Recent developments and applications of quantitative proteomics strategies for high-throughput biomolecular analyses in cancer research

Miles, Hannah N.; Delafield, Daniel G.

doi:10.1039/d1cb00039j

Cited by 8 publications

(6 citation statements)

References 153 publications

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“…Our label-free LC-MS/MS-based semiquantitative proteomic analysis succeeded in identifying quantifiable differences in protein levels across patients with and without relapsing HL. This powerful technology has been increasingly used in cancer research and biomarker discovery in recent years [ 23 ]. The small size of our cohorts prompted us to adopt a triangular strategy for plasma proteome profiling, while the more attractive idea of using larger cohorts in a rectangular strategy (described in [ 24 ]) remains a prospect for further investigations.…”

Section: Discussionmentioning

confidence: 99%

Quantitative Plasma Proteomics to Identify Candidate Biomarkers of Relapse in Pediatric/Adolescent Hodgkin Lymphoma

Repetto

Caggiari

Zorzi

et al. 2022

IJMS

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Classical pediatric Hodgkin Lymphoma (HL) is a rare malignancy. Therapeutic regimens for its management may be optimized by establishing treatment response early on. The aim of this study was to identify plasma protein biomarkers enabling the prediction of relapse in pediatric/adolescent HL patients treated under the pediatric EuroNet-PHL-C2 trial. We used untargeted liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based proteomics at the time of diagnosis—before any therapy—as semiquantitative method to profile plasma proteins specifically associated with relapse in 42 children with nodular sclerosing HL. In both the exploratory and the validation cohorts, six proteins (apolipoprotein E, C4b-binding protein α chain, clusterin, fibrinogen γ chain, prothrombin, and vitronectin) were more abundant in the plasma of patients whose HL relapsed (|fold change| ≥ 1.2, p < 0.05, Student’s t-test). Predicting protein function with the Gene Ontology classification model, the proteins were included in four biological processes (p < 0.01). Using immunoblotting and Luminex assays, we validated two of these candidate biomarkers—C4b-binding protein α chain and clusterin—linked to innate immune response function (GO:0045087). This study identified C4b-binding protein α chain and clusterin as candidate early plasma biomarkers of HL relapse, and important for the purpose of shedding light on the molecular scenario associated with immune response in patients treated under the EuroNet-PHL-C2 trial.

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Section: Discussionmentioning

confidence: 99%

Quantitative Plasma Proteomics to Identify Candidate Biomarkers of Relapse in Pediatric/Adolescent Hodgkin Lymphoma

Repetto

Caggiari

Zorzi

et al. 2022

IJMS

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“…In recent years, there have been promising advances in quantitative proteomics methodologies that play a major role in detecting novel cancer biomarkers. Quantitative proteomics operates under the principle that a signal response from any given analyte is related to its abundance within the mixture [ 9 , 10 ]. The implementation of a strong quality control and maneuvering of samples is pivotal to ensure data acquisition ( Figure 1 A,B).…”

Section: Biomarker Discovery In Cancer Proteomicsmentioning

confidence: 99%

Oncogenic Proteomics Approaches for Translational Research and HIV-Associated Malignancy Mechanisms

Alvarez-Rivera,

Ortiz-Hernández,

Lugo

et al. 2023

Proteomes

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Recent advances in the field of proteomics have allowed extensive insights into the molecular regulations of the cell proteome. Specifically, this allows researchers to dissect a multitude of signaling arrays while targeting for the discovery of novel protein signatures. These approaches based on data mining are becoming increasingly powerful for identifying both potential disease mechanisms as well as indicators for disease progression and overall survival predictive and prognostic molecular markers for cancer. Furthermore, mass spectrometry (MS) integrations satisfy the ongoing demand for in-depth biomarker validation. For the purpose of this review, we will highlight the current developments based on MS sensitivity, to place quantitative proteomics into clinical settings and provide a perspective to integrate proteomics data for future applications in cancer precision medicine. We will also discuss malignancies associated with oncogenic viruses such as Acquire Immunodeficiency Syndrome (AIDS) and suggest novel mechanisms behind this phenomenon. Human Immunodeficiency Virus type-1 (HIV-1) proteins are known to be oncogenic per se, to induce oxidative and endoplasmic reticulum stresses, and to be released from the infected or expressing cells. HIV-1 proteins can act alone or in collaboration with other known oncoproteins, which cause the bulk of malignancies in people living with HIV-1 on ART.

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“…1 Quantitative proteomic techniques, in particular, have been instrumental in advancing our understanding of protein dynamics and their involvement in diseases. 2 These techniques enable precise protein abundance measurements to unravel complex biological phenomena. 3 Differential testing is commonly used to compare conditions and identify significant differences, providing valuable insights into the underlying biology of diseases such as cancer.…”

Section: Introductionmentioning

confidence: 99%

“…Proteomics has significantly transformed biological analysis by providing quantitative insights into cellular processes . Quantitative proteomic techniques, in particular, have been instrumental in advancing our understanding of protein dynamics and their involvement in diseases . These techniques enable precise protein abundance measurements to unravel complex biological phenomena .…”

Section: Introductionmentioning

confidence: 99%

Statistical Testing for Protein Equivalence Identifies Core Functional Modules Conserved across 360 Cancer Cell Lines and Presents a General Approach to Investigating Biological Systems

Ergin,

Myung,

Lange

2024

J. Proteome Res.

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Quantitative proteomics has enhanced our capability to study protein dynamics and their involvement in disease using various techniques, including statistical testing, to discern the significant differences between conditions. While most focus is on what is different between conditions, exploring similarities can provide valuable insights. However, exploring similarities directly from the analyte level, such as proteins, genes, or metabolites, is not a standard practice and is not widely adopted. In this study, we propose a statistical framework called QuEStVar (Quantitative Exploration of Stability and Variability through statistical hypothesis testing), enabling the exploration of quantitative stability and variability of features with a combined statistical framework. QuEStVar utilizes differential and equivalence testing to expand statistical classifications of analytes when comparing conditions. We applied our method to an extensive data set of cancer cell lines and revealed a quantitatively stable core proteome across diverse tissues and cancer subtypes. The functional analysis of this set of proteins highlighted the molecular mechanism of cancer cells to maintain constant conditions of the tumorigenic environment via biological processes, including transcription, translation, and nucleocytoplasmic transport.

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Recent developments and applications of quantitative proteomics strategies for high-throughput biomolecular analyses in cancer research

Abstract: Innovations in medical technology and dedicated focus from the scientific community have inspired numerous treatment strategies for benign and invasive cancers. While these improvements often lend themselves to more positive...

Cited by 8 publications

References 153 publications

Quantitative Plasma Proteomics to Identify Candidate Biomarkers of Relapse in Pediatric/Adolescent Hodgkin Lymphoma

Quantitative Plasma Proteomics to Identify Candidate Biomarkers of Relapse in Pediatric/Adolescent Hodgkin Lymphoma

Oncogenic Proteomics Approaches for Translational Research and HIV-Associated Malignancy Mechanisms

Statistical Testing for Protein Equivalence Identifies Core Functional Modules Conserved across 360 Cancer Cell Lines and Presents a General Approach to Investigating Biological Systems

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