Acute lymphoblastic leukemia (ALL) is the most common pediatric malignancy, with an annual incidence rate of 42 cases per million children under age 15 in the United States. While overall 5-year survival rate has improved to 80% on average, the US annual incidence rate increased by 0.8% per year from 1975 to 2012 in children aged 14 years and younger.[1,2]. Like many other diseases and cancers, childhood ALL risk shows consistent sex differences.[1,3–5] Males show a higher incidence rate in comparison to females (45 cases per million children versus 39 cases per million children per year, respectively) in the United States. Relapse and secondary malignancies are also more common in males. Reasons underlying sex differences in childhood ALL risk are still unknown, as it has not been extensively evaluated.
The sex difference in susceptibility to complex disorders is an active research area, and some progress has been made in recent years.[7,8] Studies that examined sex differential for etiological clues identified several sex-specific genetic markers also for childhood ALL risk.[9–14] For example, a study that examined previously identified childhood ALL single nucleotide polymorphisms (SNPs) in genome-wide association studies (GWAS)[15,16] reported sex-specific effects of 2 statistically correlated ARID5B (AT-rich interactive domain 5b) SNPs (rs10994982 [P = 0.01] and rs10740055 [P = 0.03]), with ALL risk in males (odds ratio [OR] = 3.79 and OR = 4.35, respectively; ORs for females = 1.03 and 1.37, respectively). Candidate gene studies have also reported sex differences in genetic associations in childhood ALL. Two SNPs in ERCC1 (excision repair cross-complementation group 1) are associated with childhood ALL risk among males, but not with females. Likewise, several multiple sclerosis risk markers, such as HLA-DRA (rs3135388), HLA-C (rs9264942), HSPA1B (rs1061581), and IFNG (rs2069727), also yielded sex-specific associations with childhood ALL risk. Associations with an intronic SNP (rs12203592) in IRF4 and an intergenic SNP (rs2395185) near HLA-DRA further suggest the existence of sex-specific genetic risk variants.[9,11]
The robust and replicated sex-specific association with an IFNG (interferon gamma) polymorphism (rs2069727; Pinteraction = 0.008) in asthma suggests the existence of genetic contribution to sex differences in nonmalignant childhood disorders. The same SNP also shows a sex-specific association with childhood ALL risk. A recent genome-wide meta-analysis of asthma revealed sex-specific candidate risk markers in interferon regulatory factor 1 (IRF1) and RAP1 GTPase activating protein 2 (RAP1GAP2) with regulatory effects in gene regulation as expression quantitative trait loci (eQTL). Yao et al examined 11,672 disease-associated polymorphisms from the National Human Genome Research Institute GWAS catalog and dbGaP database in relation to gene expression levels in the whole blood derived RNA and identified 14 sex-specific eQTLs. A recent survey of correlations between SNPs and gene expression levels by sex showed that up to 15% of autosomal SNPs have a gender bias in their correlations. Similarly, another study reported that 582 autosomal genes have sex-specific differences in their expression levels. Such sex-specific signals are likely to be diluted or cancelled out when both genders are analyzed simultaneously. Likewise, reanalysis of GWAS datasets revealed that coronary artery diseases, Crohn disease, rheumatoid arthritis, and type1 diabetes show sex-specific associations with increased risk in only 1 gender.[22,23] In the present GWAS, we explored the genetic basis of sex differences by comparing genotype frequencies between male and female cases with childhood ALL using a case-only study design.
2 Materials and methods
2.1 Study design
We used a case-only design because of its greater statistical power in detecting effect modification, in this case, sex and genotype interactions, in comparison to other traditional epidemiological designs. An assumption of the case-only design requires genotype and sex to be independent in the healthy population. There is no reason to believe that healthy male and female controls have different genotype frequencies in autosomal chromosomes. Recruitment of cases in a rare disease like childhood ALL is a challenge; hence, preferring a statistically more powerful research design was more practical.
2.2 Subject recruitment
Institutional review board approval was granted by Florida International University (FIU), Office of Research Integrity, and Baylor College of Medicine (BCM) prior to the study. Samples for the current study were obtained from a parent case-control study of childhood ALL and has been explained elsewhere in detail. Briefly, incident cases of childhood ALL were consecutively recruited at the Texas Children's Cancer Center (TXCCC), BCM, in Houston, Texas from 2007 to 2012. The present study used 236 cases from the parent study for the case-only analysis. They were all non-Hispanic whites, aged 1 to 10 years, and diagnosed with confirmed B-cell precursor ALL. Genomic DNA was extracted from blood samples at TXCCC, BCM. Established childhood ALL risk associations identified in previous GWAS[14–16] have been replicated to validate the parent case-control study sample using TaqMan allelic discrimination assays at FIU.
