The UCLA Center in Early Detection of Liver Cancer
Project Number5U01CA230705-07
Former Number5U01CA230705-05
Contact PI/Project LeaderZHOU, XIANGHONG JASMINE Other PIs
Awardee OrganizationUNIVERSITY OF CALIFORNIA LOS ANGELES
Description
Abstract Text
Project Summary
Liver cancer is the second most frequent cause of cancer deaths. While cancer mortality has been declining
overall, liver cancer mortality has been rising steadily due to the increasing incidence of obesity and alcohol
abuse. This high mortality is mainly due to challenges in early detection. The goal of this project is to integrate
molecular, imaging, and clinical information for the early detection of Hepatocellular Carcinoma (HCC). In the
previous grant period, we developed a highly sensitive and cost-effective method of using cell-free DNA
methylome for cancer detection, and have successfully validated the technology on a pilot HCC versus cirrhosis
cohort. We have also established multiple clinical cohorts to study HCC detection, including a prospective
longitudinal HCC screening cohort of patients with cirrhosis or HBV. The availability of blood samples, imaging
scans, and comprehensive clinical information of patients in those cohorts provide us with unique opportunities
to develop an integrated cancer detection method in the next funding period. The UCLA center includes a
multidisciplinary team of clinical and translational researchers, and we have the following foci in the next funding
period: (1) Continued expansion of multiple clinical cohorts to facilitate early detection of liver cancer. These
cohorts shall provide an invaluable resource for the proposed research and other consortium-wide projects. (2)
Continued experimental and computational development to refine the cfDNA methylome assay for early cancer
detection. Experimentally, we will address a key challenge in cfDNA-based early cancer detection, namely, the
limited cfDNA input, and computationally we will extract additional features, in addition to DNA methylation, from
the assay data to enhance cancer detection. (3) We will integrate epigenomic, imaging, and clinical information
into a complementary approach for the detection of HCC. We will build etiology-specific models for the multi-
modality data to maximize performance. (4) We will contribute to collaborative consortium activities. We have
been enthusiastically participating in collaborations within and beyond the consortium. We are an active
enrollment site for the consortium-wide clinical cohorts, and we will collaborate with other centers to develop
multi-omics and multi-modality markers to exploit the diagnostic potential of blood samples and imaging data.
Public Health Relevance Statement
PROJECT NARRATIVE
This project will develop and validate a method to integrate blood, imaging, and clinical data for the early
detection of liver cancer. This test could fill an urgent demand to benefit patients, who are at risk of developing
liver cancer.
NIH Spending Category
No NIH Spending Category available.
Project Terms
AddressAlcohol abuseBiological AssayBiological MarkersBloodBlood specimenCancer DetectionCancer EtiologyCessation of lifeCirrhosisClassificationClinicalClinical DataCollaborationsCopy Number PolymorphismDNADNA MethylationDNA methylation profilingDataDetectionDevelopmentDiagnosticEarly DiagnosisEnrollmentEtiologyFundingGoalsGrantHepatitis B VirusImageImmuneIncidenceLearningLibrariesLiver diseasesLongitudinal cohortMagnetic Resonance ImagingMalignant NeoplasmsMalignant neoplasm of liverMethodsModelingMolecularNamesNoduleObesityPatientsPerformancePhasePreparationPrimary carcinoma of the liver cellsProspective cohortProteinsProtocols documentationRNAResearchResourcesRetrospective cohortRiskScanningScreening for Hepatocellular CancerScreening for cancerSiteStructureTestingTrainingValidationbioinformatics toolbisulfite sequencingcell free DNAcohortcostcost effectivedata modalitiesdetection methodepigenomicsexosomegenome wide methylationimprovedmethylomemicrobial compositionmortalitymultidisciplinarymultimodal datamultimodal modelingmultimodalitymultiple omicsparticipant enrollmentprospectivescreeningtechnology validationtraining datatranslational scientistultrasoundwhole genome
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Publications
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Outcomes
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Clinical Studies
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History
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