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Active NON-SBIR/STTR RPGS NIH (US)

Evaluation of treatment predictors reflecting beta-catenin activation in hepatocellular carcinoma

$5.87M USD

Funder NATIONAL CANCER INSTITUTE
Recipient Organization Queen's Medical Center
Country United States
Start Date Jul 01, 2021
End Date Jun 30, 2028
Duration 2,556 days
Number of Grantees 1
Roles Principal Investigator
Data Source NIH (US)
Grant ID 10693135
Grant Description

PROJECT SUMMARY/ABSTRACT Hepatocellular carcinoma (HCC) is a leading cause of cancer mortality worldwide and its incidence is rising in both men and women in the United States. Anti-PD1 and anti-PD-L1 immune checkpoint inhibitor (ICI) antibodies are now FDA approved for advanced HCC, however, as few as 20% of patients receiving these agents

will show an objective response to therapy. Because immune-related adverse events are non-trivial, predictive biomarkers that can explain the variability in immunotherapy response are needed to optimize patient selection. Several lines of research have recently converged to associate oncogenic activation of the Wnt/beta-

catenin signaling pathway with tumor immune-evasion and poor clinical response to ICI therapy in HCC. In previous research, we found that HCC exhibiting high uptake of the positron emission tomography / computed tomography (PET/CT) imaging agent 18F- fluorocholine (FCH) often belonged to molecular tumor sub-types

associated with beta-catenin activation and immune avoidance. Liquid biopsy based on targeted sequencing of cell-free DNA (cfDNA) has also made it possible to identify patients who have tumors that harbor mutations associated with increased Wnt/beta-catenin signaling. This project comprises a phase 2 biomarker clinical trial to prospectively evaluate these specific

embodiments of PET/CT and liquid biopsy as tools for detecting HCC recalcitrant to ICI therapy on the basis of beta-catenin activation. In addition to characterizing and comparing the predictive capabilities of FCH PET/CT and cfDNA mutation profiling based on phase 2 clinical endpoints, this project will utilize decision tree based

machine learning to estimate the predictive performance of an integrative imaging-genomic biomarker while also further examining how tumor mutations are related to PET metabolic phenotype and immunotherapy response. Furthermore, because tumor 18F-fluorodeoxyglucose (FDG) uptake is incongruent with FCH uptake in HCC, a

third aim will utilize the trial as a molecular screening process to create an enriched sub-cohort of patients with FDG-avid tumors. These patients will undergo serial FDG PET/CT to evaluate FDG as a source of predictive biomarkers of ICI response for an orthogonal molecular sub-type of HCC. If these diagnostic tests are found

reliable at predicting tumor resistance/response, they could significantly enhance the clinical precision and overall benefit of immunotherapy for HCC and possibly other cancers.

All Grantees

Queen's Medical Center

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