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Inferring Clonal Heterogeneity in Chronic Lymphocytic Leukemia From High-Throughput Data

Zucker, Mark Raymond

Abstract Details

2019, Doctor of Philosophy, Ohio State University, Biomedical Sciences.
Clonal heterogeneity is common in many types of cancer, including chronic lymphocytic leukemia (CLL). Previous research suggests that the presence of multiple distinct cancer clones is associated with clinical outcome. Detection of clonal heterogeneity from high throughput data, such as sequencing or single nucleotide polymorphism (SNP) array data, is important for gaining a better understanding of cancer and may improve prediction of clinical outcome or response to treatment. Here, I present a new method, CloneSeeker, for inferring clinical heterogeneity from sequencing data, SNP array data, or both in order to infer the clonal architecture (defined as the number of cancer clones, the fraction of cancer cells belonging to each, and the aberrations each clone possesses) of tumors. In order to validate this method, we developed a tool to generate simulated tumor data (SNP array and single nucleotide variant (SNV) data). I demonstrate the use of this tool by generating a large sample set of simulated tumors with copy number variants (CNV) and apply multiple segmentation algorithms to them to determine the circumstances under which each algorithm – or segmentation algorithms in general – is most or least effective. Next, I applied our clonal heterogeneity algorithm to simulated data with known corresponding truth for validation and to our real CLL data set and assess the clinical implications of multiclonality in CLL. Lastly, using results of the CloneSeeker analysis, I present novel associations between particular CNVs and clinical outcome, and examine whether, for clinically relevant CNVs, the clinical association is continuous or dichotomous in nature.
Kevin Coombes, R (Advisor)
103 p.

Recommended Citations

Citations

  • Zucker, M. R. (2019). Inferring Clonal Heterogeneity in Chronic Lymphocytic Leukemia From High-Throughput Data [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1554049121307262

    APA Style (7th edition)

  • Zucker, Mark. Inferring Clonal Heterogeneity in Chronic Lymphocytic Leukemia From High-Throughput Data. 2019. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1554049121307262.

    MLA Style (8th edition)

  • Zucker, Mark. "Inferring Clonal Heterogeneity in Chronic Lymphocytic Leukemia From High-Throughput Data." Doctoral dissertation, Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1554049121307262

    Chicago Manual of Style (17th edition)