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Sufficient evidence supports a relationship between certain myeloid neoplasms and exposure to benzene or formaldehyde. DNA methylation could underlie benzene- and formaldehyde-induced health outcomes, but data in exposed human populations are limited. We conducted two cross-sectional epigenome-wide association studies (EWAS), one in workers exposed to benzene and another in workers exposed to formaldehyde. Using HumanMethylation450 BeadChips, we investigated differences in blood cell DNA methylation among 50 benzene-exposed subjects and 48 controls, and among 31 formaldehyde-exposed subjects and 40 controls. We performed CpG-level and regional-level analyses. In the benzene EWAS, we found genome-wide significant alterations, i.e., FWER-controlled

Number of deletions in each gene tested amongst 559 ALL cases from the CCLS, assessed by MLPA.

Supplementary Methods for miR Extraction, qPCR, and statistical analysis

Revised Supplemental Material

Association analyses stratified by sex between tobacco smoke exposures and the number of deletions in the child's leukemia using multivariable Poisson regressions, the California Childhood Leukemia Study, 1995-2015.

Supplementary Figure S2A and S2B: miR qPCR Assay Comparison

Background: Blood miRNAs are a new promising area of disease research, but variability in miRNA measurements may limit detection of true-positive findings. Here, we measured sources of miRNA variability and determine whether repeated measures can improve power to detect fold-change differences between comparison groups.Methods: Blood from healthy volunteers (N = 12) was collected at three time points. The miRNAs were extracted by a method predetermined to give the highest miRNA yield. Nine different miRNAs were quantified using different qPCR assays and analyzed using mixed models to identify sources of variability. A larger number of miRNAs from a publicly available blood miRNA microarray dataset with repeated measures were used for a bootstrapping procedure to investigate effects of repeated measures on power to detect fold changes in miRNA expression for a theoretical case–control study.Results: Technical variability in qPCR replicates was identified as a significant source of variability (P < 0.05) for all nine miRNAs tested. Variability was larger in the TaqMan qPCR assays (SD = 0.15–0.61) versus the qScript qPCR assays (SD = 0.08–0.14). Inter- and intraindividual and extraction variability also contributed significantly for two miRNAs. The bootstrapping procedure demonstrated that repeated measures (20%–50% of N) increased detection of a 2-fold change for approximately 10% to 45% more miRNAs.Conclusion: Statistical power to detect small fold changes in blood miRNAs can be improved by accounting for sources of variability using repeated measures and choosing appropriate methods to minimize variability in miRNA quantification.Impact: This study demonstrates the importance of including repeated measures in experimental designs for blood miRNA research.See all the articles in this CEBP Focus section, “Biomarkers, Biospecimens, and New Technologies in Molecular Epidemiology.”Cancer Epidemiol Biomarkers Prev; 23(12); 2658–66. ©2014 AACR.

Supplementary Results for Figures S1-S4

Tobacco smoke exposure has been associated with risk of childhood acute lymphoblastic leukemia (ALL). Understanding the relationship between tobacco exposures and specific mutations may yield etiologic insights. We carried out a case-only analysis to explore whether prenatal and early-life tobacco smoke exposure influences the formation of leukemogenic genomic deletions. Somatic copy number of 8 genes frequently deleted in ALL (CDKN2A, ETV6, IKZF1, PAX5, RB1, BTG1, PAR1 region, and EBF1) was assessed in 559 pretreatment tumor samples from the California Childhood Leukemia Study. Parent and child's passive tobacco exposure was assessed using interview-assisted questionnaires as well as DNA methylation in aryl-hydrocarbon receptor repressor (AHRR), a sentinel epigenetic biomarker of exposure to maternal smoking during pregnancy. Multivariable Poisson regressions were used to test the association between the smoking exposures and total number of deletions. Deletion burden varied by subtype, with a lower frequency in high-hyperdiploid and higher frequency in ETV6–RUNX1 fusion ALL. The total number of deletions per case was positively associated with tobacco smoke exposure, in particular for maternal ever-smoking (ratio of means, RM, 1.31; 95% CI, 1.08–1.59), maternal smoking during pregnancy (RM, 1.48; 95% CI, 1.12–1.94), and during breastfeeding (RM, 2.11; 95% CI, 1.48–3.02). The magnitude of association with maternal ever-smoking was stronger in male children compared with females (Pinteraction = 0.04). The total number of deletions was also associated with DNA methylation at the AHRR epigenetic biomarker (RM, 1.32; 95% CI, 1.02–1.69). Our results suggest that prenatal and early-life tobacco smoke exposure increase the frequency of somatic deletions in children who develop ALL. Cancer Res; 77(7); 1674–83. ©2017 AACR.

