Background: It has been suggested that fetal carcinogenic exposure might lead to predisposition to develop cancer during childhood or in later life possibly through modulation of the fetal transcriptome. Because gender effects in the incidence of childhood cancers have been described, we hypothesized differences at the transcriptomic level in cord blood between male and female newborns as a consequence of fetal carcinogenic exposure. The objective was to investigate whether transcriptomic responses to dietary genotoxic and nongenotoxic carcinogens show gender-specific mechanisms-of-action relevant for chemical carcinogenesis.

Methods: Global gene expression was applied in umbilical cord blood samples, the CALUX-assay was used for measuring dioxin(-like), androgen(-like), and estrogen(-like) internal exposure, and acrylamide–hemoglobin adduct levels were determined by mass spectrometry adduct-FIRE-procedureTM. To link gene expression to an established phenotypic biomarker of cancer risk, micronuclei frequencies were investigated.

Results: While exposure levels did not differ between sexes at birth, important gender-specific differences were observed in gene expressions associated with these exposures linked with cell cycle, the immune system and more general cellular processes such as posttranslation. Moreover, oppositely correlating leukemia/lymphoma genes between male and female newborns were identified in relation to the different biomarkers of exposure that might be relevant to male-specific predisposition to develop these cancers in childhood.

Conclusions/Impact: This study reveals different transcriptomic responses to environmental carcinogens between the sexes. In particular, male-specific TNF-alpha-NF-kB signaling upon dioxin exposure and activation of the Wnt-pathway in boys upon acrylamide exposure might represent possible mechanistic explanations for gender specificity in the incidence of childhood leukemia. Cancer Epidemiol Biomarkers Prev; 21(10); 1756–67. ©2012 AACR.

A gender effect in the incidence of childhood cancers has been well described (1, 2). The male-to-female incidence varies for several types of cancers; in particular, a male predominance in the age-adjusted incidence of all types of leukemias and lymphomas is apparent, with the highest ratio (M:F: 3.0) for non-Hodgkin lymphoma (2). Potential gender-specific risk factors include the faster growth rate of the male embryo because of accelerated rates of cell division and proliferation (3). For both sexes, birth weight have been positively associated with childhood leukemia, whereas the maternal dietary intake of DNA topoisomerase II inhibitors was associated with infant leukemia, altogether, they are the most common pediatric neoplasm (4). However, the etiology of sex-specific childhood cancer still needs to be fully clarified.

In this context, a link between environmental/dietary exposure to carcinogenic chemicals and infant leukemia has been proposed (5). Statistics show significant increases in the incidence of cancer including leukemia, among children in Europe over the last decades (6) suggesting that (maternal) exposure to carcinogenic factors present an etiological factor. Thus, investigations on possible mechanisms-of-action through which exposure to environmental carcinogens predispose to childhood cancer risk, is warranted. The latent period of, for example, leukemia, its very early onset in childhood might indicate the fetal period to be a critical window of exposure. It has been suggested that such fetal exposure might lead to predisposition to develop disease such as cancer and immune diseases during childhood or in later life possibly through modulation of the fetal transcriptome (7, 8).

Upon ingestion or inhalation by pregnant women, many environmental carcinogens are capable of crossing the placental barrier and reaching the fetus (9). It has been shown that the placental barrier does not protect the unborn child in case of maternal exposure to acrylamide, dioxins, and estrogen-like compounds such as organochlorine pesticides and amphenones (10–13).

Over the last decade, microarray-based genomics technology has significantly progressed enabling whole genome screening at the gene expression level, making this highly suitable for investigating mechanisms underlying, for example, the molecular effects of carcinogenic exposure. Because a gender effect in the incidence of childhood cancers exists, with cancer of the blood system being far more frequent in males than females, we hypothesized that differences at the transcriptomic level in cord blood will occur between male and female newborns as a consequence of fetal carcinogenic exposure, possibly leading to gender-specific predisposition to develop childhood leukemia.

For this, global transcriptomic responses in umbilical cord blood samples from Norwegian male and female newborns were examined in relation to markers of carcinogenic exposure and effect. Investigated nongenotoxic carcinogen exposures determined by means of DR CALUX, include aryl hydrocarbon (Ah) receptor-mediated compounds such as polychlorinated dibenzodioxins (PCDD), polychlorinated dibenzofurans (PCDF), and dioxin-like polychlorinated biphenyls (PCB; ref. 14). Measurements by CALUX assay of ERα and AR receptor activation represent exposure to endocrine disruptors such as carcinogenic pesticides like 1,1,1-trichloro-2,2-bis(p-chlorophenyl)ethane (DDT) and antiandrogens (15).

Global transcriptomic responses in male and female newborns were also examined in relation to genotoxic acrylamide exposure. Humans are exposed to relatively high doses of acrylamide because this compound is formed in food during cooking (16, 17). Acrylamide can be converted to the epoxide glycidamide by cytochrome P450 2E1 (18). Both compounds are electrophilically reactive. The mutagenicity from acrylamide exposure arises from the reaction of glycidamide with nucleophilic centers in DNA, as the direct interaction of acrylamide with DNA is slow (19). Acrylamide and glycidamide also bind to circulating proteins such as hemoglobin (Hb) and form adducts. This Hb-bound acrylamide represents a well-accepted measure of internal dose and was, as well as the Hb-bound glycidamide, used as a biomarker of exposure to acrylamide in the present study. A recently developed liquid chromatography-tandem mass spectrometry method was used for measurement of the Hb adducts (20).

Gene expression was associated to a validated biomarker of cancer risk, that is, micronuclei frequencies (MN). MN is a well established biomarker of chromosomal breakage and/or whole chromosome loss that are unrepaired, misrepaired, or malsegregated because of chromosome, cellular, and nuclear dysfunction (21–23), and increased micronucleus frequencies in peripheral blood lymphocytes have been shown in prospective studies to predict the risk of cancer in humans (24).

Sample collection

Umbilical cord blood samples were collected immediately after birth from the cord vein of 200 babies whose mothers participated in the Norwegian BraMat cohort. As detailed below in section “Gene expression analysis”, 45 male and 66 female newborns were included in the current study (Table 1). This BraMat subcohort is nested within the Norwegian Mother and Child cohort (MoBa). Study protocols were approved by the Regional Committee for Ethics in Medical Research in Oslo, Norway. Informed consent was obtained from all participating mothers before sample collection. The samples were collected by trained nurses at the maternity wards of the Ulleval and Akershus hospitals in Oslo, Norway. For preserving RNA for microarray analysis, aliquots of heparin-anticoagulated whole blood of 0.4 mL were mixed with 1.2 mL of RNAlater (Ambion/Applied Biosystems, Nieuwerkerk aan den Ijssel, the Netherlands) as soon as possible after blood collection. Samples were kept at −80°C until shipment on dry ice to the research laboratory. The remainder of the whole blood was utilized for CALUX measurements, assessment of acrylamide- and glycidamide-Hb adducts, and analysis of MN.

Table 1.

