Greg Dyson

Genes Associated with Prostate Cancer Are Differentially Expressed in African American and European American Men

Background: Despite more aggressive screening across all demographics and gradual declines in mortality related to prostate cancer (PCa) in the United States, disparities among populations persist. A substantial proportion of African American men (AAM) have a higher overall incidence, earlier age of onset, increased proportion of clinically advanced disease, and increased bone metastases and mortality from PCa compared to European American men (EAM). Limited early evidence indicates that underlying causes for disparities may be observed in tumor-specific gene expression programs. Methods: This study used microarray-based methods to measure expression levels for 517 genes that were previously associated with PCa in archived formalin-fixed paraffin embedded (FFPE) specimens; testing the hypothesis that gene expression features of functional consequence to cancer distinguish PCa from AAM and EAM. A t test was conducted comparing AAM to EAM expression levels for each probe on the array. Results: Analysis of 639 tumor samples (270 AAM, 369 EAM) showed that 95 genes were overexpressed specifically in PCa from AAM relative to EAM and 132 were overexpressed in PCa from EAM relative to AAM. Furthermore, systems-level analyses highlight the relevant signaling pathways and functions associated with the EAM- or AAM-specific overexpressed gene sets, for example, inflammation and lipid metabolism. Conclusions: Results here bring further understanding to the potential for molecular differences for PCa in AAM versus EAM. Impact: The results support the notion that therapeutic benefits will be realized when targeted treatments are designed to acknowledge and address a greater spectrum of PCa subtypes and molecular distinctions. Cancer Epidemiol Biomarkers Prev; 22(5); 891–7. ©2013 AACR.

Evaluating methods for classifying expression data.

Abstract An attractive application of expression technologies is to predict drug efficacy or safety using expression data of biomarkers. To evaluate the performance of various classification methods for building predictive models, we applied these methods on six expression datasets. These datasets were from studies using microarray technologies and had either two or more classes. From each of the original datasets, two subsets were generated to simulate two scenarios in biomarker applications. First, a 50-gene subset was used to simulate a candidate gene approach when it might not be practical to measure a large number of genes/biomarkers. Next, a 2000-gene subset was used to simulate a whole genome approach. We evaluated the relative performance of several classification methods by using leave-one-out cross-validation and bootstrap cross-validation. Although all methods perform well in both subsets for a relative easy dataset with two classes, differences in performance do exist among methods for other datasets. Overall, partial least squares discriminant analysis (PLS-DA) and support vector machines (SVM) outperform all other methods. We suggest a practical approach to take advantage of multiple methods in biomarker applications.

Context-Dependent Associations Between Variation in Risk of Ischemic Heart Disease and Variation in the 5′ Promoter Region of the Apolipoprotein E Gene in Danish Women

Objective—Variations in the noncoding single-nucleotide polymorphisms (SNPs) at positions 560 and 832 in the 5′ promoter region of the apolipoprotein E gene define genotypes that distinguish between high and low concentrations of plasma total and high-density lipoprotein cholesterol and triglycerides. We addressed whether these genotypes improve the prediction of ischemic heart disease (IHD) in subsamples of individuals defined by traditional risk factors and the genotypes defined by the &egr;2, &egr;3, and &egr;4 alleles in exon 4 of the apolipoprotein E gene. Methods and Results—In a sample of 3686 female and 2772 male participants of the Copenhagen City Heart Study who were free of IHD events, 576 individuals (257 women, 7.0% and 319 men, 11.5%) were diagnosed as having developed IHD in 6.5 years of follow-up. Using a stepwise Patient Rule-Induction Method modeling strategy that acknowledges the complex pathobiology of IHD, we identified a subsample of 764 elderly women (≥65 years) with hypertriglyceridemia who had a history of smoking, a history of hypertension, or a history of both in which the A560T832/A560T832 and A560T832/A560G832 5′ 2-SNP genotypes had a higher cumulative incidence of IHD (172/1000) compared to the incidence of 70/1000 in the total sample of women. Conclusions—Our study validates that 5′ apolipoprotein E genotypes improve the prediction of IHD and documents that the improvement is greatest in a subset defined by a particular combination of traditional risk factors in Copenhagen City Heart Study female participants. We discuss the use of these genotypes in medical risk assessment of IHD in the population represented by the Copenhagen City Heart Study.

