Epigenetic alterations are a common event in lung cancer and their identification can serve to inform on the carcinogenic process and provide clinically relevant biomarkers. Using paired tumor and non-tumor lung tissues from 146 individuals from three independent populations we sought to identify common changes in DNA methylation associated with the development of non-small cell lung cancer. Pathologically normal lung tissue taken at the time of cancer resection was matched to tumorous lung tissue and together were probed for methylation using Illumina GoldenGate arrays in the discovery set (n = 47 pairs) followed by bisulfite pyrosequencing for validation sets (n = 99 pairs). For each matched pair the change in methylation at each CpG was calculated (the odds ratio), and these ratios were averaged across individuals and ranked by magnitude to identify the CpGs with the greatest change in methylation associated with tumor development. We identified the top gene-loci representing an increase in methylation (HOXA9, 10.3-fold and SOX1, 5.9-fold) and decrease in methylation (DDR1, 8.1-fold). In replication testing sets, methylation was higher in tumors for HOXA9 (p < 2.2 × 10−16) and SOX1 (p < 2.2 × 10−16) and lower for DDR1 (p < 2.2 × 10−16). The magnitude and strength of these changes were consistent across squamous cell and adenocarcinoma tumors. Our data indicate that the identified genes consistently have altered methylation in lung tumors. Our identified genes should be included in translational studies that aim to develop screening for early disease detection.
DNA Methylation; goldengate; lung cancer; molecular epidemiology; pyrosequencing
Although much is known about molecular and chromosomal characteristics that distinguish glioma histological subtypes, DNA methylation patterns of gliomas and their association with other tumor features such as mutation of isocitrate dehydrogenase (IDH) genes have only recently begun to be investigated.
DNA methylation of glioblastomas, astrocytomas, oligodendrogliomas, oligoastrocytomas, ependymomas, and pilocytic astrocytomas (n = 131) from the Brain Tumor Research Center at the University of California San Francisco, as well as nontumor brain tissues (n = 7), was assessed with the Illumina GoldenGate methylation array. Methylation data were subjected to recursively partitioned mixture modeling (RPMM) to derive methylation classes. Differential DNA methylation between tumor and nontumor was also assessed. The association between methylation class and IDH mutation (IDH1 and IDH2) was tested using univariate and multivariable analysis for tumors (n = 95) with available substrate for sequencing. Survival of glioma patients carrying mutant IDH (n = 57) was compared with patients carrying wild-type IDH (n = 38) using a multivariable Cox proportional hazards model and Kaplan–Meier analysis. All statistical tests were two-sided.
We observed a statistically significant association between RPMM methylation class and glioma histological subtype (P < 2.2 × 10−16). Compared with nontumor brain tissues, across glioma tumor histological subtypes, the differential methylation ratios of CpG loci were statistically significantly different (permutation P < .0001). Methylation class was strongly associated with IDH mutation in gliomas (P = 3.0 × 10−16). Compared with glioma patients whose tumors harbored wild-type IDH, patients whose tumors harbored mutant IDH showed statistically significantly improved survival (hazard ratio of death = 0.27, 95% confidence interval = 0.10 to 0.72).
The homogeneity of methylation classes for gliomas with IDH mutation, despite their histological diversity, suggests that IDH mutation is associated with a distinct DNA methylation phenotype and an altered metabolic profile in glioma.
Acute lymphoblastic leukemia (ALL) likely has a multistep etiology, with initial genetic aberrations occurring early in life. An abnormal immune response to common infections has emerged as a plausible candidate for triggering the proliferation of pre-leukemic clones and the fixation of secondary genetic mutations and epigenetic alterations. We investigated whether evidence of infection with a specific common myelotropic childhood virus, parvovirus B19 (PVB19), relates to patterns of gene promoter DNA methylation in ALL patients. We serologically tested bone marrow samples at diagnosis of B-cell ALL for PVB19 infection and DNA methylation using a high-throughput bead array and found that 4.2% and 36.7% of samples were seroreactive to PVB19 IgM and IgG, respectively. Leukemia samples were grouped by DNA methylation pattern. Controlling for age and immunophenotype, unsupervised modeling confirmed that the DNA methylation pattern was associated with history of PVB19 (assessed by IgG, p = 0.02), but not recent infection (assessed by IgM). Replication assays on single genes were consistent with the association. The data indicate that a common viral illness may drive specific DNA methylation patterns in susceptible B-precursor cells, contributing to the leukemogenic potential of such cells. Infections may impact childhood leukemia by altering DNA methylation patterns and specific key genes in susceptible cells; these changes may be retained even after the clearance of infection.
