Extra-cellular vesicles carry proteome of cancer hallmarks

Matthiesen, Rune

doi:10.2741/4811

Cited by 18 publications

(7 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The possibility to diagnose and screen DLBCL patients for treatment based on liquid biopsies by targeting the proteome of extracellular vesicles (EVs) is unexplored and provides clear advantages. Minimally invasive liquid biopsy-based diagnosis and patient screen opens new avenues for fast and longitudinal follow up for minimal residual disease assessment [11,12].…”

Section: Introductionmentioning

confidence: 99%

“…EVs have been shown to play fundamental roles in intercellular communication by carrying and selectively deliver bioactive molecules (e.g., lipids, proteins, and nucleic acids). EVs are tailor-made specialized mini-maps of their cell of origin and of both physiological and pathological relevance [11]. This feature leads to the potential to use EVs as blood-based biomarkers of diagnosis and prognosis for hematopoietic cancers.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Proteomic Landscape of Extracellular Vesicles for Diffuse Large B-Cell Lymphoma Subtyping

Carvalho

Baeta

Henriques

et al. 2021

IJMS

Self Cite

View full text Add to dashboard Cite

The role of extracellular vesicles (EVs) proteome in diffuse large B-cell lymphoma (DLBCL) pathology, subclassification, and patient screening is unexplored. We analyzed by state-of-the-art mass spectrometry the whole cell and secreted extracellular vesicles (EVs) proteomes of different molecular subtypes of DLBCL, germinal center B cell (GCB subtype), and activated B cell (ABC subtype). After quality control assessment, we compared whole-cell and secreted EVs proteomes of the two cell-of-origin (COO) categories, GCB and ABC subtypes, resulting in 288/1115 significantly differential expressed proteins from the whole-cell proteome and 228/608 proteins from EVs (adjust p-value < 0.05/p-value < 0.05). In our preclinical model system, we demonstrated that the EV proteome and the whole-cell proteome possess the capacity to separate cell lines into ABC and GCB subtypes. KEGG functional analysis and GO enrichment analysis for cellular component, molecular function, and biological process of differential expressed proteins (DEP) between ABC and GCB EVs showed a significant enrichment of pathways involved in immune response function. Other enriched functional categories for DEPs constitute cellular signaling and intracellular trafficking such as B-cell receptor (BCR), Fc_gamma R-mediated phagocytosis, ErbB signaling, and endocytosis. Our results suggest EVs can be explored as a tool for patient diagnosis, follow-up, and disease monitoring. Finally, this study proposes novel drug targets based on highly expressed proteins, for which antitumor drugs are available suggesting potential combinatorial therapies for aggressive forms of DLBCL. Data are available via ProteomeXchange with identifier PXD028267.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Proteomic Landscape of Extracellular Vesicles for Diffuse Large B-Cell Lymphoma Subtyping

Carvalho

Baeta

Henriques

et al. 2021

IJMS

Self Cite

View full text Add to dashboard Cite

show abstract

“…A previous meta-analysis has already analyzed the proteome of NCI-60 EVs, but with different assumptions [84]. In this research, the investigation aimed to determine the potential support of EV proteomes in facilitating the functional transfer of cancer hallmarks.…”

Section: Discussionmentioning

confidence: 99%

Machine learning-based analysis of cancer cell-derived vesicular proteins revealed significant tumor-specificity and predictive potential of extracellular vesicles for cell invasion and proliferation – A meta-analysis

Bukva,

Dobra,

Gyukity-Sebestyen

et al. 2023

Cell Commun Signal

View full text Add to dashboard Cite

Background Although interest in the role of extracellular vesicles (EV) in oncology is growing, not all potential aspects have been investigated. In this meta-analysis, data regarding (i) the EV proteome and (ii) the invasion and proliferation capacity of the NCI-60 tumor cell lines (60 cell lines from nine different tumor types) were analyzed using machine learning methods. Methods On the basis of the entire proteome or the proteins shared by all EV samples, 60 cell lines were classified into the nine tumor types using multiple logistic regression. Then, utilizing the Least Absolute Shrinkage and Selection Operator, we constructed a discriminative protein panel, upon which the samples were reclassified and pathway analyses were performed. These panels were validated using clinical data (n = 4,665) from Human Protein Atlas. Results Classification models based on the entire proteome, shared proteins, and discriminative protein panel were able to distinguish the nine tumor types with 49.15%, 69.10%, and 91.68% accuracy, respectively. Invasion and proliferation capacity of the 60 cell lines were predicted with R2 = 0.68 and R2 = 0.62 (p < 0.0001). The results of the Reactome pathway analysis of the discriminative protein panel suggest that the molecular content of EVs might be indicative of tumor-specific biological processes. Conclusion Integrating in vitro EV proteomic data, cell physiological characteristics, and clinical data of various tumor types illuminates the diagnostic, prognostic, and therapeutic potential of EVs.

show abstract

“…In in vitro models, Carvalho et al [92] explored two public proteome data sets of the NCI-60 cancer cell lines panel, analyzing whole cell [93] and secreted EVs [93] proteomes. Proteins annotated to genes such as SLC18A2, CPM, ATP6V1B1, HECTD1, GPN3, REG4, ZNF784, KIF4B, PKIA, RRAGB, PEMT, RNF115, and HAUS7 were uniquely identified in cellular proteomes of PCa compared to proteins identified in the other cell lines from the NCI 60 cancer cell line panel data set.…”

Section: Proteins Identified In Pca Proteomics Studiesmentioning

confidence: 99%

Extracellular Vesicle Proteome in Prostate Cancer: A Comparative Analysis of Mass Spectrometry Studies

Bernardino

Leão

Henrique

et al. 2021

IJMS

Self Cite

View full text Add to dashboard Cite

Molecular diagnostics based on discovery research holds the promise of improving screening methods for prostate cancer (PCa). Furthermore, the congregated information prompts the question whether the urinary extracellular vesicles (uEV) proteome has been thoroughly explored, especially at the proteome level. In fact, most extracellular vesicles (EV) based biomarker studies have mainly targeted plasma or serum. Therefore, in this study, we aim to inquire about possible strategies for urinary biomarker discovery particularly focused on the proteome of urine EVs. Proteomics data deposited in the PRIDE archive were reanalyzed to target identifications of potential PCa markers. Network analysis of the markers proposed by different prostate cancer studies revealed moderate overlap. The recent throughput improvements in mass spectrometry together with the network analysis performed in this study, suggest that a larger standardized cohort may provide potential biomarkers that are able to fully characterize the heterogeneity of PCa. According to our analysis PCa studies based on urinary EV proteome presents higher protein coverage compared to plasma, plasma EV, and voided urine proteome. This together with a direct interaction of the prostate gland and urethra makes uEVs an attractive option for protein biomarker studies. In addition, urinary proteome based PCa studies must also evaluate samples from bladder and renal cancers to assess specificity for PCa.

show abstract

Extra-cellular vesicles carry proteome of cancer hallmarks

Cited by 18 publications

References 54 publications

Proteomic Landscape of Extracellular Vesicles for Diffuse Large B-Cell Lymphoma Subtyping

Proteomic Landscape of Extracellular Vesicles for Diffuse Large B-Cell Lymphoma Subtyping

Machine learning-based analysis of cancer cell-derived vesicular proteins revealed significant tumor-specificity and predictive potential of extracellular vesicles for cell invasion and proliferation – A meta-analysis

Extracellular Vesicle Proteome in Prostate Cancer: A Comparative Analysis of Mass Spectrometry Studies

Contact Info

Product

Resources

About