Reduced healthcare access contributes to delay of care in endometrial cancer.

Objective: We aimed to characterize delays to care in patients with endometrioid endometrial cancer and the role healthcare access plays in these delays.

Methods: A chart review was performed of patients with endometrioid endometrial cancer who presented with postmenopausal bleeding at a diverse, urban medical center between 2006 and 2018. The time from symptom onset to treatment was abstracted from the medical record.

Age of first exposure to soccer heading: Associations with cognitive, clinical, and imaging outcomes in the Einstein Soccer Study.

Introduction: The objective of this study is to assess the role of age at first exposure (AFE) to soccer heading as a predictor of known adverse associations of recent and longer-term heading with brain microstructure, cognitive, and behavioral features among adult amateur soccer players.

Methods: The sample included 276 active amateur soccer players (196 male and 81 female) aged 18-53 years old. AFE to soccer heading was treated as a binary variable, dichotomized at ≤ 10 years vs.

Low molecular weight serum cell-free DNA concentration is associated with clinicopathologic indices of poor prognosis in women with uterine cancer.

Background: Serum cell-free DNA (cfDNA) holds promise as a non-invasive cancer biomarker. The objective of this study was to evaluate the association of cfDNA concentration with clinicopathologic variables of poor prognosis and overall survival among women with uterine cancer compared to benign cancer-free controls.

Methods: cfDNA was extracted from the serum of 91 women with multiple uterine cancer histologies and 22 post-menopausal controls without cancer.

Nanoparticle-Encapsulated Doxorubicin Demonstrates Superior Tumor Cell Kill in Triple Negative Breast Cancer Subtypes Intrinsically Resistant to Doxorubicin.

The effect of size and release kinetics of doxorubicin-nanoparticles on anti-tumor efficacy was evaluated in a panel of human cancer cell lines, including triple-negative breast cancer (TNBC) cells that frequently demonstrate resistance to doxorubicin. Different nano-formulations of sol-gel-based Doxorubicin containing nanoparticles were synthesized. Increased cell kill in chemoreffactory triple-negative breast cancer cells was associated with the smallest size of nanoparticles and the slowest release of Dox.

Detecting, quantifying, and discriminating the mechanism of mosaic chromosomal aneuploidies using MAD-seq.

Current approaches to detect and characterize mosaic chromosomal aneuploidy are limited by sensitivity, efficiency, cost, or the need to culture cells. We describe the mosaic aneuploidy detection by massively parallel sequencing (MAD-seq) capture assay and the analytical approach that allow low (<10%) levels of mosaicism for chromosomal aneuploidy or regional loss of heterozygosity to be detected, assigned to a meiotic or mitotic origin, and quantified as a proportion of the cells in the sample. We show results from a multi-ethnic MAD-seq (meMAD-seq) capture design that works equally well in populations of diverse racial and ethnic origins and how the analytical approach can be applied to exome or whole-genome sequencing data, revealing previously unrecognized aneuploidy or copy number neutral loss of heterozygosity in samples studied by the 1000 Genomes Project, cell lines from public repositories, and one of the Illumina Platinum Genomes samples.

Genotype copy number variations using Gaussian mixture models: theory and algorithms.

Copy number variations (CNVs) are important in the disease association studies and are usually targeted by most recent microarray platforms developed for GWAS studies. However, the probes targeting the same CNV regions could vary greatly in performance, with some of the probes carrying little information more than pure noise. In this paper, we investigate how to best combine measurements of multiple probes to estimate copy numbers of individuals under the framework of Gaussian mixture model (GMM).

Detecting multiple causal rare variants in exome sequence data.

Recent advances in sequencing technology have presented both opportunities and challenges, with limited statistical power to detect a single causal rare variant with practical sample sizes. To overcome this, the contributors to Group 1 of Genetic Analysis Workshop 17 sought to develop methods to detect the combined signal of multiple causal rare variants in a biologically meaningful way. The contributors used genes, genome location proximity, or genetic pathways as the basic unit in combining the information from multiple variants.

Computing power and sample size for case-control association studies with copy number polymorphism: application of mixture-based likelihood ratio test.

Recent studies suggest that copy number polymorphisms (CNPs) may play an important role in disease susceptibility and onset. Currently, the detection of CNPs mainly depends on microarray technology. For case-control studies, conventionally, subjects are assigned to a specific CNP category based on the continuous quantitative measure produced by microarray experiments, and cases and controls are then compared using a chi-square test of independence.

Using mixture models to characterize disease-related traits.

We consider 12 event-related potentials and one electroencephalogram measure as disease-related traits to compare alcohol-dependent individuals (cases) to unaffected individuals (controls). We use two approaches: 1) two-way analysis of variance (with sex and alcohol dependency as the factors), and 2) likelihood ratio tests comparing sex adjusted values of cases to controls assuming that within each group the trait has a 2 (or 3) component normal mixture distribution. In the second approach, we test the null hypothesis that the parameters of the mixtures are equal for the cases and controls.

A gene-model-free method for linkage analysis of a disease-related-trait based on analysis of proband/sibling pairs.

In this paper we investigate the power of finding linkage to a disease locus through analysis of the disease-related traits. We propose two family-based gene-model-free linkage statistics. Both involve considering the distribution of the number of alleles identical by descent with the proband and comparing siblings with the disease-related trait to those without the disease-related-trait.

Locating disease genes using Bayesian variable selection with the Haseman-Elston method.

Background: We applied stochastic search variable selection (SSVS), a Bayesian model selection method, to the simulated data of Genetic Analysis Workshop 13. We used SSVS with the revisited Haseman-Elston method to find the markers linked to the loci determining change in cholesterol over time. To study gene-gene interaction (epistasis) and gene-environment interaction, we adopted prior structures, which incorporate the relationship among the predictors.

Data mining and computationally intensive methods: summary of Group 7 contributions to Genetic Analysis Workshop 13.

The Framingham Heart Study data, as well as a related simulated data set, were generously provided to the participants of the Genetic Analysis Workshop 13 in order that newly developed and emerging statistical methodologies could be tested on that well-characterized data set. The impetus driving the development of novel methods is to elucidate the contributions of genes, environment, and interactions between and among them, as well as to allow comparison between and validation of methods. The seven papers that comprise this group used data-mining methodologies (tree-based methods, neural networks, discriminant analysis, and Bayesian variable selection) in an attempt to identify the underlying genetics of cardiovascular disease and related traits in the presence of environmental and genetic covariates.

A method for evaluating the results of Bayesian model selection: application to linkage analyses of attributes determined by two or more genes.

Objectives: We apply and evaluate the intrinsic Bayes factor (IBF) of Berger and Pericchi [J Am Stat Assoc 1996;91:109-122; Bayesian Statistics, Oxford University Press, vol 5, 1996] to linkage analyses done using the stochastic search variable selection (SSVS) method of George and McCulloch [J Am Stat Assoc 1993;88:881-889] as proposed by Suh et al. [Genet Epidemiol 2001;21(suppl 1):S706-S711].

Methods: We consider 20 simulations of linkage data obtained under two different generating models.