Reconstructing Tumor Evolution and Progression in Structurally Variant Cancer Cells

摘要

Cancer is disease governed by the process of evolution, in which a process of accelerated genomic diversification and selection leads to the formation of tumors and a process of generally increasing aggressiveness over time. As a result, computational algorithms for reconstructing evolution have become a crucial tool for making sense of the immense complexity of tumor genomic data and the molecular mechanisms that produce them. While cancers are evolutionary systems, though, they follow very different rules than standard species evolution. A large body of research known as cancer phylogenetics has arisen to develop evolutionary tree reconstructions adapted to the peculiar mechanisms of tumor evolution and the limitations of the data sources available for studying it. Here, we will explore computational challenges in developing phylogenetic methods for reconstructing evolution of tumors by copy number variations (CNVs) and structural variations (SVs). CNVs and SVs are the primary mechanisms by which tumors functionally adapt during their evolution, but require very different models and algorithms than are used in traditional species phylogenetics. We will examine variants of this problem for handling several forms of tumor genomic data, including particular challenges of working with various bulk genomic and single-cell technologies for profiling tumor genetic variation. We will further see how the resulting models can help us develop new insight into how tumors develop and progress and how we can predict their future behavior.