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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
Published on: October 19, 2021
Utilization of Proteomic Technologies for Precision Oncology Applications
Mariaelena Pierobon1, Julie Wulfkuhle1, Lance A Liotta1
1Center for Applied Proteomics and Molecular Medicine, George Mason University, 20110, Manassas, VA, USA.
Abstract:
Genomic analysis of tumor specimens has revealed that cancer is fundamentally a proteomic disease at the functional level: driven by genomically defined derangements, but selected for in the proteins that are encoded and the aberrant activation of signaling and biochemical networks. This activation is measured by posttranslational modifications such as phosphorylation and other modifications that modulate cellular signaling, and these events cannot be effectively measured by genomic analysis alone. Moreover, these signaling networks by and large represent the targets for many FDA-approved and experimental molecularly targeted therapeutics. Consequently, it is important that we consider new classification schemas for oncology based not on tumor site of origin or histology under the microscope but on the functional protein signaling architecture. There are numerous proteomic technologies that could be discussed from a purely technological standpoint, but this chapter will concentrate on an overview of the main proteomic technologies available for conducting protein pathway activation analysis of clinical specimens such as multiplex immunoassays, phospho-specific flow cytometry, reverse phase protein microarrays, quantitative immunohistochemistry, and mass spectrometry. This chapter will focus on the application of these technologies to cancer-based clinical studies evaluating prognostic/predictive markers or for stratifying patients to personalized treatments.
Insights
Cancer is a proteomic disease driven by protein signaling errors, not just genetics. New classifications and proteomic technologies are crucial for personalized cancer treatments and identifying predictive markers.
Area of Science:
- Oncology
- Proteomics
- Molecular Biology
Background:
- Cancer is functionally a proteomic disease, driven by aberrant protein signaling networks.
- Genomic analysis alone cannot fully capture these critical protein modifications and signaling events.
- Targeted cancer therapeutics often act on these dysregulated signaling pathways.
Purpose of the Study:
- To propose a shift in cancer classification from histology to functional protein signaling architecture.
- To provide an overview of proteomic technologies for analyzing protein pathway activation in clinical specimens.
- To highlight the application of these technologies in cancer clinical studies for marker evaluation and patient stratification.
Main Methods:
- Overview of key proteomic technologies: multiplex immunoassays, phospho-specific flow cytometry, reverse phase protein microarrays, quantitative immunohistochemistry, and mass spectrometry.
- Focus on protein pathway activation analysis in clinical cancer specimens.
- Application in evaluating prognostic/predictive markers and stratifying patients for personalized treatments.
Main Results:
- Genomic derangements lead to functional protein-level alterations in cancer.
- Posttranslational modifications (e.g., phosphorylation) are key indicators of aberrant signaling.
- Proteomic technologies enable measurement of these critical events, complementing genomic data.
Conclusions:
- A functional protein signaling-based classification is essential for modern oncology.
- Proteomic technologies are vital tools for understanding cancer biology and developing personalized therapies.
- These methods facilitate the identification of biomarkers for patient stratification and treatment selection.
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