Genetic influences may be discerned in families that have multiple affected members and may manifest as an earlier age of cancer diagnosis. In this study we determine whether cancers develop at an earlier age in multiplex Familial Barrett’s Esophagus (FBE) kindreds, defined by 3 or more members affected by Barrett’s esophagus (BE) or esophageal adenocarcinoma (EAC).
Information on BE/EAC risk factors and family history was collected from probands at eight tertiary care academic hospitals. Age of cancer diagnosis and other risk factors were compared between non-familial (no affected relatives), duplex (two affected relatives), and multiplex (three or more affected relatives) FBE kindreds.
The study included 830 non-familial, 274 duplex and 41 multiplex FBE kindreds with 274, 133 and 43 EAC and 566, 288 and 103 BE cases, respectively. Multivariable mixed models adjusting for familial correlations showed that multiplex kindreds were associated with a younger age of cancer diagnosis (p = 0.0186). Median age of cancer diagnosis was significantly younger in multiplex compared to duplex and non-familial kindreds (57 vs. 62 vs. 63 yrs, respectively, p = 0.0448). Mean body mass index (BMI) was significantly lower in multiplex kindreds (p = 0.0033) as was smoking (p < 0.0001), and reported regurgitation (p = 0.0014).
Members of multiplex FBE kindreds develop EAC at an earlier age compared to non-familial EAC cases. Multiplex kindreds do not have a higher proportion of common risk factors for EAC, suggesting that this aggregation might be related to a genetic factor.
These findings indicate that efforts to identify susceptibility genes for BE and EAC will need to focus on multiplex kindreds.
Esophageal adenocarcinoma; Barrett’s esophagus; genetics; family history
There is a critical need to identify molecular markers that can reliably aid in stratifying esophageal adenocarcinoma (EAC) risk in patients with Barrett's esophagus. MicroRNAs (miRNA/miR) are one such class of biomolecules. In the present cross-sectional study, we characterized miRNA alterations in progressive stages of neoplastic development, i.e., metaplasia–dysplasia–adenocarcinoma, with an aim to identify candidate miRNAs potentially associated with progression. Using next generation sequencing (NGS) as an agnostic discovery platform, followed by quantitative real-time PCR (qPCR) validation in a total of 20 EACs, we identified 26 miRNAs that are highly and frequently deregulated in EACs (≥4-fold in >50% of cases) when compared to paired normal esophageal squamous (nSQ) tissue. We then assessed the 26 EAC-derived miRNAs in laser microdissected biopsy pairs of Barrett's metaplasia (BM)/nSQ (n = 15), and high-grade dysplasia (HGD)/nSQ (n = 14) by qPCR, to map the timing of deregulation during progression from BM to HGD and to EAC. We found that 23 of the 26 candidate miRNAs were deregulated at the earliest step, BM, and therefore noninformative as molecular markers of progression. Two miRNAs, miR-31 and –31*, however, showed frequent downregulation only in HGD and EAC cases suggesting association with transition from BM to HGD. A third miRNA, miR-375, showed marked downregulation exclusively in EACs and in none of the BM or HGD lesions, suggesting its association with progression to invasive carcinoma. Taken together, we propose miR-31 and –375 as novel candidate microRNAs specifically associated with early- and late-stage malignant progression, respectively, in Barrett's esophagus.
Gliomas, which generally have a poor prognosis, are the most common primary malignant brain tumors in adults. Recent genome-wide association studies have demonstrated that inherited susceptibility plays a role in the development of glioma. Although first-degree relatives of patients exhibit a two-fold increased risk of glioma, the search for susceptibility loci in familial forms of the disease has been challenging because the disease is relatively rare, fatal, and heterogeneous, making it difficult to collect sufficient biosamples from families for statistical power. To address this challenge, the Genetic Epidemiology of Glioma International Consortium (Gliogene) was formed to collect DNA samples from families with two or more cases of histologically confirmed glioma. In this study, we present results obtained from 46 U.S. families in which multipoint linkage analyses were undertaken using nonparametric (model-free) methods. After removal of high linkage disequilibrium SNPs, we obtained a maximum nonparametric linkage score (NPL) of 3.39 (P=0.0005) at 17q12–21.32 and the Z-score of 4.20 (P=0.000007). To replicate our findings, we genotyped 29 independent U.S. families and obtained a maximum NPL score of 1.26 (P=0.008) and the Z-score of 1.47 (P=0.035). Accounting for the genetic heterogeneity using the ordered subset analysis approach, the combined analyses of 75 families resulted in a maximum NPL score of 3.81 (P=0.00001). The genomic regions we have implicated in this study may offer novel insights into glioma susceptibility, focusing future work to identify genes that cause familial glioma.
Glioma; family studies; linkage; haplotype pattern; NPL
In recent years, many algorithms have been developed for network-based analysis of differential gene expression in complex diseases. These algorithms use protein-protein interaction (PPI) networks as an integrative framework and identify subnetworks that are coordinately dysregulated in the phenotype of interest.
