Axonal guidance signaling pathway interacting with smoking in modifying the risk of pancreatic cancer: a gene- and pathway-based interaction analysis of GWAS data

Novel DNA methylation changes in mouse lungs associated with chronic smoking

Smoking is a potent cause of asthma exacerbations, chronic obstructive pulmonary disease (COPD) and many other health defects, and changes in DNA methylation (DNAm) have been identified as a potential link between smoking and these health outcomes. However, most studies of smoking and DNAm have been done using blood and other easily accessible tissues in humans, while evidence from more directly affected tissues such as the lungs is lacking. Here, we identified DNAm patterns in the lungs that are altered by smoking. We used an established mouse model to measure the effects of chronic smoke exposure first on lung phenotype immediately after smoking and then after a period of smoking cessation. Next, we determined whether our mouse model recapitulates previous DNAm patterns observed in smoking humans, specifically measuring DNAm at a candidate gene responsive to cigarette smoke, Cyp1a1. Finally, we carried out epigenome-wide DNAm analyses using the newly released Illumina mouse methylation microarrays. Our results recapitulate some of the phenotypes and DNAm patterns observed in human studies but reveal 32 differentially methylated genes specific to the lungs which have not been previously associated with smoking. The affected genes are associated with nicotine dependency, tumorigenesis and metastasis, immune cell dysfunction, lung function decline, and COPD. This research emphasizes the need to study CS-mediated DNAm signatures in directly affected tissues like the lungs, to fully understand mechanisms underlying CS-mediated health outcomes.

scParser: sparse representation learning for scalable single-cell RNA sequencing data analysis

The rapid rise in the availability and scale of scRNA-seq data needs scalable methods for integrative analysis. Though many methods for data integration have been developed, few focus on understanding the heterogeneous effects of biological conditions across different cell populations in integrative analysis. Our proposed scalable approach, scParser, models the heterogeneous effects from biological conditions, which unveils the key mechanisms by which gene expression contributes to phenotypes. Notably, the extended scParser pinpoints biological processes in cell subpopulations that contribute to disease pathogenesis. scParser achieves favorable performance in cell clustering compared to state-of-the-art methods and has a broad and diverse applicability.

Identification of novel susceptibility methylation loci for pancreatic cancer in a two-phase epigenome-wide association study

The role of DNA methylation and its interplay with gene expression in the susceptibility to pancreatic cancer (PanC) remains largely unexplored. To fill in this gap, we conducted an integrative two-phase epigenome-wide association study (EWAS) of PanC using genomic DNA from 44 cases and 556 controls (20 local controls and 536 public controls in the Framingham Heart Study) in phase 1 and 23 cases and 22 controls in phase 2. We validated the findings using pre-diagnostic blood samples from 13 cases and 26 controls in the Women's Health Initiative (WHI) Study. We further examined gene expression in peripheral leukocytes of 47 cases and 31 controls involved in the methylation study using RNA sequencing and performed bidirectional Mendelian randomization (MR) analysis using existing single nucleotide polymorphism (SNP) data. In phase 1, we identified 2776 significantly differentially methylated CpG sites (DMPs) and 154 significantly differentially methylated regions (DMRs). In phase 2, we validated six DMPs (in or near AIM2, DGKA, STK39, and TNFSF8) and three DMRs (in or near nc886, LY6G5C, and HLA-DPB1). The DMR near nc886 was further validated in the WHI samples (P = 6.69 × 10-5). MR analysis suggested that the CpG sites cg00308130 and cg16684184 for nc886 and cg16875057 for STK39 were causally related to PanC susceptibility and that PanC influenced methylation of cg15354065 for DGKA. This first integrative EWAS of PanC provides novel insights into the role of DNA methylation and its interplay with SNPs and gene expression in the disease susceptibility.

Investigating miRNA-mRNA interactions and gene regulatory networks from VTA dopaminergic neurons following perinatal nicotine and alcohol exposure using Bayesian network analysis

MicroRNAs play an important role in gene regulation for many biological systems, including nicotine and alcohol addiction. However, the underlying mechanism behind miRNAs and mRNA interaction is not well characterized. Microarrays are commonly used to quantify the expression levels of mRNAs and/or miRNAs simultaneously. In this study, we performed a Bayesian network analysis to identify mRNA and miRNA interactions following perinatal exposure to nicotine and/or alcohol. We utilized three sets of microarray data to predict the regulation relationship between mRNA and miRNAs. Following perinatal alcohol exposure, we identified two miRNAs: miR-542-5p and miR-874-3p, that exhibited a strong mutual influence on several mRNA in gene regulatory pathways, mainly Axon guidance and Dopaminergic synapses. Finally, we confirmed our predicted addiction pathways based on the Bayesian network analysis with the widely used Kyoto Encyclopedia of Genes and Genomes (KEGG)-based database and identified comparable relevant miRNA-mRNA pairs. We believe the Bayesian network can provide insight into the complexity biological process related to addiction and can potentially be applied to other diseases.

ROBO1 protein expression is independently associated with biochemical recurrence in prostate cancer patients who underwent radical prostatectomy in Asian patients

Background

The purpose of this study is to investigate the correlation between ROBO1 expression and prostate cancer aggressiveness.

Methods

ROBO1 expression was evaluated in normal prostate epithelial cells (PrEC) and different prostate cancer cell lines by Western blot analysis. The migration and invasion of native and ROBO1 knockdown cells were evaluated using migration chambers and a Matrigel-coated membrane, respectively. Samples from 145 patients who underwent radical prostatectomy between June 2000 and June 2008, were retrieved from the paraffin files for tissue microarray (TMA) with immunohistochemical analysis. Biochemical recurrence (BCR)-free survival curves were estimated using the Kaplan-Meier and Cox regression methods in two groups of patients classified according to the degree of ROBO1 expression (low or high expression).

