Ovarian cancer is notoriously difficult to detect early, with most patients diagnosed only once the disease has spread throughout the abdomen. At this advanced stage, treatment options are limited and survival is poor. Unlike many other cancers, ovarian cancer lacks a validated screening approach, and current risk-reduction strategies, such as preventive surgery for BRCA carriers, are invasive and life-altering. This creates a major gap in care during the period when the disease is biologically active but essentially invisible to clinicians. While recent work has shown that early tumors can release genetic material into the bloodstream, existing blood-based tests have insufficient sensitivity to capture ovarian cancer, especially at its earliest precursor states. Addressing this challenge requires a new approach to identify the genomic hallmarks of transformation at the very start of disease.
This award will advance such a strategy by using whole-genome plasma sequencing to detect two powerful signals of malignant transformation: large-scale aneuploidy and extrachromosomal DNA (ecDNA), highly amplified circular DNA fragments that appear early and frequently in ovarian cancer. The project will merge single-cell genomic and chromatin-accessibility profiling of precursor lesions with a next-generation allele frequency framework to pinpoint when these abnormalities first arise and when they begin to shed into plasma. Integrating these multimodal insights will enable the development of plasma-only classifiers capable of registering even subtle chromosomal imbalances or ecDNA-driven amplifications. Rigorous testing in longitudinal and high-risk cohorts will determine their sensitivity across the entire precursor-to-cancer continuum. If successful, this approach could reimagine ovarian cancer surveillance, inform targeted prevention, and eventually support population-level screening for a disease that urgently needs earlier detection options.