Characteristics of the study population (A), RIN and bench time (B), and data distribution of the various parameters (C)

A
AllMalesFemales
N 111 45 66 
Birth weight 3629 ± 434 3722 ± 416 3564 ± 438 
Gestation 39.8 ± 1.1 39.9 ± 1.1 39.7 ± 1.1 
Age 
 Mother 31.3 ± 4.2 31.0 ± 4.3 31.5 ± 4.2 
 Father 32.8 ± 6.4 32.3 ± 4.3 33.7 ± 5.7 
Mother BMI BP 23.4 ± 3.9 23.1 ± 3.6 23.6 ± 4.1 
Mother was pregnant before 
 no 35% 34% 36% 
 yes 65% 66% 64% 
Mother smoked BP 
 no 84% 82% 85% 
 occasional 9% 11% 8% 
 daily 7% 7% 6% 
Delivery start 
 Spontaneous 81% 86% 77% 
 Induced 12% 7% 16% 
 Caesarean section 7% 7% 8% 
Caesarean sections 
 Planned 
 Emergency 
B 
RIN (0–10) 8.0 ± 1.3 7.98 ± 1.30 7.97 ± 1.30 
Bench time 77.8 ± 172.4 58.77 ± 116.66 90.63 ± 201.29 
C 
DR CALUX 36.4± 9.8 (n = 43) 37.4 ± 10.5 (n = 14) 35.9 ± 9.6 (n = 29) 
ERα CALUX 15.6 ± 14.4 (n = 34) 16.1 ± 10.6 (n = 17) 15.2 ± 17.8 (n = 17) 
AR CALUX 0.08 ± 0.06 (n = 31) 0.07 ± 0.03 (n = 16) 0.09 ± 0.08 (n = 15) 
AA Hb-adducts 16.5 ± 6.6 (n = 84) 16.0 ± 7.1 (n = 34) 16.7 ± 6.4 (n = 50) 
GA Hb-adducts 10.0 ± 4.0 (n = 84) 9.5 ± 3.8 (n = 34) 10.3 ± 4.0 (n = 50) 
‰MNBN 1.2 ± 0.9 (n = 33) 1.1 ± 0.8 (n = 13) 1.2 ± 0.9 (n = 16) 
A
AllMalesFemales
N 111 45 66 
Birth weight 3629 ± 434 3722 ± 416 3564 ± 438 
Gestation 39.8 ± 1.1 39.9 ± 1.1 39.7 ± 1.1 
Age 
 Mother 31.3 ± 4.2 31.0 ± 4.3 31.5 ± 4.2 
 Father 32.8 ± 6.4 32.3 ± 4.3 33.7 ± 5.7 
Mother BMI BP 23.4 ± 3.9 23.1 ± 3.6 23.6 ± 4.1 
Mother was pregnant before 
 no 35% 34% 36% 
 yes 65% 66% 64% 
Mother smoked BP 
 no 84% 82% 85% 
 occasional 9% 11% 8% 
 daily 7% 7% 6% 
Delivery start 
 Spontaneous 81% 86% 77% 
 Induced 12% 7% 16% 
 Caesarean section 7% 7% 8% 
Caesarean sections 
 Planned 
 Emergency 
B 
RIN (0–10) 8.0 ± 1.3 7.98 ± 1.30 7.97 ± 1.30 
Bench time 77.8 ± 172.4 58.77 ± 116.66 90.63 ± 201.29 
C 
DR CALUX 36.4± 9.8 (n = 43) 37.4 ± 10.5 (n = 14) 35.9 ± 9.6 (n = 29) 
ERα CALUX 15.6 ± 14.4 (n = 34) 16.1 ± 10.6 (n = 17) 15.2 ± 17.8 (n = 17) 
AR CALUX 0.08 ± 0.06 (n = 31) 0.07 ± 0.03 (n = 16) 0.09 ± 0.08 (n = 15) 
AA Hb-adducts 16.5 ± 6.6 (n = 84) 16.0 ± 7.1 (n = 34) 16.7 ± 6.4 (n = 50) 
GA Hb-adducts 10.0 ± 4.0 (n = 84) 9.5 ± 3.8 (n = 34) 10.3 ± 4.0 (n = 50) 
‰MNBN 1.2 ± 0.9 (n = 33) 1.1 ± 0.8 (n = 13) 1.2 ± 0.9 (n = 16) 

Abbreviation: BP, before pregnancy; RIN, RNA integrity number; Bench time, time from blood collection to RNA stabilization.

Data are presented as mean ± SD unless stated otherwise. Birth weight expressed in g, gestation in weeks, age in years, bench time in minutes, DR CALUX in pg TEQ/gr fat, ERα CALUX (ng EEQ/mL plasma), AR CALUX (ng AEQ/mL plasma), Hb-adducts in pmol/g Hb, ‰MNBN in MN per 1,000 binucleated cells.

RNA isolation

Total RNA was isolated from the RNAlater-preserved whole blood samples using the RiboPure-Blood system (Ambion) according to the manufacturer's instructions. RNA yield and purity was measured by means of NanoDrop equipment and integrity was verified by automated gel electrophoresis (2100 BioAnalyzer, Agilent Technologies).

Gene expression analysis

From the 200 BraMat samples, only RNA samples with a RNA integrity number (RIN) 6 or greater and a bench time (time from collection until RNA stabilization) of 6 or less hours, that is, 120 samples were used for transcriptomic analysis. Total RNA (1 microgram) was used to generate Cyanine-labeled cRNA using the Agilent Low RNA Input Linear Amplification Kit according to the manufacturer's instructions. Each individual cord blood sample was labeled by means of Cyanine-5 and competitively hybridized against a common reference sample (pooled RNA cord blood samples, labeled with Cyanine-3) onto Agilent 4 × 44 k human oligonucleotide microarrays (Agilent Technologies) according to the manufacturer's instructions. After hybridization, microarrays were washed and scanned immediately using an Axon GenePix 4000B Microarray Scanner (Molecular Devices). Laser power was set to 100%. The photo multiplier tube (PMT) gain was set to a saturation tolerance of 0.02% to minimize background and saturated spots.

Additional sample exclusions were based on technical performance of the microarrays, as well as on the absence of informed consent forms or reported smoking of the mother at 30 weeks of pregnancy. The number of cases left in the study after exclusions was 111.

Internal measurements of pollutants

Dioxin-like activity in plasma was determined in a total of 43 samples using the dioxin responsive (DR) CALUX reporter gene bioassay. The bioassay comprises a genetically modified H4IIE rat hepatoma cell line containing the luciferase gene under control of the AhR. Approximately 3 grams of plasma from umbilical cord blood was extracted by means of shake-solvent extraction (hexane:diethylether, 97:3). Extracted fat was used for clean-up on an acid silica column (20% and 33% H2SO4), topped with sodium sulphate. Cleaned extracts were dissolved in dimethyl sulfoxide (DMSO; 8 μL); the DR CALUX activity was determined following exposure to dilution series of cleaned extracts (0.8% DMSO) for 24 hours. Estrogenic and androgenic activity in umbilical cord blood were determined in a total of 34 and 31 samples, respectively, using a human U2OS cell line stably transfected with the luciferase gene that is under control of the estrogen receptor alpha (ERα CALUX) or androgen receptor (AR CALUX). Approximately 0.5 mL of cord blood plasma was extracted by means of shake-solvent extraction (MTBE; methyltertiairbutylether). Extracts were dissolved in DMSO (40 μL); the ERα and AR CALUX activity was determined following 24 hours of exposure at various dilutions of the redissolved extracts (0.1% DMSO). All bioassays were corrected for procedure blanks and conducted including a reference compound calibration curve. By interpolation, final results are expressed as reference compound equivalents (eq.): DR CALUX are expressed in pilogram 2,3,7,8-TCDD-like eq. (TEQ)/gram fat, ERα CALUX in ng 17β-estradiol-like eq. (EEQ)/mL plasma, AR CALUX in nanogram dihydrotestosterone-like eq. (AEQ)/mL plasma (25, 26).

Hemoglobin-adduct levels

Hb-adducts from acrylamide (AA Hb-adducts) and glycidamide (GA Hb-adducts) were measured as internal dose markers of exposure to acrylamide from food. Red blood cells were isolated and stored at −20°C until further processing. Adduct levels to N-terminal valines in Hb were measured in a total of 84 samples applying the “adduct FIRE procedure” using liquid chromatography–tandem mass spectrometry (LC–MS/MS; Shimadzu Prominence, AB SCIEX 3200 qtrap) for final detection (20). Method performance parameters and its application to measurements of acrylamide and glycidamide Hb-adducts in cord blood samples has previously been described (20, 27).

