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      A four-marker signature of TNF-RII, TGF-α, TIMP-1 and CRP is prognostic of worse survival in high-risk surgically resected melanoma

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          Abstract

          Background

          E1694 tested GM2-KLH-QS21 vaccine versus high-dose interferon-α2b (HDI) as adjuvant therapy for operable stage IIB-III melanoma. We tested banked serum specimens from patients in the vaccine arm of E1694 for prognostic biomarkers.

          Methods

          Aushon Multiplex Platform was used to quantitate baseline serum levels of 115 analytes from 40 patients. Least absolute shrinkage and selection operator proportional hazard regression (Lasso PH) was used to select markers that are most informative for relapse-free survival (RFS) and overall survival (OS). Regular Cox PH models were then fit with the markers selected by the Lasso PH. Survival receiver operating characteristic (ROC) analysis was used to evaluate the ability of the models to predict 1-year RFS and 5-year OS.

          Results

          Four markers that include Tumor Necrosis Factor alpha Receptor II (TNF-RII), Transforming Growth Factor alpha (TGF-α), Tissue Inhibitor of Metalloproteinases 1 (TIMP-1), and C-reactive protein (CRP) were found to be most informative for the prediction of OS (high levels correlate with worse prognosis). The dichotomized risk score based on the four markers could significantly separate the OS curves (p = 0.0005). When using the four-marker PH model to predict 5-year OS, we achieved an area under the curve (AUC) of 89% (cross validated AUC = 72%). High baseline TNF-RII was also significantly associated with worse RFS. The RFS with high (above median) TNF-RII was significantly lower than low TNF-RII (p = 0.01).

          Conclusions

          The biomarker signature consisting of TNFR-II, TGF-α, TIMP-1 and CRP is significantly prognostic of survival in patients with high-risk melanoma and warrants further investigation.

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          Most cited references41

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          Final version of 2009 AJCC melanoma staging and classification.

          To revise the staging system for cutaneous melanoma on the basis of data from an expanded American Joint Committee on Cancer (AJCC) Melanoma Staging Database. The melanoma staging recommendations were made on the basis of a multivariate analysis of 30,946 patients with stages I, II, and III melanoma and 7,972 patients with stage IV melanoma to revise and clarify TNM classifications and stage grouping criteria. Findings and new definitions include the following: (1) in patients with localized melanoma, tumor thickness, mitotic rate (histologically defined as mitoses/mm(2)), and ulceration were the most dominant prognostic factors. (2) Mitotic rate replaces level of invasion as a primary criterion for defining T1b melanomas. (3) Among the 3,307 patients with regional metastases, components that defined the N category were the number of metastatic nodes, tumor burden, and ulceration of the primary melanoma. (4) For staging purposes, all patients with microscopic nodal metastases, regardless of extent of tumor burden, are classified as stage III. Micrometastases detected by immunohistochemistry are specifically included. (5) On the basis of a multivariate analysis of patients with distant metastases, the two dominant components in defining the M category continue to be the site of distant metastases (nonvisceral v lung v all other visceral metastatic sites) and an elevated serum lactate dehydrogenase level. Using an evidence-based approach, revisions to the AJCC melanoma staging system have been made that reflect our improved understanding of this disease. These revisions will be formally incorporated into the seventh edition (2009) of the AJCC Cancer Staging Manual and implemented by early 2010.
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            Time-dependent ROC curves for censored survival data and a diagnostic marker.

            ROC curves are a popular method for displaying sensitivity and specificity of a continuous diagnostic marker, X, for a binary disease variable, D. However, many disease outcomes are time dependent, D(t), and ROC curves that vary as a function of time may be more appropriate. A common example of a time-dependent variable is vital status, where D(t) = 1 if a patient has died prior to time t and zero otherwise. We propose summarizing the discrimination potential of a marker X, measured at baseline (t = 0), by calculating ROC curves for cumulative disease or death incidence by time t, which we denote as ROC(t). A typical complexity with survival data is that observations may be censored. Two ROC curve estimators are proposed that can accommodate censored data. A simple estimator is based on using the Kaplan-Meier estimator for each possible subset X > c. However, this estimator does not guarantee the necessary condition that sensitivity and specificity are monotone in X. An alternative estimator that does guarantee monotonicity is based on a nearest neighbor estimator for the bivariate distribution function of (X, T), where T represents survival time (Akritas, M. J., 1994, Annals of Statistics 22, 1299-1327). We present an example where ROC(t) is used to compare a standard and a modified flow cytometry measurement for predicting survival after detection of breast cancer and an example where the ROC(t) curve displays the impact of modifying eligibility criteria for sample size and power in HIV prevention trials.
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              C-reactive protein: ligands, receptors and role in inflammation.

              C-reactive protein (CRP) is the prototypical acute phase serum protein, rising rapidly in response to inflammation. CRP binds to phosphocholine (PC) and related molecules on microorganisms and plays an important role in host defense. However, a more important role may be the binding of CRP to PC in damaged membranes. CRP increases clearance of apoptotic cells, binds to nuclear antigens and by masking autoantigens from the immune system or enhancing their clearance, CRP may prevent autoimmunity. CRP binds to both the stimulatory receptors, FcgammaRI and FcgammaRIIa, increasing phagocytosis and the release of inflammatory cytokines; and to the inhibitory receptor, FcgammaRIIb, blocking activating signals. We have shown that, in two animal models of systemic lupus erythematosus (SLE), the (NZB x NZW)F1 mouse and the MRL/lpr mouse, a single injection of CRP before onset of proteinuria delayed disease development and late treatment reversed proteinuria. Thus, in these models, CRP plays an anti-inflammatory role.
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                Author and article information

                Journal
                J Transl Med
                J Transl Med
                Journal of Translational Medicine
                BioMed Central
                1479-5876
                2014
                23 January 2014
                : 12
                : 19
                Affiliations
                [1 ]University of Pittsburgh Cancer Institute, UPMC Cancer Pavilion, 5150 Centre Avenue, 5th Floor, Suite 555, Pittsburgh, PA 15232, USA
                [2 ]Biostatistics Facility, University of Pittsburgh Cancer Institute, 201 North Craig St., Sterling Plaza, Suite 325, Pittsburgh, PA 15213, USA
                [3 ]UPMC Montefiore/Presbyterian Internal Medicine Residency Program, Department of Medicine, University of Pittsburgh, UPMC Montefiore Hospital, N-715, 200 Lothrop Street, Pittsburgh, PA 15213, USA
                [4 ]University of Pittsburgh Cancer Institute, UPMC Shadyside Hospital, 5230 Centre Avenue, Rm. WG02.11, Pittsburgh, PA 15213, USA
                [5 ]Visiting Scholar, Department of Medicine, University of Pittsburgh Cancer Institute, 5117 Centre Avenue, Pittsburgh, PA 15213, USA
                [6 ]Biostatistics and Computational Biology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA 02215, USA
                [7 ]University of Pittsburgh Cancer Institute, 5117 Centre Avenue, Suite 1.32, Pittsburgh, PA 15213, USA
                Article
                1479-5876-12-19
                10.1186/1479-5876-12-19
                3909384
                24457057
                33e9fc0c-4588-47be-8721-e2614586f6f8
                Copyright © 2014 Tarhini et al.; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 5 December 2013
                : 18 January 2014
                Categories
                Research

                Medicine
                melanoma,tgf-α,e1694,adjuvant,timp-1,crp,tnf-rii
                Medicine
                melanoma, tgf-α, e1694, adjuvant, timp-1, crp, tnf-rii

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