Genotype data were generated using the Illumina HumanCoreExome BeadChip (Illumina, San Diego, CA) on the Illumina Infinium platform at the John P. Hussman Institute of Human Genomics, University of Miami, Florida.
2.4 Data analysis
Quality control (QC) steps were performed in a hierarchial fashion using PLINK and R packages. Sample QC was done prior to SNP QC. The Hardy–Weinberg equilibrium (HWE) test was used retrospectively in the whole group of subjects for sex-specific analysis. Unconditional logistic regression was used to calculate allelic ORs and 95% confidence intervals (CIs) after coding males as cases and females as controls. The coding of male and female cases for statistical analysis meant that an OR greater than 1.0 indicates greater risk for males compared with females. Power analysis and sample software was used to calculate statistical power for various risk genotype frequencies. The study was statistically powered to detect qualitative interactions, that is sex-specific associations differing in direction (risk or protection); rather than associations in the same direction and differing in magnitude (quantitative interactions). Permutation testing (10,000 permutations) was performed for all sex-specific results to rule out chance findings using a threshold of P <0.05. Results were adjusted for potential population stratification using the genomic control method. A quantile-quantile (Q-Q) plot was used to rule out systematic errors (Supplementary Figure 1, http://links.lww.com/MD/B374). Besides the top 100 statistically most significant results (Supplementary Table 1, http://links.lww.com/MD/B375), results were also analyzed by the OR with a nominal statistical significance (P <0.05) (Supplementary Table 2, http://links.lww.com/MD/B376).
2.5 Bioinformatic analysis
To functionally annotate the SNPs that have shown sex-specific associations, a number of bioinformatic tools were used. Variants were annotated for their effects on the gene expression, protein, and the predicted function using several bioinformatic and empirical tools. ANNOVAR was used to obtain functionality scores (DANN, FATHMM, GWAVA, SIFT, PolyPhen2, Mutation Taster, Mutation Assessor, LRT, FATHMM, and MetaLR), as well as conservation scores. ANNOVAR and SNPnexus were used to predict the effect of variants on transcription factor binding sites (TFBS), microRNA binding sites and identification of variants that disrupt enhancers, repressors, and promoters. Complementary annotations were performed using CADD, RegulomeDB, HaploReg v4, SNiPA, and rVarBase. GWAS3D was used to predict genetic variants or variants in linkage disequilibrium (LD) affecting regulatory pathways and essential disease/trait associations by integrating functional genomics, chromatin state, sequence motif, and conservation information. It also provides visualization tools to comprehend the results. To assess correlations of SNPs with gene expression levels in peripheral blood cells, we used SNP and Copy Number Annotation (SCAN) database  and Blood eQTL  databases. For each SNP, the Blood eQTL database and SCAN provided information from experimental data on gene expression regulation in peripheral blood cells and lymphoblastoid cell lines, respectively. Likewise, we screened mQTLdb for methylation-QTLs (meQTL) to examine effects of sex-specific variants on CpG islands at birth, during childhood and adolescence as well as during pregnancy. Statistically similar (proxy) SNP sets for each SNP were obtained from HaploReg v4, and were submitted to the tools listed above for a number of analyses. For direct observation of TFBSs nearby SNPs, we examined the Swiss Regulon browser. Gene set enrichment analysis (GSEA) was performed using DAVID v6.7. The interactions of candidate genes with other genes and noncoding RNAs were investigated using NPinter v3.0, which provides experimental data. Computational miRNA targets on protein-coding genes were explored using TargetScan. To map the HLA-DQA1 SNPs to HLA-DQA1 types, we examined complete sequences of HLA-DQA1 alleles in the IPD-IMGT/HLA Database (http://www.ebi.ac.uk/ipd/imgt/hla).
2.6 The microarray innovations in leukemia (MILE) study
The MILE study generated microarray-based gene expression profiles from 2096 patients with standard subtypes of acute and chronic leukemia (and myelodysplastic syndromes) in 11 laboratories on 3 continents. The aim of the study was to examine gene expression profiles for diagnosis and subclassification. The individual level data from the 2096 patients were available to compare expression levels of different genes of interest in different leukemia subtypes.
