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Abstract Background The risk of common diseases is likely determined by the complex interplay between environmental and genetic factors, including single nucleotide polymorphisms (SNPs). Traditional methods of data analysis are poorly suited for detecting complex interactions due to sparseness of data in high dimensions, which often occurs when data are available for a large number of SNPs for a relatively small number of samples. Validation of associations observed using multiple methods should be implemented to minimize likelihood of false-positive associations. Moreover, high-throughput genotyping methods allow investigators to genotype thousands of SNPs at one time. Investigating associations for each individual SNP or interactions between SNPs using traditional approaches is inefficient and prone to false positives. Results We developed the Polymorphism Interaction Analysis tool (PIA version 2.0) to include different approaches for ranking and scoring SNP combinations, to account for imbalances between case and control ratios, stratify on particular factors, and examine associations of user-defined pathways (based on SNP or gene) with case status. PIA v. 2.0 detected 2-SNP interactions as the highest ranking model 77% of the time, using simulated data sets of genetic models of interaction (minor allele frequency = 0.2; heritability = 0.01; N = 1600) generated previously [Velez DR, White BC, Motsinger AA, Bush WS, Ritchie MD, Williams SM, Moore JH: A balanced accuracy function for epistasis modeling in imbalanced datasets using multifactor dimensionality reduction. Genet Epidemiol 2007, 31:306–315.]. Interacting SNPs were detected in both balanced (20 SNPs) and imbalanced data (case:control 1:2 and 1:4, 10 SNPs) in the context of non-interacting SNPs. Conclusion PIA v. 2.0 is a useful tool for exploring gene*gene or gene*environment interactions and identifying a small number of putative associations which may be investigated further using other statistical methods and in replication study populations.

Abstract Background Two functional single nucleotide polymorphisms (SNPs) in the 5,10-methylenetetrahydrofolate reductase (MTHFR) gene, C677T and A1298C, lead to decreased enzyme activity and affect chemosensitivity of tumor cells. We investigated whether these MTHFR SNPs were associated with breast cancer survival in African-American and Caucasian women. Methods African-American (n = 143) and Caucasian (n = 105) women, who had incident breast cancer with surgery, were recruited between 1993 and 2003 from the greater Baltimore area, Maryland, USA. Kaplan-Meier survival and multivariate Cox proportional hazards regression analyses were used to examine the relationship between MTHFR SNPs and disease-specific survival. Results We observed opposite effects of the MTHFR polymorphisms A1298C and C677T on breast cancer survival. Carriers of the variant allele at codon 1298 (A/C or C/C) had reduced survival when compared to homozygous carriers of the common A allele [Hazard ratio (HR) = 2.05; 95% confidence interval (CI), 1.05–4.00]. In contrast, breast cancer patients with the variant allele at codon 677 (C/T or T/T) had improved survival, albeit not statistically significant, when compared to individuals with the common C/C genotype (HR = 0.65; 95% CI, 0.31–1.35). The effects were stronger in patients with estrogen receptor-negative tumors (HR = 2.70; 95% CI, 1.17–6.23 for A/C or C/C versus A/A at codon 1298; HR = 0.36; 95% CI, 0.12–1.04 for C/T or T/T versus C/C at codon 677). Interactions between the two MTHFR genotypes and race/ethnicity on breast cancer survival were also observed (A1298C, pinteraction = 0.088; C677T, pinteraction = 0.026). Conclusion We found that the MTHFR SNPs, C677T and A1298C, were associated with breast cancer survival. The variant alleles had opposite effects on disease outcome in the study population. Race/ethnicity modified the association between the two SNPs and breast cancer survival.

Many complex disorders are impacted by the interplay of genetic and environmental factors. In gene-environment interactions (GxE), an individual's genetic and epigenetic makeup impacts the response to environmental exposures. Understanding GxE can impact health at the individual, community, and population levels. The rapid expansion of GxE research in biomedical studies for complex diseases raises many unique ethical, legal, and social implications (ELSIs) that have not been extensively explored and addressed. This review article builds on discussions originating from a workshop held by the National Institute of Environmental Health Sciences (NIEHS) and the National Human Genome Research Institute (NHGRI) in January 2022, entitled: "Ethical, Legal, and Social Implications of Gene-Environment Interaction Research." We expand upon multiple key themes to inform broad recommendations and general guidance for addressing some of the most unique and challenging ELSI in GxE research. Key takeaways include strategies and approaches for establishing sustainable community partnerships, incorporating social determinants of health and environmental justice considerations into GxE research, effectively communicating and translating GxE findings, and addressing privacy and discrimination concerns in all GxE research going forward. Additional guidelines, resources, approaches, training, and capacity building are required to further support innovative GxE research and multidisciplinary GxE research teams., (© 2024 The Author(s). Genetic Epidemiology published by Wiley Periodicals LLC.)

Purpose: Genetic testing is a tool used in a variety of settings for medical and nonhealth related purposes. The goal of this analysis was to better understand the awareness and use of genetic testing in the United States., Methods: Data from the 2020 Health Information National Trends Survey 5 cycle 4 were used to assess the awareness and use of genetic testing by demographic characteristics, personal cancer history, and family cancer history., Results: Overall, 75% of participants were aware of genetic testing and 19% of participants had genetic testing. Ancestry testing was the most common type of testing that the participants were aware of and had received. Non-Hispanic Asian, Non-Hispanic Black, and Hispanic respondents and participants with incomes less than $20,000 were less likely to be aware of and have received any type of genetic testing than the Non-Hispanic White participants and participants with higher income, respectively. Participants with a family history of cancer were more likely to be aware of cancer genetic testing than those without, and participants with a personal history of cancer were more likely to have had cancer genetic testing., Conclusion: It appears awareness of genetic testing is increasing in the United States, and differences in awareness persist by race/ethnicity and income., Competing Interests: Conflict of Interest All the authors are employed by the National Institutes of Health. The authors declare no conflict of interest., (Published by Elsevier Inc.)

Systems epidemiology offers a more comprehensive and holistic approach to studies of cancer in populations by considering high dimensionality measures from multiple domains, assessing the inter-relationships among risk factors, and considering changes over time. These approaches offer a framework to account for the complexity of cancer and contribute to a broader understanding of the disease. Therefore, NCI sponsored a workshop in February 2019 to facilitate discussion about the opportunities and challenges of the application of systems epidemiology approaches for cancer research. Eight key themes emerged from the discussion: transdisciplinary collaboration and a problem-based approach; methods and modeling considerations; interpretation, validation, and evaluation of models; data needs and opportunities; sharing of data and models; enhanced training practices; dissemination of systems models; and building a systems epidemiology community. This manuscript summarizes these themes, highlights opportunities for cancer systems epidemiology research, outlines ways to foster this research area, and introduces a collection of papers, "Cancer System Epidemiology Insights and Future Opportunities" that highlight findings based on systems epidemiology approaches., Competing Interests: The authors have declared that no competing interests exist.

