Tobacco Alkaloids and Tobacco-Specific Nitrosamines in Dust from Homes of Smokeless Tobacco Users, Active Smokers, and Nontobacco Users

Smokeless tobacco products, such as moist snuff or chewing tobacco, contain many of the same carcinogens as tobacco smoke; however, the impact on children of indirect exposure to tobacco constituents via parental smokeless tobacco use is unknown. As part of the California Childhood Leukemia Study, dust samples were collected from 6 homes occupied by smokeless tobacco users, 6 homes occupied by active smokers, and 20 tobacco-free homes. To assess children’s potential for exposure to tobacco constituents, vacuum-dust concentrations of five tobacco-specific nitrosamines, including <i>N</i>′-nitrosonornicotine [NNN] and 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone [NNK], as well as six tobacco alkaloids, including nicotine and myosmine, were quantified by liquid chromatography-tandem mass spectrometry (LC-MS/MS). We used generalized estimating equations derived from a multivariable marginal model to compare levels of tobacco constituents between groups, after adjusting for a history of parental smoking, income, home construction date, and mother’s age and race/ethnicity. The ratio of myosmine/nicotine was used as a novel indicator of the source of tobacco contamination, distinguishing between smokeless tobacco products and tobacco smoke. Median dust concentrations of NNN and NNK were significantly greater in homes with smokeless tobacco users compared to tobacco-free homes. In multivariable models, concentrations of NNN and NNK were 4.8- and 6.9-fold higher, respectively, in homes with smokeless tobacco users compared to tobacco-free homes. Median myosmine/nicotine ratios were lower in homes with smokeless tobacco users (1.8%) compared to homes of active smokers (7.7%), confirming that cigarette smoke was not the predominant source of tobacco constituents in homes with smokeless tobacco users. Children living with smokeless tobacco users may be exposed to carcinogenic tobacco-specific nitrosamines via contact with contaminated dust and household surfaces

Concentrations of Persistent Organic Pollutants in California Children’s Whole Blood and Residential Dust

We evaluated relationships between persistent organic pollutant (POP) levels in the blood of children with leukemia and POP levels in dust from their household vacuum cleaners. Blood and dust were collected from participants of the California Childhood Leukemia Study at various intervals from 1999 to 2007 and analyzed for two polybrominated diphenyl ethers (PBDEs), two polychlorinated biphenyls (PCBs), and two organochlorine pesticides using gas chromatography–mass spectrometry. Due to small blood sample volumes (100 μL), dichlorodiphenyldichloroethylene (DDE) and BDE-153 were the only analytes with detection frequencies above 70%. For each analyte, depending on its detection frequency, a multivariable linear or logistic regression model was used to evaluate the relationship between POP levels in blood and dust, adjusting for child’s age, ethnicity, and breastfeeding duration; mother’s country of origin; household annual income; and blood sampling date. In linear regression, concentrations of BDE-153 in blood and dust were positively associated; whereas, DDE concentrations in blood were positively associated with breastfeeding, maternal birth outside the U.S., and Hispanic ethnicity, but not with corresponding dust-DDE concentrations. The probability of PCB-153 detection in a child’s blood was marginally associated with dust-PCB-153 concentrations (<i>p</i> = 0.08) in logistic regression and significantly associated with breastfeeding. Our findings suggest that dust ingestion is a source of children’s exposure to certain POPs

Risk allele proportions at genomic loci with somatic gain (<i>i</i>.<i>e</i>. hyperdiploid chromosomes).

