Qualitative and quantitative differences in estrogen biotransformation in human breast glandular and adipose tissues: implications for studies using mammary biospecimens.

Because of its assumed role in breast cancer etiology, estrogen biotransformation (and interaction of compounds therewith) has been investigated in human biospecimens for decades. However, little attention has been paid to the well-known fact that large inter-individual variations exist in the proportion of breast glandular (GLT) and adipose (ADT) tissues and less to adequate tissue characterization. To assess the relevance of this, the present study compares estrogen biotransformation in GLT and ADT. GLT and ADT were isolated from 47 reduction mammoplasty specimens derived from women without breast cancer and were characterized histologically and by their percentages of oil. Levels of 12 unconjugated and five conjugated estrogens were analyzed by GC- and UHPLC-MS/MS, respectively, and levels of 27 transcripts encoding proteins involved in estrogen biotransformation by Taqman® probe-based PCR. Unexpectedly, one-third of specimens provided neat GLT only after cryosection. Whereas 17ß-estradiol, estrone, and estrone-3-sulfate were detected in both tissues, estrone-3-glucuronide and 2-methoxy-estrone were detected predominately in GLT and ADT, respectively. Estrogen levels as well as ratios 17ß-estradiol/estrone and estrone-3-sulfate/estrone differed significantly between GLT and ADT, yet less than between individuals. Furthermore, estrogen levels in GLT and ADT correlated significantly with each other. In contrast, levels of most transcripts encoding enzymes involved in biotransformation differed more than between individuals and did not correlate between ADT and GLT. Thus, mixed breast tissues (and plasma) will not provide meaningful information on local estrogen biotransformation (and interaction of compounds therewith) whereas relative changes in 17ß-estradiol levels may be investigated in the more abundant ADT.

Phase II dose-response trials: A simulation study to compare analysis method performance under design considerations.

Phase II trials are intended to provide information about the dose-response relationship and to support the choice of doses for a pivotal phase III trial. Recently, new analysis methods have been proposed to address these objectives, and guidance is needed to select the most appropriate analysis method in specific situations. We set up a simulation study to evaluate multiple performance measures of one traditional and three more recent dose-finding approaches under four design options and illustrate the investigated analysis methods with an example from clinical practice. Our results reveal no general recommendation for a particular analysis method across all design options and performance measures. However, we also demonstrate that the new analysis methods are worth the effort compared to the traditional ANOVA-based approach.

Unimodal regression using Bernstein-Schoenberg splines and penalties.

Research in the field of nonparametric shape constrained regression has been intensive. However, only few publications explicitly deal with unimodality although there is need for such methods in applications, for example, in dose-response analysis. In this article, we propose unimodal spline regression methods that make use of Bernstein-Schoenberg splines and their shape preservation property. To achieve unimodal and smooth solutions we use penalized splines, and extend the penalized spline approach toward penalizing against general parametric functions, instead of using just difference penalties. For tuning parameter selection under a unimodality constraint a restricted maximum likelihood and an alternative Bayesian approach for unimodal regression are developed. We compare the proposed methodologies to other common approaches in a simulation study and apply it to a dose-response data set. All results suggest that the unimodality constraint or the combination of unimodality and a penalty can substantially improve estimation of the functional relationship.

Optimal strategies for sequential validation of significant features from high-dimensional genomic data.

High-dimensional genomic studies play a key role in identifying critical features that are significantly associated with a phenotypic outcome. The two most important examples are the detection of (1) differentially expressed genes from genome-wide gene expression studies and (2) single-nucleotide polymorphisms (SNPs) from genome-wide association studies. Such experiments are often associated with high noise levels, and the validity of statistical conclusions suffers from low sample size compared to large number of features. The corresponding multiple testing problem calls for the identification of optimal strategies for controlling the numbers of false discoveries and false nondiscoveries. In addition, a frequent validation problem is that features identified as important in one study are often less so in another study. Adjustment for multiple testing in both studies separately increases the risk of missing the crucial features even further. These problems can be addressed by sequential validation strategies, where only significant features identified in one study enter as candidates in the next study. The quality associated with different studies, for example, in terms of noise levels, may vary considerably. By performing simulation studies it is possible to demonstrate that the optimal order for this stepwise procedure is to sort experimental studies according to their quality in descending order. The impact of the method for multiple testing adjustment (Bonferroni-Holm, FDR) was also analyzed. Finally, the sequential validation strategy was applied to three large breast cancer studies with gene expression measurements, confirming the crucial impact of the order of the validation steps in a real-world application.