Objectives: Systems epidemiology approaches may lead to a better understanding of the complex and dynamic multi-level constellation of contributors to cancer risk and outcomes and help target interventions. This grant portfolio analysis aimed to describe the National Institutes of Health (NIH) and the National Cancer Institute (NCI) investments in systems epidemiology and to identify gaps in the cancer systems epidemiology portfolio., Methods: The analysis examined grants funded (2013-2018) through seven NIH systems science Funding Opportunity Announcements (FOAs) as well as cancer-specific systems epidemiology grants funded by NCI during that same time. Study characteristics were extracted from the grant abstracts and specific aims and coded., Results: Of the 137 grants awarded under the NIH FOAs, 52 (38%) included systems epidemiology. Only five (4%) were focused on cancer systems epidemiology. The NCI-wide search (N = 453 grants) identified 35 grants (8%) that included cancer systems epidemiology in their specific aims. Most of these grants examined epidemiology and surveillance-based questions (60%); fewer addressed clinical care or clinical trials (37%). Fifty-four percent looked at multiple scales within the individual (e.g., cell, tissue, organ), 49% looked beyond the individual (e.g., individual, community, population), and few (9%) included both. Across all grants examined, the systems epidemiology grants primarily focused on discovery or prediction, rather than on impacts of intervention or policy., Conclusions: The most notable finding was that grants focused on cancer versus other diseases reflected a small percentage of the portfolio, highlighting the need to encourage more cancer systems epidemiology research. Opportunities include encouraging more multiscale research and continuing the support for broad examination of domains in these studies. Finally, the nascent discipline of systems epidemiology could benefit from the creation of standard terminology and definitions to guide future progress., Competing Interests: The authors have declared that no competing interests exist.

In silico simulations play an indispensable role in the development and application of statistical models and methods for genetic studies. Simulation tools allow for the evaluation of methods and investigation of models in a controlled manner. With the growing popularity of evolutionary models and simulation-based statistical methods, genetic simulations have been applied to a wide variety of research disciplines such as population genetics, evolutionary genetics, genetic epidemiology, ecology, and conservation biology. In this review, we surveyed 1409 articles from five journals that publish on major application areas of genetic simulations. We identified 432 papers in which genetic simulations were used and examined the targets and applications of simulation studies and how these simulation methods and simulated data sets are reported and shared. Whereas a large proportion (30%) of the surveyed articles reported the use of genetic simulations, only 28% of these genetic simulation studies used existing simulation software, 2% used existing simulated data sets, and 19% and 12% made source code and simulated data sets publicly available, respectively. Moreover, 15% of articles provided no information on how simulation studies were performed. These findings suggest a need to encourage sharing and reuse of existing simulation software and data sets, as well as providing more information regarding the performance of simulations., (© 2020 The Authors. Genetic Epidemiology published by Wiley Periodicals LLC. This article has been contributed to by US Government employees and their work is in the public domain in the USA.)

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., (©2020 American Association for Cancer Research.)

The application of next-generation sequencing (NGS) technologies in cancer research has accelerated the discovery of somatic mutations; however, progress in the identification of germline variation associated with cancer risk is less clear. We conducted a systematic literature review of cancer genetic susceptibility studies that used NGS technologies at an exome/genome-wide scale to obtain a fuller understanding of the research landscape to date and to inform future studies. The variability across studies on methodologies and reporting was considerable. Most studies sequenced few high-risk (mainly European) families, used a candidate analysis approach, and identified potential cancer-related germline variants or genes in a small fraction of the sequenced cancer cases. This review highlights the importance of establishing consensus on standards for the application and reporting of variants filtering strategies. It also describes the progress in the identification of cancer-related germline variation to date. These findings point to the untapped potential in conducting studies with appropriately sized and racially diverse families and populations, combining results across studies and expanding beyond a candidate analysis approach to advance the discovery of genetic variation that accounts for the unexplained cancer heritability., (©2020 American Association for Cancer Research.)

Recently, many new approaches, study designs, and statistical and analytical methods have emerged for studying gene-environment interactions (G×Es) in large-scale studies of human populations. There are opportunities in this field, particularly with respect to the incorporation of -omics and next-generation sequencing data and continual improvement in measures of environmental exposures implicated in complex disease outcomes. In a workshop called "Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases," held October 17-18, 2014, by the National Institute of Environmental Health Sciences and the National Cancer Institute in conjunction with the annual American Society of Human Genetics meeting, participants explored new approaches and tools that have been developed in recent years for G×E discovery. This paper highlights current and critical issues and themes in G×E research that need additional consideration, including the improved data analytical methods, environmental exposure assessment, and incorporation of functional data and annotations., (Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health 2017.)

Genetic and environmental factors are both known to contribute to susceptibility to complex diseases. Therefore, the study of gene-environment interaction (G×E) has been a focus of research for several years. In this article, select examples of G×E from the literature are described to highlight different approaches and underlying principles related to the success of these studies. These examples can be broadly categorized as studies of single metabolism genes, genes in complex metabolism pathways, ranges of exposure levels, functional approaches and model systems, and pharmacogenomics. Some studies illustrated the success of studying exposure metabolism for which candidate genes can be identified. Moreover, some G×E successes depended on the availability of high-quality exposure assessment and longitudinal measures, study populations with a wide range of exposure levels, and the inclusion of ethnically and geographically diverse populations. In several examples, large population sizes were required to detect G×Es. Other examples illustrated the impact of accurately defining scale of the interactions (i.e., additive or multiplicative). Last, model systems and functional approaches provided insights into G×E in several examples. Future studies may benefit from these lessons learned., (© The Author(s) 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

A growing number and increasing diversity of factors are available for epidemiological studies. These measures provide new avenues for discovery and prevention, yet they also raise many challenges for adoption in epidemiological investigations. Here, we evaluate 1) designs to investigate diseases that consider heterogeneous and multidimensional indicators of exposure and behavior, 2) the implementation of numerous methods to capture indicators of exposure, and 3) the analytical methods required for discovery and validation. We find that case-control studies have provided insights into genetic susceptibility but are insufficient for characterizing complex effects of environmental factors on disease development. Prospective and two-phase designs are required but must balance extended data collection with follow-up of study participants. We discuss innovations in assessments including the microbiome; mass spectrometry and metabolomics; behavioral assessment; dietary, physical activity, and occupational exposure assessment; air pollution monitoring; and global positioning and individual sensors. We claim the the availability of extensive correlated data raises new challenges in disentangling specific exposures that influence cancer risk from among extensive and often correlated exposures. In conclusion, new high-dimensional exposure assessments offer many new opportunities for environmental assessment in cancer development. Cancer Epidemiol Biomarkers Prev; 26(9); 1370-80. ©2017 AACR ., (©2017 American Association for Cancer Research.)

