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Although the function of the BRCA1 gene has been extensively studied, the relationship between BRCA1 gene expression and tumor aggressiveness remains controversial in sporadic breast cancers. Because the BRCA1 protein is known to regulate estrogen signaling, we selected microarray data of ER+ breast cancers from the GEO public repository to resolve previous conflicting findings. The BRCA1 gene expression level in highly proliferative luminal B tumors was shown to be higher than that in luminal A tumors. Survival analysis using a cure model indicated that patients of early ER+ breast cancers with high BRCA1 expression developed rapid distant metastasis. In addition, the proliferation marker genes MKI67 and PCNA, which are characteristic of aggressive tumors, were also highly expressed in patients with high BRCA1 expression. The associations among high BRCA1 expression, high proliferation marker expression, and high risk of distant metastasis emerged in independent datasets, regardless of tamoxifen treatment. Tamoxifen therapy could improve the metastasis-free fraction of high BRCA1 expression patients. Our findings link BRCA1 expression with proliferation and possibly distant metastasis via the ER signaling pathway. We propose a testable hypothesis based on these consistent results and offer an interpretation for our reported associations. [ABSTRACT FROM AUTHOR]

The Pearson product-moment correlation coefficient was generalized to determine the association between the failure time and the truncation time in the observable region of truncated data. Specifically, it was shown that a truncated bivariate normal distribution for both time variables results in equivalence of the quasi-independence and a zero value of the generalized correlation coefficient. Consequently, it was applied to regression parameter estimation in a linear model using truncated data., The random truncation model has been considered extensively in the literature. Tsai has noted that many previous results hold under the weaker assumption of quasi-independence between the failure time and [...]

Abstract Background The development of microarrays permits us to monitor transcriptomes on a genome-wide scale. To validate microarray measurements, quantitative-real time-reverse transcription PCR (Q-RT-PCR) is one of the most robust and commonly used approaches. The new challenge in gene quantification analysis is how to explicitly incorporate statistical estimation in such studies. In the realm of statistical analysis, the various available methods of the probe level normalization for microarray analysis may result in distinctly different target selections and variation in the scores for the correlation between microarray and Q-RT-PCR. Moreover, it remains a major challenge to identify a proper internal control for Q-RT-PCR when confirming microarray measurements. Results Sixty-six Affymetrix microarray slides using lung adenocarcinoma tissue RNAs were analyzed by a statistical re-sampling method in order to detect genes with minimal variation in gene expression. By this approach, we identified DDX5 as a novel internal control for Q-RT-PCR. Twenty-three genes, which were differentially expressed between adjacent normal and tumor samples, were selected and analyzed using 24 paired lung adenocarcinoma samples by Q-RT-PCR using two internal controls, DDX5 and GAPDH. The percentage correlation between Q-RT-PCR and microarray were 70% and 48% by using DDX5 and GAPDH as internal controls, respectively. Conclusion Together, these quantification strategies for Q-RT-PCR data processing procedure, which focused on minimal variation, ought to significantly facilitate internal control evaluation and selection for Q-RT-PCR when corroborating microarray data.

Summary: Lan and DeMets (1983) proposed the alpha spending function for group sequential trials to permit the use of unspecified frequencies and timings of interim analyses in the trial design. Regarding a trial with censored time to endpoint, Lan and DeMets (1989) later defined information time at an interim analysis in a maximum duration trial. To compare two survival curves utilizing such a design, information times for group sequential logrank and Wilcoxon‐type statistics have been developed by assuming that the survival time follows an exponential distribution or a Weibull distribution without considering the censoring distribution. To better address the practical concerns inherent in clinical trials with survival endpoints, we present a new approach to adequately design a group sequential trial using the Harrington–Fleming (1982) test based on our proposed information fractions by assuming the censoring distribution depends on the patient's accrual time according to various entry distributions and by extending the underlying survival distribution to the generalized gamma distribution. We also determine associated sample sizes, expected number of events and expected stopping time. Two phase III trials of non‐small‐cell lung cancer originally designed using fixed‐sample tests are utilized to illustrate the potential advantages of using a group sequential design with the proposed approach. This enhanced method facilitates the design and analysis of group sequential clinical trials studying survival endpoints by increasing implemental flexibility. [ABSTRACT FROM AUTHOR]