Automated Quantitative Analysis of Tissue Microarrays Reveals an Association between High Bcl-2 Expression and Improved Outcome in Melanoma

DiVito, Kyle A.; Berger, Aaron J.; Camp, Robert L.; Dolled‐Filhart, Marisa; Rimm, David L.; Kluger, Harriet M.

doi:10.1158/0008-5472.can-04-1387

Cited by 70 publications

(45 citation statements)

References 30 publications

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“…In other cases [GRP78 (26), cyclin B1 (32), CDK1 (32), nm23 (33,34), and topoisomerase II (32)], the opposite was observed; gene transcript levels differ while no significant outcome-related effect was observed at the protein level. For the remaining 16 molecules [BCL2 (32,38), survivin (18,32) (28,44), and SPP1/osteopontin (29,45,46)], concordance could not be assessed due to between-study differences making a direct comparison untenable. In addition, there were 4 molecules for which disparate findings were observed between different measures of clinical outcome.…”

Section: Bioinformatic Analysismentioning

confidence: 99%

Melanoma Prognosis: A REMARK-Based Systematic Review and Bioinformatic Analysis of Immunohistochemical and Gene Microarray Studies

Schramm

Mann

2011

Molecular Cancer Therapeutics

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Despite intensive research efforts, within-stage survival rates for melanoma vary widely. Pursuit of molecular biomarkers with improved prognostic significance over clinicohistological measures has produced extensive literature. Reviews have synthesized these data, but none have systematically partitioned highquality studies from the remainder across different molecular methods nor examined system properties of that output. Databases were searched for studies analyzing protein expression by immunohistochemistry (n ¼ 617, extending the only systematic review to date by 102 studies) or for gene expression microarray studies (n ¼ 45) in melanoma in relation to outcome. REMARK-derived criteria were applied to identify highquality studies. Biomarkers and pathways were functionally assessed by using gene ontology software. Most manuscripts did not meet REMARK-based criteria, an ongoing trend that can impede translational research. Across REMARK-compliant literature, 41 proteins were significantly associated with outcome. Multimarker tests consistently emerged among the most promising potential biomarkers, indicating a need to continue assessing candidates in that composite setting. Twenty-one canonical pathways were populated by outcomerelated proteins but not by those that failed to show such an association; we propose that this set of pathways warrants closer investigation to understand drivers of poor outcome in melanoma. Two-gene expression microarray studies met REMARK-based criteria reflecting a genuine paucity of literature in the area. The 254 outcome-related genes were examined for correspondences with the systematically identified protein signature. This analysis highlighted proliferating cell nuclear antigen and survivin as priorities for further examination as biomarkers in melanoma prognosis, and illustrated ongoing need to integrate alternative approaches to biomarker discovery in melanoma translational research.

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Section: Bioinformatic Analysismentioning

confidence: 99%

Melanoma Prognosis: A REMARK-Based Systematic Review and Bioinformatic Analysis of Immunohistochemical and Gene Microarray Studies

Schramm

Mann

2011

Molecular Cancer Therapeutics

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“…Besides the above mentioned LDH, a number of prognostic biomarkers have been tested both in the primary tumor (e.g., protein kinase-B [PKB/Akt], 9 Bcl-2 10 ) and in the peripheral blood (e.g., melanoma inhibitory activity [MIA] protein, 11 circulating tumor cells [CTC] 12 ): however, no consensus exist on their utility in the routine clinical practice, with particular regard to the therapeutic management of patients with TNM stage I-III cutaneous melanoma.…”

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confidence: 99%

The prognostic value of serum S100B in patients with cutaneous melanoma: A meta‐analysis

Mocellin

Zavagno

Nitti

2008

Intl Journal of Cancer

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S100B protein detected in the serum of patients with cutaneous melanoma has been long reported as a prognostic biomarker. However, no consensus exists on its implementation in the routine clinical setting. This study aimed to comprehensively and quantitatively summarize the evidence on the suitability of serum S100B to predict patients' survival. Twenty-two series enrolling 3393 patients with TNM stage I to IV cutaneous melanoma were reviewed. Standard meta-analysis methods were applied to evaluate the overall relationship between S100B serum levels and patients' survival (meta-risk). Serum S100B positivity was associated with significantly poorer survival (hazard ratio [HR] 5 2.23, 95% CI: 1.92-2.58, p < 0.0001). Between-study heterogeneity was significant, which appeared to be related mainly to dissemination bias and the inclusion of patients with stage IV disease. Considering stage I to III melanoma (n 5 1594), the meta-risk remained highly significant (HR 5 2.28, 95% CI: 1.8-2.89; p < 0.0001) and studies' estimates were homogeneous. Subgroup analysis of series reporting multivariate survival analysis supported S100B as a prognostic factor independent of the TNM staging system. Our findings suggest that serum S100B detection has a clinically valuable independent prognostic value in patients with melanoma, with particular regard to stage I-III disease. Further investigation focusing on this subset of patients is justified and warranted before S100B can be implemented in the routine clinical management of melanoma.

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“…TMA experiments measure tumor-specific protein expression via high-density immunohistochemical staining assays, allowing simultaneous evaluation of hundreds of patient samples in a single experiment [1]. Since their initial development, TMA-based expression studies have quickly become an integral part of cancer biomarker development [2][3][4].…”

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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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Automated Quantitative Analysis of Tissue Microarrays Reveals an Association between High Bcl-2 Expression and Improved Outcome in Melanoma

Cited by 70 publications

References 30 publications

Melanoma Prognosis: A REMARK-Based Systematic Review and Bioinformatic Analysis of Immunohistochemical and Gene Microarray Studies

Melanoma Prognosis: A REMARK-Based Systematic Review and Bioinformatic Analysis of Immunohistochemical and Gene Microarray Studies

The prognostic value of serum S100B in patients with cutaneous melanoma: A meta‐analysis

Modeling intra‐tumor protein expression heterogeneity in tissue microarray experiments

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