After stringent QC, 209 subjects and 271,069 SNPs were included in the statistical analysis. In the final dataset, genotype call rates were >99.9%. All QC steps and their results are reported in Table 1. The final sample included 116 males (55.5%) and 93 females (44.5%) with no statistically significant difference in age distribution (P = 0.43). The mean age was 4.45 (±2.58) years for males and 4.73 (±2.32) years for females. The genomic inflation factor (λ), an indicator of population stratification, was 1.0, which suggested no population stratification, as confirmed by the Q-Q plot (Supplementary Figure 1, http://links.lww.com/MD/B374). The design was ideal to detect sex-specific associations as the reflection of allele frequencies higher in one sex than the other. Naturally, the allele frequencies in the pooled sample would be in between sex-specific frequencies. We checked this assumption by using the frequencies for the European sample in the HapMap project (Tables 2 and 3). At least for the higher ranking results, HapMap frequencies were between the frequencies observed in male and female cases.
When results were ranked according to their P values, the smallest P value was 3.8 × 10−6 for RASSF2 (Ras association domain family member 2), rs4813720 association, ORinteraction = 0.30) indicating males with the minor allele were less likely to develop ALL compared with females with the minor allele (Table 2 and Supplementary Table 1, http://links.lww.com/MD/B375). The SNP rs4813720 correlates with RASSF2 expression levels (P = 2.1 × 10−6) in peripheral blood cells (Supplementary Table 3, http://links.lww.com/MD/B377). Bioinformatic analysis also yielded high functionality scores (CADD = 5.13; DANN = 0.7). According to rVarBase analysis, this SNP maps to active enhancers in highly relevant cell types: primary hematopoietic cells, primary B cells, primary T-helper cells, primary neutrophils, and monocytes. HaploReg v4 analysis revealed that a nearby (552 bp away) SNP, rs7271897, is in LD with rs4813720 (r2 = 0.70), and alters an estrogen receptor (ER)-alpha binding site. Likewise, rs13045004 (within 3 kb) is in LD with rs4813720 (r2 = 0.70), and alters an NF-kB binding site. In HaploReg analysis, there were 3 SNPs in strong LD (r2 ≥ 0.80) with rs4813720, but most bioinformatic analyses predicted rs4813720 as the putative causal SNP. An indel SNP rs3215695 was statistically correlated (r2 = 0.98) with rs4813720 yielding a high functional score (2b) for RegulomeDB. This SNP is also located in an active transcription start site.
Having observed that the statistically most significant association (RASSF2 rs4813720) was likely to be causal, we performed extended in silico analysis of rs4813720. Besides being an eQTL for RASSF2 in peripheral blood cells, this SNP is also located within enhancers in cell types relevant to leukemia. Since RASSF2 is frequently methylated in tumors, we also examined whether rs4813720 is a meQTL. Screening of the mQTLdb showed that rs4813720 is a very strong meQTL for the CpG site cg22485289 (Table 4) in the promoter region of RASSF2 at most time points examined (pregnancy, birth, childhood, and adolescence; P ≤ 9 × 10−14), and at middle age, but not as significantly (P = 2 × 10−10).
Having established that our top hit rs4813720 is an eQTL for RASSF2 and a meQTL for a CpG site within the same gene, we examined whether it may be involved in any other mechanism that would affect the expression levels of RASSF2. The examination of the NPinter database revealed that RASSF2 physically interacts with mir-19a/19b. TargetScan also predicts mir-19b binding to RASSF2 3’UTR. However, none of the SNPs in the statistically similar SNP set of rs4813720 were within the microRNA binding site in 3’ UTR. To gain further insight into RASSF2 function, we obtained the list of experimentally confirmed target gene list for mir-19a/19b (n = 298) from NPinter database, and subjected this list to GSEA on DAVID. The highest fold enrichment (12.4) was observed for RAS association category, which was also the statistically most significant (false discovery rate [FDR] = 0.004). Apoptosis was also one of the most highly significant categories (fold enrichment = 3.5; FDR = 0.007). Thus, the only microRNA that had an experimentally confirmed physical interaction with RASSF2 had targets cumulatively involved in RAS signaling and apoptosis (mir-19a/19b belongs to the mir-17-92 cluster).
The second ranking SNP rs23127 (ORinteraction = 3.0), located in the genes PROSER3/HSPB6, yielded a high risk for males compared with females for childhood ALL (Table 2). Our analysis suggested that rs23127 might have multiple regulatory functions as it affects the expression level of several genes (COX6B1; P = 2.2 × 10−6, UPK1A; P = 1.2 × 10−7 and U2AF1LA; P = 3.1 x 10−9) (Supplementary Table 3, http://links.lww.com/MD/B377) and is also located in an active promoter region (RegulomeDB = 2b; CADD = 7.8; DANN = 0.71). Another top ranking SNP, rs798292, is associated with expression of KAT7 (K(lysine) acetyltransferase 7), a histone acetyltransferase that represses androgen receptor-mediated transcription in lymphoblastoid cells (Supplementary Table 3, http://links.lww.com/MD/B377). Results ranked by statistical significance showed more eQTLs in lymphoblastoid cell lines in comparison to the results based on effect size (Supplementary Table 3, http://links.lww.com/MD/B377).
