Inherited predisposition of lung cancer: a hierarchical modeling approach to DNA repair and cell cycle control pathways

Spinal Metastasis in Multiple Primary Malignancies Involving Lung Cancer: Clinical Characteristics and Survival

OBJECTIVE

The incidence of multiple primary malignancies (MPM) has increased in recent decades. Our aim was to evaluate incidence, clinical features, and survival in cases of spinal metastases from MPM in which one of the malignancies is lung cancer.

METHODS

We retrospectively reviewed an institutional database of lung cancer patients with spinal metastasis and extracted all cases of MPM.

RESULTS

Among 275 patients who had spinal metastasis with lung cancer as one of the diagnoses, 21 (7.6%) patients with MPM were identified. Mean patient age was 68.5 years (95% confidence interval [CI], 65.3-71.7). The most common cancers diagnosed in addition to lung cancer were breast cancer (5 patients, 24%), upper aerodigestive tract cancer (4 patients, 19%), and prostate cancer (4 patients, 19%). Eighteen (86%) patients walked independently, and 3 (14%) patients walked with help. Seventeen (80.9%) patients had a good Karnofsky performance scale score. The median survivals from the date of first cancer diagnosis, last cancer diagnosis, and spinal metastasis diagnosis were 109.8 months (95% CI, 23.5-196.1), 17.8 months (95% CI, 5.8-29.8), and 10.3 months (95% CI, 5.4-15.2), respectively. Actual rates of survival at 6 months, 12 months, and 24 months from the date of spinal metastasis diagnosis were 81%, 42.9%, and 23.8%, respectively.

CONCLUSIONS

The present study is the first series to our knowledge to show that survival of patients with spinal metastasis and MPM involving lung cancer is not clearly inferior to that of patients with spinal metastasis and lung cancer alone.

Multiple primary malignancies involving lung cancer: a single-center experience

OBJECTIVE

Currently, unlike earlier years, patients affected by multiple primary malignancies (MPM) are significantly increased, thus representing a clinical-pathologic category worthy of attention. Their clinical features and prognosis still need to be studied thoroughly, and this is the aim of our study.

METHODS

Patients with MPM involving lung cancer admitted in our center between January 2006 and December 2016 were considered. Parametric and nonparametric testing was used for statistical comparisons. Univariate and multivariate analysis was used to evaluate the variables associated with a prognostic value.

RESULTS

MPM incidence was 19.8%. Among the 222 patients with MPM enrolled, 204 (91.8%) had two malignancies, while 18 (8.2%) had three malignancies, 38 (17.1%) were synchronous, 41 (18.5%) had lung cancer first (LCF) and 181 (81.5%) had other cancer first (OCF). A significant difference between the time of first cancer diagnosis to the second cancer diagnosis in the LCF vs OCF group was found (median 32 vs 51 months; p-value: 0.038). The most frequent anatomical sites of malignancies preceding or following lung cancer were prostate, colorectal, bladder, and larynx. Multivariate analysis revealed that sex, histologic pattern, and time and order of occurrence were independent factors for overall survival, with male sex, squamous cell lung carcinoma, synchronous and LCF MPM significantly associated with poorer overall survival.

CONCLUSIONS

Prostate, colorectal, bladder, and larynx were the most frequent anatomical sites of malignancies preceding or following lung cancer. Male sex, squamous cell lung carcinoma, synchronous and LCF MPM might be associated with poorer prognosis.

The prognostic significance of the 8th edition AJCC TNM staging system for non-small-cell lung cancer is not applicable to lung cancer as a second primary malignancy

BACKGROUND AND OBJECTIVES

It is unclear whether the prognostic significance of the 8th American Joint Committee on Cancer (AJCC) tumor, node, metastasis (TNM) staging system for non-small-cell lung cancer (NSCLC) is applicable to lung cancer as a second primary malignancy (LCSPM). This study used a population-based database to evaluate this relationship.

METHODS

Patients diagnosed with second primary lung cancer after a nonpulmonary malignancy were identified from the Surveillance, Epidemiology and End Results (SEER) registry from 2004 to 2015. Cumulative incidence function (CIF) and multivariable CIF regression analyses were performed to estimate the difference in disease-specific mortality (DSM) among different TNM stages.

RESULTS

Our cohort included 2687 patients from the SEER database. After CIF analysis, although rates of 1-year, 3-year, and 5-year DSM trended higher with increasing TNM stages, the DSM curves overlapped for many subcategories. In a multivariable regression analysis, hazards ratios (HRs) for subcategories of stage Ι demonstrated no significant difference compared with the reference stage ΙA1 ([ΙA2 HR = 1.120; 95% confidence interval [CI], 0.477-2.626]; [ΙA3 HR = 1.762; 95% CI, 0.752-4.126]; [ΙB HR = 2.003; 95% CI, 0.804-4.911]). The following HRs trended higher for increasing TNM stages but with overlapping CIs among adjacent stage groupings.

CONCLUSION

The 8th edition AJCC TNM staging system fails to provide accurate prognostic value for LCSPM.

The Role of O6-methylguanine-DNA Methyltransferase Polymorphisms in Prostate Cancer Susceptibility: a Meta-Analysis

To assess the associations between O⁶-methylguanine-DNA methyltransferase(MGMT) polymorphisms and prostate cancer risk. We retrieved PubMed, Cochrane Library and Embase electronic database to search for all eligible studies published from Jan 1, 1970 to Sep 31, 2017 to conduct a Meta-analysis. we identified 11 independent studies in 5 eligible reports, including 5143 cases and 8118 controls. The data suggested that rs12917 was associated with higher PCa risk under the contrast of TT vs CC and recessive model in overall population (TT vs CC: OR = 1.599, 95%CI: 1.007-2.539, P = 0.047; TT vs CC + CT: OR = 1.627, 95%CI: 1.026-2.580, P = 0.038). In subgroup analyses stratified by ethnicity, the remarkable association with higher PCa risk was detected under allelic model, dominant model, the contrast of TC vs CC, and the contrast of TC vs CC + TT in Asian population. (T vs C: OR = 1.911, 95%CI: 1.182-3.090, P = 0.008; TC vs CC: OR = 1.948, 95%CI: 1.152-3.295, P = 0.013; TC + TT vs CC: OR = 1.994, 95%CI: 1.190-3.342, P = 0.009; TC vs CC + TT: OR = 1.926, 95%CI: 1.140-3.255, P = 0.014). However, the data suggest the rs2308327 and rs2308321 polymorphisms of the MGMT gene were nor associated with the susceptibility of prostate cancer. Based on the meta-analysis, MGMT rs12917 polymorphism increase the susceptibility to prostate cancer, which can be taken for a diagnosis and screening molecular biomarker for prostate cancer patients.