childhood leukemia; DNA methylation; parvovirus B19; serology
Approximately 500,000 individuals diagnosed with bladder cancer in the U.S. require routine cystoscopic follow-up to monitor for disease recurrences or progression, resulting in over $2 billion in annual expenditures. Identification of new diagnostic and monitoring strategies are clearly needed, and markers related to DNA methylation alterations hold great promise due to their stability, objective measurement, and known associations with the disease and with its clinical features. To identify novel epigenetic markers of aggressive bladder cancer, we utilized a high-throughput DNA methylation bead-array in two distinct population-based series of incident bladder cancer (n = 73 and n = 264, respectively). We then validated the association between methylation of these candidate loci with tumor grade in a third population (n = 245) through bisulfite pyrosequencing of candidate loci. Array based analyses identified 5 loci for further confirmation with bisulfite pyrosequencing. We identified and confirmed that increased promoter methylation of HOXB2 is significantly and independently associated with invasive bladder cancer and methylation of HOXB2, KRT13 and FRZB together significantly predict high-grade non-invasive disease. Methylation of these genes may be useful as clinical markers of the disease and may point to genes and pathways worthy of additional examination as novel targets for therapeutic treatment.
Motivation: Integration of various genome-scale measures of molecular alterations is of great interest to researchers aiming to better define disease processes or identify novel targets with clinical utility. Particularly important in cancer are measures of gene copy number DNA methylation. However, copy number variation may bias the measurement of DNA methylation. To investigate possible bias, we analyzed integrated data obtained from 19 head and neck squamous cell carcinoma (HNSCC) tumors and 23 mesothelioma tumors.
Results: Statistical analysis of observational data produced results consistent with those anticipated from theoretical mathematical properties. Average beta value reported by Illumina GoldenGate (a bead-array platform) was significantly smaller than a similar measure constructed from the ratio of average dye intensities. Among CpGs that had only small variations in measured methylation across tumors (filtering out clearly biological methylation signatures), there were no systematic copy number effects on methylation for three and more than four copies; however, one copy led to small systematic negative effects, and no copies led to substantial significant negative effects.
Conclusions: Since mathematical considerations suggest little bias in methylation assayed using bead-arrays, the consistency of observational data with anticipated properties suggests little bias. However, further analysis of systematic copy number effects across CpGs suggest that though there may be little bias when there are copy number gains, small biases may result when one allele is lost, and substantial biases when both alleles are lost. These results suggest that further integration of these measures can be useful for characterizing the biological relationships between these somatic events.
Supplementary information: Supplementary data are available at Bioinformatics online.
Pathologic differentiation of tissue of origin in tumors found in the lung can be challenging, with differentiation of mesothelioma and lung adenocarcinoma emblematic of this problem. Indeed, proper classification is essential for determination of treatment regimen for these diseases, making accurate and early diagnosis critical. Here we investigate the potential of epigenetic profiles of lung adenocarcinoma, mesothelioma, and non-malignant pulmonary tissues (n=285) as differentiation markers in an analysis of DNA methylation at 1413 autosomal CpG loci associated with 773 cancer-related genes. Using an unsupervised recursively-partitioned mixture modeling technique for all samples, the derived methylation profile classes were significantly associated with sample type (P < 0.0001). In a similar analysis restricted to tumors, methylation profile classes significantly predicted tumor type (P < 0.0001). Random forests classification of CpG methylation of tumors - which splits the data into training and test sets - accurately differentiated MPM from lung adenocarcinoma over 99% of the time (P < 0.0001). In a locus-by-locus comparison of CpG methylation between tumor types, 1266 CpG loci had significantly different methylation between tumors following correction for multiple comparisons (Q < 0.05); 61% had higher methylation in adenocarcinoma. Using the CpG loci with significant differential methylation in a pathways analysis revealed significant enrichment of methylated gene-loci in Cell Cycle Regulation, DNA Damage Response, PTEN Signaling, and Apoptosis Signaling pathways in lung adenocarcinoma when compared to mesothelioma. Methylation-profile-based differentiation of lung adenocarcinoma and mesothelioma is highly accurate, informs on the distinct etiologies of these diseases, and holds promise for clinical application.