Background: Risk of cancer is determined by a complex interplay of genetic and environmental factors. Although the study of gene-environment interactions (G×E) has been an active area of research, little is reported about the known findings in the literature., Methods: To examine the state of the science in G×E research in cancer, we performed a systematic review of published literature using gene-environment or pharmacogenomic flags from two curated databases of genetic association studies, the Human Genome Epidemiology (HuGE) literature finder and Cancer Genome-Wide Association and Meta Analyses Database (CancerGAMAdb), from January 1, 2001, to January 31, 2011. A supplemental search using HuGE was conducted for articles published from February 1, 2011, to April 11, 2013. A 25% sample of the supplemental publications was reviewed., Results: A total of 3,019 articles were identified in the original search. From these articles, 243 articles were determined to be relevant based on inclusion criteria (more than 3,500 interactions). From the supplemental search (1,400 articles identified), 29 additional relevant articles (1,370 interactions) were included. The majority of publications in both searches examined G×E in colon, rectal, or colorectal; breast; or lung cancer. Specific interactions examined most frequently included environmental factors categorized as energy balance (e.g., body mass index, diet), exogenous (e.g., oral contraceptives) and endogenous hormones (e.g., menopausal status), chemical environment (e.g., grilled meats), and lifestyle (e.g., smoking, alcohol intake). In both searches, the majority of interactions examined were using loci from candidate genes studies and none of the studies were genome-wide interaction studies (GEWIS). The most commonly reported measure was the interaction P-value, of which a sizable number of P-values were considered statistically significant (i.e., <0.05). In addition, the magnitude of interactions reported was modest., Conclusion: Observations of published literature suggest that opportunity exists for increased sample size in G×E research, including GWAS-identified loci in G×E studies, exploring more GWAS approaches in G×E such as GEWIS, and improving the reporting of G×E findings., (Published 2016. This article is a U.S. Government work and is in the public domain in the USA.)

Background: African Americans have a higher risk of developing lung cancer than European Americans. Previous studies suggested that certain circulating cytokines were associated with lung cancer. We hypothesized that variations in serum cytokine levels exist between African Americans and European Americans, and increased circulating cytokine levels contribute to lung cancer differently in the two races., Methods: Differences in 10 serum cytokine levels, IL1β, IL4, IL5, IL6, IL8, IL10, IL12, granulocyte macrophage colony-stimulating factor, IFNγ, and TNFα, between 170 African-American and 296 European-American controls from the National Cancer Institute-Maryland (NCI-MD) case-control study were assessed. Associations of the serum cytokine levels with lung cancer were analyzed. Statistically significant results were replicated in the prospective Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial and the Wayne State University Karmanos Cancer Institute case-control study., Results: Six cytokines, IL4, IL5, IL8, IL10, IFNγ, and TNFα, were significantly higher among European-American as compared with African-American controls. Elevated IL6 and IL8 levels were associated with lung cancer among both races in all three studies. Elevated IL1β, IL10, and TNFα levels were associated with lung cancer only among African Americans. The association between elevated TNFα levels and lung cancer among European Americans was significant after adjustment for additional factors., Conclusions: Serum cytokine levels vary by race and might contribute to lung cancer differently between African Americans and European Americans., Impact: Future work examining risk prediction models of lung cancer can measure circulating cytokines to accurately characterize risk within racial groups., (©2015 American Association for Cancer Research.)

Next Generation Sequencing (NGS) technologies are used to detect somatic mutations in tumors and study germ line variation. Most NGS studies use DNA isolated from whole blood or fresh frozen tissue. However, formalin-fixed paraffin-embedded (FFPE) tissues are one of the most widely available clinical specimens. Their potential utility as a source of DNA for NGS would greatly enhance population-based cancer studies. While preliminary studies suggest FFPE tissue may be used for NGS, the feasibility of using archived FFPE specimens in population based studies and the effect of storage time on these specimens needs to be determined. We conducted a study to determine whether DNA in archived FFPE high-grade ovarian serous adenocarcinomas from Surveillance, Epidemiology and End Results (SEER) registries Residual Tissue Repositories (RTR) was present in sufficient quantity and quality for NGS assays. Fifty-nine FFPE tissues, stored from 3 to 32 years, were obtained from three SEER RTR sites. DNA was extracted, quantified, quality assessed, and subjected to whole exome sequencing (WES). Following DNA extraction, 58 of 59 specimens (98%) yielded DNA and moved on to the library generation step followed by WES. Specimens stored for longer periods of time had significantly lower coverage of the target region (6% lower per 10 years, 95% CI: 3-10%) and lower average read depth (40x lower per 10 years, 95% CI: 18-60), although sufficient quality and quantity of WES data was obtained for data mining. Overall, 90% (53/59) of specimens provided usable NGS data regardless of storage time. This feasibility study demonstrates FFPE specimens acquired from SEER registries after varying lengths of storage time and under varying storage conditions are a promising source of DNA for NGS.

Validation of early detection cancer biomarkers has proven to be disappointing when initial promising claims have often not been reproducible in diagnostic samples or did not extend to prediagnostic samples. The previously reported lack of rigorous internal validity (systematic differences between compared groups) and external validity (lack of generalizability beyond compared groups) may be effectively addressed by utilizing blood specimens and data collected within well-conducted cohort studies. Cohort studies with prediagnostic specimens (eg, blood specimens collected prior to development of clinical symptoms) and clinical data have recently been used to assess the validity of some early detection biomarkers. With this background, the Division of Cancer Control and Population Sciences (DCCPS) and the Division of Cancer Prevention (DCP) of the National Cancer Institute (NCI) held a joint workshop in August 2013. The goal was to advance early detection cancer research by considering how the infrastructure of cohort studies that already exist or are being developed might be leveraged to include appropriate blood specimens, including prediagnostic specimens, ideally collected at periodic intervals, along with clinical data about symptom status and cancer diagnosis. Three overarching recommendations emerged from the discussions: 1) facilitate sharing of existing specimens and data, 2) encourage collaboration among scientists developing biomarkers and those conducting observational cohort studies or managing healthcare systems with cohorts followed over time, and 3) conduct pilot projects that identify and address key logistic and feasibility issues regarding how appropriate specimens and clinical data might be collected at reasonable effort and cost within existing or future cohorts., (© Published by Oxford University Press 2015.)

Computer simulations have played an indispensable role in the development and applications of statistical models and methods for genetic studies across multiple disciplines. The need to simulate complex evolutionary scenarios and pseudo-datasets for various studies has fueled the development of dozens of computer programs with varying reliability, performance, and application areas. To help researchers compare and choose the most appropriate simulators for their studies, we have created the genetic simulation resources (GSR) website, which allows authors of simulation software to register their applications and describe them with more than 160 defined attributes. This article summarizes the properties of 93 simulators currently registered at GSR and provides an overview of the development and applications of genetic simulators. Unlike other review articles that address technical issues or compare simulators for particular application areas, we focus on software development, maintenance, and features of simulators, often from a historical perspective. Publications that cite these simulators are used to summarize both the applications of genetic simulations and the utilization of simulators., (© 2014 WILEY PERIODICALS, INC.)

