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Multiplicity: an organizing principle for cancers and somatic mutations
Lewis J Frey1, Stephen R Piccolo, Mary E Edgerton
1Department of Biomedical Informatics, University of Utah, 26 South 2000 East, Salt Lake City, UT 84112, USA. lewis.frey@hsc.utah.edu
We developed multiplicity measures to organize cancer genomic data, revealing similarities in mutations across cancer types. This method effectively clusters causal genes and segregates blood tumors, aiding in understanding cancer progression.
Area of Science:
- Genomics
- Cancer Biology
- Bioinformatics
Background:
- Whole-genome analysis generates vast amounts of tumor genomic data.
- Organizing this data requires principled methods for clinical relevance.
- Multiplicity measures are proposed to organize cancer and gene networks.
Purpose of the Study:
- To introduce multiplicity measures for organizing somatic mutations and cancers.
- To identify clinically meaningful clusters of cancer types and genes.
- To extend the multi-hit genetic model across diverse cancers.
Main Methods:
- Constructed cancer and gene networks using the Catalogue of Somatic Mutations in Cancer (COSMIC).
- Calculated multiplicity based on linked cancers and genes per somatic mutation.
- Employed the Kamada-Kawai algorithm for network visualization and hierarchical clustering in 3D.
Main Results:
- Gene clustering revealed shared mutations across cancer types and separated known causal mutations.
- Multiplicity clustering identified causal genes with high accuracy (AUC 0.84).
- Cancer type clustering segregated blood tumors, indicating distinct mutation patterns.
Conclusions:
- Multiplicity measures effectively organize somatic mutations and cancers into clinically relevant clusters.
- These clusters highlight key genes driving cancer progression.
- The approach provides a framework for understanding inter-cancer mutation similarities.
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