Table 3 and Supplementary Table 2 (http://links.lww.com/MD/B376) show the results based on the highest effect sizes. Two statistically correlated SNPs (r2 = 1), rs12722042 (CADD = 16.02 and DANN = 1) and rs12722039 (CADD = 5.02 and DANN = 0.49), from the HLA-DQA1 (major histocompatibility complex, class II, DQ alpha 1) gene and a SNP, rs11992342, from the ADAM28 (ADAM metallopeptidase domain 28) region yielded the highest effect sizes (ORinteraction > 14; P ≤0.01) for sex-specific results. Both HLA-DQA1 SNPs were missense variants and marked by several histone modification marks, such as Histone-3 lysine-4 monomethylation (H3K4me1), Histone-3 lysine-4 trimethylation (H3K4me3), Histone-3 lysine-9 acetylation (H3K9ac), and Histone-3 lysine-27acetylation (H3K27ac) indicating the location within an active promoter site. When we examined whether there are crucial TFBSs in the vicinity of these SNPs located at chromosome 6 positions 32,605,284 and 32,605,309 (hg19 coordinates), Swiss Regulon browser indicated that there was an androgen receptor binding site within 100 bp of these SNPs (32,605,225 to 32,605,2546). ADAM28 rs11992342 maps to an active enhancer region, and affects binding affinity of several TFs of STAT family. Top ranking 100 results based on OR included associations with more missense variants than the results based on P values (34 vs 2) (Supplementary Tables 1 and 2, http://links.lww.com/MD/B375, http://links.lww.com/MD/B376). Our results predicted several of these missense variants may have deleterious effects on final protein structure (Supplementary Table 4, http://links.lww.com/MD/B378). Most of the identified sex-specific SNPs were located in cis-regulatory elements, such as promoters and enhancers or may have eQTL effects (Supplementary Tables 1 and 2, http://links.lww.com/MD/B375, http://links.lww.com/MD/B376).
Our mQTLdb screening results showed that several variants were meQTLs at birth, during childhood and adolescence, as well as during pregnancy (Supplementary Table 5, http://links.lww.com/MD/B379). We examined the involvement of the CpG sites linked to our GWAS associations in B-cell differentiation and leukemogenesis by using the data provided by Kulis et al. A total of 24 variants were reported to be pivotal for B-lymphocyte differentiation and their target CpG sites found to be either hypermethylated or hypomethylated in ALL (Table 4). Most notably, the top ranking SNP rs4813720 is an meQTL for the CpG island cg22485289 within RASSF2. Both HLA-DQA1 SNPs (rs12722042 and rs12722039) are meQTLs for the CpG island cg24593918 within HLA-DQB1.
GWAS3D analysis for the top 100 ranking sets of SNPs unraveled a number of long-range interactions between the regions of SNP locations and other genes, some of which being on different chromosomes (Supplementary Figures 2 and 3, http://links.lww.com/MD/B374). GWAS3D reported 84 variants with TF binding affinity changes or regulatory signals based on the GM12878 cell line and HapMap CEU population for 100 statistically most significant results. A total of 66 variants have been detected affecting long-range interactions (Supplementary Figure 2, http://links.lww.com/MD/B374). Cumulatively, GWAS3D results suggested a potential role for STAT and MYC TFs in sex-differences in childhood ALL risk. Among the top 100 results ranked by the effect size, GWAS3D analysis showed that 68 of the variants were associated with regulatory signals and TF binding affinity changes on the GM12878 cell line and HapMap CEU population. Long-range interactions were noted for 60 variants (Supplementary Figure 3, http://links.lww.com/MD/B374).
As shown in Table 5, in all ALL subtypes examined in the MILE study, RASSF2 expression was lower than controls, and mir-17-92 levels were higher with statistical significance varying from P = 0.02 to 6.8 × 10−80. The statistically most significant change in RASSF2 was in T-ALL with the expression ratio to controls being 0.23 (mir-17-92 showed an expression ratio of 1.71 in T-ALL). The statistically most significant change in mir-17-92 was in ALL with t(12;21) with the expression ratio to controls being 3.17 (RASSF2 showed an expression ratio of 0.50 in the same ALL subtype). In all subtypes, RASSSF2 and mir-17-92 expression levels showed an inverse correlation (Table 5). The expression levels of MYC did not appear to correlate with mir-17-92 levels except in pro-B and mature B-ALL subtypes (Table 5). While the strong inverse correlation between RASSF2 and mir-17-92 confirmed the expected relationship between them, the lack of a correlation between MYC and mir-17-92 suggested that mir-17-92 increase in ALL is not exclusively caused by MYC, but may be due to genomic amplifications as observed in other malignancies.