<p>Allelic copy number was measured in constitutional DNA and leukemia bone marrow (tumor) DNA from HeH ALL patients heterozygous for ALL-associated SNPs on chromosomes frequently gained in HeH ALL: <i>CEBPE</i> SNP rs2239633 (A), <i>ARID5B</i> SNP rs7089424 (B), <i>PIP4K2A</i> SNP rs10764338 (C), and <i>GATA3</i> SNP rs3824662 (D). Risk allele proportions are displayed as a fraction of the total allelic copy number measured in each patient using ddPCR. Each subject was assayed in duplicate, and error bars represent the standard error of the mean (some error bars not visible due to their range falling within boundaries of the data point). Upper/lower thresholds of allelic imbalance (AI) were +/- 3 SDs from the mean allelic proportion from repeat measurements in constitutional DNA samples (white squares). For rs2239633, 19 tumor samples showed AI favoring the risk allele versus 13 patients with AI favoring the protective allele (P = 0.19). For rs7089424, 20 tumor samples showed AI favoring the risk allele versus 15 patients with AI favoring the protective allele (P = 0.25). For rs10764338, 4 tumor samples showed AI favoring the risk allele versus 5 patients with AI favoring the protective allele (P = 0.50). For rs3824662, 10 tumor samples showed AI favoring the risk allele versus 9 patients with AI favoring the protective allele (P = 0.50). Data points clustering at ~0.66 and ~0.33 represent a 3:2 or 2:3 risk:protective allele proportion due to chromosomal copy number shifting from diploid (n = 2) to triploid (n = 3). Data points at ~0.75 represents a 3:1 risk:protective allele proportion due to a diploid to tetraploid (n = 4) shift in chromosome ploidy. Data points at 1 and 0 likely represent HeH ALL that has arisen via near-haploidy, leading to chromosomal LOH (Paulsson <i>et al</i>. 2005) [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0143343#pone.0143343.ref031" target="_blank">31</a>].</p

Candidates for tumor PAI: recurrent SCNA loci from TCGA that overlap cancer-associated SNPs (NHGRI GWAS Catalog) identified in matching tumor types.

<p>SNP associations and tumor types highlighted in bold indicate those whereby cancer type of SNP associations matches tumor type in which recurrent SCNAs were identified.</p><p>* Chromosomal locations based on human genome build hg19.</p><p>** Cancer type of SNP association loci that overlap SCNA regions (SNPs retrieved from January 2015 version of NHGRI GWAS catalog).</p><p>‡ Tumor type in which recurrent SCNAs were detected in TCGA.</p><p>ALL = acute lymphoblastic leukemia; BLCA = bladder; BRCA = breast; CLL = chronic lymphoblastic leukemia; CRC = colorectal; GBM = glioblastoma multiforme; HNSC = head and neck squamous cell carcinoma; KIRC = kidney renal cell carcinoma; LAML = acute myeloid leukemia; LUAD = lung adenocarcinoma; LUSC = lung squamous cell carcinoma; OV = serous ovarian carcinoma; UCEC = endometrial (uterine).</p><p>Candidates for tumor PAI: recurrent SCNA loci from TCGA that overlap cancer-associated SNPs (NHGRI GWAS Catalog) identified in matching tumor types.</p

Risk allele proportions at genomic loci with somatic loss.

<p>Allelic copy number was measured in constitutional DNA and leukemia bone marrow (tumor) DNA from ALL patients heterozygous for <i>CDKN2A</i> tagging SNP rs3731217 (A), and <i>IKZF1</i> SNP rs4132601 (B). Risk allele proportions are displayed as a fraction of the total allelic copy number measured in each patient using ddPCR. Each subject was assayed in duplicate, and error bars represent the standard error of the mean (some error bars not visible due to their range falling within boundaries of the data point). Upper/lower thresholds of allelic imbalance (AI) were +/- 3 SDs from the mean allelic proportion from repeat measurements in constitutional DNA samples (white squares). For rs3731217, 11 tumor samples showed AI favoring the risk allele versus 6 patients with AI favoring the protective allele (P = 0.17). For rs4132601, 17 tumor samples showed AI favoring the risk allele versus 12 patients with AI favoring the protective allele (P = 0.23).</p

<i>CDKN2A</i> and <i>IKZF1</i> SNP allele proportions in tumor DNA relative to genomic control copy number.

<p>Stacked histograms showing tumor DNA copy number of (A) <i>CDKN2A</i> and (B) <i>IKZF1</i> SNPs relative to a genomic control locus (<i>SLC24A3</i>). Black and grey bars represent the proportions of normalized SNP copy number accounted for by the risk and protective alleles respectively. White bars represent the difference between <i>CDKN2A</i>/<i>IKZF1</i> SNP copy number and the genomic control gene copy number. SMART-ddPCR was used to measure copy number of SNP risk/protective alleles, as well as the genomic control locus, in 35 leukemia bone marrow (tumor) DNA samples for <i>CDKN2A</i> (SNP rs3731249) and 75 tumor DNA samples for <i>IKZF1</i> (SNP rs4132601). Samples are grouped into those with allelic imbalance (AI) and those without AI, and arranged in order of normalized gene copy number relative to the genomic control.</p

Summary of the childhood ALL-associated SNPs investigated and the corresponding tumor DNA allelic imbalance results.

