Review of the Gene-Environment Interaction Literature in Cancer: What Do We Know?

Elemental biomapping of human tissues suggests toxic metals such as mercury play a role in the pathogenesis of cancer

Toxic metals such as mercury, lead, and cadmium have multiple carcinogenic capacities, including the ability to damage DNA and incite inflammation. Environmental toxic metals have long been suspected to play a role in the pathogenesis of cancer, but convincing evidence from epidemiological studies that toxic metals are risk factors for common neoplasms has been difficult to gain. Another approach is to map the location of potentially toxic elements in normal human cells where common cancers originate, as well as in the cancers themselves. In this Perspective, studies are summarized that have used elemental biomapping to detect toxic metals such as mercury in human cells. Two elemental biomapping techniques, autometallography and laser ablation-inductively coupled-mass spectrometry imaging, have shown that multiple toxic metals exist in normal human cells that are particularly prone to developing cancer, and are also seen in neoplastic cells of breast and pancreatic tumors. Biomapping studies of animals exposed to toxic metals show that these animals take up toxic metals in the same cells as humans. The finding of toxic metals such as mercury in human cells prone to cancer could explain the increasing global incidence of many cancers since toxic metals continue to accumulate in the environment. The role of toxic metals in cancer remains to be confirmed experimentally, but to decrease cancer risk a precautionary approach would be to reduce emissions of mercury and other toxic metals into the environment from industrial and mining activities and from the burning of fossil fuels.

GbyE: an integrated tool for genome widely association study and genome selection based on genetic by environmental interaction

BACKGROUND

The growth and development of organism were dependent on the effect of genetic, environment, and their interaction. In recent decades, lots of candidate additive genetic markers and genes had been detected by using genome-widely association study (GWAS). However, restricted to computing power and practical tool, the interactive effect of markers and genes were not revealed clearly. And utilization of these interactive markers is difficult in the breeding and prediction, such as genome selection (GS).

RESULTS

Through the Power-FDR curve, the GbyE algorithm can detect more significant genetic loci at different levels of genetic correlation and heritability, especially at low heritability levels. The additive effect of GbyE exhibits high significance on certain chromosomes, while the interactive effect detects more significant sites on other chromosomes, which were not detected in the first two parts. In prediction accuracy testing, in most cases of heritability and genetic correlation, the majority of prediction accuracy of GbyE is significantly higher than that of the mean method, regardless of whether the rrBLUP model or BGLR model is used for statistics. The GbyE algorithm improves the prediction accuracy of the three Bayesian models BRR, BayesA, and BayesLASSO using information from genetic by environmental interaction (G × E) and increases the prediction accuracy by 9.4%, 9.1%, and 11%, respectively, relative to the Mean value method. The GbyE algorithm is significantly superior to the mean method in the absence of a single environment, regardless of the combination of heritability and genetic correlation, especially in the case of high genetic correlation and heritability.

CONCLUSIONS

Therefore, this study constructed a new genotype design model program (GbyE) for GWAS and GS using Kronecker product. which was able to clearly estimate the additive and interactive effects separately. The results showed that GbyE can provide higher statistical power for the GWAS and more prediction accuracy of the GS models. In addition, GbyE gives varying degrees of improvement of prediction accuracy in three Bayesian models (BRR, BayesA, and BayesCpi). Whatever the phenotype were missed in the single environment or multiple environments, the GbyE also makes better prediction for inference population set. This study helps us understand the interactive relationship between genomic and environment in the complex traits. The GbyE source code is available at the GitHub website ( https://github.com/liu-xinrui/GbyE ).

Understanding and applying gene-environment interactions: a guide for nutrition professionals with an emphasis on integration in African research settings

Noncommunicable diseases (NCDs) are influenced by the interplay between genetics and environmental exposures, particularly diet. However, many healthcare professionals, including nutritionists and dietitians, have limited genetic background and, therefore, they may lack understanding of gene-environment interactions (GxEs) studies. Even researchers deeply involved in nutrition studies, but with a focus elsewhere, can struggle to interpret, evaluate, and conduct GxE studies. There is an urgent need to study African populations that bear a heavy burden of NCDs, demonstrate unique genetic variability, and have cultural practices resulting in distinctive environmental exposures compared with Europeans or Americans, who are studied more. Although diverse and rapidly changing environments, as well as the high genetic variability of Africans and difference in linkage disequilibrium (ie, certain gene variants are inherited together more often than expected by chance), provide unparalleled potential to investigate the omics fields, only a small percentage of studies come from Africa. Furthermore, research evidence lags behind the practices of companies offering genetic testing for personalized medicine and nutrition. We need to generate more evidence on GxEs that also considers continental African populations to be able to prevent unethical practices and enable tailored treatments. This review aims to introduce nutrition professionals to genetics terms and valid methods to investigate GxEs and their challenges, and proposes ways to improve quality and reproducibility. The review also provides insight into the potential contributions of nutrigenetics and nutrigenomics to the healthcare sphere, addresses direct-to-consumer genetic testing, and concludes by offering insights into the field's future, including advanced technologies like artificial intelligence and machine learning.

Mass Spectral Library for DNA Adductomics

Endogenous electrophiles, ionizing and non-ionizing radiation, and hazardous chemicals present in the environment and diet can damage DNA by forming covalent adducts. DNA adducts can form in critical cancer driver genes and, if not repaired, may induce mutations during cell division, potentially leading to the onset of cancer. The detection and quantification of specific DNA adducts are some of the first steps in studying their role in carcinogenesis, the physiological conditions that lead to their production, and the risk assessment of exposure to specific genotoxic chemicals. Hundreds of different DNA adducts have been reported in the literature, and there is a critical need to establish a DNA adduct mass spectral database to facilitate the detection of previously observed DNA adducts and characterize newly discovered DNA adducts. We have collected synthetic DNA adduct standards from the research community, acquired MSn (n = 2, 3) fragmentation spectra using Orbitrap and Quadrupole-Time-of-Flight (Q-TOF) MS instrumentation, processed the spectral data and incorporated it into the MassBank of North America (MoNA) database, and created a DNA adduct portal Web site (https://sites.google.com/umn.edu/dnaadductportal) to serve as a central location for the DNA adduct mass spectra and metadata, including the spectral database downloadable in different formats. This spectral library should prove to be a valuable resource for the DNA adductomics community, accelerating research and improving our understanding of the role of DNA adducts in disease.

Systematic review on gene-sun exposure interactions in skin cancer

BACKGROUND

The risk of skin cancer is determined by environmental factors like ultraviolet radiation (UVR), personal habits like time spent outdoors and genetic factors. This review aimed to survey existing studies in gene-environment (GxE) interaction on skin cancer risk, and report on GxE effect estimates.

METHODS

We searched Embase, Medline (Ovid) and Web of Science (Core Collection) and included only primary research that reported on GxE on the risk of the three most common types of skin cancer: basal cell carcinoma (BCC), squamous cell carcinoma (SCC) and melanoma. Quality assessment followed the Newcastle-Ottawa Scale. Meta-analysis was not possible because no two studies examined the same interaction. This review was registered on PROSPERO (CRD42021238064).

RESULTS

In total 260 records were identified after exclusion of duplicates. Fifteen studies were included in the final synthesis-12 used candidate gene approach. We found some evidence of GxE interactions with sun exposure, notably, with MC1R, CAT and NOS1 genes in melanoma, HAL and IL23A in BCC and HAL and XRCC1 in SCC.

CONCLUSION

Sun exposure seems to interact with genes involved in pigmentation, oxidative stress and immunosuppression, indicating that excessive UV exposure might exhaust oxidative defence and repair systems differentially, dependent on genetic make-up. Further research is warranted to better understand skin cancer epidemiology and develop sun exposure recommendations. A genome-wide approach is recommended as it might uncover unknown disease pathways dependent on UV radiation.

A genome-wide gene-environment interaction study of breast cancer risk for women of European ancestry

BACKGROUND

Genome-wide studies of gene-environment interactions (G×E) may identify variants associated with disease risk in conjunction with lifestyle/environmental exposures. We conducted a genome-wide G×E analysis of ~ 7.6 million common variants and seven lifestyle/environmental risk factors for breast cancer risk overall and for estrogen receptor positive (ER +) breast cancer.

