In vivo phosphoproteomics reveals kinase activity profiles that predict treatment outcome in triple-negative breast cancer

Abstract

Triple-negative breast cancer (TNBC) lacks prognostic and predictive markers. Here, we use high-throughput phosphoproteomics to build a functional TNBC taxonomy. A cluster of 159 phosphosites is upregulated in relapsed cases of a training set (n = 34 patients), with 11 hyperactive kinases accounting for this phosphoprofile. A mass-spectrometry-to-immunohistochemistry translation step, assessing 2 independent validation sets, reveals 6 kinases with preserved independent prognostic value. The kinases split the validation set into two patterns: one without hyperactive kinases being associated with a >90% relapse-free rate, and the other one showing ≥1 hyperactive kinase and being associated with an up to 9.5-fold higher relapse risk. Each kinase pattern encompasses different mutational patterns, simplifying mutation-based taxonomy. Drug regimens designed based on these 6 kinases show promising antitumour activity in TNBC cell lines and patient-derived xenografts. In summary, the present study elucidates phosphosites and kinases implicated in TNBC and suggests a target-based clinical classification system for TNBC.

Kinase Inhibitor Treatment of Patients with Advanced Cancer Results in High Tumor Drug Concentrations and in Specific Alterations of the Tumor Phosphoproteome

“…In particular, phosphotyrosine-(pTyr)-phospho-proteomics provides an opportunity for the identification of patient subgroups likely to benefit from tyrosine kinase inhibitors [10]. The potential of this high-throughput method has first been evidenced by the identification of the Anaplastic lymphoma kinase (ALK), Reactive oxygen species (ROS) and Platelet derived growth factor alpha (PDGFRα) mediated Non-small cell lung cancer (NSCLC) subtypes in 2007 [11] and just recently by identification of 6 kinases prognostic for the outcome of triple negative breast cancer [12]. We previously demonstrated that MS-based profiling of the tyrosine phosphoproteome in tumor biopsies is feasible and provides patient-specific profiles, enabling its further development for treatment selection purposes [13].…”

Recent advances in mass spectrometry based clinical proteomics: applications to cancer research

“…Many studies have shown important roles in understanding the molecular mechanism of cancers governed by phosphorylation-mediated pathways [108,109]. Zagorac et al [110] performed label-free quantitative analysis of the triple negative breast cancer (TNBC) phosphoproteome and compared relapsed and non-relapsed patients. In total 34 patient samples were lysed, digested and enriched for phosphopeptides.…”

‘Omics Approaches to Explore the Breast Cancer Landscape

“…MS-based proteomics has been used to characterize cell lines (Huang F. K. et al, 2017), to reveal novel layers of breast cancer classification (Tyanova et al, 2016;Yanovich et al, 2018), and to identify proteins involved in drug resistance (Liu et al, 2006). Furthermore, phosphoproteomics that identify phosphorylated proteins (von Stechow et al, 2015) has been used to connect somatic mutations to signaling (proteogenomics) (Mertins et al, 2016), to identify kinases signatures in TNBC (Zagorac et al, 2018), and to map drug targets for personalized treatments (Pierobon et al, 2018). These discoveries have diagnostic and prognostic potential which is worth further exploring and implementing in the clinic when phosphoproteomics methods will become common practice.…”