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Elevated circulating branched chain amino acids are an early event in pancreatic adenocarcinoma development

1 , 2 , 3 , 4 , 5 , 4 , 6 , 1 , 1 , 3 , 7 , 7 , 4 , 7 , 1 , 1 , 8 , 1 , 3 , 4 , 9 , 4 , 7 , 10 , 4 , 7 , 10 , 3 , 3 , 4 , 7 , 4 , 11 , 11 , 12 , 13 , 14 , 15 , 4 , 7 , 11 , 16 , 8 , 5 , 5 , 17 , 5 , 18 , 3 , 7 , 1 , 3 , 5 , , 3 , 19 ,

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      Abstract

      Most patients with pancreatic ductal adenocarcinoma (PDAC) are diagnosed with advanced disease and survive less than 12 months 1 . PDAC has been linked with obesity and glucose intolerance 2- 4 , but whether changes in circulating metabolites are associated with early cancer progression is unknown. To better understand metabolic derangements associated with early disease, we profiled metabolites in prediagnostic plasma from pancreatic cancer cases and matched controls from four prospective cohort studies. We find that elevated plasma levels of branched chain amino acids (BCAAs) are associated with a greater than 2–fold increased risk of future pancreatic cancer diagnosis. This elevated risk was independent of known predisposing factors, with the strongest association observed among subjects with samples collected 2 to 5 years prior to diagnosis when occult disease is likely present. We show that plasma BCAAs are also elevated in mice with early stage pancreatic cancers driven by mutant Kras expression, and that breakdown of tissue protein accounts for the increase in plasma BCAAs that accompanies early stage disease. Together, these findings suggest that increased whole–body protein breakdown is an early event in development of PDAC.

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      The meaning and use of the area under a receiver operating characteristic (ROC) curve.

      A representation and interpretation of the area under a receiver operating characteristic (ROC) curve obtained by the "rating" method, or by mathematical predictions based on patient characteristics, is presented. It is shown that in such a setting the area represents the probability that a randomly chosen diseased subject is (correctly) rated or ranked with greater suspicion than a randomly chosen non-diseased subject. Moreover, this probability of a correct ranking is the same quantity that is estimated by the already well-studied nonparametric Wilcoxon statistic. These two relationships are exploited to (a) provide rapid closed-form expressions for the approximate magnitude of the sampling variability, i.e., standard error that one uses to accompany the area under a smoothed ROC curve, (b) guide in determining the size of the sample required to provide a sufficiently reliable estimate of this area, and (c) determine how large sample sizes should be to ensure that one can statistically detect differences in the accuracy of diagnostic techniques.
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        Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach.

        Methods of evaluating and comparing the performance of diagnostic tests are of increasing importance as new tests are developed and marketed. When a test is based on an observed variable that lies on a continuous or graded scale, an assessment of the overall value of the test can be made through the use of a receiver operating characteristic (ROC) curve. The curve is constructed by varying the cutpoint used to determine which values of the observed variable will be considered abnormal and then plotting the resulting sensitivities against the corresponding false positive rates. When two or more empirical curves are constructed based on tests performed on the same individuals, statistical analysis on differences between curves must take into account the correlated nature of the data. This paper presents a nonparametric approach to the analysis of areas under correlated ROC curves, by using the theory on generalized U-statistics to generate an estimated covariance matrix.
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          Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond.

          Identification of key factors associated with the risk of developing cardiovascular disease and quantification of this risk using multivariable prediction algorithms are among the major advances made in preventive cardiology and cardiovascular epidemiology in the 20th century. The ongoing discovery of new risk markers by scientists presents opportunities and challenges for statisticians and clinicians to evaluate these biomarkers and to develop new risk formulations that incorporate them. One of the key questions is how best to assess and quantify the improvement in risk prediction offered by these new models. Demonstration of a statistically significant association of a new biomarker with cardiovascular risk is not enough. Some researchers have advanced that the improvement in the area under the receiver-operating-characteristic curve (AUC) should be the main criterion, whereas others argue that better measures of performance of prediction models are needed. In this paper, we address this question by introducing two new measures, one based on integrated sensitivity and specificity and the other on reclassification tables. These new measures offer incremental information over the AUC. We discuss the properties of these new measures and contrast them with the AUC. We also develop simple asymptotic tests of significance. We illustrate the use of these measures with an example from the Framingham Heart Study. We propose that scientists consider these types of measures in addition to the AUC when assessing the performance of newer biomarkers.
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            Author and article information

            Affiliations
            [1 ] Koch Institute for Integrative Cancer Research and Department of Biology, Massachusetts Institute of Technology, Cambridge, MA
            [2 ] Department of Etiology and Carcinogenesis, Cancer Institute and Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
            [3 ] Department of Medical Oncology, Dana–Farber Cancer Institute and Harvard Medical School, Boston, MA
            [4 ] Department of Epidemiology, Harvard School of Public Health, Boston, MA
            [5 ] Broad Institute of MIT and Harvard University, Cambridge, MA
            [6 ] Department of Biostatistics, Harvard School of Public Health, Boston, MA
            [7 ] Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
            [8 ] Division of Genomic Stability and DNA repair, Department of Radiation Oncology, Dana– Farber Cancer Institute, Boston, MA 02215
            [9 ] Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
            [10 ] Department of Nutrition, Harvard School of Public Health, Boston, MA
            [11 ] Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA
            [12 ] Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System
            [13 ] University of Washington School of Nursing, Seattle, WA
            [14 ] Departments of Epidemiology and Medicine, Brown University, Providence, RI
            [15 ] Department of Social and Preventive Medicine, University at Buffalo, SUNY, Buffalo, NY
            [16 ] Departments of Oncology and Medicine, McGill University, Montreal, QC, Canada
            [17 ] Division of Cardiovascular Medicine, Vanderbilt University, Nashville, TN
            [18 ] Cardiology Division, Massachusetts General Hospital, and Harvard Medical School, Boston, MA
            [19 ] Department of Medicine, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA
            Author notes
            [] Correspondence: Brian Wolpin or Matthew Vander Heiden +1–617–632–6942 or +1–617–715–4471 bwolpin@ 123456partners.org or mvh@ 123456mit.edu
            Journal
            9502015
            8791
            Nat Med
            Nat. Med.
            Nature medicine
            1078-8956
            1546-170X
            9 August 2014
            28 September 2014
            October 2014
            01 April 2015
            : 20
            : 10
            : 1193-1198
            25261994 4191991 10.1038/nm.3686 NIHMS619476
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            Medicine

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