Results

ROBO1 is highly expressed in the prostate cancer cell lines. All ROBO1 knockdown cells (PC3, 22Rv1 and DU 145) showed markedly decreased migration and invasiveness compared to native cells. In 145 patients with radical prostatectomy, the Kaplan-Meier curves and log-rank test for BCR-free survival stratified by ROBO1 expression in organ-confined (pT2) or not (pT3), showed significant differences in 10-year survival between the ROBO1 high and low expression groups (87.2% versus 52.6% in pT2; P=0.047, 51.0% versus 36.9% in pT3; P=0.033). The multivariable-adjusted model showed a markedly increased hazard ratio (HR) in patients with high ROBO1 expression compared to the patients with low ROBO1expression in every model.

Conclusions

ROBO1 may play an important role in the migration and invasion of prostate cancer cells, and was independently associated with BCR.

Smoking Modifies Pancreatic Cancer Risk Loci on 2q21.3

Germline variation and smoking are independently associated with pancreatic ductal adenocarcinoma (PDAC). We conducted genome-wide smoking interaction analysis of PDAC using genotype data from four previous genome-wide association studies in individuals of European ancestry (7,937 cases and 11,774 controls). Examination of expression quantitative trait loci data from the Genotype-Tissue Expression Project followed by colocalization analysis was conducted to determine whether there was support for common SNP(s) underlying the observed associations. Statistical tests were two sided and P < 5 × 10-8 was considered statistically significant. Genome-wide significant evidence of qualitative interaction was identified on chr2q21.3 in intron 5 of the transmembrane protein 163 (TMEM163) and upstream of the cyclin T2 (CCNT2). The most significant SNP using the Empirical Bayes method, in this region that included 45 significantly associated SNPs, was rs1818613 [per allele OR in never smokers 0.87, 95% confidence interval (CI), 0.82-0.93; former smokers 1.00, 95% CI, 0.91-1.07; current smokers 1.25, 95% CI 1.12-1.40, P interaction = 3.08 × 10-9). Examination of the Genotype-Tissue Expression Project data demonstrated an expression quantitative trait locus in this region for TMEM163 and CCNT2 in several tissue types. Colocalization analysis supported a shared SNP, rs842357, in high linkage disequilibrium with rs1818613 (r 2 = 0. 94) driving both the observed interaction and the expression quantitative trait loci signals. Future studies are needed to confirm and understand the differential biologic mechanisms by smoking status that contribute to our PDAC findings. SIGNIFICANCE: This large genome-wide interaction study identifies a susceptibility locus on 2q21.3 that significantly modified PDAC risk by smoking status, providing insight into smoking-associated PDAC, with implications for prevention.

Identification of novel drug resistance mechanisms by genomic and transcriptomic profiling of glioblastoma cells with mutation-activated EGFR

AIMS

Epidermal growth factor receptor (EGFR) is not only involved in carcinogenesis, but also in chemoresistance. We characterized U87.MGΔEGFR glioblastoma cells with constitutively active EGFR due to deletion at the ligand binding domain in terms of gene expression profiling and chromosomal aberrations. Wild-type U87.MG cells served as control.

MATERIALS AND METHODS

RNA sequencing and network analyses (Ingenuity Pathway Analysis) were performed to identify novel drug resistance mechanisms related to expression of mutation activated EGFR. Chromosomal aberrations were characterized by multicolor fluorescence in situ hybridization (mFISH) and array comparative genomic hybridization (aCGH).

KEY FINDINGS

U87.MGΔEGFR cells presented much more chromosomal aberrations, amplifications and deletions than wild-type U87.MG cells. Still, both cell lines were near-triploid. Numerous genes were overexpressed in U87.MGΔEGFR cells, some of which have been already linked to drug resistance. PXDN, which is associated with epithelial mesenchymal transition, was the most upregulated gene (901.8-fold). TENM1 was 331.6-fold upregulated, and it was previously reported to modulate neural development. EGFR-AS1 (161.2-fold upregulated) has been reported to increase the EGFR mRNA stability and its expression - in accordance with that of EGFR - was upregulated (85.5-fold). In addition to well-known resistance genes, numerous novel genes and genomic aberrations were identified. ANGPT2 upregulation and CPM downregulation were validated by Western blotting.

SIGNIFICANCE

Transcriptomics and genomics analyses in U87.MGΔEGFR cells unraveled a range of novel drug resistance mechanisms including apoptosis, DNA repair, ferroptosis, glutathione related gene activities, heat shock, oxidative stress, transcription factor activities, which may have important implications for future treatment strategies.

Controlled Delivery of Slit3 Proteins from Alginate Microbeads Inhibits In Vitro Angiogenesis

BACKGROUND

The Slit-Robo pathway is a key regulator of angiogenesis and cellular function in experimental models. Slit3 proteins exhibit both proangiogenic and antiangiogenic properties, but the exact mechanism remains unclear. It is theorized that Slit3 may be a potential treatment for vascular diseases and cancer.

METHODS

Slit3 labeled with I-125 was encapsulated in microbeads composed of low-viscosity alginate of high-glucuronic acid content, first coated with poly-L-ornithine for various durations and finally with low-viscosity high mannuronic acid. Gamma counter was used to measure microbead encapsulation efficiency and Slit3 release. Markers of angiogenesis were assessed with Boyden chamber, scratch wound, and Matrigel tube formation assays using human umbilical vein and mouse endothelial cells.

