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Motivation DNA methylation is a key epigenetic factor regulating gene expression. While promoter methylation has been well studied, recent publications have revealed that functionally important methylation also occurs in intergenic and distal regions, and varies across genes and tissue types. Given the growing importance of inter-platform integrative genomic analyses, there is an urgent need to develop methods to discover and characterize gene-level relationships between methylation and expression. Results We introduce a novel sequential penalized regression approach to identify methylation-expression quantitative trait loci (methyl-eQTLs), a term that we have coined to represent, for each gene and tissue type, a sparse set of CpG loci best explaining gene expression and accompanying weights indicating direction and strength of association. Using TCGA and MD Anderson colorectal cohorts to build and validate our models, we demonstrate our strategy better explains expression variability than current commonly used gene-level methylation summaries. The methyl-eQTLs identified by our approach can be used to construct gene-level methylation summaries that are maximally correlated with gene expression for use in integrative models, and produce a tissue-specific summary of which genes appear to be strongly regulated by methylation. Our results introduce an important resource to the biomedical community for integrative genomics analyses involving DNA methylation. Availability and implementation We produce an R Shiny app (https://rstudio-prd-c1.pmacs.upenn.edu/methyl-eQTL/) that interactively presents methyl-eQTL results for colorectal, breast and pancreatic cancer. The source R code for this work is provided in the Supplementary Material. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

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Motivation: DNA methylation is a key epigenetic modification that can modulate gene expression. Over the past decade, a lot of studies have focused on profiling DNA methylation and investigating its alterations in complex diseases such as cancer. While early studies were mostly restricted to CpG islands or promoter regions, recent findings indicate that many of important DNA methylation changes can occur in other regions and DNA methylation needs to be examined on a genome-wide scale. In this article, we apply the wavelet-based functional mixed model methodology to analyze the high-throughput methylation data for identifying differentially methylated loci across the genome. Contrary to many commonly-used methods that model probes independently, this framework accommodates spatial correlations across the genome through basis function modeling as well as correlations between samples through functional random effects, which allows it to be applied to many different settings and potentially leads to more power in detection of differential methylation. Results: We applied this framework to three different high-dimensional methylation data sets (CpG Shore data, THREE data and NIH Roadmap Epigenomics data), studied previously in other works. A simulation study based on CpG Shore data suggested that in terms of detection of differentially methylated loci, this modeling approach using wavelets outperforms analogous approaches modeling the loci as independent. For the THREE data, the method suggests newly detected regions of differential methylation, which were not reported in the original study. [ABSTRACT FROM AUTHOR]

The authors were able to obtain results and patient-level data from 9 large multicenter randomized trials from the ACCENT database (1), providing sufficient power to attack the important problem of validating surrogacy for this specific setting. The authors considered surrogacy of 3-year DFS for OS at other time points (1); sadly however, they did not consider whether DFS at earlier time points would retain strong surrogacy for OS. I congratulate Yin et al. (1) on their well-conducted study validating the surrogacy of 3-year disease-free survival (DFS) for 5-year overall survival (OS) in the setting of adjuvant colorectal cancer studies. [Extracted from the article]

Medical and public health research increasingly involves the collection of complex and high dimensional data. In particular, functional data-where the unit of observation is a curve or set of curves that are finely sampled over a grid-is frequently obtained. Moreover, researchers often sample multiple curves per person resulting in repeated functional measures. A common question is how to analyze the relationship between two functional variables. We propose a general function-on-function regression model for repeatedly sampled functional data on a fine grid, presenting a simple model as well as a more extensive mixed model framework, and introducing various functional Bayesian inferential procedures that account for multiple testing. We examine these models via simulation and a data analysis with data from a study that used event-related potentials to examine how the brain processes various types of images. [ABSTRACT FROM AUTHOR]

