Breast Cancer Classification Based on Proteotypes Obtained by SWATH Mass Spectrometry

Abstract

Summary Accurate classification of breast tumors is vital for patient management decisions and enables more precise cancer treatment. Here, we present a quantitative proteotyping approach based on sequential windowed acquisition of all theoretical fragment ion spectra (SWATH) mass spectrometry and establish key proteins for breast tumor classification. The study is based on 96 tissue samples representing five conventional breast cancer subtypes. SWATH proteotype patterns largely recapitulate these subtypes; however, they also reveal varying heterogeneity within the conventional subtypes, with triple negative tumors being the most heterogeneous. Proteins that contribute most strongly to the proteotype-based classification include INPP4B, CDK1, and ERBB2 and are associated with estrogen receptor (ER) status, tumor grade status, and HER2 status. Although these three key proteins exhibit high levels of correlation with transcript levels (R > 0.67), general correlation did not exceed R = 0.29, indicating the value of protein-level measurements of disease-regulated genes. Overall, this study highlights how cancer tissue proteotyping can lead to more accurate patient stratification.

Proteomics Identification and Validation of Desmocollin‐1 and Catechol‐O‐Methyltransferase as Proteins Associated with Breast Cancer Cell Migration and Metastasis

“…A detailed description of the sample set is available in Supplemental file 1, Supporting Information, of a previous publication . Tissue sample processing, spectral library preparation, and SWATH measurements were described recently by Bouchal and co‐workers …”

“A Proteogenomic workflow reveals distinct molecular phenotypes related to breast cancer appearance”

“…The resulting output consisted of an OpenSWATH compatible Generic Transition List (GTL) in tsv format. We applied this workflow to three sets of raw DDA files: the dataset generated in this study, the Tyanova (Tyanova et al, 2016a) dataset, and the Bouchal (Bouchal et al, 2019) dataset.…”

“…So far, most proteogenomics studies in BC have relied on peptide-fractionation followed by extensive data dependent acquisition (DDA) MS analysis, typically associated with high instrument usage. However, a recent study employed data independent acquisition (DIA) MS to define high-quality protein maps of BC subtypes, identifying the protein markers INPP4B, CDK1, and ERBB2 as discriminatory of key BC histopathological features, pinpointing biological pathways tied to tumor phenotypes, and assessing expression similarities between transcript and protein abundance (Bouchal et al, 2019). These results show that DIA MS constitute an alternative strategy for the quantitative investigation of protein expression in a proteogenomic context, by generating tissue specific spectral libraries and achieving high identification rates out of singleshot analysis.…”

A mouse SWATH-MS reference spectral library enables deconvolution of species-specific proteomic alterations in human tumour xenografts

“…The ability to capture all peptide precursor ions dramatically improves reproducibility in protein identification across multiple samples. These quantitative digital proteome maps have been shown to have wide applications in medical research, such as the discovery of potential biomarkers and therapeutic targets (Cecchettini et al, 2019;Gao et al, 2017;Miyauchi et al, 2018;Hou et al, 2016;Xu et al, 2018), as well as tumour proteotyping for cancer stratification and classification (Bouchal et al, 2019;Zhu et al, 2019).…”