METHODS

Analyses were conducted using 72,285 breast cancer cases and 80,354 controls of European ancestry from the Breast Cancer Association Consortium. Gene-environment interactions were evaluated using standard unconditional logistic regression models and likelihood ratio tests for breast cancer risk overall and for ER + breast cancer. Bayesian False Discovery Probability was employed to assess the noteworthiness of each SNP-risk factor pairs.

RESULTS

Assuming a 1 × 10-5 prior probability of a true association for each SNP-risk factor pairs and a Bayesian False Discovery Probability < 15%, we identified two independent SNP-risk factor pairs: rs80018847(9p13)-LINGO2 and adult height in association with overall breast cancer risk (ORint = 0.94, 95% CI 0.92-0.96), and rs4770552(13q12)-SPATA13 and age at menarche for ER + breast cancer risk (ORint = 0.91, 95% CI 0.88-0.94).

CONCLUSIONS

Overall, the contribution of G×E interactions to the heritability of breast cancer is very small. At the population level, multiplicative G×E interactions do not make an important contribution to risk prediction in breast cancer.

Robust Bayesian variable selection for gene-environment interactions

Gene-environment (G× E) interactions have important implications to elucidate the etiology of complex diseases beyond the main genetic and environmental effects. Outliers and data contamination in disease phenotypes of G× E studies have been commonly encountered, leading to the development of a broad spectrum of robust regularization methods. Nevertheless, within the Bayesian framework, the issue has not been taken care of in existing studies. We develop a fully Bayesian robust variable selection method for G× E interaction studies. The proposed Bayesian method can effectively accommodate heavy-tailed errors and outliers in the response variable while conducting variable selection by accounting for structural sparsity. In particular, for the robust sparse group selection, the spike-and-slab priors have been imposed on both individual and group levels to identify important main and interaction effects robustly. An efficient Gibbs sampler has been developed to facilitate fast computation. Extensive simulation studies, analysis of diabetes data with single-nucleotide polymorphism measurements from the Nurses' Health Study, and The Cancer Genome Atlas melanoma data with gene expression measurements demonstrate the superior performance of the proposed method over multiple competing alternatives.

Environmental Exposure Science and Human Health

Human health and environmental exposure form an inseparable binomial [...].

Smoking as a risk factor for colorectal neoplasms in young individuals? A systematic meta-analysis

BACKGROUND AND AIMS

Early-onset colorectal neoplasms (EoCRN) include both benign and malign colorectal tumors, which occur before the age of 50. The incidence of EoCRN is rising worldwide. Tobacco smoking has previously been proven to be related to the development of various tumor types. However, its relationship with EoCRN is not clearly defined. Hence, we carried out a systematic review and a meta-analysis to evaluate the relationship between smoking status and the risk of EoCRN.

METHODS

A systematic search of PubMed, EMBASE, and Web of Science up to September 7, 2022, was performed for studies that evaluated the association of smoking status with EoCRN. The quality of the case-control study was evaluated with the Newcastle‒Ottawa Scale. The quality of the cross-sectional studies was evaluated with the American Health Care Research and Quality checklist. Fixed-effects models were used to pool odds ratios (ORs) to evaluate the relationship between the risk of developing EoCRN and smoking status. The meta-analyses were performed with Review Manager version 5.4, and funnel plots and publication bias tests were produced by STATA software.

RESULTS

A total of six studies were included in this meta-analysis. After pooling the results of these six studies, we found that current smokers carry a relatively high risk of developing EoCRN (OR, 1.33; 95% confidence interval [CI], 1.17-1.52) compared to never-smokers. Ex-smokers were not at a significantly increased risk for developing EoCRN (OR, 1.00; 95% CI, 0.86-1.18).

DISCUSSION

Smoking behavior is significantly associated with an increased risk for developing EoCRN and might be one of the reasons for the increasing incidence. Ex-smokers who quit are not at significant risk of developing EoCRN.

Common variants of pro-inflammatory gene IL1B and interactions with PPP1R13L and POLR1G in relation to lung cancer among Northeast Chinese

Lung cancer is a complex disease influenced by a variety of genetic and environmental factors. The cytokine interleukin 1 encoded by IL1B is an important mediator of the inflammatory response, and is involved in a variety of cellular activities. The effect of single nucleotide polymorphisms (SNP) at IL1B has been investigated in relation to cancer with inconsistent results. This Northeastern-Chinese case-control study involving 627 cases and 633 controls evaluated the role of three haplotype-tagging single nucleotide polymorphisms (htSNP) (rs1143633, rs3136558 and rs1143630) representing 95% of the common haplotype diversity across the IL1B gene and assessed interactions with IL1B, PPP1R13L, POLR1G and smoking duration in relation to lung cancer risk. The analyses of five genetic models showed associations with lung cancer risk for rs1143633 in the dominant model [adjusted-OR (95% CI) = 0.67 (0.52-0.85), P = 0.0012] and rs3136558 in the recessive model [adjusted-OR (95% CI) = 1.44 (1.05-1.98), P = 0.025]. Haplotype4 was associated with increased lung cancer risk [adjusted-OR (95% CI) = 1.55 (1.07-2.24), P = 0.021]. The variant G-allele of rs1143633 was protective in smoking sub-group of > 20 years. Using multifactor dimensionality reduction (MDR) analyses, we identified the three best candidate models of interactions and smoking-duration or IL1B rs1143633 as main effect. In conclusion, our findings suggest that IL1B SNP rs1143633 may associate with lower risk of lung cancer, confirming previously identified marker; IL1B SNP rs3136558 and haplotype4 consisting of IL1B htSNPs may associate with increasing risk of lung cancer; interactions of IL1B with POLR1G or PPP1R13L or smoking-duration, which is independent or combined, may involve in risk of lung cancer and lung squamous cell carcinoma.

Integrative toxicogenomics: Advancing precision medicine and toxicology through artificial intelligence and OMICs technology

More information about a person's genetic makeup, drug response, multi-omics response, and genomic response is now available leading to a gradual shift towards personalized treatment. Additionally, the promotion of non-animal testing has fueled the computational toxicogenomics as a pivotal part of the next-gen risk assessment paradigm. Artificial Intelligence (AI) has the potential to provid new ways analyzing the patient data and making predictions about treatment outcomes or toxicity. As personalized medicine and toxicogenomics involve huge data processing, AI can expedite this process by providing powerful data processing, analysis, and interpretation algorithms. AI can process and integrate a multitude of data including genome data, patient records, clinical data and identify patterns to derive predictive models anticipating clinical outcomes and assessing the risk of any personalized medicine approaches. In this article, we have studied the current trends and future perspectives in personalized medicine & toxicology, the role of toxicogenomics in connecting the two fields, and the impact of AI on personalized medicine & toxicology. In this work, we also study the key challenges and limitations in personalized medicine, toxicogenomics, and AI in order to fully realize their potential.

Gene-environment interactions and their impact on human health

The molecular processes underlying human health and disease are highly complex. Often, genetic and environmental factors contribute to a given disease or phenotype in a non-additive manner, yielding a gene-environment (G × E) interaction. In this work, we broadly review current knowledge on the impact of gene-environment interactions on human health. We first explain the independent impact of genetic variation and the environment. We next detail well-established G × E interactions that impact human health involving environmental toxicants, pollution, viruses, and sex chromosome composition. We conclude with possibilities and challenges for studying G × E interactions.

Integrating Cutting-Edge Methods to Oral Cancer Screening, Analysis, and Prognosis

Oral cancer (OC) has become a significant barrier to health worldwide due to its high morbidity and mortality rates. OC is among the most prevalent types of cancer that affect the head and neck region, and the overall survival rate at 5 years is still around 50%. Moreover, it is a multifactorial malignancy instigated by genetic and epigenetic variabilities, and molecular heterogeneity makes it a complex malignancy. Oral potentially malignant disorders (OPMDs) are often the first warning signs of OC, although it is challenging to predict which cases will develop into malignancies. Visual oral examination and histological examination are still the standard initial steps in diagnosing oral lesions; however, these approaches have limitations that might lead to late diagnosis of OC or missed diagnosis of OPMDs in high-risk individuals. The objective of this review is to present a comprehensive overview of the currently used novel techniques viz., liquid biopsy, next-generation sequencing (NGS), microarray, nanotechnology, lab-on-a-chip (LOC) or microfluidics, and artificial intelligence (AI) for the clinical diagnostics and management of this malignancy. The potential of these novel techniques in expanding OC diagnostics and clinical management is also reviewed.