Supplemental Figures S1-S7

Supplementary Legends for Figures S1-S4

Supplementary Table 1 from Chromosome 12p Deletions in TEL-AML1 Childhood Acute Lymphoblastic Leukemia Are Associated with Retrotransposon Elements and Occur Postnatally

Benzene is a recognized hematotoxin and leukemogen; however, its mechanism of action in humans remain unclear. To provide insight into the processes underlying benzene hematotoxicity, we performed high-resolution metabolomic profiling of plasma collected from a cross-sectional study of 33 healthy workers exposed to benzene (median 8-h time-weighted average exposure; 20 ppma), and 25 unexposed controls in Shanghai, China. Metabolic features associated with benzene were identified using a metabolome-wide association study (MWAS) that tested for the relationship between feature intensity and benzene exposure. MWAS identified 478 mass spectral features associated with benzene exposure at false discovery rate < 20%. Comparison to a list of 13 known benzene metabolites and metabolites predicted using a multi-component biotransformation algorithm showed five metabolites were detected, which included the known metabolites phenol and benzene diolepoxide. Metabolic pathway enrichment identified 41 pathways associated with benzene exposure, with altered pathways including carnitine shuttle, fatty acid metabolism, sulfur amino acid metabolism, glycolysis, gluconeogenesis and branched chain amino acid metabolism. These results suggest disruption to fatty acid uptake, energy metabolism and increased oxidative stress, and point towards pathways related to mitochondrial dysfunction, which has previously been linked to benzene exposure in animal models and human studies. Taken together, these results suggest benzene exposure is associated with disruption of mitochondrial pathways, and provide promising, systems biology biomarkers for risk assessment of benzene-induced hematotoxicity in humans.

Summary Background Non-Hodgkin lymphoma comprises a heterogeneous group of cancers with unresolved aetiology, although risk factors include environmental exposures to toxic chemicals. Although the ubiquitous pollutant benzene is an established leukemogen, its potential to cause non-Hodgkin lymphoma has been widely debated. We aimed to examine the potential link between benzene exposure and risk of non-Hodgkin lymphoma in humans by evaluating a wide array of cohort and case-control studies using electronic systematic review. Methods We did a comprehensive systematic review and meta-analysis of all qualified human epidemiological studies that assessed the relationship between benzene exposure and non-Hodgkin lymphoma. We queried the PubMed and Embase databases for relevant articles published before June 5, 2019, and applied the SysRev platform for study selection. All peer-reviewed human cohort and case-control studies that reported non-Hodgkin lymphoma risk estimates specifically for benzene exposure were eligible for inclusion. Studies that calculated relative risks (RRs) for industries or job types without identifying those specifically exposed to benzene, that combined non-Hodgkin lymphoma with other cancer types, or that reported many different solvent exposures together were excluded. From each study, two investigators independently extracted information on the study design, location, years, sample size, participation rates, age, sex, sources of cases and controls, diagnosis, histological verification, exposure assessment, results, adjustment, and statistical analysis, and subsequently assessed study quality. We calculated the meta-analysis relative risk (meta-RR) and CIs using the fixed effect and random effect models, as well as assessing publication bias. Findings Our search yielded 2481 articles. After screening and removal of duplicates, 20 case-control studies and eight cohort studies were included in our meta-analysis, which included a total of 9587 patients with non-Hodgkin lymphoma. We reported an increased meta-relative risk (meta-RR) of 33% in highly exposed groups, when data were available (meta-RR 1·33 [95% CI 1·13–1·57], n=28). The meta-RR rose to 1·51 (1·22–1·87, n=18) in the studies that provided results specifically for highly exposed individuals. In particular, we reported a doubling of this risk for diffuse large B-cell lymphoma, a major non-Hodgkin lymphoma subtype (1·67 [1·01–2·77]). We also detected increased risks for follicular lymphoma (1·47 [0·95–2·27]) and hairy cell leukaemia (1·77 [0·99–3·16]), though they were not statistically significant. Funnel plot, Egger's test (p=0·77) and Begg's test (p=0·98) did not show evidence of publication bias. We evaluated the major aspects of causal inference and found evidence to support all the Hill considerations for assigning causation. Interpretation Our findings suggest a causal link between benzene exposure and non-Hodgkin lymphoma, especially for diffuse large B-cell lymphoma. Funding National Institute of Environmental Health Sciences.

