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Choice of High-Throughput Proteomics Method Affects Data Integration with Transcriptomics and the Potential Use in Biomarker Discovery

Mosquim Junior, Sergio LU ; Siino, Valentina LU ; Rydén, Lisa LU orcid ; Vallon-Christersson, Johan LU orcid and Levander, Fredrik LU (2022) In Cancers 14(23).
Abstract

In recent years, several advances have been achieved in breast cancer (BC) classification and treatment. However, overdiagnosis, overtreatment, and recurrent disease are still significant causes of complication and death. Here, we present the development of a protocol aimed at parallel transcriptome and proteome analysis of BC tissue samples using mass spectrometry, via Data Dependent and Independent Acquisitions (DDA and DIA). Protein digestion was semi-automated and performed on flowthroughs after RNA extraction. Data for 116 samples were acquired in DDA and DIA modes and processed using MaxQuant, EncyclopeDIA, or DIA-NN. DIA-NN showed an increased number of identified proteins, reproducibility, and correlation with matching RNA-seq... (More)

In recent years, several advances have been achieved in breast cancer (BC) classification and treatment. However, overdiagnosis, overtreatment, and recurrent disease are still significant causes of complication and death. Here, we present the development of a protocol aimed at parallel transcriptome and proteome analysis of BC tissue samples using mass spectrometry, via Data Dependent and Independent Acquisitions (DDA and DIA). Protein digestion was semi-automated and performed on flowthroughs after RNA extraction. Data for 116 samples were acquired in DDA and DIA modes and processed using MaxQuant, EncyclopeDIA, or DIA-NN. DIA-NN showed an increased number of identified proteins, reproducibility, and correlation with matching RNA-seq data, therefore representing the best alternative for this setup. Gene Set Enrichment Analysis pointed towards complementary information being found between transcriptomic and proteomic data. A decision tree model, designed to predict the intrinsic subtypes based on differentially abundant proteins across different conditions, selected protein groups that recapitulate important clinical features, such as estrogen receptor status, HER2 status, proliferation, and aggressiveness. Taken together, our results indicate that the proposed protocol performed well for the application. Additionally, the relevance of the selected proteins points to the possibility of using such data as a biomarker discovery tool for personalized medicine.

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author
; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
breast cancer, classification, multi-omics, proteomics
in
Cancers
volume
14
issue
23
article number
5761
publisher
MDPI AG
external identifiers
  • scopus:85143615065
  • pmid:36497242
ISSN
2072-6694
DOI
10.3390/cancers14235761
language
English
LU publication?
yes
id
3a0231c3-7c57-445c-8710-00236011a005
date added to LUP
2022-12-23 08:51:23
date last changed
2024-05-28 22:42:47
@article{3a0231c3-7c57-445c-8710-00236011a005,
  abstract     = {{<p>In recent years, several advances have been achieved in breast cancer (BC) classification and treatment. However, overdiagnosis, overtreatment, and recurrent disease are still significant causes of complication and death. Here, we present the development of a protocol aimed at parallel transcriptome and proteome analysis of BC tissue samples using mass spectrometry, via Data Dependent and Independent Acquisitions (DDA and DIA). Protein digestion was semi-automated and performed on flowthroughs after RNA extraction. Data for 116 samples were acquired in DDA and DIA modes and processed using MaxQuant, EncyclopeDIA, or DIA-NN. DIA-NN showed an increased number of identified proteins, reproducibility, and correlation with matching RNA-seq data, therefore representing the best alternative for this setup. Gene Set Enrichment Analysis pointed towards complementary information being found between transcriptomic and proteomic data. A decision tree model, designed to predict the intrinsic subtypes based on differentially abundant proteins across different conditions, selected protein groups that recapitulate important clinical features, such as estrogen receptor status, HER2 status, proliferation, and aggressiveness. Taken together, our results indicate that the proposed protocol performed well for the application. Additionally, the relevance of the selected proteins points to the possibility of using such data as a biomarker discovery tool for personalized medicine.</p>}},
  author       = {{Mosquim Junior, Sergio and Siino, Valentina and Rydén, Lisa and Vallon-Christersson, Johan and Levander, Fredrik}},
  issn         = {{2072-6694}},
  keywords     = {{breast cancer; classification; multi-omics; proteomics}},
  language     = {{eng}},
  number       = {{23}},
  publisher    = {{MDPI AG}},
  series       = {{Cancers}},
  title        = {{Choice of High-Throughput Proteomics Method Affects Data Integration with Transcriptomics and the Potential Use in Biomarker Discovery}},
  url          = {{http://dx.doi.org/10.3390/cancers14235761}},
  doi          = {{10.3390/cancers14235761}},
  volume       = {{14}},
  year         = {{2022}},
}