Deriving consensus tumor trees using integer linear programming

Matthew Smith-Erb, Ziyun Guang, Layla Oesper

Abstract: The acquisition of somatic mutations by a tumor can be modeled by a type of evolutionary tree. Although many methods have been developed to infer a tumor’s evolutionary history, they can produce conflicting results for a single patient. A consensus tree that reconciles these possible trees is important for understanding the tumor’s evolutionary process. We use integer linear programming to find a consensus tree among multiple plausible tumor evolutionary histories.