The potential role of MGMT rs12917 polymorphism in cancer risk: an updated pooling analysis with 21010 cases and 34018 controls

In the present study, we aimed at determining the potential role of rs12917 polymorphism of the O-6-methylguanine-DNA methyltransferase (MGMT) gene in the occurrence of cancer. Based on the available data from the online database, we performed an updated meta-analysis. We retrieved 537 articles from our database research and finally selected a total of 54 case–control studies (21010 cases and 34018 controls) for a series of pooling analyses. We observed an enhanced risk in cancer cases compared with controls, using the genetic models T/T compared with C/C (P-value of association test <0.001; odds ratio (OR) = 1.29) and T/T compared with C/C+C/T (P<0.001; OR = 1.32). We detected similar positive results in the subgroups ‘Caucasian’, and ‘glioma’ (all P<0.05; OR > 1). However, we detected negative results in our analyses of most of the other subgroups (P>0.05). Begg’s and Egger’s tests indicated that the results were free of potential publication bias, and sensitivity analysis suggested the stability of the pooling results. In summary, the T/T genotype of MGMT rs12917 is likely to be linked to an enhanced susceptibility to cancer overall, especially glioma, in the Caucasian population.

XPG rs17655 G>C polymorphism associated with cancer risk: evidence from 60 studies

Xeroderma pigmentosum group G (XPG), a key component in nucleotide excision repair pathway, functions to cut DNA lesions during DNA repair. Genetic variations that alter DNA repair gene expression or function may decrease DNA repair ability and impair genome integrity, thereby predisposing to cancer. The association between XPG rs17655 G>C polymorphism and cancer risk has been investigated extensively, but the results remain contradictory. To get a more accurate conclusion, we performed a comprehensive meta-analysis of 60 case-control studies, involving 27,098 cancer cases and 30,535 healthy controls. Crude odds ratios (ORs) and 95% confidence interval (CIs) were calculated to determine the association of interest. Pooled analysis indicated that the XPG rs17655 G>C polymorphism increased the risk of overall cancer (CC vs. GG: OR=1.10, 95% CI=1.00-1.20; CG vs. GG: OR=1.06, 95% CI=1.02-1.11; CG+CC vs. GG: OR=1.07, 95% CI=1.02-1.12; C vs. G: OR=1.05, 95% CI=1.01-1.09). Stratification analysis by cancer type further showed that this polymorphism was associated with increased risk of gastric cancer and colorectal cancer. This meta-analysis indicated that the XPG gene rs17655 G>C polymorphism was associated with increased overall cancer risk, especially the risk of gastric cancer and colorectal cancer. Further validation experiments are needed to strength our conclusion.

Identification of lung cancer histology-specific variants applying Bayesian framework variant prioritization approaches within the TRICL and ILCCO consortia

Large-scale genome-wide association studies (GWAS) have likely uncovered all common variants at the GWAS significance level. Additional variants within the suggestive range (0.0001> P > 5×10(-8)) are, however, still of interest for identifying causal associations. This analysis aimed to apply novel variant prioritization approaches to identify additional lung cancer variants that may not reach the GWAS level. Effects were combined across studies with a total of 33456 controls and 6756 adenocarcinoma (AC; 13 studies), 5061 squamous cell carcinoma (SCC; 12 studies) and 2216 small cell lung cancer cases (9 studies). Based on prior information such as variant physical properties and functional significance, we applied stratified false discovery rates, hierarchical modeling and Bayesian false discovery probabilities for variant prioritization. We conducted a fine mapping analysis as validation of our methods by examining top-ranking novel variants in six independent populations with a total of 3128 cases and 2966 controls. Three novel loci in the suggestive range were identified based on our Bayesian framework analyses: KCNIP4 at 4p15.2 (rs6448050, P = 4.6×10(-7)) and MTMR2 at 11q21 (rs10501831, P = 3.1×10(-6)) with SCC, as well as GAREM at 18q12.1 (rs11662168, P = 3.4×10(-7)) with AC. Use of our prioritization methods validated two of the top three loci associated with SCC (P = 1.05×10(-4) for KCNIP4, represented by rs9799795) and AC (P = 2.16×10(-4) for GAREM, represented by rs3786309) in the independent fine mapping populations. This study highlights the utility of using prior functional data for sequence variants in prioritization analyses to search for robust signals in the suggestive range.

Multiple primary malignancies involving lung cancer

Background

The incidence of multiple primary malignancies (MPM) has increased sharply in recent decades. However, the clinical characteristics and prognosis of MPM patients involving lung cancer were not fully elucidated. This retrospective study was designed to explore the clinical characteristics and prognosis of MPM patients involving lung cancer in the People’s Republic of China.

Methods

Of 5405 lung cancer cases diagnosed at the Guangdong Lung Cancer Institute between 2005 and 2013, we analyzed 185 patients (3.4 %) with MPM involving lung cancer.

Results

Among 185 patients with MPM involving lung cancer, 10 (5.4 %)had three malignancies and 175 (94.6 %) had two malignancies. 10 patients with three malignancies were excluded from the analysis to avoid misunderstanding. Of 175 accompanying malignancies, 64 (36.6 %) were synchronous MPM patients and 111 (63.4 %) were metachronous MPM patients; 49 (28.0 %) were lung cancer first MPM patients and 126 (72.0 %) were other cancer first MPM patients. The most frequent accompanying malignancy was colon cancer (25/175), followed by rectal cancer (18/175), esophageal cancer (17/175), and thyroid cancer (13/175). Metachronous MPM patients showed significantly better overall survival (OS) than synchronous MPM, with a median OS of 72.8 (range 12.2–391.0) and 12.9 (range 0.8–86.3)months, respectively (P < 0.001). Cox regression analysis revealed that time of occurrence and stage were independent factors for OS.

