Robust Tests for Additive Gene-Environment Interaction in Case-Control Studies Using Gene-Environment Independence

There have been recent proposals advocating the use of additive gene-environment interaction instead of the widely used multiplicative scale, as a more relevant public health measure. Using gene-environment independence enhances statistical power for testing multiplicative interaction in case-control studies. However, under departure from this assumption, substantial bias in the estimates and inflated type I error in the corresponding tests can occur. In this paper, we extend the empirical Bayes (EB) approach previously developed for multiplicative interaction, which trades off between bias and efficiency in a data-adaptive way, to the additive scale. An EB estimator of the relative excess risk due to interaction is derived, and the corresponding Wald test is proposed with a general regression setting under a retrospective likelihood framework. We study the impact of gene-environment association on the resultant test with case-control data. Our simulation studies suggest that the EB approach uses the gene-environment independence assumption in a data-adaptive way and provides a gain in power compared with the standard logistic regression analysis and better control of type I error when compared with the analysis assuming gene-environment independence. We illustrate the methods with data from the Ovarian Cancer Association Consortium.

Abstract AS13: Epidemiologic predictors of pre-treatment CA125 in women with ovarian cancer

Background: CA125 is elevated in 80% of ovarian cancer cases and has proven utility in assessing response to therapy and prognosis. Unfortunately, CA125 is elevated in a variety of benign conditions and only in 50% of early stage ovarian cancers, resulting in a sensitivity and specificity unacceptable for population-based screening. Therefore, understanding factors that influence CA125 at presentation could provide important insights for interpreting CA125 values.

Methods: Using pre-treatment CA125 and detailed epidemiologic data from 12 studies participating in the Ovarian Cancer Association Consortium (OCAC), we evaluated factors previously reported to influence CA125 levels, including age, race, oral contraceptive use, parity, tubal ligation, endometriosis, body mass index (BMI), personal history of breast cancer, or a family history of breast or ovarian cancer. We used linear regression to estimate the association between each variable and CA125 probit scores, which we used to standardize values between studies. Secondary analyses included adjustment for histologic subtype. We also estimated the associations within each study using log-transformed CA125 as the outcome and estimated summary measures using random effects meta-analyses.

Results: Of the 4417 cases included in the analysis, 2918 (66%) were serous, 227 (5%) were mucinous, 484 (11%) were endometrioid, and 258 (6%) were clear cell carcinomas. Median CA125 values varied between studies with a high of 831 U/mL and a low of 271 U/mL. We observed no association between race, oral contraceptive use, tubal ligation, endometriosis, prior breast cancer, and family history of breast cancer and pre-treatment CA125. However, we observed increased pre-treatment CA125 levels with older age (>70 vs. < 50, p=0.03), parity (p=0.01), number of children (p=0.02), obesity (BMI>30 vs. 21-25, p=0.03), and family history of ovarian cancer (p=0.05). Results were similar but attenuated when we calculated summary estimates of the association using meta-analysis of study specific estimates of the association with log-transformed CA125 rather than probit scores. Age and BMI remained predictive of pre-treatment CA125 values after adjustment for histologic subtype. Our data are limited by between site variability in CA125 assays; therefore, validation of these findings is needed with adjustment for type of assay used or in a study population in which all cases had pre-treatment CA125 values measured with the same assay.

Conclusions: Despite these limitations, results from this analysis of over 4000 ovarian cancer cases suggest that age and body size may influence pre-treatment CA125 values.

Citation Format: KL Terry, A Babic, BY Karlan, MT Goodman, D Lambrechts, F Heitz, K Matsuo, I McNeish, T Pejovic, S Kruger Kjaer, PM Webb, E Hogdall, EL Goode, DW Cramer, for the Ovarian Cancer Association Consortium. Epidemiologic predictors of pre-treatment CA125 in women with ovarian cancer [abstract]. In: Proceedings of the 10th Biennial Ovarian Cancer Research Symposium; Sep 8-9, 2014; Seattle, WA. Philadelphia (PA): AACR; Clin Cancer Res 2015;21(16 Suppl):Abstract nr AS13.