Head and neck squamous cell carcinomas (HNSCCs) represent clinically and etiologically heterogeneous tumors affecting >40 000 patients per year in the USA. Previous research has identified individual epigenetic alterations and, in some cases, the relationship of these alterations with carcinogen exposure or patient outcomes, suggesting that specific exposures give rise to specific types of molecular alterations in HNSCCs. Here, we describe how different etiologic factors are reflected in the molecular character and clinical outcome of these tumors. In a case series of primary, incident HNSCC (n = 68), we examined the DNA methylation profile of 1413 autosomal CpG loci in 773 genes, in relation to exposures and etiologic factors. The overall pattern of epigenetic alteration could significantly distinguish tumor from normal head and neck epithelial tissues (P < 0.0001) more effectively than specific gene methylation events. Among tumors, there were significant associations between specific DNA methylation profile classes and tobacco smoking and alcohol exposures. Although there was a significant association between methylation profile and tumor stage (P < 0.01), we did not observe an association between these profiles and overall patient survival after adjustment for stage; although methylation of a number of specific loci falling in different cellular pathways was associated with overall patient survival. We found that the etiologic heterogeneity of HNSCC is reflected in specific patterns of molecular epigenetic alterations within the tumors and that the DNA methylation profiles may hold clinical promise worthy of further study.
Mechanisms of action of non-mutagenic carcinogens such as asbestos remain poorly characterized. As pleural mesothelioma is known to have limited numbers of genetic mutations, we aimed to characterize the relationships among gene-locus specific methylation alterations, disease status, asbestos burden, and survival in this rapidly-fatal asbestos-associated tumor. Methylation of 1505 CpG loci associated with 803 cancer-related genes were studied in 158 pleural mesotheliomas and 18 normal pleura. After false-discovery rate correction, 969 CpG loci were independently associated with disease status (Q < 0.05). Classifying samples based upon CpG methylation profile with a mixture model approach, methylation classes discriminated tumor from normal pleura (permutation P < 0.0001). In a random forests classification the overall misclassification error rate was 3.4%, with <1% (n=1) of tumors misclassified as normal (P < 0.0001). Among tumors, methylation class membership was significantly associated with lung tissue asbestos body burden (P < 0.03), and significantly predicted survival (likelihood ratio P < 0.01). Consistent with prior work, asbestos burden was associated with an increased risk of death (HR = 1.4, 95% CI, 1.1 – 1.8). Our results have shown that methylation profiles powerfully differentiate diseased pleura from non-tumor pleura and that asbestos burden and methylation profiles are independent predictors of mesothelioma patient survival. We have added to the growing body of evidence that cellular epigenetic dysregulation is a critical mode of action for asbestos in the induction of pleural mesothelioma. Importantly, these findings hold great promise for using epigenetic profiling in the diagnosis and prognosis of human cancers.
Methylation; asbestos; mesothelioma
Although tumor size and lymph node involvement are the current cornerstones of breast cancer prognosis, they have not been extensively explored in relation to tumor methylation attributes in conjunction with other tumor and patient dietary and hormonal characteristics. Using primary breast tumors from 162 (AJCC stage I–IV) women from the Kaiser Division of Research Pathways Study and the Illumina GoldenGate methylation bead-array platform, we measured 1,413 autosomal CpG loci associated with 773 cancer-related genes and validated select CpG loci with Sequenom EpiTYPER. Tumor grade, size, estrogen and progesterone receptor status, and triple negative status were significantly (Q-values <0.05) associated with altered methylation of 209, 74, 183, 69, and 130 loci, respectively. Unsupervised clustering, using a recursively partitioned mixture model (RPMM), of all autosomal CpG loci revealed eight distinct methylation classes. Methylation class membership was significantly associated with patient race (P<0.02) and tumor size (P<0.001) in univariate tests. Using multinomial logistic regression to adjust for potential confounders, patient age and tumor size, as well as known disease risk factors of alcohol intake and total dietary folate, were all significantly (P<0.0001) associated with methylation class membership. Breast cancer prognostic characteristics and risk-related exposures appear to be associated with gene-specific tumor methylation, as well as overall methylation patterns.
