oestrogen synthesis in human aromatase.

Aromatase cytochrome P450 is the only enzyme in vertebrates known to catalyse the biosynthesis of all oestrogens from androgens. Aromatase inhibitors therefore constitute a frontline therapy for oestrogen-dependent breast cancer. In a three-step process, each step requiring 1 mol of O(2), 1 mol of NADPH, and coupling with its redox partner cytochrome P450 reductase, aromatase converts androstenedione, testosterone and 16alpha-hydroxytestosterone to oestrone, 17beta-oestradiol and 17beta,16alpha-oestriol, respectively.

Discovery of transcriptional regulators and signaling pathways in the developing pituitary gland by bioinformatic and genomic approaches.

We report a catalog of the mouse embryonic pituitary gland transcriptome consisting of five cDNA libraries including wild type tissue from E12.5 and E14.5, Prop1(df/df) mutant at E14.

A homozygous missense mutation in the IRBP gene (RBP3) associated with autosomal recessive retinitis pigmentosa.

Purpose: Interphotoreceptor retinoid-binding protein (IRBP) has been considered essential for normal rod and cone function, as it mediates the transport of retinoids between the photoreceptors and the retinal pigment epithelium. This study was performed to determine whether mutations in the IRBP gene (RBP3) are associated with photoreceptor degeneration.

Methods: A consanguineous family was ascertained in which four children had autosomal recessive retinitis pigmentosa (RP).

Reconstructing tumor-wise protein expression in tissue microarray studies using a Bayesian cell mixture model.

Motivation: Tissue microarrays (TMAs) quantify tissue-specific protein expression of cancer biomarkers via high-density immuno-histochemical staining assays. Standard analysis approach estimates a sample mean expression in the tumor, ignoring the complex tissue-specific staining patterns observed on tissue arrays.

Methods: In this article, a cell mixture model (CMM) is proposed to reconstruct tumor expression patterns in TMA experiments.

The effect of age at migration on cardiovascular mortality among elderly Mexican immigrants.

Purpose: In this study, we evaluated the influence of age at migration on cardiovascular mortality among older Mexican Americans immigrants.

Methods: A population-based cohort of Mexican-origin (N = 907) participants ages 60+ was followed up to 8 years. The association between migration before age 20 compared with after age 20 and mortality was analyzed with the use of multivariate Cox proportional models.

On assessing surrogacy in a single trial setting using a semicompeting risks paradigm.

There has been a recent emphasis on the identification of biomarkers and other biologic measures that may be potentially used as surrogate endpoints in clinical trials. We focus on the setting of data from a single clinical trial. In this article, we consider a framework in which the surrogate must occur before the true endpoint.

"Omics" data and levels of evidence for biomarker discovery.

With the development of new technologies for assaying biological activity on a global basis in experimental samples, various new "-omics" signatures have been developed to predict disease progression. Such signatures hold the potential to alter the nature of clinical management of human disease. In this article, we describe some necessary statistical considerations needed to take these signatures from the discovery phase to a clinically useful assay.

Pathway analysis reveals functional convergence of gene expression profiles in breast cancer.

Background: A recent study has shown high concordance of several breast-cancer gene signatures in predicting disease recurrence despite minimal overlap of the gene lists. It raises the question if there are common themes underlying such prediction concordance that are not apparent on the individual gene-level. We therefore studied the similarity of these gene-signatures on the basis of their functional annotations.

Estimation and testing for the effect of a genetic pathway on a disease outcome using logistic kernel machine regression via logistic mixed models.

Background: Growing interest on biological pathways has called for new statistical methods for modeling and testing a genetic pathway effect on a health outcome. The fact that genes within a pathway tend to interact with each other and relate to the outcome in a complicated way makes nonparametric methods more desirable. The kernel machine method provides a convenient, powerful and unified method for multi-dimensional parametric and nonparametric modeling of the pathway effect.

Genomic outlier profile analysis: mixture models, null hypotheses, and nonparametric estimation.

In most analyses of large-scale genomic data sets, differential expression analysis is typically assessed by testing for differences in the mean of the distributions between 2 groups. A recent finding by Tomlins and others (2005) is of a different type of pattern of differential expression in which a fraction of samples in one group have overexpression relative to samples in the other group. In this work, we describe a general mixture model framework for the assessment of this type of expression, called outlier profile analysis.

Inference for constrained estimation of tumor size distributions.

In order to develop better treatment and screening programs for cancer prevention programs, it is important to be able to understand the natural history of the disease and what factors affect its progression. We focus on a particular framework first outlined by Kimmel and Flehinger (1991, Biometrics, 47, 987-1004) and in particular one of their limiting scenarios for analysis. Using an equivalence with a binary regression model, we characterize the nonparametric maximum likelihood estimation procedure for estimation of the tumor size distribution function and give associated asymptotic results.

Combining multiple biomarker models in logistic regression.

In medical research, there is great interest in developing methods for combining biomarkers. We argue that selection of markers should also be considered in the process. Traditional model/variable selection procedures ignore the underlying uncertainty after model selection.

Modeling intra-tumor protein expression heterogeneity in tissue microarray experiments.

Tissue microarrays (TMAs) measure tumor-specific protein expression via high-density immunohistochemical staining assays. They provide a proteomic platform for validating cancer biomarkers emerging from large-scale DNA microarray studies. Repeated observations within each tumor result in substantial biological and experimental variability.

A powerful and flexible multilocus association test for quantitative traits.

Association mapping of complex traits typically employs tagSNP genotype data to identify a trait locus within a region of interest. However, considerable debate exists regarding the most powerful strategy for utilizing such tagSNP data for inference. A popular approach tests each tagSNP within the region individually, but such tests could lose power as a result of incomplete linkage disequilibrium between the genotyped tagSNP and the trait locus.

A transcriptional fingerprint of estrogen in human breast cancer predicts patient survival.

Estrogen signaling plays an essential role in breast cancer progression, and estrogen receptor (ER) status has long been a marker of hormone responsiveness. However, ER status alone has been an incomplete predictor of endocrine therapy, as some ER+ tumors, nevertheless, have poor prognosis. Here we sought to use expression profiling of ER+ breast cancer cells to screen for a robust estrogen-regulated gene signature that may serve as a better indicator of cancer outcome.

On the Plackett distribution with bivariate censored data.

In the analysis of dependence of bivariate correlated failure time data, a popular model is a gamma frailty model proposed by Clayton and Oakes. An alternative approach is using a Plackett distribution, whose dependence parameter has a very appealing odds ratio interpretation for dependence between the two failure times. In this article, we develop novel semiparametric estimation and inference procedures for the model.