Genetic simulation programs are used to model data under specified assumptions to facilitate the understanding and study of complex genetic systems. Standardized data sets generated using genetic simulation are essential for the development and application of novel analytical tools in genetic epidemiology studies. With continuing advances in high-throughput genomic technologies and generation and analysis of larger, more complex data sets, there is a need for updating current approaches in genetic simulation modeling. To provide a forum to address current and emerging challenges in this area, the National Cancer Institute (NCI) sponsored a workshop, entitled "Genetic Simulation Tools for Post-Genome Wide Association Studies of Complex Diseases" at the National Institutes of Health (NIH) in Bethesda, Maryland on March 11-12, 2014. The goals of the workshop were to (1) identify opportunities, challenges, and resource needs for the development and application of genetic simulation models; (2) improve the integration of tools for modeling and analysis of simulated data; and (3) foster collaborations to facilitate development and applications of genetic simulation. During the course of the meeting, the group identified challenges and opportunities for the science of simulation, software and methods development, and collaboration. This paper summarizes key discussions at the meeting, and highlights important challenges and opportunities to advance the field of genetic simulation., (© 2014 WILEY PERIODICALS, INC.)

Cancer is characterized by a diversity of genetic and epigenetic alterations occurring in both the germline and somatic (tumor) genomes. Hundreds of germline variants associated with cancer risk have been identified, and large amounts of data identifying mutations in the tumor genome that participate in tumorigenesis have been generated. Increasingly, these two genomes are being explored jointly to better understand how cancer risk alleles contribute to carcinogenesis and whether they influence development of specific tumor types or mutation profiles. To understand how data from germline risk studies and tumor genome profiling is being integrated, we reviewed 160 articles describing research that incorporated data from both genomes, published between January 2009 and December 2012, and summarized the current state of the field. We identified three principle types of research questions being addressed using these data: (i) use of tumor data to determine the putative function of germline risk variants; (ii) identification and analysis of relationships between host genetic background and particular tumor mutations or types; and (iii) use of tumor molecular profiling data to reduce genetic heterogeneity or refine phenotypes for germline association studies. We also found descriptive studies that compared germline and tumor genomic variation in a gene or gene family, and papers describing research methods, data sources, or analytical tools. We identified a large set of tools and data resources that can be used to analyze and integrate data from both genomes. Finally, we discuss opportunities and challenges for cancer research that integrates germline and tumor genomics data., (Published by Oxford University Press 2014.)

Over the past two decades, researchers have increasingly used human biospecimens to evaluate hypotheses related to disease risk, outcomes and treatment. We conducted an analysis of population-science cancer research grants funded by the National Cancer Institute (NCI) to gain a more comprehensive understanding of biospecimens and common derivatives involved in those studies and identify opportunities for advancing the field. Data available for 1,018 extramural, peer-reviewed grants (active as of July 2012) supported by the Division of Cancer Control and Population Sciences (DCCPS), the NCI Division that supports cancer control and population-science extramural research grants, were analyzed. 455 of the grants were determined to involve biospecimens or derivatives. The most common specimen types included were whole blood (51% of grants), serum or plasma (40%), tissue (39%), and the biospecimen derivative, DNA (66%). While use of biospecimens in molecular epidemiology has become common, biospecimens for behavioral and social research is emerging, as observed in our analysis. Additionally, we found the majority of grants were using already existing biospecimens (63%). Grants that involved use of existing biospecimens resulted in lower costs (studies that used existing serum/plasma biospecimens were 4.2 times less expensive) and more publications per year (1.4 times) than grants collecting new biospecimens. This analysis serves as a first step at understanding the types of biospecimen collections supported by NCI DCCPS. There is room to encourage increased use of archived biospecimens and new collections of rarer specimen and cancer types, as well as for behavioral and social research. To facilitate these efforts, we are working to better catalogue our funded resources and make that data available to the extramural community.

Cancer pharmacogenomics have contributed a number of important discoveries to current cancer treatment, changing the paradigm of treatment decisions. Both somatic and germline mutations are utilized to better understand the underlying biology of cancer growth and treatment response. The level of evidence required to fully translate pharmacogenomic discoveries into the clinic has relied heavily on randomized control trials. In this review, the use of observational studies, as well as, the use of adaptive trials and next generation sequencing to develop the required level of evidence for clinical implementation are discussed.

Purpose: On 11 and 12 June 2012, the National Cancer Institute hosted a think tank concerning the identifiability of biospecimens and "omic" data in order to explore challenges surrounding this complex and multifaceted topic., Methods: The think tank brought together 46 leaders from several fields, including cancer genomics, bioinformatics, human subject protection, patient advocacy, and commercial genetics., Results: The first day involved presentations regarding the state of the science of reidentification; current and proposed regulatory frameworks for assessing identifiability; developments in law, industry, and biotechnology; and the expectations of patients and research participants. The second day was spent by think tank participants in small breakout groups designed to address specific subtopics under the umbrella issue of identifiability, including considerations for the development of best practices for data sharing and consent, and targeted opportunities for further empirical research., Conclusion: We describe the outcomes of this 2-day meeting, including two complementary themes that emerged from moderated discussions following the presentations on day 1, and ideas presented for further empirical research to discern the preferences and concerns of research participants about data sharing and individual identifiability.

Summary: Many simulation methods and programs have been developed to simulate genetic data of the human genome. These data have been widely used, for example, to predict properties of populations retrospectively or prospectively according to mathematically intractable genetic models, and to assist the validation, statistical inference and power analysis of a variety of statistical models. However, owing to the differences in type of genetic data of interest, simulation methods, evolutionary features, input and output formats, terminologies and assumptions for different applications, choosing the right tool for a particular study can be a resource-intensive process that usually involves searching, downloading and testing many different simulation programs. Genetic Simulation Resources (GSR) is a website provided by the National Cancer Institute (NCI) that aims to help researchers compare and choose the appropriate simulation tools for their studies. This website allows authors of simulation software to register their applications and describe them with well-defined attributes, thus allowing site users to search and compare simulators according to specified features., Availability: http://popmodels.cancercontrol.cancer.gov/gsr.

Background: Genetic and environmental factors jointly influence cancer risk. The NIH has made the study of gene-environment (GxE) interactions a research priority since the year 2000., Methods: To assess the current status of GxE research in cancer, we analyzed the extramural grant portfolio of the National Cancer Institute (NCI) from Fiscal Years 2007 to 2009. Publications attributed to selected grants were also evaluated., Results: From the 1,106 research grants identified in our portfolio analysis, a random sample of 450 grants (40%) was selected for data abstraction; of these, 147 (33%) were considered relevant. The most common cancer type was breast (20%, n = 29), followed by lymphoproliferative (10%, n = 14), colorectal (9%, n = 13), melanoma/other skin (9%, n = 13), and lung/upper aerodigestive tract (8%, n = 12) cancers. The majority of grants were studies of candidate genes (68%, n = 100) compared with genome-wide association studies (GWAS) (8%, n = 12). Approximately one-third studied environmental exposures categorized as energy balance (37%, n = 54) or drugs/treatment (29%, n = 43). From the 147 relevant grants, 108 publications classified as GxE or pharmacogenomic were identified. These publications were linked to 37 of the 147 grant applications (25%)., Conclusion: The findings from our portfolio analysis suggest that GxE studies are concentrated in specific areas. There is room for investments in other aspects of GxE research, including, but not limited to developing alternative approaches to exposure assessment, broadening the spectrum of cancer types investigated, and conducting GxE within GWAS., Impact: This portfolio analysis provides a cross-sectional review of NCI support for GxE research in cancer.

