Powerful Tukey’s One Degree-of-Freedom Test for Detecting Gene–Gene and Gene–Environment Interactions
Genome-wide association studies (GWASs) have identified thousands of single nucleotide polymorphisms (SNPs) robustly associated with hundreds of complex human diseases including cancers. However, the large number of GWAS-identified genetic loci only explains a small proportion of the disease heritability. This “missing heritability” problem has been partly attributed to the yet-to-be-identified gene–gene (G × G) and gene–environment (G × E) interactions. In spite of the important roles of G × G and G × E interactions in understanding disease mechanisms and filling in the missing heritability, straightforward GW...
Source: Cancer Informatics - June 4, 2015 Category: Cancer & Oncology Authors: Yaping WangDonghui LiPeng Wei Source Type: research
Penalized Ordinal Regression Methods for Predicting Stage of Cancer in High-Dimensional Covariate Spaces
The pathological description of the stage of a tumor is an important clinical designation and is considered, like many other forms of biomedical data, an ordinal outcome. Currently, statistical methods for predicting an ordinal outcome using clinical, demographic, and high-dimensional correlated features are lacking. In this paper, we propose a method that fits an ordinal response model to predict an ordinal outcome for high-dimensional covariate spaces. Our method penalizes some covariates (high-throughput genomic features) without penalizing others (such as demographic and/or clinical covariates). We demonstrate the appl...
Source: Cancer Informatics - May 27, 2015 Category: Cancer & Oncology Authors: Amanda Elswick GentryColleen K. Jackson-CookDebra E. LyonKellie J. Archer Source Type: research
Inferring Stable Acquisition Durations for Applications of Perfusion Imaging in Oncology
Tissue perfusion plays a critical role in oncology. Growth and migration of cancerous cells requires proliferation of networks of new blood vessels through the process of tumor angiogenesis. Many imaging technologies developed recently attempt to measure characteristics pertaining to the passage of fluid through blood vessels, thereby providing a noninvasive means for cancer detection, as well as treatment prognostication, prediction, and monitoring. However, because these techniques require a sequence of successive imaging scans under administration of intravenous imaging tracers, the quality of the resulting perfusion da...
Source: Cancer Informatics - May 25, 2015 Category: Cancer & Oncology Authors: Brian P. HobbsChaan S. Ng Source Type: research
Identifying CDKN3 Gene Expression as a Prognostic Biomarker in Lung Adenocarcinoma via Meta-analysis
In this study, we investigated the expression of CDKN3 and its association with prognosis in lung adenocarcinoma (ADC) and squamous cell carcinoma (SCC) using datasets in Lung Cancer Explorer (LCE; http://qbrc.swmed.edu/lce/). We found that CDKN3 was up-regulated in ADC and SCC compared to normal tissues. We also found that CDKN3 was expressed at a higher level in SCC than in ADC, which was further validated through meta-analysis (coefficient = 2.09, 95% CI = 1.50–2.67, P < 0.0001). In addition, based on meta-analysis for the prognostic value of CDKN3, we found that higher CDKN3 expression was associated with poorer surv...
Source: Cancer Informatics - May 24, 2015 Category: Cancer & Oncology Authors: Xiao ZangMin ChenYunyun ZhouGuanghua XiaoYang Xieand Xinlei Wang Source Type: research
A Hypothesis-Directed Approach to the Targeted Development of a Multiplexed Proteomic Biomarker Assay for Cancer
In recent years, hundreds of candidate protein biomarkers have been identified using discovery-based proteomics. Despite the large number of candidate biomarkers, few proteins advance to clinical validation. We propose a hypothesis-driven approach to identify candidate biomarkers, previously characterized in the literature, with the highest probability of clinical applicability. A ranking method, called the “hypothesis-directed biomarker ranking” (HDBR) system, was developed to score candidate biomarkers based on seven criteria deemed important in the selection of clinically useful biomarkers. To demonstrate its applic...
Source: Cancer Informatics - May 17, 2015 Category: Cancer & Oncology Authors: Emily M. MackayJennifer KoppelPooja DasJoanna WooDavid C. SchriemerOliver F. Bathe Source Type: research
Integrative Analyses of Cancer Data: A Review from a Statistical Perspective
It has become increasingly common for large-scale public data repositories and clinical settings to have multiple types of data, including high-dimensional genomics, epigenomics, and proteomics data as well as survival data, measured simultaneously for the same group of biological samples, which provides unprecedented opportunities to understand cancer mechanisms from a more comprehensive scope and to develop new cancer therapies. Nevertheless, how to interpret a wealth of data into biologically and clinically meaningful information remains very challenging. In this paper, I review recent development in statistics for inte...
Source: Cancer Informatics - May 14, 2015 Category: Cancer & Oncology Authors: Yingying Wei Source Type: research
Assessment of Weighted Quantile Sum Regression for Modeling Chemical Mixtures and Cancer Risk
In evaluation of cancer risk related to environmental chemical exposures, the effect of many chemicals on disease is ultimately of interest. However, because of potentially strong correlations among chemicals that occur together, traditional regression methods suffer from collinearity effects, including regression coefficient sign reversal and variance inflation. In addition, penalized regression methods designed to remediate collinearity may have limitations in selecting the truly bad actors among many correlated components. The recently proposed method of weighted quantile sum (WQS) regression attempts to overcome these ...
