Black/white disparities in lung cancer incidence and mortality mandate an evaluation of underlying biological differences. We have previously shown higher risks of lung cancer associated with prior emphysema in African American compared with white patients with lung cancer.
We therefore evaluated a panel of 1440 inflammatory gene variants in a two-phase analysis (discovery and replication), added top genome-wide association studies (GWAS) lung cancer hits from white populations, and 28 single-nucleotide polymorphisms (SNPs) from a published gene panel. The discovery set (477 self-designated African Americans cases, 366 controls matched on age, ethnicity, and gender) were from Houston, Texas. The external replication set (330 cases and 342 controls) was from the EXHALE study at Wayne State University.
In discovery, 154 inflammation SNPs were significant (p < 0.05) on univariate analysis, as was one of the gene panel SNPs (rs308738 in REV1, p = 0.0013), and three GWAS hits, rs16969968 p = 0.0014 and rs10519203 p = 0.0003 in the 15q locus and rs2736100, in the HTERT locus, p = 0.0002. One inflammation SNP, rs950286, was successfully replicated with a concordant odds ratio of 1.46 (1.14-1.87) in discovery, 1.37 (1.05-1.77) in replication, and a combined odds ratio of 1.40 (1.17-1.68). This SNP is intergenic between IRF4 and EXOC2 genes. We also constructed and validated epidemiologic and extended risk prediction models. The area under the curve (AUC) for the epidemiologic discovery model was 0.77 and 0.80 for the extended model. For the combined datasets, the AUC values were 0.75 and 0.76, respectively.
As has been reported for other cancer sites and populations, incorporating top genetic hits into risk prediction models, provides little improvement in model performance and no clinical relevance.
"Etzel et al.51 have previously shown higher risks of lung cancer associated with prior emphysema in African-American populations compared with white patients with lung cancer. Spitz et al.52 further evaluated a panel of 1,440 inflammatory gene variants in a two-phase analysis (discovery and replication), adding top GWAS lung cancer hits from white populations, and 28 SNPs from a published gene panel. The discovery set (477 self-designated African-Americans cases, 366 controls matched on age, ethnicity, and gender) was from Houston, Texas. "
[Show abstract][Hide abstract] ABSTRACT: Cancer risk prediction models are important in identifying individuals at high risk of developing cancer, which could result in targeted screening and interventions to maximize the treatment benefit and minimize the burden of cancer. The cancer-associated genetic variants identified in genome-wide or candidate gene association studies have been shown to collectively enhance cancer risk prediction, improve our understanding of carcinogenesis, and possibly result in the development of targeted treatments for patients. In this article, we review the cancer risk prediction models that have been developed for popular cancers and assess their applicability, strengths, and weaknesses. We also discuss the factors to be considered for future development and improvement of models for cancer risk prediction.
Cancer informatics 09/2014; 13(Suppl 2):19-28. DOI:10.4137/CIN.S13788
"Both Troung et al. and Landi et al. noted a histology-specific role of rs2736100 in adenocarcinoma. This locus was also recently implicated in lung cancer risk in African American patients . There is biologic plausibility for this finding because mean relative telomere length has been associated with four genetic variants of the hTERT gene, including rs2736100 , and TERT gene amplification is responsible for TERT mRNA overexpression in a majority of lung adenocarcinomas . "
[Show abstract][Hide abstract] ABSTRACT: Lung cancer is the leading cause of cancer death worldwide in part due to our inability to identify which smokers are at highest risk and the lack of effective tools to detect the disease at its earliest and potentially curable stage. Recent results from the National Lung Screening Trial have shown that annual screening of high-risk smokers with low-dose helical computed tomography of the chest can reduce lung cancer mortality. However, molecular biomarkers are needed to identify which current and former smokers would benefit most from annual computed tomography scan screening in order to reduce the costs and morbidity associated with this procedure. Additionally, there is an urgent clinical need to develop biomarkers that can distinguish benign from malignant lesions found on computed tomography of the chest given its very high false positive rate. This review highlights recent genetic, transcriptomic and epigenomic biomarkers that are emerging as tools for the early detection of lung cancer both in the diagnostic and screening setting.
BMC Medicine 07/2013; 11(1):168. DOI:10.1186/1741-7015-11-168 · 7.25 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Deaths from lung cancer exceed those from any other type of malignancy, with 1·5 million deaths in 2010. Prevention and smoking cessation are still the main methods to reduce the death toll. The US National Lung Screening Trial, which compared CT screening with chest radiograph, yielded a mortality advantage of 20% to participants in the CT group. International debate is ongoing about whether sufficient evidence exists to implement CT screening programmes. When questions about effectiveness and cost-effectiveness have been answered, which will await publication of the largest European trial, NELSON, and pooled analysis of European CT screening trials, we discuss the main topics that will need consideration. These unresolved issues are risk prediction models to identify patients for CT screening; radiological protocols that use volumetric analysis for indeterminate nodules; options for surgical resection of CT-identified nodules; screening interval; and duration of screening. We suggest that a demonstration project of biennial screening over a 4-year period should be undertaken.
The Lancet 08/2013; 382(9893):732-41. DOI:10.1016/S0140-6736(13)61614-1 · 45.22 Impact Factor
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