Background: The primary risk factor for chronic obstructive pulmonary disease (COPD) and non-small cell lung cancer (NSCLC) is cigarette smoking but shared susceptibility factors, such as variations in the matrix metalloproteinase-1 (MMP1) gene, may also underlie both diseases., Materials and Methods: Cases with prevalent COPD (n=167), incident NSCLC (n=242), or prevalent COPD plus incident NSCLC (n=128) were compared to disease-free controls (n=338) to assess six MMP1 polymorphisms. The association between these polymorphisms and survival in NSCLC was also evaluated., Results: Rs11292517 among African-Americans [odds ratio (OR)=5.48, 95% confidence interval (CI)=1.17-25.72] and rs2071230 among Caucasians (OR=2.51, 95% CI=1.09-5.77) appeared to be associated with NSCLC risk in the presence of COPD. Rs470558 appeared to be associated with survival in NSCLC among African-Americans (hazard ratio=3.94; 95%CI=1.14-13.63). No associations remained after adjusting for multiple comparisons., Conclusion: Polymorphisms in MMP1 were not consistently associated with prevalent COPD or incident NSCLC nor with survival in NSCLC.

Background: Through their roles in tissue remodeling, variants in the genes that encode alpha1-antitrypsin (AAT) and neutrophil elastase (NE) were hypothesized to be associated with the risk of both chronic obstructive pulmonary disease (COPD) and non-small cell lung cancer (NSCLC)., Materials and Methods: Cases with prevalent COPD (n=145), incident NSCLC (n=203) or prevalent COPD plus NSCLC (n=118) were compared to disease-free controls (n=317), to assess two functional polymorphisms in serpin peptidase inhibitor, clade A, member 1 (SERPINA1), which encodes AAT, and eleven tagging polymorphisms in and around elastase 2 (ELA2), which encodes NE. All analyses were stratified by race., Results: Among African-Americans, the less efficient SERPINA1 variant appeared to be associated with increased risk of prevalent COPD but only in the presence of NSCLC (odds ratio=7.39; 95% confidence interval=1.03-53.21) and not after correcting for multiple comparisons., Conclusion: Variations in SERPINA1 and ELA2 were not consistently or strongly associated with the risk of either COPD or NSCLC in either race.

Background: Patients with lung cancer with mutations in EGF receptor (EGFR) tyrosine kinase have improved prognosis when treated with EGFR inhibitors. We hypothesized that EGFR mutations may be related to residential radon or passive tobacco smoke., Methods: This hypothesis was investigated by analyzing EGFR mutations in 70 lung tumors from a population of never and long-term former female smokers from Missouri with detailed exposure assessments. The relationship with passive smoking was also examined in never-smoking female lung cancer cases from the Mayo clinic., Results: Overall, the frequency of EGFR mutation was 41% [95% confidence interval (CI), 32%-49%]. Neither radon nor passive-smoking exposure was consistently associated with EGFR mutations in lung tumors., Conclusions: The results suggest that EGFR mutations are common in female, never-smoking lung cancer cases from the United States, and EGFR mutations are unlikely due to exposure to radon or passive smoking.

LKB1/STK11 is a tumor suppressor and a negative regulator of mammalian target of rapamycin signaling. It is inactivated in 30% of lung cancer cell lines but only 5-15% of primary lung adenocarcinomas. There is evidence that homozygous deletion (HD) of chromosome 19p at the LKB locus contributes to the inactivation of the gene in primary human lung cancers. Here, we used several complementary genetic approaches to assess the LKB1 locus in primary non-small cell lung cancers (NSCLCs). We first analyzed 124 NSCLC cases for allelic imbalance using eight microsatellite markers on chromosome 19p, which revealed an overall rate of 65% (80 of 124) loss of heterozygosity (LOH). We next used chromogenic in situ hybridization (CISH) to directly examine the chromosomal status of the LKB1 locus. In all, 65 of 124 LOH tested samples were available for CISH and 58 of those (89%) showed either loss of one copy of chromosome 19p (LOH, 40 of 65 cases, 62%) or both copies (HD 18 of 65 cases, 28%). The occurrence of HD was significantly more frequent in Caucasian (35%) than in African-American patients (6%) (P=0.04). A total of 62 of 124 samples with LOH at one or both markers immediately flanking the LKB1 gene were further analyzed by directly sequencing the complete coding region, which identified 7 of 62 (11%) tumors with somatic mutations in the gene. Jointly, our data identified total inactivation of the LKB1 gene by either HD or LOH with somatic mutation in 39% of tested samples, whereas loss of chromosome 19p region by HD or LOH at the LKB1 region occured in 90% of NSCLC.

Background: Previous studies that were based primarily on small numbers of patients suggested that certain circulating proinflammatory cytokines may be associated with lung cancer; however, large independent studies are lacking., Methods: Associations between serum interleukin 6 (IL-6) and interleukin 8 (IL-8) levels and lung cancer were analyzed among 270 case patients and 296 control subjects participating in the National Cancer Institute-Maryland (NCI-MD) case-control study. Results were validated in 532 case patients and 595 control subjects in a nested case-control study within the prospective Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial. Association with C-reactive protein (CRP), a systemic inflammation biomarker, was also analyzed. Associations between biomarkers and lung cancer were estimated using logistic regression models adjusted for smoking, stage, histology, age, and sex. The 10-year standardized absolute risks of lung cancer were estimated using a weighted Cox regression model., Results: Serum IL-6 and IL-8 levels in the highest quartile were associated with lung cancer in the NCI-MD study (IL-6, odds ratio [OR] = 3.29, 95% confidence interval [CI] = 1.88 to 5.77; IL-8, OR = 2.06, 95% CI = 1.19 to 3.57) and with lung cancer risk in the PLCO study (IL-6, OR = 1.48, 95% CI = 1.04 to 2.10; IL-8, OR = 1.57, 95% CI = 1.10 to 2.24), compared with the lowest quartile. In the PLCO study, increased IL-6 levels were only associated with lung cancer diagnosed within 2 years of blood collection, whereas increased IL-8 levels were associated with lung cancer diagnosed more than 2 years after blood collection (OR = 1.57, 95% CI = 1.15 to 2.13). The 10-year standardized absolute risks of lung cancer in the PLCO study were highest among current smokers with high IL-8 and CRP levels (absolute risk = 8.01%, 95% CI = 5.77% to 11.05%)., Conclusions: Although increased levels of both serum IL-6 and IL-8 are associated with lung cancer, only IL-8 levels are associated with lung cancer risk several years before diagnosis. Combination of IL-8 and CRP are more robust biomarkers than either marker alone in predicting subsequent lung cancer.