Multidimensional molecular measurements-environment interaction analysis for disease outcomes

Multiple types of molecular (genetic, genomic, epigenetic, etc.) measurements, environmental risk factors, and their interactions have been found to contribute to the outcomes and phenotypes of complex diseases. In each of the previous studies, only the interactions between one type of molecular measurement and environmental risk factors have been analyzed. In recent biomedical studies, multidimensional profiling, in which data from multiple types of molecular measurements are collected from the same subjects, is becoming popular. A myriad of recent studies have shown that collectively analyzing multiple types of molecular measurements is not only biologically sensible but also leads to improved estimation and prediction. In this study, we conduct an M-E interaction analysis, with M standing for multidimensional molecular measurements and E standing for environmental risk factors. This can accommodate multiple types of molecular measurements and sufficiently account for their overlapping as well as independent information. Extensive simulation shows that it outperforms several closely related alternatives. In the analysis of TCGA (The Cancer Genome Atlas) data on lung adenocarcinoma and cutaneous melanoma, we make some stable biological findings and achieve stable prediction.

Environmental pathways affecting gene expression (E.PAGE) as an R package to predict gene-environment associations

The purpose of this study is to manually and semi-automatically curate a database and develop an R package that will act as a comprehensive resource to understand how biological processes are dysregulated due to interactions with environmental factors. The initial database search run on the Gene Expression Omnibus and the Molecular Signature Database retrieved a total of 90,018 articles. After title and abstract screening against pre-set criteria, a total of 237 datasets were selected and 522 gene modules were manually annotated. We then curated a database containing four environmental factors, cigarette smoking, diet, infections and toxic chemicals, along with a total of 25,789 genes that had an association with one or more of gene modules. The database and statistical analysis package was then tested with the differentially expressed genes obtained from the published literature related to type 1 diabetes, rheumatoid arthritis, small cell lung cancer, COVID-19, cobalt exposure and smoking. On testing, we uncovered statistically enriched biological processes, which revealed pathways associated with environmental factors and the genes. The curated database and enrichment tool are available as R packages at https://github.com/AhmedMehdiLab/E.PATH and https://github.com/AhmedMehdiLab/E.PAGE respectively.

Estimates of incidence, prevalence, mortality, and disability-adjusted life years of lung cancer in Iran, 1990-2019: A systematic analysis from the global burden of disease study 2019

BACKGROUND

Lung cancer is one of the leading cancers, with a high burden worldwide. As a developing country, Iran is facing with population growth, widespread tobacco use, demographic and epidemiologic changes, and environmental exposures, which lead to cancers becoming a severe concern of public health in Iran. We aimed to examine the burden of lung cancer and its risk factors in Iran.

METHODS

We utilized the Global Burden of Disease 2019 data and analyzed the total burden of the lung cancer and seven related risk factors by sex, age at national and sub-national levels from 1990 to 2019.

RESULTS

The lung cancer age-standardized death rate increased from 11.8 (95% Uncertainty Interval: 9.7-14.4) to 12.9 (11.9-13.9) per 100,000 between 1990 and 2019. This increase was among women from 5 (4.2-7.1) to 8 (7.2-8.8) per 100,000; in contrast, there was a decline among men from 18.5 (14.8-22.6) to 17.8 (16.2-19.4) per 100,000. The burden of lung cancer is concentrated in the advanced age groups. Smoking with 53.5% of total attributable deaths (51.0%-55.9%) was the leading risk factor. At the provincial level, there was a wide range between the lowest and highest, from 8.3 (7.0-10.0) to 19.1 (16.4-22.0) per 100,000 population in the incidence rate and from 8.7 (7.3-10.3) to 20.6 (17.7-24.0) per 100,000 population in mortality rate, respectively in Tehran and West Azerbaijan provinces in 2019.

CONCLUSION

The increasing trend of lung cancer burden among the entire Iranian population, the inter-provincial disparities, and the significant rise in burden of this cancer in women necessitate the urgent implementation and development of policies to prevent and manage lung cancer burden and strategies to reduce exposure to risk factors.

Intake Patterns of Specific Alcoholic Beverages by Prostate Cancer Status

Simple Summary

Previous studies have shown heavy intake of different alcoholic beverages affects prostate cancer (PCa) clinical outcomes differently. However, the intake patterns of specific alcoholic beverages for PCa status are understudied. The study’s objective is to evaluate intake patterns of total alcohol and three types of alcoholic beverage (beer, wine, and spirits) by PCa risk and aggressiveness status. This study included 10,029 men with European ancestry (4676 non-PCa men and 5353 PCa patients). We found PCa patients had a similar total heavy alcohol intake compared with non-PCa men. However, PCa patients were likely to drink more wine and spirits than non-PCa men. Patients with aggressive PCa drank more beer but not wine and spirits. Interestingly, heavy wine intake was inversely associated with PCa aggressiveness. These findings suggest that the intake patterns of specific alcoholic beverages differ by PCa status, and this information might help develop personalized alcohol intervention for PCa patients.

Abstract

Background: Previous studies have shown that different alcoholic beverage types impact prostate cancer (PCa) clinical outcomes differently. However, intake patterns of specific alcoholic beverages for PCa status are understudied. The study’s objective is to evaluate intake patterns of total alcohol and the three types of beverage (beer, wine, and spirits) by the PCa risk and aggressiveness status. Method: This is a cross-sectional study using 10,029 men (4676 non-PCa men and 5353 PCa patients) with European ancestry from the PCa consortium. Associations between PCa status and alcohol intake patterns (infrequent, light/moderate, and heavy) were tested using multinomial logistic regressions. Results: Intake frequency patterns of total alcohol were similar for non-PCa men and PCa patients after adjusting for demographic and other factors. However, PCa patients were more likely to drink wine (light/moderate, OR = 1.11, p = 0.018) and spirits (light/moderate, OR = 1.14, p = 0.003; and heavy, OR = 1.34, p = 0.04) than non-PCa men. Patients with aggressive PCa drank more beer than patients with non-aggressive PCa (heavy, OR = 1.48, p = 0.013). Interestingly, heavy wine intake was inversely associated with PCa aggressiveness (OR = 0.56, p = 0.009). Conclusions: The intake patterns of some alcoholic beverage types differed by PCa status. Our findings can provide valuable information for developing custom alcohol interventions for PCa patients.

Hydrogels and Hydrogel Nanocomposites: Enhancing Healthcare through Human and Environmental Treatment

Humans are constantly exposed to exogenous chemicals throughout their life, which can lead to a multitude of negative health impacts. Advanced materials can play a key role in preventing or mitigating these impacts through a wide variety of applications. The tunable properties of hydrogels and hydrogel nanocomposites (e.g., swelling behavior, biocompatibility, stimuli responsiveness, functionality, etc.) have deemed them ideal platforms for removal of environmental contaminants, detoxification, and reduction of body burden from exogenous chemical exposures for prevention of disease initiation, and advanced treatment of chronic diseases, including cancer, diabetes, and cardiovascular disease. In this review, three main junctures where the use of hydrogel and hydrogel nanocomposite materials can intervene to positively impact human health are highlighted: 1) preventing exposures to environmental contaminants, 2) prophylactic treatments to prevent chronic disease initiation, and 3) treating chronic diseases after they have developed.

Identifying Gene-Environment Interactions With Robust Marginal Bayesian Variable Selection

In high-throughput genetics studies, an important aim is to identify gene–environment interactions associated with the clinical outcomes. Recently, multiple marginal penalization methods have been developed and shown to be effective in G×E studies. However, within the Bayesian framework, marginal variable selection has not received much attention. In this study, we propose a novel marginal Bayesian variable selection method for G×E studies. In particular, our marginal Bayesian method is robust to data contamination and outliers in the outcome variables. With the incorporation of spike-and-slab priors, we have implemented the Gibbs sampler based on Markov Chain Monte Carlo (MCMC). The proposed method outperforms a number of alternatives in extensive simulation studies. The utility of the marginal robust Bayesian variable selection method has been further demonstrated in the case studies using data from the Nurse Health Study (NHS). Some of the identified main and interaction effects from the real data analysis have important biological implications.