Cytokinesis-block micronucleus assay

MNs per 1,000 binucleated cells (‰MNBN) were analyzed in peripheral blood T-lymphocytes of 33 samples. MNBN, in addition to in vivo-accumulated MN, reflect damage present on DNA or key proteins and expressed as MN during in vitro cell division. MN in mononucleated cells (MNMONO) only reflect chromosomal damage before the start of the assay. Because gene expression is not purely a cumulative measure, and MNMONO frequencies were low, MNBN were used. The in vitro cytokinesis-blocked MN assay (CBMN assay) was carried out according to the standardized protocol developed for semiautomated image analysis system. (Decordier et al. 2009) (28). Sampling and making of cultures occurred in Oslo, Norway. The protocol was conducted as recently described by Vande Loock and colleagues (29).

Data analysis

The data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus (30) and are accessible through GEO Series accession number GSE31836 (31). Scan images of Cy5- and Cy3- channels were loaded into ImaGene software version 8.0.1 (BioDiscovery) for the extraction of raw pixel intensities and local backgrounds. The data were preprocessed using the Refiner Array module from the Genedata Expressionist system (Genedata AG). Raw data were assessed for quality and preprocessed as follows. Signals were corrected by subtracting backgrounds without producing negative values. For each feature and array, the contrast, that is, the quality for each signal according to its signal-to-noise ratio, the distortion, and imbalance were determined. Defective or saturated features detected by the scanner software or by the Refiner Array module were diagnosed and masked. LOWESS correction was used to correct all features so that the signal distortion and imbalance of the 2 channels is minimized. Quality criteria for the data analysis were set as follows: signal-to-noise ratios more than 2, relative errors less than 0.5, and saturated and features flagged as masked filtered out.

Dose–response relationships were investigated with the available samples by Pearson's correlation analyses of individual gene expressions (Cy5/Cy3 ratios) and individual values for CALUX measurements, Hb-adducts, and MN, as mentioned in Table 2. Correlation analyses were conducted only for transcripts with least 50% valid expression values.

Table 2.

Numbers of genes correlating with CALUX, Hb-adducts, and micronuclei

No. of genesPos. correlationNeg. correlationGenderNo. of genesPos. correlationNeg. correlationOverlap boys-girls
DR CALUX 3 Boys (n = 14) 371 163 208 
    Girls (n = 29) 39 29 10  
ERα CALUX 20 14 Boys (n = 17) 493 311 182 
    Girls (n = 17) 626 459 167  
AR CALUX 83 69 14 Boys (n = 16) 1293 946 347 29 
    Girls (n = 15) 508 366 142  
AA Hb-adducts 0 Boys (n = 34) 23 18 
    Girls (n = 50) 0  
GA Hb-adducts 0 Boys (n = 34) 6 
    Girls (n = 50) 0  
‰MNBN 22 18 Boys (n = 13) 1397 662 735 
    Girls (n = 16) 95 28 67  
No. of genesPos. correlationNeg. correlationGenderNo. of genesPos. correlationNeg. correlationOverlap boys-girls
DR CALUX 3 Boys (n = 14) 371 163 208 
    Girls (n = 29) 39 29 10  
ERα CALUX 20 14 Boys (n = 17) 493 311 182 
    Girls (n = 17) 626 459 167  
AR CALUX 83 69 14 Boys (n = 16) 1293 946 347 29 
    Girls (n = 15) 508 366 142  
AA Hb-adducts 0 Boys (n = 34) 23 18 
    Girls (n = 50) 0  
GA Hb-adducts 0 Boys (n = 34) 6 
    Girls (n = 50) 0  
‰MNBN 22 18 Boys (n = 13) 1397 662 735 
    Girls (n = 16) 95 28 67  

Numbers are presented for sexes grouped together and separately, correlation coefficients more than 0.60 or less than -0.60, and an unadjusted P value ≤ 0.05.

Significantly correlating transcripts were selected using 2 cut-offs. Minimal correlation coefficients were set to 0.60 or more or −0.60 or less and an unadjusted P value of less than 0.05. To ensure maximum specificity, a higher cut-off of 0.75 or more or −0.75 or less was used. No false discovery rates were used, because correlation coefficients were considered more important.

To identify biological processes in which significantly correlating genes are involved, the software suite Metacore™ was used. Gene ontology (GO) terms, pathways, and gene networks containing at least 2 significantly correlating genes and a P-value 0.01 or less were considered significantly enriched. In addition, a false discovery-rate cut-off of 0.25 was applied. For enabling further biological interpretation of correlation data, correlation coefficients were uploaded in Tox-Profiler without any statistical pre-selection of correlating genes. Tox-Profiler uses the T-test to score differences between mean input values (usually expression values, but in our case correlation coefficients) of predefined gene sets and that of all other genes (32). For biological interpretation, gene sets based on annotations from different databases were used, that is, GO terms, KEGG, Biocarta, and WikiPathways. To identify significant effects, a Wilcoxon nonparametric statistical test was used, using an unadjusted E-value cut-off of 0.05.

To evaluate differences at the transcriptomic level between male and female newborns in relation to blood cancer risk, the effects on human cancer genes, that is, genes in which mutations that are causally implicated in oncogenesis have been reported, were investigated (33). For this, only genes connected to leukemia and lymphoma were included which showed an opposite correlation coefficient between male and female neonates. Additionally, the correlation coefficient had to be 0.60 or more or −0.60 or less in either male or female newborns.

Biomarker analyses

From a total of 200 samples from the BraMat cohort, 120 RNA samples were selected on the basis of a RIN 6 or more and a bench time (time from collection until RNA stabilization) of 6 or less hours. These 120 samples were hybridized onto microarrays. Sample/microarray exclusions were based on technical performance of the microarrays, as well as on the absence of informed consent forms or reported smoking of the mother at 30 weeks of pregnancy. The number of cases left in the study after exclusions was 111. Characteristics of the remaining study population are presented in Table 1A, for the whole study population as well as for male and female infants separately. All mothers of the included 111 newborns are nonsmokers. Table 1B presents RIN and bench time, that is, time from collection until RNA stabilization, whereas Table 1C presents the data distributions for the investigated parameters on CALUX, Hb-adducts, and MN. The number of available samples for each biomarker varies as presented in Table 1C. For all these measurements, no significant differences were observed between male and female newborns.

Number of significantly correlating genes

Correlation analyses of gene expression levels with biomarkers of exposure and with MN measured showed variable numbers of significantly correlating genes (Table 2). Because of missing values, the numbers of available samples for correlation analysis vary per biomarker and were significantly lower than the 111 mentioned in Table 1. Overall, separate analyses for male and female newborns resulted in higher numbers of significantly correlating genes per gender with low overlap of similarly expressed genes between the 2 sexes, thus indicating a clear gender-specific toxicogenomic response.

Biologic evaluation

DR CALUX.

Correlation analysis showed that male infants show a higher number of significantly correlating transcripts in relation to dioxin and dioxin-like exposure, assessed by DR CALUX assay, when compared with females. For males, functional interpretation revealed significantly affected processes relevant to carcinogenicity, such as programmed cell death/apoptosis, spindle/nuclear division, and the ubiquitination–proteasome pathway (Table 3). In females, mainly epigenetic responses were found, such as nucleotide and chromatin assembly (Table 4). The mitotic cell cycle appeared to be affected in both males and females.

Table 3.