While such dysregulated subnetworks have demonstrated significant improvement over individual gene markers for classifying phenotype, the current state-of-the-art in dysregulated subnetwork discovery is almost exclusively limited to binary phenotype classes. However, many clinical applications require identification of molecular markers for multiple classes.
We consider the problem of discovering groups of genes whose expression signatures can discriminate multiple phenotype classes. We consider two alternate formulations of this problem (i) an all-vs-all approach that aims to discover subnetworks distinguishing all classes, (ii) a one-vs-all approach that aims to discover subnetworks distinguishing each class from the rest of the classes. For the one-vs-all formulation, we develop a set-cover based algorithm, which aims to identify groups of genes such that at least one gene in the group exhibits differential expression in the target class.
We test the proposed algorithms in the context of predicting stages of colorectal cancer. Our results show that the set-cover based algorithm identifying "stage-specific" subnetworks outperforms the all-vs-all approaches in classification. We also investigate the merits of utilizing PPI networks in the search for multiple markers, and show that, with correct parameter settings, network-guided search improves performance. Furthermore, we show that assessing statistical significance when selecting features greatly improves classification performance.
Single nucleotide polymorphisms (SNPs) in alcohol metabolism genes are associated with squamous cell carcinoma of the head and neck (SCCHN), and may influence cancer risk in conjunction with alcohol. Genetic variation in the oxidative stress pathway may impact the carcinogenic effect of reactive oxygen species produced by ethanol metabolism. We hypothesized that alcohol interacts with these pathways to affect SCCHN incidence.
Interview and genotyping data for 64 SNPs were obtained from 2552 European- and African-American subjects (1227 cases, 1325 controls) from the Carolina Head and Neck Cancer Epidemiology study, a population-based case-control study of SCCHN conducted in North Carolina from 2002–2006. We estimated odds ratios and 95% confidence intervals for SNPs and haplotypes, adjusting for age, sex, race, and duration of cigarette smoking. P-values were adjusted for multiple testing using Bonferroni correction.
Two SNPs were associated with SCCHN risk: ADH1B rs1229984 A allele (OR=0.7, 95%CI=0.6–0.9) and ALDH2 rs2238151 C allele (OR=1.2, 95%CI=1.1–1.4). Three were associated with sub-site tumors: ADH1B rs17028834 C allele (larynx, OR=1.5, 95%CI=1.1–2.0), SOD2 rs4342445 A allele (oral cavity, OR=1.3, 95%CI=1.1–1.6), and SOD2 rs5746134 T allele (hypopharynx, OR=2.1, 95%CI=1.2–3.7). Four SNPs in alcohol metabolism genes interacted additively with alcohol consumption: ALDH2 rs2238151, ADH1B rs1159918, ADH7 rs1154460, and CYP2E1 rs2249695. No alcohol interactions were found for oxidative stress SNPs.
Conclusions and Impact
Previously unreported associations of SNPs in ALDH2, CYP2E1, GPX2, SOD1, and SOD2 with SCCHN and sub-site tumors provide evidence that alterations in alcohol and oxidative stress pathways influence SCCHN carcinogenesis, and warrant further investigation.
Head and Neck Neoplasms; Head and Neck Neoplasms/epidemiology; Gene-environment interaction; Alcohol Drinking/metabolism; Oxidative Stress
Genetic epidemiological studies of complex diseases often rely on data from the International HapMap Consortium for identification of single nucleotide polymorphisms (SNPs), particularly those that tag haplotypes. However, little is known about the relevance of the African populations used to collect HapMap data for study populations conducted elsewhere in Africa. Toll-like receptor (TLR) genes play a key role in susceptibility to various infectious diseases, including tuberculosis. We conducted full-exon sequencing in samples obtained from Uganda (n = 48) and South Africa (n = 48), in four genes in the TLR pathway: TLR2, TLR4, TLR6, and TIRAP. We identified one novel TIRAP SNP (with minor allele frequency [MAF] 3.2%) and a novel TLR6 SNP (MAF 8%) in the Ugandan population, and a TLR6 SNP that is unique to the South African population (MAF 14%). These SNPs were also not present in the 1000 Genomes data. Genotype and haplotype frequencies and linkage disequilibrium patterns in Uganda and South Africa were similar to African populations in the HapMap datasets. Multidimensional scaling analysis of polymorphisms in all four genes suggested broad overlap of all of the examined African populations. Based on these data, we propose that there is enough similarity among African populations represented in the HapMap database to justify initial SNP selection for genetic epidemiological studies in Uganda and South Africa. We also discovered three novel polymorphisms that appear to be population-specific and would only be detected by sequencing efforts.
