Enhancing cancer clonality analysis with integrative genomics.

Area of Science:

• Oncology
• Genomics
• Bioinformatics

Background:

• Cancer is a clonal disease where metastasis and therapeutic resistance arise from Darwinian evolution of cancer cell clones.
• Tumor adaptability and clonal expansion drive relapse, treatment resistance, and mortality.
• Understanding tumor clonality is crucial for developing effective cancer therapies.

Purpose of the Study:

• To develop and demonstrate a detailed clonality analysis approach using integrative genomics.
• To enhance existing clonality analysis tools by incorporating multi-omic data.
• To reveal tumor clonal complexity and evolutionary dynamics.

Main Methods:

• Whole Exome Sequencing (WES), RNA-sequencing (RNA-seq), and DNA methylation profiling of patient tumor samples.
• Utilized STAR and Haplotype Caller for RNA-seq processing.
• Developed custom approaches for multi-omic data integration and analysis using enhanced CloneViz (iCloneViz).

Main Results:

• Introduced iCloneViz, enabling multi-dimensional tumor clonality analysis by integrating DNA mutations, RNA-expressed mutations, and DNA methylation data.
• Achieved integration of RNA and DNA methylation data for clonality analysis, a novel capability.
• Demonstrated iCloneViz's ability to provide integrative genomic mutational dissection and traceability across molecular layers.

Conclusions:

• The iCloneViz approach facilitates the analysis of clonal evolution and mutational dynamics in multi-omic datasets.
• Provides an integrative and quantitative method to reveal tumor clonal complexity.
• Aids in improved mutational characterization, understanding, and therapeutic assignments for cancer patients.