Conclusions

Colorectal cancer, esophageal cancer, and thyroid cancer were the tumors that most frequently accompanying lung cancer. Metachronous MPM patients showed significantly better OS compared with synchronous MPM patients.

Genetic Association Between Angiotensinogen Polymorphisms and Lung Cancer Risk

Earlier published studies investigating the association between polymorphisms in the angiotensinogen gene and lung cancer risk showed no consistent results. In this study, we have summarized all currently available data to examine the correlation by meta-analysis. Case-control studies addressing the association being examined were identified through Embase, the Cochrane Library, ISI Web of Science (Web of Knowledge), Google Scholar, PubMed, and CNKI databases. Risk of lung cancer (odds ratio [OR] and 95% confidence interval [CI]) was estimated with the fixed or the random effects model assuming homozygous, allele, heterozygous, dominant, and recessive models for all angiotensinogen polymorphisms. We identified a total of 10 articles in this meta-analysis, including 7 for Leu84Phe, 4 for Ile143Val, and 3 for Leu53Leu. In the meta-analysis of Leu84Phe polymorphism, the homozygous model provided an OR of 1.44 (Phe/Phe vs Ile/Ile: OR = 1.44, 95% CI = 1.04-1.99, P values for heterogeneity test (Q-test) [P(Het)] = 0.382). The significantly increased risk was similarly indicated in the recessive model (Phe/Phe vs Phe/Ile + Ile/Ile: OR = 1.41, 95% CI = 1.02-1.95, P(Het) = 0.381). We also observed a positive association in the Caucasian subgroup. The heterozygous model and the dominant model tested for the Ile143Val polymorphism showed a marginally increased risk (Ile/Val vs Ile/Ile: OR = 1.16, 95% CI = 1.00-1.36, P(Het) = 0.323; Val/Val + Ile/Val vs Ile/Ile: OR = 1.15, 95% CI = 0.99-1.34, P(Het) = 0.253). These data suggest that Leu84Phe and Ile143Val polymorphisms in the angiotensinogen gene may be useful biomarkers for lung cancer in some specific populations.

Functional annotation signatures of disease susceptibility loci improve SNP association analysis

BACKGROUND

Genetic association studies are conducted to discover genetic loci that contribute to an inherited trait, identify the variants behind these associations and ascertain their functional role in determining the phenotype. To date, functional annotations of the genetic variants have rarely played more than an indirect role in assessing evidence for association. Here, we demonstrate how these data can be systematically integrated into an association study's analysis plan.

RESULTS

We developed a Bayesian statistical model for the prior probability of phenotype-genotype association that incorporates data from past association studies and publicly available functional annotation data regarding the susceptibility variants under study. The model takes the form of a binary regression of association status on a set of annotation variables whose coefficients were estimated through an analysis of associated SNPs in the GWAS Catalog (GC). The functional predictors examined included measures that have been demonstrated to correlate with the association status of SNPs in the GC and some whose utility in this regard is speculative: summaries of the UCSC Human Genome Browser ENCODE super-track data, dbSNP function class, sequence conservation summaries, proximity to genomic variants in the Database of Genomic Variants and known regulatory elements in the Open Regulatory Annotation database, PolyPhen-2 probabilities and RegulomeDB categories. Because we expected that only a fraction of the annotations would contribute to predicting association, we employed a penalized likelihood method to reduce the impact of non-informative predictors and evaluated the model's ability to predict GC SNPs not used to construct the model. We show that the functional data alone are predictive of a SNP's presence in the GC. Further, using data from a genome-wide study of ovarian cancer, we demonstrate that their use as prior data when testing for association is practical at the genome-wide scale and improves power to detect associations.

CONCLUSIONS

We show how diverse functional annotations can be efficiently combined to create 'functional signatures' that predict the a priori odds of a variant's association to a trait and how these signatures can be integrated into a standard genome-wide-scale association analysis, resulting in improved power to detect truly associated variants.

Replication of breast cancer susceptibility loci in whites and African Americans using a Bayesian approach

Genome-wide association studies (GWAS) and candidate gene analyses have led to the discovery of several dozen genetic polymorphisms associated with breast cancer susceptibility, many of which are considered well-established risk factors for the disease. Despite attempts to replicate these same variant-disease associations in African Americans, the evaluable populations are often too small to produce precise or consistent results. We estimated the associations between 83 previously identified single nucleotide polymorphisms (SNPs) and breast cancer among Carolina Breast Cancer Study (1993-2001) participants using maximum likelihood, Bayesian, and hierarchical methods. The selected SNPs were previous GWAS hits (n = 22), near-hits (n = 19), otherwise well-established risk loci (n = 5), or located in the same genes as selected variants (n = 37). We successfully replicated 18 GWAS-identified SNPs in whites (n = 2,352) and 10 in African Americans (n = 1,447). SNPs in the fibroblast growth factor receptor 2 gene (FGFR2) and the TOC high mobility group box family member 3 gene (TOX3) were strongly associated with breast cancer in both races. SNPs in the mitochondrial ribosomal protein S30 gene (MRPS30), mitogen-activated protein kinase kinase kinase 1 gene (MAP3K1), zinc finger, MIZ-type containing 1 gene (ZMIZ1), and H19, imprinted maternally expressed transcript gene (H19) were associated with breast cancer in whites, and SNPs in the estrogen receptor 1 gene (ESR1) and H19 gene were associated with breast cancer in African Americans. We provide precise and well-informed race-stratified odds ratios for key breast cancer-related SNPs. Our results demonstrate the utility of Bayesian methods in genetic epidemiology and provide support for their application in small, etiologically driven investigations.