The current standard prognostic indicator for breast cancer is tumor-node-metastasis staging; though, as population-based studies and clinical trials are conducted, molecular characterization of disease is beginning to allow improved markers of prognosis and assist clinicians in choosing the most appropriate therapies. We investigated DNA methylation profiles in over 160 well annotated breast tumor samples and found significant relationships with standard and other known predictors of prognosis, as well as established risk factors for disease: alcohol intake and dietary folate. Recently the United States National Cancer Institute Cancer Biomarkers Research Group articulated a need for a “Strategic Approach to Validating Methylated Genes as Biomarkers for Breast Cancer,” and our work is extremely responsive to this call for a national strategy. Recognizing the increasing use of pre-operative chemotherapy for patients with operable, early-stage disease, there is added complexity in breast cancer staging. Since chemotherapy can considerably decrease tumor size, it is still unclear whether pre-operative or post-operative stage best informs prognosis and treatment decisions for patients electing pre-operative chemotherapy. However, our data clearly illustrate the promise of tumor DNA methylation for augmenting tumor staging and can be attained with minimal tissue in a pre-operative context.
Epigenetic control of gene transcription is critical for normal human development and cellular differentiation. While alterations of epigenetic marks such as DNA methylation have been linked to cancers and many other human diseases, interindividual epigenetic variations in normal tissues due to aging, environmental factors, or innate susceptibility are poorly characterized. The plasticity, tissue-specific nature, and variability of gene expression are related to epigenomic states that vary across individuals. Thus, population-based investigations are needed to further our understanding of the fundamental dynamics of normal individual epigenomes. We analyzed 217 non-pathologic human tissues from 10 anatomic sites at 1,413 autosomal CpG loci associated with 773 genes to investigate tissue-specific differences in DNA methylation and to discern how aging and exposures contribute to normal variation in methylation. Methylation profile classes derived from unsupervised modeling were significantly associated with age (P<0.0001) and were significant predictors of tissue origin (P<0.0001). In solid tissues (n = 119) we found striking, highly significant CpG island–dependent correlations between age and methylation; loci in CpG islands gained methylation with age, loci not in CpG islands lost methylation with age (P<0.001), and this pattern was consistent across tissues and in an analysis of blood-derived DNA. Our data clearly demonstrate age- and exposure-related differences in tissue-specific methylation and significant age-associated methylation patterns which are CpG island context-dependent. This work provides novel insight into the role of aging and the environment in susceptibility to diseases such as cancer and critically informs the field of epigenomics by providing evidence of epigenetic dysregulation by age-related methylation alterations. Collectively we reveal key issues to consider both in the construction of reference and disease-related epigenomes and in the interpretation of potentially pathologically important alterations.
The causes and extent of tissue-specific interindividual variation in human epigenomes are underappreciated and, hence, poorly characterized. We surveyed over 200 carefully annotated human tissue samples from ten anatosites at 1,413 CpGs for methylation alterations to appraise the nature of phenotypically, and hence potentially clinically important epigenomic alterations. Within tissue types, across individuals, we found variation in methylation that was significantly related to aging and environmental exposures such as tobacco smoking. Individual variation in age- and exposure-related methylation may significantly contribute to increased susceptibility to several diseases. As the NIH–funded HapMap project is critically contributing to annotating the human reference genome defining normal genetic variability, our work raises key issues to consider in the construction of reference epigenomes. It is well recognized that understanding genetic variation is essential to understanding disease. Our work, and the known interplay of epigenetics and genetics, makes it equally clear that a more complete characterization of epigenetic variation and its sources must be accomplished to reach the goal of a complete understanding of disease. Additional research is absolutely necessary to define the mechanisms controlling epigenomic variation. We have begun to lay the foundations for essential normal tissue controls for comparison to diseased tissue, which will allow the identification of the most crucial disease-related alterations and provide more robust targets for novel treatments.
Skin cancer survivors experience an increased risk for subsequent malignancies but the associated risk factors are poorly understood. This study examined the risk of a new primary cancer following an initial skin cancer and assessed risk factors associated with second primary cancers.
All invasive cutaneous malignant melanomas (CMM, N = 28 069) and squamous cell carcinomas (SCC, N = 24 620) diagnosed in Norway during 1955–2008 were included. Rates of new primary cancers in skin cancer survivors were compared to rates of primary malignancies in the general population using standardized incidence ratios (SIR). Discrete-time logistic regression models were applied to individual-level data to estimate cancer risk among those with and without a prior skin cancer, accounting for residential region, education, income, parenthood, marital status and parental cancer status, using a 20% random sample of the entire Norwegian population as reference. Further analyses of the skin cancer cohort were undertaken to determine risk factors related to subsequent cancers.
