Literature DB >> 24453208

Proteomic profiles of human lung adeno and squamous cell carcinoma using super-SILAC and label-free quantification approaches.

Wen Zhang1, Yuhong Wei, Vladimir Ignatchenko, Lei Li, Shingo Sakashita, Nhu-An Pham, Paul Taylor, Ming Sound Tsao, Thomas Kislinger, Michael F Moran.   

Abstract

Nonsmall cell lung cancer (NSCLC) accounts for 85% of lung cancers, and is subdivided into two major histological subtypes: adenocarcinoma (ADC) and squamous cell carcinoma (SCC). There is an unmet need to further subdivide NSCLC according to distinctive molecular features that may be associated with responsiveness to therapies. Four primary tumor-derived xenograft proteomes (two-each ADC and SCC) were quantitatively compared by using a super-SILAC labeling approach together with ultrahigh-resolution MS. Proteins highly differentially expressed in the two subtypes were identified, including 30 that were validated in an independent cohort of 12 NSCLC primary tumor-derived xenograft tumors whose proteomes were quantified by an alternative, label-free shotgun MS methodology. The 30-protein signature contains metabolism enzymes including phosphoglycerate dehydrogenase, which is more highly expressed in SCC, as well as a comprehensive set of cytokeratins and other components of the epithelial barrier, which is therefore distinctly different between ADC and SCC. These results demonstrate the utility of the super-SILAC method for the characterization of primary tissues, and compatibility with datasets derived from different MS-based platforms. The validation of proteome signatures of NSCLC subtypes supports the further development and application of MS-based quantitative proteomics as a basis for precision classifications and treatments of tumors. All MS data have been deposited in the ProteomeXchange with identifier PXD000438 (http://proteomecentral.proteomexchange.org/dataset/PXD000438).
© 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Entities:  

Keywords:  Biomedicine; Label-free; Lung cancer; Shotgun proteomics; Super-SILAC; Xenograft

Mesh:

Substances:

Year:  2014        PMID: 24453208     DOI: 10.1002/pmic.201300382

Source DB:  PubMed          Journal:  Proteomics        ISSN: 1615-9853            Impact factor:   3.984


  13 in total

Review 1.  A Biologist's Field Guide to Multiplexed Quantitative Proteomics.

Authors:  Corey E Bakalarski; Donald S Kirkpatrick
Journal:  Mol Cell Proteomics       Date:  2016-02-12       Impact factor: 5.911

2.  Relative protein quantification and accessible biology in lung tumor proteomes from four LC-MS/MS discovery platforms.

Authors:  Paul A Stewart; Bin Fang; Robbert J C Slebos; Guolin Zhang; Adam L Borne; Katherine Fellows; Jamie K Teer; Y Ann Chen; Eric Welsh; Steven A Eschrich; Eric B Haura; John M Koomen
Journal:  Proteomics       Date:  2017-03       Impact factor: 3.984

3.  In Vivo Evaluation of Combined CK2 Inhibition and Irradiation in Human WiDr Tumours.

Authors:  Felix Zwicker; Henrik Hauswald; Klaus-Josef Weber; JÜrgen Debus; Peter E Huber
Journal:  In Vivo       Date:  2021 Jan-Feb       Impact factor: 2.155

4.  Reliable Entity Subtyping in Non-small Cell Lung Cancer by Matrix-assisted Laser Desorption/Ionization Imaging Mass Spectrometry on Formalin-fixed Paraffin-embedded Tissue Specimens.

Authors:  Mark Kriegsmann; Rita Casadonte; Jörg Kriegsmann; Hendrik Dienemann; Peter Schirmacher; Jan Hendrik Kobarg; Kristina Schwamborn; Albrecht Stenzinger; Arne Warth; Wilko Weichert
Journal:  Mol Cell Proteomics       Date:  2016-07-29       Impact factor: 5.911

5.  Kinome Profiling of Primary Endometrial Tumors Using Multiplexed Inhibitor Beads and Mass Spectrometry Identifies SRPK1 as Candidate Therapeutic Target.

Authors:  Alison M Kurimchak; Vikas Kumar; Carlos Herrera-Montávez; Katherine J Johnson; Nishi Srivastava; Karthik Davarajan; Suraj Peri; Kathy Q Cai; Gina M Mantia-Smaldone; James S Duncan
Journal:  Mol Cell Proteomics       Date:  2020-09-29       Impact factor: 5.911

6.  Managing a Large-Scale Multiomics Project: A Team Science Case Study in Proteogenomics.

Authors:  Paul A Stewart; Eric A Welsh; Bin Fang; Victoria Izumi; Tania Mesa; Chaomei Zhang; Sean Yoder; Guolin Zhang; Ling Cen; Fredrik Pettersson; Yonghong Zhang; Zhihua Chen; Chia-Ho Cheng; Ram Thapa; Zachary Thompson; Melissa Avedon; Marek Wloch; Michelle Fournier; Katherine M Fellows; Jewel M Francis; James J Saller; Theresa A Boyle; Y Ann Chen; Eric B Haura; Jamie K Teer; Steven A Eschrich; John M Koomen
Journal:  Methods Mol Biol       Date:  2021

7.  From Proteomic Analysis to Potential Therapeutic Targets: Functional Profile of Two Lung Cancer Cell Lines, A549 and SW900, Widely Studied in Pre-Clinical Research.

Authors:  Luís Korrodi-Gregório; Vanessa Soto-Cerrato; Rui Vitorino; Margarida Fardilha; Ricardo Pérez-Tomás
Journal:  PLoS One       Date:  2016-11-04       Impact factor: 3.240

8.  Protein abundance of AKT and ERK pathway components governs cell type-specific regulation of proliferation.

Authors:  Lorenz Adlung; Sandip Kar; Marie-Christine Wagner; Bin She; Sajib Chakraborty; Jie Bao; Susen Lattermann; Melanie Boerries; Hauke Busch; Patrick Wuchter; Anthony D Ho; Jens Timmer; Marcel Schilling; Thomas Höfer; Ursula Klingmüller
Journal:  Mol Syst Biol       Date:  2017-01-24       Impact factor: 11.429

9.  A Pilot Proteogenomic Study with Data Integration Identifies MCT1 and GLUT1 as Prognostic Markers in Lung Adenocarcinoma.

Authors:  Paul A Stewart; Katja Parapatics; Eric A Welsh; André C Müller; Haoyun Cao; Bin Fang; John M Koomen; Steven A Eschrich; Keiryn L Bennett; Eric B Haura
Journal:  PLoS One       Date:  2015-11-05       Impact factor: 3.240

Review 10.  Proteomic-Based Approaches for the Study of Cytokines in Lung Cancer.

Authors:  Ángela Marrugal; Laura Ojeda; Luis Paz-Ares; Sonia Molina-Pinelo; Irene Ferrer
Journal:  Dis Markers       Date:  2016-06-30       Impact factor: 3.434

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