The GO terms, pathways, and networks enriched among the significantly correlating genes for the various biomarkers for males

GO term/Pathway/Network# significant processes (CC ≥ 0.60/CC ≥ 0.75)P-value (# genes affected/total # in pathway)Database
DR CALUX 29/16   
 Cell cycle  3.76E-04 (31/679) GO 
 Apoptosis  5.63E-04 (26/543) GO 
ERα CALUX 50/9   
 Antigen processing and presentation  2.92E-05 (9/61) GO 
 Regulation of adaptive immune response  1.69E-04 (8/60) GO 
AR CALUX 7/1   
 Cell cycle_G1-S  6.64E-03 (17/122) Pathways 
 Proteolysis_Ubiquitin–proteasomal proteolysis  7.11E-03 (19/143) Pathways 
AA Hb-adducts 4/0   
 Regulation of developmental process  5.51E-03 (4/637) GO 
 Regulation of T-cell activation  8.89E-03 (2/121) GO 
GA Hb-adducts 0/0   
‰MNBN 22/53   
 DNA damage_Checkpoint  9.22E-03 (16/92) Networks 
 Regulation of cell cyclea  3.49E-04 (14/314) GO 
 Apoptosis and survival_p53-dependent apoptosisa  1.06E-03 (4/25) Pathways 
GO term/Pathway/Network# significant processes (CC ≥ 0.60/CC ≥ 0.75)P-value (# genes affected/total # in pathway)Database
DR CALUX 29/16   
 Cell cycle  3.76E-04 (31/679) GO 
 Apoptosis  5.63E-04 (26/543) GO 
ERα CALUX 50/9   
 Antigen processing and presentation  2.92E-05 (9/61) GO 
 Regulation of adaptive immune response  1.69E-04 (8/60) GO 
AR CALUX 7/1   
 Cell cycle_G1-S  6.64E-03 (17/122) Pathways 
 Proteolysis_Ubiquitin–proteasomal proteolysis  7.11E-03 (19/143) Pathways 
AA Hb-adducts 4/0   
 Regulation of developmental process  5.51E-03 (4/637) GO 
 Regulation of T-cell activation  8.89E-03 (2/121) GO 
GA Hb-adducts 0/0   
‰MNBN 22/53   
 DNA damage_Checkpoint  9.22E-03 (16/92) Networks 
 Regulation of cell cyclea  3.49E-04 (14/314) GO 
 Apoptosis and survival_p53-dependent apoptosisa  1.06E-03 (4/25) Pathways 

Only processes mentioned in the text are presented; for complete lists refer to Supplementary data Table S1.

aFound when using the 0.75 correlation coefficient cut-off.

Table 4.

The GO terms, pathways, and networks enriched for males among the significantly correlating genes for the various biomarkers for females

GO term/Pathway/Network# Significant Processes (CC ≥ 0.60/CC ≥ 0.75)P-Value (# Genes Affected/Total # in Pathway)Database
DR CALUX 18/0   
 Nucleosome assembly  1.22E-05 (4/69) GO 
 Cell cycle_Initiation of mitosis  6.37E-03 (2/18) Pathways 
ERα CALUX 74/88   
 Immune system process  8.49E-06 (58/863) GO 
 Regulation of lymphocyte activation  1.99E-05 (18/155) GO 
AR CALUX 39/33   
 Translational elongation  8.72E-19 (28/104) GO 
 Ubiquitin-dependent protein catabolic process  4.70E-06 (21/214) GO 
AA Hb-adducts 0/0   
GA Hb-adducts 0/0   
‰MNBN 3/6   
 Nucleotide-excision repair, DNA damage removal  1.30E-04 (3/18) GO 
 Cell cycle_Initiation of mitosis  6.37E-03 (2/18) Pathways 
GO term/Pathway/Network# Significant Processes (CC ≥ 0.60/CC ≥ 0.75)P-Value (# Genes Affected/Total # in Pathway)Database
DR CALUX 18/0   
 Nucleosome assembly  1.22E-05 (4/69) GO 
 Cell cycle_Initiation of mitosis  6.37E-03 (2/18) Pathways 
ERα CALUX 74/88   
 Immune system process  8.49E-06 (58/863) GO 
 Regulation of lymphocyte activation  1.99E-05 (18/155) GO 
AR CALUX 39/33   
 Translational elongation  8.72E-19 (28/104) GO 
 Ubiquitin-dependent protein catabolic process  4.70E-06 (21/214) GO 
AA Hb-adducts 0/0   
GA Hb-adducts 0/0   
‰MNBN 3/6   
 Nucleotide-excision repair, DNA damage removal  1.30E-04 (3/18) GO 
 Cell cycle_Initiation of mitosis  6.37E-03 (2/18) Pathways 

Only processes mentioned in the text are presented; for complete lists refer to Supplementary data Table S2.

Tox-Profiler results showed more processes to be significantly affected in female neonates compared with males (Table 5). Female-specific processes included translation and posttranslational modification. Tox-Profiler also confirmed the epigenetic responses in females, although these were also (not-significantly) positively correlated in males. T- and B-cell receptor signaling pathways were found to be suppressed in females. Translational processes were also found for male newborns. Interestingly, these were oppositely regulated when compared with females. Also, proteasome degradation was found to be oppositely regulated between the sexes as well as the TNF-alpha-NF-kB signaling pathway.

Table 5.

GO terms and pathways enriched for males among all correlating genes using Tox-Profiler

BiomarkerProcess# Significant Processes Males/FemalesT-Value MalesP-Value MalesT-Value FemalesP-Value FemalesDatabase
DR CALUX  5/29      
 Nucleosome assembly  4.1 0.154 6.4 <0.001 GO 
 T-cell receptor signaling pathway  −2.7 1.000 −4.5 0.001 KEGG 
 B-cell receptor signaling pathway  −0.6 1.000 −4.1 0.005 KEGG 
 TNF-alpha-NF-kB Signaling Pathway  2.9 0.551 −4.2 0.010 WikiPathways 
ERα CALUX  2/41      
 Immune response  −1.4 1.000 6.8 <0.001 GO 
 T-cell receptor signaling pathway  −1.5 1.000 6.8 <0.001 WikiPathways 
 B-cell receptor signaling pathway  −1.9 1.000 4.9 <0.001 WikiPathways 
AR CALUX  12/24      
 Immune response  −2.5 1.000 −6.6 <0.001 GO 
 Ubiquitin-dependent protein catabolic process  0.9 1.000 5.9 <0.001 GO 
 Nucleosome  −4.7 0.020 −1.2 1.000 GO 
AA Hb-adducts  4/17      
 Translational elongation  −1.7 1.000 8.8 <0.001 GO 
 RNA splicing  −5.6 <0.001 1.7 1.0000 GO 
        
GA Hb-adducts  8/12      
 Wnt signaling pathway  4.2 0.032 0.2 1.000 KEGG 
        
‰MNBN  30/13      
 Translational elongation  8.5 <0.001 −5.7 <0.001 GO 
 Spliceosome  4.6 0.002 −5.4 <0.001 KEGG 
 mRNA processing  4.0 0.012 −4.1 0.002 WikiPathways 
 Pathways in cancer  4.8 0.001 1.8 1.000 KEGG 
 Translational elongation  8.5 <0.001 −5.7 <0.001 GO 
BiomarkerProcess# Significant Processes Males/FemalesT-Value MalesP-Value MalesT-Value FemalesP-Value FemalesDatabase
DR CALUX  5/29      
 Nucleosome assembly  4.1 0.154 6.4 <0.001 GO 
 T-cell receptor signaling pathway  −2.7 1.000 −4.5 0.001 KEGG 
 B-cell receptor signaling pathway  −0.6 1.000 −4.1 0.005 KEGG 
 TNF-alpha-NF-kB Signaling Pathway  2.9 0.551 −4.2 0.010 WikiPathways 
ERα CALUX  2/41      
 Immune response  −1.4 1.000 6.8 <0.001 GO 
 T-cell receptor signaling pathway  −1.5 1.000 6.8 <0.001 WikiPathways 
 B-cell receptor signaling pathway  −1.9 1.000 4.9 <0.001 WikiPathways 
AR CALUX  12/24      
 Immune response  −2.5 1.000 −6.6 <0.001 GO 
 Ubiquitin-dependent protein catabolic process  0.9 1.000 5.9 <0.001 GO 
 Nucleosome  −4.7 0.020 −1.2 1.000 GO 
AA Hb-adducts  4/17      
 Translational elongation  −1.7 1.000 8.8 <0.001 GO 
 RNA splicing  −5.6 <0.001 1.7 1.0000 GO 
        
