e16617 Background: Hepatocellular carcinoma (HCC) is the most common primary malignancy of the liver and has significant cancer-related mortality worldwide. Molecularly matched therapy has an emerging role in HCC treatment; however, HCC poses a unique challenge as patients are frequently diagnosed radiographically rather than by tissue biopsy, limiting our ability to utilize tumor tissue data for therapy selection. The purpose of this study is to establish a multi-omic database with matched demographic and clinical data to identify patterns of shared molecular aberrations in HCC tumors. Methods: An institutional HCC cancer registry was created, and a retrospective review was performed using data from 2017-2020 at a tertiary cancer center. 56 patients with advanced HCC were identified, and tissue samples were analyzed with multi-omic profiling assays (next generation DNA sequencing, RNA sequencing, protein expression via immunohistochemistry) versus ctDNA from peripheral blood. Results: Next generation sequencing was obtained for 38 (76%) patients using either tissue or ctDNA. While the results were similar, a higher number of total mutations were identified using tumor tissues rather than ctDNA. The average variant allele fraction for tumor tissues was approximately 10x higher than the variant allele fractions identified by ctDNA. However, ctDNA sequencing identified a higher number of potentially actionable mutations, on average, than did tissue testing. The most commonly mutated genes included TERT (42% of patients), TP53 (47%), CTNNB1 (34%), ARID1A (18%), CCND1 (16%), and NEF2L2 (13%). Several patients had serial ctDNA sequencing, which showed an increase in actionable and total mutations over time as well as changes in variant allele fractions in response to therapy. Conclusions: Next generation sequencing of ctDNA can be used in lieu of tumor tissue in patients diagnosed with HCC radiographically to identify actionable mutations, but it may underestimate the mutational burden of HCC tumors. Serial analyses with cell free DNA may be useful to monitor clonal evolution in response to targeted treatment and may identify new actionable mutations as patients progress on systemic therapies. [Table: see text]
Next Generation Cancer Registry; Opportunities of Web 3.0 and Physician Perspective
