High-order Gene-gene Interactions Research Articles

Synergistic polymorphic interactions of phase II metabolizing genes and their association toward lung cancer susceptibility in North Indians

ABSTRACT Lung cancer is a multifactorial carcinoma with diverse heterogeneity. Genetic variations in drug-metabolizing enzymes may lead to defective detoxification and clearance of carcinogenic compounds. The high-order gene-gene interaction has been carried out between different genotypes of Phase II detoxification genes (NQO1, SULT1A1, NAT2, and EPHX1). Our results depict the genetic combination of SULT1A1 R 213 H with NAT2 × 5B L 161 L, SULT1A1 R 213 H with NAT2 × 5C K 268 R, EPHX1 H 139 R and NAT2 × 5B L 161 L exhibit a protective effect towards lung cancer risk. Further, the triple combinations of NQO1 P 187 S, EPHX1 Y 113 H, and EPHX1 H 139 R; NQO1 P 187 S, EPHX1 Y 113 H, and NAT2 × 6 R 197 Q; NQO1 P 187 S, EPHX1 Y 113 H, and NAT2 × 7 G 286 E; SULT1A1 R 213 H, EPHX1 H 139 R, and NAT2 × 7 G 286 E suggested a two-fold increased risk of lung cancer for subjects. Genetic polymorphisms of phase II detoxifying genes (NAT2, NQO1, EPHX1, SULT1A1) are prognostic markers for lung cancer.

Determining dependency and redundancy for identifying gene-gene interaction associated with complex disease.

As interactions among genetic variants in different genes can be an important factor for predicting complex diseases, many computational methods have been proposed to detect if a particular set of genes has interaction with a particular complex disease. However, even though many such methods have been shown to be useful, they can be made more effective if the properties of gene-gene interactions can be better understood. Towards this goal, we have attempted to uncover patterns in gene-gene interactions and the patterns reveal an interesting property that can be reflected in an inequality that describes the relationship between two genotype variables and a disease-status variable. We show, in this paper, that this inequality can be generalized to [Formula: see text] genotype variables. Based on this inequality, we establish a conditional independence and redundancy (CIR)-based definition of gene-gene interaction and the concept of an interaction group. From these new definitions, a novel measure of gene-gene interaction is then derived. We discuss the properties of these concepts and explain how they can be used in a novel algorithm to detect high-order gene-gene interactions. Experimental results using both simulated and real datasets show that the proposed method can be very promising.

DNA repair genes hOGG1, XRCC1 and ERCC2 polymorphisms and their molecular mapping in breast cancer patients from India.

Identification of modifier genes predisposing to breast cancer (BC) phenotype remains a significant challenge and varies with ethnicity. The genetic variability observed in DNA repair genes may modulate the cell's ability to repair the damaged DNA and hence, evaluation of genetic variants in crucial DNA damage repair genes is of clinical importance. We performed the present study to evaluate the role of ERCC2-Lys751Gln, hOGG1-Ser326Cys, and XRCC1-Arg399Gln gene polymorphisms on the risk of BC development and its molecular profile in Indian women. Three non-synonymous variants (rs13181, rs1052133, and rs25487) were genotyped in 464 BC patients and 450 healthy controls. Logistic regression was employed to evaluate the association of genotypes with BC risk. Also, in silico analysis was carried out to map the Arg399Gln variant on the BRCT1 domain of XRCC1 protein. XRCC1 Gln/Gln genotype frequency was significantly elevated in BC patients [odd ratio (OR) = 1.73; 95% confidence interval (CI) = 1.13-2.65]. No significant association was observed between hOGG1-Ser326Cys and ERCC2-Lys751Gln variants and BC risk. Subgroup analysis revealed that ERCC2-Lys751Gln and XRCC1-Arg399Gln variants contributed towards tumor progression. A positive interaction between the investigated SNPs and BC was revealed by MDR analysis. Arg399Gln variant resulted in a change in the surface charge of XRCC1 protein. The rs25487 variant of XRCC1 might be associated with an elevated risk of BC. Furthermore, we demonstrated that high order gene-gene interaction plays a significant role in BC etiology. Hence, understanding the impact of low penetrant gene polymorphisms might enable a better understanding of the genetic background of breast cancer.

