Biological correlates of biochemical recurrence free survival using multiple markers in a large tissue microarray cohort.

BACKGROUND: High-throughput analyses yielded a large number of predictive biomarkers in prostatic cancer (PCa) patients. Combinations of these biomarkers and with clinical features could improve on prediction. MATERIALS AND METHODS: Tissue microarrays (640 patients) with triplicate cores of non-neoplastic prostate, benign prostatic hyperplasia (BPH), and index tumor were immunostained with antibodies to numerous biomarkers, digitized, and quantified. We used tree-based classification algorithms to stratify patients into 3 risk strata on the basis of their clinical and pathologic data. Markers were tested for prognostic ability in each stratum (stratum 1 had <10% risk of recurrence; stratum 3 had >60% likelihood of recurrence over a period >12 years). Sub stratification of the clinico-pathologic strata was also pursued. RESULTS: We identified a number of significant predictors for PSA recurrence free survival, which were used to construct a predictive model that combines clinical and biomarker data. In the low-risk clinico-pathologic stratum, the markers were predominantly related to non-neoplastic tissues, in the moderate-risk stratum to stromal-epithelial interactions and angiogenesis, while those in the high-risk stratum were mostly oncogenes. Substratification of the intermediate risk group using stromal quantitation and proliferative index successfully, up or down, staged the risk strata for most patients. CONCLUSIONS: The fact that different biomarkers are most predictive of disease recurrence within different risk subgroups suggests an association between biological processes and prognostic ability. This is the first time that subgroup analysis of markers finds that prognostic ability is associated with biological processes and is proof of concept that distinct phenotypes are associated with risk of recurrence in different types of cancer.

Impact of PSA screening on the incidence of advanced stage prostate cancer in the United States: a surveillance modeling approach.

BACKGROUND: and objective. Both the detection and the treatment of prostate cancer have undergone important clinical advances. Simultaneously, both distant stage incidence and disease-specific mortality have fallen in the United States. A recent study suggests that if prostate-specific antigen (PSA) testing explains the decline in distant stage incidence, then it may be largely responsible for the decline in mortality. The objective was to quantify this link between PSA screening and the decline in distant stage incidence. METHODS: A fixed-cohort simulation model of prostate cancer progression and screening was adapted to a population-based model that integrates new data on trends in testing and biopsy practices. The model was calibrated to pre-PSA incidence and then screening was superimposed, obtaining incidence projections in the absence and presence of testing. This approach permits calculation of clinically relevant measures for model validation and direct assessment of the role of testing in the distant stage incidence decline. RESULTS: The model validated well with prior studies of natural history, and the sensitivity analysis indicated that the findings were robust to variation in model parameters. Model results indicate that PSA screening accounts for approximately 80% of the observed decline in distant stage incidence. CONCLUSIONS: PSA screening contributed to the observed declines in distant stage incidence and mortality in the 1990s. However, additional factors, such as increasing awareness of prostate cancer and advances in treatment, have probably also played a role in these trends.

Quantifying the role of PSA screening in the US prostate cancer mortality decline.

OBJECTIVE: To quantify the plausible contribution of prostate-specific antigen (PSA) screening to the nearly 30% decline in the US prostate cancer mortality rate observed during the 1990s. METHODS: Two mathematical modeling teams of the US National Cancer Institute's Cancer Intervention and Surveillance Modeling Network independently projected disease mortality in the absence and presence of PSA screening. Both teams relied on Surveillance, Epidemiology, and End Results (SEER) registry data for disease incidence, used common estimates of PSA screening rates, and assumed that screening, by shifting disease from distant to local-regional clinical stage, confers a corresponding improvement in disease-specific survival. RESULTS: The teams projected similar mortality increases in the absence of screening and decreases in the presence of screening after 1985. By 2000, the models projected that 45% (Fred Hutchinson Cancer Research Center) to 70% (University of Michigan) of the observed decline in prostate cancer mortality could be plausibly attributed to the stage shift induced by screening. CONCLUSIONS: PSA screening may account for much, but not all, of the observed drop in prostate cancer mortality. Other factors, such as changing treatment practices, may also have played a role in improving prostate cancer outcomes.

Graphs in molecular biology.

Graph theoretical concepts are useful for the description and analysis of interactions and relationships in biological systems. We give a brief introduction into some of the concepts and their areas of application in molecular biology. We discuss software that is available through the Bioconductor project and present a simple example application to the integration of a protein-protein interaction and a co-expression network.

GGtools: analysis of genetics of gene expression in bioconductor.

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.

Combining results of microarray experiments: a rank aggregation approach.

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.

Regulation of hepatocyte activator inhibitor-1 expression by androgen and oncogenic transformation in the prostate.

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.

Prostate-specific antigen and free prostate-specific antigen in the early detection of prostate cancer: do combination tests improve detection?

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.