Literature DB >> 26697917

Proteogenomics for understanding oncology: recent advances and future prospects.

Yashwanth Subbannayya1,2, Sneha M Pinto1,2, Harsha Gowda1,2, T S Keshava Prasad1,2,3.   

Abstract

The concept of proteogenomics has emerged rapidly as a valuable approach to integrate mass spectrometry-derived proteomic data with genomic and transcriptomic data. It is used to harness the full potential of the former dataset in the discovery of potential biomarkers, therapeutic targets and novel proteins associated with various biological processes including diseases. Proteogenomic strategies have been successfully utilized to identify novel genes and redefine annotation of existing gene models in various genomes. In recent years, this approach has been extended to the field of cancer biology to unravel complexities in the tumor genomes and proteomes. Standard proteomics workflows employing translated cancer genomes and transcriptomes can potentially identify peptides from mutant proteins, splice variants and fusion proteins in the tumor proteome, which in addition to the currently available biomarker panels can serve as potential diagnostic and prognostic biomarkers, besides having therapeutic utility. This review focuses on the role of proteogenomics to understand cancer biology.

Entities:  

Keywords:  Carcinogenesis; NextGen sequencing; Oncoproteogenomics; Proteomics; Systems biology

Mesh:

Substances:

Year:  2016        PMID: 26697917     DOI: 10.1586/14789450.2016.1136217

Source DB:  PubMed          Journal:  Expert Rev Proteomics        ISSN: 1478-9450            Impact factor:   3.940


  7 in total

Review 1.  Moonshot Objectives: Catalyze New Scientific Breakthroughs-Proteogenomics.

Authors:  Karin D Rodland; Paul Piehowski; Richard D Smith
Journal:  Cancer J       Date:  2018 May/Jun       Impact factor: 3.360

Review 2.  Clinical potential of mass spectrometry-based proteogenomics.

Authors:  Bing Zhang; Jeffrey R Whiteaker; Andrew N Hoofnagle; Geoffrey S Baird; Karin D Rodland; Amanda G Paulovich
Journal:  Nat Rev Clin Oncol       Date:  2019-04       Impact factor: 66.675

3.  Personalized Proteome: Comparing Proteogenomics and Open Variant Search Approaches for Single Amino Acid Variant Detection.

Authors:  Renee Salz; Robbin Bouwmeester; Ralf Gabriels; Sven Degroeve; Lennart Martens; Pieter-Jan Volders; Peter A C 't Hoen
Journal:  J Proteome Res       Date:  2021-05-17       Impact factor: 4.466

4.  Detecting protein variants by mass spectrometry: a comprehensive study in cancer cell-lines.

Authors:  Javier A Alfaro; Alexandr Ignatchenko; Vladimir Ignatchenko; Ankit Sinha; Paul C Boutros; Thomas Kislinger
Journal:  Genome Med       Date:  2017-07-18       Impact factor: 11.117

5.  Translation and evaluation of a pre-clinical 5-protein response prediction signature in a breast cancer phase Ib clinical trial.

Authors:  Axel Ducret; Ian James; Sabine Wilson; Martina Feilke; Andreas Tebbe; Nikolaj Dybowski; Sarah Elschenbroich; Martin Klammer; Adele Blackler; Wei-Li Liao; Yuan Tian; Thomas Friess; Birgit Bossenmaier; Gabriele Dietmann; Christoph Schaab; Todd Hembrough; Maurizio Ceppi
Journal:  PLoS One       Date:  2019-03-21       Impact factor: 3.240

6.  CusVarDB: A tool for building customized sample-specific variant protein database from next-generation sequencing datasets.

Authors:  Sandeep Kasaragod; Varshasnata Mohanty; Ankur Tyagi; Santosh Kumar Behera; Arun H Patil; Sneha M Pinto; T S Keshava Prasad; Prashant Kumar Modi; Harsha Gowda
Journal:  F1000Res       Date:  2020-05-11

7.  Deepening into Intracellular Signaling Landscape through Integrative Spatial Proteomics and Transcriptomics in a Lymphoma Model.

Authors:  Alicia Landeira-Viñuela; Paula Díez; Pablo Juanes-Velasco; Quentin Lécrevisse; Alberto Orfao; Javier De Las Rivas; Manuel Fuentes
Journal:  Biomolecules       Date:  2021-11-26
  7 in total

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