Source: Cancer Informatics - May 13, 2015 Category: Cancer & Oncology Authors: Jenna CzarnotaChris GenningsDavid C. Wheeler Source Type: research
The Standardized Histogram Shift of T2 Magnetic Resonance Image (MRI) Signal Intensities of Nephroblastoma Does Not Predict Histopathological Diagnostic Information
The objective of this study is to assess standardized histograms of signal intensities of T2-weighted magnetic resonance image (MRI) modality before and after preoperative chemotherapy for nephroblastoma (Wilms' tumor). All analyzed patients are enrolled in the International Society of Paediatric Oncology (SIOP) 2001/GPOH trial.1 The question to be answered is whether the comparison of the histograms can add new knowledge by comparing them with the histology of the tumor after preoperative chemotherapy. Twenty-three unilateral nephroblastoma cases were analyzed. All patients were examined by MRI before and after preoperati...
Source: Cancer Informatics - May 12, 2015 Category: Cancer & Oncology Authors: Sabine MüllerRuslan DavidKostas MariasNorbert Graf Source Type: research
Recent Enhancements to the Genetic Risk Prediction Model BRCAPRO
BRCAPRO is a widely used model for genetic risk prediction of breast cancer. It is a function within the R package BayesMendel and is used to calculate the probabilities of being a carrier of a deleterious mutation in one or both of the BRCA genes, as well as the probability of being affected with breast and ovarian cancer within a defined time window. Both predictions are based on information contained in the counselee’s family history of cancer. During the last decade, BRCAPRO has undergone several rounds of successive refinements: the current version is part of release 2.1 of BayesMendel. In this review, we showcase s...
Source: Cancer Informatics - May 10, 2015 Category: Cancer & Oncology Authors: Emanuele MazzolaAmanda BlackfordGiovanni ParmigianiSwati Biswas Source Type: research
Identifying Molecular Features Associated with Psychoneurological Symptoms in Women with Breast Cancer Using Multivariate Mixed Models
In conclusion, when analyzing correlated psychoneurological symptom outcomes, multivariate models are more powerful and thus are recommended. (Source: Cancer Informatics)
Source: Cancer Informatics - May 7, 2015 Category: Cancer & Oncology Authors: Qing ZhouColleen Jackson-CookDebra LyonRobert PereraKellie J. Archer Source Type: research
Prediction of Early Breast Cancer Metastasis from DNA Microarray Data Using High-Dimensional Cox Regression Models
Conclusions: High-dimensional regression methods are attractive in prognostic studies because finding a small subset of genes may facilitate the transfer to the clinic, and also because they strengthen the robustness of the model by limiting the selection of false-positive predictive genes. With only six genes, the CoxBoost classifier predicted the 4-year status of metastatic disease with 93% sensitivity. Selecting a few genes related to ontologies other than cell proliferation might further improve the overall sensitivity performance. (Source: Cancer Informatics)
Source: Cancer Informatics - May 5, 2015 Category: Cancer & Oncology Authors: Christophe ZemmourFrançois BertucciPascal FinettiBernard ChetritDaniel BirnbaumThomas FilleronJean-Marie Boher Source Type: research
Evaluating Geographically Weighted Regression Models for Environmental Chemical Risk Analysis
In the evaluation of cancer risk related to environmental chemical exposures, the effect of many correlated chemicals on disease is often of interest. The relationship between correlated environmental chemicals and health effects is not always constant across a study area, as exposure levels may change spatially due to various environmental factors. Geographically weighted regression (GWR) has been proposed to model spatially varying effects. However, concerns about collinearity effects, including regression coefficient sign reversal (ie, reversal paradox), may limit the applicability of GWR for environmental chemical risk...
Source: Cancer Informatics - May 5, 2015 Category: Cancer & Oncology Authors: Jenna CzarnotaDavid C. WheelerChris Gennings Source Type: research
Evaluation of the Performance of Smoothing Functions in Generalized Additive Models for Spatial Variation in Disease
This study evaluates the ability of different smoothing functions to detect overall spatial variation of risk and elevated risk in diverse geographical areas at various risk levels using a simulation study. We created five scenarios with different true risk area shapes (circle, triangle, linear) in a square study region. We applied four different smoothing functions in the GAMs, including two types of thin plate regression splines (TPRS) and two versions of locally weighted scatterplot smoothing (loess). We tested the null hypothesis of constant risk and detected areas of elevated risk using analysis of deviance with permu...
Source: Cancer Informatics - April 29, 2015 Category: Cancer & Oncology Authors: Umaporn SiangphoeDavid C. Wheeler Source Type: research
Generalized Monotone Incremental Forward Stagewise Method for Modeling Count Data: Application Predicting Micronuclei Frequency
The cytokinesis-block micronucleus (CBMN) assay can be used to quantify micronucleus (MN) formation, the outcome measured being MN frequency. MN frequency has been shown to be both an accurate measure of chromosomal instability/DNA damage and a risk factor for cancer. Similarly, the Agilent 4 × 44k human oligonucleotide microarray can be used to quantify gene expression changes. Despite the existence of accepted methodologies to quantify both MN frequency and gene expression, very little is known about the association between the two. In modeling our count outcome (MN frequency) using gene expression levels from the high-...
Source: Cancer Informatics - April 29, 2015 Category: Cancer & Oncology Authors: Mateusz MakowskiKellie J. Archer Source Type: research
Selecting Spatial Scale of Covariates in Regression Models of Environmental Exposures
Environmental factors or socioeconomic status variables used in regression models to explain environmental chemical exposures or health outcomes are often in practice modeled at the same buffer distance or spatial scale. In this paper, we present four model selection algorithms that select the best spatial scale for each buffer-based or area-level covariate. Contamination of drinking water by nitrate is a growing problem in agricultural areas of the United States, as ingested nitrate can lead to the endogenous formation of N-nitroso compounds, which are potent carcinogens. We applied our methods to model nitrate levels in ...
Source: Cancer Informatics - April 27, 2015 Category: Cancer & Oncology Authors: Lauren P. GrantChris GenningsDavid C. Wheeler Source Type: research