Background Child-Turcotte-Pugh (CTP) score is the standard tool to assess hepatic reserve in hepatocellular carcinoma (HCC), and CTP-A is the classic group for active therapy. However, CTP stratification accuracy has been questioned. We hypothesized that plasma insulin-like growth factor 1 (IGF-1) is a valid surrogate for hepatic reserve to replace the subjective parameters in CTP score to improve its prognostic accuracy. Methods We retrospectively tested plasma IGF-1 levels in the training set (n = 310) from MD Anderson Cancer Center. Recursive partitioning identified three optimal IGF-1 ranges that correlated with overall survival (OS): greater than 50ng/mL = 1 point; 26 to 50ng/mL = 2 points; and less than 26ng/mL = 3 points. We modified the CTP score by replacing ascites and encephalopathy grading with plasma IGF-1 value (IGF-CTP) and subjected both scores to log-rank analysis. Harrell's C-index and U-statistics were used to compare the prognostic performance of both scores in both the training and validation cohorts (n = 155). All statistical tests were two-sided. Results Patients' stratification was statistically significantly stronger for IGF-CTP than CTP score for the training (P= .003) and the validation cohort (P= .005). Patients reclassified by IGF-CTP relative to their original CTP score were better stratified by their new risk groups. Most important, patients classified as A by CTP but B by IGF-CTP had statistically significantly worse OS than those who remained under class A by IGF-CTP in both cohorts (P= .03 and P< .001, respectively, from Cox regression models). AB patients had a worse OS than AA patients in both the training and validation set (hazard ratio [HR] = 1.45, 95% confidence interval [CI] = 1.03 to 2.04, P= .03; HR = 2.83, 95% CI = 1.65 to 4.85, P< .001, respectively). Conclusions The IGF-CTP score is simple, blood-based, and cost-effective, stratified HCC better than CTP score, and validated well on two independent cohorts. International validation studies are warranted. [ABSTRACT FROM AUTHOR]

Background. Appendiceal adenocarcinomas (AAs) are rare and this has limited their molecular understanding. The purpose of our study was to characterize the molecular profile of AA and explore the role of targeted therapy against cyclooxygenase- 2 (COX-2) and epidermal growth factor receptor (EGFR). Patients and Methods.We performed a retrospective review of 607patients with AA at a single institution. A total of 149 patients Underwent molecular testing for at least one of the following: activating mutations in KRAS, BRAF, cKIT, EGFR, or PI3K; protein expression of c-KIT or COX-2; or microsatellite instability (MSI) status by immunohistochemistry. Kaplan-Meier product limit method and log-rank test were used to estimate overall survival (OS) and to determine associations among OS,COX-2 expression, KRAS mutations, and other characteristics. Results. Age, grade, stage, signet ring cells, mucinous histology, and completeness of cytoreduction score correlated with survival outcomes. COX-2 expression, KRAS, PI3K, and BRAF mutations were seen in 61%, 55%, 17%,and 4%of patients, respectively. High MSI was seen in 6% of patients. KRAS mutation was strongly associated with well differentiated or moderately differentiated AA (p <.01). COX-2 expression ( p= .33) and the presence of KRAS mutation (p = .91) had no impact on OS. The use of celecoxib in patients whose tumors expressed COX-2 (p = .84) and the use of cetuximab or panitumumab in patients with KRAS wild-type tumors (p = .83) also had no impact on OS. Conclusion. In this cohort, we demonstrated that COX-2 expression and KRAS mutations were frequently seen in AA, although neither exhibited any prognostic significance. MSI was infrequent in AA. Targeted therapy against COX-2 and EGFR appeared to provide no clinical benefit. Well and moderately differentiated AA were molecularly distinct from poorly differentiated AA. [ABSTRACT FROM AUTHOR]

Motivation: Analyzing data from multi-platform genomics experiments combined with patients’ clinical outcomes helps us understand the complex biological processes that characterize a disease, as well as how these processes relate to the development of the disease. Current data integration approaches are limited in that they do not consider the fundamental biological relationships that exist among the data obtained from different platforms.Statistical Model: We propose an integrative Bayesian analysis of genomics data (iBAG) framework for identifying important genes/biomarkers that are associated with clinical outcome. This framework uses hierarchical modeling to combine the data obtained from multiple platforms into one model.Results: We assess the performance of our methods using several synthetic and real examples. Simulations show our integrative methods to have higher power to detect disease-related genes than non-integrative methods. Using the Cancer Genome Atlas glioblastoma dataset, we apply the iBAG model to integrate gene expression and methylation data to study their associations with patient survival. Our proposed method discovers multiple methylation-regulated genes that are related to patient survival, most of which have important biological functions in other diseases but have not been previously studied in glioblastoma.Availability: http://odin.mdacc.tmc.edu/∼vbaladan/.Contact: veera@mdanderson.orgSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