RESULTS

On incubation of Slit3-loaded microbeads, there was an initial burst phase release of Slit3 for the first 24 h followed by sustained release for 6 to 12 d. Microbead composition determined encapsulation efficiency and rate of release; Slit3 encapsulation was most efficient in microbeads with lower low-viscosity alginate of high-glucuronic acid content concentrations (1.5%) and no poly-L-ornithine coating. Compared with controls (media alone), Slit3 microbeads significantly inhibited in vitro cellular migration, endothelial cell migration for wound closure at 24 and 48 h and endothelial tube formation (P < 0.001, respectively).

CONCLUSIONS

Slit3 can be effectively encapsulated and delivered via a controlled release pattern using alginate microbeads. Microbead encapsulation reduces in vitro endothelial tube formation and inhibits cellular migration to impair angiogenesis. Thus, Slit3 microparticles may be explored as a therapeutic option to mitigate tumor proliferation.

Incorporating multiple sets of eQTL weights into gene-by-environment interaction analysis identifies novel susceptibility loci for pancreatic cancer

It is of great scientific interest to identify interactions between genetic variants and environmental exposures that may modify the risk of complex diseases. However, larger sample sizes are usually required to detect gene-by-environment interaction (G × E) than required to detect genetic main association effects. To boost the statistical power and improve the understanding of the underlying molecular mechanisms, we incorporate functional genomics information, specifically, expression quantitative trait loci (eQTLs), into a data-adaptive G × E test, called aGEw. This test adaptively chooses the best eQTL weights from multiple tissues and provides an extra layer of weighting at the genetic variant level. Extensive simulations show that the aGEw test can control the Type 1 error rate, and the power is resilient to the inclusion of neutral variants and noninformative external weights. We applied the proposed aGEw test to the Pancreatic Cancer Case-Control Consortium (discovery cohort of 3,585 cases and 3,482 controls) and the PanScan II genome-wide association study data (replication cohort of 2,021 cases and 2,105 controls) with smoking as the exposure of interest. Two novel putative smoking-related pancreatic cancer susceptibility genes, TRIP10 and KDM3A, were identified. The aGEw test is implemented in an R package aGE.

Genome-Wide Gene-Diabetes and Gene-Obesity Interaction Scan in 8,255 Cases and 11,900 Controls from PanScan and PanC4 Consortia

BACKGROUND

Obesity and diabetes are major modifiable risk factors for pancreatic cancer. Interactions between genetic variants and diabetes/obesity have not previously been comprehensively investigated in pancreatic cancer at the genome-wide level.

METHODS

We conducted a gene-environment interaction (GxE) analysis including 8,255 cases and 11,900 controls from four pancreatic cancer genome-wide association study (GWAS) datasets (Pancreatic Cancer Cohort Consortium I-III and Pancreatic Cancer Case Control Consortium). Obesity (body mass index ≥30 kg/m2) and diabetes (duration ≥3 years) were the environmental variables of interest. Approximately 870,000 SNPs (minor allele frequency ≥0.005, genotyped in at least one dataset) were analyzed. Case-control (CC), case-only (CO), and joint-effect test methods were used for SNP-level GxE analysis. As a complementary approach, gene-based GxE analysis was also performed. Age, sex, study site, and principal components accounting for population substructure were included as covariates. Meta-analysis was applied to combine individual GWAS summary statistics.

RESULTS

No genome-wide significant interactions (departures from a log-additive odds model) with diabetes or obesity were detected at the SNP level by the CC or CO approaches. The joint-effect test detected numerous genome-wide significant GxE signals in the GWAS main effects top hit regions, but the significance diminished after adjusting for the GWAS top hits. In the gene-based analysis, a significant interaction of diabetes with variants in the FAM63A (family with sequence similarity 63 member A) gene (significance threshold P < 1.25 × 10-6) was observed in the meta-analysis (P GxE = 1.2 ×10-6, P Joint = 4.2 ×10-7).

CONCLUSIONS

This analysis did not find significant GxE interactions at the SNP level but found one significant interaction with diabetes at the gene level. A larger sample size might unveil additional genetic factors via GxE scans.

IMPACT

This study may contribute to discovering the mechanism of diabetes-associated pancreatic cancer.

Multi-omics Data Analyses Construct TME and Identify the Immune-Related Prognosis Signatures in Human LUAD

Lung cancer has been the focus of attention for many researchers in recent years for the leading contribution to cancer-related death worldwide, in which lung adenocarcinoma (LUAD) is the most common histological type. However, the potential mechanism behind LUAD initiation and progression remains unclear. Aiming to dissect the tumor microenvironment of LUAD and to discover more informative prognosis signatures, we investigated the immune-related differences in three types of genetic or epigenetic characteristics (expression status, somatic mutation, and DNA methylation) and considered the potential roles that these alterations have in the immune response and both the immune-related metabolic and neural systems by analyzing the multi-omics data from The Cancer Genome Atlas (TCGA) portal. Additionally, a four-step strategy based on lasso regression and Cox regression was used to construct the prognostic prediction model. For the prognostic predictions on the independent test set, the performance of the trained models (average concordance index [C-index] = 0.839) is satisfied, with average 1-year, 3-year, and 5-year areas under the curve (AUCs) equal to 0.796, 0.786, and 0.777. Finally, the overall model was constructed based on all samples, which comprised 27 variables and achieved a high degree of accuracy on the 1-year (AUC = 0.861), 3-year (AUC = 0.850), and 5-year (AUC = 0.916) survival predictions.

