Development of a genotyping microarray for studying the role of gene-environment interactions in risk for lung cancer

Quantification of Plasma 8-Isoprostane by High-Performance Liquid Chromatography with Tandem Mass Spectrometry in a Case-Control Study of Lung Cancer

AIM

8-iso-prostaglandin F2α is a biomarker of lipid peroxidation, and one of the most commonly used measures of oxidative stress. It is an established biomarker of lung cancer risk. It is commonly measured by enzyme-linked immunosorbent assay. Given its importance, we developed a stable isotope dilution UPLC-tandem mass spectrometric method for the rapid determination of 8-isoprostane in blood.

METHODS

We tested the discriminatory capability of the method in 49 lung cancer patients, 55 benign lung nodule patients detected by chest X-ray, and 41 patients with chronic obstructive pulmonary disease (COPD) or asthma.

RESULTS

Significant differences were found in mean 8-isoprostane levels between the three groups (p = 0.027), and post-hoc tests found higher levels in the lung cancer patients than in patients with benign nodules (p = 0.032) and COPD/asthma (p = 0.014). The receiving operating characteristic area under the curve (AUC) was 0.69 for differentiating the lung cancer group from the benign nodule group, and 0.7 for differentiating from the COPD/asthma group.

CONCLUSIONS

The UPLC-MS/MS-based method is an efficient analytical tool for measuring 8-isoprostane plasma concentrations. The results suggest exploring its utility as a marker for early lung cancer screening.

Environmental exposomics and lung cancer risk assessment in the Philadelphia metropolitan area using ZIP code-level hazard indices

To illustrate methods for assessing environmental exposures associated with lung cancer risk, we investigated anthropogenic based air pollutant data in a major metropolitan area using United States-Environmental Protection Agency (US-EPA) Toxic Release Inventory (TRI) (1987-2017), and PM2.5 (1998-2016) and NO2 (1996-2012) concentrations from NASA satellite data. We studied chemicals reported according to the following five exposome features: (1) International Agency for Research on Cancer (IARC) cancer grouping; (2) priority EPA polycyclic aromatic hydrocarbons (PAHs); (3) component of diesel exhaust; (4) status as a volatile organic compound (VOC); and (5) evidence of lung carcinogenesis. Published articles from PubChem were tallied for occurrences of 10 key characteristics of cancer-causing agents on those chemicals. Zone Improvement Plan (ZIP) codes with higher exposures were identified in two ways: (1) combined mean exposure from all features, and (2) hazard index derived through a multi-step multi-criteria decision analysis (MMCDA) process. VOCs, IARC Group 1 carcinogens consisted 82.3% and 11.5% of the reported TRI emissions, respectively. ZIP codes along major highways tended to have greater exposure. The MMCDA approach yielded hazard indices based on imputed toxicity, occurrence, and persistence for risk assessment. Despite many studies describing environmental exposures and lung cancer risk, this study develops a method to integrate these exposures into population-based exposure estimates that could be incorporated into future lung cancer screening trials and benefit public health surveillance of lung cancer incidence. Our methodology may be applied to probe other hazardous exposures for other cancers.

On meta- and mega-analyses for gene-environment interactions

Gene-by-environment (G × E) interactions are important in explaining the missing heritability and understanding the causation of complex diseases, but a single, moderately sized study often has limited statistical power to detect such interactions. With the increasing need for integrating data and reporting results from multiple collaborative studies or sites, debate over choice between mega- versus meta-analysis continues. In principle, data from different sites can be integrated at the individual level into a "mega" data set, which can be fit by a joint "mega-analysis." Alternatively, analyses can be done at each site, and results across sites can be combined through a "meta-analysis" procedure without integrating individual level data across sites. Although mega-analysis has been advocated in several recent initiatives, meta-analysis has the advantages of simplicity and feasibility, and has recently led to several important findings in identifying main genetic effects. In this paper, we conducted empirical and simulation studies, using data from a G × E study of lung cancer, to compare the mega- and meta-analyses in four commonly used G × E analyses under the scenario that the number of studies is small and sample sizes of individual studies are relatively large. We compared the two data integration approaches in the context of fixed effect models and random effects models separately. Our investigations provide valuable insights in understanding the differences between mega- and meta-analyses in practice of combining small number of studies in identifying G × E interactions.

Pretreatment Red Blood Cell Total Folate Concentration Is Associated With Response to Pemetrexed in Stage IV Nonsquamous Non-Small-cell Lung Cancer

INTRODUCTION

Pemetrexed inhibits folate-dependent enzymes involved in pyrimidine and purine synthesis. Previous studies of genetic variation in these enzymes as predictors of pemetrexed efficacy have yielded inconsistent results. We investigated whether red blood cell (RBC) total folate, a phenotypic rather than genotypic, marker of cellular folate status was associated with the response to pemetrexed-based chemotherapy in advanced nonsquamous non-small-cell lung cancer (NSCLC).

MATERIALS AND METHODS

We conducted a prospective cohort study of patients with stage IV nonsquamous NSCLC receiving first-line chemotherapy containing pemetrexed. The pretreatment RBC total folate level was quantified using liquid chromatography mass spectrometry. We then compared the objective response rate (ORR) between patients with RBC total folate concentrations greater than and less than an optimal cutoff value determined from the receiver operating characteristic curve. A logistic regression model was used to adjust for age, sex, and the use of bevacizumab.

RESULTS

The ORR was 62% (32 of 52 patients). Receiver operating characteristic analysis was used to establish that a RBC total folate cutoff value of 364.6 nM optimally discriminated between pemetrexed responders and nonresponders. Patients with RBC total folate < 364.5 nM had an ORR of 27% compared with 71% for patients with RBC total folate > 364.5 nM (P = .01). This difference persisted after adjusting for age, sex, and the use of bevacizumab (odds ratio, 0.07; 95% confidence interval, 0.01-0.57; P = .01).

CONCLUSION

A low pretreatment RBC total folate was associated with an inferior response to pemetrexed-based chemotherapy in stage IV nonsquamous NSCLC. Larger, multicenter studies are needed to validate RBC total folate as a predictive marker of pemetrexed response.