This article introduces new methods for performing classification of complex, high-dimensional functional data using the functional mixed model (FMM) framework. The FMM relates a functional response to a set of predictors through functional fixed and random effects, which allows it to account for various factors and between-function correlations. The methods include training and prediction steps. In the training steps we train the FMM model by treating class designation as one of the fixed effects, and in the prediction steps we classify the new objects using posterior predictive probabilities of class. Through a Bayesian scheme, we are able to adjust for factors affecting both the functions and the class designations. While the methods can be used in any FMM framework, we provide details for two specific Bayesian approaches: the Gaussian, wavelet-based FMM (G-WFMM) and the robust, wavelet-based FMM (R-WFMM). Both methods perform modeling in the wavelet space, which yields parsimonious representations for the functions, and can naturally adapt to local features and complex nonstationarities in the functions. The R-WFMM allows potentially heavier tails for features of the functions indexed by particular wavelet coefficients, leading to a down-weighting of outliers that makes the method robust to outlying functions or regions of functions. The models are applied to a pancreatic cancer mass spectroscopy data set and compared with other recently developed functional classification methods. [ABSTRACT FROM AUTHOR]

Expression of 15-lipoxygenase-1 (15-LOX-1) is decreased in many human cancers; however, the mechanistic significance of its decreased expression has been difficult to determine because its mouse homolog 12/15-LOX has opposing functions. We generated a mouse model in which expression of a human 15-LOX-1 transgene was targeted to the intestinal epithelium via the villin promoter. Targeted expression was confirmed by real-time reverse transcription–polymerase chain reaction and immunoblotting. When the 15-LOX-1 transgene was expressed in colonic epithelial cells of two independent mouse lines (B6 and FVB), azoxymethane-inducible colonic tumorigenesis was suppressed (mean number of tumors: wild type [WT] = 8.2, 15-LOX-1+/− = 4.91, 15-LOX-1+/+ = 3.57; WT vs 15-LOX-1+/− two-sided P = .003, WT vs 15-LOX-1+/+ two-sided P < .001; n = 10–14 mice per group). 15-LOX-1 transgene expression was always decreased in the tumors that did develop. In the presence of expression of the 15-LOX-1 transgene, expression of tumor necrosis factor alpha and its target inducible nitric oxide synthase were decreased and activation of nuclear factor-kappa B in colonic epithelial cells was inhibited. [ABSTRACT FROM PUBLISHER]

Frequently, exposure data are measured over time on a grid of discrete values that collectively define a functional observation. In many applications, researchers are interested in using these measurements as covariates to predict a scalar response in a regression setting, with interest focusing on the most biologically relevant time window of exposure. One example is in panel studies of the health effects of particulate matter (PM), where particle levels are measured over time. In such studies, there are many more values of the functional data than observations in the data set so that regularization of the corresponding functional regression coefficient is necessary for estimation. Additional issues in this setting are the possibility of exposure measurement error and the need to incorporate additional potential confounders, such as meteorological or co-pollutant measures, that themselves may have effects that vary over time. To accommodate all these features, we develop wavelet-based linear mixed distributed lag models that incorporate repeated measures of functional data as covariates into a linear mixed model. A Bayesian approach to model fitting uses wavelet shrinkage to regularize functional coefficients. We show that, as long as the exposure error induces fine-scale variability in the functional exposure profile and the distributed lag function representing the exposure effect varies smoothly in time, the model corrects for the exposure measurement error without further adjustment. Both these conditions are likely to hold in the environmental applications we consider. We examine properties of the method using simulations and apply the method to data from a study examining the association between PM, measured as hourly averages for 1–7 days, and markers of acute systemic inflammation. We use the method to fully control for the effects of confounding by other time-varying predictors, such as temperature and co-pollutants. [ABSTRACT FROM PUBLISHER]

Peroxisome proliferator-activated receptor-delta (PPAR-δ) is overexpressed in human colon cancer, but its contribution to colonic tumorigenesis is controversial. We generated a mouse model in which PPAR-δ was genetically disrupted in colonic epithelial cells by targeted deletion of exon 4. Elimination of colon-specific PPAR-δ expression vas confirmed by real-time reverse transcription-polymerase chain reaction (real-time RT-PCR), immunoblotting, and activity assays. Mice with and without targeted PPAR-δ genetic disruption (10-11 mice per group) were tested for incidence of azoxymethane-induced colon tumors. The effects of targeted PPAR-δ deletion on vascular endothelial growth factor expression were determined by realtime RT-PCR. Targeted PPAR-δ genetic disruption inhibited colonic carcinogenesis: Mice with PPAR-δ[-/-] colons developed 98.5% fewer tumors than wild-type mice (PPAR-δ[-/-] vs wild-type, mean = 0.1 tumors per mouse vs 6.6 tumors per mouse, difference = 6.5 tumors per mouse, 95% confidence interval = 4.9 to 8.0 tumors per mouse, P .001, two-sided test). Increased expression of vascular endothelial growth factor in colon tumors vs normal colon was suppressed by loss of PPAR4-δ expression. These findings indicate that PPAR-δ has a crucial role in promoting colonic tumorigenesis. [ABSTRACT FROM AUTHOR]

