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BubbleTree: an intuitive visualization to elucidate tumoral aneuploidy and clonality using next generation sequencing
Wei Zhu1, Michael Kuziora2, Todd Creasy2
1Translational Bioinformatics, MedImmune, Gaithersburg, MD 20878, USA weizhu365@gmail.com.
Nucleic Acids Research
|November 19, 2015
Summary
BubbleTree, a novel framework using next-generation sequencing data, effectively characterizes tumor clonality and heterogeneity. It outperforms existing tools in identifying subclonal populations and tracking tumor evolution.
Area of Science:
- Oncology
- Bioinformatics
- Computational Biology
Background:
- Tumors exhibit genetic instability, heterogeneity, and oligoclonality, making intratumoral heterogeneity difficult to study.
- Understanding tumor complexity is crucial for effective cancer treatment and research.
Purpose of the Study:
- To introduce BubbleTree, a novel framework for characterizing tumor clonality using next-generation sequencing (NGS) data.
- To provide an intuitive graphical representation of tumor complexity, including cancer cell purity, ploidy, and allele-specific copy number.
Main Methods:
- Developed BubbleTree, an R-based framework utilizing NGS data to analyze tumor clonality.
- Implemented a three-step heuristic method for automated BubbleTree graph interpretation.
- Compared BubbleTree's performance against THetA2, ABSOLUTE, AbsCN-seq, and ASCAT using simulated and patient-derived data.
Main Results:
- BubbleTree accurately estimates cancer cell purity, tumor ploidy, allele-specific copy number, and clonality.
- BubbleTree demonstrated superior performance compared to existing tools, especially in identifying tumor subclones and polyploidy.
- The framework successfully tracked clonality changes between primary and metastatic tumors and dated somatic variants.
Conclusions:
- BubbleTree offers a powerful approach for a comprehensive analysis of heterogeneous tumor karyotypes.
- The intuitive graph and automated interpretation facilitate a deeper understanding of tumor evolution.
- BubbleTree is freely available, promoting its adoption in cancer research.
