UCSF

Motivation: Prostate cancer remains the most commonly diagnosed neoplasm in American men, with existing biomarkers (i.e. PSA, nomograms, MRI) having varying levels of sensitivity and specificity in identifying more advanced and potentially aggressive disease. Tumor tissue biopsies remain the gold standard for confirming the presence of prostate cancer, as well as evaluating the genomic heterogeneity and clonal architecture that may be predictive of poor outcomes (i.e. recurrence and metastasis). However, tissue biopsies are limited in their ability to comprehensively assess tumors, and may lead to underestimation of disease grade and stage. These hurdles may be overcome with cell-free DNA (cfDNA), which allows for minimally invasive, repeated sampling through blood draws. This is particularly important when tumor tissue is unavailable during active surveillance or disease monitoring for the detection of residual disease or progression. Additionally, genomic interrogation via cfDNA sequencing typically requires prior knowledge of existing mutations from a patient’s tumor. The work presented here leverages a number of methods to ensure broad, yet sensitive detection of cfDNA variants for patients with localized prostate cancer, including sequencing with a machine-learning guided 2.5Mb targeted panel. In this dissertation, I investigate the use of cfDNA concentration, fragment size, and sequencing to identify advanced prostate cancer, as well as detect somatic mutations present in patient-matched tumors.

Methods: The patient cohort included in these studies are composed of 268 individuals: 34 healthy individuals, 112 men with localized prostate cancer who underwent radical prostatectomy (RP), and 122 men with metastatic castration-resistant prostate cancer (mCRPC). Plasma cfDNA concentration and fragment size were quantified with a Qubit fluorometer or Bioanalyzer utilizing a chip-based capillary electrophoresis method for nucleic acid analysis. Low-pass whole-genome and targeted sequencing were used to identify single nucleotide variants (SNVs), small insertions and deletions (indels), and copy number alterations (CNAs) for a subset of patients. Plasma cfDNA was barcoded with duplex Unique Molecular Identifiers (UMIs) to construct consensus reads and improve variant detection by leveraging duplicate reads and sequence complementarity of the two DNA strands. Extensive tissue sampling was used to capture tumor heterogeneity and provide a patient-specific gold standard for comparison of matched cfDNA.

Results and Conclusions: Patients with advanced mCRPC had higher cfDNA concentration than men with localized disease or healthy controls, and those with localized disease had shorter average fragment sizes than controls. Importantly, cfDNA concentration and fragment size remained independent predictors after adjusting for age and PSA. We found that targeted sequencing of cfDNA—without a priori patient-specific tumor mutation information—identified somatic alterations found in matched tumor tissue from multiple regions, potentially allowing for dynamic monitoring of emerging resistant subclones throughout the course of disease. Detection of these concordant variants was associated with seminal vesicle invasion and the number of somatic variants found in the tumor tissue samples, predicating its use for patients with poor prognostic factors in a localized setting. Similar to cfDNA concentration, plasma cfDNA mutational burden was also found to increase with disease severity. The results from our studies demonstrate the ability of cfDNA to identify somatic variants in patients with heterogeneous, localized prostate cancer.