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Updated: Aug 17, 2026

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
Published on: October 19, 2021
Mapping molecular networks using proteomics: a vision for patient-tailored combination therapy
Emanuel F Petricoin1, Verena E Bichsel, Valerie S Calvert
1US Food and Drug Administration-National Cancer Institute Clinical Proteomics Program, Office of Cellular and Gene Therapy, Center for Biologics Evaluation and Research, FDA, Bethesda, MD, USA. epetrico@gmu.edu
Abstract:
Mapping tumor cell protein networks in vivo will be critical for realizing the promise of patient-tailored molecular therapy. Cancer can be defined as a dysregulation or hyperactivity in the network of intracellular and extracellular signaling cascades. These protein signaling circuits are the ultimate targets of molecular therapy. Each patient's tumor may be driven by a distinct series of molecular pathogenic defects. Thus, for any single molecular targeted therapy, only a subset of cancer patients may respond. Individualization of therapy, which tailors a therapeutic regimen to a tumor molecular portrait, may be the solution to this dilemma. Until recently, the field lacked the technology for molecular profiling at the genomic and proteomic level. Emerging proteomic technology, used concomitantly with genomic analysis, promises to meet this need and bring to reality the clinical adoption of molecular stratification. The activation state of kinase-driven signal networks contains important information relative to cancer pathogenesis and therapeutic target selection. Proteomic technology offers a means to quantify the state of kinase pathways, and provides post-translational phosphorylation data not obtainable by gene arrays. Case studies using clinical research specimens are provided to show the feasibility of generating the critical information needed to individualize therapy. Such technology can reveal potential new pathway interconnections, including differences between primary and metastatic lesions. We provide a vision for individualized combinatorial therapy based on proteomic mapping of phosphorylation end points in clinical tissue material.
Insights
Molecular profiling using proteomic technology is key for personalized cancer therapy. This approach maps tumor protein networks to guide individualized treatment strategies for better patient outcomes.
Area of Science:
- Oncology
- Molecular Biology
- Proteomics
Background:
- Cancer is characterized by dysregulated intracellular and extracellular signaling cascades.
- Protein signaling circuits are primary targets for molecular cancer therapies.
- Tumor heterogeneity necessitates individualized treatment approaches.
Purpose of the Study:
- To highlight the critical role of in vivo tumor cell protein network mapping for patient-tailored molecular therapy.
- To demonstrate the feasibility of proteomic technology for molecular profiling in clinical settings.
- To present a vision for individualized combinatorial therapy based on proteomic data.
Main Methods:
- Utilizing emerging proteomic technology alongside genomic analysis for molecular profiling.
- Quantifying the activation state of kinase-driven signal networks through proteomic analysis.
- Analyzing post-translational phosphorylation data from clinical research specimens.
Main Results:
- Proteomic technology enables quantification of kinase pathway activation states.
- Phosphorylation data provides insights not obtainable through gene arrays.
- Case studies demonstrate the feasibility of generating critical data for individualized therapy.
- Potential new pathway interconnections, including differences between primary and metastatic lesions, can be revealed.
Conclusions:
- Proteomic mapping of tumor cell protein networks is essential for advancing personalized cancer medicine.
- Individualized therapy, guided by proteomic profiling, offers a solution to treatment non-responsiveness.
- This technology facilitates the clinical adoption of molecular stratification for targeted cancer treatments.
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