This is the first GWAS designed to identify sex-specific childhood ALL risk markers. As a second exploratory approach, we also analyzed results based on OR, to examine clinically meaningful results that may not have reached the strict statistical significance due to their lower frequencies. These results are free from multiple comparison issues, as each permutation on each SNP assesses the role of chance.
The statistically most significant association was with an intronic SNP in RASSF2, which is a novel tumor suppressor gene and a member of the RAS family which regulates a wide range of biological processes, including KRAS signaling.[46,47] It induces apoptosis and cell cycle arrest, shows frequent methylation in several cancers, and rs4813720 correlates with RASSF2 expression levels.RASSF2 ablation down regulates genes involved in the immune response, hematological development, as well as genes activating nuclear factor (NF)-κB signaling.
RASSF2 has emerged as a candidate gene involved in sex-specific modification of risk for childhood ALL in the present study for the first time. This result is biologically plausible. RASSF2 is a tumor suppressor gene via its inhibitory effect on the proto-oncogene KRAS.KRAS is one of the most frequently mutated proto-oncogenes in childhood ALL.[44,49] The top ranking sex-specific risk marker for childhood ALL, rs4813720, correlates with RASSF2 expression level, as well as the methylation levels in a key CpG site cg22485289 within the promoter of RASSF2 at birth and childhood. This particular CpG site is one of the key sites found to be hypomethylated in childhood ALL compared with pre-B cells. Thus, rs4813720 appears to be involved in the prevention of childhood ALL in males via maintaining the expression levels of tumor suppressive RASSF2 in pre-B cells presumably via a methylation-related mechanism. Linkage of DNA methylation quantitative trait loci to human cancer risk is already well documented, and our results represent yet another example.
The sex effect in the prevention of childhood ALL by RASSF2 expression may also be biologically plausible. The key to the sex effect may be the events upstream of RASSF2. The miR-17-92 cluster, which is in an amplified genomic region in human malignant B-cell lymphomas, has been shown to be an oncomicroRNA also in c-Myc-induced lymphomagenesis in mice. mir-17-92 also plays a major role in normal B-cell development.[52,53] In an experimental study, RASSF2 has been found to be a target gene for the mir-92 polycistron. Our bioinformatics work also confirmed a physical interaction with one of the micro RNAs that derive from mir-17-92 polycistron, mir-19a/19b, and revealed a predicted binding site for it (see Results). Crucially, estrogen administration increases mir-17-92 levels via MYC overexpression[55,56] and downregulates RASSF2 expression. Thus, in addition to the already known involvement of sex hormones in the gender effect in cancer, we propose a novel mechanism for childhood ALL: estrogen exposure increases the oncogenic microRNA species mir-19a/19b levels, which in turn downregulates RASSF2 (as well as another tumor suppressor PTEN). Downregulation of RASSF2 abolishes KRAS inhibition, and the KRAS oncogene contributes to childhood ALL development. Males are already less susceptible to this mechanism due to lesser exposure to estrogen, as well as due to the presence of binding sites to Y chromosome-linked transcription factor SRY, and are further protected if they possess the minor allele of rs4813720, which increases RASSF2 expression levels. If experimentally confirmed, this would be the first demonstration of the mechanism of a sexually antagonistic association of a SNP with cancer susceptibility. Although sex hormone levels are very low during childhood in both males and females, programming of autosomal gene expression by sex hormones during prenatal development or during the temporary androgen surge in early infancy (called mini-puberty) is a possibility. Mini-puberty is particularly interesting as inter individual variability in sex hormone levels, which may rise to the puberty levels but temporarily, may be very remarkable.[60,61] Unfortunately, there are no studies on the long-term effects of these sex hormone exposures in early infancy on biological systems although behavioral studies continue to show the relevance of this exposure.[60,62]
Having a marker that is associated with prevention for childhood ALL in males has clinical importance. Males develop childhood ALL more frequently, have a higher relapse rate, and worse prognosis. Due to having clinically more aggressive form of the disease, they also receive more intensive treatment resulting in higher toxicity. The marker is within RASSF2 and acts by increasing its expression, which in turn, binds to and inactivates KRAS. KRAS is frequently mutated in childhood ALL[44,49] and mutant KRAS worsens the prognosis. Thus, any pharmacological intervention to increase the expression of RASSF2 should be beneficial for male patients with childhood ALL, especially for those with KRAS mutations, to counter the deleterious effects of mutant KRAS.