Peroxisome proliferator-activated receptor-delta (PPAR-delta) is overexpressed in human colon cancer, but its contribution to colonic tumorigenesis is controversial. We generated a mouse model in which PPAR-delta was genetically disrupted in colonic epithelial cells by targeted deletion of exon 4. Elimination of colon-specific PPAR-delta expression was confirmed by real-time reverse transcription-polymerase chain reaction (real-time RT-PCR), immunoblotting, and activity assays. Mice with and without targeted PPAR-delta genetic disruption (10-11 mice per group) were tested for incidence of azoxymethane-induced colon tumors. The effects of targeted PPAR-delta deletion on vascular endothelial growth factor expression were determined by real-time RT-PCR. Targeted PPAR-delta genetic disruption inhibited colonic carcinogenesis: Mice with PPAR-delta((-/-)) colons developed 98.5% fewer tumors than wild-type mice (PPAR-delta((-/-)) vs wild-type, mean = 0.1 tumors per mouse vs 6.6 tumors per mouse, difference = 6.5 tumors per mouse, 95% confidence interval = 4.9 to 8.0 tumors per mouse, P < .001, two-sided test). Increased expression of vascular endothelial growth factor in colon tumors vs normal colon was suppressed by loss of PPAR-delta expression. These findings indicate that PPAR-delta has a crucial role in promoting colonic tumorigenesis. [ABSTRACT FROM AUTHOR]

In this article, we apply the recently developed Bayesian wavelet-based functional mixed model methodology to analyze MALDI-TOF mass spectrometry proteomic data. By modeling mass spectra as functions, this approach avoids reliance on peak detection methods. The flexibility of this framework in modeling nonparametric fixed and random effect functions enables it to model the effects of multiple factors simultaneously, allowing one to perform inference on multiple factors of interest using the same model fit, while adjusting for clinical or experimental covariates that may affect both the intensities and locations of peaks in the spectra. For example, this provides a straightforward way to account for systematic block and batch effects that characterize these data. From the model output, we identify spectral regions that are differentially expressed across experimental conditions, in a way that takes both statistical and clinical significance into account and controls the Bayesian false discovery rate to a prespecified level. We apply this method to two cancer studies. [ABSTRACT FROM AUTHOR]

Increasingly, scientific studies yield functional data, in which the ideal units of observation are curves and the observed data consist of sets of curves that are sampled on a fine grid. We present new methodology that generalizes the linear mixed model to the functional mixed model framework, with model fitting done by using a Bayesian wavelet-based approach. This method is flexible, allowing functions of arbitrary form and the full range of fixed effects structures and between-curve covariance structures that are available in the mixed model framework. It yields nonparametric estimates of the fixed and random-effects functions as well as the various between-curve and within-curve covariance matrices. The functional fixed effects are adaptively regularized as a result of the non-linear shrinkage prior that is imposed on the fixed effects’ wavelet coefficients, and the random-effect functions experience a form of adaptive regularization because of the separately estimated variance components for each wavelet coefficient. Because we have posterior samples for all model quantities, we can perform pointwise or joint Bayesian inference or prediction on the quantities of the model. The adaptiveness of the method makes it especially appropriate for modelling irregular functional data that are characterized by numerous local features like peaks. [ABSTRACT FROM AUTHOR]

Proteomic expression patterns derived from mass spectrometry have been put forward as potential biomarkers for the early diagnosis of cancer and other diseases. This approach has generated much excitement and has led to a large number of new experiments and vast amounts of new data. The data, derived at great expense, can have very little value if careful attention is not paid to the experimental design and analysis. Using examples from surface-enhanced laser desorption/ionisation time-of-flight (SELDI-TOF) and matrix-assisted laser desorption--ionisation/time-of-flight (MALDI-TOF) experiments, we describe several experimental design issues that can corrupt a dataset. Fortunately, the problems we identify can be avoided if attention is paid to potential sources of bias before the experiment is run. With an appropriate experimental design, proteomics technology can be a useful tool for discovering important information relating protein expression to disease. [ABSTRACT FROM AUTHOR]