Grid-based stochastic search for hierarchical gene-gene interactions in population-based genetic studies of common human diseases

BackgroundLarge-scale genetic studies of common human diseases have focused almost exclusively on the independent main effects of single-nucleotide polymorphisms (SNPs) on disease susceptibility. These studies have had some success, but much of the genetic architecture of common disease remains unexplained. Attention is now turning to detecting SNPs that impact disease susceptibility in the context of other genetic factors and environmental exposures. These context-dependent genetic effects can manifest themselves as non-additive interactions, which are more challenging to model using parametric statistical approaches. The dimensionality that results from a multitude of genotype combinations, which results from considering many SNPs simultaneously, renders these approaches underpowered. We previously developed the multifactor dimensionality reduction (MDR) approach as a nonparametric and genetic model-free machine learning alternative. Approaches such as MDR can improve the power to detect gene-gene interactions but are limited in their ability to exhaustively consider SNP combinations in genome-wide association studies (GWAS), due to the combinatorial explosion of the search space. We introduce here a stochastic search algorithm called Crush for the application of MDR to modeling high-order gene-gene interactions in genome-wide data. The Crush-MDR approach uses expert knowledge to guide probabilistic searches within a framework that capitalizes on the use of biological knowledge to filter gene sets prior to analysis. Here we evaluated the ability of Crush-MDR to detect hierarchical sets of interacting SNPs using a biology-based simulation strategy that assumes non-additive interactions within genes and additivity in genetic effects between sets of genes within a biochemical pathway.ResultsWe show that Crush-MDR is able to identify genetic effects at the gene or pathway level significantly better than a baseline random search with the same number of model evaluations. We then applied the same methodology to a GWAS for Alzheimer’s disease and showed base level validation that Crush-MDR was able to identify a set of interacting genes with biological ties to Alzheimer’s disease.ConclusionsWe discuss the role of stochastic search and cloud computing for detecting complex genetic effects in genome-wide data.

A multianalytical approach to evaluate the association of 55 SNPs in 28 genes with obesity risk in North Indian adults.

The aim of the study was to investigate the association of 55 SNPs in 28 genes with obesity risk in a North Indian population using a multianalytical approach. Overall, 480 subjects from the North Indian population were studied using strict inclusion/exclusion criteria. SNP Genotyping was carried out by Sequenom Mass ARRAY platform (Sequenom, San Diego, CA) and validated Taqman® allelic discrimination (Applied Biosystems® ). Statistical analyses were performed using SPSS software version 19.0, SNPStats, GMDR software (version 6) and GENEMANIA. Logistic regression analysis of 55 SNPs revealed significant associations (P < .05) of 49 SNPs with BMI linked obesity risk whereas the remaining 6 SNPs revealed no association (P > .05). The pathway-wise G-score revealed the significant role (P = .0001) of food intake-energy expenditure pathway genes. In CART analysis, the combined genotypes of FTO rs9939609 and TCF7L2 rs7903146 revealed the highest risk for BMI linked obesity. The analysis of the FTO-IRX3 locus revealed high LD and high order gene-gene interactions for BMI linked obesity. The interaction network of all of the associated genes in the present study generated by GENEMANIA revealed direct and indirect connections. In addition, the analysis with centralized obesity revealed that none of the SNPs except for FTO rs17818902 were significantly associated (P < .05). In this multi-analytical approach, FTO rs9939609 and IRX3 rs3751723, along with TCF7L2 rs7903146 and TMEM18 rs6548238, emerged as the major SNPs contributing to BMI linked obesity risk in the North Indian population.

Genetic variations in apoptosis pathway and the risk of ovarian cancer.

