Dataset Information

Breast Cancer Classification Based on Proteotypes Obtained by SWATH Mass Spectrometry.

ABSTRACT: 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.

SUBMITTER: Bouchal P

PROVIDER: S-EPMC6656695 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Breast Cancer Classification Based on Proteotypes Obtained by SWATH Mass Spectrometry.

Bouchal Pavel P Schubert Olga T OT Faktor Jakub J Capkova Lenka L Imrichova Hana H Zoufalova Karolina K Paralova Vendula V Hrstka Roman R Liu Yansheng Y Ebhardt Holger Alexander HA Budinska Eva E Nenutil Rudolf R Aebersold Ruedi R

Cell reports 20190701 3

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 subty ...[more]

PMID: 31315058

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Project description:The lack of early diagnostic maneuvers and effective pharmacotherapy for osteoarthritis (OA) is predominantly attributed to current limited understanding of its pathogenesis. In the present study, the alteration of synovial fluid (SF) proteome in OA were analyzed using SWATH?mass spectrometry (SWATH?MS) to further elucidate the pathogenesis of OA. SF samples were collected from 10 OA and 10 rheumatoid arthritis (RA) patients undergoing arthroplasty and 10 patients undergoing arthroscopy for traumatic arthritis (meniscus injury without cartilage lesion). According to the Kellgren?Lawrence (KL) radiographic grading criteria, all the OA and RA patients were classified as KL grade 4. SWATH?MS was applied to identify differentially expressed proteins specifically regulated in OA. Differentially expressed proteins identified by SWATH?MS were subjected to gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation. Proteins of interest were quantified by enzyme?linked immunosorbent assay (ELISA) following identification. With the use of SWATH?MS, 131 proteins were identified to be differentially expressed in OA, of which 93 corresponded to upregulation and 38 to downregulation. Complement C1r was the most significantly upregulated protein in OA. Twenty?eight out of the 131 proteins were specifically regulated in OA, of which 17 were upregulated and 11 were downregulated. Dickkopf?related protein 2 (DKK2) was one of the proteins specifically upregulated in OA. Furthermore, KEGG pathway annotation indicated that differentially expressed proteins in OA were enriched in complement and coagulation cascades. ELISA indicated that OA severity was positively correlated with the levels of complement C1r (r=0.549; P<0.001) and DKK2 (r=0.79; P<0.001) in the SF. The results indicate that complement and coagulation cascades are involved in the pathogenesis of OA. Differentially expressed proteins, including complement C1r and DKK2 may be used as potential biomarkers or drug targets, which may facilitate with intervention of OA.

Project description:Amyloidosis is a rare disease caused by the misfolding and extracellular aggregation of proteins as insoluble fibrillary deposits localized either in specific organs or systemically throughout the body. The organ targeted and the disease progression and outcome is highly dependent on the specific fibril-forming protein, and its accurate identification is essential to the choice of treatment. Mass spectrometry-based proteomics has become the method of choice for the identification of the amyloidogenic protein. Regrettably, this identification relies on manual and subjective interpretation of mass spectrometry data by an expert, which is undesirable and may bias diagnosis. To circumvent this, we developed a statistical model-assisted method for the unbiased identification of amyloid-containing biopsies and amyloidosis subtyping. Based on data from mass spectrometric analysis of amyloid-containing biopsies and corresponding controls. A Boruta method applied on a random forest classifier was applied to proteomics data obtained from the mass spectrometric analysis of 75 laser dissected Congo Red positive amyloid-containing biopsies and 78 Congo Red negative biopsies to identify novel "amyloid signature" proteins that included clusterin, fibulin-1, vitronectin complement component C9 and also three collagen proteins, as well as the well-known amyloid signature proteins apolipoprotein E, apolipoprotein A4, and serum amyloid P. A SVM learning algorithm were trained on the mass spectrometry data from the analysis of the 75 amyloid-containing biopsies and 78 amyloid-negative control biopsies. The trained algorithm performed superior in the discrimination of amyloid-containing biopsies from controls, with an accuracy of 1.0 when applied to a blinded mass spectrometry validation data set of 103 prospectively collected amyloid-containing biopsies. Moreover, our method successfully classified amyloidosis patients according to the subtype in 102 out of 103 blinded cases. Collectively, our model-assisted approach identified novel amyloid-associated proteins and demonstrated the use of mass spectrometry-based data in clinical diagnostics of disease by the unbiased and reliable model-assisted classification of amyloid deposits and of the specific amyloid subtype.

Project description:Proteomic analysis of extracellular matrix (ECM) and ECM-associated proteins, collectively known as the matrisome, is a challenging task due to the inherent complexity and insolubility of these proteins. Here we present sequential window acquisition of all theoretical fragment ion spectra mass spectrometry (SWATH MS) as a tool for the quantitative analysis of matrisomal proteins in both non-enriched and ECM enriched tissue without the need for prior fractionation. Utilising a spectral library containing 201 matrisomal proteins, we compared the performance and reproducibility of SWATH MS over conventional data-dependent analysis mass spectrometry (DDA MS) in unfractionated murine lung and liver. SWATH MS conferred a 15-20% increase in reproducible peptide identification across replicate experiments in both tissue types and identified 54% more matrisomal proteins in the liver versus DDA MS. We further use SWATH MS to evaluate the quantitative changes in matrisome content that accompanies ECM enrichment. Our data shows that ECM enrichment led to a systematic increase in core matrisomal proteins but resulted in significant losses in matrisome-associated proteins including the cathepsins and proteins of the S100 family. Our proof-of-principle study demonstrates the utility of SWATH MS as a versatile tool for in-depth characterisation of the matrisome in unfractionated and non-enriched tissues. SIGNIFICANCE: The matrisome is a complex network of extracellular matrix (ECM) and ECM-associated proteins that provides scaffolding function to tissues and plays important roles in the regulation of fundamental cellular processes. However, due to its inherent complexity and insolubility, proteomic studies of the matrisome typically require the application of enrichment workflows prior to MS analysis. Such enrichment strategies often lead to losses in soluble matrisome-associated components. In this study, we present sequential window acquisition of all theoretical fragment ion spectra mass spectrometry (SWATH MS) as a tool for the quantitative analysis of matrisomal proteins. We show that SWATH MS provides a more reproducible coverage of the matrisome compared to data-dependent analysis (DDA) MS. We also demonstrate that SWATH MS is capable of accurate quantification of matrisomal proteins without prior ECM enrichment and fractionation, which may simplify sample handling workflows and avoid losses in matrisome-associated proteins commonly linked to ECM enrichment.

Dataset Information

Breast Cancer Classification Based on Proteotypes Obtained by SWATH Mass Spectrometry.

Publications

Breast Cancer Classification Based on Proteotypes Obtained by SWATH Mass Spectrometry.

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