Adipocytokines are produced by visceral fat, and levels may be associated with breast cancer risk. We investigated whether single nucleotide polymorphisms (SNPs) in adipocytokine genes adiponectin (ADIPOQ), leptin (LEP), and the leptin receptor (LEPR) were associated with basal-like or luminal A breast cancer subtypes. 104 candidate and tag SNPs were genotyped in 1776 of 2022 controls and 1972 (200 basal-like, 679 luminal A) of 2311 cases from the Carolina Breast Cancer Study (CBCS), a population-based case–control study of whites and African Americans. Breast cancer molecular subtypes were determined by immunohistochemistry. Genotype odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using unconditional logistic regression. Haplotype ORs and 95% CIs were estimated using Hapstat. Interactions with waist-hip ratio were evaluated using a multiplicative interaction term. Ancestry was estimated from 144 ancestry informative markers (AIMs), and included in models to control for population stratification. Candidate SNPs LEPR K109R (rs1137100) and LEPR Q223R (rs1137101) were positively associated with luminal A breast cancer, whereas ADIPOQ +45 T/G (rs2241766), ADIPOQ +276 G/T (rs1501299), and LEPR K656N (rs8129183) were not associated with either subtype. Few patterns were observed among tag SNPs, with the exception of 3 LEPR SNPs (rs17412175, rs9436746, and rs9436748) that were in moderate LD and inversely associated with basal-like breast cancer. However, no SNP associations were statistically significant after adjustment for multiple comparisons. Haplotypes in LEP and LEPR were associated with both basal-like and luminal A subtypes. There was no evidence of interaction with waist-hip ratio. Data suggest associations between LEPR candidate SNPs and luminal A breast cancer in the CBCS and LEPR intron 2 tag SNPs and basal-like breast cancer. Replication in additional studies where breast cancer subtypes have been defined is necessary to confirm these potential associations.
Adiponectin; Leptin; Leptin receptor; Breast cancer; Subtypes; Single nucleotide polymorphism
Copy number variants (CNVs) have been implicated in many complex diseases. We examined whether inherited CNVs were associated with overall survival among women with invasive epithelial ovarian cancer. Germline DNA from 1,056 cases (494 deceased, average of 3.7 years follow-up) was interrogated with the Illumina 610 quad genome-wide array containing, after quality control exclusions, 581,903 single nucleotide polymorphisms (SNPs) and 17,917 CNV probes. Comprehensive analysis capitalized upon the strengths of three complementary approaches to CNV classification. First, to identify small CNVs, single markers were evaluated and, where associated with survival, consecutive markers were combined. Two chromosomal regions were associated with survival using this approach (14q31.3 rs2274736 p = 1.59 × 10−6, p = 0.001; 22q13.31 rs2285164 p = 4.01 × 10−5, p = 0.009), but were not significant after multiple testing correction. Second, to identify large CNVs, genome-wide segmentation was conducted to characterize chromosomal gains and losses, and association with survival was evaluated by segment. Four regions were associated with survival (1q21.3 loss p = 0.005, 5p14.1 loss p = 0.004, 9p23 loss p = 0.002, and 15q22.31 gain p = 0.002); however, again, after correcting for multiple testing, no regions were statistically significant, and none were in common with the single marker approach. Finally, to evaluate associations with general amounts of copy number changes across the genome, we estimated CNV burden based on genome-wide numbers of gains and losses; no associations with survival were observed (p > 0.40). Although CNVs that were not well-covered by the Illumina 610 quad array merit investigation, these data suggest no association between inherited CNVs and survival after ovarian cancer.
association testing; copy number variation; genotyping array; ovarian cancer; overall survival
The molecular behavior of biological systems can be described in terms of three fundamental components: (i) the physical entities, (ii) the interactions among these entities, and (iii) the dynamics of these entities and interactions. The mechanisms that drive complex disease can be productively viewed in the context of the perturbations of these components. One challenge in this regard is to identify the pathways altered in specific diseases. To address this challenge, Gene Set Enrichment Analysis (GSEA) and others have been developed, which focus on alterations of individual properties of the entities (such as gene expression). However, the dynamics of the interactions with respect to disease have been less well studied (i.e., properties of components ii and iii).
Here, we present a novel method called Gene Interaction Enrichment and Network Analysis (GIENA) to identify dysregulated gene interactions, i.e., pairs of genes whose relationships differ between disease and control. Four functions are defined to model the biologically relevant gene interactions of cooperation (sum of mRNA expression), competition (difference between mRNA expression), redundancy (maximum of expression), or dependency (minimum of expression) among the expression levels. The proposed framework identifies dysregulated interactions and pathways enriched in dysregulated interactions; points out interactions that are perturbed across pathways; and moreover, based on the biological annotation of each type of dysregulated interaction gives clues about the regulatory logic governing the systems level perturbation. We demonstrated the potential of GIENA using published datasets related to cancer.
