Testing for causality between systematically identified risk factors and glioma: a Mendelian randomization study.

BACKGROUND: Whilst epidemiological studies have provided evidence of associations between certain risk factors and glioma onset, inferring causality has proven challenging. Using Mendelian randomization (MR), we assessed whether associations of 36 reported glioma risk factors showed evidence of a causal relationship. METHODS: We performed a systematic search of MEDLINE from inception to October 2018 to identify candidate risk factors and conducted a meta-analysis of two glioma genome-wide association studies (5739 cases and 5501 controls) to form our exposure and outcome datasets. MR analyses were performed using genetic variants to proxy for candidate risk factors. We investigated whether risk factors differed by subtype diagnosis (either glioblastoma (n = 3112) or non-glioblastoma (n = 2411)). MR estimates for each risk factor were determined using multiplicative random effects inverse-variance weighting (IVW). Sensitivity analyses investigated potential pleiotropy using MR-Egger regression, the weighted median estimator, and the mode-based estimator. To increase power, trait-specific polygenic risk scores were used to test the association of a genetically predicated increase in each risk factor with glioma onset. RESULTS: Our systematic search identified 36 risk factors that could be proxied using genetic variants. Using MR, we found evidence that four genetically predicted traits increased risk of glioma, glioblastoma or non-glioblastoma: longer leukocyte telomere length, liability to allergic disease, increased alcohol consumption and liability to childhood extreme obesity (> 3 standard deviations from the mean). Two traits decreased risk of non-glioblastoma cancers: increased low-density lipoprotein cholesterol (LDLc) and triglyceride levels. Our findings were similar across sensitivity analyses that made allowance for pleiotropy (genetic confounding). CONCLUSIONS: Our comprehensive investigation provides evidence of a causal link between both genetically predicted leukocyte telomere length, allergic disease, alcohol consumption, childhood extreme obesity, and LDLc and triglyceride levels, and glioma. The findings from our study warrant further research to uncover mechanisms that implicate these traits in glioma onset.

Copy number imbalances between screen- and symptom-detected breast cancers and impact on disease-free survival.

Screening mammography results in the increased detection of indolent tumors. We hypothesized that screen- and symptom-detected tumors would show genotypic differences as copy number imbalances (CNI) that, in part, explain differences in the clinical behavior between screen- and symptom-detected breast tumors. We evaluated 850 women aged 40 and above diagnosed with stage I and II breast cancer at the University of Texas MD Anderson Cancer Center between 1985 and 2000 with information available on method of tumor detection (screen vs. symptoms). CNIs in screen- and symptom-detected tumors were identified using high-density molecular inversion probe arrays. Cox proportional modeling was used to estimate the effect of method of tumor detection on disease-free survival after adjusting for age, stage, and the CNIs. The majority of tumors were symptom detected (n = 603) compared with screen detected (n = 247). Copy number gains in chromosomes 2p, 3q, 8q, 11p, and 20q were associated with method of breast cancer detection (P < 0.00001). We estimated that 32% and 63% of the survival advantage of screen detection was accounted for by age, stage, nuclear grade, and Ki67 in women aged 50 to 70 and aged 40 to 87, respectively. In each age category, an additional 20% of the survival advantage was accounted for by CNIs associated with method of detection. Specific CNIs differ between screen- and symptom-detected tumors and explain part of the survival advantage associated with screen-detected tumors. Measurement of tumor genotype has the potential to improve discrimination between indolent and aggressive screen-detected tumors and aids patient and physician decision making about use of surgical and adjuvant treatments.