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      Breast Cancer Classification Based on Proteotypes Obtained by SWATH Mass Spectrometry

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          Summary

          Accurate classification of breast tumors is vital for patient management decisions and enables more precise cancer treatment. Here, we present a quantitative proteotyping approach based on sequential windowed acquisition of all theoretical fragment ion spectra (SWATH) mass spectrometry and establish key proteins for breast tumor classification. The study is based on 96 tissue samples representing five conventional breast cancer subtypes. SWATH proteotype patterns largely recapitulate these subtypes; however, they also reveal varying heterogeneity within the conventional subtypes, with triple negative tumors being the most heterogeneous. Proteins that contribute most strongly to the proteotype-based classification include INPP4B, CDK1, and ERBB2 and are associated with estrogen receptor (ER) status, tumor grade status, and HER2 status. Although these three key proteins exhibit high levels of correlation with transcript levels (R > 0.67), general correlation did not exceed R = 0.29, indicating the value of protein-level measurements of disease-regulated genes. Overall, this study highlights how cancer tissue proteotyping can lead to more accurate patient stratification.

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          Highlights

          • Proteotyping of 96 breast tumors by SWATH mass spectrometry

          • Three key proteins for breast tumor classification

          • Varying degrees of heterogeneity within conventional breast cancer subtypes

          • Generally modest correlation between protein and transcript levels in tumor tissue

          Abstract

          Bouchal et al. explore and confirm the suitability of SWATH-MS for proteotyping of human tumor samples at relatively high throughput. Results indicate that proteotype-based classification resolves more variability than is apparent from conventional subtyping and potentially improves current classification.

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          Most cited references41

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          Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search.

          We present a statistical model to estimate the accuracy of peptide assignments to tandem mass (MS/MS) spectra made by database search applications such as SEQUEST. Employing the expectation maximization algorithm, the analysis learns to distinguish correct from incorrect database search results, computing probabilities that peptide assignments to spectra are correct based upon database search scores and the number of tryptic termini of peptides. Using SEQUEST search results for spectra generated from a sample of known protein components, we demonstrate that the computed probabilities are accurate and have high power to discriminate between correctly and incorrectly assigned peptides. This analysis makes it possible to filter large volumes of MS/MS database search results with predictable false identification error rates and can serve as a common standard by which the results of different research groups are compared.
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            OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data.

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              Using iRT, a normalized retention time for more targeted measurement of peptides.

              Multiple reaction monitoring (MRM) has recently become the method of choice for targeted quantitative measurement of proteins using mass spectrometry. The method, however, is limited in the number of peptides that can be measured in one run. This number can be markedly increased by scheduling the acquisition if the accurate retention time (RT) of each peptide is known. Here we present iRT, an empirically derived dimensionless peptide-specific value that allows for highly accurate RT prediction. The iRT of a peptide is a fixed number relative to a standard set of reference iRT-peptides that can be transferred across laboratories and chromatographic systems. We show that iRT facilitates the setup of multiplexed experiments with acquisition windows more than four times smaller compared to in silico RT predictions resulting in improved quantification accuracy. iRTs can be determined by any laboratory and shared transparently. The iRT concept has been implemented in Skyline, the most widely used software for MRM experiments.
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                Author and article information

                Contributors
                Journal
                Cell Rep
                Cell Rep
                Cell Reports
                Cell Press
                2211-1247
                16 July 2019
                16 July 2019
                16 July 2019
                : 28
                : 3
                : 832-843.e7
                Affiliations
                [1 ]Department of Biochemistry, Faculty of Science, Masaryk University, Brno, Czech Republic
                [2 ]Regional Centre for Applied Molecular Oncology, Masaryk Memorial Cancer Institute, Brno, Czech Republic
                [3 ]Department of Biology, Institute of Molecular Systems Biology, Federal Institute of Technology (ETH) Zurich, Zurich, Switzerland
                [4 ]Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
                [5 ]Center for Human Genetics, University of Leuven, Leuven, Belgium
                [6 ]Department of Pharmacology, Yale Cancer Biology Institute, Yale University School of Medicine, West Haven, CT, USA
                [7 ]Systems Biology Ireland, University College Dublin, Dublin, Ireland
                [8 ]Research Centre for Toxic Compounds in the Environment, Faculty of Science, Masaryk University, Brno, Czech Republic
                [9 ]Faculty of Science, University of Zurich, Zurich, Switzerland
                Author notes
                []Corresponding author bouchal@ 123456chemi.muni.cz
                [∗∗ ]Corresponding author aebersold@ 123456imsb.biol.ethz.ch
                [10]

                Lead Contact

                Article
                S2211-1247(19)30819-8
                10.1016/j.celrep.2019.06.046
                6656695
                31315058
                ea353a84-cfe4-487d-aa49-67ee2f227c36
                © 2019 The Author(s)

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 26 July 2017
                : 6 March 2019
                : 12 June 2019
                Categories
                Article

                Cell biology
                breast cancer,proteomics,tumor classification,tissue,swath-ms,data independent acquisition,transcriptomics

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