GA Hb-adducts  8/12      
 Wnt signaling pathway  4.2 0.032 0.2 1.000 KEGG 
        
‰MNBN  30/13      
 Translational elongation  8.5 <0.001 −5.7 <0.001 GO 
 Spliceosome  4.6 0.002 −5.4 <0.001 KEGG 
 mRNA processing  4.0 0.012 −4.1 0.002 WikiPathways 
 Pathways in cancer  4.8 0.001 1.8 1.000 KEGG 
 Translational elongation  8.5 <0.001 −5.7 <0.001 GO 

Only processes mentioned in the text are presented; for complete lists refer to Supplementary data Table S3.

ERα CALUX.

Female newborns showed a higher number of significantly correlating transcripts in relation to estrogen and estrogen-like exposure when compared with males. Accordingly, more processes were found to be significantly affected in females, of which the majority was immune related. Other processes included more general posttranslation events, whereas estrogen (-like) exposure also appeared to affect apoptosis Table 4). Tox-Profiler-based analysis in particular showed the immune response to be positively correlated in females. Also, more general cellular processes mostly related to transcription and posttranslational processes were identified. Interestingly, all processes found using Tox-Profiler were oppositely regulated between the sexes, although mainly reaching significance in females only (Table 5).

AR CALUX.

Male newborns showed a higher number of significantly correlating genes with AR CALUX involved in immune response, cell-cycle regulation, ubiquitin–proteasomal proteolysis and cytoskeletal-related processes (Table 3). Functional enrichment analyses on significantly correlating genes for females resulted in an extensive list comprising general cellular processes like posttranslation, metabolic, and biosynthesis processes (Table 4). This finding is confirmed by the Tox-Profiler analysis, which also shows general cellular processes to be significantly affected in female newborns (Table 5).

Hemoglobin-adduct level.

Analyses revealed only weak correlation of GA Hb-adducts with gene expression, leaving too few significantly correlating genes to conduct biological interpretation analyses. For AA Hb-adducts, in particular for males, regulation of developmental process, exocytosis, and regulation of T-cell activation and neuron differentiation was found (Tables 3 and 4). Tox-Profiler results revealed general processes oppositely regulated between the 2 sexes, although significance was generally found for females, except for mRNA splicing, which was significantly (negatively) correlated with AA Hb-adducts in males (Table 5).

Micronuclei frequency.

Male newborns showed a higher number of genes significantly correlating with MN, when compared with female newborns (1397 vs. 95; Table 2). Functional enrichment analyses using a correlation coefficient cut-off of 0.60 identified general cellular processes like translation and RNA processing, next to a number of immune-related processes. A higher cut-off of 0.75, decreasing the number of genes to 270 and thus increasing specificity, resulted in more specific processes such as nuclear fragmentation during apoptosis and cell-cycle regulation (Table 3). Despite the limited number of significantly correlating genes for female newborns, cell-cycle regulation and DNA damage removal appeared to be enriched (Table 4). Tox-Profiler analysis confirmed the significant correlation of immune-related processes in males. Also, the translational and posttranslational processes found by using significantly correlating transcripts were confirmed, showing negatively correlation with MN in males, where a significant positive correlation was found in females.

No significant correlations were observed between MN and the exposure markers when the subjects were grouped together as well as for females separately. Only in males, acrylamide and glycidamide hemoglobin adducts correlated significantly with MN (CC = 0.75 with P = 0.019 and CC = 0.73 with P = 0.025, respectively).

Inversely correlating effects on leukemia- and lymphoma-associated genes.

Finally, to evaluate differences at the transcriptomic level between male and female newborns in relation to blood cancer risk, we investigated the effects on human cancer genes. Figure 1 presents the top 4 (based on deltaCC) oppositely correlating cancer genes between male and female newborns in association with the biomarkers. In total, 4 transcripts correlated inversely in relation to DR CALUX, 13 in relation to ERα CALUX, 7 in relation to AR CALUX, 18 in relation to MN, 2 in relation to acrylamide, and none in relation to glycidamide (Supplementary data Table S4).

Figure 1.

Human leukemia and lymphoma-associated genes with opposite correlations with biomarkers between male and female newborns. Only the top 4, based on deltaCC are presented; for complete lists refer to Supplementary Table S4. * indicates a significant correlation, with an unadjusted P value less than 0.05. ** indicates the query terms used to search the GO term annotations for each gene.

Figure 1.

Human leukemia and lymphoma-associated genes with opposite correlations with biomarkers between male and female newborns. Only the top 4, based on deltaCC are presented; for complete lists refer to Supplementary Table S4. * indicates a significant correlation, with an unadjusted P value less than 0.05. ** indicates the query terms used to search the GO term annotations for each gene.

Close modal

Because a gender effect in the incidence of childhood cancers is known to exist, we hypothesized differences at the transcriptomic level in cord blood between male and female newborns as a consequence of fetal carcinogenic exposure. Concordantly, different transcriptomic responses to environmental carcinogens between the sexes were observed. Taking into account that the largest differences between sexes in childhood cancer are found for leukemias and lymphomas (2), and that we measured transcriptomics, exposure biomarkers and effect biomarkers in blood of newborns, this discussion will focus on the relation between transscirptome data and childhood risks for acute lymphoblastic leukemia (ALL).

DR CALUX

The nongenotoxic carcinogen exposures determined by means of DR CALUX, comprise compounds that activate the Ah receptor, such as PCDDs, PCDFs, and dioxin-like PCBs (14). In our study, far more genes correlated with DR CALUX in males than in females (Table 2). In addition, functional interpretation has shown large gender differences in dose-dependent transcriptomics responses.

An epigenetic response to Ah-receptor-mediated exposure was found, showing nucleosome-related processes positively correlating with DR CALUX mainly in females. Effects on the nucleosome are in concordance with the known mechanism-of-action of dioxins; activation of transcription by dioxin is accompanied by changes in chromatin structure, which depend upon a functional aromatic hydrocarbon (Ah) receptor (34). It is, however, unclear why this is sex-specific and how this could affect ALL susceptibility. The (mitotic) cell cycle was mainly affected in males. Although it is generally accepted that Ah receptor activation causes toxic and carcinogenic effects through its role in detoxification by modulating expression of metabolizing enzymes, activation of the Ah receptor possibly also participates in pathways such as cell-cycle regulation, apoptosis, and immune response (35). Possibly, the activation of cell-cycle regulating genes via the Ah-receptor in male neonates is stronger than in females, and therefore males are more susceptible to develop ALL because of exposure to PCB and other Ah-receptor activators.

T- and B-cell receptor signaling pathways are negatively correlated with dioxin exposure in female neonates. Fetal TCDD exposure in animals has been linked to inhibition of cellular differentiation and maturation, primarily targeting T lymphocytes, leading to thymic atrophy and immunosuppression in offspring (36). This may suggest that the immune system is more suppressed in females exposed to Ah-activators, than in males. That would imply a higher risk in females, which is not the case in childhood ALL. However, a higher incidence has been reported in female infants (37) possibly suggesting T- and B-cell receptor signaling pathways to mechanistically underlie the increased risk of developing of infant ALL.

