Computational extraction and analysis of de-identified medical records to characterize hyperammonemia in patients with fibrolamellar carcinoma (FLC).

Abstract

e16169 Background: Rare cancers including FLC make up 25% of adult tumors, but are difficult to study due to low incidence and incomplete case identification. FLC occurs in adolescents and young adults without liver dysfunction. Hyperammonemia has been frequently reported in FLC patients, but is poorly understood. Methods: Data from three clinical trials allowed us to establish the incidence of hyperammonemia (serum ammonia value > 75 µmol/L) in FLC. In these studies, FLC patients received everolimus, estrogen deprivation therapy (EDT) with leuprolide + letrozole or everolimus + EDT (Oncologist. 2020 25(11):925-e1603), ENMD-2076 (Oncologist. 2020 25(12):e1837-e1845), or neratinib (J Clin Oncol 39, 2021 (suppl 3; abstr 310); ammonia was tested prospectively in the latter two studies. To assess impacts of cancer therapy or liver dysfunction, we studied hyperammonemia in FLC and non-FLC patients at UCSF in parallel. Using Natural Language Processing (NLP) of pathology reports and oncology notes from > 2300 liver cancer patients from the last 12 years of UCSF records, we identified a cohort of patients with FLC, contrasting their laboratory data to all UCSF patients with ammonia testing for the last 10 years. We used leiden clustering and umap dimensionality reduction to contrast FLC and other patients to assess the clinical context of hyperammonemia. Results: Data from the 3 trials showed hyperammonemia in 10 of 32 (31.3%) FLC patients during study participation, independent of the therapy received. These patients exhibited hyperammonemia with varying levels, and at different points in their treatment. NLP identified 37 patients with FLC ( < 0.1% of liver cancer patients), with 33% showing hyperammonemia. Across all UCSF patients, we found 24,000 independent visits where ammonia was tested, with > 2400 demonstrating hyperammonemia. Using leidan clustering on all encounters with ammonia > 75 µmol/L, we found distinct subsets of hyperammonemia corresponding to known metabolic and physiologic processes (e.g., fulminant liver failure, tumor lysis syndrome, etc). FLC patients clustered separately from hepatocellular carcinoma patients with hyperammonemic encephalopathy due to cirrhosis. Conclusions: NLP of large EMRs is a valuable tool to study FLC, a rare cancer. Herein, we have defined hyperammonemia as a frequent event in FLC, not directly linked to hepatic dysfunction or individual therapies. Further investigation may determine whether hyperammonemia is related to FLC tumor biology.