During follow-up, 9608 new primary cancers occurred after an initial skin cancer. SIR analyses showed 50% and 90% increased risks for any cancer after CMM and SCC, respectively (p < 0.01). The logistic regression model suggested even stronger increase after SCC (130%). The highest risk was seen for subsequent skin cancers, but several non-skin cancers were also diagnosed in excess: oral, lung, colon, breast, prostate, thyroid, leukemia, lymphoma and central nervous system. Factors that were associated with increased risk of subsequent cancers include male sex, older age, lower residential latitude, being married and low education and income. Parental cancer did not increase the risk of a subsequent cancer after SCC, but was a significant predictor among younger CMM survivors.
Our results provide information on shared environmental and genetic risk factors for first and later cancers and may help to identify individuals at high risk for subsequent cancers, which will be important as skin cancer incidence continues to rise.
Malignant Melanoma; Squamous Cell Carcinoma; Second cancer; Population-based; Sociodemographic factors; Family
Recent studies have shown two distinct non-CIMP methylation clusters in colorectal cancer, raising the possibility that DNA methylation, involving non-CIMP genes, may play a role in the conventional adenoma–carcinoma pathway. A total of 135 adenomas (65 left colon and 70 right colon) were profiled for epigenome-wide DNA methylation using the Illumina HumanMethylation450 BeadChip. A principal components analysis was performed to examine the association between variability in DNA methylation and adenoma location. Linear regression and linear mixed effects models were used to identify locus-specific differential DNA methylation in adenomas of right and left colon. A significant association was present between the first principal component and adenoma location (P = 0.007), even after adjustment for subject age and gender (P = 0.009). A total of 168 CpG sites were differentially methylated between right- and left-colon adenomas and these loci demonstrated enrichment of homeobox genes (P = 3.0 × 10−12). None of the 168 probes were associated with CIMP genes. Among CpG loci with the largest difference in methylation between right- and left-colon adenomas, probes associated with PRAC(prostate cancer susceptibility candidate) gene showed hypermethylation in right-colon adenomas whereas those associated with CDX2(caudal type homeobox transcription factor 2) showed hypermethylation in left-colon adenomas. A subgroup of left-colon adenomas enriched for current smokers (OR = 6.1, P = 0.004) exhibited a methylation profile similar to right-colon adenomas. In summary, our results indicate distinct patterns of DNA methylation, independent of CIMP genes, in adenomas of the right and left colon.
colon polyps; colorectal cancer; CpG island methylator phenotype; epigenetics
Evaluating occupational exposures in population-based case-control studies often requires exposure assessors to review each study participants' reported occupational information job-by-job to derive exposure estimates. Although such assessments likely have underlying decision rules, they usually lack transparency, are time-consuming and have uncertain reliability and validity. We aimed to identify the underlying rules to enable documentation, review, and future use of these expert-based exposure decisions.
Classification and regression trees (CART, predictions from a single tree) and random forests (predictions from many trees) were used to identify the underlying rules from the questionnaire responses and an expert's exposure assignments for occupational diesel exhaust exposure for several metrics: binary exposure probability and ordinal exposure probability, intensity, and frequency. Data were split into training (n=10,488 jobs), testing (n=2,247), and validation (n=2,248) data sets.
The CART and random forest models' predictions agreed with 92–94% of the expert's binary probability assignments. For ordinal probability, intensity, and frequency metrics, the two models extracted decision rules more successfully for unexposed and highly exposed jobs (86–90% and 57–85%, respectively) than for low or medium exposed jobs (7–71%).