We showed that GIENA identifies dysregulated pathways that are missed by traditional enrichment methods based on the individual gene properties and that use of traditional methods combined with GIENA provides coverage of the largest number of relevant pathways. In addition, using the interactions detected by GIENA, specific gene networks both within and across pathways associated with the relevant phenotypes are constructed and analyzed.
Gene-gene interaction; Dysregulated pathways; Enrichment analysis; BAD pathway
Mitochondria contribute to oxidative stress, a phenomenon implicated in ovarian carcinogenesis. We hypothesized that inherited variants in mitochondrial-related genes influence epithelial ovarian cancer (EOC) susceptibility.
Through a multi-center study of 1,815 Caucasian EOC cases and 1,900 controls, we investigated associations between EOC risk and 128 single nucleotide polymorphisms (SNPs) from 22 genes/regions within the mitochondrial genome (mtDNA) and 2,839 nuclear-encoded SNPs localized to 138 genes involved in mitochondrial biogenesis (BIO, n=35), steroid hormone metabolism (HOR, n=13), and oxidative phosphorylation (OXP, n=90) pathways. Unconditional logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals (CI) between genotype and case status. Overall significance of each gene and pathway was evaluated using Fisher’s method to combine SNP-level evidence. At the SNP-level, we investigated whether lifetime ovulation, hormone replacement therapy (HRT), and cigarette smoking were confounders or modifiers of associations.
Inter-individual variation involving BIO was most strongly associated with EOC risk (empirical P=0.050), especially for NRF1, MTERF, PPARGC1A, ESRRA, and CAMK2D. Several SNP-level associations strengthened after adjustment for non-genetic factors, particularly for MTERF. Statistical interactions with cigarette smoking and HRT use were observed with MTERF and CAMK2D SNPs, respectively. Overall variation within mtDNA, HOR, and OXP was not statistically significant (empirical P >0.10).
We provide novel evidence to suggest that variants in mitochondrial biogenesis genes may influence EOC susceptibility.
A deeper understanding of the complex mechanisms implicated in mitochondrial biogenesis and oxidative stress may aid in developing strategies to reduce morbidity and mortality from EOC.
polymorphisms; oxidative stress; genetic susceptibility; mitochondria; ovarian cancer
Inherited variability in genes that influence androgen metabolism has been associated with risk of prostate cancer. The objective of this analysis was to evaluate interactions for prostate cancer risk using classification and regression tree (CART) models (i.e. decision trees), and to evaluate whether these interactive effects add information about prostate cancer risk prediction beyond that of “traditional” risk factors.
We compared CART models to traditional logistic regression models for associations of factors with prostate cancer risk using 1084 prostate cancer cases and 941 controls. All analyses were stratified by race. We used unconditional logistic regression (LR) to complement and compare to the race-stratified CART results using the area under curve (AUC) for the receiver operating characteristic (ROC) curves.
The CART modeling of prostate cancer risk showed different interaction profiles by race. For European Americans, interactions among CYP3A43 genotype, history of benign prostate hypertrophy, family history of prostate cancer and age at consent revealed a distinct hierarchy of gene-environment and gene-gene interactions. While for African Americans, interactions among family history of prostate cancer, individual proportion of European ancestry, number of GGC AR repeats and CYP3A4/CYP3A5 haplotype revealed distinct interaction effects from those found in European Americans. For European Americans the CART model had the highest AUC while for African Americans, the LR model with the CART discovered factors had the largest AUC.
Conclusion & Impact
These results provide new insight into underlying prostate cancer biology for European Americans and African Americans.
Decision tree; classification and regression tree (CART); androgen pathway; prostate cancer risk; ancestry
The identification of very small subsets of predictive variables is an important toπc that has not often been considered in the literature. In order to discover highly predictive yet compact gene set classifiers from whole genome expression data, a non-parametric, iterative algorithm, Splitting Random Forest (SRF), was developed to robustly identify genes that distinguish between molecular subtypes. The goal is to improve the prediction accuracy while considering sparsity.
The optimal SRF 50 run (SRF50) gene classifiers for glioblastoma (GB), breast (BC) and ovarian cancer (OC) subtypes had overall prediction rates comparable to those from published datasets upon validation (80.1%-91.7%). The SRF50 sets outperformed other methods by identifying compact gene sets needed for distinguishing between tested cancer subtypes (10–200 fold fewer genes than ANOVA or published gene sets). The SRF50 sets achieved superior and robust overall and subtype prediction accuracies when compared with single random forest (RF) and the Top 50 ANOVA results (80.1% vs 77.8% for GB; 84.0% vs 74.1% for BC; 89.8% vs 88.9% for OC in SRF50 vs single RF comparison; 80.1% vs 77.2% for GB; 84.0% vs 82.7% for BC; 89.8% vs 87.0% for OC in SRF50 vs Top 50 ANOVA comparison). There was significant overlap between SRF50 and published gene sets, showing that SRF identifies the relevant sub-sets of important gene lists. Through Ingenuity Pathway Analysis (IPA), the overlap in “hub” genes between the SRF50 and published genes sets were RB1, πK3R1, PDGFBB and ERK1/2 for GB; ESR1, MYC, NFkB and ERK1/2 for BC; and Akt, FN1, NFkB, PDGFBB and ERK1/2 for OC.