Examination of associations with the highest odds ratios identified a pair of HLA region SNPs. The HLA-DQA1 SNPs (rs12722042 and rs12722039) belong to DQA1*01, confirming the previously reported male-specific association with DQA1*01, and supporting the proposed infection-related etiology in childhood ALL risk for males. Both HLA-DQA1 SNPs are located in an active promoter region and showed eQTL effects on the HLA-DQB1 gene. These SNPs have regulatory effects on gene expression rather than on protein structure. Intriguingly, the HLA-DQA1 SNPs are in close proximity (<100 bp) of an androgen receptor binding site. This observation may be relevant in their risk associations with childhood ALL in males. The SNP rs35597309, correlated statistically with rs12722042 (r2 = 0.97), has shown a risk association with esophageal squamous cell carcinoma in a Chinese population (esophageal carcinoma has a high male-to-female ratio).
The validity of the case-only design depends on the independence of exposure and genotype. Departures from this independence may affect the results. Such an association would be invalid due to the violation of the major assumption of case-only approach. In the case of gender, it seems unlikely to have different genotype frequencies in autosomal chromosomes between males and females in the healthy population. Conforming to the assumptions of the case-control design still does not make the results valid until replicated by a second independent study. The lack of a previously observed association of RASSF2 SNPs with childhood ALL does not argue against the validity of our result. This is most likely due to cancellation of associations in opposite directions in males and females when the analysis is performed on the overall sample. None of the previous childhood ALL GWAS performed sex-specific analyses. Despite the biological plausibility of our results, given the limited sample size of our study and lack of replication, it is important to reexamine these results in an independent study before proceeding to functional studies. These results cannot be generalized in any other ethnic population, as all of these samples were non-Hispanic whites.
In conclusion, our results suggest that novel sex-specific risk variants for childhood ALL exist. Functional analyses suggest that most variants (either direct effect or through LD) have regulatory effects, which increases the likelihood of causal associations. For instance, the statistically most significant association rs4813720 correlates with RASSF2 expression and may play a significant role in childhood ALL etiology. Our results also confirm the previously observed male-specific association with DQA1*01. While ranking results by OR is not a traditional approach for GWAS, this exploratory approach yielded several missense variants with effects on protein function. The statistically most significant association was with a SNP within RASSF2, which interacts with KRAS. Given the difficulties of developing drugs to inactivate KRAS directly, the demonstration of the effect of RASSF2 in prevention from childhood ALL may even offer a new target for drug development efforts. Feasibility of countering the downstream effects of mir-17–92 by pharmacological manipulation has already been shown. Prenatal programming by sex hormones, if confirmed, may shed some light on the mechanism of the differential risk with childhood ALL. These preliminary results provide a foundation for further replication and functional studies to examine the genetic basis of sex-differential in childhood ALL risk. Future studies should include functional and genetic replication of the RASSF2 association, particularly in case series with known KRAS mutation status. Given the well-established involvement of the oncogenic mir-17-92 in its development and the more exaggerated male predominance, childhood non-Hodgkin lymphoma should also be examined for RASSF2 associations.[67,68]
1. Howlader N, Noone AM, Krapcho M, et al. (eds). SEER Cancer Statistics Review, 1975-2012, National Cancer Institute. Bethesda, MD, Available at: http://seer.cancer.gov/csr/1975_2012/
, based on November 2014 SEER data submission, posted to the SEER web site, April 2015 (accessed May 31, 2016).