CART and random forest models extracted decision rules and accurately predicted an expert's exposure decisions for the majority of jobs and identified questionnaire response patterns that would require further expert review if the rules were applied to other jobs in the same or different study. This approach makes the exposure assessment process in case-control studies more transparent and creates a mechanism to efficiently replicate exposure decisions in future studies.
diesel exhaust; classification; data mining; occupational exposure
We investigate the distribution of bladder tumor category and stage in Northern New England by geographic region, smoking status and over time. 1091 incident bladder cancer cases from the New England Bladder Cancer Study (NEBCS), a large population-based case-control study carried out in Maine, New Hampshire and Vermont (2001–2004), and 680 bladder cancer cases from previous case-control studies in New Hampshire (1994–2000) were used in the analysis. Of 1091 incident bladder cancer cases from the NEBCS, 26.7% of tumors were papillary urothelial neoplasms of low malignant potential (PUNLMP), 26.8% low-grade papillary urothelial carcinomas (PUC-LG), 31.3% high-grade papillary urothelial carcinomas (PUC-HG), 9.1% non-papillary urothelial carcinomas (non-PUC), and 4.3% carcinoma in situ (CIS). Approximately 70% of cases were non-invasive (Tis/Ta), and all PUNLMP cases were of the Ta category. By contrast, half of all PUC-HG carcinomas were invasive. Short-term time trend analysis within the NEBCS (2001–2004) indicated an increase in the percentage of PUNLMP (p-trend<0.0001) paralleled by a decrease in PUC-LG (p-trend=0.02), and for PUC-LG an increase in the percentage of non-invasive tumors (p-trend 0.04). Our findings suggest possible short-term trends with an increase in the percentage of PUNLMP and a change in the percentage of PUC-LG towards non-invasive disease.
We review the applicability of Bayesian networks (BNs) for discovering relations between genes, environment, and disease. By translating probabilistic dependencies among variables into graphical models and vice versa, BNs provide a comprehensible and modular framework for representing complex systems. We first describe the Bayesian network approach and its applicability to understanding the genetic and environmental basis of disease. We then describe a variety of algorithms for learning the structure of a network from observational data. Because of their relevance to real-world applications, the topics of missing data and causal interpretation are emphasized. The BN approach is then exemplified through application to data from a population-based study of bladder cancer in New Hampshire, USA. For didactical purposes, we intentionally keep this example simple. When applied to complete data records, we find only minor differences in the performance and results of different algorithms. Subsequent incorporation of partial records through application of the EM algorithm gives us greater power to detect relations. Allowing for network structures that depart from a strict causal interpretation also enhances our ability to discover complex associations including gene-gene (epistasis) and gene-environment interactions. While BNs are already powerful tools for the genetic dissection of disease and generation of prognostic models, there remain some conceptual and computational challenges. These include the proper handling of continuous variables and unmeasured factors, the explicit incorporation of prior knowledge, and the evaluation and communication of the robustness of substantive conclusions to alternative assumptions and data manifestations.
Structural learning; Belief networks; Genetic epidemiology; Bioinformatics; Complex traits; Arsenic; SNP
A few epidemiologic studies have found that use of nonsteroidal anti-inflammatory drugs (NSAIDs) is associated with reduced risk of bladder cancer. However, the effects of specific NSAID use and individual variability in risk have not been well studied. We examined the association between NSAIDs use and bladder cancer risk, and its modification by 39 candidate genes related to NSAID metabolism. A population-based case–control study was conducted in northern New England, enrolling 1,171 newly diagnosed cases and 1,418 controls. Regular use of nonaspirin, nonselective NSAIDs was associated with reduced bladder cancer risk, with a statistically significant inverse trend in risk with duration of use (ORs of 1.0, 0.8, 0.6 and 0.6 for <5, 5–9, 10–19 and 201 years, respectively; ptrend = 0.015). This association was driven mainly by ibuprofen; significant inverse trends in risk with increasing duration and dose of ibuprofen were observed (ptrend = 0.009 and 0.054, respectively). The reduced risk from ibuprofen use was limited to individuals carrying the T allele of a single nucleotide polymorphism (rs4646450) compared to those who did not use ibuprofen and did not carry the T allele in the CYP3A locus, providing new evidence that this association might be modified by polymorphisms in genes that metabolize ibuprofen. Significant positive trends in risk with increasing duration and cumulative dose of selective cyclooxygenase (COX-2) inhibitors were observed. Our results are consistent with those from previous studies linking use of NSAIDs, particularly ibuprofen, with reduced risk. We observed a previously unrecognized risk associated with use of COX-2 inhibitors, which merits further evaluation.
bladder cancer; nonsteroidal anti-inflammatory drugs; gene–drug interaction; CYP3A
tattoo; health behavior survey; women’s health
Purpose of review
Bacterial colonization of the infant intestinal tract begins at birth. We are at the forefront of understanding complex relationships between bacteria and multiple parameters of health of the developing infant. Moreover, the establishment of the microbiome in the critical neonatal period is potentially foundational for lifelong health and disease susceptibility. Recent studies utilizing state-of-the-art culture-independent technologies have begun to increase our knowledge about the gut microbiome in infancy, the impact of multiple exposures, and its effects on immune response and clinical outcomes such as allergy and infection.
