Current status of clinical proteogenomics in lung cancer
(2019) In Expert Review of Proteomics 16(9). p.761-772- Abstract
Introduction: Lung cancer is the leading cause of cancer death worldwide. Proteogenomics, a way to integrate genomics, transcriptomics, and proteomics, have emerged as a way to understand molecular causes in cancer tumorigenesis. This understanding will help identify therapeutic targets that are urgently needed to improve individual patient outcomes. Areas covered: To explore underlying molecular mechanisms of lung cancer subtypes, several efforts have used proteogenomic approaches that integrate next generation sequencing (NGS) and mass spectrometry (MS)-based technologies. Expert opinion: A large-scale, MS-based, proteomic analysis, together with both NGS-based genomic data and clinicopathological information, will facilitate... (More)
Introduction: Lung cancer is the leading cause of cancer death worldwide. Proteogenomics, a way to integrate genomics, transcriptomics, and proteomics, have emerged as a way to understand molecular causes in cancer tumorigenesis. This understanding will help identify therapeutic targets that are urgently needed to improve individual patient outcomes. Areas covered: To explore underlying molecular mechanisms of lung cancer subtypes, several efforts have used proteogenomic approaches that integrate next generation sequencing (NGS) and mass spectrometry (MS)-based technologies. Expert opinion: A large-scale, MS-based, proteomic analysis, together with both NGS-based genomic data and clinicopathological information, will facilitate establishing extensive databases for lung cancer subtypes that can be used for further proteogenomic analyzes. Proteogenomic strategies will further be understanding of how major driver mutations affect downstream molecular networks, resulting in lung cancer progression and malignancy, and how therapy-resistant cancers resistant are molecularly structured. These strategies require advanced bioinformatics based on a dynamic theory of network systems, rather than statistics, to accurately identify mutant proteins and their affected key networks.
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- author
- Nishimura, Toshihide ; Nakamura, Haruhiko ; Végvári, Ákos LU ; Marko-Varga, György LU ; Furuya, Naoki and Saji, Hisashi
- organization
- publishing date
- 2019-08
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- clinical proteogenomics, Lung cancer, mutant identification, network-based bioinformatics, next generation sequencing, proteomics⋅mass, spectrometry
- in
- Expert Review of Proteomics
- volume
- 16
- issue
- 9
- pages
- 12 pages
- publisher
- Future Drugs Ltd
- external identifiers
-
- pmid:31402712
- scopus:85071313485
- ISSN
- 1478-9450
- DOI
- 10.1080/14789450.2019.1654861
- language
- English
- LU publication?
- yes
- id
- 69f6a2b1-6486-4800-890c-76773d74302b
- date added to LUP
- 2019-09-03 08:32:40
- date last changed
- 2024-04-16 19:14:08
@article{69f6a2b1-6486-4800-890c-76773d74302b,
abstract = {{<p>Introduction: Lung cancer is the leading cause of cancer death worldwide. Proteogenomics, a way to integrate genomics, transcriptomics, and proteomics, have emerged as a way to understand molecular causes in cancer tumorigenesis. This understanding will help identify therapeutic targets that are urgently needed to improve individual patient outcomes. Areas covered: To explore underlying molecular mechanisms of lung cancer subtypes, several efforts have used proteogenomic approaches that integrate next generation sequencing (NGS) and mass spectrometry (MS)-based technologies. Expert opinion: A large-scale, MS-based, proteomic analysis, together with both NGS-based genomic data and clinicopathological information, will facilitate establishing extensive databases for lung cancer subtypes that can be used for further proteogenomic analyzes. Proteogenomic strategies will further be understanding of how major driver mutations affect downstream molecular networks, resulting in lung cancer progression and malignancy, and how therapy-resistant cancers resistant are molecularly structured. These strategies require advanced bioinformatics based on a dynamic theory of network systems, rather than statistics, to accurately identify mutant proteins and their affected key networks.</p>}},
author = {{Nishimura, Toshihide and Nakamura, Haruhiko and Végvári, Ákos and Marko-Varga, György and Furuya, Naoki and Saji, Hisashi}},
issn = {{1478-9450}},
keywords = {{clinical proteogenomics; Lung cancer; mutant identification; network-based bioinformatics; next generation sequencing; proteomics⋅mass; spectrometry}},
language = {{eng}},
number = {{9}},
pages = {{761--772}},
publisher = {{Future Drugs Ltd}},
series = {{Expert Review of Proteomics}},
title = {{Current status of clinical proteogenomics in lung cancer}},
url = {{http://dx.doi.org/10.1080/14789450.2019.1654861}},
doi = {{10.1080/14789450.2019.1654861}},
volume = {{16}},
year = {{2019}},
}