Predictors of Population Awareness of Cancer Genetic Tests: Implications for Enhancing Equity in Engaging in Cancer Prevention and Precision Medicine

Racial and ethnic disparities in genetic awareness (GA) can diminish the impact of personalized cancer treatment and risk assessment. We assessed factors predictive of GA in a diverse population-based sample to inform awareness strategies and reduce disparities in genetic testing.

METHODS

A cross-sectional study was conducted from July 2019 to August 2019, with the survey e-mailed to 7,575 adult residents in southeastern Pennsylvania and New Jersey. Constructs from National Cancer Institute Health Information and National Trends Survey assessed cancer attitudes or beliefs, health literacy, and numeracy. Characteristics were summarized with mean ± standard deviation for numeric variables and frequency counts and percentages for categorical variables. Comparison of factors by race or ethnicity (non-Hispanic White and non-Hispanic Black) and sex was conducted by t-tests, chi-square, or Fisher's exact tests. Multivariate logistic regression models were conducted to identify factors independently predictive of GA.

RESULTS

Of 1,557 respondents, data from 940 respondents (the mean age was 45 ± 16.2 years, 35.5% males, and 23% non-Hispanic Blacks) were analyzed. Factors associated with higher GA included female gender (P < .001), non-Hispanic White (P < .001), college education (P < .001), middle-higher income (P < .001), stronger belief in genetic basis of cancer (P < .001), lower cancer fatalism (P = .004), motivation for cancer information (P < .001), and higher numeracy (P = .002). On multivariate analysis, college education (odds ratio [OR] 1.79; 95% CI, 1.22 to 2.63), higher motivation for cancer information (OR 1.56; 95% CI, 1.17 to 2.09), stronger belief in genetics of cancer (OR 2.21; 95% CI, 1.48 to 3.30), and higher medical literacy (OR 2.21; 95% CI, 1.34 to 3.65) predicted greater GA.

CONCLUSION

This population-based study conducted in the precision medicine era identified novel modifiable factors, importantly perceptions of cancer genetics and medical literacy, as predictive of GA, which informs strategies to promote equitable engagement in genetically based cancer care.

Defining the Scope of Exposome Studies and Research Needs from a Multidisciplinary Perspective

The concept of the exposome was introduced over 15 years ago to reflect the important role that the environment exerts on health and disease. While originally viewed as a call-to-arms to develop more comprehensive exposure assessment methods applicable at the individual level and throughout the life course, the scope of the exposome has now expanded to include the associated biological response. In order to explore these concepts, a workshop was hosted by the Gunma University Initiative for Advanced Research (GIAR, Japan) to discuss the scope of exposomics from an international and multidisciplinary perspective. This Global Perspective is a summary of the discussions with emphasis on (1) top-down, bottom-up, and functional approaches to exposomics, (2) the need for integration and standardization of LC- and GC-based high-resolution mass spectrometry methods for untargeted exposome analyses, (3) the design of an exposomics study, (4) the requirement for open science workflows including mass spectral libraries and public databases, (5) the necessity for large investments in mass spectrometry infrastructure in order to sequence the exposome, and (6) the role of the exposome in precision medicine and nutrition to create personalized environmental exposure profiles. Recommendations are made on key issues to encourage continued advancement and cooperation in exposomics.

Development of mammary cancer in γ-irradiated F1 hybrids of susceptible Sprague-Dawley and resistant Copenhagen rats, with copy-number losses that pinpoint potential tumor suppressors

Copenhagen rats are highly resistant to mammary carcinogenesis, even after treatment with chemical carcinogens and hormones; most studies indicate that this is a dominant genetic trait. To test whether this trait is also dominant after radiation exposure, we characterized the susceptibility of irradiated Copenhagen rats to mammary carcinogenesis, as well as its inheritance, and identified tumor-suppressor genes that, when inactivated or mutated, may contribute to carcinogenesis. To this end, mammary cancer-susceptible Sprague-Dawley rats, resistant Copenhagen rats, and their F1 hybrids were irradiated with 4 Gy of γ-rays, and tumor development was monitored. Copy-number variations and allelic imbalances of genomic DNA were studied using microarrays and PCR analysis of polymorphic markers. Gene expression was assessed by quantitative PCR in normal tissues and induced mammary cancers of F1 rats. Irradiated Copenhagen rats exhibited a very low incidence of mammary cancer. Unexpectedly, this resistance trait did not show dominant inheritance in F1 rats; rather, they exhibited intermediate susceptibility levels (i.e., between those of their parent strains). The susceptibility of irradiated F1 rats to the development of benign mammary tumors (i.e., fibroadenoma and adenoma) was also intermediate. Copy-number losses were frequently observed in chromosome regions 1q52-54 (24%), 2q12-15 (33%), and 3q31-42 (24%), as were focal (38%) and whole (29%) losses of chromosome 5. Some of these chromosomal regions exhibited allelic imbalances. Many cancer-related genes within these regions were downregulated in mammary tumors as compared with normal mammary tissue. Some of the chromosomal losses identified have not been reported previously in chemically induced models, implying a novel mechanism inherent to the irradiated model. Based on these findings, Sprague-Dawley × Copenhagen F1 rats offer a useful model for exploring genes responsible for radiation-induced mammary cancer, which apparently are mainly located in specific regions of chromosomes 1, 2, 3 and 5.

Comprehensive analysis of the effect of rs2295080 and rs2536 polymorphisms within the mTOR gene on cancer risk

There is still no conclusion on the potential effect of the rs2295080 and rs2536 polymorphisms of mTOR (mammalian target of rapamycin) gene on different cancers. Herein, we performed a comprehensive assessment using pooled analysis, FPRP (false-positive report probability), TSA (trial sequential analysis), and eQTL (expression quantitative trait loci) analysis. Eighteen high-quality articles from China were enrolled. The pooled analysis of rs2295080 with 9502 cases and 10,965 controls showed a decreased risk of urinary system tumors and specific prostate cancers [TG vs. TT, TG+GG vs. TT and G vs. T; P<0.05, OR (odds ratio) <1]. FPRP and TSA data further confirmed these results. There was an increased risk of leukemia [G vs. T, GG vs. TT, and GG vs. TT+TG genotypes; P<0.05, OR>1]. The eQTL data showed a potential correlation between the rs2295080 and mTOR expression in whole blood samples. Nevertheless, FPRP and TSA data suggested that more evidence is required to confirm the potential role of rs2295080 in leukemia risk. The pooled analysis of rs2536 (6653 cases and 7025 controls) showed a significant association in the subgroup of "population-based" control source via the allele, heterozygote, dominant, and carrier comparisons (P<0.05, OR>1). In conclusion, the TG genotype of mTOR rs2295080 may be linked to reduced susceptibility to urinary system tumors or specific prostate cancers in Chinese patients. The currently data do not strongly support a role of rs2295080 in leukemia susceptibility. Large sample sizes are needed to confirm the potential role of rs2536 in more types of cancer.

Uncovering Evidence for Endocrine-Disrupting Chemicals That Elicit Differential Susceptibility through Gene-Environment Interactions

Exposure to endocrine-disrupting chemicals (EDCs) is linked to myriad disorders, characterized by the disruption of the complex endocrine signaling pathways that govern development, physiology, and even behavior across the entire body. The mechanisms of endocrine disruption involve a complex system of pathways that communicate across the body to stimulate specific receptors that bind DNA and regulate the expression of a suite of genes. These mechanisms, including gene regulation, DNA binding, and protein binding, can be tied to differences in individual susceptibility across a genetically diverse population. In this review, we posit that EDCs causing such differential responses may be identified by looking for a signal of population variability after exposure. We begin by summarizing how the biology of EDCs has implications for genetically diverse populations. We then describe how gene-environment interactions (GxE) across the complex pathways of endocrine signaling could lead to differences in susceptibility. We survey examples in the literature of individual susceptibility differences to EDCs, pointing to a need for research in this area, especially regarding the exceedingly complex thyroid pathway. Following a discussion of experimental designs to better identify and study GxE across EDCs, we present a case study of a high-throughput screening signal of putative GxE within known endocrine disruptors. We conclude with a call for further, deeper analysis of the EDCs, particularly the thyroid disruptors, to identify if these chemicals participate in GxE leading to differences in susceptibility.

