Genetic Analysis of Mutation and Response in Cancer

Abstract

This thesis is divided into two distinct parts: mutation and treatment of cancer. Each abstract is described separately. Molecular, cellular, and clinical studies have combined to demonstrate a contribution from the DNA cytosine deaminase APOBEC3B (A3B) to the overall mutation load in breast, head/neck, lung, bladder, cervical, ovarian, and other cancer types. However, the complete landscape of mutations attributable to this enzyme has yet to be determined in a controlled human cell system. In Chapter 2, we report a conditional and isogenic system for A3B induction, genomic DNA deamination, and mutagenesis. Targeted sequencing of portions of TP53 and MYC demonstrated greater mutation accumulation in the A3B-eGFP exposed pools. Clones were generated and microarray analyses were used to identify those with the greatest number of SNP alterations for whole genome sequencing. A3B-eGFP exposed clones showed global increases in C-to-T transition mutations, enrichments for cytosine mutations within A3B-preferred trinucleotide motifs, and more copy number aberrations. The 293-based system characterized here still yielded a genome-wide view of A3B-catalyzed mutagenesis in human cells and a system for additional studies on the compounded effects of simultaneous mutation mechanisms in cancer. Personalized medicine includes the identification of the biomarkers and/or expression profiles important in the treatment of cancer. Cancer cell lines offer a representative diversity of molecular processes involved in both malignant phenotypes as well as therapeutic responses. Coupling cancer line expression with high-throughput drug screens allows for examination of perturbed pathways that influence drug sensitivities. Here we use The Sanger Institute’s Genomics of Drug Sensitivity in Cancer to develop a bioinformatic and pathway analysis approach that identifies activation of the BCR pathway as an important biomarker in response to the tyrosine kinase inhibitor, dasatinib, used in hematologic cancer therapies. This approach establishes a process by which data from cell line repositories can be used to identify biomarkers associated with drug response in the treatment of cancers.