BackgroundKey characteristics (KCs), properties of agents or exposures that confer potential hazard, have been developed for carcinogens and other toxicant classes. KCs have been used in the systematic assessment of hazards and to identify assay and data gaps that limit screening and risk assessment. Many of the mechanisms through which pharmaceuticals and occupational or environmental agents modulate immune function are well recognized. Thus KCs could be identified for immunoactive substances and applied to improve hazard assessment of immunodulatory agents.ObjectivesThe goal was to generate a consensus-based synthesis of scientific evidence describing the KCs of agents known to cause immunotoxicity and potential applications, such as assays to measure the KCs.MethodsA committee of 18 experts with diverse specialties identified 10 KCs of immunotoxic agents, namely, 1) covalently binds to proteins to form novel antigens, 2) affects antigen processing and presentation, 3) alters immune cell signaling, 4) alters immune cell proliferation, 5) modifies cellular differentiation, 6) alters immune cell-cell communication, 7) alters effector function of specific cell types, 8) alters immune cell trafficking, 9) alters cell death processes, and 10) breaks down immune tolerance. The group considered how these KCs could influence immune processes and contribute to hypersensitivity, inappropriate enhancement, immunosuppression, or autoimmunity.DiscussionKCs can be used to improve efforts to identify agents that cause immunotoxicity via one or more mechanisms, to develop better testing and biomarker approaches to evaluate immunotoxicity, and to enable a more comprehensive and mechanistic understanding of adverse effects of exposures on the immune system. https://doi.org/10.1289/EHP10800.

The ability to induce chronic inflammation and immunosuppression are two key characteristics of carcinogens and important forms of immunotoxicity. The National Toxicology Program (NTP) evaluated the immunotoxicity of two per- and polyfluoroalkyl substances (PFASs), PFOA (perfluorooctanoic acid) and PFOS (perfluorooctane sulfonate), in 2016. However, the potential pro-inflammatory and immunosuppressive effects of other PFASs remain largely uncharacterized.We developed an expanded set of search terms pertaining to the chronic inflammatory and immunosuppressive effects of PFASs based on those of the International Agency for Research on Cancer (IARC) and NTP. To confirm searching effectiveness and scope, we compared our search term results with those of IARC and NTP for both PFASs and two other known carcinogens, chromium (VI) and benzene. Systematic evidence maps (SEMs) were also produced using Tableau to visualize the distribution of study numbers and types reporting immunotoxic effects and specific biomarkers elicited by PFAS exposures.In total, 1155 PFAS studies were retrieved, of which 321 qualified for inclusion in our dataset. Using our search terms, we identified a greater number of relevant studies than those obtained using IARC and NTP's search terms. From the SEM findings, increased cytokine production strengthened an association between PFAS exposure and chronic inflammation, and decreased B-cell activation and altered levels of T-cell subtypes and immunoglobulins confirmed PFAS-induced immunosuppression.Our SEM findings confirm that several PFASs commonly found in both in the environment, including those that are lesser-known, may induce immunosuppression and chronic inflammation, two key characteristics of carcinogens. This approach, including development of search terms, study screening process, data coding, and evidence mapping visualizations, can be applied to other key characteristics of chemical carcinogens.

Matrix multiplication is one of the bottleneck computations for training the weights within deep neural networks. To speed up the training phase, we propose to use faster algorithms for matrix multiplication known as Arbitrary Precision Approximating (APA) algorithms. APA algorithms perform asymptotically fewer arithmetic operations than the classical algorithm, but they compute an approximate result with an error that can be made arbitrarily small in exact arithmetic. Practical APA algorithms provide significant reduction in computation time and still provide enough accuracy for many applications like neural network training. We demonstrate that APA algorithms can be efficiently implemented and parallelized for multicore CPUs to obtain up to 28% and 21% speedups over the fastest implementation of the classical algorithm using one core and 12 cores, respectively. Furthermore, using these algorithms to train a Multi-Layer Perceptron (MLP) network yields no significant reduction in the training or testing error. Our performance results on a large MLP network show overall sequential and multithreaded performance improvements of up to 25% and 13%, respectively. We also demonstrate up to 15% improvement when training the fully connected layers of the VGG-19 image classification network.