Postnatal exposures play a central role in the complex interactions between the nearly blank canvas of the neonatal intestine, whereas genetic factors do not appear to be a major factor. Infant microbial colonization is affected by delivery mode, dietary exposures, antibiotic exposure, and environmental toxicants. Successive microbiome acquisition in infancy is likely a determinant of early immune programming, subsequent infection, and allergy risk.
The novel investigation of the neonatal microbiome is beginning to unearth substantial information, with a focus on immune programming that coevolves with the developing microbiome early in life. Several exposures common to neonatal and infant populations could exert pressure on the development of the microbiome and major diseases including allergy and infection in large populations.
allergy; infection; microbiome; neonate
It is well-known that ultraviolet (UV) light exposure and a sun sensitive phenotype are risk factors for the development of non-melanoma skin cancer (NMSC), including basal cell carcinoma (BCC) and squamous cell carcinoma (SCC). In this New Hampshire population-based case-control study, we collected data from 5,072 individuals, including histologically-confirmed cases of BCC and SCC, and controls via a personal interview to investigate possible associations between photosensitizing medication use and NMSC. After adjustment for potentially confounding factors (e.g. lifetime number of painful sunburns), we found a modest increase in risk of SCC (OR = 1.2, 95% CI = 1.0–1.4) and BCC (OR = 1.2, 95% CI = 0.9–1.5), in particular early-onset BCC, (≤ 50 years of age) (OR = 1.5, 95% CI = 1.1–2.1) associated with photosensitizing medication use. For SCC the association was strongest amongst those with tendency to sunburn rather than tan. We also specifically found associations with BCC, and especially early-onset BCC, and photosensitizing antimicrobials. In conclusion, certain commonly prescribed photosensitizing medications may enhance the risk of developing SCC, especially in individuals with a sun sensitive phenotype, and may increase the risk of developing BCC and incidence of BCC at a younger age.
Arsenic is a carcinogen that contaminates drinking water worldwide. Accumulating evidence suggests that both exposure and genetic factors may influence susceptibility to arsenic-induced malignancies. We sought to identify novel susceptibility loci for arsenic-related bladder cancer in a US population with low to moderate drinking water levels of arsenic. We first screened a subset of bladder cancer cases using a panel of approximately 10,000 non-synonymous single nucleotide polymorphisms (SNPs). Top ranking hits on the SNP array then were considered for further analysis in our population-based case–control study (n = 832 cases and 1,191 controls). SNPs in the fibrous sheath interacting protein 1 (FSIP1) gene (rs10152640) and the solute carrier family 39, member 2 (SLC39A2) in the ZIP gene family of metal transporters (rs2234636) were detected as potential hits in the initial scan and validated in the full case–control study. The adjusted odds ratio (OR) for the FSIP1 polymorphism was 2.57 [95% confidence interval (CI) 1.13, 5.85] for heterozygote variants (AG) and 12.20 (95% CI 2.51, 59.30) for homozygote variants (GG) compared to homozygote wild types (AA) in the high arsenic group (greater than the 90th percentile), and unrelated in the low arsenic group (equal to or below the 90th percentile) (P for interaction = 0.002). For the SLC39A2 polymorphism, the adjusted ORs were 2.96 (95% CI 1.23, 7.15) and 2.91 (95% CI 1.00, 8.52) for heterozygote (TC) and homozygote (CC) variants compared to homozygote wild types (TT), respectively, and close to one in the low arsenic group (P for interaction = 0.03). Our findings suggest novel variants that may influence risk of arsenic-associated bladder cancer and those who may be at greatest risk from this widespread exposure.