BackgroundApoptosis is a highly conserved form of cell death and aberrant regulation of apoptotic cell death mechanisms leads to variety of major human diseases, especially tumor formation. Genetic variations in apoptosis genes may increase susceptibility to ovarian cancer.ResultsIn individual SNP analysis, 12 SNPs in 5 apoptosis pathway genes were significantly associated with ovarian cancer risk after adjustment for multiple comparisons at q-value <0.05. The most significant SNP was rs11152377 in the Bcl-2 gene. The homozygous variant TT genotype was associated with a significantly decreased risk of ovarian cancer (odds ratio [OR] =0.53; 95% confidence interval [CI], 0.37-0.77, P<0.001). Cumulative effect analysis showed joint effects of increased risk of ovarian cancer with increasing number of unfavorable genotypes in patients. Classification and regression tree (CART) analysis further revealed high-order gene-gene interactions and categorized the study subjects into low-, medium-, and high-risk groups. Compared with the low-risk group, medium-risk group and high-risk group conferred 1.76-fold (95% CI: 1.06–2.90) and 3.64-fold (95% CI: 2.37–5.59) increased risk of ovarian cancer (P for trend <0.001)Materials and MethodsIn a case-control study of 417 ovarian cancer patients and 417 matched controls, we evaluated the associations of 587 single nucleotide polymorphisms (SNPs) from 65 genes of the apoptosis pathway with the risk of ovarian cancer.ConclusionsOur results suggest that genetic variations in apoptosis pathway genes modulate the risk of ovarian cancer individually and jointly.

Sparse Inverse Covariance Estimation with L0 Penalty for Network Construction with Omics Data.

Constructing coexpression and association networks with omics data is crucial for studying gene-gene interactions and underlying biological mechanisms. In recent years, learning the structure of a Gaussian graphical model from high-dimensional data using L1 penalty has been well-studied and many applications in bioinformatics and computational biology have been found. However, besides the problem of biased estimators with LASSO, L1 does not always choose the true model consistently. Based on our previous work with L0 regularized regression (Liu and Li, 2014), we propose an L0 regularized sparse inverse covariance estimation (L0RICE) for structure learning with the efficient alternating direction (AD) method. The proposed method is robust and has the oracle property. The proposed method is applied to omics data including data, from next-generation sequencing technologies. Novel procedures for network construction and high-order gene-gene interaction detection with omics data are developed. Results from simulation and real omics data analysis indicate that L0 regularized structure learning can identify high-order correlation structure with lower false positive rate and outperform graphical lasso by a large margin.

High order gene-gene interactions in eight single nucleotide polymorphisms of renin-angiotensin system genes for hypertension association study.

Several single nucleotide polymorphisms (SNPs) of renin-angiotensin system (RAS) genes are associated with hypertension (HT) but most of them are focusing on single locus effects. Here, we introduce an unbalanced function based on multifactor dimensionality reduction (MDR) for multiloci genotypes to detect high order gene-gene (SNP-SNP) interaction in unbalanced cases and controls of HT data. Eight SNPs of three RAS genes (angiotensinogen, AGT; angiotensin-converting enzyme, ACE; angiotensin II type 1 receptor, AT 1 R) in HT and non-HT subjects were included that showed no significant genotype differences. In 2- to 6-locus models of the SNP-SNP interaction, the SNPs of AGT and ACE genes were associated with hypertension (bootstrapping odds ratio [Boot-OR] = 1.972~3.785; 95%, confidence interval (CI) 1.26~6.21; P < 0.005). In 7- and 8-locus model, SNP A1166C of AT 1 R gene is joined to improve the maximum Boot-OR values of 4.050 to 4.483; CI = 2.49 to 7.29; P < 1.63E − 08. In conclusion, the epistasis networks are identified by eight SNP-SNP interaction models. AGT, ACE, and AT 1 R genes have overall effects with susceptibility to hypertension, where the SNPs of ACE have a mainly hypertension-associated effect and show an interacting effect to SNPs of AGT and AT 1 R genes.

A non-parametric approach for detecting gene-gene interactions associated with age-at-onset outcomes.

BackgroundCox-regression-based methods have been commonly used for the analyses of survival outcomes, such as age-at-disease-onset. These methods generally assume the hazard functions are proportional among various risk groups. However, such an assumption may not be valid in genetic association studies, especially when complex interactions are involved. In addition, genetic association studies commonly adopt case-control designs. Direct use of Cox regression to case-control data may yield biased estimators and incorrect statistical inference.ResultsWe propose a non-parametric approach, the weighted Nelson-Aalen (WNA) approach, for detecting genetic variants that are associated with age-dependent outcomes. The proposed approach can be directly applied to prospective cohort studies, and can be easily extended for population-based case-control studies. Moreover, it does not rely on any assumptions of the disease inheritance models, and is able to capture high-order gene-gene interactions. Through simulations, we show the proposed approach outperforms Cox-regression-based methods in various scenarios. We also conduct an empirical study of progression of nicotine dependence by applying the WNA approach to three independent datasets from the Study of Addiction: Genetics and Environment. In the initial dataset, two SNPs, rs6570989 and rs2930357, located in genes GRIK2 and CSMD1, are found to be significantly associated with the progression of nicotine dependence (ND). The joint association is further replicated in two independent datasets. Further analysis suggests that these two genes may interact and be associated with the progression of ND.ConclusionsAs demonstrated by the simulation studies and real data analysis, the proposed approach provides an efficient tool for detecting genetic interactions associated with age-at-onset outcomes.