The role of tumor estrogen receptors (ERs) and serum estrogen in lung cancer is inconclusive. We investigated the hypothesis that ERs and functional single-nucleotide polymorphisms in the estrogen biosynthesis pathway are associated with poorer lung cancer survival. Lung cancer patients (n = 305) from a National Cancer Institute-Maryland (NCI-MD) case-case cohort in the Baltimore metropolitan area were used as a test cohort. To validate, 227 cases from the NCI-MD case-control cohort and 293 cases from a Norwegian lung cancer cohort were studied. Information on demographics, tobacco and reproductive histories was collected in an interviewer-administered questionnaire. Serum estrogen, progesterone, tumor messenger RNA expression of hormone receptors and germ line DNA polymorphisms were analyzed for associations with lung cancer survival. Patients in the highest tertile of serum estrogen had worse survival in all three cohorts (P combined < 0.001). Furthermore, the variant allele of estrogen receptor alpha (ER-α) polymorphism (rs2228480) was significantly associated with increased tumor ER-α levels and worse survival in all three cohorts [hazard ratio (HR) = 2.59, 95% confidence interval (CI): 1.20- 4.01; HR = 1.76, 95% CI: 1.08-2.87 and HR = 2.85, 95% CI: 1.31-4.36). Other polymorphisms associated with lower serum estrogen correlated with improved survival. Results were independent of gender and hormone replacement therapy. We report a significant association of increased serum estrogen with poorer survival among lung cancer male and female patients. Understanding the genetic control of estrogen biosynthesis and response in lung cancer could lead to improved prognosis and therapy.

Prospective studies of lifestyle and non-Hodgkin lymphoma (NHL) are conflicting, and some are inconsistent with case-control studies. The Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial was used to evaluate risk of NHL and its subtypes in association with anthropometric factors, smoking, and alcohol consumption in a prospective cohort study. Lifestyle was assessed via questionnaire among 142,982 male and female participants aged 55-74 years enrolled in the PLCO Trial during 1993-2001. Hazard ratios and 95% confidence intervals were calculated using Cox proportional hazards regression. During 1,201,074 person-years of follow-up through 2006, 1,264 histologically confirmed NHL cases were identified. Higher body mass index (BMI; weight (kg)/height (m)(2)) at ages 20 and 50 years and at baseline was associated with increased NHL risk (P(trend) < 0.01 for all; e.g., for baseline BMI > or =30 vs. 18.5-24.9, hazard ratio = 1.32, 95% confidence interval: 1.13, 1.54). Smoking was not associated with NHL overall but was inversely associated with follicular lymphoma (ever smoking vs. never: hazard ratio = 0.62, 95% confidence interval: 0.45, 0.85). Alcohol consumption was unrelated to NHL (drinks/week: P(trend) = 0.187). These data support previous studies suggesting that BMI is positively associated with NHL, show an inverse association between smoking and follicular lymphoma (perhaps due to residual confounding), and do not support a causal association between alcohol and NHL.

Background: Exposure to secondhand smoke during adulthood has detrimental health effects, including increased lung cancer risk. Compared with adults, children may be more susceptible to secondhand smoke. This susceptibility may be exacerbated by alterations in inherited genetic variants of innate immunity genes. We hypothesized a positive association between childhood secondhand smoke exposure and lung cancer risk that would be modified by genetic polymorphisms in the mannose binding lectin-2 (MBL2) gene resulting in well-known functional changes in innate immunity., Methods: Childhood secondhand smoke exposure and lung cancer risk was assessed among men and women in the ongoing National Cancer Institute-Maryland Lung Cancer (NCI-MD) study, which included 624 cases and 348 controls. Secondhand smoke history was collected via in-person interviews. DNA was used for genotyping the MBL2 gene. To replicate, we used an independent case-control study from Mayo Clinic consisting of 461 never smokers, made up of 172 cases and 289 controls. All statistical tests were two-sided., Results: In the NCI-MD study, secondhand smoke exposure during childhood was associated with increased lung cancer risk among never smokers [odds ratio (OR), 2.25; 95% confidence interval (95% CI), 1.04-4.90]. This was confirmed in the Mayo study (OR, 1.47; 95% CI, 1.00-2.15). A functional MBL2 haplotype associated with high circulating levels of MBL and increased MBL2 activity was associated with increased lung cancer risk among those exposed to childhood secondhand smoke in both the NCI-MD and Mayo studies (OR, 2.52; 95% CI, 1.13-5.60, and OR, 2.78; 95% CI, 1.18-3.85, respectively)., Conclusions: Secondhand smoke exposure during childhood is associated with increased lung cancer risk among never smokers, particularly among those possessing a haplotype corresponding to a known overactive complement pathway of the innate immune system.

Accumulating evidence suggests a role for inflammation in the development and progression of cancer. Our group recently identified a cytokine gene signature in lung tissue associated with lung cancer prognosis. Therefore, we hypothesized that concentrations of circulating cytokines in serum may be associated with lung cancer survival. Ten serum cytokines, namely, interleukin (IL)-1beta, IL-4, IL-5, IL-6, IL-8, IL-10, IL-12, granulocyte macrophage colony-stimulating factor, interferon (IFN)-gamma, and tumor necrosis factor-alpha, were assessed in 353 non-small cell lung cancer cases from a case-control study of lung cancer in the greater Baltimore, Maryland area. Cytokines were measured using an ultrasensitive electrochemiluminescence immunoassay. IL-6 serum concentrations (>or=4.0 pg/mL) were associated with significantly poorer survival in both African Americans [hazard ratio (HR), 2.71; 95% confidence interval (CI), 1.26-5.80] and Caucasians (HR, 1.71; 95% CI, 1.22-2.40). IL-10 (HR, 2.62; 95% CI, 1.33-5.15) and IL-12 (HR, 1.98; 95% CI, 1.14-3.44) were associated with lung cancer survival only in African Americans. Some evidence for an association of tumor necrosis factor-alpha levels with survival in Caucasians was observed, although these results were not significant. These hypothesis-generating findings indicate that selected serum cytokine concentrations are associated with lung cancer survival, and indicate that further research is warranted to better understand the mechanistic underpinnings of these associations.

Background: The risk of common diseases is likely determined by the complex interplay between environmental and genetic factors, including single nucleotide polymorphisms (SNPs). Traditional methods of data analysis are poorly suited for detecting complex interactions due to sparseness of data in high dimensions, which often occurs when data are available for a large number of SNPs for a relatively small number of samples. Validation of associations observed using multiple methods should be implemented to minimize likelihood of false-positive associations. Moreover, high-throughput genotyping methods allow investigators to genotype thousands of SNPs at one time. Investigating associations for each individual SNP or interactions between SNPs using traditional approaches is inefficient and prone to false positives., Results: We developed the Polymorphism Interaction Analysis tool (PIA version 2.0) to include different approaches for ranking and scoring SNP combinations, to account for imbalances between case and control ratios, stratify on particular factors, and examine associations of user-defined pathways (based on SNP or gene) with case status. PIA v. 2.0 detected 2-SNP interactions as the highest ranking model 77% of the time, using simulated data sets of genetic models of interaction (minor allele frequency = 0.2; heritability = 0.01; N = 1600) generated previously [Velez DR, White BC, Motsinger AA, Bush WS, Ritchie MD, Williams SM, Moore JH: A balanced accuracy function for epistasis modeling in imbalanced datasets using multifactor dimensionality reduction. Genet Epidemiol 2007, 31:306-315.]. Interacting SNPs were detected in both balanced (20 SNPs) and imbalanced data (case:control 1:2 and 1:4, 10 SNPs) in the context of non-interacting SNPs., Conclusion: PIA v. 2.0 is a useful tool for exploring gene*gene or gene*environment interactions and identifying a small number of putative associations which may be investigated further using other statistical methods and in replication study populations.