Associations between polymorphisms of the XPC gene and lung cancer susceptibility: a meta-analysis

Xeroderma pigmentosum complementation group C (XPC) gene plays a critical role in DNA damage recognition, and its functional single nucleotide polymorphisms (SNPs) may alter DNA repair capacity and cancer susceptibility. Numerous epidemiological studies have investigated the associations between XPC Lys939Gln and Ala499Val polymorphisms and lung cancer susceptibility, but the conclusions are inconclusive. We searched three electronic databases (MEDLINE, EMBASE and EBSCO) for eligible publications and performed a meta-analysis assessing the associations between XPC Lys939Gln and Ala499Val polymorphisms and lung cancer risk. We also analysed the genotype-mRNA expression correlation using the data of HapMap phase II release 23 with 270 individuals from 4 ethnicities for exploring biological plausibility of our findings. We included ten published studies of 3,882 cases and 5,219 controls for Lys939Gln, and five studies with 2,605 cases and 3,329 controls for Ala499Val. When all studies were pooled, we found a significantly increased overall lung cancer risk for Lys939Gln polymorphism (recessive model: OR = 1.14, 95 % CI = 1.01-1.29, P = 0.218 for heterogeneity). Stratification analysis also showed a higher lung cancer risk in Asian populations (recessive model: OR = 1.26, 95% CI = 1.04-1.52, P = 0.263 for heterogeneity). Interestingly, we found significant correlation between Lys939Gln genotypes and XPC mRNA expression for Asian populations as well. However, we did not observe any association between Ala499Val polymorphism and overall lung cancer risk, nor in further stratification analysis. This meta-analysis suggests that XPC Lys939Gln polymorphism may contribute to lung cancer risk, which needs further validation in single larger studies.

Two-phase and family-based designs for next-generation sequencing studies

The cost of next-generation sequencing is now approaching that of early GWAS panels, but is still out of reach for large epidemiologic studies and the millions of rare variants expected poses challenges for distinguishing causal from non-causal variants. We review two types of designs for sequencing studies: two-phase designs for targeted follow-up of genomewide association studies using unrelated individuals; and family-based designs exploiting co-segregation for prioritizing variants and genes. Two-phase designs subsample subjects for sequencing from a larger case-control study jointly on the basis of their disease and carrier status; the discovered variants are then tested for association in the parent study. The analysis combines the full sequence data from the substudy with the more limited SNP data from the main study. We discuss various methods for selecting this subset of variants and describe the expected yield of true positive associations in the context of an on-going study of second breast cancers following radiotherapy. While the sharing of variants within families means that family-based designs are less efficient for discovery than sequencing unrelated individuals, the ability to exploit co-segregation of variants with disease within families helps distinguish causal from non-causal ones. Furthermore, by enriching for family history, the yield of causal variants can be improved and use of identity-by-descent information improves imputation of genotypes for other family members. We compare the relative efficiency of these designs with those using unrelated individuals for discovering and prioritizing variants or genes for testing association in larger studies. While associations can be tested with single variants, power is low for rare ones. Recent generalizations of burden or kernel tests for gene-level associations to family-based data are appealing. These approaches are illustrated in the context of a family-based study of colorectal cancer.

Associations of Lys939Gln and Ala499Val polymorphisms of the XPC gene with cancer susceptibility: a meta-analysis

XPC polymorphisms may alter DNA repair capacity, thus leading to genetic instability and carcinogenesis. Numerous studies have investigated the associations of XPC Lys939Gln (rs2228001) and Ala499Val (rs2228000) polymorphisms with cancer susceptibility; however, the findings are inconclusive. We searched literature from MEDLINE and EMBASE for eligible publications that assessed the associations between these two polymorphisms and cancer risk. We also assessed genotype-mRNA expression correlation data from HapMap for rs2228001 and rs2228000 in normal cell lines derived from 270 subjects with different ethnicities. The final analysis included 62 published studies of 25,708 cases and 30,432 controls for the Lys939Gln and 34 studies with 14,877 cases and 17,888 controls for the Ala499Val. Overall, Lys939Gln was significantly associated with an increased overall cancer risk (Gln/Gln vs. Lys/Lys: OR = 1.16, 95% CI = 1.07 - 1.25, p < 0.001; recessive model: OR = 1.14, 95% CI = 1.06 - 1.22, p < 0.001; dominant model: OR = 1.06, 95% CI = 1.01 - 1.11, p = 0.015 and Gln vs. Lys: OR = 1.07, 95% CI = 1.03 - 1.10, p < 0.001) and further stratifications showed an increased risk for bladder, lung and colorectal cancer, Asian populations and population-based studies. Likewise, Ala499Val was also significantly associated with an increased overall cancer risk (Val/Val vs. Ala/Ala: OR = 1.21, 95% CI = 1.07 - 1.36, p = 0.003 and recessive model: OR = 1.20, 95% CI = 1.08 - 1.34, p = 0.001) and further stratification showed an increased risk for breast and bladder cancer, particularly in Asian populations. Interestingly, significantly correlation between XPC genotypes and mRNA expression was found only for Asian populations as well. Despite some limitations, this meta-analysis established some solid statistical evidence for an association between XPC polymorphisms and cancer risk, which warrants further validation in single large studies.

Integrated enrichment analysis of variants and pathways in genome-wide association studies indicates central role for IL-2 signaling genes in type 1 diabetes, and cytokine signaling genes in Crohn's disease

Pathway analyses of genome-wide association studies aggregate information over sets of related genes, such as genes in common pathways, to identify gene sets that are enriched for variants associated with disease. We develop a model-based approach to pathway analysis, and apply this approach to data from the Wellcome Trust Case Control Consortium (WTCCC) studies. Our method offers several benefits over existing approaches. First, our method not only interrogates pathways for enrichment of disease associations, but also estimates the level of enrichment, which yields a coherent way to promote variants in enriched pathways, enhancing discovery of genes underlying disease. Second, our approach allows for multiple enriched pathways, a feature that leads to novel findings in two diseases where the major histocompatibility complex (MHC) is a major determinant of disease susceptibility. Third, by modeling disease as the combined effect of multiple markers, our method automatically accounts for linkage disequilibrium among variants. Interrogation of pathways from eight pathway databases yields strong support for enriched pathways, indicating links between Crohn's disease (CD) and cytokine-driven networks that modulate immune responses; between rheumatoid arthritis (RA) and "Measles" pathway genes involved in immune responses triggered by measles infection; and between type 1 diabetes (T1D) and IL2-mediated signaling genes. Prioritizing variants in these enriched pathways yields many additional putative disease associations compared to analyses without enrichment. For CD and RA, 7 of 8 additional non-MHC associations are corroborated by other studies, providing validation for our approach. For T1D, prioritization of IL-2 signaling genes yields strong evidence for 7 additional non-MHC candidate disease loci, as well as suggestive evidence for several more. Of the 7 strongest associations, 4 are validated by other studies, and 3 (near IL-2 signaling genes RAF1, MAPK14, and FYN) constitute novel putative T1D loci for further study.