2. Spector LG, Pankratz N, Marcotte EL. Genetic and nongenetic risk factors for childhood cancer. Pediatr Clin North Am
3. Cook MB, Dawsey SM, Freedman ND, et al. Sex disparities in cancer incidence by period and age. Cancer Epidemiol Biomarkers Prev
4. Dorak MT, Karpuzoglu E. Gender differences in cancer susceptibility: an inadequately addressed issue. Front Appl Genet Epidemiol
5. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2016. CA Cancer J Clin
6. Mulder RL, Hudson MM, Skinner R, et al. Health problems in survivors of childhood cancer: the need for international collaboration in long-term follow-up care. Future Oncol
7. Randall JC, Winkler TW, Kutalik Z, et al. Sex-stratified Genome-wide Association Studies Including 270,000 Individuals Show Sexual Dimorphism in Genetic Loci for Anthropometric Traits. PLoS Genet
8. Gilks WP, Abbott JK, Morrow EH. Sex differences in disease genetics: evidence, evolution, and detection. Trends Genet
9. Do TN, Ucisik-Akkaya E, Davis CF, et al. 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
10. Morrison BA, Ucisik-Akkaya E, Flores H, et al. Multiple sclerosis risk markers in HLA-DRA, HLA-C, and IFNG genes are associated with sex-specific childhood leukemia risk. Autoimmunity
11. Kennedy AE, Kamdar KY, Lupo PJ, et al. Genetic markers in a multi-ethnic sample for childhood acute lymphoblastic leukemia risk. Leuk Lymphoma
12. Healy J, Richer C, Bourgey M, et al. Replication analysis confirms the association of ARID5B with childhood B-cell acute lymphoblastic leukemia. Haematologica
13. Wang SL, Zhao H, Zhou B, et al. Polymorphisms in ERCC1 and susceptibility to childhood acute lymphoblastic leukemia in a Chinese population. Leuk Res
14. Xu H, Yang W, Perez-Andreu V, et al. Novel susceptibility variants at 10p12.31-12.2 for childhood acute lymphoblastic leukemia in ethnically diverse populations. J Natl Cancer Inst
15. Trevino LR, Yang W, French D, et al. Germline genomic variants associated with childhood acute lymphoblastic leukemia. Nat Genet
16. Papaemmanuil E, Hosking FJ, Vijayakrishnan J, et al. Loci on 7p12.2, 10q21.2 and 14q11.2 are associated with risk of childhood acute lymphoblastic leukemia. Nat Genet
17. Loisel DA, Tan Z, Tisler CJ, et al. IFNG genotype and sex interact to influence the risk of childhood asthma. J Allergy Clin Immunol
18. Myers RA, Scott NM, Gauderman WJ, et al. Genome-wide interaction studies reveal sex-specific asthma risk alleles. Hum Mol Genet
19. Yao C, Joehanes R, Johnson AD, et al. Sex- and age-interacting eQTLs in human complex diseases. Hum Mol Genet
20. Dimas AS, Nica AC, Montgomery SB, et al. Sex-biased genetic effects on gene regulation in humans. Genome Res
21. Jansen R, Batista S, Brooks AI, et al. Sex differences in the human peripheral blood transcriptome. BMC Genom
22. Liu LY, Schaub MA, Sirota M, et al. Sex differences in disease risk from reported genome-wide association study findings. Hum Genet
23. Orozco G, Ioannidis JP, Morris A, et al. Sex-specific differences in effect size estimates at established complex trait loci. Int J Epidemiol
24. Clayton D, McKeigue PM. Epidemiological methods for studying genes and environmental factors in complex diseases. Lancet
25. Anderson CA, Pettersson FH, Clarke GM, et al. Data quality control in genetic case-control association studies. Nat Protoc
26. Devlin B, Bacanu SA, Roeder K. Genomic control to the extreme. Nat Genet
27. Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res
28. Chelala C, Khan A, Lemoine NR. SNPnexus: a web database for functional annotation of newly discovered and public domain single nucleotide polymorphisms. Bioinformatics
29. Kircher M, Witten DM, Jain P, et al. A general framework for estimating the relative pathogenicity of human genetic variants. Nat Genet
30. Schaub MA, Boyle AP, Kundaje A, et al. Linking disease associations with regulatory information in the human genome. Genome Res
31. Ward LD, Kellis M. HaploReg v4: systematic mining of putative causal variants, cell types, regulators and target genes for human complex traits and disease. Nucleic Acids Res
32. Arnold M, Raffler J, Pfeufer A, et al. SNiPA: an interactive, genetic variant-centered annotation browser. Bioinformatics
33. Guo L, Du Y, Qu S, et al. rVarBase: an updated database for regulatory features of human variants. Nucleic Acids Res
34. Li MJ, Wang LY, Xia Z, et al. GWAS3D: detecting human regulatory variants by integrative analysis of genome-wide associations, chromosome interactions and histone modifications. Nucleic Acids Res
35. Gamazon ER, Zhang W, Konkashbaev A, et al. SCAN: SNP and copy number annotation. Bioinformatics
36. Westra HJ, Peters MJ, Esko T, et al. Systematic identification of trans eQTLs as putative drivers of known disease associations. Nat Genet
37. Gaunt TR, Shihab HA, Hemani G, et al. Systematic identification of genetic influences on methylation across the human life course. Genome Biol
38. Pachkov M, Balwierz PJ, Arnold P, et al. SwissRegulon, a database of genome-wide annotations of regulatory sites: recent updates. Nucleic Acids Res
39. Huang da W, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res
40. Hao Y, Wu W, Li H, et al. NPInter v3.0: an upgraded database of noncoding RNA-associated interactions. Database (Oxford)
2016; pii: baw2057.