Weighted risk score-based multifactor dimensionality reduction to detect gene-gene interactions in nasopharyngeal carcinoma.

Determining the complex relationships between diseases, polymorphisms in human genes and environmental factors is challenging. Multifactor dimensionality reduction (MDR) has been proven to be capable of effectively detecting the statistical patterns of epistasis, although classification accuracy is required for this approach. The imbalanced dataset can cause seriously negative effects on classification accuracy. Moreover, MDR methods cannot quantitatively assess the disease risk of genotype combinations. Hence, we introduce a novel weighted risk score-based multifactor dimensionality reduction (WRSMDR) method that uses the Bayesian posterior probability of polymorphism combinations as a new quantitative measure of disease risk. First, we compared the WRSMDR to the MDR method in simulated datasets. Our results showed that the WRSMDR method had reasonable power to identify high-order gene-gene interactions, and it was more effective than MDR at detecting four-locus models. Moreover, WRSMDR reveals more information regarding the effect of genotype combination on the disease risk, and the result was easier to determine and apply than with MDR. Finally, we applied WRSMDR to a nasopharyngeal carcinoma (NPC) case-control study and identified a statistically significant high-order interaction among three polymorphisms: rs2860580, rs11865086 and rs2305806.

Genetic predisposition to left ventricular dysfunction: a multigenic and multi-analytical approach

Left ventricular dysfunction (LVD) is a complex, multifactorial condition, caused by mechanical, neurohormonal, and genetic factors. We have previously observed association of renin-angiotensin-aldosterone system (RAAS), matrix metalloproteinases (MMPs) and inflammatory pathway genes with LVD. Therefore the present study was undertaken to identify the combination of genetic variants and their possible interactions contributing towards genetic susceptibility to LVD in the background of coronary artery disease (CAD). The study included 230 healthy controls and 510 consecutive patients with angiographically confirmed CAD. Among them, 162 with reduced left ventricle ejection fraction (LVEF≤45%) were categorized as having LVD. We analyzed 11 polymorphisms of RAAS, MMPs and inflammatory pathways. Single locus analysis showed that AT1 A1166C (p value<0.001; OR=3.67), MMP9 R668Q (p value=0.007; OR=3.48) and NFKB1-94 ATTG ins/del (p value=0.013; OR=2.01) polymorphisms were independently associated with LVD when compared with both non-LVD patients and healthy controls. High-order gene-gene interaction analysis, using classification and regression tree (CART) and multifactor dimensionality reduction (MDR) revealed that AT1 A1166C and NFKB1-94 ATTG ins/del polymorphisms jointly increased the risk of LVD to great extent (p-value=0.001; OR=8.55) and best four-factor interaction model consisted of AT1 A1166C, MMP7 A-181G, MMP9 R668Q and NFKB1-94 ATTG ins/del polymorphisms with testing accuracy of 0.566 and cross validation consistency (CVC)=9/10 (permutation p<0.001) showed increased risk for LVD respectively. AT1 A1166C independently and in combination with MMP9 R668Q and NFKB1-94 ATTG ins/del polymorphisms plays important role in conferring genetic susceptibility to LVD in CAD patients.

Polymorphisms in DNA repair genes of XRCC1, XPA, XPC, XPD and associations with lung cancer risk in Chinese people.