The SRF approach is an effective driver of biomarker discovery research that reduces the number of genes needed for robust classification, dissects complex, high dimensional “omic” data and provides novel insights into the cellular mechanisms that define cancer subtypes.
Tree based models; High dimensional data; Cancer subtypes
We investigated the ability of several principal components analysis (PCA)-based strategies to detect and control for population stratification using data from a multi-center study of epithelial ovarian cancer among women of European-American ethnicity. These include a correction based on an ancestry informative markers (AIMs) panel designed to capture European ancestral variation and corrections utilizing un-thinned genome-wide SNP data; case-control samples were drawn from four geographically distinct North-American sites. The AIMs-only and genome-wide first principal components (PC1) both corresponded to the previously described North or Northwest-Southeast axis of European variation. We found that the genome-wide PCA captured this primary dimension of variation more precisely and identified additional axes of genome-wide variation of relevance to epithelial ovarian cancer. Associations evident between the genome-wide PCs and study site corroborate North American immigration history and suggest that undiscovered dimensions of variation lie within Northern Europe. The structure captured by the genome-wide PCA was also found within control individuals and did not reflect the case-control variation present in the data. The genome-wide PCA highlighted three regions of local LD, corresponding to the lactase (LCT) gene on chromosome 2, the human leukocyte antigen system (HLA) on chromosome 6 and to a common inversion polymorphism on chromosome 8. These features did not compromise the efficacy of PCs from this analysis for ancestry control. This study concludes that although AIMs panels are a cost-effective way of capturing population structure, genome-wide data should preferably be used when available.
Human cancers are driven by the acquisition of somatic mutations. Separating the driving mutations from those that are random consequences of general genomic instability remains a challenge. New sequencing technology makes it possible to detect mutations that are present in only a minority of cells in a heterogeneous tumor population. We sought to leverage the power of ultra-deep sequencing to study various levels of tumor heterogeneity in the serial recurrences of a single glioblastoma multiforme patient. Our goal was to gain insight into the temporal succession of DNA base-level lesions by querying intra- and inter-tumoral cell populations in the same patient over time. We performed targeted “next-generation" sequencing on seven samples from the same patient: two foci within the primary tumor, two foci within an initial recurrence, two foci within a second recurrence, and normal blood. Our study reveals multiple levels of mutational heterogeneity. We found variable frequencies of specific EGFR, PIK3CA, PTEN, and TP53 base substitutions within individual tumor regions and across distinct regions within the same tumor. In addition, specific mutations emerge and disappear along the temporal spectrum from tumor at the time of diagnosis to second recurrence, demonstrating evolution during tumor progression. Our results shed light on the spatial and temporal complexity of brain tumors. As sequencing costs continue to decline and deep sequencing technology eventually moves into the clinic, this approach may provide guidance for treatment choices as we embark on the path to personalized cancer medicine.
Most individuals throughout the Americas are admixed descendants of Native American, European, and African ancestors. Complex historical factors have resulted in varying proportions of ancestral contributions between individuals within and among ethnic groups. We developed a panel of 446 ancestry informative markers (AIMs) optimized to estimate ancestral proportions in individuals and populations throughout Latin America. We used genome-wide data from 953 individuals from diverse African, European, and Native American populations to select AIMs optimized for each of the three main continental populations that form the basis of modern Latin American populations. We selected markers on the basis of locus-specific branch length to be informative, well distributed throughout the genome, capable of being genotyped on widely available commercial platforms, and applicable throughout the Americas by minimizing within-continent heterogeneity. We then validated the panel in samples from four admixed populations by comparing ancestry estimates based on the AIMs panel to estimates based on genome-wide association study (GWAS) data. The panel provided balanced discriminatory power among the three ancestral populations and accurate estimates of individual ancestry proportions (R2>0.9 for ancestral components with significant between-subject variance). Finally, we genotyped samples from 18 populations from Latin America using the AIMs panel and estimated variability in ancestry within and between these populations. This panel and its reference genotype information will be useful resources to explore population history of admixture in Latin America and to correct for the potential effects of population stratification in admixed samples in the region.
Individuals from Latin America are descendants of multiple ancestral populations, primarily Native American, European, and African ancestors. The relative proportions of these ancestries can be estimated using genetic markers, known as ancestry informative markers (AIMs), whose allele frequency varies between the ancestral groups. Once determined, these ancestral proportions can be correlated with normal phenotypes, can be associated with disease, can be used to control for confounding due to population stratification, or can inform on the history of admixture in a population. In this study, we identified a panel of AIMs relevant to Latin American populations, validated the panel by comparing estimates of ancestry using the panel to ancestry determined from genome-wide data, and tested the panel in a diverse set of populations from the Americas. The panel of AIMs produces ancestry estimates that are highly accurate and appropriately controlled for population stratification, and it was used to genotype 18 populations from throughout Latin America. We have made the panel of AIMs available to any researcher interested in estimating ancestral proportions for populations from the Americas.
