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      An introduction to biomarkers: applications to chronic kidney disease

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      Pediatric Nephrology (Berlin, Germany)
      Springer Berlin Heidelberg
      CKD, Surrogate endpoint, Cross validation, Proteomics, ROC curve

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          Abstract

          Diagnosis and management of chronic kidney disease (CKD) will be characterized in the future by an increasing use of biomarkers—quantitative indicators of biologic or pathologic processes that vary continuously with progression of the process. “Classical” biomarkers of CKD progression include quantitative proteinuria, the percentage of sclerotic glomeruli or fractional interstitial fibrosis. New candidate biomarkers (e.g., urinary proteomic patterns) are being developed based on both mechanistic and “shotgun” approaches. Validation of potential biomarkers in prospective studies as surrogate endpoints for hard clinical outcomes is often complicated by the long lag time to the ultimate clinical outcome (e.g., end-stage renal disease). The very dense data sets that result from shotgun approaches on small numbers of patients carry a significant risk of model overfitting, leading to spurious associations. New analytic methods can help to decrease this risk. It is likely that clinical practice will come to depend increasingly on multiplex (vector) biomarkers used in conjunction with risk markers in early diagnosis as well as to guide therapy.

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          Most cited references48

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          Use of proteomic patterns in serum to identify ovarian cancer.

          New technologies for the detection of early-stage ovarian cancer are urgently needed. Pathological changes within an organ might be reflected in proteomic patterns in serum. We developed a bioinformatics tool and used it to identify proteomic patterns in serum that distinguish neoplastic from non-neoplastic disease within the ovary. Proteomic spectra were generated by mass spectroscopy (surface-enhanced laser desorption and ionisation). A preliminary "training" set of spectra derived from analysis of serum from 50 unaffected women and 50 patients with ovarian cancer were analysed by an iterative searching algorithm that identified a proteomic pattern that completely discriminated cancer from non-cancer. The discovered pattern was then used to classify an independent set of 116 masked serum samples: 50 from women with ovarian cancer, and 66 from unaffected women or those with non-malignant disorders. The algorithm identified a cluster pattern that, in the training set, completely segregated cancer from non-cancer. The discriminatory pattern correctly identified all 50 ovarian cancer cases in the masked set, including all 18 stage I cases. Of the 66 cases of non-malignant disease, 63 were recognised as not cancer. This result yielded a sensitivity of 100% (95% CI 93--100), specificity of 95% (87--99), and positive predictive value of 94% (84--99). These findings justify a prospective population-based assessment of proteomic pattern technology as a screening tool for all stages of ovarian cancer in high-risk and general populations.
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            Prediction of cancer outcome with microarrays: a multiple random validation strategy

            General studies of microarray gene-expression profiling have been undertaken to predict cancer outcome. Knowledge of this gene-expression profile or molecular signature should improve treatment of patients by allowing treatment to be tailored to the severity of the disease. We reanalysed data from the seven largest published studies that have attempted to predict prognosis of cancer patients on the basis of DNA microarray analysis. The standard strategy is to identify a molecular signature (ie, the subset of genes most differentially expressed in patients with different outcomes) in a training set of patients and to estimate the proportion of misclassifications with this signature on an independent validation set of patients. We expanded this strategy (based on unique training and validation sets) by using multiple random sets, to study the stability of the molecular signature and the proportion of misclassifications. The list of genes identified as predictors of prognosis was highly unstable; molecular signatures strongly depended on the selection of patients in the training sets. For all but one study, the proportion misclassified decreased as the number of patients in the training set increased. Because of inadequate validation, our chosen studies published overoptimistic results compared with those from our own analyses. Five of the seven studies did not classify patients better than chance. The prognostic value of published microarray results in cancer studies should be considered with caution. We advocate the use of validation by repeated random sampling.
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              Predictors of the progression of renal disease in the Modification of Diet in Renal Disease Study.

              The Modification of Diet in Renal Disease (MDRD) Study examined the effects of dietary protein restriction and strict blood pressure control on the decline in glomerular filtration rate (GFR) in 840 patients with diverse renal diseases. We describe a systematic analysis to determine baseline factors that predict the decline in GFR, or which alter the efficacy of the diet or blood pressure interventions. Univariate analysis identified 18 of 41 investigated baseline factors as significant (P < 0.05) predictors of GFR decline. In multivariate analysis, six factors--greater urine protein excretion, diagnosis of polycystic kidney disease (PKD), lower serum transferrin, higher mean arterial pressure, black race, and lower serum HDL cholesterol--independently predicted a faster decline in GFR. Together with the study interventions, these six factors accounted for 34.5% and 33.9% of the variance between patients in GFR slopes in Studies A and B, respectively, with proteinuria and PKD playing the predominant role. The mean rate of GFR decline was not significantly related to baseline GFR, suggesting an approximately linear mean GFR decline as renal disease progresses. The 41 baseline predictors were also assessed for their interactions with the diet and blood pressure interventions. A greater benefit of the low blood pressure intervention was found in patients with higher baseline urine protein. None of the 41 baseline factors were shown to predict a greater or lesser effect of dietary protein restriction.
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                Author and article information

                Contributors
                klemley@chla.usc.edu
                Journal
                Pediatr Nephrol
                Pediatr. Nephrol
                Pediatric Nephrology (Berlin, Germany)
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                0931-041X
                1432-198X
                30 March 2007
                30 March 2007
                2007
                : 22
                : 11
                : 1849-1859
                Affiliations
                GRID grid.239546.f, ISNI 0000000121536013, Division of Nephrology MS 40, , Childrens Hospital Los Angeles, ; 4650 Sunset Blvd, Los Angeles, CA 90027 USA
                Article
                455
                10.1007/s00467-007-0455-9
                6949205
                17394023
                c0e2de98-9d69-43e1-b402-9d3a41738e00
                © IPNA 2007

                This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 3 December 2006
                : 31 January 2007
                : 31 January 2007
                Categories
                Educational Feature
                Custom metadata
                © IPNA 2007

                Nephrology
                ckd,surrogate endpoint,cross validation,proteomics,roc curve
                Nephrology
                ckd, surrogate endpoint, cross validation, proteomics, roc curve

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