Interestingly, the TNF-alpha-NF-kB signaling pathway is negatively correlated with dioxin exposure in females, whereas (not significantly) positively correlated in male newborns. The association of inflammation and cancer has been studied extensively (38) showing TNF-α acts as a key mediator of inflammation and cancer (39). Although this pathway is involved in general cancer susceptibility, NF-kB activation has been consistently associated with childhood ALL (40, 41). The involvement of the NF-kB pathway has been suggested to play a role in the sex-specific character of childhood leukemia based on the association of the IRF4 gene with higher male-specific risk with childhood ALL (42). IRF4 was, however, not found to correlate with dioxin exposure, although we found several other genes showing differential gender-specific correlation within the TNF-alpha-NF-kB signaling pathway, including, for example, TRAF1, TRAF3, MAP3k3, MAP3k14, and CASP8. This might further indicate the TNF-alpha-NF-kB signaling pathway as a possible contributing factor in relation to the male predominance in childhood ALL. Moreover, the unaffected or even nonsignificantly increased signaling by tumor necrosis factor (TNF)-α in males might also result in a failure of the lymphocytes to undergo apoptosis (43), possibly contributing to the increased risk of childhood ALL in males. This is underlined by the male-specific negative correlation of CASP8 with dioxin exposure. Accordingly, apoptosis was found to be exclusively affected in males. Nine human blood cancer-associated genes were found to be inversely correlating strongly indicating a gender-specific response to dioxins in relation to tumorigenesis. The human antiapoptotic cancer genes JAK2 and VHL are positively correlated with dioxin exposure in males, whereas negatively in females contributing to apoptosis blockade, possibly predisposing males to develop childhood ALL. These cancer genes have been associated with childhood ALL (44–45).

In conclusion, the female-specic suppression of the TNF-alpha-NF-kB signaling pathway upon dioxin exposure might protect female neonates from developing childhood ALL, whereas their male counterparts are at higher risk, possibly because of consequent blockade of apoptosis.

ERα CALUX

Estrogens have been linked to B- and T-cell activation, enhanced antigen presentation by dendritic cells, and cytokine production (46). Accordingly, we found immune-related processes to be affected, with a higher response in females compared with males. In fact, results using unfiltered correlation data suggest an inverse regulation upon estrogen receptor activation with a significant stimulation of the GO term “immune system” in females and (nonsignificant) suppression in males. GO terms like B- and T-cell signaling show similar inverse trends. The male-specific immune suppression may contribute to the higher incidence of childhood ALL because leukemia is associated with immune dysfunction (47). Further confirming gender-specificity, we found leukemia- and lymphoma-related genes differentially correlating with estrogen exposure (Figure 1). Not all genes conclusively point to a male predisposition for developing ALL, in fact we found a female-specific-positive correlation of MllT6. Mixed lineage leukemias are histone methyl-transferases that regulate gene activation and are also well known to be rearranged in acute myeloid and lymphoid leukemias (48). Interestingly, the female-specific correlation might be in line with the literature. Unlike childhood leukemia diagnosed in childhood, where there is a predominance in males, high frequencies of MLL rearrangements in female Infant leukemia have been reported, as well as a higher risk for females to develop infant ALL (37, 49).

AR CALUX

Next to androgens, certain environmental pollutants may influence AR signaling pathways by acting as or interfering with endogenous androgens, regulating the expression of various genes involved in cell-cycle control, apoptosis, cell growth, and differentiation (50). We found the cell-cycle GO term to correlate significantly to AR CALUX in males. Possibly, the activation of cell-cycle genes via the AR-receptor in male neonates is stronger than in females, and leading to a higher males-specific susceptibility. In contrast, the immune response was significantly suppressed in only females implying a higher cancer risk in females. Concordantly, differentially correlating leukemia and lymphoma genes with AR receptor activation do not conclusively support a male predisposition. In fact, next to the MLLT6 gene correlating with the ERα receptor, a female-specific positive correlation of MllT3 with AR receptor activation was found which might be related to female predisposition to develop infant leukemia (37, 48, 49).

Hb-adducts

Tox-Profiler revealed the Wnt signaling pathway and a number of general processes oppositely regulated between the 2 sexes upon acrylamide exposure. Aberrations of the Wnt pathway have been associated with various types of cancer including ALL. A male-specific activation of the Wnt pathway might thus contribute to the higher incidence of cancer in the blood system.

Micronuclei frequencies

Micronuclei formation in peripheral blood T-lymphocytes may be regarded as a phenotypic endpoint that reflects the deleterious effects on chromosomes because of the (combined) exposure to genotoxic carcinogens (51). Previous studies report that the baseline MN frequency is relatively low in newborns and no differences in gender have been identified (29, 52), which is confirmed in our study. Despite this, the sex-specific transcriptomic profiles associated with MN differ dramatically. Overall, a higher number of significantly correlating genes in male newborns in relation to MN was observed. Correlations were frequently opposite between males and females. The male-specific profile contains many more genes relevant to ALL and carcinogenicity in general. This might contribute to the higher childhood cancer risk in males. Interestingly, 6 genes of our MN correlating genes are also present in the recently published MN gene network (53). They were found to be oppositely correlating between male and female neonates, that is, BCL2, CDC20, PTTG1, BAX, PCNA, and LMNA, suggesting sex-specific contribution to the network. Finally, MN-associated sex-specific differences in general cellular processes such as transcription, posttranslational processes, RNA processing, and splicing might be of relevance with regard to the reported gender differences in the incidence of childhood cancers, however, interpreting these findings is difficult.

While exposure to acrylamide and endocrine disruptors did not differ significantly between sexes at birth, distinct gender-specific dose- and response-related transcriptomic profiles were identified. In particular, male-specific TNF-alpha-NF-kB signaling upon dioxin exposure and subsequent failure of the lymphocytes to undergo apoptosis, and activation of the Wnt pathway in boys upon acrylamide exposure might represent possible mechanistic explanations for the gender-specificity in the incidence of childhood ALL. Interestingly, oppositely correlating leukemia and lymphoma genes between male and female newborns were identified in relation to the different biomarkers of exposure which might be relevant to male-specific predisposition to develop childhood ALL and female-specific predisposition to develop infant ALL.

Our study has the strength of considering multiple parameters in relation to gene expression, although we recognize that the correlation analyses in the current study of gene expression with the various biomarkers are based on a relatively small number of samples making multivariate correlation analyses impractical. This is because of the limited availability of samples with biomarker measurements and gender-specific analyses, which reduces statistical power. Nevertheless, because the gender-specific responses occur for multiple biomarkers, we are confident about our conclusions. To confirm the generated hypotheses, future research based on higher number of samples is needed.

No potential conflicts of interest were disclosed.

