First passage time analysis of spatial mutation patterns reveals sub-clonal evolutionary dynamics in colorectal...

Area of Science:

• Computational Biology
• Cancer Research
• Spatial Analysis

Background:

• Understanding early cancer dynamics is crucial for effective treatment.
• The spatial arrangement of tumor cells encodes information about sub-clone growth.
• Novel methods are needed to quantify spatial tumor data at the cellular scale.

Purpose of the Study:

• To develop a framework using first passage times of random walks to quantify tumor cell population mixing.
• To link evolutionary dynamics to the spatial architecture of tumors.
• To infer parameters of early sub-clonal dynamics in human colorectal cancer.

Main Methods:

• Utilizing first passage times of random walks to quantify spatial patterns.
• Applying the method to simulated agent-based tumor growth models.
• Analyzing experimentally measured human colorectal cancer data.

Main Results:

• First passage time statistics effectively distinguish between different spatial pattern structures.
• Inferred mutant cell division rates ranged from 1 to 4 times that of non-mutated cells.
• Sub-clone emergence times varied significantly, from 100 to 50,000 non-mutant cell divisions.
• Inferred dynamics were consistent with boundary-driven growth or short-range cell pushing.

Conclusions:

• First passage time analysis is an effective new methodology for spatial analysis of solid tumor tissue.
• Patterns of sub-clonal mixing provide valuable insights into early cancer dynamics.
• The distribution of inferred dynamics can inform about initial mutational events.