CAREER: Algorithmic Approaches for Phylogenetic Analysis of Tumor Evolution

In order to improve clinical diagnosis and treatment of patients afflicted with cancer, there is a need for an improved understanding of how tumors grow and develop over time. This work seeks specifically to understand what order genetic alterations occur, and what alterations occur together or in separate cell lineages. Such knowledge will be fundamental towards lessening the impact of this disease on those affected.

For example, treatment plans designed to target genetic alterations occurring in separate cell lineages may be more effective in preventing disease relapse. Recent advances in DNA sequencing technologies and methods designed to analyze the data produced by these technologies has enabled glimpses into the mutational processes underlying cancers. Researchers are now better equipped to infer information about the history of a recently diagnosed tumor, including the order that events took place during its development over months or even years prior to diagnosis.

However, there is still much uncertainty and variation in the tumor histories inferred using these methods. This project will address these issues through the development of critically needed approaches that enable comparison, summarization, and visualization of tumor histories inferred from sequencing data. Numerous opportunities will be provided for undergraduate students and recent graduates, especially those from underrepresented groups, to gain hands-on research experience.

This will be combined with several educational activities that enable undergraduate students with a minimal computer science background to learn about real world applications of how their skills can be applied to important biological problems - an area that is likely to need a growing workforce in the coming years.

The phylogenetic history of how a tumor developed is typically described using a labeled, rooted tree called a clonal tree. This project will result in the development of methods that compare, organize, and communicate information about clonal trees. Specifically, the intellectual aims of the project are: (1) Develop new distance measures for comparing clonal trees and means for assessing such measures; (2) Design algorithms that allow for the summarization of both small sets of clonal trees and the larger space of all such trees; and (3) Design a visualization tool for experts to explore and compare clonal trees.

These goals are integrated with an educational plan that focuses on expanding and broadening undergraduate involvement in computational biology: through workshops, innovative classroom experiences, and numerous opportunities for undergraduates to be directly involved in the research process.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.