Quantification of subclonal selection in cancer from bulk sequencing data.

Area of Science:

• Oncology
• Evolutionary Biology
• Genomics

Background:

• Subclonal architectures are common in human cancers.
• The evolutionary processes driving the emergence of tumor subclones are not well understood.

Purpose of the Study:

• To develop a computational framework for measuring subclone dynamics and evolutionary trajectories in human cancers.
• To quantify the selective advantage and emergence time of subclones using high-throughput sequencing data.

Main Methods:

• Computational modeling of subclonal selection.
• Application of theoretical population genetics to high-throughput sequencing data.
• Analysis of breast, gastric, blood, colon, and lung cancer samples.

Main Results:

• The developed method accurately determines subclonal architecture, selective advantage, and emergence time.
• Detectable subclones under selection consistently emerged early in tumor development.
• These early-emerging subclones possess a substantial fitness advantage (>20%).

Conclusions:

• This quantitative framework provides novel insights into the evolutionary dynamics of human cancers.
• The findings facilitate predictive measurements of cancer evolution from sequencing data.
• Understanding early subclone emergence is crucial for cancer progression.