Biomedical subjects
Publications and source records attributed to Seth Falcon.
UNLABELLED: This paper reviews the central concepts and implementation of data structures and methods for studying genetics of gene expression with the GGtools package of Bioconductor. Illustration with a HapMap+expression dataset is provided. AVAILABILITY: Package GGtools is part of Bioconductor 1.9 (http://bioconductor.org). Open source with Artistic License.
As technology for microarray analysis becomes widespread, it is becoming increasingly important to be able to compare and combine the results of experiments that explore the same scientific question. In this article, we present a rank-aggregation approach for combining results from several microarray studies. The motivation for this approach is twofold; first, the final results of microarray studies are typically expressed as lists of genes, rank-ordered by a measure of the strength of evidence that they are functionally involved in the disease process, and second, using the information on this rank-ordered metric means that we do not have to concern ourselves with data on the actual expression levels, which may not be comparable across experiments. Our approach draws on methods for combining top-k lists from the computer science literature on meta-search. The meta-search problem shares several important features with that of combining microarray experiments, including the fact that there are typically few lists with many elements and the elements may not be common to all lists. We implement two meta-search algorithms, which use a Markov chain framework to convert pairwise preferences between list elements into a stationary distribution that represents an aggregate ranking (Dwork et al, 2001). We explore the behavior of the algorithms in hypothetical examples and a simulated dataset and compare their performance with that of an algorithm based on the order-statistics model of Thurstone (Thurstone, 1927). We apply all three algorithms to aggregate the results of five microarray studies of prostate cancer.
Hepatocyte activator inhibitor-1 (HAI-1) is a transmembrane serine protease inhibitor that regulates the conversion of latent to active hepatocyte growth factor (HGF). Studies supporting a role for the HGF pathway in prostate carcinogenesis prompted an analysis of HAI-1 expression in the prostate. Here we analyze the regulation of HAI-1 expression by androgen, oncogenic transformation, and cancer progression. Immunohistochemical analysis revealed that HAI-1 expression was restricted to prostate epithelium, where staining occurred primarily in basal and atrophic luminal epithelial cells. Compared to normal glands, HAI-1 expression was significantly increased in localized prostate cancer and was present in most prostate cancer metastases. HAI-1 protein expression levels were sensitive to androgen in normal epithelium but not in cancer. Although androgen did not increase HAI-1 protein expression levels in LNCaP cells, it decreased HAI-1 surface expression, consistent with previous data from our group (Martin DB, Gifford DR, Wright ME, Keller A, Yi E, Goodlett DR, Aebersold R, Nelson PS: Quantitative proteomic analysis of proteins released by neoplastic prostate epithelium. Cancer Res 2004, 64:347-355). HAI-1 overexpression in cancer was predictive of prostate-specific antigen recurrence (relative risk, 1.24). These results suggest that HAI-1 regulates the HGF Met axis on prostate epithelial cells and influences HGF mediated tumor invasion and metastasis.
BACKGROUND: The combined use of free and total prostate-specific antigen (PSA) in early detection of prostate cancer has been controversial. This article systematically evaluates the discriminating capacity of a large number of combination tests. METHODS: Free and total PSA were analyzed in stored serum samples taken prior to diagnosis in 429 cases and 1,640 controls from the Physicians' Health Study. We used a classification algorithm called logic regression to search for clinically useful tests combining total and percent free PSA and receiver operating characteristic analysis and compared these tests with those based on total and complexed PSA. Data were divided into training and test subsets. For robustness, we considered 35 test-train splits of the original data and computed receiver operating characteristic curves for each test data set. RESULTS: The average area under the receiver operating characteristic curve across test data sets was 0.74 for total PSA and 0.76 for the combination tests. Combination tests with higher sensitivity and specificity than PSA > 4.0 ng/mL were identified 29 out of 35 times. All these tests extended the PSA reflex range to below 4.0 ng/mL. Receiver operating characteristic curve analysis indicated that the overall diagnostic performance as expressed by the area under the curve did not differ significantly for the different tests. CONCLUSIONS: Tests combining total and percent free PSA show modest overall improvements over total PSA. However, utilization of percent free PSA below a PSA threshold of 4 ng/mL could translate into a practically important reduction in unnecessary biopsies without sacrificing cancers detected.