Molecular characterization of triple negative breast cancer formaldehyde‐fixed paraffin‐embedded samples by data‐independent acquisition proteomics

Abstract

Triple negative breast cancer accounts for 15%–20% of all breast carcinomas and is clinically characterized by an aggressive phenotype and poor prognosis. Triple negative tumors do not benefit from targeted therapies, so further characterization is needed to define subgroups with potential therapeutic value. In this work, the proteomes of 125 formalin‐fixed paraffin‐embedded samples from patients diagnosed with non‐metastatic triple negative breast cancer were analyzed using data‐independent acquisition + in a LTQ‐Orbitrap Fusion Lumos mass spectrometer coupled to an EASY‐nLC 1000. 1206 proteins were identified in at least 66% of the samples. Hierarchical clustering, probabilistic graphical models and Significance Analysis of Microarrays were combined to characterize proteomics‐based molecular groups. Two molecular groups were defined with differences in biological processes such as glycolysis, translation and immune response. These two molecular groups showed also several differentially expressed proteins. This clinically homogenous dataset may serve to design new therapeutic strategies in the future.

Advances in the application of label‐free quantitative proteomics techniques in malignancy research

“…It is insensitive to conventional targeted therapies and endocrine therapies, with high invasiveness and relapse rates and poor patient outcomes (Cao et al, 2021). To identify the subgroups of therapeutic value, García‐Adrián et al (2022) conducted a proteomics analysis of formalin‐fixed paraffin‐coated samples from 125 people with nonmetastatic TNBC. A total of 1206 proteins were obtained in at least 66% of the samples, and two subgroups were identified via hierarchical clustering, probabilistic graphical modeling, and microarray significance analysis.…”

Recent Contributions of Mass Spectrometry-Based “Omics” in the Studies of Breast Cancer

“…Krasny and Huang (2021) highlighted the strategic approaches of DIA for elucidations of cancer resistance proteomes and characterizations of post-translational modifications (PTMs based on soft ionization-based adoptions) . Data independent mass spectrometry acquisition was used to characterize TNBC FFPE samples where several differentially expressed proteins were found to arrive at two molecular groups which differentiated in biological processes like glycolysis, translation, and immune responses . Novel identification strategies of protein quantifications that are done in numerous advancements of various paradigms of MS in clinical research are reviewed by Macklin (2020) .…”

“…72 Data independent mass spectrometry acquisition was used to characterize TNBC FFPE samples where several differentially expressed proteins were found to arrive at two molecular groups which differentiated in biological processes like glycolysis, translation, and immune responses. 10 Novel identification strategies of protein quantifications that are done in numerous advancements of various paradigms of MS in clinical research are reviewed by Macklin (2020). 73 Various configurations of MS-based instrumentation setups, databases, software packages, and machine learning algorithms used that are required for identifying novel proteins in these approaches are reviewed by Huang (2020).…”

“…This review aims to explore the latest developments in mass spectrometry instrumentation and their implications within current and evolving research approaches in breast cancer (BC) research. Various MS-based platforms of targeted and nontargeted MS and hybrid approaches are answering various questions of BC on a global level. , MS-based proteomics and metabolomics have been extensively used with various acquisition strategies, including data dependent acquisition (DDA), − data independent acquisition (DIA), − and hybrid approaches. Diverse “omic” features of subtypes are revealed in this incredibly heterogeneous group of cancer.…”

Clinical applications of mass spectrometry‐based proteomics in cancer: Where are we?

“…Unsupervised clustering revealed two groups with differences in biological pathways including immune response. This, however, did not translate to a meaningful difference in clinical outcome parameters including disease‐free survival in this cohort [47].…”