15
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Molecular and serologic markers of HPV 16 infection are associated with local recurrence in patients with oral cavity squamous cell carcinoma

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Human papillomavirus (HPV) infections predict mortality in Taiwanese patients with oral cavity squamous cell carcinoma (OCSCC). To address their prognostic significance for local recurrence (LR), in this retrospective cohort study we investigated different serologic and molecular markers of HPV 16 infection in 85 consecutive patients with primary OCSCC who received standard treatment and had their sera stored before treatment. Resected tumor specimens were examined with PCR-based assays for HPV 16 E6/E7 mRNA expression. Sera were tested with suspension arrays for the presence of HPV-specific antibodies using synthetic L1 and E6 peptides as well as a synthetic E7 protein. HPV 16 E6/E7 mRNA, anti-L1, anti-E6, and anti-E7 antibodies tested positive in 12%, 25%, 38%, and 41% of the study patients, respectively. Multivariate analysis identified pathological T3/T4, E6/E7 mRNA, and anti-E7 antibodies as independent risk factors for LR, whereas anti-E6 antibodies were an independent protective factor. In patients with ≥ 3 (high-risk group), 2 (intermediate-risk), and ≤ 1 (low-risk) independent risk factors (predictors), the 5-year LR rates were 75%, 42%, and 4%, respectively. Results were validated in an independent cohort. Together, our preliminary data indicate that HPV 16 infections as well as low and high serum levels of anti-E6 and anti-E7 antibodies, respectively, can serve as biomarkers of LR in patients with OCSCC, whereas the clinical usefulness of anti-HPV 16 antibodies for risk stratification of newly diagnosed cases deserves further scrutiny.

          Related collections

          Most cited references67

          • Record: found
          • Abstract: found
          • Article: not found

          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.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            What you see may not be what you get: a brief, nontechnical introduction to overfitting in regression-type models.

            Statistical models, such as linear or logistic regression or survival analysis, are frequently used as a means to answer scientific questions in psychosomatic research. Many who use these techniques, however, apparently fail to appreciate fully the problem of overfitting, ie, capitalizing on the idiosyncrasies of the sample at hand. Overfitted models will fail to replicate in future samples, thus creating considerable uncertainty about the scientific merit of the finding. The present article is a nontechnical discussion of the concept of overfitting and is intended to be accessible to readers with varying levels of statistical expertise. The notion of overfitting is presented in terms of asking too much from the available data. Given a certain number of observations in a data set, there is an upper limit to the complexity of the model that can be derived with any acceptable degree of uncertainty. Complexity arises as a function of the number of degrees of freedom expended (the number of predictors including complex terms such as interactions and nonlinear terms) against the same data set during any stage of the data analysis. Theoretical and empirical evidence--with a special focus on the results of computer simulation studies--is presented to demonstrate the practical consequences of overfitting with respect to scientific inference. Three common practices--automated variable selection, pretesting of candidate predictors, and dichotomization of continuous variables--are shown to pose a considerable risk for spurious findings in models. The dilemma between overfitting and exploring candidate confounders is also discussed. Alternative means of guarding against overfitting are discussed, including variable aggregation and the fixing of coefficients a priori. Techniques that account and correct for complexity, including shrinkage and penalization, also are introduced.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Deintensification candidate subgroups in human papillomavirus-related oropharyngeal cancer according to minimal risk of distant metastasis.

              To define human papillomavirus (HPV) -positive oropharyngeal cancers (OPC) suitable for treatment deintensification according to low risk of distant metastasis (DM). OPC treated with radiotherapy (RT) or chemoradiotherapy (CRT) from 2001 to 2009 were included. Outcomes were compared for HPV-positive versus HPV-negative patients. Univariate and multivariate analyses identified outcome predictors. Recursive partitioning analysis (RPA) stratified the DM risk. HPV status was ascertained in 505 (56%) of 899 consecutive OPCs. Median follow-up was 3.9 years. HPV-positive patients (n = 382), compared with HPV-negative patients (n = 123), had higher local (94% v 80%, respectively, at 3 years; P 10 reduced overall survival (HR, 1.72; 95% CI, 1.1 to 2.7; P = .03) but did not impact RFS (HR, 1.1; 95% CI, 0.7 to 1.9; P = .65). RPA segregated HPV-positive patients into low (T1-3N0-2c; DC, 93%) and high DM risk (N3 or T4; DC, 76%) groups and HPV-negative patients into different low (T1-2N0-2c; DC, 93%) and high DM risk (T3-4N3; DC, 72%) groups. The DC rates for HPV-positive, low-risk N0-2a or less than 10 pack-year N2b patients were similar for RT alone and CRT, but the rate was lower in the N2c subset managed by RT alone (73% v 92% for CRT; P = .02). HPV-positive T1-3N0-2c patients have a low DM risk, but N2c patients from this group have a reduced DC when treated with RT alone and seem less suited for deintensification strategies that omit chemotherapy.
                Bookmark

                Author and article information

                Journal
                Oncotarget
                Oncotarget
                Oncotarget
                ImpactJ
                Oncotarget
                Impact Journals LLC
                1949-2553
                23 May 2017
                31 March 2017
                : 8
                : 21
                : 34820-34835
                Affiliations
                1 Department of Laboratory Medicine, Head and Neck Oncology Group, Linkou Chang Gung Memorial Hospital, Taoyuan, Taiwan, ROC
                2 Department of Medical Biotechnology and Laboratory Science, Chang Gung University, Taoyuan, Taiwan, ROC
                3 Graduate Institute of Biomedical Sciences, Chang Gung University, Taoyuan, Taiwan, ROC
                4 Faculty of Medicine, Chang Gung University, Taoyuan, Taiwan, ROC
                5 Department of Otorhinolaryngology - Head and Neck Surgery, Head and Neck Oncology Group, Linkou Chang Gung Memorial Hospital, Taoyuan, Taiwan, ROC
                6 Molecular Imaging Center, Head and Neck Oncology Group, Linkou Chang Gung Memorial Hospital, Taoyuan, Taiwan, ROC
                7 Department of Pathology, Head and Neck Oncology Group, Linkou Chang Gung Memorial Hospital, Taoyuan, Taiwan, ROC
                Author notes
                Correspondence to: Kuo-Chien Tsao, kctsao@ 123456cgmh.org.tw
                Article
                16747
                10.18632/oncotarget.16747
                5471014
                28422732
                c1ef6814-7d7d-4c9e-9f4b-d230f7959240
                Copyright: © 2017 Huang et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC-BY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 5 October 2016
                : 20 March 2017
                Categories
                Research Paper

                Oncology & Radiotherapy
                oral cavity squamous cell carcinoma,human papillomavirus,mrna expression,serology,local recurrence

                Comments

                Comment on this article