Single nucleotide polymorphisms (SNPs) in microRNA-related genes have been associated with epithelial ovarian cancer (EOC) risk in two reports, yet associated alleles may be inconsistent across studies.
We conducted a pooled analysis of previously-identified SNPs by combining genotype data from 3,973 invasive EOC cases and 3,276 controls from the Ovarian Cancer Association Consortium. We also conducted imputation to obtain dense coverage of genes and comparable genotype data for all studies. In total, 226 SNPs within 15 kilobases of 4 miRNA biogenesis genes (DDX20, DROSHA, GEMIN4, and XPO5) and 23 SNPs located within putative miRNA binding sites of 6 genes (CAV1, COL18A1, E2F2, IL1R1, KRAS, and UGT2A3) were genotyped or imputed and analyzed in the entire dataset.
After adjustment for European ancestry, no overall association was observed between any of the analyzed SNPs and EOC risk.
Common variants in these evaluated genes do not appear to be strongly associated with EOC risk.
This analysis suggests earlier associations between EOC risk and SNPs in these genes may have been chance findings, possibly confounded by population admixture. To more adequately evaluate the relationship between genetic variants and cancer risk, large sample sizes are needed, adjustment for population stratification should be performed, and use of imputed SNP data should be considered.
miRNA processing; binding sites; inherited susceptibility; ovarian cancer; genetic variants
Invasive melanoma of the skin is the third most common cancer diagnosed among adolescents and young adults (aged 15-39 years) in the United States. Understanding the burden of melanoma in this age group is important to identifying areas for etiologic research and in developing effective prevention approaches aimed at reducing melanoma risk.
Melanoma incidence data reported from 38 National Program of Cancer Registries and/or Surveillance Epidemiology and End Results statewide cancer registries covering nearly 67.2% of the US population were used to estimate age-adjusted incidence rates for persons 15-39 years of age. Incidence rate ratios were calculated to compare rates between demographic groups.
Melanoma incidence was higher among females (age-adjusted incidence rates = 9.74; 95% confidence interval 9.62-9.86) compared with males (age-adjusted incidence rates = 5.77; 95% confidence interval 5.68-5.86), increased with age, and was higher in non-Hispanic white compared with Hispanic white and black, American Indians/Alaskan Natives, and Asian and Pacific Islanders populations. Melanoma incidence rates increased with year of diagnosis in females but not males. The majority of melanomas were diagnosed on the trunk in all racial and ethnic groups among males but only in non-Hispanic whites among females. Most melanomas were diagnosed at localized stage, and among those melanomas with known histology, the majority were superficial spreading.
Accuracy of melanoma cases reporting was limited because of some incompleteness (delayed reporting) or nonspecific reporting including large proportion of unspecified histology.
Differences in incidence rates by anatomic site, histology, and stage among adolescents and young adults by race, ethnicity, and sex suggest that both host characteristics and behaviors influence risk. These data suggest areas for etiologic research around gene-environment interactions and the need for targeted cancer control activities specific to adolescents and young adult populations.
adolescents; cancer; incidence; melanoma; National Program of Cancer Registries; surveillance; Surveillance Epidemiology and End Results; young adults
Purpose: Family history is associated with gliomas, but this association has not been established for benign brain tumors. Using information from newly diagnosed primary brain tumor patients, we describe patterns of family cancer histories in patients with benign brain tumors and compare those to patients with gliomas. Methods: Newly diagnosed primary brain tumor patients were identified as part of the Ohio Brain Tumor Study. Each patient was asked to participate in a telephone interview about personal medical history, family history of cancer, and other exposures. Information was available from 33 acoustic neuroma (65%), 78 meningioma (65%), 49 pituitary adenoma (73.1%), and 152 glioma patients (58.2%). The association between family history of cancer and each subtype was compared with gliomas using unconditional logistic regression models generating odds ratios (ORs) and 95% confidence intervals. Results: There was no significant difference in family history of cancer between patients with glioma and benign subtypes. Conclusion: The results suggest that benign brain tumor may have an association with family history of cancer. More studies are warranted to disentangle the potential genetic and/or environmental causes for these diseases.