The carcinogenic chemicals and reactive oxygen species in tobacco can result in DNA damage. DNA repair genes play an important role in maintaining genome integrity. Genetic polymorphisms of DNA repair genes and smoking may contribute to susceptibility of lung cancer. In this hospital-based case-control study, we investigated the relationship between 13 tagging single nucleotide polymorphisms (SNPs) in base excision repair pathway and nucleotide excision repair pathway genes, smoking, and lung cancer susceptibility. Thirteen tag SNPs were genotyped in 265 lung cancer patients and 301 healthy controls. Logistic regression and multifactor dimensionality reduction method were applied to explore the association and high-order gene-gene and gene-smoking interaction. In single tag SNP analysis, XPA rs2808668, XPC rs2733533, and XPD rs1799787 were significantly associated with lung cancer susceptibility. Joint effects analysis of XPA rs2808668, XPC rs2733533 and XPD rs1799787 showed that there was an increased risk of lung cancer with increasing numbers of risk alleles. Haplotype analysis showed that XRCC1 (rs25487, rs1799782, rs3213334) GCC had a positive association with lung cancer. Analysis of gene-gene and gene-smoking interaction by multifactor dimensionality reduction showed that a positive interaction existed between the four genes and smoking. The two-factor model, including XPC rs2755333 and smoking, had the best prediction ability for lung cancer. Compared with the C/C genotype of XPC rs2733533 and no smoking, the combination of genotype A carriers with XPC rs2733533 and heavy smokers (≥30 pack-year) had a 13.32-fold risk of lung cancer. Our results suggest multiple genetic variants in multiple DNA repair genes may jointly contribute to lung cancer risk through gene-gene and gene-smoking interactions.

An information-gain approach to detecting three-way epistatic interactions in genetic association studies

BackgroundEpistasis has been historically used to describe the phenomenon that the effect of a given gene on a phenotype can be dependent on one or more other genes, and is an essential element for understanding the association between genetic and phenotypic variations. Quantifying epistasis of orders higher than two is very challenging due to both the computational complexity of enumerating all possible combinations in genome-wide data and the lack of efficient and effective methodologies.ObjectivesIn this study, we propose a fast, non-parametric, and model-free measure for three-way epistasis.MethodsSuch a measure is based on information gain, and is able to separate all lower order effects from pure three-way epistasis.ResultsOur method was verified on synthetic data and applied to real data from a candidate-gene study of tuberculosis in a West African population. In the tuberculosis data, we found a statistically significant pure three-way epistatic interaction effect that was stronger than any lower-order associations.ConclusionOur study provides a methodological basis for detecting and characterizing high-order gene-gene interactions in genetic association studies.

Gene Polymorphisms of ADIPOQ +45T&gt;G, UCP2 -866G&gt;A, and FABP2 Ala54Thr on the Risk of Colorectal Cancer: A Matched Case-Control Study

As insulin resistance (IR) is an established risk factor for colorectal cancer (CRC), we explored the association between each of the IR-related gene polymorphisms of adiponectin (ADIPOQ) rs2241766, uncoupling protein 2 (UCP2) rs659366, and fatty acid-binding protein (FABP2) rs1799883 and CRC risk. Genotyping of blood samples and collection of lifestyle and dietary habits were performed for 400 case-control pairs. Unconditional logistic regression (ULR) was applied to assess the effects of the three single nucleotide polymorphisms (SNP), environmental factors. Both ULR and generalized multifactor dimensionality reduction (GMDR) were used to test the gene-gene and gene-environment interactions on CRC risk. Subjects carrying the ADIPOQ rs2241766 TG+GG genotype had a higher CRC risk than those carrying the TT genotype (OR = 1.429, 95% CI 1.069–1.909). The additive and multiplicative interactions between ADIPOQ rs2241766 and FABP2 rs1799883 on CRC were found by ULR (RERI = 0.764, 95%CI 0.218∼1.311, AP = 0.514, 95%CI 0.165∼0.864, S = −1.745, 95%CI is unachievable, and Pmulti = 0.017, respectively). Furthermore, the high order gene-gene interaction of the three SNPs were found by GMDR (P = 0.0107). A significant dosage effect with an increasing number of risk genotypes was observed as the risk of CRC increased (Ptrend = 0.037). In GMDR, the gene-environment interaction among the three SNPs and red meat consumption on CRC risk was significant (P = 0.0107). Compared with subjects with low red meat consumption and null risk genotypes, those with high-red meat consumption and three risk genotypes had 3.439-fold CRC risk (95% CI 1.410–8.385). In conclusion, the results showed that the ADIPOQ rs2241766 TG+GG genotype increased CRC risk. Given the complexity of the carcinogen for CRC, ADIPOQ rs2241766, UCP2 rs659366, FABP2 rs1799883 and red meat consumption potentially worked together in affecting CRC risk.

