Defining Aggressive Prostate Cancer Using a 12-Gene Model

Bismar, Tarek A.; Demichelis, Francesca; Riva, Alberto; Kim, Robert; Varambally, Sooryanarayana; He, Lan; Kutok, Jeff; Aster, JC; Tang, Jun; Kuefer, Rainer; Hofer, Matthias; Febbo, Phillip G.; Chinnaiyan, Arul M.; Rubin, Mark A.

doi:10.1593/neo.05664

Cited by 97 publications

(80 citation statements)

References 59 publications

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“…A recent example from our group is the evaluation of a-methylacyl CoA racemase for men with clinically localized cancer using PCA-specific death as the outcome. 81 We also recently developed a 12-gene model 82 building on work that used a combination of proteomic and expression array data. 83 We identified a set of 40 genes with concordant dysregulation that could be evaluated by quantitative immunohistochemistry.…”

Section: Effects Of Common Androgen Ablation Treatment On the Prostatementioning

confidence: 99%

“…Through integration of array data at the transcriptome and proteome, our group identified two molecular signatures of advanced PCA, the 12-gene (described above) and 9-gene models, 82,83 which were significantly associated with PSA-failure after prostatectomy. We tested these two signatures in relation to PCA death in the Orebro Watchful Waiting cohort (Mucci et al, submitted).…”

Section: Validation Of Molecular Signatures Of Pca Progression Using mentioning

confidence: 99%

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Targeted therapy of cancer: new roles for pathologists—prostate cancer

Rubin

2008

Modern Pathology

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The clinical dilemma today in the management of prostate cancer (PCA) is to distinguish men who need definitive treatment from men who have indolent disease. As demonstrated most recently by the randomized Scandinavian trial evaluating the benefit of prostatectomy over Watchful Waiting, surgery significantly decreased the risk of death from PCA. However, this same study also suggests that 19 men need to be treated to benefit one man. Given the high prevalence of the disease, the aging of the population, and the potential morbidity of treatment, the ability to distinguish aggressive from indolent forms of PCA is critical. Treatment for advanced PCA begins with androgen ablation, but eventually hormone-refractory (HR) PCA emerges. Novel therapies are in various stages of clinical trials, including kinase inhibitors, antisense oligonucleotides, and inhibitors of heat-shock proteins. The discovery of novel therapeutic approaches is an active area of clinical research. Eliminating HR PCA before it advances is a high priority in the biomarker field. Therefore, the development of molecular signatures of lethal PCA are critical. In addition, the recent discovery that a significant percentage of PCAs harbor a TMPRSS2-ETS gene fusion suggests that targeting either the ETS transcription factors or the fusion product may offer a novel approach to therapy. However, in 2007, the mainstay of treatment for advanced PCA remains androgen ablation therapy as originally introduced in the early 1940s.

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Section: Effects Of Common Androgen Ablation Treatment On the Prostatementioning

confidence: 99%

Section: Validation Of Molecular Signatures Of Pca Progression Using mentioning

confidence: 99%

Targeted therapy of cancer: new roles for pathologists—prostate cancer

Rubin

2008

Modern Pathology

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“…BM28 encodes a highly conserved mini-chromosome maintenance protein (MCM) that is involved in the initiation of genome replication. Bismar et al [27] profiled a total of 41 genes (including BM28) in a TMA-based proteomic study. They identified a 12-gene model showing the expression combination of the twelve genes significantly associates with tumor progression and PSA failure in a set of 79 men following surgery for clinically localized prostate cancer.…”

Section: Data Descriptionmentioning

confidence: 99%

Modeling intra‐tumor protein expression heterogeneity in tissue microarray experiments

Shen¹,

Ghosh²,

Taylor³

2008

Statistics in Medicine

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SUMMARYTissue Microarrays (TMAs) measure tumor-specific protein expression via high-density immunohistochemical staining assays. They provide a proteomic platform for validating cancer biomarkers emerging from large-scale DNA microarray studies. Repeated observations within each tumor result in substantial biological and experimental variability. This variability is usually ignored when associating the TMA expression data with patient survival outcome. It generates biased estimates of hazard ratio in proportional hazards models. We propose a Latent Expression Index (LEI) as a surrogate protein expression estimate in a two-stage analysis. Several estimators of LEI are compared: an Empirical Bayes (EB), a Full Bayes (FB), and a Varying Replicate Number (VRN) estimator. In addition, we jointly model survival and TMA expression data via a shared random effects model. Bayesian estimation is carried out using a Markov Chain Monte Carlo (MCMC) method. Simulation studies were conducted to compare the two-stage methods and the joint analysis in estimating the Cox regression coefficient. We show the two-stage methods reduce bias relative to the naive approach, but still lead to under-estimated hazard ratios. The joint model consistently outperforms the two-stage methods in terms of both bias and coverage property in various simulation scenarios. In case studies using prostate cancer TMA data sets, the two stage methods yields a good approximation in one data set while an insufficient one in the other. A general advice is to use the joint model inference whenever results differ between the two-stage methods and the joint analysis.

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“…14 Interestingly, while a number of studies have associated increased TPD52 expression with poor survival in breast [15][16][17] and prostate cancers, 18 increased TPD52 expression has been reported as a favorable independent prognostic marker in ovarian carcinoma, which is usually treated with DNA damaging agents. 19 TPD52 promotes proliferation, migration/invasion, and metastasis in different cell models, 17,20,21 whereas knockdown of TPD52 induced apoptosis in breast and prostate cancer cell lines.…”

Section: Introductionmentioning

confidence: 99%