Alcohol Intake and Alcohol-SNP Interactions Associated with Prostate Cancer Aggressiveness

Excessive alcohol intake is a well-known modifiable risk factor for many cancers. It is still unclear whether genetic variants or single nucleotide polymorphisms (SNPs) can modify alcohol intake's impact on prostate cancer (PCa) aggressiveness. The objective is to test the alcohol-SNP interactions of the 7501 SNPs in the four pathways (angiogenesis, mitochondria, miRNA, and androgen metabolism-related pathways) associated with PCa aggressiveness. We evaluated the impacts of three excessive alcohol intake behaviors in 3306 PCa patients with European ancestry from the PCa Consortium. We tested the alcohol-SNP interactions using logistic models with the discovery-validation study design. All three excessive alcohol intake behaviors were not significantly associated with PCa aggressiveness. However, the interactions of excessive alcohol intake and three SNPs (rs13107662 [CAMK2D, p = 6.2 × 10-6], rs9907521 [PRKCA, p = 7.1 × 10-5], and rs11925452 [ROBO1, p = 8.2 × 10-4]) were significantly associated with PCa aggressiveness. These alcohol-SNP interactions revealed contrasting effects of excessive alcohol intake on PCa aggressiveness according to the genotypes in the identified SNPs. We identified PCa patients with the rs13107662 (CAMK2D) AA genotype, the rs11925452 (ROBO1) AA genotype, and the rs9907521 (PRKCA) AG genotype were more vulnerable to excessive alcohol intake for developing aggressive PCa. Our findings support that the impact of excessive alcohol intake on PCa aggressiveness was varied by the selected genetic profiles.

Integrating Multi–Omics Data for Gene-Environment Interactions

Gene-environment (G×E) interaction is critical for understanding the genetic basis of complex disease beyond genetic and environment main effects. In addition to existing tools for interaction studies, penalized variable selection emerges as a promising alternative for dissecting G×E interactions. Despite the success, variable selection is limited in terms of accounting for multidimensional measurements. Published variable selection methods cannot accommodate structured sparsity in the framework of integrating multiomics data for disease outcomes. In this paper, we have developed a novel variable selection method in order to integrate multi-omics measurements in G×E interaction studies. Extensive studies have already revealed that analyzing omics data across multi-platforms is not only sensible biologically, but also resulting in improved identification and prediction performance. Our integrative model can efficiently pinpoint important regulators of gene expressions through sparse dimensionality reduction, and link the disease outcomes to multiple effects in the integrative G×E studies through accommodating a sparse bi-level structure. The simulation studies show the integrative model leads to better identification of G×E interactions and regulators than alternative methods. In two G×E lung cancer studies with high dimensional multi-omics data, the integrative model leads to an improved prediction and findings with important biological implications.

Gene-Environment Interaction: A Variable Selection Perspective

Gene-environment interactions have important implications for elucidating the genetic basis of complex diseases beyond the joint function of multiple genetic factors and their interactions (or epistasis). In the past, G × E interactions have been mainly conducted within the framework of genetic association studies. The high dimensionality of G × E interactions, due to the complicated form of environmental effects and the presence of a large number of genetic factors including gene expressions and SNPs, has motivated the recent development of penalized variable selection methods for dissecting G × E interactions, which has been ignored in the majority of published reviews on genetic interaction studies. In this article, we first survey existing studies on both gene-environment and gene-gene interactions. Then, after a brief introduction to the variable selection methods, we review penalization and relevant variable selection methods in marginal and joint paradigms, respectively, under a variety of conceptual models. Discussions on strengths and limitations, as well as computational aspects of the variable selection methods tailored for G × E studies, have also been provided.

Opportunities for Gene and Environment Research in Cancer: An Updated Review of NCI's Extramural Grant Portfolio

BACKGROUND

The study of gene-environment (GxE) interactions is a research priority for the NCI. Previously, our group analyzed NCI's extramural grant portfolio from fiscal years (FY) 2007 to 2009 to determine the state of the science in GxE research. This study builds upon our previous effort and examines changes in the landscape of GxE cancer research funded by NCI.

METHODS

The NCI grant portfolio was examined from FY 2010 to 2018 using the iSearch application. A time-trend analysis was conducted to explore changes over the study interval.

RESULTS

A total of 107 grants met the search criteria and were abstracted. The most common cancer types studied were breast (19.6%) and colorectal (18.7%). Most grants focused on GxE using specific candidate genes (69.2%) compared with agnostic approaches using genome-wide (26.2%) or whole-exome/whole-genome next-generation sequencing (NGS) approaches (19.6%); some grants used more than one approach to assess genetic variation. More funded grants incorporated NGS technologies in FY 2016-2018 compared with prior FYs. Environmental exposures most commonly examined were energy balance (46.7%) and drugs/treatment (40.2%). Over the time interval, we observed a decrease in energy balance applications with a concurrent increase in drug/treatment applications.

CONCLUSIONS

Research in GxE interactions has continued to concentrate on common cancers, while there have been some shifts in focus of genetic and environmental exposures. Opportunities exist to study less common cancers, apply new technologies, and increase racial/ethnic diversity.

IMPACT

This analysis of NCI's extramural grant portfolio updates previous efforts and provides a review of NCI grant support for GxE research.

Impact of Gene-Environment Interactions on Cancer Development

Several epidemiological and experimental studies have demonstrated that many human diseases are not only caused by specific genetic and environmental factors but also by gene-environment interactions. Although it has been widely reported that genetic polymorphisms play a critical role in human susceptibility to cancer and other chronic disease conditions, many single nucleotide polymorphisms (SNPs) are caused by somatic mutations resulting from human exposure to environmental stressors. Scientific evidence suggests that the etiology of many chronic illnesses is caused by the joint effect between genetics and the environment. Research has also pointed out that the interactions of environmental factors with specific allelic variants highly modulate the susceptibility to diseases. Hence, many scientific discoveries on gene-environment interactions have elucidated the impact of their combined effect on the incidence and/or prevalence rate of human diseases. In this review, we provide an overview of the nature of gene-environment interactions, and discuss their role in human cancers, with special emphases on lung, colorectal, bladder, breast, ovarian, and prostate cancers.

Alterations of Lipid Metabolism in Cancer: Implications in Prognosis and Treatment

Cancer remains the second leading cause of mortality worldwide. In the course of this multistage and multifactorial disease, a set of alterations takes place, with genetic and environmental factors modulating tumorigenesis and disease progression. Metabolic alterations of tumors are well-recognized and are considered as one of the hallmarks of cancer. Cancer cells adapt their metabolic competences in order to efficiently supply their novel demands of energy to sustain cell proliferation and metastasis. At present, there is a growing interest in understanding the metabolic switch that occurs during tumorigenesis. Together with the Warburg effect and the increased glutaminolysis, lipid metabolism has emerged as essential for tumor development and progression. Indeed, several investigations have demonstrated the consequences of lipid metabolism alterations in cell migration, invasion, and angiogenesis, three basic steps occurring during metastasis. In addition, obesity and associated metabolic alterations have been shown to augment the risk of cancer and to worsen its prognosis. Consequently, an extensive collection of tumorigenic steps has been shown to be modulated by lipid metabolism, not only affecting the growth of primary tumors, but also mediating progression and metastasis. Besides, key enzymes involved in lipid-metabolic pathways have been associated with cancer survival and have been proposed as prognosis biomarkers of cancer. In this review, we will analyze the impact of obesity and related tumor microenviroment alterations as modifiable risk factors in cancer, focusing on the lipid alterations co-occurring during tumorigenesis. The value of precision technologies and its application to target lipid metabolism in cancer will also be discussed. The degree to which lipid alterations, together with current therapies and intake of specific dietary components, affect risk of cancer is now under investigation, and innovative therapeutic or preventive applications must be explored.