meningioma; acoustic neuroma; pituitary tumor; glioma; family history of cancer
Defective miRNA biogenesis contributes to the development and progression of epithelial ovarian cancer (EOC). In this study, we examined the hypothesis that single nucleotide polymorphisms (SNPs) in miRNA biogenesis genes may influence EOC risk. In an initial investigation, 318 SNPs in 18 genes were evaluated among 1,815 EOC cases and 1,900 controls, followed up by a replicative joint meta-analysis of data from an additional 2,172 cases and 3,052 controls. Of 23 SNPs from 9 genes associated with risk (empirical P<0.05) in the initial investigation, the meta-analysis replicated 6 SNPs from the DROSHA, FMR1, LIN28, and LIN28B genes, including rs12194974 (G>A), a SNP in a putative transcription factor binding site in the LIN28B promoter region (summary OR=0.90, 95% CI: 0.82–0.98; P=0.015) which has been recently implicated in age of menarche and other phenotypes. Consistent with reports that LIN28B over-expression in EOC contributes to tumorigenesis by repressing tumor suppressor let-7 expression, we provide data from luciferase reporter assays and quantitative RT-PCR to suggest that the inverse association among rs12194974 A allele carriers may be due to reduced LIN28B expression. Our findings suggest that variants in LIN28B and possibly other miRNA biogenesis genes may influence EOC susceptibility.
miRNA processing; inherited susceptibility; ovarian cancer; genetic variants
In genome-wide association studies, inherited risk of glioma has been demonstrated for rare familial syndromes and with common variants from 3–5 chromosomal regions. To assess the degree of familial aggregation of glioma, the authors performed a pooled analysis of data from 2 large glioma case-control studies in the United States (MD Anderson Cancer Center, Houston, Texas (1994–2006) and University of California, San Francisco (1991–2004)) and from the Swedish Cancer Registry (1958–2006) to measure excess cases of cancer among first-degree relatives of glioma probands. This analysis included 20,377 probands with glioma and 52,714 first-degree relatives. No overall increase was found in the expected number of cancers among family members; however, there were 77% more gliomas than expected. There were also significantly more sarcoma and melanoma cases than expected, which is supported by evidence in the literature, whereas there were significantly fewer-than-expected cases of leukemia, non-Hodgkin lymphoma, and bladder, lung, pancreatic, prostate, and uterine cancers. This large pooled analysis provided sufficient numbers of related family members to examine the genetic mechanisms involved in the aggregation of glioma with other cancers in these families. However, misclassification due to unvalidated cancers among family members could account for the differences seen by study site.
family; glioma; meta-analysis; neoplasms
Dysfunctional lipid metabolism plays a central role in pathogenesis of major chronic diseases, and genetic factors are important determinants of individual lipid profiles. We analyzed the associations of two well-established functional polymorphisms (FABP2 A54T and APOE isoforms) with past and family histories of 1492 population samples. FABP2-T54 allele was associated with an increased risk of past history of myocardial infarction (odds ratio (OR) = 1.51). Likewise, the subjects with APOE4, compared with E2 and E3, had a significantly increased risk of past history myocardial infarction (OR = 1.89). The OR associated with APOE4 was specifically increased in women for past history of myocardial infarction but decreased for gallstone disease. Interactions between gender and APOE isoforms were also significant or marginally significant for these two conditions. FABP2-T54 allele may be a potential genetic marker for myocardial infarction, and APOE4 may exert sex-dependent effects on myocardial infarction and gallbladder disease.
Inconsistent observations in epidemiologic studies on the association between total fat intake and colorectal cancer may be ascribed to opposing effects of individual fatty acids and the presence of other dietary constituents that modify luminal or systemic lipid exposure. We analyzed the data from a population-based case-control study that included 1163 cases and 1501 controls to examine the effects of individual fatty acid groups on colorectal cancer risk as well as their interactions with calcium and fiber intake. Odds ratios (OR) and 95% confidence intervals (CI) were estimated by unconditional logistic regression model according to quartile levels of energy-adjusted fatty acid intake. In the bivariable analyses, the risk of colorectal cancer increased with trans fatty acid (TFA) intake (OR for top vs bottom quartile =1.46, 95% CI 1.17-1.59, p-value for a trend <0.001 ), but the associations was substantially attenuated in multivariable analyses (p-value for a trend =0.176). However, a significant linear trend in the multivariable OR (p=0.029) for TFA was present for subjects with lower calcium intake. Furthermore, multivariable ORs progressively decreased with increasing both omega-3 and omega-6 polyunsaturated fatty acid intake (P-values for linear trend: 0.033 and 0.011, respectively) for subjects with lower dietary fiber intake. These interactions were also significant or marginally significant (P = 0.085 for TFA, 0.029 for omega-3 and 0.068 for omega-6). Our results suggest that populations with lower intake of luminal modifiers, i.e., calcium and fiber, may have differential risks of colorectal cancer associated with dietary fatty acid intake.