ZBED2 is an antagonist of interferon regulatory factor 1 and modifies cell identity in pancreatic cancer

Lineage plasticity is a prominent feature of pancreatic ductal adenocarcinoma (PDA) cells, which can occur via deregulation of lineage-specifying transcription factors. Here, we show that the zinc finger protein ZBED2 is aberrantly expressed in PDA and alters tumor cell identity in this disease. Unexpectedly, our epigenomic experiments reveal that ZBED2 is a sequence-specific transcriptional repressor of IFN-stimulated genes, which occurs through antagonism of IFN regulatory factor 1 (IRF1)-mediated transcriptional activation at cooccupied promoter elements. Consequently, ZBED2 attenuates the transcriptional output and growth arrest phenotypes downstream of IFN signaling in multiple PDA cell line models. We also found that ZBED2 is preferentially expressed in the squamous molecular subtype of human PDA, in association with inferior patient survival outcomes. Consistent with this observation, we show that ZBED2 can repress the pancreatic progenitor transcriptional program, enhance motility, and promote invasion in PDA cells. Collectively, our findings suggest that high ZBED2 expression is acquired during PDA progression to suppress the IFN response pathway and to promote lineage plasticity in this disease.

A powerful and data-adaptive test for rare-variant-based gene-environment interaction analysis

As whole-exome/genome sequencing data become increasingly available in genetic epidemiology research consortia, there is emerging interest in testing the interactions between rare genetic variants and environmental exposures that modify the risk of complex diseases. However, testing rare-variant-based gene-by-environment interactions (GxE) is more challenging than testing the genetic main effects due to the difficulty in correctly estimating the latter under the null hypothesis of no GxE effects and the presence of neutral variants. In response, we have developed a family of powerful and data-adaptive GxE tests, called "aGE" tests, in the framework of the adaptive powered score test, originally proposed for testing the genetic main effects. Using extensive simulations, we show that aGE tests can control the type I error rate in the presence of a large number of neutral variants or a nonlinear environmental main effect, and the power is more resilient to the inclusion of neutral variants than that of existing methods. We demonstrate the performance of the proposed aGE tests using Pancreatic Cancer Case-Control Consortium Exome Chip data. An R package "aGE" is available at http://github.com/ytzhong/projects/.

Neuroproteins in Cancer: Assumed Bystanders Become Culprits

Recent breakthrough discoveries have highlighted the stimulatory role of nerves in cancer initiation and progression, through the release of neurotransmitters and growth factors by nerve terminals in the tumor microenvironment. Intriguingly, neuroproteins such as neuronal membrane proteins, synaptic proteins, neurotransmitters, and neurotrophic growth factors as well as their corresponding receptors, to name only a few, are frequently found in proteomic analyses of cancer tissues external to the brain and central nervous system. While the usual explanation was that neuroproteins were actually not specific to the nervous system and were therefore also expressed in cancer cells, it now appears that the presence of neuroproteins in cancer is largely due to the infiltration of nerves in the tumor microenvironment. Given the newly identified function of nerves as promoters of cancer growth and metastasis, neuroproteins should be considered with great attention because they may actually represent innovative biomarkers and therapeutic targets in oncology.

Genetic variants in the platelet-derived growth factor subunit B gene associated with pancreatic cancer risk

The platelet-derived growth factor (PDGF) signaling pathway plays important roles in development and progression of human cancers. In our study, we aimed to identify genetic variants of the PDGF pathway genes associated with pancreatic cancer (PC) risk in European populations using three published genome-wide association study datasets, which consisted of 9,381 cases and 7,719 controls. The expression quantitative trait loci (eQTL) analysis was also performed using data from the 1000 Genomes, TCGA and GTEx projects. As a result, we identified two potential susceptibility loci (rs5757573 and rs6001516) of PDGFB associated with PC risk [odds ratio (OR) = 1.10, 95% confidence interval (CI) = 1.05-1.16, and p = 4.70 × 10-5 for the rs5757573 C allele and 1.21, 1.11-1.32, and 2.01 × 10-5 for the rs6001516 T allele]. Haplotype analysis revealed that the C-T haplotype carriers had a significantly increased risk of PC than those carrying the T-C haplotype (OR = 1.23, 95% CI = 1.12-1.34, p =5.00 × 10-6 ). The multivariate regression model incorporating the number of unfavorable genotypes (NUGs) with age and sex showed that carriers with 1-2 NUGs, particularly among 60-70 age group or males, had an increased risk of PC, compared to those without NUG. Furthermore, the eQTL analysis revealed that both loci were correlated with a decreased mRNA expression level of PDGFB in lymphoblastoid cell lines and pancreatic tumor tissues (p = 0.015 and 0.071, respectively). Our results suggest that genetic variants in PDGFB may play a role in susceptibility to PC. Further population and functional validations of our findings are warranted.

Molecular mechanisms of Ellis‑van Creveld gene variations in ventricular septal defect

The Ellis-van Creveld (EVC) gene is associated with various congenital heart diseases. However, studies on EVC gene variations in ventricular septal defect (VSD) and the underlying molecular mechanisms are sparse. The present study detected 11 single-nucleotide polymorphisms (SNPs) in 65 patients with VSD and 210 control patients from the Chinese Han population. Of the identified SNPs only the c.1727G>A SNP site was positively associated with the development of VSD (P<0.007). A known mutation, c.343C>G, was also identified, which causes a leucine to valine substitution at amino acid 115 of the EVC protein (p.L115V). The results of functional prediction indicated that c.343C>G may be a pathogenic mutation. In addition, in NIH3T3 mouse embryonic fibroblast cells, the EVC c.343C>G mutation significantly decreased cell proliferation and increased apoptosis. Further investigation demonstrated that in NIH3T3 cells, overexpression of EVC c.343C>G mutation reduced the binding between EVC and smoothened, which further downregulated the activity of the hedgehog (Hh) signaling pathway and the expression of downstream cyclin D1 and B-cell lymphoma 2 proteins with SAG. The c.1727G>A SNP of the EVC gene increased VSD susceptibility in patients from the Chinese Han population. The molecular mechanism underlying the development of VSD induced by the EVC c.343C>G mutation may be due to a reduction in the anti-apoptotic and proliferative abilities of cardiomyocytes via downregulation of Hh pathway activity. The results of the present study may provide novel targets for the diagnosis and treatment of patients with VSD.