Background: The relationship among chronic inflammation, innate immunity, and cancer is well established. Mannose-binding lectin (MBL) is a key player in innate immunity. Five polymorphisms in the promoter and first exon of the MBL2 gene alter the expression and function of MBL in humans and are associated with inflammation-related disease susceptibility. These five polymorphisms create six well-characterized haplotypes that result in lower (i.e., LYB, LYC, HYD, and LXA) or higher (i.e., HYA and LYA) serum MBL concentrations. We investigated whether survival of patients with lung cancer was associated with these polymorphisms., Methods: We used a multivariable Cox proportional hazards model to study the association between MBL2 polymorphisms and their haplotypes and diplotypes in 558 white and 173 African American patients with non-small-cell lung cancer in the Baltimore, MD, area and lung cancer mortality. Smoking history and race were obtained from interviews, tumor stage was obtained from medical records, and cause of death was obtained from the National Death Index. All statistical tests were two-sided., Results: We found a statistically significant association between the X allele of the promoter Y/X polymorphism (which results in a lower serum MBL concentration) and improved lung cancer survival among white patients (risk ratio [RR] of death from lung cancer with X/X or X/Y genotype compared with Y/Y genotype = 0.61, 95% confidence interval [CI] = 0.46 to 0.81) but not among African American patients (RR = 1.11, 95% CI = 0.69 to 1.77). The associations among white patients were strongest in heavy smokers and were independent of stage. We also found a statistically significant interaction between the Y/X polymorphism and race for lung cancer survival (P(interaction) = .019). The MBL2 LXA haplotype and XA/B diplotype, which are also associated with low serum MBL levels, were statistically significantly associated with improved lung cancer survival among white patients., Conclusion: The functional Y/X polymorphism of the innate-immunity gene MBL2 and MBL2 haplotypes and diplotypes appear to be associated with lung cancer survival among white patients.

Background: A 17-cytokine gene expression signature in noncancerous hepatic tissue from patients with metastatic hepatocellular carcinoma (HCC) was recently found to predict HCC metastasis and recurrence. We examined whether the cytokine gene expression profile of noncancerous lung tissue could predict the metastatic capability of adjacent lung adenocarcinoma., Methods: We analyzed a 15-cytokine gene expression profile in noncancerous lung tissue and corresponding lung tumor tissue from 80 US lung adenocarcinoma patients using real-time quantitative reverse transcription-polymerase chain reaction. We then used unsupervised hierarchical clustering and Prediction Analysis of Microarray classification to test the prognostic ability of the 15-cytokine gene profile in the US patients and in an independent validation set comprising 50 Japanese patients with stage I disease. Survival was analyzed by the Kaplan-Meier method using the log-rank test, and univariate and multivariable Cox proportional hazards modeling were used to analyze the association of clinical variables with patient survival. All statistical tests were two-sided., Results: A 15-cytokine gene signature in noncancerous lung tissue primarily reflected the lymph node status of 80 lung adenocarcinoma patients, whereas the gene signature of the corresponding lung tumor tissue was associated with prognosis independent of lymph node status. Cytokine Lung Adenocarcinoma Survival Signature of 11 genes (CLASS-11), a refined 11-gene signature, accurately classified patients, including those with stage I disease, according to risk of death from adenocarcinoma. CLASS-11 prognostic classification was statistically significantly associated with survival and was an independent prognostic factor for stage I patients (hazard ratio for death in the high-risk CLASS-11 group compared with the low-risk CLASS-11 reference group = 7.46, 95% confidence interval = 2.14 to 26.05; P = .002). CLASS-11 also classified patients in the validation set according to risk of recurrence., Conclusion: CLASS-11, which consists of genes for pro- and anti-inflammatory cytokines, identifies stage I lung adenocarcinoma patients who have a poor prognosis.

Common genetic variants in cancer-related genes contribute to breast cancer. The innate immune system plays a crucial role in the immune surveillance against malignancies, thus it is plausible that genetic variations in key genes of the innate immunity such as the mannose-binding lectin (MBL), MBL2, could influence the risk for breast cancer. We investigated the association of MBL2 genotypes with breast cancer and conducted a comprehensive genotype and haplotype analysis of 26 MBL2 single nucleotide polymorphisms (SNPs) in a case-control study of breast cancer [166 African-American (AA) case patients versus 180 controls and 127 Caucasian (CAU) case patients versus 137 controls]. We observed that the A allele of the 3'-UTR SNP Ex4-1067 (NCBI SNP ID: rs10824792) was significantly associated with a decreased disease risk in AA women [odds ratio (OR) = 0.47, 95% confidence interval (CI) = 0.27-0.81]. Haplotype analysis of MBL2 showed that the frequency of the corresponding 3' haplotype TATAAC (Ex4-1483, Ex4-1067, Ex4-1047, Ex4-901, Ex4-710, 3238bp 3' STP) was lower in cases than controls among AA women (0.15 versus 0.21; P = 0.02) suggesting a protective effect after adjusting for covariates (OR = 0.51, 95% CI = 0.29-0.88, P = 0.018). In conclusion, this study presents preliminary evidence that common genetic variants in the 3'-UTR of MBL2 might influence the risk for breast cancer in AA women, probably in interaction with the 5' secretor haplotypes that are associated with high concentrations of MBL.

Lung cancer is primarily caused by tobacco smoking, but susceptibility is likely modified by common genetic variation. In response to many forms of cellular stress, including DNA damage, the p53 protein functions to induce cell cycle arrest, DNA repair, senescence, or apoptosis. We hypothesized that common TP53 haplotypes modulate pathways of lung carcinogenesis and lung cancer susceptibility or prognosis. To investigate our hypothesis, 14 polymorphisms in TP53, including haplotype tagging and coding single nucleotide polymorphisms, were genotyped in two studies from the greater Baltimore, Maryland area. One study is a case-control study and the second is a case-only study for which TP53 mutational spectra data are available. African Americans with Pro-T-A-G-G haplotypes of the combined TP53 polymorphisms TP53&#95;01 (rs1042522), TP53&#95;65 (rs9895829), TP53&#95;66 (rs2909430), TP53&#95;16 (rs1625895), and TP53&#95;11 (rs12951053) had both an increased risk for lung cancer (odds ratio, 2.32; 95% confidence interval, 1.18-4.57) and a worsened lung cancer prognosis (hazards ratio, 2.38; 95% confidence interval, 1.38-4.10) compared with those with Arg-T-A-G-T haplotypes. No associations of TP53 polymorphisms with lung cancer were observed in Caucasians. In the case-only study, several polymorphisms in TP53 and TP53 haplotypes, overlapping regions of TP53 associated with risk and prognosis in African Americans, were associated with increased odds of somatic TP53 mutation in lung tumors in Caucasians. In conclusion, common genetic variation in TP53 could modulate lung cancer pathways, as suggested by the association with lung cancer in African Americans and somatic TP53 mutation frequency in lung tumors.

