Levonorgestrel-releasing intrauterine system use is associated with a decreased risk of ovarian and endometrial cancer, without increased risk of breast cancer. Results from the NOWAC Study.

OBJECTIVE: Women with ovarian cancer have poor survival rates, which have proven difficult to improve; therefore primary prevention is important. The levonorgestrel-releasing intrauterine system (LNG-IUS) prevents endometrial cancer, and recent studies suggested that it may also prevent ovarian cancer, but with a concurrent increased risk of breast cancer. We compared adjusted risks of ovarian, endometrial, and breast cancer in ever users and never users of LNG-IUS. METHODS: Our study cohort consisted of 104,318 women from the Norwegian Women and Cancer Study, 9144 of whom were ever users and 95,174 of whom were never users of LNG-IUS. Exposure information was taken from self-administered questionnaires, and cancer cases were identified through linkage to the Cancer Registry of Norway. Relative risks (RRs) with 95% confidence intervals (CIs) were estimated with Poisson regression using robust error estimates. RESULTS: Median age at inclusion was 52years and mean follow-up time was 12.5 (standard deviation 3.7) years, for a total of 1,305,435 person-years. Among ever users of LNG-IUS there were 18 cases of epithelial ovarian cancer, 15 cases of endometrial cancer, and 297 cases of breast cancer. When ever users were compared to never users of LNG-IUS, the multivariable RR of ovarian, endometrial, and breast cancer was 0.53 (95% CI: 0.32, 0.88), 0.22 (0.13, 0.40), and 1.03 (0.91, 1.17), respectively. CONCLUSION: In this population-based prospective cohort study, ever users of LNG-IUS had a strongly reduced risk of ovarian and endometrial cancer compared to never users, with no increased risk of breast cancer.

Does an epidemiological comparison support a common cellular lineage for similar subtypes of postmenopausal uterine and ovarian carcinoma? The Norwegian Women and Cancer Study.

Uterine and ovarian carcinomas have the same major histological subtypes, but whether they originate from the same cell types is a matter of ongoing debate. Uterine and ovarian endometrioid and clear cell carcinoma (ECC) and uterine and ovarian serous carcinoma (SC) may originate in the same location, or share a common lineage of differentiation. Epidemiologically, a common cellular lineage should be reflected in similar risk associations, and we explored the similarity of uterine and ovarian ECC and uterine and ovarian SC. We included 146,316 postmenopausal participants from the Norwegian Women and Cancer Study. Exposure information was taken from self-administered questionnaires, and cancer cases were identified through linkage to the Cancer Registry of Norway. Hazard ratios with 95% confidence intervals for uterine and ovarian carcinoma and their subtypes were calculated using multivariable Cox regression models, and a Wald test was used to check for heterogeneity. During 1.6 million person-years, 1,006 uterine and 601 ovarian carcinomas were identified. Parity, total menstrual lifespan, body mass index and smoking were differentially associated with total uterine and total ovarian carcinoma (pheterogeneity  = 0.041, 0.027, <0.001 and 0.001, respectively). The corresponding associations for uterine and ovarian ECC did not differ significantly (pheterogeneity  > 0.05). Smoking was differentially associated with uterine and ovarian SC (pheterogeneity  = 0.021). Our epidemiological analyses do not contradict a common differentiation lineage for uterine and ovarian ECC. Uterine and ovarian SC are less likely to be of a common lineage of differentiation, based on their difference in risk associated with smoking.

Assessment of evaluation criteria for survival prediction from genomic data.

Survival prediction from high-dimensional genomic data is dependent on a proper regularization method. With an increasing number of such methods proposed in the literature, comparative studies are called for and some have been performed. However, there is currently no consensus on which prediction assessment criterion should be used for time-to-event data. Without a firm knowledge about whether the choice of evaluation criterion may affect the conclusions made as to which regularization method performs best, these comparative studies may be of limited value. In this paper, four evaluation criteria are investigated: the log-rank test for two groups, the area under the time-dependent ROC curve (AUC), an R²-measure based on the Cox partial likelihood, and an R²-measure based on the Brier score. The criteria are compared according to how they rank six widely used regularization methods that are based on the Cox regression model, namely univariate selection, principal components regression (PCR), supervised PCR, partial least squares regression, ridge regression, and the lasso. Based on our application to three microarray gene expression data sets, we find that the results obtained from the widely used log-rank test deviate from the other three criteria studied. For future studies, where one also might want to include non-likelihood or non-model-based regularization methods, we argue in favor of AUC and the R²-measure based on the Brier score, as these do not suffer from the arbitrary splitting into two groups nor depend on the Cox partial likelihood.

Survival prediction from clinico-genomic models--a comparative study.

BACKGROUND: Survival prediction from high-dimensional genomic data is an active field in today's medical research. Most of the proposed prediction methods make use of genomic data alone without considering established clinical covariates that often are available and known to have predictive value. Recent studies suggest that combining clinical and genomic information may improve predictions, but there is a lack of systematic studies on the topic. Also, for the widely used Cox regression model, it is not obvious how to handle such combined models. RESULTS: We propose a way to combine classical clinical covariates with genomic data in a clinico-genomic prediction model based on the Cox regression model. The prediction model is obtained by a simultaneous use of both types of covariates, but applying dimension reduction only to the high-dimensional genomic variables. We describe how this can be done for seven well-known prediction methods: variable selection, unsupervised and supervised principal components regression and partial least squares regression, ridge regression, and the lasso. We further perform a systematic comparison of the performance of prediction models using clinical covariates only, genomic data only, or a combination of the two. The comparison is done using three survival data sets containing both clinical information and microarray gene expression data. Matlab code for the clinico-genomic prediction methods is available at http://www.med.uio.no/imb/stat/bmms/software/clinico-genomic/. CONCLUSIONS: Based on our three data sets, the comparison shows that established clinical covariates will often lead to better predictions than what can be obtained from genomic data alone. In the cases where the genomic models are better than the clinical, ridge regression is used for dimension reduction. We also find that the clinico-genomic models tend to outperform the models based on only genomic data. Further, clinico-genomic models and the use of ridge regression gives for all three data sets better predictions than models based on the clinical covariates alone.