Incorporation of Biological Knowledge Into the Study of Gene-Environment Interactions

A growing knowledge base of genetic and environmental information has greatly enabled the study of disease risk factors. However, the computational complexity and statistical burden of testing all variants by all environments has required novel study designs and hypothesis-driven approaches. We discuss how incorporating biological knowledge from model organisms, functional genomics, and integrative approaches can empower the discovery of novel gene-environment interactions and discuss specific methodological considerations with each approach. We consider specific examples where the application of these approaches has uncovered effects of gene-environment interactions relevant to drug response and immunity, and we highlight how such improvements enable a greater understanding of the pathogenesis of disease and the realization of precision medicine.

Nerve Dependence: From Regeneration to Cancer

Nerve dependence has long been described in animal regeneration, where the outgrowth of axons is necessary to the reconstitution of lost body parts and tissue remodeling in various species. Recent discoveries have demonstrated that denervation can suppress tumor growth and metastasis, pointing to nerve dependence in cancer. Regeneration and cancer share similarities in regard to the stimulatory role of nerves, and there are indications that the stem cell compartment is a preferred target of innervation. Thus, the neurobiology of cancer is an emerging discipline that opens new perspectives in oncology.

Validity of using ad hoc methods to analyze secondary traits in case-control association studies

Case-control association studies often collect from their subjects information on secondary phenotypes. Reusing the data and studying the association between genes and secondary phenotypes provide an attractive and cost-effective approach that can lead to discovery of new genetic associations. A number of approaches have been proposed, including simple and computationally efficient ad hoc methods that ignore ascertainment or stratify on case-control status. Justification for these approaches relies on the assumption of no covariates and the correct specification of the primary disease model as a logistic model. Both might not be true in practice, for example, in the presence of population stratification or the primary disease model following a probit model. In this paper, we investigate the validity of ad hoc methods in the presence of covariates and possible disease model misspecification. We show that in taking an ad hoc approach, it may be desirable to include covariates that affect the primary disease in the secondary phenotype model, even though these covariates are not necessarily associated with the secondary phenotype. We also show that when the disease is rare, ad hoc methods can lead to severely biased estimation and inference if the true disease model follows a probit model instead of a logistic model. Our results are justified theoretically and via simulations. Applied to real data analysis of genetic associations with cigarette smoking, ad hoc methods collectively identified as highly significant (P<10-5) single nucleotide polymorphisms from over 10 genes, genes that were identified in previous studies of smoking cessation.

Associations between Nine Polymorphisms in EXO1 and Cancer Susceptibility: A Systematic Review and Meta-Analysis of 39 Case-control Studies

An increasing number of studies have highlighted the potential link between EXO1 polymorphisms and cancer risk, although no consensus has yet been obtained. Thus, we aimed to obtain a thorough and current assessment of EXO1 polymorphisms and cancer susceptibility by performing a meta-analysis. A comprehensive literature retrieval was performed on PubMed, EMbase, Web of Science and Wanfang databases. The odds ratio (OR) and 95% confidence interval (CI) were applied to assess the results. Finally, 39 case-control studies of the nine EXO1 polymorphisms that involved 21,651 cases and 21,348 controls met our inclusion criteria. The pooled analysis indicated that the rs1047840 polymorphism conferred a significantly increased susceptibility to cancer in an allelic model. Similarly, the rs3754093, rs1776177, rs9350, rs10802996, rs1635498, rs1776148 and rs851797 polymorphisms were also associated with an increased susceptibility to cancer in an allelic model, respectively, while no significant association was identified for rs1635517 polymorphism. For the rs1047840 polymorphism, in an ethnicity subgroup analysis, a significantly increased susceptibility to cancer for Asians was identified in all the genetic models, and for Caucasians in an allelic model. Our findings provide the evidence that the rs1047840, rs9350, rs10802996, rs1635498, rs1776148, rs1776177, rs3754093 and rs851797 polymorphisms may act as risk factors for cancer.

Genome-wide association studies and epigenome-wide association studies go together in cancer control

Completion of the human genome a decade ago laid the foundation for: using genetic information in assessing risk to identify individuals and populations that are likely to develop cancer, and designing treatments based on a person's genetic profiling (precision medicine). Genome-wide association studies (GWAS) completed during the past few years have identified risk-associated single nucleotide polymorphisms that can be used as screening tools in epidemiologic studies of a variety of tumor types. This led to the conduct of epigenome-wide association studies (EWAS). This article discusses the current status, challenges and research opportunities in GWAS and EWAS. Information gained from GWAS and EWAS has potential applications in cancer control and treatment.

Mithramycin Depletes Specificity Protein 1 and Activates p53 to Mediate Senescence and Apoptosis of Malignant Pleural Mesothelioma Cells

PURPOSE

Specificity protein 1 (SP1) is an oncogenic transcription factor overexpressed in various human malignancies. This study sought to examine SP1 expression in malignant pleural mesotheliomas (MPM) and ascertain the potential efficacy of targeting SP1 in these neoplasms.