MGMT Leu84Phe polymorphism contributes to cancer susceptibility: evidence from 44 case-control studies

Background

O⁶-methylguanine-DNA methyltransferase is one of the few proteins to directly remove alkylating agents in the human DNA direct reversal repair pathway. A large number of case-control studies have been conducted to explore the association between MGMT Leu84Phe polymorphism and cancer risk. However, the results were not consistent.

Methods

We carried out a meta-analysis of 44 case-control studies to clarify the association between the Leu84Phe polymorphism and cancer risk.

Results

Overall, significant association of the T allele with cancer susceptibility was verified with meta-analysis under a recessive genetic model (P<0.001, OR=1.30, 95%CI 1.24-1.50) and TT versus CC comparison (P=0.001, OR=1.29, 95% CI 1.12-1.50). In subgroup analysis, a significant increased risk was found for lung cancer (TT versus CC, P=0.027, OR=1.67, 95% CI 1.06-2.63; recessive genetic model, P=0.32, OR=1.64, 95% CI 1.04-2.58), whereas risk of colorectal cancer was significantly low under a dominant genetic model (P=0.019, OR=0.84, 95% CI 0.72-0.97). Additionally, a significant association between TT genetic model and total cancer risk was found in the Caucasian population (TT versus CC, P=0.014, OR=1.29, 95% CI 1.05-1.59; recessive genetic model, P=0.009, OR=1.31, 95% CI 1.07-1.61), but not in the Asian population. An increased risk for lung cancer was also verified in the Caucasian population (TT versus CC, P=0.035, OR=1.62, 95% CI 1.04-2.53; recessive genetic model, P=0.048, OR=1.57, 95% CI 1.01-2.45).

Conclusions

These results suggest that MGMT Leu84Phe polymorphism might contribute to the susceptibility of certain cancers.

Epidemiology of lung cancer: Diagnosis and management of lung cancer, 3rd ed: American College of Chest Physicians evidence-based clinical practice guidelines

BACKGROUND

Ever since a lung cancer epidemic emerged in the mid-1900 s, the epidemiology of lung cancer has been intensively investigated to characterize its causes and patterns of occurrence. This report summarizes the key findings of this research.

METHODS

A detailed literature search provided the basis for a narrative review, identifying and summarizing key reports on population patterns and factors that affect lung cancer risk.

RESULTS

Established environmental risk factors for lung cancer include smoking cigarettes and other tobacco products and exposure to secondhand tobacco smoke, occupational lung carcinogens, radiation, and indoor and outdoor air pollution. Cigarette smoking is the predominant cause of lung cancer and the leading worldwide cause of cancer death. Smoking prevalence in developing nations has increased, starting new lung cancer epidemics in these nations. A positive family history and acquired lung disease are examples of host factors that are clinically useful risk indicators. Risk prediction models based on lung cancer risk factors have been developed, but further refinement is needed to provide clinically useful risk stratification. Promising biomarkers of lung cancer risk and early detection have been identified, but none are ready for broad clinical application.

CONCLUSIONS

Almost all lung cancer deaths are caused by cigarette smoking, underscoring the need for ongoing efforts at tobacco control throughout the world. Further research is needed into the reasons underlying lung cancer disparities, the causes of lung cancer in never smokers, the potential role of HIV in lung carcinogenesis, and the development of biomarkers.

The polymorphisms in the MGMT gene and the risk of cancer: a meta-analysis

Polymorphisms in the MGMT gene have been implicated in susceptibility to cancer, but the published studies have reported inconclusive results. The objective of the current study was to investigate the genetic risk of polymorphisms in the MGMT gene for cancer. A meta-analysis was carried out to analyze the association between polymorphisms in the MGMT gene and cancer risk. Five polymorphisms (Leu84Phe, Leu53Leu, Ile143Val, Lys178Arg, and -485C/A) with 98 case-control studies from 49 articles were analyzed. The results indicated that individuals who carried the Phe/Phe homozygote genotype of Leu84Phe had a 31 % increased risk of cancer compared with the Leu allele (Leu + Leu/Phe) carriers (odds ratio [OR] = 1.32, 95 % confidence interval [CI] = 1.15-1.52, P < 0.0001 for Phe/Phe vs. Phe/Leu + Leu/Leu). However, there was no significant association between the risk of cancer and the other four polymorphisms (Leu53Leu, Ile143Val, Lys178Arg, and -485C/A). In further stratified analyses for the Leu84Phe and Ile143Val polymorphisms, the increased risk of cancer remained in subgroups of Caucasians, patients with esophageal cancer for the Leu84Phe polymorphism, and patients with lung cancer for the Ile143Val polymorphism. Results from the current meta-analysis suggested that Leu84Phe and Ile143Val in the MGMT gene are risk factors for cancer. In the future, more studies should be performed to validate our results.