Serial analysis of gene expression (SAGE) is technology for quantifying gene expression in biological tissue that yields count data that can be modeled by multinomial distribution with two characteristics: skewness in the relative frequencies and small sample size relative to the dimension. As result of these characteristics, given SAGE sample may fail to capture large number of expressed mRNA species present in the tissue. Empirical estimators of mRNA species' relative abundance effectively ignore these missing species, and as result tend to overestimate the abundance of the scarce observed species comprising vast majority of the total. We have developed new Bayesian estimation procedure that quantifies our prior information about these characteristics,yielding nonlinear shrinkage estimator with efficiency advantages over the MLE. Our prior is mixture of Dirichlets, whereby species are stochastically partitioned into abundant and scarce classes, each with its own multivariate prior. Simulation studies reveal our estimator has lower integrated mean squared error (IMSE) than the MLE for the SAGE scenarios simulated, and yields relative abundance profiles closer in Euclidean distance to the truth for all samples simulated. We apply our method to SAGE library of normal colon tissue,and discuss its implications for assessing differential expression. [ABSTRACT FROM AUTHOR]

There is now general agreement that the etiology of proximal and distal colon cancers may differ, thus prompting renewed interest in understanding anatomical site-specific molecular mechanisms of tumor development. Using a 2×2×2 factorial design with male Sprague–Dawley rats (corn oil, fish oil; pectin, cellulose; plus or minus azoxymethane injection) we found a greater than 2-fold difference (P < 0.001) in tumor incidence proximally versus distally (prox/dist ratio: corn oil, 2.25; fish oil, 2.61). The purpose of the present study was to determine if the higher degree of proximal versus distal tumors in our model system could be accounted for by differences between these two sites in initial DNA damage, response to that damage or an effect of diet at one site but not the other. DNA damage was assessed by quantitative immunohistochemistry of O6-methylguanine adducts; repair by measurement of O6-methylguanine-DNA alkyltransferase and removal was determined by measurement of targeted apoptosis. Although overall initial DNA damage was similar at both sites, in the distal colon there was a greater expression of repair protein (P < 0.001) and a greater degree of targeted apoptosis (P < 0.0001). There was also a reduction in DNA damage in the distal colon of rats consuming fish oil. Together, these results suggest that the lower tumor incidence in the distal colon may be a result of the capacity to deal with initial DNA damage by the distal colon, as compared with the proximal colon. Therefore, the determination of site-specific mechanisms in tumor development is important because distinct strategies may be required to protect against cancer at different sites. [ABSTRACT FROM PUBLISHER]

We have recently demonstrated that overexpression of PKC βII renders transgenic mice more susceptible to carcinogen-induced colonic hyperproliferation and aberrant crypt foci formation. In order to further investigate the ability of PKC βII to modulate colonocyte cytokinetics, we determined the localization of PKC βII with respect to cell proliferation and apoptosis along the entire colonic crypt axis following carcinogen and diet manipulation. Rats were provided diets containing either corn oil [containing n-6 polyunsaturated fatty acids (PUFA)] or fish oil (containing n-3 PUFA), cellulose (non-fermentable fiber) or pectin (fermentable fiber) and injected with azoxymethane (AOM) or saline. After 16 weeks, an intermediate time point when no macroscopic tumors are detected, colonic sections were utilized for immunohistochemical image analysis and immunoblotting. Cell proliferation was measured by incorporation of bromodeoxyuridine into DNA and apoptosis by terminal deoxynucleotidyl transferase-mediated dUTP-biotin nick end-labeling. In the distal colon, PKC βII staining was localized to the upper portion of the crypt. In comparison, proximal crypts had more (P < 0.05) staining in the lower tertile. AOM enhanced (P < 0.05) PKC βII expression in all regions of the distal colonic crypt (upper, middle and lower tertiles). There was also an interaction (P < 0.05) between dietary fat and fiber on PKC βII expression (corn/pectin > fish/cellulose, fish/pectin > corn/cellulose) in all regions of the distal colonic crypt. With respect to colonic cell kinetics, proliferation paralleled the increase in PKC βII expression in carcinogen-treated animals. In contrast, apoptosis at the lumenal surface was inversely proportional to PKC βII expression in the upper tertile. These results suggest that an elevation in PKC βII expression along the crypt axis in the distal colon is linked to enhancement of cell proliferation and suppression of apoptosis, predictive intermediate biomarkers of tumor development. Therefore, select dietary factors may confer protection against colon carcinogenesis in part by blocking carcinogen-induced PKC βII expression. [ABSTRACT FROM PUBLISHER]