EXPERIMENTAL DESIGN

qRT-PCR, immunoblotting, and immunohistochemical techniques were used to evaluate SP1 expression in cultured MPM cells and MPM specimens and normal mesothelial cells/pleura. MTS, chemotaxis, soft agar, β-galactosidase, and Apo-BrdUrd techniques were used to assess proliferation, migration, clonogenicity, senescence, and apoptosis in MPM cells following SP1 knockdown, p53 overexpression, or mithramycin treatment. Murine subcutaneous and intraperitoneal xenograft models were used to examine effects of mithramycin on MPM growth in vivo. Microarray, qRT-PCR, immunoblotting, and chromatin immunoprecipitation techniques were used to examine gene expression profiles mediated by mithramycin and combined SP1 knockdown/p53 overexpression and correlate these changes with SP1 and p53 levels within target gene promoters.

RESULTS

MPM cells and tumors exhibited higher SP1 mRNA and protein levels relative to control cells/tissues. SP1 knockdown significantly inhibited proliferation, migration, and clonogenicity of MPM cells. Mithramycin depleted SP1 and activated p53, dramatically inhibiting proliferation and clonogenicity of MPM cells. Intraperitoneal mithramycin significantly inhibited growth of subcutaneous MPM xenografts and completely eradicated mesothelioma carcinomatosis in 75% of mice. Mithramycin modulated genes mediating oncogene signaling, cell-cycle regulation, senescence, and apoptosis in vitro and in vivo. The growth-inhibitory effects of mithramycin in MPM cells were recapitulated by combined SP1 knockdown/p53 overexpression.

CONCLUSIONS

These findings provide preclinical rationale for phase II evaluation of mithramycin in patients with mesothelioma.

Association between three exonuclease 1 polymorphisms and cancer risks: a meta-analysis

To date, the results of studies exploring the relation between exonuclease 1 (Exo1) polymorphisms and cancer risks have differed. In this study, we performed a meta-analysis to investigate the effect of the three most extensively studied Exo1 polymorphisms (Pro757Leu, Glu589Lys, and Glu670Gly) on cancer susceptibility. The related studies published before August 5, 2015, were collected by searching the PubMed and EMBASE databases. We found 16 publications containing studies that were eligible for our study, including 10 studies for Pro757Leu polymorphism (4,093 cases and 3,834 controls), 12 studies for Glu589Lys polymorphism (6,479 cases and 6,550 controls), and 7 studies for Glu670Gly polymorphism (3,700 cases and 3,496 controls). Pooled odds ratios and 95% confidence intervals were used to assess the strength of the associations, and all the statistical analyses were calculated using the software program STATA version 12.0. Our results revealed that the Pro757Leu polymorphism was significantly associated with a reduced cancer risk, whereas an inverse association was found for the Glu589Lys polymorphism. Furthermore, subgroup analysis of smoking status indicated that the Glu589Lys polymorphism was significantly associated with an increased cancer risk in smokers, but not in nonsmokers. However, no evidence was found for an association between the Glu670Gly polymorphism and cancer risk. In conclusion, this meta-analysis suggests that the Pro757Leu polymorphism may provide protective effects against cancer, while the Glu589Lys polymorphism may be a risk factor for cancer. Moreover, the Glu670Gly polymorphism may have no influence on cancer susceptibility. In the future, large-scaled and well-designed studies are needed to achieve a more precise and comprehensive result.

The emerging role of Slit-Robo pathway in gastric and other gastro intestinal cancers

Gastric cancer remains one of the most common cancers worldwide and one of the leading cause for cancer-related deaths. Due to the high frequency of metastasis, it is still one of the most lethal malignancies in which kinds of signaling pathways are involved in. The Roundabout (ROBO) receptors and their secreted SLIT glycoprotein ligands, which were originally identified as important axon guidance molecules, have implication in the regulation of neurons and glia, leukocytes, and endothelial cells migration. Recent researches also put high emphasis on the important roles of the Slit-Robo pathway in tumorigenesis, cancer progression and metastasis. Herein we provide a comprehensive review on the role of these molecules and their associated signaling pathway in gastric and other gastrointestinal cancers. Improved knowledge of the Slit-Robo signaling pathway in gastric carcinoma will be useful for deep understanding the mechanisms of tumor development and identifying ideal targets of anticancer therapy in gastric carcinoma.

Influence of a single-nucleotide polymorphism of the DNA mismatch repair-related gene exonuclease-1 (rs9350) with prostate cancer risk among Chinese people

In this study, we aimed to identify the influence of exonuclease 1 (EXO1) single-nucleotide polymorphism rs9350, which is involved in DNA mismatch repair, on prostate cancer risk in Chinese people. In our hospital-based case-control study, 214 prostate cancer patients and 253 cancer-free control subjects were enrolled from three hospitals in China. Genotyping for rs9350 was performed by the SNaPshot(®) method using peripheral blood samples. Consequently, a significantly higher prostate cancer risk was observed in patients with the CC genotype [odds ratio (OR) = 1.678, 95 % confidence interval (CI) = 1.130-2.494, P = 0.010] than in those with the CT genotype. Further, the CT/TT genotypes were significantly associated with increased prostate cancer risk (adjusted OR = 1.714, 95 % CI = 1.176-2.500, P = 0.005), and the C allele had a statistically significant compared with T allele (P = 0.009) of EXO1 (rs9350). Through stratified analysis, significant associations were revealed for the CT/TT genotype in the subgroup with diagnosis age >72 (adjusted OR = 1.776, 95 % CI = 1.051-3.002, P = 0.032) and in patients with localized disease subgroup (adjusted OR = 1.798, 95 % CI = 1.070-3.022, P = 0.027). In addition, we observed that patients with prostate-specific antigen (PSA) levels of ≤10 ng/mL were more likely to have the CT/TT genotypes than those with PSA levels of >10 ng/mL (P = 0.006). For the first time, we present evidence that the inherited EXO1 polymorphism rs9350 may have a substantial influence on prostate cancer risk in Chinese people. We believe that the rs9350 could be a useful biomarker for assessing predisposition for and early diagnosis of prostate cancer.