CHEK2*1100delC homozygosity in the Netherlands--prevalence and risk of breast and lung cancer

The 1100delC mutation in the CHEK2 gene has a carrier frequency of up to 1.5% in individuals from North-West Europe. Women heterozygous for 1100delC have an increased breast cancer risk (odds ratio 2.7). To explore the prevalence and clinical consequences of 1100delC homozygosity in the Netherlands, we genotyped a sporadic breast cancer hospital-based cohort, a group of non-BRCA1/2 breast cancer families, and breast tumors from a tumor tissue bank. Three 1100delC homozygous patients were found in the cohort of 1434 sporadic breast cancer patients, suggesting an increased breast cancer risk for 1100delC homozygotes (odds ratio 3.4, 95% confidence interval 0.4-32.6, P=0.3). Another 1100delC homozygote was found in 592 individuals from 108 non-BRCA1/2 breast cancer families, and two more were found after testing 1706 breast tumors and confirming homozygosity on their wild-type DNA. Follow-up data was available for five homozygous patients, and remarkably, three of them had developed contralateral breast cancer. A possible relationship between 1100delC and lung cancer risk was investigated in 457 unrelated lung cancer patients but could not be confirmed. Due to the small number of 1100delC homozygotes identified, the breast cancer risk estimate associated with this genotype had limited accuracy but is probably higher than the risk in heterozygous females. Screening for CHEK2 1100delC could be beneficial in countries with a relatively high allele frequency.

Hierarchical modeling identifies novel lung cancer susceptibility variants in inflammation pathways among 10,140 cases and 11,012 controls

Recent evidence suggests that inflammation plays a pivotal role in the development of lung cancer. In this study, we used a two-stage approach to investigate associations between genetic variants in inflammation pathways and lung cancer risk based on genome-wide association study (GWAS) data. A total of 7,650 sequence variants from 720 genes relevant to inflammation pathways were identified using keyword and pathway searches from Gene Cards and Gene Ontology databases. In Stage 1, six GWAS datasets from the International Lung Cancer Consortium were pooled (4,441 cases and 5,094 controls of European ancestry), and a hierarchical modeling (HM) approach was used to incorporate prior information for each of the variants into the analysis. The prior matrix was constructed using (1) role of genes in the inflammation and immune pathways; (2) physical properties of the variants including the location of the variants, their conservation scores and amino acid coding; (3) LD with other functional variants and (4) measures of heterogeneity across the studies. HM affected the priority ranking of variants particularly among those having low prior weights, imprecise estimates and/or heterogeneity across studies. In Stage 2, we used an independent NCI lung cancer GWAS study (5,699 cases and 5,818 controls) for in silico replication. We identified one novel variant at the level corrected for multiple comparisons (rs2741354 in EPHX2 at 8p21.1 with p value = 7.4 × 10(-6)), and confirmed the associations between TERT (rs2736100) and the HLA region and lung cancer risk. HM allows for prior knowledge such as from bioinformatic sources to be incorporated into the analysis systematically, and it represents a complementary analytical approach to the conventional GWAS analysis.

Joint analysis for integrating two related studies of different data types and different study designs using hierarchical modeling approaches

BACKGROUND

A chronic disease such as asthma is the result of a complex sequence of biological interactions involving multiple genes and pathways in response to a multitude of environmental exposures. However, methods to model jointly all factors are still evolving. Some of the current challenges include how to integrate knowledge from different data types and different disciplines, as well as how to utilize relevant external information such as gene annotation to identify novel disease genes and gene-environment inter-actions.

METHODS

Using a Bayesian hierarchical modeling framework, we developed two alternative methods for joint analysis of an epidemiologic study of a disease endpoint and an experimental study of intermediate phenotypes, while incorporating external information.

RESULTS

Our simulation studies demonstrated superior performance of the proposed hierarchical models compared to separate analysis with the standard single-level regression modeling approach. The combined analyses of the Southern California Children's Health Study and challenge study data suggest that these joint analytical methods detected more significant genetic main and gene-environment interaction effects than the conventional analysis.

CONCLUSION

The proposed prior framework is very flexible and can be generalized for an integrative analysis of diverse sources of relevant biological data.

Association between the ERCC5 Asp1104His polymorphism and cancer risk: a meta-analysis

BACKGROUND

Excision repair cross complementing group 5 (ERCC5 or XPG) plays an important role in regulating DNA excision repair, removal of bulky lesions caused by environmental chemicals or UV light. Mutations in this gene cause a rare autosomal recessive syndrome, and its functional single nucleotide polymorphisms (SNPs) may alter DNA repair capacity phenotype and cancer risk. However, a series of epidemiological studies on the association between the ERCC5 Asp1104His polymorphism (rs17655, G>C) and cancer susceptibility generated conflicting results.

METHODOLOGY/PRINCIPAL FINDINGS

To derive a more precise estimation of the association between the ERCC5 Asp1104His polymorphism and overall cancer risk, we performed a meta-analysis of 44 published case-control studies, in which a total of 23,490 cases and 27,168 controls were included. To provide additional biological plausibility, we also assessed the genotype-gene expression correlation from the HapMap phase II release 23 data with 270 individuals from 4 ethnic populations. When all studies were pooled, we found no statistical evidence for a significantly increased cancer risk in the recessive genetic models (His/His vs. Asp/Asp: OR = 0.99, 95% CI: 0.92-1.06, P = 0.242 for heterogeneity or His/His vs. Asp/His + Asp/Asp: OR = 0.98, 95% CI: 0.93-1.03, P = 0.260 for heterogeneity), nor in further stratified analyses by cancer type, ethnicity, source of controls and sample size. In the genotype-phenotype correlation analysis from 270 individuals, we consistently found no significant correlation of the Asp1104His polymorphism with ERCC5 mRNA expression.

CONCLUSIONS/SIGNIFICANCE

This meta-analysis suggests that it is unlikely that the ERCC5 Asp1104His polymorphism may contribute to individual susceptibility to cancer risk.

Joint association testing of common and rare genetic variants using hierarchical modeling

New sequencing technologies provide an opportunity for assessing the impact of rare and common variants on complex diseases. Several methods have been developed for evaluating rare variants, many of which use weighted collapsing to combine rare variants. Some approaches require arbitrary frequency thresholds below which to collapse alleles, and most assume that effect sizes for each collapsed variant are either the same or a function of minor allele frequency. Some methods also further assume that all rare variants are deleterious rather than protective. We expect that such assumptions will not hold in general, and as a result performance of these tests will be adversely affected. We propose a hierarchical model, implemented in the new program CHARM, to detect the joint signal from rare and common variants within a genomic region while properly accounting for linkage disequilibrium between variants. Our model explores the scale, rather than the center of the odds ratio distribution, allowing for both causative and protective effects. We use cross-validation to assess the evidence for association in a region. We use model averaging to widen the range of disease models under which we will have good power. To assess this approach, we simulate data under a range of disease models with effects at common and/or rare variants. Overall, our method had more power than other well-known rare variant approaches; it performed well when either only rare, or only common variants were causal, and better than other approaches when both common and rare variants contributed to disease.