Indoor and outdoor air pollution is known to contribute to increased lung cancer incidence. This study is the first to address the contribution of home heating fuel and geographical course particulate matter (PM10) concentrations to lung cancer rates in New Hampshire, U.S. First, Pearson correlation analysis and Geographically weighted regression were used to investigate spatial relationships between outdoor PM10 and lung cancer rates. While the aforementioned analyses did not indicate a significant contribution of PM10 to lung cancer in the state, there was a trend towards a significant association in the northern and southwestern regions of the state. Second, case-control data were used to estimate the contributions of indoor pollution and second hand smoke to risk of lung cancer with adjustment for confounders. Increased risk was found among those who used wood or coal to heat their homes for more than 10 winters before the age of 18, with a significant increase in risk per winter. Resulting data suggest that further investigation of the relationship between heating-related air pollution levels and lung cancer risk is needed.
Most human exposure to mercury (Hg) in the United States is from consuming marine fish and shellfish. The Gulf of Maine is a complex marine ecosystem comprised of twelve physioregions, including the Bay of Fundy, coastal shelf areas and deeper basins that contain highly productive fishing grounds. Here we review available data on spatial and temporal Hg trends to better understand the drivers of human and biological exposures. Atmospheric Hg deposition from U.S. and Canadian sources has declined since the mid-1990s in concert with emissions reductions but deposition from global sources has increased. Oceanographic circulation is the dominant source of total Hg inputs to the entire Gulf of Maine region (59%), followed by atmospheric deposition (28%), wastewater/industrial sources (8%), and rivers (5%). Resuspension of sediments increases MeHg inputs to overlying waters raising concerns about benthic trawling activities in shelf regions. In the near coastal areas, elevated sediment and mussel Hg levels are co-located in urban embayments and near large historical point sources. Temporal patterns in sentinel species (mussels and birds) have in some cases declined in response to localized point source mercury reductions but overall Hg trends do not show consistent declines. For example, levels of Hg have either declined or remained stable in eggs from four seabird species collected in the Bay of Fundy since 1972. Quantitatively linking Hg exposures from fish harvested from the Gulf of Maine to human health risks is challenging at this time because no data are available on the geographic origin of seafood consumed by coastal residents. In addition, there is virtually no information on Hg levels in commercial species for offshore regions of the Gulf of Maine where some of the most productive fisheries are located. Both of these data gaps should be priorities for future research.
Gulf of Maine; North Atlantic; methylmercury; fish; risk
Genetic association studies have become standard approaches to characterize the genetic and epigenetic variability associated with cancer development, including predispositions and mutations. However, the bewildering genetic and phenotypic heterogeneity inherent in cancer both magnifies the conceptual and methodological problems associated with these approaches and renders the translation of available genetic information into a knowledge that is both biologically sound and clinically relevant difficult. Here, we elaborate on the underlying causes of this complexity, illustrate why it represents a challenge for genetic association studies, and briefly discuss how it can be reconciled with the ultimate goal of identifying targetable disease pathways and successfully treating individual patients.
cancer heterogeneity; genetic predispositions; somatic mutations; genetic association studies
Epistasis is recognized ubiquitous in the genetic architecture of complex traits such as disease susceptibility. Experimental studies in model organisms have revealed extensive evidence of biological interactions among genes. Meanwhile, statistical and computational studies in human populations have suggested non-additive effects of genetic variation on complex traits. Although these studies form a baseline for understanding the genetic architecture of complex traits, to date they have only considered interactions among a small number of genetic variants. Our goal here is to use network science to determine the extent to which non-additive interactions exist beyond small subsets of genetic variants. We infer statistical epistasis networks to characterize the global space of pairwise interactions among approximately 1500 Single Nucleotide Polymorphisms (SNPs) spanning nearly 500 cancer susceptibility genes in a large population-based study of bladder cancer.
The statistical epistasis network was built by linking pairs of SNPs if their pairwise interactions were stronger than a systematically derived threshold. Its topology clearly differentiated this real-data network from networks obtained from permutations of the same data under the null hypothesis that no association exists between genotype and phenotype. The network had a significantly higher number of hub SNPs and, interestingly, these hub SNPs were not necessarily with high main effects. The network had a largest connected component of 39 SNPs that was absent in any other permuted-data networks. In addition, the vertex degrees of this network were distinctively found following an approximate power-law distribution and its topology appeared scale-free.
In contrast to many existing techniques focusing on high main-effect SNPs or models of several interacting SNPs, our network approach characterized a global picture of gene-gene interactions in a population-based genetic data. The network was built using pairwise interactions, and its distinctive network topology and large connected components indicated joint effects in a large set of SNPs. Our observations suggested that this particular statistical epistasis network captured important features of the genetic architecture of bladder cancer that have not been described previously.