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Quantitative Mass Spectrometric Profiling of Cancer-cell Proteomes Derived From Liquid and Solid Tumors
Published on: February 27, 2015
Kinome profiling of clinical cancer specimens
Kaushal Parikh1, Maikel P Peppelenbosch
1University Medical Center Groningen, A. Deusinglaan 1, Groningen, 9713 AV, the Netherlands. k.parikh@med.umcg.nl
Abstract:
Over the past years novel technologies have emerged to enable the determination of the transcriptome and proteome of clinical samples. These data sets will prove to be of significant value to our elucidation of the mechanisms that govern pathophysiology and may provide biological markers for future guidance in personalized medicine. However, an equally important goal is to define those proteins that participate in signaling pathways during the disease manifestation itself or those pathways that are made active during successful clinical treatment of the disease: the main challenge now is the generation of large-scale data sets that will allow us to define kinome profiles with predictive properties on the outcome-of-disease and to obtain insight into tissue-specific analysis of kinase activity. This review describes the current techniques available to generate kinome profiles of clinical tissue samples and discusses the future strategies necessary to achieve new insights into disease mechanisms and treatment targets.
Insights
Novel technologies allow transcriptome and proteome analysis of clinical samples, aiding in understanding disease mechanisms and personalized medicine. Defining kinome profiles is key for predicting disease outcomes and identifying therapeutic targets.
Area of Science:
- Biochemistry
- Molecular Biology
- Clinical Research
Background:
- Emerging technologies enable transcriptome and proteome analysis of clinical samples.
- These datasets are valuable for elucidating disease pathophysiology and identifying biomarkers for personalized medicine.
- Understanding signaling pathways in disease and treatment is crucial.
Purpose of the Study:
- To review current techniques for generating kinome profiles from clinical tissue samples.
- To discuss future strategies for gaining insights into disease mechanisms and treatment targets.
- To highlight the importance of kinome profiling for predicting disease outcomes.
Main Methods:
- Review of existing technologies for kinome profiling.
- Discussion of data generation strategies for large-scale kinome datasets.
- Analysis of tissue-specific kinase activity.
Main Results:
- Current techniques for kinome profiling are available.
- Large-scale datasets are needed for predictive kinome profiles.
- Tissue-specific kinase activity analysis is an area for future research.
Conclusions:
- Kinome profiling holds significant potential for personalized medicine and understanding disease.
- Further development of large-scale data generation is necessary.
- Future strategies should focus on tissue-specific kinase activity for novel therapeutic targets.
