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Lighting Up the Pathways to Caspase Activation Using Bimolecular Fluorescence Complementation
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High-Throughput Mutation Data Now Complement Transcriptomic Profiling: Advances in Molecular Pathway Activation
Anton Buzdin1,2,3, Maxim Sorokin1,2,3, Elena Poddubskaya1,4
1Institute for Personalized Medicine, I.M. Sechenov First Moscow State Medical University, Moscow, Russia.
Cancer Informatics
|April 3, 2019
Summary
Quantitative pathway analysis using omics data offers new tumor biomarker applications. Recent advancements include Pathway Instability for mutation burden, Shambhala for gene expression harmonization, and FloWPS for enhanced treatment response prediction.
Area of Science:
- Bioinformatics
- Cancer Biology
- Genomics
Background:
- High-throughput molecular "omics" data are increasingly used for quantitative pathway activation analysis.
- These metrics hold significant potential as tumor biomarkers.
- Previous reviews focused on established applications; this update covers novel conceptual findings.
Purpose of the Study:
- To provide an update on recent conceptual findings in pathway analysis for tumor biology.
- To highlight novel methods and their applications in cancer research and treatment.
Main Methods:
- Introduction of "Pathway Instability" for calculating pathway-scale tumor mutation burden.
- Description of the Shambhala technique for harmonizing diverse gene expression profiles.
- Explanation of the FLOating-Window Projective Separator (FloWPS) for enhancing biomarker value.
Main Results:
- Pathway Instability enables scoring of anticancer target drugs.
- Shambhala facilitates merging and comparison of gene expression datasets.
- FloWPS reduces sample size requirements for machine learning classifiers predicting treatment response.
- Clinical cases demonstrate successful gene-expression-based pathway analysis for personalized drug prescription.
Conclusions:
- Recent advancements significantly enhance the utility of pathway analysis in oncology.
- Novel bioinformatics tools offer improved methods for tumor mutation burden assessment, data integration, and biomarker development.
- Gene-expression-based pathway analysis is proving effective for personalized cancer treatment strategies.
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