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Updated: Sep 28, 2025
Quantitative Mass Spectrometric Profiling of Cancer-cell Proteomes Derived From Liquid and Solid Tumors
Published on: February 27, 2015
Integrating computational pathology and proteomics to address tumor heterogeneity
Anglin Dent1,2, Phedias Diamandis1,2,3,4
1Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada.
Abstract:
Despite numerous advances in our molecular understanding of cancer biology, success in precision medicine trials has remained elusive for many malignancies. Emerging evidence now supports that these challenges are partly driven by proteogenomic discordances across molecular readouts and heterogeneous biology that is spatially distributed across tumors. Here we discuss these key limitations and how integrating the promise of mass-spectrometry-based global proteomics and computational imaging can help prioritize and direct regional sampling to help overcome these important challenges of biologic variation in cancer. © 2022 The Pathological Society of Great Britain and Ireland.
Insights
Precision medicine faces challenges due to proteogenomic discordances in cancer. Integrating mass spectrometry-based proteomics and computational imaging can guide regional sampling to overcome tumor heterogeneity.
Area of Science:
- Oncology
- Proteomics
- Computational Pathology
Background:
- Despite molecular advances, precision medicine struggles with cancer treatment efficacy.
- Proteogenomic discordances and spatial tumor heterogeneity limit therapeutic success.
- Understanding tumor biology variation is crucial for effective cancer therapies.
Purpose of the Study:
- To address limitations in precision medicine caused by tumor heterogeneity.
- To explore the integration of proteomics and computational imaging for cancer research.
- To improve regional sampling strategies in cancer studies.
Main Methods:
- Discusses mass spectrometry-based global proteomics for molecular profiling.
- Highlights computational imaging for spatial analysis of tumor biology.
- Proposes integrating these techniques for targeted regional sampling.
Main Results:
- Proteogenomic discordances contribute to challenges in precision medicine.
- Spatial heterogeneity within tumors complicates molecular profiling.
- Integrated approaches offer a path to overcome biologic variation.
Conclusions:
- Integrating proteomics and computational imaging can enhance understanding of tumor heterogeneity.
- This integrated approach can guide regional sampling for more effective cancer research.
- Addressing proteogenomic discordances is key to advancing precision oncology.
