Gould Rothberg BE, Bracken MB, Rimm DLTissue biomarkers for prognosis in cutaneous melanoma: a systematic review and meta-analysis. J Natl Cancer Inst 101: 452-474

Department of Pathology, Yale University School of Medicine, New Haven, CT 06520, USA.
CancerSpectrum Knowledge Environment (Impact Factor: 15.16). 05/2009; 101(7):452-74. DOI: 10.1093/jnci/djp038
Source: PubMed

ABSTRACT In the clinical management of early-stage cutaneous melanoma, it is critical to determine which patients are cured by surgery alone and which should be treated with adjuvant therapy. To assist in this decision, many groups have made an effort to use molecular information. However, although there are hundreds of studies that have sought to assess the potential prognostic value of molecular markers in predicting the course of cutaneous melanoma, at this time, no molecular method to improve risk stratification is part of recommended clinical practice. To help understand this disconnect, we conducted a systematic review and meta-analysis of the published literature that reported immunohistochemistry-based protein biomarkers of melanoma outcome. Three parallel search strategies were applied to the PubMed database through January 15, 2008, to identify cohort studies that reported associations between immunohistochemical expression and survival outcomes in melanoma that conformed to the REMARK criteria. Of the 102 cohort studies, we identified only 37 manuscripts, collectively describing 87 assays on 62 distinct proteins, which met all inclusion criteria. Promising markers that emerged included melanoma cell adhesion molecule (MCAM)/MUC18 (all-cause mortality [ACM] hazard ratio [HR] = 16.34; 95% confidence interval [CI] = 3.80 to 70.28), matrix metalloproteinase-2 (melanoma-specific mortality [MSM] HR = 2.6; 95% CI = 1.32 to 5.07), Ki-67 (combined ACM HR = 2.66; 95% CI = 1.41 to 5.01), proliferating cell nuclear antigen (ACM HR = 2.27; 95% CI = 1.56 to 3.31), and p16/INK4A (ACM HR = 0.29; 95% CI = 0.10 to 0.83, MSM HR = 0.4; 95% CI = 0.24 to 0.67). We further noted incomplete adherence to the REMARK guidelines: 14 of 27 cohort studies that failed to adequately report their methods and nine studies that failed to either perform multivariable analyses or report their risk estimates were published since 2005.