Effect of vitamin D receptor polymorphisms on plasma oxidative stress and apoptotic biomarkers among breast cancer survivors supplemented vitamin D3

We investigated whether plasma oxidative stress and apoptotic biomarkers were associated with the VDR polymorphisms in breast cancer survivors supplemented with vitamin D3. Two hundred fourteen breast cancer survivors received 4000 IU of vitamin D3 daily for 12 weeks. Linear regression was used to analyze whether the effect of vitamin D3 supplementation on response variables was associated with the selected VDR single nucleotide polymorphisms executing by 'association' function in the R package 'SNPassoc'. Linear regression analyses adjusted for age, BMI and on-study plasma 25(OH)D changes indicated that the aa genotype of the ApaI [codominant model (aa vs. AA): -0.21 (-0.39 to -0.03); recessive model (aa vs. AA and Aa): -0.20 (-0.37 to -0.03)] and bb genotypes of the BsmI [recessive model (bb vs. BB and Bb): -0.20 (-0.39 to -0.01)] on VDR were associated with greater decrease in plasma Bcl2. Our findings indicated that, the Ff genotype of FokI was accompanied by higher increase in plasma MDA levels [codominant model (Ff vs. FF): 0.64 (0.18-1.11); dominant model (ff and Ff vs. FF): 0.52 (0.09-0.05)]. This observed association was not remained statistically significant after correction for multiple testing. Haplotype score analyses revealed statistically significant association between the FokI BsmI ApaI haplotype and circulating MDA changes (P-value for global score = 0.001) after false-discovery rate correction. Our study suggests that genetic variations in the VDR do not powerfully modify the effects of vitamin D3 intake on biomarkers associated with antioxidant activity, oxidative stress and apoptosis in breast cancer survivors.

Characterization of the Cancer Spectrum in Men With Germline BRCA1 and BRCA2 Pathogenic Variants: Results From the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA)

Importance

The limited data on cancer phenotypes in men with germline BRCA1 and BRCA2 pathogenic variants (PVs) have hampered the development of evidence-based recommendations for early cancer detection and risk reduction in this population.

Objective

To compare the cancer spectrum and frequencies between male BRCA1 and BRCA2 PV carriers.

Design, Setting, and Participants

Retrospective cohort study of 6902 men, including 3651 BRCA1 and 3251 BRCA2 PV carriers, older than 18 years recruited from cancer genetics clinics from 1966 to 2017 by 53 study groups in 33 countries worldwide collaborating through the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA). Clinical data and pathologic characteristics were collected.

Main Outcomes and Measures

BRCA1/2 status was the outcome in a logistic regression, and cancer diagnoses were the independent predictors. All odds ratios (ORs) were adjusted for age, country of origin, and calendar year of the first interview.

Results

Among the 6902 men in the study (median [range] age, 51.6 [18-100] years), 1634 cancers were diagnosed in 1376 men (19.9%), the majority (922 of 1,376 [67%]) being BRCA2 PV carriers. Being affected by any cancer was associated with a higher probability of being a BRCA2, rather than a BRCA1, PV carrier (OR, 3.23; 95% CI, 2.81-3.70; P < .001), as well as developing 2 (OR, 7.97; 95% CI, 5.47-11.60; P < .001) and 3 (OR, 19.60; 95% CI, 4.64-82.89; P < .001) primary tumors. A higher frequency of breast (OR, 5.47; 95% CI, 4.06-7.37; P < .001) and prostate (OR, 1.39; 95% CI, 1.09-1.78; P = .008) cancers was associated with a higher probability of being a BRCA2 PV carrier. Among cancers other than breast and prostate, pancreatic cancer was associated with a higher probability (OR, 3.00; 95% CI, 1.55-5.81; P = .001) and colorectal cancer with a lower probability (OR, 0.47; 95% CI, 0.29-0.78; P = .003) of being a BRCA2 PV carrier.

Conclusions and Relevance

Significant differences in the cancer spectrum were observed in male BRCA2, compared with BRCA1, PV carriers. These data may inform future recommendations for surveillance of BRCA1/2-associated cancers and guide future prospective studies for estimating cancer risks in men with BRCA1/2 PVs.

The Tyranny of Content: "Content Coverage" as a Barrier to Evidence-Based Teaching Approaches and Ways to Overcome It

Instructors have inherited a model for conscientious instruction that suggests they must cover all the material outlined in their syllabus, and yet this model frequently diverts time away from allowing students to engage meaningfully with the content during class. We outline the historical forces that may have conditioned this teacher-centered model as well as the disciplinary pressures that inadvertently reward it. As a way to guide course revision and move to a learner-centered teaching approach, we propose three evidence-based strategies that instructors can adopt: 1) identify the core concepts and competencies for your course; 2) create an organizing framework for the core concepts and competencies; and 3) teach students how to learn in your discipline. We further outline examples of actions that instructors can incorporate to implement each of these strategies. We propose that moving from a content-coverage approach to these learner-centered strategies will help students better learn and retain information and apply it to new situations.

Familial cancer risk in family members and spouses of patients with early-onset head and neck cancer

BACKGROUND

Reported patterns of familial aggregation of head and neck cancer (HNC) vary greatly, with many studies hampered by the limited number of subjects.

METHODS

Altogether 923 early-onset (≤40 years old) HNC probands, their first-degree relatives, spouses, and siblings' offspring were ascertained. Cumulative risk and standardized incidence ratios (SIRs) were estimated.

RESULTS

Of all early-onset HNC families, only 21 (2.3%) had familial HNC cancers at any age and less than five familial early onset HNC cancers among first-degree relatives. The cumulative risk of HNC for siblings by age 60 (0.52%) was at population level (0.33%). No increased familial risk of early-onset HNC could be discerned in family members (SIR 2.68, 95% CI 0.32-9.68 for first-degree relatives).

CONCLUSIONS

Our study indicates that the cumulative and relative familial risk of early-onset HNC is modest in the Finnish population and, at most, only a minor proportion of early-onset HNCs are due solely to inherited genetic mutations.

A Fast and Accurate Method for Genome-wide Scale Phenome-wide G × E Analysis and Its Application to UK Biobank

The etiology of most complex diseases involves genetic variants, environmental factors, and gene-environment interaction (G × E) effects. Compared with marginal genetic association studies, G × E analysis requires more samples and detailed measure of environmental exposures, and this limits the possible discoveries. Large-scale population-based biobanks with detailed phenotypic and environmental information, such as UK-Biobank, can be ideal resources for identifying G × E effects. However, due to the large computation cost and the presence of case-control imbalance, existing methods often fail. Here we propose a scalable and accurate method, SPAGE (SaddlePoint Approximation implementation of G × E analysis), that is applicable for genome-wide scale phenome-wide G × E studies. SPAGE fits a genotype-independent logistic model only once across the genome-wide analysis in order to reduce computation cost, and SPAGE uses a saddlepoint approximation (SPA) to calibrate the test statistics for analysis of phenotypes with unbalanced case-control ratios. Simulation studies show that SPAGE is 33-79 times faster than the Wald test and 72-439 times faster than the Firth's test, and SPAGE can control type I error rates at the genome-wide significance level even when case-control ratios are extremely unbalanced. Through the analysis of UK-Biobank data of 344,341 white British European-ancestry samples, we show that SPAGE can efficiently analyze large samples while controlling for unbalanced case-control ratios.

Identification of gene-environment interactions with marginal penalization

Gene-environment (G-E) interaction analysis has been extensively conducted for complex diseases. In marginal analysis, the common practice is to conduct likelihood-based (and other "standard") estimation with each marginal model, and then select significant G-E interactions and main effects based on p values and multiple comparisons adjustment. One limitation of this approach is that the identification results often do not respect the "main effects, interactions" hierarchy, which has been stressed in recent G-E interaction analyses. There is some recent effort tackling this problem, however, with very complex formulations. Another limitation of the common practice is that it may not perform well when regularization is needed, for example, because of "non-normal" distributions. In this article, we propose a marginal penalization approach which adopts a novel penalty to directly tackle the aforementioned problems. The proposed approach has a framework more coherent with that of the recently developed joint analysis methods and an intuitive formulation, and can be effectively realized. In simulation, it outperforms the popular significance-based analysis and simple penalization-based alternatives. Promising findings are made in the analysis of a single-nucleotide polymorphism and a gene expression data.

