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

      research-article
      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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            Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression.

            Multiple, complex molecular events characterize cancer development and progression. Deciphering the molecular networks that distinguish organ-confined disease from metastatic disease may lead to the identification of critical biomarkers for cancer invasion and disease aggressiveness. Although gene and protein expression have been extensively profiled in human tumours, little is known about the global metabolomic alterations that characterize neoplastic progression. Using a combination of high-throughput liquid-and-gas-chromatography-based mass spectrometry, we profiled more than 1,126 metabolites across 262 clinical samples related to prostate cancer (42 tissues and 110 each of urine and plasma). These unbiased metabolomic profiles were able to distinguish benign prostate, clinically localized prostate cancer and metastatic disease. Sarcosine, an N-methyl derivative of the amino acid glycine, was identified as a differential metabolite that was highly increased during prostate cancer progression to metastasis and can be detected non-invasively in urine. Sarcosine levels were also increased in invasive prostate cancer cell lines relative to benign prostate epithelial cells. Knockdown of glycine-N-methyl transferase, the enzyme that generates sarcosine from glycine, attenuated prostate cancer invasion. Addition of exogenous sarcosine or knockdown of the enzyme that leads to sarcosine degradation, sarcosine dehydrogenase, induced an invasive phenotype in benign prostate epithelial cells. Androgen receptor and the ERG gene fusion product coordinately regulate components of the sarcosine pathway. Here, by profiling the metabolomic alterations of prostate cancer progression, we reveal sarcosine as a potentially important metabolic intermediary of cancer cell invasion and aggressivity.
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              Use and misuse of the receiver operating characteristic curve in risk prediction.

              The c statistic, or area under the receiver operating characteristic (ROC) curve, achieved popularity in diagnostic testing, in which the test characteristics of sensitivity and specificity are relevant to discriminating diseased versus nondiseased patients. The c statistic, however, may not be optimal in assessing models that predict future risk or stratify individuals into risk categories. In this setting, calibration is as important to the accurate assessment of risk. For example, a biomarker with an odds ratio of 3 may have little effect on the c statistic, yet an increased level could shift estimated 10-year cardiovascular risk for an individual patient from 8% to 24%, which would lead to different treatment recommendations under current Adult Treatment Panel III guidelines. Accepted risk factors such as lipids, hypertension, and smoking have only marginal impact on the c statistic individually yet lead to more accurate reclassification of large proportions of patients into higher-risk or lower-risk categories. Perfectly calibrated models for complex disease can, in fact, only achieve values for the c statistic well below the theoretical maximum of 1. Use of the c statistic for model selection could thus naively eliminate established risk factors from cardiovascular risk prediction scores. As novel risk factors are discovered, sole reliance on the c statistic to evaluate their utility as risk predictors thus seems ill-advised.
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                Author and article information

                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
                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
                Article
                NIHMS619476
                10.1038/nm.3686
                4191991
                25261994
                8ba0cc39-c220-4bec-9dc6-06dded8af26f
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