Background: Two functional single nucleotide polymorphisms (SNPs) in the 5,10-methylenetetrahydrofolate reductase (MTHFR) gene, C677T and A1298C, lead to decreased enzyme activity and affect chemosensitivity of tumor cells. We investigated whether these MTHFR SNPs were associated with breast cancer survival in African-American and Caucasian women., Methods: African-American (n = 143) and Caucasian (n = 105) women, who had incident breast cancer with surgery, were recruited between 1993 and 2003 from the greater Baltimore area, Maryland, USA. Kaplan-Meier survival and multivariate Cox proportional hazards regression analyses were used to examine the relationship between MTHFR SNPs and disease-specific survival., Results: We observed opposite effects of the MTHFR polymorphisms A1298C and C677T on breast cancer survival. Carriers of the variant allele at codon 1298 (A/C or C/C) had reduced survival when compared to homozygous carriers of the common A allele [Hazard ratio (HR) = 2.05; 95% confidence interval (CI), 1.05-4.00]. In contrast, breast cancer patients with the variant allele at codon 677 (C/T or T/T) had improved survival, albeit not statistically significant, when compared to individuals with the common C/C genotype (HR = 0.65; 95% CI, 0.31-1.35). The effects were stronger in patients with estrogen receptor-negative tumors (HR = 2.70; 95% CI, 1.17-6.23 for A/C or C/C versus A/A at codon 1298; HR = 0.36; 95% CI, 0.12-1.04 for C/T or T/T versus C/C at codon 677). Interactions between the two MTHFR genotypes and race/ethnicity on breast cancer survival were also observed (A1298C, pinteraction = 0.088; C677T, pinteraction = 0.026)., Conclusion: We found that the MTHFR SNPs, C677T and A1298C, were associated with breast cancer survival. The variant alleles had opposite effects on disease outcome in the study population. Race/ethnicity modified the association between the two SNPs and breast cancer survival.

In lung tumors, the p53 tumor suppressor gene is commonly mutated with a characteristic mutation spectrum. The amount of and alterations in plasma DNA, such as mutations in p53, were associated with several cancers. Few studies used quantitative methods of high sensitivity. Previously, we observed p53 mutations in the noncancerous tissue that differed from those in lung tumors using the highly sensitive p53 mutation load assay. Based on our observation of an increased p53 mutation load in nontumorous lung tissue in smokers, we hypothesized that plasma DNA may contain mutant p53 indicative of tobacco smoke exposure and will be an effective biomarker of lung cancer or smoking exposure. We modified the p53 mutation load assay to detect mutations at p53 codons 248 and 249, common mutations in lung cancer, in plasma DNA samples with a sensitivity of 1:5,000. The assay was applied to a set of lung cancer cases (n = 39), hospital controls (n = 21), and population controls (n = 20) from a larger study. Controls were selected to consist of equal numbers of both ever and never smokers. The p53 mutation load (mutated p53 copies per total number of p53 copies) was associated with smoking (P = 0.06), but not with lung cancer (P = 0.59). Most of the individuals with p53 mutations observed in plasma DNA were ever smokers and the p53 mutation load was higher in those who smoked for longer durations (P = 0.04). In summary, we were able to detect p53 mutations in plasma DNA from healthy individuals and our data suggest that p53 mutations in plasma DNA may be a marker of carcinogen exposure from tobacco smoke.

A single nucleotide polymorphism (SNP) in the MDM2 promoter (a T to G exchange at nucleotide 309) has been found to be associated with tumor formation. Publication of this null report is important because an association between MDM2 SNP309 and lung cancer was previously reported in two independent studies. Our findings suggest that MDM2 SNP309 is not a strong factor in lung carcinogenesis. In addition, this is the first MDM2 SNP309 report on a population consisting of Caucasians in the United States and African-Americans. A strength of the study design is that the controls consist of both population and hospital controls.

Polymorphisms exist in several genes involved in nucleotide excision repair (NER), the principal pathway for removal of smoking-induced DNA damage. An epidemiologic study was conducted to determine whether these polymorphisms modify the association between smoking and breast cancer. DNA samples and exposure histories were analyzed as part of a large population-based case-control study of breast cancer in North Carolina. The study population included 2311 cases (894 African Americans, 1417 whites) and 2022 controls (788 African Americans, 1234 whites). Odds ratios (ORs) were calculated for breast cancer and smoking, and for breast cancer and nine non-synonymous coding polymorphisms in six NER genes (XPD codons 312 and 751, RAD23B codon 249, XPG codon 1104, XPC codon 939, XPF codons 415 and 662, and ERCC6 codons 1213 and 1230). Modification of ORs for smoking by single and combined NER genotypes was investigated. In this study population, smoking was more strongly associated with breast cancer in African American women compared with white women. Among African American women, the association of breast cancer and smoking was strongest among women with specific combinations of NER genotypes. Evidence for multiplicative interaction was found between combined NER genotypes and smoking dose (likelihood ratio test P = 0.06), duration (P = 0.09), time since cessation (P = 0.02), age at initiation (P = 0.04) and former smoking (P = 0.03). No interactions were observed in white women. Therefore, polymorphisms in NER genes may modify the relationship between breast cancer and smoking. These results are consistent with previous evidence of exposure-specific p53 mutations in breast tumors from current and former smokers, suggesting that smoking may play a role in breast cancer etiology.

Several single nucleotide polymorphisms (SNPs) in genes derived from distinct pathways are associated with colon cancer risk; however, few studies have examined SNP-SNP interactions concurrently. We explored the association between colon cancer and 94 SNPs, using a novel approach, polymorphism interaction analysis (PIA). We developed PIA to examine all possible SNP combinations, based on the 94 SNPs studied in 216 male colon cancer cases and 255 male controls, employing 2 separate functions that cross-validate and minimize false-positive results in the evaluation of SNP combinations to predict colon cancer risk. PIA identified previously described null polymorphisms in glutathione-S-transferase T1 (GSTT1) as the best predictor of colon cancer among the studied SNPs, and also identified novel polymorphisms in the inflammation and hormone metabolism pathways that singly or jointly predict cancer risk. PIA identified SNPs that may interact with the GSTT1 polymorphism, including coding polymorphisms in TP53 (Arg72Pro in p53) and CASP8 (Asp302His in caspase 8), which may modify the association between this polymorphism and colon cancer. This was confirmed by logistic regression, as the GSTT1 null polymorphism in combination with either the TP53 or the CASP8 polymorphism significantly alter colon cancer risk (p(interaction) < 0.02 for both). GSTT1 prevents DNA damage by detoxifying mutagenic compounds, while the p53 protein facilitates repair of DNA damage and induces apoptosis, and caspase 8 is activated in p53-mediated apoptosis. Our results suggest that PIA is a valid method for suggesting SNP-SNP interactions that may be validated in future studies, using more traditional statistical methods on different datasets.