Treating triple negative breast cancer cells with erlotinib plus a select antioxidant overcomes drug resistance by targeting cancer cell heterogeneity

Among breast cancer patients, those diagnosed with the triple-negative breast cancer (TNBC) subtype have the worst prog-nosis. TNBC does not express estrogen receptor-alpha, progesterone receptor, or the HER2 oncogene; therefore, TNBC lacks targets for molecularly-guided therapies. The concept that EGFR oncogene inhibitor drugs could be used as targeted treatment against TNBC has been put forth based on estimates that 30–60% of TNBC express high levels of EGFR. However, results from clinical trials testing EGFR inhibitors, alone or in combination with cytotoxic chemotherapy, did not improve patient outcomes. Results herein offer an explanation as to why EGFR inhibitors failed TNBC patients and support how combining a select antioxidant and an EGFR-specific small molecule kinase inhibitor (SMKI) could be an effective, novel therapeutic strategy. Treatment with CAT-SKL—a re-engineered protein form of the antioxidant enzyme catalase—inhibited cancer stem-like cells (CSCs), and treatment with the EGFR-specific SMKI erlotinib inhibited non-CSCs. Thus, combining the antioxidant CAT-SKL with erlotinib targeted both CSCs and bulk cancer cells in cultures of EGFR-expressing TNBC-derived cells. We also report evidence that the mechanism for CAT-SKL inhibition of CSCs may depend on antioxidant-induced downregulation of a short alternative mRNA splicing variant of the methyl-CpG binding domain 2 gene, isoform MBD2c.

Validated context-dependent associations of coronary heart disease risk with genotype variation in the chromosome 9p21 region: The Atherosclerosis Risk in Communities study

AbstractMarkers of the chromosome 9p21 region are regarded as the strongest and most reliably significant genome-wide association study (GWAS) signals for Coronary heart disease (CHD) risk; this was recently confirmed by the CARDIoGRAMplusC4D Consortium meta-analysis. However, while these associations are significant at the population level, they may not be clinically relevant predictors of risk for all individuals. We describe here the results of a study designed to address the question: What is the contribution of context defined by traditional risk factors in determining the utility of DNA sequence variations marking the 9p21 region for explaining variation in CHD risk? We analyzed a sample of 7,589 (3,869 females and 3,720 males) European American participants of the Atherosclerosis Risk in Communities study. We confirmed CHD-SNP genotype associations for two 9p21 region marker SNPs previously identified by the CARDIoGRAMplusC4D Consortium study, of which ARIC was a part. We then tested each marker SNP genotype effect on prediction of CHD within sub-groups of the ARIC sample defined by traditional CHD risk factors by applying a novel multi-model strategy, PRIM. We observed that the effects of SNP genotypes in the 9p21 region were strongest in a sub-group of hypertensives. We subsequently validated the effect of the region in an independent sample from the Copenhagen City Heart Study. Our study suggests that marker SNPs identified as predictors of CHD risk in large population based GWAS may have their greatest utility in explaining risk of disease in particular sub-groups characterized by biological and environmental effects measured by the traditional CHD risk factors.

Modifications to the patient rule-induction method that utilize non-additive combinations of genetic and environmental effects to define partitions that predict ischemic heart disease

This article extends the Patient Rule‐Induction Method (PRIM) for modeling cumulative incidence of disease developed by Dyson et al. (Genet Epidemiol 31:515–527) to include the simultaneous consideration of non‐additive combinations of predictor variables, a significance test of each combination, an adjustment for multiple testing and a confidence interval for the estimate of the cumulative incidence of disease in each partition. We employ the partitioning algorithm component of the Combinatorial Partitioning Method to construct combinations of predictors, permutation testing to assess the significance of each combination, theoretical arguments for incorporating a multiple testing adjustment and bootstrap resampling to produce the confidence intervals. An illustration of this revised PRIM utilizing a sample of 2,258 European male participants from the Copenhagen City Heart Study is presented that assesses the utility of genetic variants in predicting the presence of ischemic heart disease beyond the established risk factors. Genet. Epidemiol. 2009.