Abstract 4843: Genetic variants in Notch signaling pathway and ovarian cancer.

Abstract The Notch signaling pathway drives proliferation, differentiation, apoptosis, cell fate choices, and has a key role in the regulation of stem cells in a variety of cancers. In this study, we applied a pathway-based approach to evaluate the associations between single nucleotide polymorphisms in Notch signaling pathway and ovarian cancer risk, survival, and response to chemotherapy. Towards this, we systematically genotyped 139 SNPs from 10 genes in the pathway in 339 Caucasian ovarian cancer cases and 349 Caucasian healthy controls. Individually, four SNPs were significantly associated with the ovarian cancer risk. The most significant SNP was NOTCH4: rs397081, with an adjusted odds ratio (OR) of 0.38 (95% CI = 0.22-0.66, P=0.00057). A highly significant gene-dosage effect was observed for these top variants (P for trend &amp;lt; 0.00001) with those carrying ≥ 3 risk genotypes having a nearly 6-fold increase in risk (OR = 5.67, 95% CI = 2.58 - 12.44] . Classification and regression tree (CART) analysis further revealed potential higher order gene-gene interactions between these four SNPs, with the highest risk group having almost 7-fold increased risk. Ten SNPs were significantly associated with overall survival, of which the strongest association was identified for NOTCH4: rs3131290 (OR = 0.48, 95% CI = 0.27-0.84). Cumulative effect analysis showed that multiple adverse genotypes had a significant dose-dependent effect on overall survival. The median survival times for individuals carrying ≤3, 4 to 6, and ≥7 of these adverse genotypes were &amp;gt;186.3, 62.82, and 28.58 months, respectively. Similarly, a significant association was identified between a poor response to platinum-based chemotherapy and the number of unfavorable SNPs (P for trend &amp;lt; 0.0001). Our findings suggest that genetic variants in the Notch signaling pathway are associated with ovarian cancer risk and could serve as potential predictors of clinical outcomes in ovarian cancer patients. Citation Format: Enping Xu, Karen H. Lu, Michelle AT Hildebrandt, Maosheng Huang, Xifeng Wu, Dong Liang. Genetic variants in Notch signaling pathway and ovarian cancer. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 4843. doi:10.1158/1538-7445.AM2013-4843

Abstract 1350: Genetic variations in apoptosis pathway are associated with survival in late stage non-small cell lung cancer.

Abstract Dysregulation of apoptosis pathway plays an essential role in carcinogenesis, tumor progression, and resistance to chemotherapy. We hypothesized that genetic variations in genes involved in the apoptosis pathway may affect the survival in late stage non-small cell lung cancer (NSCLC) patients. To test this hypothesis, we genotyped 583 tagging SNPs in 598 late stage NSCLC patients and analyzed their associations with survival individually and jointly. Seven SNPs were statistically significantly associated with overall survival after correction for multiple comparisons (q&amp;lt;0.1). The most significant SNP was rs17592236 on FOXO1 gene under the dominant model (OR: 2.54, 95%CI: 1.71-3.78, p=4.15x10−6). We also observed a significant cumulative effect. Compared to the reference group (patients with no more than two risk alleles), those having five or more risk alleles were much more likely to die within seven months (HR: 3.58, 95% CI: 2.55-5.04, p&amp;lt;0.001). Notably, the median survival time (MST) was significantly reduced by up to 15 months in high risk patients when compared to the reference group (MST: 22.2 months, p&amp;lt;0.001). Survival tree analysis revealed potential higher order gene-gene interactions among these SNPs. Our results provided evidence that genetic variations in the apoptosis pathway modulate NSCLC survival and may serve as biomarkers to differentiate NSCLC patients with different prognosis. Citation Format: Xiang Shu, Charles Lu, Joe Y. Chang, Jack A. Roth, Xifeng Wu. Genetic variations in apoptosis pathway are associated with survival in late stage non-small cell lung cancer. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 1350. doi:10.1158/1538-7445.AM2013-1350

Abstract 4582: Genome-wide association study of toxicity following definitive radiotherapy in non-small cell lung cancer patients.

