Deciphering the heterogeneity and spatial architecture of tumors

Cancer is caused by the accumulation of somatic mutations that form distinct populations of cells, called clones. The resulting intra-tumor heterogeneity evolves temporally, as well as spatially, and is the main cause of relapse and resistance to treatment. With decreasing costs in DNA sequencing technology, rich cancer genomics datasets that effectively capture mutational signals in cancer have become available, allowing researchers to closely examine the underlying mechanisms that shape the tumor landscape. In this thesis, we explore the multi-faceted elements of intra-tumor heterogeneity via visualization, quantification, and detection. We begin by introducing ClonArch, a tool which interactively visualizes the evolutionary relationships and spatial distribution of clones in a single tumor mass. ClonArch fills the gap for visualizations that address spatial aspects of clonal architecture. We then adapt a cancer genomics pipeline to quantify intra-tumor heterogeneity in a porcine model, showing its potential impact on translational clinical studies. Finally, we attempt to detect negative selection in the cancer exome by performing a depletion analysis on neoantigens.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2020

Author dc:creator

• Wu, Jiaqi

Contributors dc:contributor

• El-Kebir, Mohammed

• intra-tumor heterogeneity, cancer genomics, bioinformatics, genome analysis, spatial analytics, visualization

Statement dc:rights

• Copyright 2020 Jiaqi Wu

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/108185

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/108185