Genetics and Genetic Testing in Pancreatic Cancer

Genetic testing of germline DNA is used in patients suspected of being at risk of pancreatic ductal adenocarcinoma (PDAC) to better define the individual's risk and to determine the mechanism of risk. A high genetic risk increases the pretest probability that a biomarker of early cancer is a true positive and warrants further investigation. The highest PDAC risk is generally associated with a hereditary predisposition. However, the majority of PDAC results from complex, progressive gene-environment interactions that currently fall outside the traditional risk models. Over many years, the combination of inflammation, exposure to DNA-damaging toxins, and failed DNA repair promote the accumulation of somatic mutations in pancreatic cells; PDAC risk is further increased by already present oncogenic germline mutations. Predictive models and new technologies are needed to classify patients into more accurate and mechanistic PDAC risk categories that can be linked to improved surveillance and preventative strategies.

Maximizing Effectiveness Trials in PTSD and SUD Through Secondary Analysis: Benefits and Limitations Using the National Institute on Drug Abuse Clinical Trials Network "Women and Trauma" Study as a Case Example

Recent federal legislation and a renewed focus on integrative care models underscore the need for economical, effective, and science-based behavioral health care treatment. As such, maximizing the impact and reach of treatment research is of great concern. Behavioral health issues, including the frequent co-occurrence of substance use disorders (SUD) and posttraumatic stress disorder (PTSD), are often complex, with a myriad of factors contributing to the success of interventions. Although treatment guides for comorbid SUD/PTSD exist, most patients continue to suffer symptoms following the prescribed treatment course. Further, the study of efficacious treatments has been hampered by methodological challenges (e.g., overreliance on "superiority" designs (i.e., designs structured to test whether or not one treatment statistically surpasses another in terms of effect sizes) and short term interventions). Secondary analyses of randomized controlled clinical trials offer potential benefits to enhance understanding of findings and increase the personalization of treatment. This paper offers a description of the limits of randomized controlled trials as related to SUD/PTSD populations, highlights the benefits and potential pitfalls of secondary analytic techniques, and uses a case example of one of the largest effectiveness trials of behavioral treatment for co-occurring SUD/PTSD conducted within the National Drug Abuse Treatment Clinical Trials Network (NIDA CTN) and producing 19 publications. The paper concludes with implications of this secondary analytic approach to improve addiction researchers' ability to identify best practices for community-based treatment of these disorders. Innovative methods are needed to maximize the benefits of clinical studies and better support SUD/PTSD treatment options for both specialty and non-specialty healthcare settings. Moving forward, planning for and description of secondary analyses in randomized trials should be given equal consideration and care to the primary outcome analysis.

EVC gene polymorphisms and risks of isolated hypospadias - a preliminary study

Introduction

Hypospadias has a complex etiology with both genetic and environmental factors contributing to the condition. Urogenital abnormalities including hypospadias, are found in 22% of cases with Ellis van Creveld syndrome (EvC). Mutations in the EVC gene can cause major and minor anomalies, which form phenotypes that partially overlap with those present in EvC.

The aim of this study was to evaluate the association between nucleotide variants of the EVC gene and the risk of hypospadias.

Material and methods

Four single nucleotide polymorphisms (SNPs) of the EVC gene (rs3774856, rs2302075, rs1383180, rs7680768) were taken under investigation in 96 patients with isolated hypospadias and 284 matched controls. Genotyping of all polymorphisms was carried out by PCR and followed by appropriate restriction enzyme digestion (PCR-RFLP).

Results

Individuals homozygous for the SNP rs2302075 (p.Thr449Lys) showed an elevated risk for hypospadias. Haplotypes containing the rs2302075 variant also revealed modest associations with hypospadias, which did not survive multiple testing corrections. None of the other tested EVC polymorphisms displayed significant association with the risk of hypospadias, either in dominant or recessive inheritance models.

Conclusions

The results of this study suggest that polymorphic variants of the EVC gene do not substantially contribute to the risk of hypospadias based on our study population. However, further studies should help to clarify the relationship between polymorphisms of EVC and hypospadias.

The cancer cell map initiative: defining the hallmark networks of cancer

Progress in DNA sequencing has revealed the startling complexity of cancer genomes, which typically carry thousands of somatic mutations. However, it remains unclear which are the key driver mutations or dependencies in a given cancer and how these influence pathogenesis and response to therapy. Although tumors of similar types and clinical outcomes can have patterns of mutations that are strikingly different, it is becoming apparent that these mutations recurrently hijack the same hallmark molecular pathways and networks. For this reason, it is likely that successful interpretation of cancer genomes will require comprehensive knowledge of the molecular networks under selective pressure in oncogenesis. Here we announce the creation of a new effort, The Cancer Cell Map Initiative (CCMI), aimed at systematically detailing these complex interactions among cancer genes and how they differ between diseased and healthy states. We discuss recent progress that enables creation of these cancer cell maps across a range of tumor types and how they can be used to target networks disrupted in individual patients, significantly accelerating the development of precision medicine.