Gene-environment interactions and colorectal cancer risk: An umbrella review of systematic reviews and meta-analyses of observational studies

The cause of colorectal cancer (CRC) is multifactorial, involving both genetic variants and environmental risk factors. We systematically searched the MEDLINE, EMBASE, China National Knowledge Infrastructure (CNKI) and Wanfang databases from inception to December 2016, to identify systematic reviews and meta-analyses of observational studies that investigated gene-environment (G×E) interactions in CRC risk. Then, we critically evaluated the cumulative evidence for the G×E interactions using an extension of the Human Genome Epidemiology Network's Venice criteria. Overall, 15 articles reporting systematic reviews of observational studies on 89 G×E interactions, 20 articles reporting meta-analyses of candidate gene- or single-nucleotide polymorphism-based studies on 521 G×E interactions, and 8 articles reporting 33 genome-wide G×E interaction analyses were identified. On the basis of prior and observed scores, only the interaction between rs6983267 (8q24) and aspirin use was found to have a moderate overall credibility score as well as main genetic and environmental effects. Though 5 other interactions were also found to have moderate evidence, these interaction effects were tenuous due to the lack of main genetic effects and/or environmental effects. We did not find highly convincing evidence for any interactions, but several associations were found to have moderate strength of evidence. Our conclusions are based on application of the Venice criteria which were designed to provide a conservative assessment of G×E interactions and thus do not include an evaluation of biological plausibility of an observed joint effect.

Case-Only Analysis of Gene-Environment Interactions Using Polygenic Risk Scores

Investigations of gene (G)-environment (E) interactions have led to limited findings to date, possibly due to weak effects of individual genetic variants. Polygenic risk scores (PRS), which capture the genetic susceptibility associated with a set of variants, can be a powerful tool for detecting global patterns of interaction. Motivated by the case-only method for evaluating interactions with a single variant, we propose a case-only method for the analysis of interactions with a PRS in case-control studies. Assuming the PRS and E are independent, we show how a linear regression of the PRS on E in a sample of cases can be used to efficiently estimate the interaction parameter. Furthermore, if an estimate of the mean of the PRS in the underlying population is available, the proposed method can estimate the PRS main effect. Extensions allow for PRS-E dependence due to associations between variants in the PRS and E. Simulation studies indicate the proposed method offers appreciable gains in efficiency over logistic regression and can recover much of the efficiency of a cohort study. We applied the proposed method to investigate interactions between a PRS and epidemiologic factors on breast cancer risk in the UK Biobank (United Kingdom, recruited 2006-2010).

Diverse tumour susceptibility in Collaborative Cross mice: identification of a new mouse model for human gastric tumourigenesis

OBJECTIVE

The Collaborative Cross (CC) is a mouse population model with diverse and reproducible genetic backgrounds used to identify novel disease models and genes that contribute to human disease. Since spontaneous tumour susceptibility in CC mice remains unexplored, we assessed tumour incidence and spectrum.

DESIGN

We monitored 293 mice from 18 CC strains for tumour development. Genetic association analysis and RNA sequencing were used to identify susceptibility loci and candidate genes. We analysed genomes of patients with gastric cancer to evaluate the relevance of genes identified in the CC mouse model and measured the expression levels of ISG15 by immunohistochemical staining using a gastric adenocarcinoma tissue microarray. Association of gene expression with overall survival (OS) was assessed by Kaplan-Meier analysis.

RESULTS

CC mice displayed a wide range in the incidence and types of spontaneous tumours. More than 40% of CC036 mice developed gastric tumours within 1 year. Genetic association analysis identified Nfκb1 as a candidate susceptibility gene, while RNA sequencing analysis of non-tumour gastric tissues from CC036 mice showed significantly higher expression of inflammatory response genes. In human gastric cancers, the majority of human orthologues of the 166 mouse genes were preferentially altered by amplification or deletion and were significantly associated with OS. Higher expression of the CC036 inflammatory response gene signature is associated with poor OS. Finally, ISG15 protein is elevated in gastric adenocarcinomas and correlated with shortened patient OS.

CONCLUSIONS

CC strains exhibit tremendous variation in tumour susceptibility, and we present CC036 as a spontaneous laboratory mouse model for studying human gastric tumourigenesis.

Systems Biology of Selenium and Complex Disease

Selenium is an essential trace element for maintenance of overall health, whose deficiency and dyshomeostasis have been linked to a variety of diseases and disorders. The majority of previous researches focused on characterization of genes encoding selenoproteins or proteins involved in selenium metabolism as well as their functions. Many studies in humans also investigated the relationship between selenium and complex diseases, but their results have been inconsistent. In recent years, systems biology and "-omics" approaches have been widely used to study complex and global variations of selenium metabolism and function in physiological and different pathological conditions. The present paper reviews recent progress in large-scale and systematic analyses of the relationship between selenium status or selenoproteins and several complex diseases, mainly including population-based cohort studies and meta-analyses, genetic association studies, and some other omics-based studies. Advances in ionomics and its application in studying the interaction between selenium and other trace elements in human health and diseases are also discussed.

Robust semiparametric gene-environment interaction analysis using sparse boosting

For the pathogenesis of complex diseases, gene-environment (G-E) interactions have been shown to have important implications. G-E interaction analysis can be challenging with the need to jointly analyze a large number of main effects and interactions and to respect the "main effects, interactions" hierarchical constraint. Extensive methodological developments on G-E interaction analysis have been conducted in recent literature. Despite considerable successes, most of the existing studies are still limited as they cannot accommodate long-tailed distributions/data contamination, make the restricted assumption of linear effects, and cannot effectively accommodate missingness in E variables. To directly tackle these problems, a semiparametric model is assumed to accommodate nonlinear effects, and the Huber loss function and Qn estimator are adopted to accommodate long-tailed distributions/data contamination. A regression-based multiple imputation approach is developed to accommodate missingness in E variables. For model estimation and selection of relevant variables, we adopt an effective sparse boosting approach. The proposed approach is practically well motivated, has intuitive formulations, and can be effectively realized. In extensive simulations, it significantly outperforms multiple direct competitors. The analysis of The Cancer Genome Atlas data on stomach adenocarcinoma and cutaneous melanoma shows that the proposed approach makes sensible discoveries with satisfactory prediction and stability.

Germline Risk Contribution to Genomic Instability in Multiple Myeloma

Genomic instability, a well-established hallmark of human cancer, is also a driving force in the natural history of multiple myeloma (MM) - a difficult to treat and in most cases fatal neoplasm of immunoglobulin producing plasma cells that reside in the hematopoietic bone marrow. Long recognized manifestations of genomic instability in myeloma at the cytogenetic level include abnormal chromosome numbers (aneuploidy) caused by trisomy of odd-numbered chromosomes; recurrent oncogene-activating chromosomal translocations that involve immunoglobulin loci; and large-scale amplifications, inversions, and insertions/deletions (indels) of genetic material. Catastrophic genetic rearrangements that either shatter and illegitimately reassemble a single chromosome (chromotripsis) or lead to disordered segmental rearrangements of multiple chromosomes (chromoplexy) also occur. Genomic instability at the nucleotide level results in base substitution mutations and small indels that affect both the coding and non-coding genome. Sometimes this generates a distinctive signature of somatic mutations that can be attributed to defects in DNA repair pathways, the DNA damage response (DDR) or aberrant activity of mutator genes including members of the APOBEC family. In addition to myeloma development and progression, genomic instability promotes acquisition of drug resistance in patients with myeloma. Here we review recent findings on the genetic predisposition to myeloma, including newly identified candidate genes suggesting linkage of germline risk and compromised genomic stability control. The role of ethnic and familial risk factors for myeloma is highlighted. We address current research gaps that concern the lack of studies on the mechanism by which germline risk alleles promote genomic instability in myeloma, including the open question whether genetic modifiers of myeloma development act in tumor cells, the tumor microenvironment (TME), or in both. We conclude with a brief proposition for future research directions, which concentrate on the biological function of myeloma risk and genetic instability alleles, the potential links between the germline genome and somatic changes in myeloma, and the need to elucidate genetic modifiers in the TME.

Robust genetic interaction analysis

For the risk, progression, and response to treatment of many complex diseases, it has been increasingly recognized that genetic interactions (including gene-gene and gene-environment interactions) play important roles beyond the main genetic and environmental effects. In practical genetic interaction analyses, model mis-specification and outliers/contaminations in response variables and covariates are not uncommon, and demand robust analysis methods. Compared with their nonrobust counterparts, robust genetic interaction analysis methods are significantly less popular but are gaining attention fast. In this article, we provide a comprehensive review of robust genetic interaction analysis methods, on their methodologies and applications, for both marginal and joint analysis, and for addressing model mis-specification as well as outliers/contaminations in response variables and covariates.