Fatty acids; colorectal cancer; case-control study; calcium; fiber
Twenty-nine single-nucleotide polymorphisms (SNPs) from previously published genome-wide association studies (GWAS) and multiple ancestry informative markers were genotyped in the Carolina Breast Cancer Study (CBCS) (742 African-American (AA) cases, 1230 White cases; 658 AA controls, 1118 White controls). In the entire study population, 9/10 SNPs in fibroblast growth factor receptor 2 (FGFR2) were significantly associated with breast cancer after adjusting for age, race and European ancestry [odds ratios (OR) range 1.17–1.81]. Associations were observed for SNPs in FGFR2, LSP1, H19, TLR1/TLR6 and RELN for AA; FGFR2, TNRC9, H19 and MAP3K1 for Whites; FGFR2, TNRC9, Msc5A1 and chromosome 8q for women ≥50 years old and FGFR2 and TNRC9 for women <50 years old. FGFR2 haplotypes based upon rs11200014, rs2981579, rs1219648 and rs2420946 were associated with increased risk of breast cancer, including the GTGT haplotype in AAs [OR = 1.27, 95% confidence interval (CI) 1.04–1.56] and younger women of either race [OR = 1.35, 95% CI 1.02–1.78) and the ATGT haplotype in Whites (OR = 1.30, 95% CI 1.15–1.46). Recent GWAS hits for breast cancer in Europeans and Whites (i.e. women of European descent) thus showed evidence of replication among AAs and Whites in the CBCS. Several new haplotypes were associated with breast cancer in AA and younger women, particularly the FGFR2 GTGT haplotype. These results highlight the need to conduct GWAS among younger women and in a variety of racial–ethnic populations.
As genome-wide association studies expand beyond populations of European ancestry, the role of admixture will become increasingly important in the continued discovery and fine-mapping of variation influencing complex traits. Although admixture is commonly viewed as a confounding influence in association studies, approaches such as admixture mapping have demonstrated its ability to highlight disease susceptibility regions of the genome. In this study, we illustrate a powerful two-stage testing strategy designed to uncover trait-associated single nucleotide polymorphisms in the presence of ancestral allele frequency differentiation. In the first stage, we conduct an association scan by using predicted genotypic values based on regional admixture estimates. We then select a subset of promising markers for inclusion in a second-stage analysis, where association is tested between the observed genotype and the phenotype conditional on the predicted genotype. We prove that, under the null hypothesis, the test statistics used in each stage are orthogonal and asymptotically independent. Using simulated data designed to mimic African-American populations in the case of a quantitative trait, we show that our two-stage procedure maintains appropriate control of the family wise error rate and has higher power under realistic effect sizes than the one-stage testing procedure in which all markers are tested for association simultaneously with control of admixture. We apply the proposed procedure to a study of height in 201 African-Americans genotyped at 108 ancestry informative markers. The two-stage procedure identified two statistically significant markers rs1985080 (PTHB1/BBS9) and rs952718 (ABCA12). PTHB1/BBS9 is downregulated by parathyroid hormone in osteoblastic cells and is thought to be involved in parathyroid hormone action in bones and may play a role in height. ABCA12 is a member of the superfamily of ATP-binding cassette transporters and its potential involvement in height is unclear.
two-stage; structured association testing; admixture mapping; regional admixture estimate; genome-wide association studies
In this study, we assessed association of genome-wide association studies (GWAS) “hits” by race with adjustment for potential population stratification (PS) in two large, diverse study populations; the Carolina Breast Cancer Study (CBCS; N total = 3693 individuals) and the University of Pennsylvania Study of Clinical Outcomes, Risk, and Ethnicity (SCORE; N total = 1135 individuals). In both study populations, 136 ancestry information markers and GWAS “hits” (CBCS: FGFR2, 8q24; SCORE: JAZF1, MSMB, 8q24) were genotyped. Principal component analysis was used to assess ancestral differences by race. Multivariable unconditional logistic regression was used to assess differences in cancer risk with and without adjustment for the first ancestral principal component (PC1) and for an interaction effect between PC1 and the GWAS “hit” (SNP) of interest. PC1 explained 53.7% of the variance for CBCS and 49.5% of the variance for SCORE. European Americans and African Americans were similar in their ancestral structure between CBCS and SCORE and cases and controls were well matched by ancestry. In the CBCS European Americans, 9/11 SNPs were significant after PC1 adjustment, but after adjustment for the PC1 by SNP interaction effect, only one SNP remained significant (rs1219648 in FGFR2); for CBCS African Americans, 6/11 SNPs were significant after PC1 adjustment and after adjustment for the PC1 by SNP interaction effect, all six SNPs remained significant and an additional SNP now became significant. In the SCORE European Americans, 0/9 SNPs were significant after PC1 adjustment and no changes were seen after additional adjustment for the PC1 by SNP interaction effect; for SCORE African Americans, 2/9 SNPs were significant after PC1 adjustment and after adjustment for the PC1 by SNP interaction effect, only one SNP remained significant (rs16901979 at 8q24). We show that genetic associations by race are modified by interaction between individual SNPs and PS.
population stratification; ancestry; prostate cancer; breast cancer; GWAS “hits”