[Effects of Chinese herbal medicine Feiyanning decoction on expressions of nucleosome conformation-regulating factors H3-K56, Rtt109, Asf1 and E2F1 in Lewis-bearing mice]

OBJECTIVE

Malignant tumor cells were found with an abnormal cell cycle. Previous in vivo experiment had confirmed the Feiyanning's intervention effect on checkpoint signaling of G1/S in the cell cycle. This study was to further observe the expressions of nucleosome conformation-regulating factors intervened by Feiyanning decoction in S phase.

METHODS

Lewis lung carcinoma models of C57BL/6 mice were established. Sixty mice were randomly divided into four groups: normal control group, model control group, Feiyanning group, and cisplatin group. There were 15 mice in each group. Tumor weight and tumor inhibition rate were observed. In addition, the cell cycle distribution was detected by flow cytometry and the proliferation index was calculated. Furthermore, mRNA and protein expressions of H3-K56, regulator of Ty1 transposition 109 (Rtt109), antisilencing function 1 (Asf1) and E2F1 were analyzed by real-time polymerase chain reaction and Western blot methods, respectively.

RESULTS

The tumor weights of mice in the Feiyanning group and the cisplatin group were lower than those in the model group (P<0.01), with tumor inhibition rates of 27.92% and 42.50%, respectively. Cancer cell proliferation index and proportion of cancer cell population in S phase in the Feiyanning group were significantly lower than those in the cisplatin group (P<0.01). The mRNA and protein levels of H3-K56, Rtt109, Asf1, E2F1 in the Feiyanning group were lower than those in the model group (P<0.05).

CONCLUSION

Feiyanning plays a role in intervening in the abnormal cell cycle by nucleosome conformation-regulating factors and thus inhibits the lung cancer cell proliferation.

Variant allele of CHEK2 is associated with a decreased risk of esophageal cancer lymph node metastasis in a Chinese population

Growing evidence suggests that the checkpoint kinase 2 (CHEK2) signaling pathway occupies a central position in the signaling networks of DNA-damage signaling. Many functional and molecular epidemiological studies have evaluated the association between genetic variants of CHEK2 and various cancers. To evaluate the relationship between CHEK2 functional genetic variants and esophageal cancer risk and the risk of lymph node metastasis among a Chinese population. We genotyped CHEK2 rs738722, rs2236141 and rs2236142 single nucleotide polymorphisms (SNPs) using the matrix assisted laser desorption/ionization time-of-flight mass spectrometry assay in a case-controlled study, including 380 esophageal cancer cases and 380 healthy controls in a Chinese population. We found that none of the three polymorphisms achieved significant difference in their distributions between esophageal cancer cases and controls. Multiple logistic regression analyses revealed that esophageal cancer risk was not associated significantly with the variant genotypes of the three CHEK2 polymorphisms as compared with their wild-type genotypes. However, we found that functional variant rs738722 and rs2236142 in CHEK2 might contribute to susceptibility to lymph node metastasis. Our data did not support a significant association between CHEK2 SNPs and the risk of esophageal cancer. Functional variant CHEK2 rs738722 and rs2236142 might contribute to lymph node metastasis susceptibility. The CT allele of SNP rs738722 and the GC allele of SNP rs2236142 might be a protective factor of the risk for lymph node metastasis of esophageal cancer.

Polymorphisms associated with the risk of lung cancer in a healthy Mexican Mestizo population: Application of the additive model for cancer

Lung cancer is the leading cause of cancer mortality in Mexico and worldwide. In the past decade, there has been an increase in the number of lung cancer cases in young people, which suggests an important role for genetic background in the etiology of this disease. In this study, we genetically characterized 16 polymorphisms in 12 low penetrance genes (AhR, CYP1A1, CYP2E1, EPHX1, GSTM1, GSTT1, GSTPI, XRCC1, ERCC2, MGMT, CCND1 and TP53) in 382 healthy Mexican Mestizos as the first step in elucidating the genetic structure of this population and identifying high risk individuals. All of the genotypes analyzed were in Hardy-Weinberg equilibrium, but different degrees of linkage were observed for polymorphisms in the CYP1A1 and EPHX1 genes. The genetic variability of this population was distributed in six clusters that were defined based on their genetic characteristics. The use of a polygenic model to assess the additive effect of low penetrance risk alleles identified combinations of risk genotypes that could be useful in predicting a predisposition to lung cancer. Estimation of the level of genetic susceptibility showed that the individual calculated risk value (iCRV) ranged from 1 to 16, with a higher iCRV indicating a greater genetic susceptibility to lung cancer.

Assessment of rare BRCA1 and BRCA2 variants of unknown significance using hierarchical modeling

Current evidence suggests that the genetic risk of breast cancer may be caused primarily by rare variants. However, while classification of protein-truncating mutations as deleterious is relatively straightforward, distinguishing as deleterious or neutral the large number of rare missense variants is a difficult on-going task. In this article, we present one approach to this problem, hierarchical statistical modeling of data observed in a case-control study of contralateral breast cancer (CBC) in which all the participants were genotyped for variants in BRCA1 and BRCA2. Hierarchical modeling permits leverage of information from observed correlations of characteristics of groups of variants with case-control status to infer with greater precision the risks of individual rare variants. A total of 181 distinct rare missense variants were identified among the 705 cases with CBC and the 1,398 controls with unilateral breast cancer. The model identified three bioinformatic hierarchical covariates, align-GV, align-GD, and SIFT scores, each of which was modestly associated with risk. Collectively, the 11 variants that were classified as adverse on the basis of all the three bioinformatic predictors demonstrated a stronger risk signal. This group included five of six missense variants that were classified as deleterious at the outset by conventional criteria. The remaining six variants can be considered as plausibly deleterious, and deserving of further investigation (BRCA1 R866C; BRCA2 G1529R, D2665G, W2626C, E2663V, and R3052W). Hierarchical modeling is a strategy that has promise for interpreting the evidence from future association studies that involve sequencing of known or suspected cancer genes.