Tumor autoantibodies as biomarkers for predicting ovarian cancer recurrence

Ovarian cancer (OVCA) has a high incidence of recurrence and a high rate of mortality. We performed a pilot study to evaluate the usefulness of tumor autoantibodies to tumor associated antigens (TAA) to predict OVCA recurrence. A validation study with 56 antigens, previously identified in the initial phase of the study, along with 13 known tumor antigens on protein arrays was performed on an independent cohort of recurrent and non-recurrent OVCA patients. Statistical analyses revealed that a panel of 3 antigens predicted recurrence at a median time of 9.07 months prior to clinical recurrence in a study population, where majority of patients had CA125 values less than 35 U/ml, with an average sensitivity, specificity and accuracy of 94.7%, 86.7% and 93.3% respectively. One of the top 3 antigens has been associated with the development of polymyositis (PM) which has been shown in some cases to precede the occurrence of ovarian carcinoma. Our results indicate that these 3 antigens have potential for predicting recurrence at an early time and may have better prognostic utility than CA125 alone for early therapeutic intervention. These biomarkers could guide us to identify those patients that could benefit most from maintenance or consolidation therapy.

Whole-exome sequencing reveals genetic variability among lung cancer cases subphenotyped for emphysema

Lung cancer continues to be a major public health challenge in the United States despite efforts to decrease the prevalence of smoking; outcomes are especially poor for African-American patients compared to other races/ethnicities. Chronic obstructive pulmonary disease (COPD) co-occurs with lung cancer frequently, but not always, suggesting both shared and distinct risk factors for these two diseases. To identify germline genetic variation that distinguishes between lung cancer in the presence and absence of emphysema, we performed whole-exome sequencing on 46 African-American lung cancer cases (23 with and 23 without emphysema frequency matched on age, sex, histology and pack years). Using conditional logistic regression, we found 6305 variants (of 168 150 varying sites) significantly associated with lung cancer subphenotype (P ≤ 0.05). Next, we validated 10 of these variants in an independent set of 612 lung cancer cases (267 with emphysema and 345 without emphysema) from the same population of inference as the sequenced cases. We found one variant that was significantly associated with lung cancer subphenotype in the validation sample. These findings contribute to teasing apart shared genetic factors from independent genetic factors for lung cancer and COPD.

Five advanced pancreatic cancer patients in a Phase I study of anti-CD3 x anti-EGFR bispecific antibody armed activated T cells (BATS)

Meeting abstracts Conventional chemotherapy for locally advanced pancreatic cancer (LAPC) and metastatic PC (MPC) is associated with clinical toxicities, dismal response, and poor survival rates. Novel approaches are needed. Preclinical studies show that bispecific antibody armed T cells exhibit

Efficient identification of context dependent subgroups of risk from genome-wide association studies

Abstract We have developed a modified Patient Rule-Induction Method (PRIM) as an alternative strategy for analyzing representative samples of non-experimental human data to estimate and test the role of genomic variations as predictors of disease risk in etiologically heterogeneous sub-samples. A computational limit of the proposed strategy is encountered when the number of genomic variations (predictor variables) under study is large (>500) because permutations are used to generate a null distribution to test the significance of a term (defined by values of particular variables) that characterizes a sub-sample of individuals through the peeling and pasting processes. As an alternative, in this paper we introduce a theoretical strategy that facilitates the quick calculation of Type I and Type II errors in the evaluation of terms in the peeling and pasting processes carried out in the execution of a PRIM analysis that are under-estimated and non-existent, respectively, when a permutation-based hypothesis test is employed. The resultant savings in computational time makes possible the consideration of larger numbers of genomic variations (an example genome-wide association study is given) in the selection of statistically significant terms in the formulation of PRIM prediction models.