Inferring Tumor Proliferative Organization from Phylogenetic Tree Measures in a Computational Model

Area of Science:

• Oncology
• Computational Biology
• Evolutionary Biology

Background:

• The cancer stem cell hypothesis posits a hierarchy driving tumor growth and therapeutic resistance.
• Neutral evolution models provide a framework for understanding tumor genetic diversity.
• Multiregion sampling enables inference of tumor evolutionary history.

Purpose of the Study:

• To computationally model and infer the symmetric division probability of cancer stem cells.
• To explore the relationship between symmetric division probability and tumor evolutionary dynamics.
• To identify statistical measures for stratifying tumors based on this biological parameter.

Main Methods:

• Utilizing computational modeling to simulate tumor evolution.
• Analyzing phylogenetic trees derived from simulated tumor growth.
• Applying statistical measures to differentiate evolutionary patterns based on symmetric division probability and spatial constraints.

Main Results:

• Distinct patterns in phylogenetic tree measures were observed with varying symmetric division probabilities.
• The inclusion of spatial constraints was crucial for identifying these differentiating patterns.
• Computational models successfully linked symmetric division probability to observable evolutionary metrics.

Conclusions:

• It is possible to computationally infer cancer stem cell symmetric division probability.
• Symmetric division probability significantly influences tumor evolution, especially with spatial considerations.
• Findings provide a basis for tumor stratification and generate hypotheses for therapy and evolutionary studies.