The study aimed to validate admission clinical and radiographic features of pediatric patients with traumatic epidural hematoma (EDH) that lead to safe observation. A Level I trauma center radiology and electronic medical record databases were retrospectively queried for pediatric patients with EDH on CT scan between 1/1/2016 and 10/1/2016. Patient imaging, treatment and outcome variables were abstracted. Characteristics of the cohort were compared to an external cohort used to develop prediction rules for surgical intervention. External validity of the prediction rules was assessed. 195 eligible subjects were included in the study, 37 of which failed observation and required surgery while 158 underwent successful observation. The surgical cohort had significantly thicker (p <.001) and higher volume (p <.001) EDH, increased midline shift (p <.001) and higher likelihood of mass effect (p <.001). There was significantly higher residual neurologic deficit rate (54% vs 23%, p <.001) and hospital mortality (5% vs 0%, p =.035) amongst the surgical group. There were significant differences in patient demographic, clinical and imaging characteristics between the internal and external cohorts. The predictive rules externally developed yielded positive predictive value of 97.7% (95% CI = 93.3–99.5%), negative predictive value of 24.5% (95% CI = 16.2–34.4%), specificity of 88.5% (95% CI = 69.9–97.6%), and sensitivity of 63.8% (95% CI = 56.6–70.5%) for successful observation. The current study validates previously developed prediction rules for safe observation of pediatric EDH in a cohort with distinct characteristics from the external cohort. Specifically, patients with no mass effect, EDH volume <15 ml and no neurological deficits are less likely to fail observation. The current study validates prediction rules for safe observation of pediatric EDH in a distinct pediatric cohort that provides further support to conservative management in these circumstances. [ABSTRACT FROM AUTHOR]

Proteomic profiling of serum initially appeared to be dramatically effective for diagnosis of early-stage ovarian cancer, but these results have proven difficult to reproduce. A recent publication reported good classification in one dataset using results from training on a much earlier dataset, but the authors have since reported that they did not perform the analysis as described. We examined the reproducibility of the proteomic patterns across datasets in more detail. Our analysis reveals that the pattern that enabled successful classification is biologically implausible and that the method, properly applied, does not classify the data accurately. We show that the method used in previously published studies does not establish reproducibility and performs no better than chance for classifying the second dataset, in part because the second dataset is easy to classify correctly. We conclude that the reproducibility of the proteomic profiling approach has yet to be established. [ABSTRACT FROM AUTHOR]

Background The nuclear receptor peroxisome proliferator-activated receptor-δ/β (PPAR-d) is upregulated in human colorectal cancers, but its role in colonic tumorigenesis remains controversial. Methods We generated a novel mouse model of intestinally targeted PPAR-d overexpression to simulate PPAR-d upregulation in human colon carcinogenesis. Colon-specific PPAR-d overexpression was confirmed by real-time reverse transcription polymerase chain reaction, immunoblotting, and activity assays. Mice with and without targeted PPAR-d overexpression were tested for azoxymethane (AOM)–induced colonic tumorigenesis. Mouse whole-genome transcriptome microarray analyses were performed to identify PPAR-d target genes to promote tumorigenesis. We used linear models to test for PPAR-d overexpression trend effects on tumor multiplicity. All statistical tests were two-sided. Results Targeted PPAR-d overexpression markedly increased colonic tumor incidence (from 0 of 10 wild-type [WT] littermate mice to 9 of 10 mice [P < .001] in 2 FVB/N background mouse lines [villin-PPAR-d-1 and villin-PPAR-d-2] at a 5-mg/kg AOM dose) and multiplicity (number of tumors per mouse per mg/kg dose of AOM increased from 0.47 [95% confidence interval [CI] = 0.22 to 0.72] for the WT littermates to 2.15 [95% CI = 1.90 to 2.40] [P < .001] for the villin-PPAR-d-1 mice and from 0.44 [95% CI = 0.09 to 0.79] for the WT littermates to 1.91 [95% CI = 1.57 to 2.25] [P < .001] for the villin-PPAR-d-2 mice). PPAR-d overexpression reversed resistance to AOM-induced colonic tumorigenesis in C57BL/6 mice. PPAR-d overexpression modulated expression of several novel PPAR-d target genes in normal-appearing colonic epithelial cells of mice with PPAR-d overexpression in a pattern that matched the changes in colonic tumors. Conclusions Our finding that PPAR-d upregulation profoundly enhances susceptibility to colonic tumorigenesis should impact the development of strategies of molecularly targeting PPAR-d in cancer and noncancerous diseases. [ABSTRACT FROM AUTHOR]