Abstract Radiation therapy in combination with chemotherapy is the primary treatment for lung cancer patients with surgically unresectable tumors or locally advanced diseases. Unfortunately, about one third of these patients develop clinically significant toxicities. This not only severely limits the dosage of radiation that can be delivered, but also negatively impacts patients’ quality of life. Commonly used clinical variables are insufficient to predict the occurrence of these toxicities prior to treatment. This has led to interest in examining the influence of genetic variability in patient toxicity following radiotherapy. Several small scale association studies with only limited samples and markers have provided evidence of genetic contribution to the risk of developing radiation-induced toxicity, while most lack validation. To identify reliable genetic variants associated with risk of developing clinically significant radiation-induced toxicities, we conducted a genome wide scan of 300,977 single-nucleotide polymorphisms (SNPs) in 393 non-small cell lung cancer (NSCLC) Caucasian patients who received definitive radiation therapy at the University of Texas MD Anderson Cancer Center (discovery phase). We assessed the association of each SNP with the two major forms of radiation-induced toxicities (pneumonitis and esophagitis) via a multivariate logistic regression model. A total of 30,474 SNPs were significantly associated with pneumonitis (651 SNPs reached P&amp;lt;10−3, 45 SNPs reached P&amp;lt;10−4) and 31,086 with esophagitis (687 SNPs reached P&amp;lt;10−3, 59 SNPs reached P&amp;lt;10−4). The most significant SNPs for pneumonitis and esophagitis were located in chromosome 10q (OR=3.24, 95%CI=2.06-5.08, P=3.18×10−7) and chromosome 4p (OR=0.17, 95%CI=0.08-0.37, P=9.42×10−6), respectively. The results from discovery phase showed no evidence for inflation of the test statistics (inflation factor of dominant model =1.065 for both outcomes). We then performed classification and regression tree (CART) analysis to explore high-order gene-gene interactions among top SNPs (P&amp;lt;10−3) in modulating toxicities. Four SNPs for pneumonitis and four SNPs for esophagitis were identified by CART to have significant interactions in modulating the risk of developing toxicities. A validation phase for the most significant SNPs in an additional 275 NSCLC patients treated in a similar fashion is currently ongoing. To our knowledge, this is the first genome-wide association study to identify SNPs associated with toxicities resulting from definitive radiation therapy in NSCLC patients. With this relatively large sample size, we expect to identify novel susceptibility loci that may play a significant role in contributing to the development of radiation-induced toxicities. This will assist in the development of more personalized radiotherapy delivery and symptom management. Citation Format: Xia Pu, Heath D. Skinner, Yuanqing Ye, Michelle AT Hildebrandt, Joe Y. Chang, Charles Lu, Ritsuko Komaki, John D. Minna, Jack A. Roth, Xifeng Wu. Genome-wide association study of toxicity following definitive radiotherapy in non-small cell lung cancer patients. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 4582. doi:10.1158/1538-7445.AM2013-4582

Genetic Variations in the Transforming Growth Factor Beta Pathway as Predictors of Bladder Cancer Risk

Bladder cancer is the fifth most common cancer in the United States, and identifying genetic markers that may predict susceptibility in high-risk population is always needed. The purpose of our study is to determine whether genetic variations in the transforming growth factor-beta (TGF-β) pathway are associated with bladder cancer risk. We identified 356 single-nucleotide polymorphisms (SNPs) in 37 key genes from this pathway and evaluated their association with cancer risk in 801 cases and 801 controls. Forty-one SNPs were significantly associated with cancer risk, and after adjusting for multiple comparisons, 9 remained significant (Q-value ≤0.1). Haplotype analysis further revealed three haplotypes within VEGFC and two haplotypes in EGFR were significantly associated with increased bladder cancer risk compared to the most common haplotype. Classification and regression tree analysis further revealed potential high-order gene-gene interactions, with VEGFC: rs3775194 being the initial split, which suggests that this variant is responsible for the most variation in risk. Individuals carrying the common genotype for VEGFC: rs3775194 and EGFR: rs7799627 and the variant genotype for VEGFR: rs4557213 had a 4.22-fold increase in risk, a much larger effect magnitude than that conferred by common genotype for VEGFR: rs4557213. Our study provides the first epidemiological evidence supporting a connection between TGF-β pathway variants and bladder cancer risk.

