A processual model for functional analyses of carcinogenesis in the prospective cohort design

Published version also available at http://dx.doi.org/10.1016/j.mehy.2015.07.006Traditionally, the prospective design has been chosen for risk factor analyses of lifestyle and cancer using mainly estimation by survival analysis methods. With new technologies, epidemiologists can expand their prospective studies to include functional genomics given either as transcriptomics, mRNA and microRNA, or epigenetics in blood or other biological materials. The novel functional analyses should not be assessed using classical survival analyses since the main goal is not risk estimation, but the analysis of functional genomics as part of the dynamic carcinogenic process over time, i.e., a ‘‘processual’’ approach. In the risk factor model, time to event is analysed as a function of exposure variables known at start of follow-up (fixed covariates) or changing over the follow-up period (time-dependent covariates). In the processual model, transcriptomics or epigenetics is considered as functions of time and exposures. The success of this novel approach depends on the development of new statistical methods with the capacity of describing and analysing the time-dependent curves or trajectories for tens of thousands of genes simultaneously. This approach also focuses on multilevel or integrative analyses introducing novel statistical methods in epidemiology. The processual approach as part of systems epidemiology might represent in a near future an alternative to human in vitro studies using human biological material for understanding the mechanisms and pathways involved in carcinogenesis

A new statistical method for curve group analysis of longitudinal gene expression data illustrated for breast cancer in the NOWAC postgenome cohort as a proof of principle

International audienceA new statistical method for curve group analysis of longitudinal gene expression data illustrated for breast cancer in the NOWAC postgenome cohort as a proof of principle Abstract Background: The understanding of changes in temporal processes related to human carcinogenesis is limited. One approach for prospective functional genomic studies is to compile trajectories of differential expression of genes, based on measurements from many case-control pairs. We propose a new statistical method that does not assume any parametric shape for the gene trajectories. Methods: The trajectory of a gene is defined as the curve representing the changes in gene expression levels in the blood as a function of time to cancer diagnosis. In a nested case–control design it consists of differences in gene expression levels between cases and controls. Genes can be grouped into curve groups, each curve group corresponding to genes with a similar development over time. The proposed new statistical approach is based on a set of hypothesis testing that can determine whether or not there is development in gene expression levels over time, and whether this development varies among different strata. Curve group analysis may reveal significant differences in gene expression levels over time among the different strata considered. This new method was applied as a " proof of concept " to breast cancer in the Norwegian Women and Cancer (NOWAC) postgenome cohort, using blood samples collected prospectively that were specifically preserved for transcriptomic analyses (PAX tube). Cohort members diagnosed with invasive breast cancer through 2009 were identified through linkage to the Cancer Registry of Norway, and for each case a random control from the postgenome cohort was also selected, matched by birth year and time of blood sampling, to create a case-control pair. After exclusions, 441 case-control pairs were available for analyses, in which we considered strata of lymph node status at time of diagnosis and time of diagnosis with respect to breast cancer screening visits. Results: The development of gene expression levels in the NOWAC postgenome cohort varied in the last years before breast cancer diagnosis, and this development differed by lymph node status and participation in the Norwegian Breast Cancer Screening Program. The differences among the investigated strata appeared larger in the year before breast cancer diagnosis compared to earlier years.ConclusionsThis approach shows good properties in term of statistical power and type 1 error under minimal assumptions. When applied to a real data set it was able to discriminate between groups of genes with non-linear similar patterns before diagnosis

Survival prediction from clinico-genomic models - a comparative study

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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 <url>http://www.med.uio.no/imb/stat/bmms/software/clinico-genomic/</url>.</p> <p>Conclusions</p> <p>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.</p

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 52 years 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

Transcriptomic signals in blood prior to lung cancer focusing on time to diagnosis and metastasis

Recent studies have indicated that there are functional genomic signals that can be detected in blood years before cancer diagnosis. This study aimed to assess gene expression in prospective blood samples from the Norwegian Women and Cancer cohort focusing on time to lung cancer diagnosis and metastatic cancer using a nested case–control design. We employed several approaches to statistically analyze the data and the methods indicated that the case–control differences were subtle but most distinguishable in metastatic case–control pairs in the period 0–3 years prior to diagnosis. The genes of interest along with estimated blood cell populations could indicate disruption of immunological processes in blood. The genes identified from approaches focusing on alterations with time to diagnosis were distinct from those focusing on the case–control differences. Our results support that explorative analyses of prospective blood samples could indicate circulating signals of disease-related processes

Reproducible Data Management and Analysis using R

Standardizing and documenting computational analyses is necessary to ensure reproducible results. We describe an R-based implementation of data management and preprocessing that is well integrated with the analysis tools typically used for statistical analysis of omics data. We have used these tools to organize data storage and documentation, and to standardize the analysis of gene expression data, in the Norwegian Women and Cancer study