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Quantifying local malignant adaptation in tissue-specific evolutionary trajectories by harnessing cancer's
Natsuki Tokutomi1, Caroline Moyret-Lalle2, Alain Puisieux2
1Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences The University of Tokyo Tokyo Japan.
Cancer evolution involves distinct genetic changes, yet follows repeatable patterns. Understanding local malignant adaptation (LMA) helps predict tumor progression and identify key genetic drivers.
Area of Science:
- Evolutionary biology
- Genomics
- Cancer research
Background:
- Cancer arises from distinct genetic alterations, even in similar patient profiles.
- Tumorigenesis involves complex evolutionary processes.
- Understanding cancer evolution is key to predicting progression.
Purpose of the Study:
- Reconstruct tumor initiation's evolutionary process using cancer genome data.
- Develop a framework to score local malignant adaptation (LMA) of genetic clones.
- Investigate system-level evolutionary properties of cancer.
Main Methods:
- Analyzed 2,742 cancer genomes from nine tumor types.
- Developed a framework based on cancer development repeatability to score LMA.
- Utilized network analyses for system-level evolutionary properties.
Main Results:
- Premalignant lesions show local adaptation but not full cancerous transformation.
- Metastatic clones are more adapted to the origin site than invaded tissue.
- Different tumor types exhibit distinct malignant progression dynamics.
Conclusions:
- Cancer development repeatability can predict tumor trajectories.
- Occurrence-based methods can model cancer initiation and progression.
- LMA framework aids in evaluating genetic clone adaptation and predicting cancer evolution.
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