Conception and design: D. van Leeuwen, M. Løvik, B. Granum, M. Kirsch-Volders, J. C.S. Kleinjans, H. van Loveren, J. H.M. van Delft

Development of methodology: D. van Leeuwen, H. Besselink, P. Rydberg, J. C.S. Kleinjans, H. van Loveren, J. H.M. van Delft

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): K. Hochstenbach, M. Løvik, B. Granum, U. C. Nygaard, E. Namork, K. vande Loock, H. Besselink, M. Törnqvist, P. Rydberg

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): K. Hochstenbach, D. van Leeuwen, H. Gmuender, M. Kirsch-Volders, M. Törnqvist, H. von Stedingk, H. van Loveren

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): R. W. Gottschalk, B. Granum

Writing, review, and/or revision of the manuscript: K. Hochstenbach, M. Løvik, B. Granum, U. C. Nygaard, E. Namork, M., J. H.M. van Delft Kirsch-Volders, I. Decordier, H. Besselink, M. Törnqvist, H. von Stedingk, J. C.S. Kleinjans, H. van Loveren

Study supervision: D. van Leeuwen, M. Løvik, H. van Loveren, J. H.M. van Delft

This work was supported by the EU Integrated Project NewGeneris, 6th Framework Programme, Priority 5: Food Quality and Safety. NewGeneris is the acronym of the project “Newborns and Genotoxic exposure risks” [FOOD-CT-2005-016320]; the Norwegian Institute of Public Health; and Netherlands Genomics Initiative/Netherlands Organisation for Scientific Research (NOW; 050-060-510).