The Role of nAChR and Calcium Signaling in Pancreatic Cancer Initiation and Progression

Pancreatic cancer shows a strong correlation with smoking and the current therapeutic strategies have been relatively ineffective in improving the survival of patients. Efforts have been made over the past many years to understand the molecular events that drive the initiation and progression of pancreatic cancer, especially in the context of smoking. It has become clear that components of tobacco smoke not only initiate these cancers, especially pancreatic ductal adenocarcinomas (PDACs) through their mutagenic properties, but can also promote the growth and metastasis of these tumors by stimulating cell proliferation, angiogenesis, invasion and epithelial-mesenchymal transition. Studies in cell culture systems, animal models and human samples have shown that nicotinic acetylcholine receptor (nAChR) activation enhances these tumor-promoting events by channeling signaling through multiple pathways. In this context, signaling through calcium channels appear to facilitate pancreatic cancer growth by itself or downstream of nAChRs. This review article highlights the role of nAChR downstream signaling events and calcium signaling in the growth, metastasis as well as drug resistance of pancreatic cancer.

Powerful Tukey's One Degree-of-Freedom Test for Detecting Gene-Gene and Gene-Environment Interactions

Genome-wide association studies (GWASs) have identified thousands of single nucleotide polymorphisms (SNPs) robustly associated with hundreds of complex human diseases including cancers. However, the large number of GWAS-identified genetic loci only explains a small proportion of the disease heritability. This “missing heritability” problem has been partly attributed to the yet-to-be-identified gene–gene (G × G) and gene–environment (G × E) interactions. In spite of the important roles of G × G and G × E interactions in understanding disease mechanisms and filling in the missing heritability, straightforward GWAS scanning for such interactions has very limited statistical power, leading to few successes. Here we propose a two-step statistical approach to test G × G/G × E interactions: the first step is to perform principal component analysis (PCA) on the multiple SNPs within a gene region, and the second step is to perform Tukey’s one degree-of-freedom (1-df) test on the leading PCs. We derive a score test that is computationally fast and numerically stable for the proposed Tukey’s 1-df interaction test. Using extensive simulations we show that the proposed approach, which combines the two parsimonious models, namely, the PCA and Tukey’s 1-df form of interaction, outperforms other state-of-the-art methods. We also demonstrate the utility and efficiency gains of the proposed method with applications to testing G × G interactions for Crohn’s disease using the Wellcome Trust Case Control Consortium (WTCCC) GWAS data and testing G × E interaction using data from a case–control study of pancreatic cancer.

Slit/Robo pathway: a promising therapeutic target for cancer

Axon guidance molecules, slit glycoprotein (Slit) and Roundabout receptor (Robo), have implications in the regulation of physiological processes. Recent studies indicate that Slit and Robo also have important roles in tumorigenesis, cancer progression and metastasis. The Slit/Robo pathway can be considered a master regulator for multiple oncogenic signaling pathways. Herein, we provide a comprehensive review on the role of these molecules and their associated signaling pathways in cancer progression and metastasis. Overall, the current available data suggest that the Slit/Robo pathway could be a promising target for development of anticancer drugs.

Shared molecular pathways and gene networks for cardiovascular disease and type 2 diabetes mellitus in women across diverse ethnicities

BACKGROUND

Although cardiovascular disease (CVD) and type 2 diabetes mellitus (T2D) share many common risk factors, potential molecular mechanisms that may also be shared for these 2 disorders remain unknown.

METHODS AND RESULTS

Using an integrative pathway and network analysis, we performed genome-wide association studies in 8155 blacks, 3494 Hispanic American, and 3697 Caucasian American women who participated in the national Women's Health Initiative single-nucleotide polymorphism (SNP) Health Association Resource and the Genomics and Randomized Trials Network. Eight top pathways and gene networks related to cardiomyopathy, calcium signaling, axon guidance, cell adhesion, and extracellular matrix seemed to be commonly shared between CVD and T2D across all 3 ethnic groups. We also identified ethnicity-specific pathways, such as cell cycle (specific for Hispanic American and Caucasian American) and tight junction (CVD and combined CVD and T2D in Hispanic American). In network analysis of gene-gene or protein-protein interactions, we identified key drivers that included COL1A1, COL3A1, and ELN in the shared pathways for both CVD and T2D. These key driver genes were cross-validated in multiple mouse models of diabetes mellitus and atherosclerosis.

CONCLUSIONS

Our integrative analysis of American women of 3 ethnicities identified multiple shared biological pathways and key regulatory genes for the development of CVD and T2D. These prospective findings also support the notion that ethnicity-specific susceptibility genes and process are involved in the pathogenesis of CVD and T2D.

Functional logistic regression approach to detecting gene by longitudinal environmental exposure interaction in a case-control study

Most complex human diseases are likely the consequence of the joint actions of genetic and environmental factors. Identification of gene-environment (G × E) interactions not only contributes to a better understanding of the disease mechanisms, but also improves disease risk prediction and targeted intervention. In contrast to the large number of genetic susceptibility loci discovered by genome-wide association studies, there have been very few successes in identifying G × E interactions, which may be partly due to limited statistical power and inaccurately measured exposures. Although existing statistical methods only consider interactions between genes and static environmental exposures, many environmental/lifestyle factors, such as air pollution and diet, change over time, and cannot be accurately captured at one measurement time point or by simply categorizing into static exposure categories. There is a dearth of statistical methods for detecting gene by time-varying environmental exposure interactions. Here, we propose a powerful functional logistic regression (FLR) approach to model the time-varying effect of longitudinal environmental exposure and its interaction with genetic factors on disease risk. Capitalizing on the powerful functional data analysis framework, our proposed FLR model is capable of accommodating longitudinal exposures measured at irregular time points and contaminated by measurement errors, commonly encountered in observational studies. We use extensive simulations to show that the proposed method can control the Type I error and is more powerful than alternative ad hoc methods. We demonstrate the utility of this new method using data from a case-control study of pancreatic cancer to identify the windows of vulnerability of lifetime body mass index on the risk of pancreatic cancer as well as genes that may modify this association.