Genetic architecture of cancer and other complex diseases: lessons learned and future directions

Genome-wide association studies have broadened our understanding of the genetic architecture of cancer to include common variants, in addition to the rare variants previously identified by linkage analysis. We review current knowledge on the genetic architecture of four cancers--breast, lung, prostate and colorectal--for which the balance of common and rare alleles identified ranges from fewer common alleles (lung cancer) to more common alleles (prostate cancer). Although most variants are cancer specific, pleiotropy has been observed for several variants, for example, variants at the 8q24 locus and breast, ovarian and prostate cancers or variants in KITLG in relation to hair color and testicular cancer. Although few studies have been adequately powered to investigate heterogeneity among ancestry groups, effect sizes associated with common variants have been reported to be fairly homogenous among ethnic groups. Some associations appear to be ancestry specific, such as HNF1B, which is associated with prostate cancer in European Americans and Latinos but not in African-Americans. Studies of cancer and other complex diseases suggest that a simple dichotomy between rare and common allelic architectures may be too simplistic and that future research is needed to characterize a fuller spectrum of allele frequency (common (>5%), uncommon (1-5%) and rare (<<1%) alleles) and effect size. In addition, a broadening of the concept of genetic architecture to encompass both population architecture, which reflects differences in exposures, genetic factors and population level risk among diverse groups of people, and genomic architecture, which includes structural, epigenomic and somatic variation, is envisioned.

Use of pathway information in molecular epidemiology

Candidate gene studies are generally motivated by some form of pathway reasoning in the selection of genes to be studied, but seldom has the logic of the approach been carried through to the analysis. Marginal effects of polymorphisms in the selected genes, and occasionally pairwise gene-gene or gene-environment interactions, are often presented, but a unified approach to modelling the entire pathway has been lacking. In this review, a variety of approaches to this problem is considered, focusing on hypothesis-driven rather than purely exploratory methods. Empirical modelling strategies are based on hierarchical models that allow prior knowledge about the structure of the pathway and the various reactions to be included as 'prior covariates'. By contrast, mechanistic models aim to describe the reactions through a system of differential equations with rate parameters that can vary between individuals, based on their genotypes. Some ways of combining the two approaches are suggested and Bayesian model averaging methods for dealing with uncertainty about the true model form in either framework is discussed. Biomarker measurements can be incorporated into such analyses, and two-phase sampling designs stratified on some combination of disease, genes and exposures can be an efficient way of obtaining data that would be too expensive or difficult to obtain on a full candidate gene sample. The review concludes with some thoughts about potential uses of pathways in genome-wide association studies.

DeltaN133p53 expression levels in relation to haplotypes of the TP53 internal promoter region

The transcription of the DeltaN133p53 isoform of the TP53 gene is controlled by an internal promoter region (IPR) containing eight polymorphisms in 11 common haplotypes, following a resequencing of 47 Caucasians. We assayed the functional effects of the commonest six haplotypes on the promoter activity with a luciferase reporter system, in HeLa and 293T cells. These studies showed that different IPR haplotypes are associated with differences in the promoter activity resulting in marked variation in the baseline expression of DeltaN133p53. In vivo quantitative-polymerase chain reaction (PCR) on human tissues confirmed that the baseline levels of DeltaN133p53 showed haplotype specific differences that paralleled those seen in vitro. When cell lines were treated with camptothecin, the fold-increase in DeltaN133p53 levels was dose-dependent but haplotype-independent (i.e., similar for all the haplotypes). Finally, we used an electrophoretic mobility shift assay to analyze the rs1794287 polymorphism and found changes in the pattern of protein binding. This partially confirmed our in silico analysis showing that the polymorphism rs1794287 can affect the function of the internal promoter by changing its affinity for several transcription factors. Thus, we showed that the expression of DeltaN133p53 is under genetic control, and suggested the presence of interindividual differences underlying this mechanism.

Gene--environment-wide association studies: emerging approaches

Despite the yield of recent genome-wide association (GWA) studies, the identified variants explain only a small proportion of the heritability of most complex diseases. This unexplained heritability could be partly due to gene--environment (G×E) interactions or more complex pathways involving multiple genes and exposures. This Review provides a tutorial on the available epidemiological designs and statistical analysis approaches for studying specific G×E interactions and choosing the most appropriate methods. I discuss the approaches that are being developed for studying entire pathways and available techniques for mining interactions in GWA data. I also explore methods for marrying hypothesis-driven pathway-based approaches with 'agnostic' GWA studies.

Repair system of 7, 8-dihydro-8-oxoguanine as a defense line against carcinogenesis

Reactive oxygen species (ROS) are essentially harmful for living organisms, including human beings. It is well known that ROS-induced damage of cellular components may lead to human diseases, such as inflammatory diseases, degenerative diseases, or cancer. In particular, oxidative DNA damage is premutagenic, and thus, the generation of DNA damage and the failure of its removal are critical events for tumorigenesis or carcinogenesis. To prevent this disadvantage, living organisms have defense mechanisms against ROS-induced gene instability. Studies of 8-oxo-Gua and its main repair enzyme, 8-oxoguanine DNA glycosylase 1 (OGG1), are informative and useful, because 8-oxo-Gua is commonly observed in DNA, and OGG1 enzymes exist in a wide variety of living organisms. The importance of OGG1 was confirmed by polymorphism analyses and studies using knockout mice. Moreover, analyses of the influences of environmental factors on DNA damage and repair systems have confirmed the effects of heavy metals on 8-oxo-Gua formation and OGG1 expression. These studies revealed that the 8-oxo-Gua repair system is crucial for the prevention of mutation-related diseases, such as cancer. In this review, the advances in this field during the last two decades are described.