PROJECT SUMMARY
Ovarian cancer is the deadliest gynecological cancer, in part because there is no widely used screening test and 80% of
tumors are diagnosed at a late stage. Though ovarian cancer screening has the poten>al to diagnose tumors at early
stages when pa>ent outcomes are far be?er (90% survival for stage I cancer vs. <20% for stage III/IV), recent screening
trials of protein biomarkers have failed to show a mortality benefit. This has highlighted three main challenges in ovarian
cancer screening: (1) Avoiding false posi>ve results leading to unnecessary procedures in a disease with low popula>on
prevalence, (2) Achieving high enough sensi>vity to be a clinically useful test, and (3) Ensuring accessibility and pa>ent
compliance with screening. Much of my early PhD work, and work in my mentor’s lab, has pioneered high performance
blood tests that use cell-free DNA fragmenta>on pa?erns to detect cancer. These fragmentomic assays can detect subtle
changes to DNA shed from tumor cells even using low-coverage sequencing of <1mL of blood, and their non-invasive
nature makes them accessible and appealing to pa>ents. Given the cri7cal clinical need, the overarching goal of my
work is to advance our understanding of ovarian tumorigenesis and develop an accessible, high-performance liquid
biopsy for ovarian cancer screening. A limita>on of current liquid biopsies is that they don’t incorporate informa>on
from the ~50% of the genome comprising repeat elements, which have long been implicated in carcinogenesis but have
been difficult to study due to incomplete genome references and technical genome alignment challenges. In preliminary
work, I show a novel approach I developed that allows the study of repeat landscapes in short-read sequencing and that
has iden>fied over 800 new elements not previously implicated in cancer. The ability to use this approach in short-read
sequencing opens the door to studying repeat landscapes in cell-free DNA, where most fragments are 150-180bp in
length. I propose three specific aims: In Aim 1, I will expand this approach to develop novel bioinforma>cs approaches
enabling the study of repeat elements in cell-free DNA. In Aim 2, I will develop and test sensi>ve and specific cell-free
DNA liquid biopsies for ovarian cancer. In Aim 3, I will characterize genomic changes to repeat elements during ovarian
tumorigenesis, from normal >ssue to pre-cancerous lesion to tumor. Successful execu7on of these aims has the
poten7al to advance our understanding of the role of the so-called “dark genome” in ovarian tumorigenesis, change
the paradigm for liquid biopsies by illumina7ng cancer-related changes to repeat elements, and apply these
discoveries to a screening technology for ovarian cancer. This work may also serve as a basis for future pan-cancer
screening methods. The proposed work will provide me with outstanding mentorship and scien>fic training in basic and
transla>onal science and help me advance my career as a future physician-scien>st and independent inves>gator.
Public Health Relevance Statement
PROJECT NARRATIVE
Ovarian cancer is the deadliest gynecologic cancer with 80% of tumors diagnosed at a late stage though there is
dras9cally higher survival for early-stage disease. This study proposes to develop novel methodologies to study the >50%
of the genome comprising repeat elements in the context of ovarian tumor evolu9on and cell-free DNA, and to use
genome-wide approaches to enable a non-invasive screening technology for ovarian cancer. Development of accessible
screening and characteriza9on techniques for ovarian cancer has the poten9al to revolu9onize clinical prac9ce for this
devasta9ng disease.
NIH Spending Category
No NIH Spending Category available.
Project Terms
BenignBiologicalBiological AssayBiological MarkersBiologyBloodBlood TestsCA-125 AntigenCancer DetectionCancerousCell DeathCellsClinicalDNADNA Transposable ElementsDarknessDataDevelopmentDiagnosisDiseaseDoctor of PhilosophyEarly DiagnosisElementsEnsureEnvironmentEvaluable DiseaseFacultyFailureFutureGenomeGenomicsGoalsHumanImageLengthLesionMachine LearningMalignant - descriptorMalignant Female Reproductive System NeoplasmMalignant NeoplasmsMalignant neoplasm of lungMalignant neoplasm of ovaryMammalian OviductsMentorsMentorshipMethodologyMethodsMovementNatureOncogenesOutcomeOvarianOvarian MassPerformancePhysiciansPrevalenceQualifyingRecordsRiskRoleScienceScreening for Ovarian CancerScreening for cancerSpecificitySymptomsTechniquesTechnologyTestingTrainingUnnecessary ProceduresWomanWorkcancer genomecarcinogenesiscareercell free DNAcostdetection assaygenome-widegenome-wide analysisinsightliquid biopsyliver cancer modelmortalityneoplastic cellnovelnovel strategiesovarian neoplasmpremalignantprofessorprotein biomarkersreference genomescreeningtranscriptometumortumor diagnosistumorigenesis
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