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      Proteins in stool as biomarkers for non‐invasive detection of colorectal adenomas with high risk of progression

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          Screening to detect colorectal cancer (CRC) in an early or premalignant state is an effective method to reduce CRC mortality rates. Current stool‐based screening tests, e.g. fecal immunochemical test (FIT), have a suboptimal sensitivity for colorectal adenomas and difficulty distinguishing adenomas at high risk of progressing to cancer from those at lower risk. We aimed to identify stool protein biomarker panels that can be used for the early detection of high‐risk adenomas and CRC. Proteomics data (LC–MS/MS) were collected on stool samples from adenoma ( n = 71) and CRC patients ( n = 81) as well as controls ( n = 129). Colorectal adenoma tissue samples were characterized by low‐coverage whole‐genome sequencing to determine their risk of progression based on specific DNA copy number changes. Proteomics data were used for logistic regression modeling to establish protein biomarker panels. In total, 15 of the adenomas (15.8%) were defined as high risk of progressing to cancer. A protein panel, consisting of haptoglobin (Hp), LAMP1, SYNE2, and ANXA6, was identified for the detection of high‐risk adenomas (sensitivity of 53% at specificity of 95%). Two panels, one consisting of Hp and LRG1 and one of Hp, LRG1, RBP4, and FN1, were identified for high‐risk adenomas and CRCs detection (sensitivity of 66% and 62%, respectively, at specificity of 95%). Validation of Hp as a biomarker for high‐risk adenomas and CRCs was performed using an antibody‐based assay in FIT samples from a subset of individuals from the discovery series ( n = 158) and an independent validation series ( n = 795). Hp protein was significantly more abundant in high‐risk adenoma FIT samples compared to controls in the discovery ( p = 0.036) and the validation series ( p = 9e‐5). We conclude that Hp, LAMP1, SYNE2, LRG1, RBP4, FN1, and ANXA6 may be of value as stool biomarkers for early detection of high‐risk adenomas and CRCs. © 2019 Authors. Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

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          Global Cancer Statistics 2018: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries

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            MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.

            Efficient analysis of very large amounts of raw data for peptide identification and protein quantification is a principal challenge in mass spectrometry (MS)-based proteomics. Here we describe MaxQuant, an integrated suite of algorithms specifically developed for high-resolution, quantitative MS data. Using correlation analysis and graph theory, MaxQuant detects peaks, isotope clusters and stable amino acid isotope-labeled (SILAC) peptide pairs as three-dimensional objects in m/z, elution time and signal intensity space. By integrating multiple mass measurements and correcting for linear and nonlinear mass offsets, we achieve mass accuracy in the p.p.b. range, a sixfold increase over standard techniques. We increase the proportion of identified fragmentation spectra to 73% for SILAC peptide pairs via unambiguous assignment of isotope and missed-cleavage state and individual mass precision. MaxQuant automatically quantifies several hundred thousand peptides per SILAC-proteome experiment and allows statistically robust identification and quantification of >4,000 proteins in mammalian cell lysates.
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              pROC: an open-source package for R and S+ to analyze and compare ROC curves

              Background Receiver operating characteristic (ROC) curves are useful tools to evaluate classifiers in biomedical and bioinformatics applications. However, conclusions are often reached through inconsistent use or insufficient statistical analysis. To support researchers in their ROC curves analysis we developed pROC, a package for R and S+ that contains a set of tools displaying, analyzing, smoothing and comparing ROC curves in a user-friendly, object-oriented and flexible interface. Results With data previously imported into the R or S+ environment, the pROC package builds ROC curves and includes functions for computing confidence intervals, statistical tests for comparing total or partial area under the curve or the operating points of different classifiers, and methods for smoothing ROC curves. Intermediary and final results are visualised in user-friendly interfaces. A case study based on published clinical and biomarker data shows how to perform a typical ROC analysis with pROC. Conclusions pROC is a package for R and S+ specifically dedicated to ROC analysis. It proposes multiple statistical tests to compare ROC curves, and in particular partial areas under the curve, allowing proper ROC interpretation. pROC is available in two versions: in the R programming language or with a graphical user interface in the S+ statistical software. It is accessible at under the GNU General Public License. It is also distributed through the CRAN and CSAN public repositories, facilitating its installation.

                Author and article information

                J Pathol
                J. Pathol
                The Journal of Pathology
                John Wiley & Sons, Ltd (Chichester, UK )
                13 January 2020
                March 2020
                : 250
                : 3 ( doiID: 10.1002/path.v250.3 )
                : 288-298
                [ 1 ] Department of Pathology The Netherlands Cancer Institute Amsterdam The Netherlands
                [ 2 ] Department of Medical Oncology Amsterdam UMC, VU University Medical Center Amsterdam The Netherlands
                [ 3 ] Department of Epidemiology and Biostatistics Amsterdam UMC, VU University Medical Center Amsterdam The Netherlands
                [ 4 ] Department of Pathology Amsterdam UMC, VU University Medical Center Amsterdam The Netherlands
                [ 5 ] Department of Gastroenterology and Hepatology Amsterdam UMC, VU University Medical Center Amsterdam The Netherlands
                [ 6 ] Department of Gastroenterology and Hepatology Amsterdam UMC, University of Amsterdam Amsterdam The Netherlands
                [ 7 ] Department of Gastroenterology and Hepatology Erasmus MC University Medical Center Rotterdam The Netherlands
                Author notes
                [* ] Correspondence to: M de Wit, Department of Pathology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands. E‐mail: m.d.wit@

                © 2019 Authors. Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

                This is an open access article under the terms of the License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                Page count
                Figures: 5, Tables: 1, Pages: 11, Words: 6291
                Funded by: European Cooperation in Science and Technology , open-funder-registry 10.13039/501100000921;
                Award ID: CA17118
                Funded by: Health∼Holland
                Award ID: LSHM15040
                Funded by: KWF Kankerbestrijding , open-funder-registry 10.13039/501100004622;
                Award ID: 2013‐6025
                Funded by: Maag Lever Darm Stichting PPP Allowance made available by Health∼Holland, Top Sector Life Sciences & Health
                Award ID: LSHM15040 CRC Bioscreen2.0
                Funded by: SU2C‐DCS International Translational Cancer Research Dream Team Grant
                Award ID: SU2C‐AACR‐DT1415
                Award ID: MEDOCC
                Funded by: VU University Medical Center, Cancer Center Amsterdam
                Original Paper
                Original Papers
                Custom metadata
                March 2020
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.7.7 mode:remove_FC converted:11.03.2020


                early detection, biomarkers, high‐risk adenomas, colorectal cancer


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