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© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Simple Summary

Omics analyses provide possibilities for molecular classification of cancers to enable personalized medicine. To allow for multi-layered molecular analysis, we developed an automated protocol for the generation of proteomics data of breast cancer tumor tissue that is subjected to parallel transcriptome analysis. We compare different data acquisition strategies for proteomics and settle on data-independent acquisition, achieving high correlation with RNA between samples. The proteomics data were further used for functional analyses and tumor classification, showing the potential of the methodology.

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 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.

Details

Title
Choice of High-Throughput Proteomics Method Affects Data Integration with Transcriptomics and the Potential Use in Biomarker Discovery
Author
Sergio Mosquim Junior 1   VIAFID ORCID Logo  ; Siino, Valentina 1   VIAFID ORCID Logo  ; Rydén, Lisa 2   VIAFID ORCID Logo  ; Vallon-Christersson, Johan 3   VIAFID ORCID Logo  ; Levander, Fredrik 4   VIAFID ORCID Logo 

 Department of Immunotechnology, Lund University, 223 81 Lund, Sweden 
 Division of Surgery, Department of Clinical Sciences Lund, Lund University, 223 81 Lund, Sweden; Department of Surgery and Gastroenterology, Skåne University Hospital, 214 28 Malmö, Sweden 
 Division of Oncology, Department of Clinical Sciences Lund, Lund University, 223 81 Lund, Sweden 
 Department of Immunotechnology, Lund University, 223 81 Lund, Sweden; National Bioinformatics Infrastructure Sweden (NBIS), Science for Life Laboratory, Lund University, 223 81 Lund, Sweden 
First page
5761
Publication year
2022
Publication date
2022
Publisher
MDPI AG
e-ISSN
20726694
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2748516349
Copyright
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.