<p>* Number of heterozygous samples (for each SNP) with available bone marrow (i.e. tumor) DNA.</p><p>‡ % of HeH ALL samples with gains of that chromosome, based on data from Paulsson <i>et al</i>. (2010) [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0143343#pone.0143343.ref021" target="_blank">21</a>] and Dastugue <i>et al</i>. (2013) [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0143343#pone.0143343.ref022" target="_blank">22</a>].</p><p>† High hyperdiploid samples only.</p><p>Significant p-values highlighted in bold.</p><p>Summary of the childhood ALL-associated SNPs investigated and the corresponding tumor DNA allelic imbalance results.</p

Comparison between deletion gene copy number measurements made by SMART-ddPCR and MLPA.

<p>Copy number measurements were available from ddPCR and MLPA assays for SNPs at the two deletion genes <i>CDKN2A</i> (SNP rs3731249) and <i>IKZF1</i> (SNP rs4132601) in 27 and 75 tumor DNA samples respectively. (A) High correlation (R2 = 0.91) between the combined deletion gene copy number measurements made by ddPCR and MLPA. (B) Bland-Altman plot displaying the difference between measurements made in the same individual against their mean, as measured by two different methodologies (<i>i</i>.<i>e</i>. ddPCR and MLPA). There is very close agreement between the copy number measurements made by the two assays, as demonstrated by the narrow limits of agreement (-0.170 to 0.138) either side of the observed average agreement (-0.016).</p

Polychlorinated Biphenyls in Residential Dust: Sources of Variability

We characterized the variability in concentrations of polychlorinated biphenyls (PCBs) measured in residential dust. Vacuum cleaner samples were collected from 289 homes in the California Childhood Leukemia Study during two sampling rounds from 2001 to 2010 and 15 PCBs were measured by high resolution gas chromatography–mass spectrometry. Median concentrations of the most abundant PCBs (i.e., PCBs 28, 52, 101, 105, 118, 138, 153, and 180) ranged from 1.0–5.8 ng per g of dust in the first sampling round and from 0.8–3.4 ng/g in the second sampling round. For each of these eight PCBs, we used a random-effects model to apportion total variation into regional variability (6–11%), intraregional between-home variability (27–56%), within-home variability over time (18–52%), and within-sample variability (9–16%). In mixed-effects models, differences in PCB concentrations between homes were explained by home age, with older homes having higher PCB levels. Differences in PCB concentrations within homes were explained by decreasing time trends. Estimated half-lives ranged from 5–18 years, indicating that PCBs are removed very slowly from the indoor environment. Our findings suggest that it may be feasible to use residential dust for retrospective assessment of PCB exposures in studies of children’s health

SNP Association Mapping across the Extended Major Histocompatibility Complex and Risk of B-Cell Precursor Acute Lymphoblastic Leukemia in Children

<div><p>The extended major histocompatibility complex (xMHC) is the most gene-dense region of the genome and harbors a disproportionately large number of genes involved in immune function. The postulated role of infection in the causation of childhood B-cell precursor acute lymphoblastic leukemia (BCP-ALL) suggests that the xMHC may make an important contribution to the risk of this disease. We conducted association mapping across an approximately 4 megabase region of the xMHC using a validated panel of single nucleotide polymorphisms (SNPs) in childhood BCP-ALL cases (n=567) enrolled in the Northern California Childhood Leukemia Study (NCCLS) compared with population controls (n=892). Logistic regression analyses of 1,145 SNPs, adjusted for age, sex, and Hispanic ethnicity indicated potential associations between several SNPs and childhood BCP-ALL. After accounting for multiple comparisons, one of these included a statistically significant increased risk associated with rs9296068 (OR=1.40, 95% CI=1.19-1.66, corrected p=0.036), located in proximity to <i>HLA-DOA</i>. Sliding window haplotype analysis identified an additional locus located in the extended class I region in proximity to <i>TRIM27</i> tagged by a haplotype comprising rs1237485, rs3118361, and rs2032502 (corrected global p=0.046). Our findings suggest that susceptibility to childhood BCP-ALL is influenced by genetic variation within the xMHC and indicate at least two important regions for future evaluation.</p> </div