Author ORCID Identifier

Date of Graduation


Document Type

Dissertation (PhD)

Program Affiliation

Biostatistics, Bioinformatics and Systems Biology

Degree Name

Doctor of Philosophy (PhD)

Advisor/Committee Chair

Dr. Nicholas Navin

Committee Member

Dr. Amado Zurita

Committee Member

Dr. Ana Aparicio

Committee Member

Dr. Ken Chen

Committee Member

Dr. Scott Kopetz

Committee Member

Dr. Patricia Troncoso


Investigating genome evolution in response to therapy is difficult in human tissue samples due to the difficulty in accessing metastatic tumor sites and logistical challenges of collecting longitudinal samples. To overcome these issues, we developed an unbiased whole-genome plasma DNA sequencing approach called PEGASUS that concurrently measures genomic copy number and exome mutations from archival cryostored plasma samples. This approach was applied to study longitudinal blood plasma samples from prostate cancer patients. A molecular characterization of archival plasma DNA from 233 patients and genomic profiling of 101 patients identified clinical correlations of aneuploid plasma DNA profiles with poor survival, increased plasma DNA concentrations, and lower plasma DNA size distributions. Deep-exome sequencing and genomic copy number profiling were performed on 23 patients, including 9 patients with matched metastatic tissues and 12 patients with serial plasma samples. Our data show a high concordance in genomic alterations between the plasma DNA and metastatic tissue samples, suggesting the plasma DNA is highly representative of the genomic alterations in tissues. Longitudinal sequencing of 12 patients with 2–5 serial plasma samples revealed clonal dynamics and genome evolution in response to hormonal and chemotherapy. By performing an integrated evolutionary analysis, minor subclones were identified in 9 patients that expanded in response to therapy and harbored mutations associated with resistance. Furthermore, we applied PEGASUS to profile a larger cohort of 79 prostate cancer patients with 2-10 longitudinal samples. A single time point analysis of 49 patients revealed extensive interpatient-heterogeneity and recurrent aberrations in genes including TP53, PTEN, RB1 and AR. The copy number and mutational data showed four different evolutionary responses to therapy: clonal extinction, clonal persistence, intrinsic resistance and partial response. Survival analysis of these 4 classes show that clonal extinction patients have a longer survival compared to clonal persistence patients. Overall, this dissertation provides an unbiased approach to non-invasively monitor clonal dynamics in response to therapy in prostate cancer patients, which improved our understanding of therapeutic resistance in this devastating disease.


bioinformatics, cell-free DNA, tumor evolution, DNA sequencing



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