41. Agarwal V, Bell GW, Nam JW, et al. Predicting effective microRNA target sites in mammalian mRNAs. Elife
42. Haferlach T, Kohlmann A, Wieczorek L, et al. Clinical utility of microarray-based gene expression profiling in the diagnosis and subclassification of leukemia: report from the International Microarray Innovations in Leukemia Study Group. J Clin Oncol
43. Richter AM, Pfeifer GP, Dammann RH. The RASSF proteins in cancer; from epigenetic silencing to functional characterization. Biochim Biophys Acta
44. Case M, Matheson E, Minto L, et al. Mutation of genes affecting the RAS pathway is common in childhood acute lymphoblastic leukemia. Cancer Res
45. Volinia S, Galasso M, Costinean S, et al. Reprogramming of miRNA networks in cancer and leukemia. Genome Res
46. Song H, Kim H, Lee K, et al. Ablation of Rassf2 induces bone defects and subsequent haematopoietic anomalies in mice. EMBO J
47. Volodko N, Gordon M, Salla M, et al. RASSF tumor suppressor gene family: biological functions and regulation. FEBS Lett
48. Heyn H, Sayols S, Moutinho C, et al. Linkage of DNA methylation quantitative trait loci to human cancer risk. Cell Rep
49. Irving J, Matheson E, Minto L, et al. Ras pathway mutations are prevalent in relapsed childhood acute lymphoblastic leukemia and confer sensitivity to MEK inhibition. Blood
50. Kulis M, Merkel A, Heath S, et al. Whole-genome fingerprint of the DNA methylome during human B cell differentiation. Nat Genet
51. He L, Thomson JM, Hemann MT, et al. A microRNA polycistron as a potential human oncogene. Nature
52. Ventura A, Young AG, Winslow MM, et al. Targeted deletion reveals essential and overlapping functions of the miR-17 through 92 family of miRNA clusters. Cell
53. Lai M, Gonzalez-Martin A, Cooper AB, et al. Regulation of B-cell development and tolerance by different members of the miR-17∼92 family microRNAs. Nat Commun
54. Li Z, Luo RT, Mi S, et al. Consistent deregulation of gene expression between human and murine MLL rearrangement leukemias. Cancer Res
55. Castellano L, Giamas G, Jacob J, et al. The estrogen receptor-alpha-induced microRNA signature regulates itself and its transcriptional response. Proc Natl Acad Sci U S A
56. Wang C, Mayer JA, Mazumdar A, et al. Estrogen induces c-myc gene expression via an upstream enhancer activated by the estrogen receptor and the AP-1 transcription factor. Mol Endocrinol
57. Ren CE, Zhu X, Li J, et al. Microarray analysis on gene regulation by estrogen, progesterone and tamoxifen in human endometrial stromal cells. Int J Mol Sci
58. Clocchiatti A, Cora E, Zhang Y, et al. Sexual dimorphism in cancer. Nat Rev Cancer
59. Olive V, Bennett MJ, Walker JC, et al. miR-19 is a key oncogenic component of mir-17-92. Genes Dev
60. Alexander GM, Wilcox T, Farmer ME. Hormone-behavior associations in early infancy. Horm Behav
61. Tomlinson C, Macintyre H, Dorrian CA, et al. Testosterone measurements in early infancy. Arch Dis Child Fetal Neonatal Ed
62. Hines M, Spencer D, Kung KT, et al. The early postnatal period, mini-puberty, provides a window on the role of testosterone in human neurobehavioural development. Curr Opin Neurobiol
63. Taylor GM, Dearden S, Payne N, et al. Evidence that an HLA-DQA1-DQB1 haplotype influences susceptibility to childhood common acute lymphoblastic leukaemia in boys provides further support for an infection-related aetiology. Br J Cancer
64. Kong KL, Kwong DL, Fu L, et al. Characterization of a candidate tumor suppressor gene uroplakin 1A in esophageal squamous cell carcinoma. Cancer Res
65. Boraska V, Jeroncic A, Colonna V, et al. Genome-wide meta-analysis of common variant differences between men and women. Hum Mol Genet
66. McCormick F. K-Ras protein as a drug target. J Mol Med (Berl)
67. Jin HY, Lai M, Xiao C. microRNA-17∼92 is a powerful cancer driver and a therapeutic target. Cell Cycle
68. Jin HY, Oda H, Lai M, et al. MicroRNA-17∼92 plays a causative role in lymphomagenesis by coordinating multiple oncogenic pathways. EMBO J