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1.
Cartwright
RA
,
Gurney
KA
,
Moorman
AV
. 
Sex ratios and the risks of haematological malignancies
.
Br J Haematol
2002
;
118
:
1071
7
.
2.
Linet
MS
,
Wacholder
S
,
Zahm
SH
. 
Interpreting epidemiologic research: lessons from studies of childhood cancer
.
Pediatrics
2003
;
112
:
218
32
.
3.
Preston-Martin
S
,
Pike
MC
,
Ross
RK
,
Jones
PA
,
Henderson
BE
. 
Increased cell division as a cause of human cancer
.
Cancer Res
1990
;
50
:
7415
21
.
4.
Tower
RL
,
Spector
LG
. 
The epidemiology of childhood leukemia with a focus on birth weight and diet
.
Crit Rev Clin Lab Sci
2007
;
44
:
203
42
.
5.
Pombo-de-Oliveira
MS
,
Koifman
S
. 
Infant acute leukemia and maternal exposures during pregnancy
.
Cancer Epidemiol Biomarkers Prev
2006
;
15
:
2336
41
.
6.
Steliarova-Foucher
E
,
Stiller
C
,
Kaatsch
P
,
Berrino
F
,
Coebergh
JW
,
Lacour
B
, et al
Geographical patterns and time trends of cancer incidence and survival among children and adolescents in Europe since the 1970s (the ACCISproject): an epidemiological study
.
Lancet
2004
;
364
:
2097
105
.
7.
Wild
CP
,
Kleinjans
J
. 
Children and increased susceptibility to environmental carcinogens: evidence or empathy?
Cancer Epidemiol Biomarkers Prev
2003
;
12
:
1389
94
.
8.
Gluckman
PD
,
Hanson
MA
,
Cooper
C
,
Thornburg
KL
. 
Effect of in utero and early-life conditions on adult health and disease
.
N Eng J Med
2008
:
359
:
61
73
.
9.
Mose
T
,
Mathiesen
L
,
Karttunen
V
,
Nielsen
JK
,
Sieppi
E
,
Kummu
M
, et al
Meta-analysis of data from human ex vivo placental perfusion studies on genotoxic and immunotoxic agents within the integrated European project NewGeneris
.
Placenta
2012
;
33
:
433
9
.
10.
Annola
K
,
Keski-Rahkonen
P
,
Vahakangas
K
,
Lehtonen
M
. 
Simultaneous determination of acrylamide, its metabolite glycidamide and antipyrine in human placental perfusion fluid and placental tissue by liquid chromatography-electrospray tandem mass spectrometry
.
J Chromatogr B Analyt Technol Biomed Life Sci
2008
;
876
:
191
7
.
11.
Balakrishnan
B
,
Henare
K
,
Thorstensen
EB
,
Ponnampalam
AP
,
Mitchell
MD
. 
Transfer of bisphenol A across the human placenta
.
Am J Obstet Gynecol
2010
;
202
:
e1
7
.
12.
Sala
M
,
Ribas-Fitó
N
,
Cardo
E
,
de Muga
ME
,
Marco
E
,
Mazón
C
, et al
Levels of hexachlorobenzene and other organochlorine compounds in cord blood: exposure across placenta
.
Chemosphere
2001
;
43
:
895
901
.
13.
Wang
SL
,
Lin
CY
,
Guo
YL
,
Lin
LY
,
Chou
WL
,
Chang
LW
. 
Infant exposure to polychlorinated dibenzo-p-dioxins, dibenzofurans and biphenyls (PCDD/Fs, PCBs)—correlation between prenatal and postnatal exposure
.
Chemosphere
2004
;
54
:
1459
73
.
14.
Gizzi
G
,
Vincent
U
,
von Holst
C
,
de Jong
J
,
Genouel
C
. 
Validation of an analytical method for the determination of carbadox and olaquindox in feedstuff by liquid chromatography coupled to UV and/or diode array detection
.
Food Addit Contam
2007
;
24
:
1226
35
.
15.
Choi
SM
,
Yoo
SD
,
Lee
BM
. 
Toxicological characteristics of endocrine-disrupting chemicals: developmental toxicity, carcinogenicity, and mutagenicity
.
J Toxicol Environ Health B Crit Rev
2004
;
7
:
1
24
.
16.
Tareke
E
,
Rydberg
P
,
Karlsson
P
,
Eriksson
S
,
Tornqvist
M
. 
Analysis of acrylamide, a carcinogen formed in heated foodstuffs
.
J Agric Food Chem
2002
;
50
:
4998
5006
.
17.
Tornqvist
M
. 
Acrylamide in food: the discovery and its implications: a historical perspective
.
Adv Exp Med Biol
2005
;
561
:
1
19
.
18.
Ghanayem
BI
,
Witt
KL
,
Kissling
GE
,
Tice
RR
,
Recio
L
. 
Absence of acrylamide-induced genotoxicity in CYP2E1-null mice: evidence consistent with a glycidamide-mediated effect
.
Mutat Res
2005
;
578
:
284
97
.
19.
Besaratinia
A
,
Pfeifer
GP
. 
A review of mechanisms of acrylamide carcinogenicity
.
Carcinogenesis
2007
;
28
:
519
28
.
20.
von Stedingk
H
,
Rydberg
P
,
Tornqvist
M
. 
A new modified Edman procedure for analysis of N-terminal valine adducts in hemoglobin by LC-MS/MS
.
J Chromatogr B Analyt Technol Biomed Life Sci
2010
;
878
:
2483
90
.
21.
Fenech
M
. 
Cytokinesis-block micronucleus cytome assay
.
Nat Protoc
2007
;
2
:
1084
104
.
22.
Kirsch-Volders
M
,
Elhajouji
A
,
Cundari
E
,
Van Hummelen
P
. 
The in vitro micronucleus test: a multi-endpoint assay to detect simultaneously mitotic delay, apoptosis, chromosome breakage, chromosome loss and non-disjunction
.
Mutat Res
1997
;
392
:
19
30
.
23.
Kirsch-Volders
M
,
Plas
G
,
Elhajouji
A
,
Lukamowicz
M
,
Gonzalez
L
,
Vande Loock
K
, et al
The in vitro MN assay in 2011: origin and fate, biological significance, protocols, high throughput methodologies and toxicological relevance
.
Arch Toxicol
2011
;
85
:
873
99
.
24.
Bonassi
S
,
Znaor
A
,
Ceppi
M
,
Lando
C
,
Chang
WP
,
Holland
N
, et al
An increased micronucleus frequency in peripheral blood lymphocytes predicts the risk of cancer in humans
.
Carcinogenesis
2007
;
28
:
625
31
.
25.
Brouwers
MM
,
Besselink
H
,
Bretveld
RW
,
Anzion
R
,
Scheepers
PT
,
Brouwer
A
, et al
Estrogenic and androgenic activities in total plasma measured with reporter-gene bioassays: relevant exposure measures for endocrine disruptors in epidemiologic studies?
Environ Int
2011
;
37
:
557
64
.
26.
Pedersen
M
,
Halldorsson
TI
,
Mathiesen
L
,
Mose
T
,
Brouwer
A
,
Hedegaard
M
, et al
Dioxin-like exposures and effects on estrogenic and androgenic exposures and micronuclei frequency in mother-newborn pairs
.
Environ Int
2010
;
36
:
344
51
.
27.
von Stedingk
H
,
Vikström
AC
,
Rydberg
P
,
Pedersen
M
,
Nielsen
JK
,
Segerbäck
D
, et al
Analysis of hemoglobin adducts from acrylamide, glycidamide, and ethylene oxide in paired mother/cord blood samples from Denmark
.
Chem Res Toxicol
2011
;
24
:
1957
65
.
28.
Decordier
I
Papine
A
Plas
G
Roesems
S
Vande Loock
K
Moreno-Palomo
J
et al 
Automated image analysis of cytokinesis-blocked micronuclei: an adapted protocol and a validated scoring procedure for biomonitoring
.
Mutagenesis
2009
;
24(1)
:
85
93
.
29.
Vande Loock
K
,
Fthenou
E
,
Decordier
I
,
Chalkiadaki
G
,
Keramarou
M
,
Plas
G
, et al
Maternal and gestational factors and micronuclei frequencies in umbilical blood: the NewGeneris Rhea Cohort in Crete
.
Environ Health Perspect
2011
;
119
:
1460
5
.
30.
Edgar
R
,
Domrachev
M
,
Lash
AE
. 
Gene Expression Omnibus: NCBI gene expression and hybridization array data repository
.
Nucleic Acids Res
2002
;
30
:
207
10
.
32.
Boorsma
A
,
Foat
BC
,
Vis
D
,
Klis
F
,
Bussemaker
HJ
. 
T-profiler: scoring the activity of predefined groups of genes using gene expression data
.
Nucleic Acids Res
2005
;
33
:
W592
5
.
33.
Futreal
PA
,
Coin
L
,
Marshall
M
,
Down
T
,
Hubbard
T
,
Wooster
R
, et al
A census of human cancer genes
.
Nat Rev Cancer
2004
;
4
:
177
83
.
34.
Morgan
JE
,
Whitlock
JP
 Jr
. 
Transcription-dependent and transcription-independent nucleosome disruption induced by dioxin
.
Proc Natl Acad Sci U S A
1992
;
89
:
11622
6
.
35.
Puga
A
,
Ma
C
,
Marlowe
JL
. 
The aryl hydrocarbon receptor cross-talks with multiple signal transduction pathways
.
Biochem Pharmacol
2009
;
77
:
713
22
.
36.
Yoshizawa
K
,
Heatherly
A
,
Malarkey
DE
,
Walker
NJ
,
Nyska
A
. 
A critical comparison of murine pathology and epidemiological data of TCDD, PCB126, and PeCDF
.
Toxicol Pathol
2007
;
35
:
865
79
.
37.
Pieters
R
. 
Biology and treatment of infant leukemias
. In:
Pui
ChinHon
,
editor
. 
Treatment of acute leukemias
.
New directions for Clinical Research. Totowa
.
Humana Press
; 
2003
:
p.
61
75
.
38.
Mantovani
A
,
Garlanda
C
,
Allavena
P
. 
Molecular pathways and targets in cancer-related inflammation
.
Ann Med
2010
;
42
:
161
70
.
39.
Balkwill
F
. 
Tumour necrosis factor and cancer
.
Nat Rev Cancer
2009
;
9
:
361
71
.
40.
Jost
PJ
,
Ruland
J
. 
Aberrant NF-kappaB signaling in lymphoma: mechanisms, consequences, and therapeutic implications
.
Blood
2007
;
109
:
2700
7
.
41.
Kordes
U
,
Krappmann
D
,
Heissmeyer
V
,
Ludwig
WD
,
Scheidereit
C
. 
Transcription factor NF-kappaB is constitutively activated in acute lymphoblastic leukemia cells
.
Leukemia
2000
;
14
:
399
402
.
42.
Do
TN
,
Ucisik-Akkaya
E
,
Davis
CF
,
Morrison
BA
,
Dorak
MT
. 
An intronic polymorphism of IRF4 gene influences gene transcription in vitro and shows a risk association with childhood acute lymphoblastic leukemia in males
.
Biochim Biophys Acta
2010
;
1802
:
292
300
.
43.
Kast
RE
,
Altschuler
EL
. 
Anti-apoptosis function of TNF-alpha in chronic lymphocytic leukemia: lessons from Crohn's disease and the therapeutic potential of bupropion to lower TNF-alpha
.
Arch Immunol Ther Exp (Warsz)
2005
;
53
:
143
7
.
44.
Mullighan
CG
,
Zhang
J
,
Harvey
RC
,
Collins-Underwood
 Jr
,
Schulman
BA
,
Phillips
LA
, et al
JAK mutations in high-risk childhood acute lymphoblastic ##leukemia
.
Proc Natl Acad Sci U S A
2009
;
106
:
9414
8
.
45.
Benetatos
L
,
Dasoula
A
,
Syed
N
,
Hatzimichael
E
,
Crook
T
,
Bourantas
KL
. 
Methylation analysis of the von Hippel–Lindau gene in acute myeloid leukaemia and myelodysplastic syndromes
.
Leukemia
2008
;
22
:
1293
5
.
46.
Pierdominici
M
,
Maselli
A
,
Colasanti
T
,
Giammarioli
AM
,
Delunardo
F
,
Vacirca
D
, et al
Estrogen receptor profiles in human peripheral blood lymphocytes
.
Immunol Lett
2010
;
132
:
79
85
.
47.
Ramsay
AG
,
Gribben
JG
. 
Immune dysfunction in chronic lymphocytic leukemia T cells and lenalidomide as an immunomodulatory drug
.
Haematologica
2009
;
94
:
1198
202
.
48.
Ansari
KI
,
Mishra
BP
,
Mandal
SS
. 
MLL histone methylases in gene expression, hormone signaling and cell cycle
.
Front Biosci
2009
;
14
:
3483
95
.
49.
Ross
JA
,
Robison
LL
. 
MLL rearrangements in infant leukemia: is there a higher frequency in females?
Leuk Res
1997
;
21
:
793
5
.
50.
Heisler
LE
,
Evangelou
A
,
Lew
AM
,
Trachtenberg
J
,
Elsholtz
HP
,
Brown
TJ
. 
Androgen-dependent cell cycle arrest and apoptotic death in PC-3 prostatic cell cultures expressing a full-length human androgen receptor
.
Mol Cell Endocrinol
1997
;
126
:
59
73
.
51.
Holland
N
,
Fucic
A
,
Merlo
DF
,
Sram
R
,
Kirsch-Volders
M
. 
Micronuclei in neonates and children: effects of environmental, genetic, demographic and disease variables
.
Mutagenesis
2011
;
26
:
51
6
.
52.
Milosevic-Djordjevic
O
,
Grujicic
D
,
Arsenijevic
S
,
Brkic
M
,
Ugrinovic
S
,
Marinkovic
D
. 
Micronuclei in cord blood lymphocytes as a biomarker of transplacental exposure to environmental pollutants
.
Tohoku J Exp Med
2007
;
213
:
231
9
.
53.
van Leeuwen
DM
,
Pedersen
M
,
Knudsen
LE
,
Bonassi
S
,
Fenech
M
,
Kleinjans
JC
, et al
Transcriptomic network analysis of micronuclei-related genes: a case study
.
Mutagenesis
2011
;
26
:
27
32
.