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Available from: Bonnie Gould Rothberg, Jun 23, 2014
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    • "Biomarkers could provide additional prognostic information based on the molecular mechanisms of transformation and disease progression. A systematic review summarizes the use of protein biomarkers visualized by immunohistochemistry to predict melanoma outcome (Rothberg et al., 2009). Gene expression profiling of melanoma has yielded an enormous amount of information leading to the definition of molecular signatures for disease progression (Haqq et al., 2005; Jaeger et al., 2007; Timar et al., 2010), recurrence, and survival (Bogunovic et al., 2009). "
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    ABSTRACT: Outcomes for melanoma patients with stage III disease, differ widely even within the same sub-category. Molecular signatures that more accurately predict prognosis are needed to stratify patients according to risk. Proteomic analyses were used to identify differentially abundant proteins in extracts of surgically excised samples from patients with stage IIIc melanoma lymph node metastases. Analysis of samples from patients with poor (n = 14, < 1 year) and good (n = 19, > 4 years) survival outcomes identified 84 proteins that were differentially abundant between prognostic groups. Subsequent selected reaction monitoring analysis verified 21 proteins as potential biomarkers for survival. Poor prognosis patients are characterized by increased levels of proteins involved in protein metabolism, nucleic acid metabolism, angiogenesis, deregulation of cellular energetics and methylation processes, and decreased levels of proteins involved in apoptosis and immune response. These proteins are able to classify stage IIIc patients into prognostic sub-groups (p < 0.02). This is the first report of potential prognostic markers from stage III melanoma using proteomic analyses. Validation of these protein markers in larger patient cohorts should define protein signatures that enable better stratification of stage III melanoma patients.This article is protected by copyright. All rights reserved.
    Pigment Cell & Melanoma Research 07/2014; 27(6). DOI:10.1111/pcmr.12290 · 5.64 Impact Factor
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    • "Our report was written to comply with REMARK criteria (McShane et al., 2005; Gould Rothberg et al., 2009b); therefore, we provide relevant information about study design, hypotheses, patient and specimen characteristics, assay methods, and statistical analysis methods. In addition, we conducted a replication study. "
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    ABSTRACT: The genetic alterations contributing to melanoma pathogenesis are incompletely defined, and few independent prognostic features have been identified beyond the clinicopathological characteristics of the primary tumor. We used transcriptome profiling of 46 primary melanomas, 12 melanoma metastases, and 16 normal skin samples to find novel genes associated with melanoma development and progression. Results were confirmed using immunohistochemistry and real-time PCR and replicated in an independent set of 330 melanomas using AQUA analysis of tissue microarray. Transcriptome profiling revealed that transcription factor HMGA2, previously unrecognized in melanoma pathogenesis, is significantly upregulated in primary melanoma and metastases (P-values=1.2 × 10(-7) and 9 × 10(-5)) compared to normal skin. HMGA2 overexpression is associated with BRAF/NRAS mutations (P=0.0002). Cox-proportional hazard regression model and log-rank test showed that HMGA2 is independently associated with DFS (hazard ratio (HR)=6.3, 95% confidence interval (CI)=1.8-22.3, P=0.004), OS (stratified log-rank P=0.008), and DMFS (HR=6.4, 95%CI=1.4-29.7, P=0.018) after adjusting for AJCC stage and age at diagnosis. Survival analysis in an independent replication tissue microarray (TMA) of 330 melanomas confirmed the association of HMGA2 expression with OS (P=0.0211). Our study implicates HMGA2 in melanoma progression and demonstrates that HMGA2 overexpression can serve as an independent predictor of survival in melanoma.Journal of Investigative Dermatology accepted article preview online, 30 April 2013; doi:10.1038/jid.2013.197.
    Journal of Investigative Dermatology 04/2013; 133(11). DOI:10.1038/jid.2013.197 · 6.37 Impact Factor
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    • "Tissue microarrays can be used to validate cDNA microarray profiling findings [10] but the immunohistochemical data may need to be as rigorously processed or normalized [11] as for other high-throughput data sets. For example, a metaanalysis of melanoma biomarkers showed a wide variation of cut-off points for the expression of multiple markers and their utility in determining survival [12] which creates some difficulty in comparing outcome analysis. Furthermore markers to differentiate pulmonary from breast cancer used 7 antibodies but did not normalize the intensity scores before data analysis [13]. "
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    ABSTRACT: Tissue microarray based immunohistochemical staining and proteomics are important tools to create and validate clinically relevant cancer biomarkers. Immunohistochemical stains using formalin-fixed tissue microarray sections for protein expression are scored manually and semi-quantitatively. Digital image analysis methods remove some of the drawbacks of manual scoring but may need other methods such as normalization to provide across the board utility. In the present study, quantitative proteomics-based global normalization method was used to evaluate its utility in the analysis of p53 protein expression in mixed human normal and cancer tissue microarray. Global normalization used the mean or median of β-actin to calculate ratios of individual core stain intensities, then log transformed the ratios, calculate a mean or median and subtracted the value from the log of ratios. In the absence of global normalization of p53 protein expression, 44% (42 of 95) of tissue cores were positive using the median of intensity values and 40% (38 of 95) using the mean of intensities as cut-off points. With global normalization, p53 positive cores changed to 20% (19 of 95) when using median of intensities and 15.8%(15 of 95) when the mean of intensities were used. In conclusion, the global normalization method helped to define positive p53 staining in the tissue microarray set used. The method used helped to define clear cut-off points and confirmed all negatively stained tissue cores. Such normalization methods should help to better define clinically useful biomarkers.
    International journal of clinical and experimental pathology 06/2011; 4(5):505-12. · 1.78 Impact Factor
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