Abstract A93: Genetic variations in aging related genes/pathways and colorectal cancer risk

Abstract Advanced age is arguably the most important risk factor for developing cancer. About 77% of all cancers are diagnosed in individuals 55 years of age and older. Aging is especially a dominant risk factor for colorectal cancer (CRC), with 91% of cases diagnosed in individuals 50 years of age and older. A number of genes and signaling pathways have been indicated in the aging process in model organisms including mammals, e.g. the insulin/IGF-1 signaling pathway, mTOR pathway and telomere and telomerase pathway. Recently, genome-wide association studies also identified several single nucleotide polymorphisms (SNPs) associated with longevity. Therefore, we hypothesized that genetic variations of the genes and pathways related to aging might be associated with CRC risk. We systematically identified 189 putative functional SNPs in 109 aging-related genes and 20 longevity-associated SNPs from GWAS. In the discovery phase, we genotyped these SNPs in 797 Caucasian CRC cases and 797 matched healthy control subjects. In main effect analysis, 10 SNPs were significantly associated with altered CRC risk. When evaluating combined effects of these SNPs, we found a significant gene dosage effect for increased CRC risk with increasing number of unfavorable genotypes. Compared with individuals carrying fewer than three unfavorable genotypes, individuals with six and more than six of these unfavorable genotypes exhibited a 2.06-fold (95% CI: 1.24-3.43, P = 0.006) and a 2.44-fold (95% CI: 1.76-3.38, P = 7.56×10−8) increased risk of CRC, respectively (P for trend 1.25×10−9). Higher order gene-gene interaction analysis also revealed potential relationships among SNPs in TP53, ERCC2, GPR133, PRKAG3, TEP1, TSC1 and LMNA that further defined risk groups for CRC. Our results suggest that genetic polymorphisms in aging-related genes and pathways may modify CRC susceptibility both individually and jointly. Replication is currently underway to validate these findings. Citation Format: Fanmao Zhang, Cathy Eng, Moubin Lin, Michelle A.T. Hildebrandt, Yonggang He, Jie Lin, Maosheng Huang, Jian Gu, Xifeng Wu. Genetic variations in aging-related genes/pathways and colorectal cancer risk. [abstract]. In: Proceedings of the Eleventh Annual AACR International Conference on Frontiers in Cancer Prevention Research; 2012 Oct 16-19; Anaheim, CA. Philadelphia (PA): AACR; Cancer Prev Res 2012;5(11 Suppl):Abstract nr A93.

Multi‐loci analysis reveals the importance of genetic variations in sensitivity of platinum‐based chemotherapy in non‐small‐cell lung cancer

Polymorphisms in DNA repair and apoptotic pathways may cause variations in chemosensitivity of non-small-cell lung cancer (NSCLC) through complex gene-gene and gene-environment interactions. A total of 200 advanced NSCLC patients who received platinum-based chemotherapies were recruited. The short-term clinical outcomes were classified as chemosensitive group, including complete remission (CR) and partial remission (PR), and chemoresistant group, namely stable disease (SD) and progression disease (PD) at the end of treatment. We applied multifactor dimensionality reduction (MDR), classification and regression tree (CART) and traditional logistic regression (LR) to explore high-order gene-gene and gene-environment interactions among 11 functional single nucleotide polymorphisms (SNPs), smoking status, cancer stages and treatment regimens in the response to chemotherapy. Multi-loci analyses consistently indicated that interactions among XRCC1 Arg194Trp, XPC PAT, FAS G-1377A, and FASL T-844C were associated with sensitivity to platinum-based chemotherapy. In MDR analysis, the four-factor model yielded the highest test accuracy of 0.72 (permutation P = 0.001). In CART analysis, these four SNPs were the determinant nodes of the growth of regression tree. Patients carrying XRCC1 Arg194Arg, FAS-1377GG, and FASL-844T allele displayed completely no response to platinum, whereas patients with XRCC1 194Trp allele and XPC PAT +/+ had 68.8% response rate to platinum. In LR analysis, a significant gene-dosage effect was detected along with the increasing number of favorable genotypes of these four polymorphisms (P trend = 0.00002). Multi-loci analysis reveals the importance of genetic variations involved in DNA repair and apoptotic pathways in sensitivity of platinum-based chemotherapy in NSCLC.