Reconstructing tumor clonal heterogeneity and evolutionary relationships based on tumor DNA sequencing data

Area of Science:

• Oncology
• Computational Biology
• Genomics

Background:

• Tumor heterogeneity drives cancer evolution, complicating treatment strategies.
• Bulk DNA sequencing faces challenges in accurately inferring tumor evolution due to mutation multiplicity misidentification and reconstruction uncertainties.

Purpose of the Study:

• To introduce REconstructing Tumor Clonal Heterogeneity and Evolutionary Relationships (RETCHER), a novel method for precise tumor evolution inference.
• To accurately characterize cancer cell fractions by identifying mutation multiplicity and considering reconstruction uncertainties.

Main Methods:

• RETCHER identifies mutation multiplicity while accounting for uncertainty in reconstruction and subclone clustering.
• The method comprehensively infers tumor clonal heterogeneity and phylogenetic relationships.

Main Results:

• RETCHER demonstrates superior performance on simulated data compared to existing methods.
• The approach infers clearer subclone structures and evolutionary relationships in real multisample tumor sequencing data from five cancer types.

Conclusions:

• RETCHER offers a robust approach for analyzing complex tumor clonal heterogeneity and evolutionary dynamics.
• This method provides scientific evidence for developing precise, personalized cancer treatment strategies and aids in clinical diagnosis.