Extracellular vesicles (EVs) are a potential source of disease-associated biomarkers for diagnosis. In breast cancer, comprehensive analyses of EVs could yield robust and reliable subtype-specific biomarkers that are still critically needed to improve diagnostic routines and clinical outcome. Here, we show that proteome profiles of EVs secreted by different breast cancer cell lines are highly indicative of their respective molecular subtypes, even more so than the proteome changes within the cancer cells. Moreover, we detected molecular evidence for subtype-specific biological processes and molecular pathways, hyperphosphorylated receptors and kinases in connection with the disease, and compiled a set of protein signatures that closely reflect the associated clinical pathophysiology. These unique features revealed in our work, replicated in clinical material, collectively demonstrate the potential of secreted EVs to differentiate between breast cancer subtypes and show the prospect of their use as non-invasive liquid biopsies for diagnosis and management of breast cancer patients.
Acquired resistance to MAPK inhibitors limits the clinical efficacy in melanoma treatment. We and others have recently shown that BRAF inhibitor (BRAFi)-resistant melanoma cells can develop a dependency on the therapeutic drugs to which they have acquired resistance, creating a vulnerability for these cells that can potentially be exploited in cancer treatment. In drug-addicted melanoma cells, it was shown that this induction of cell death was preceded by a specific ERK2-dependent phenotype switch; however, the underlying molecular mechanisms are largely lacking. To increase the molecular understanding of this drug dependency, we applied a mass spectrometry-based proteomic approach on BRAFi-resistant BRAF MUT 451Lu cells, in which ERK1, ERK2, and JUNB were silenced separately using CRISPR-Cas9. Inactivation of ERK2 and, to a lesser extent, JUNB prevents drug addiction in these melanoma cells, while, conversely, knockout of ERK1 fails to reverse this phenotype, showing a response similar to that of control cells. Our analysis reveals that ERK2 and JUNB share comparable proteome responses dominated by reactivation of cell division. Importantly, we find that EMT activation in drug-addicted melanoma cells upon drug withdrawal is affected by silencing ERK2 but not ERK1. Moreover, transcription factor (regulator) enrichment shows that PIR acts as an effector of ERK2 and phosphoproteome analysis reveals that silencing of ERK2 but not ERK1 leads to amplification of GSK3 kinase activity. Our results depict possible mechanisms of drug addiction in melanoma, which may provide a guide for therapeutic strategies in drug-resistant melanoma.
Many essential cellular functions are carried out by multi-protein complexes that can be characterized by their protein–protein interactions. The interactions between protein subunits are critically dependent on the strengths of their interactions and their cellular abundances, both of which span orders of magnitude. Despite many efforts devoted to the global discovery of protein complexes by integrating large-scale protein abundance and interaction features, there is still room for improvement. Here, we integrated >7000 quantitative proteomic samples with three published affinity purification/co-fractionation mass spectrometry datasets into a deep learning framework to predict protein–protein interactions (PPIs), followed by the identification of protein complexes using a two-stage clustering strategy. Our deep-learning-technique-based classifier significantly outperformed recently published machine learning prediction models and in the process captured 5010 complexes containing over 9000 unique proteins. The vast majority of proteins in our predicted complexes exhibited low or no tissue specificity, which is an indication that the observed complexes tend to be ubiquitously expressed throughout all cell types and tissues. Interestingly, our combined approach increased the model sensitivity for low abundant proteins, which amongst other things allowed us to detect the interaction of MCM10, which connects to the replicative helicase complex via the MCM6 protein. The integration of protein abundances and their interaction features using a deep learning approach provided a comprehensive map of protein–protein interactions and a unique perspective on possible novel protein complexes.
In chapter one, a general introduction to the basic principles and techniques of MS-based proteomics, quantification strategies, and a generalized shotgun proteomics workflow are given. Moreover, I also outline how to analyze proteomics data from a bioinformatics perspective including normalization, dealing with missing values, differential analysis, functional annotation, as well as how to reveal the biology from post-translational modification data. Furthermore, I generalized the basics of machine learning algorithms from the perspective of supervised and unsupervised machine learning, along with that the application of machine learning algorithms to the identification of protein complexes. In chapter two, we are seeking to explore the drug addiction mechanism in melanoma cells that carry BRAF mutation. We present a proteomics and phosphoproteomics study of BRAFi-addicted melanoma cells (i.e., 451Lu cell line) in response to BRAFi withdrawal, in which ERK1, ERK2, and JUNB were genetically silenced separately using CRISPR-Cas9. We show that inactivation of ERK2 and, to a lesser extent, JUNB prevents drug addiction in these melanoma cells, while, conversely, knockout of ERK1 fails to reverse this phenotype, showing a response similar to that of control cells. Our data indicate that ERK2 and JUNB share comparable proteome responses dominated by the reactivation of cell division. Importantly, we find that EMT activation in drug-addicted melanoma cells upon drug withdrawal is affected by silencing ERK2 but not ERK1. Moreover, we reveal that PIR acts as an effector of ERK2, and phosphoproteome analysis reveals that silencing of ERK2 but not ERK1 leads to the amplification of GSK3 kinase activity. Our results depict possible mechanisms of drug addiction in melanoma, which may provide a guide for therapeutic strategies in drug-resistant melanoma. In chapter three, we are dedicated to exploring the role of PD-1 in T cell activation by comparing the proteome and phosphoproteome profiles in resting and activated CD8+ T cells, in which PD-1 was silenced using CRISPR–Cas9. Our data reveal that the activated T cells reprogrammed their proteome and phosphoproteome marked by activating of mTORC1 pathway. Moreover, we find that silencing of PD-1 altered the expression of E3 ubiquitin-- protein ligases, and increased glucose and lactate transporters. On the phosphoproteomics level, it evokes phosphorylation events in the mTORC1 pathway and activates the epidermal growth factor and its downstream MAPK pathway. Therefore, the data presented in this chapter depicts mechanisms of PD-1 in response to TCR stimulation in CD8+ T cells, which may provide a guide in immune homeostasis and immune checkpoint therapy. In chapter four, we construct a comprehensive map of human protein complexes through the integration of protein-protein interactions and protein abundance features. A deep learning framework was built to predict protein-protein interactions (PPIs), followed by a two-stage clustering to identify protein complexes. Our deep learning technique-based classifier significantly outperformed recently published machine learning prediction models with an F1-measure of 0.68 and captured in the process 5,010 complexes containing over 9,000 unique proteins. Moreover, this deep learning model enables us to capture poorly characterized interactions and the co-expressed protein involved interactions.
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