The Japan Public Health Center-based Prospective Study for the Next Generation (JPHC-NEXT): Study Design and Participants

Background

Lifestyle and life-environment factors have undergone drastic changes in Japan over the last few decades. Further, many molecular epidemiologic studies have reported that genetic, epigenetic, and other biomarker information may be useful in predicting individual disease risk.

Methods

The Japan Public Health Center-based Prospective Study for the Next Generation (JPHC-NEXT) was launched in 2011 to identify risk factors for lifestyle-related disease, elucidate factors that extend healthy life expectancy, and contribute toward personalized healthcare based on our more than 20 years’ experience with the JPHC Study. From 2011 through 2016, a baseline survey was conducted at 16 municipalities in seven prefectures across the country. A self-administered questionnaire was distributed to all registered residents aged 40–74, which mainly asked about lifestyle factors, such as socio-demographic situation, personal medical history, smoking, alcohol and dietary habits. We obtained informed consent from each participant to participate in this long follow-up study of at least 20 years, including consent to the potential use of their residence registry, medical records, medical fee receipts, care insurance etc., and to the provision of biospecimens (blood and urine), including genomic analysis.

Results

As of December 31, 2016, we have established a population-based cohort of 115,385 persons (Response rate 44.1%), among whom 55,278 (47.9% of participants) have provided blood and urine samples. The participation rate was slightly higher among females and in the older age group.

Conclusion

We have established a large-scale population-based cohort for next-generation epidemiological study in Japan.

Alcohol drinking, ADH1B and ADH1C genotypes and the risk of postmenopausal breast cancer by hormone receptor status: the Netherlands Cohort Study on diet and cancer

Alcohol consumption has consistently been shown to increase breast cancer (BC) risk. This association may be modified by single nucleotide polymorphisms (SNPs) in alcohol dehydrogenase (ADH) isoenzymes ADH1B and ADH1C. The Netherlands Cohort Study comprises 62 573 women, aged 55-69 years at baseline (1986). Follow-up for postmenopausal BC for 20.3 years was available. Genotyping of six tag SNPs in ADH1B and ADH1C was performed on DNA from toenails. A case-cohort approach was used for analysis (complete data available for nsubcohort = 1301; ncases = 1630). Cox regression models for postmenopausal BC were applied to determine marginal effects of alcohol intake and SNPs using a dominant genetic model, as well as multiplicative interaction of the two. Results were also obtained for subtypes by estrogen receptor (ER) and progesterone receptor (PR) status. Multiple testing was adjusted for by applying the false discovery rate (FDR). Alcohol intake (categorical) increased the risk of postmenopausal BC (Ptrend = 0.031). Trends for ER and PR subgroups followed a similar pattern. Continuous modeling of alcohol resulted in a hazard rate ratio (HR) for overall postmenopausal BC of 1.09 (95% confidence interval: 1.01-1.19) per 10 g/day of alcohol. SNPs were not associated with BC risk. No effect modification of the alcohol-BC association by SNP genotype was seen after FDR correction in overall BC and ER/PR subgroups. In conclusion, alcohol consumption was shown to increase the risk of postmenopausal BC. This association was not significantly modified by common SNPs in ADH1B and ADH1C, neither in overall BC nor in hormone receptor-defined subtypes.

Consensus report of the 8 and 9th Weinman Symposia on Gene x Environment Interaction in carcinogenesis: novel opportunities for precision medicine

The relative contribution of intrinsic genetic factors and extrinsic environmental ones to cancer aetiology and natural history is a lengthy and debated issue. Gene-environment interactions (G x E) arise when the combined presence of both a germline genetic variant and a known environmental factor modulates the risk of disease more than either one alone. A panel of experts discussed our current understanding of cancer aetiology, known examples of G × E interactions in cancer, and the expanded concept of G × E interactions to include somatic cancer mutations and iatrogenic environmental factors such as anti-cancer treatment. Specific genetic polymorphisms and genetic mutations increase susceptibility to certain carcinogens and may be targeted in the near future for prevention and treatment of cancer patients with novel molecularly based therapies. There was general consensus that a better understanding of the complexity and numerosity of G × E interactions, supported by adequate technological, epidemiological, modelling and statistical resources, will further promote our understanding of cancer and lead to novel preventive and therapeutic approaches.

Alcohol intake, ADH1B and ADH1C genotypes, and the risk of colorectal cancer by sex and subsite in the Netherlands Cohort Study

The alcohol-colorectal cancer (CRC) association may differ by sex and ADH1B and ADH1C genotypes. ADH enzymes oxidize ethanol to acetaldehyde, both of which are human carcinogens. The Netherlands Cohort Study includes 120 852 participants, aged 55-69 years at baseline (1986), and has 20.3 years follow-up (case-cohort: nsubcohort = 4774; ncases = 4597). The baseline questionnaire included questions on alcohol intake at baseline and 5 years before. Using toenail DNA, available for ~75% of the cohort, we successfully genotyped six ADH1B and six ADH1C SNPs (nsubcohort = 3897; ncases = 3558). Sex- and subsite-specific Cox hazard ratios and 95% confidence intervals for CRC were estimated comparing alcohol categories, genotypes within drinkers and alcohol categories within genotype strata. We used a dominant genetic model and adjusted for multiple testing. Alcohol intake increased CRC risk in both sexes, though in women only in the (proximal) colon when in excess of 30 g/day. In male drinkers, ADH1B rs4147536 increased (distal) colon cancer risk. In female drinkers, ADH1C rs283415 increased proximal colon cancer risk. ADH1B rs3811802 and ADH1C rs4147542 decreased CRC risk in heavy (>30 g/day) and stable drinkers (compared to 5 years before baseline), respectively. Rs3811802 and rs4147542 significantly modified the alcohol-colon cancer association in women (Pfor interaction = 0.004 and 0.02, respectively). A difference in associations between genotype strata was generally clearer in men than women. In conclusion, men showed increased CRC risks across subsites and alcohol intake levels, while only colon cancer risk was increased in women at heavy intake levels. ADH1B rs3811802 and ADH1C rs4147542 significantly modified the alcohol-colon cancer association in women.

Robust gene-environment interaction analysis using penalized trimmed regression

In biomedical and epidemiological studies, gene-environment (G-E) interactions have been shown to importantly contribute to the etiology and progression of many complex diseases. Most existing approaches for identifying G-E interactions are limited by the lack of robustness against outliers/contaminations in response and predictor spaces. In this study, we develop a novel robust G-E identification approach using the trimmed regression technique under joint modeling. A robust data-driven criterion and stability selection are adopted to determine the trimmed subset which is free from both vertical outliers and leverage points. An effective penalization approach is developed to identify important G-E interactions, respecting the "main effects, interactions" hierarchical structure. Extensive simulations demonstrate the better performance of the proposed approach compared to multiple alternatives. Interesting findings with superior prediction accuracy and stability are observed in the analysis of TCGA data on cutaneous melanoma and breast invasive carcinoma.

Robust identification of gene-environment interactions for prognosis using a quantile partial correlation approach

Gene-environment (G-E) interactions have important implications for the etiology and progression of many complex diseases. Compared to continuous markers and categorical disease status, prognosis has been less investigated, with the additional challenges brought by the unique characteristics of survival outcomes. Most of the existing G-E interaction approaches for prognosis data share the limitation that they cannot accommodate long-tailed or contaminated outcomes. In this study, for prognosis data, we develop a robust G-E interaction identification approach using the censored quantile partial correlation (CQPCorr) technique. The proposed approach is built on the quantile regression technique (and hence has a solid statistical basis), uses weights to easily accommodate censoring, and adopts partial correlation to identify important interactions while properly controlling for the main genetic and environmental effects. In simulation, it outperforms multiple competitors with more accurate identification. In the analysis of TCGA data on lung cancer and melanoma, biologically sensible findings different from using the alternatives are made.

New Directions in Cancer Control and Population Sciences

Cancer control science has been evolving since it first became a focus for cancer research and program activities a century ago. The evolution of the field has responded to historical megatrends along the way that keep it relevant to the cancer-related needs of society. This commentary describes some of the key trends and developments now influencing cancer control and population sciences that reflect societal change and new tools and concepts in modern biomedical science. New directions include the impact of climate change, health care delivery research, the growth of population health science, data science, precision medicine, data sharing, implementation science, and new technologies, including social media and new geospatial methodologies. Cancer Epidemiol Biomarkers Prev; 26(8); 1165-9. ©2017 AACR.