March, 2023
PLAIN LANGUAGE SUMMARY
Defining incidence and complications of fibrolamellar liver cancer through tiered computational analysis of clinical data
Fibrolamellar cancer is generally regarding as rare. Why does that matter? Underestimating the incidence of a disease can bias caregivers to miss the diagnosis. It can cause them to confuse it with an apparently similar disease, resulting in ineffective or even harmful treatment. It can diminish public attention to the disease, thus reducing the resources provided for its study and development of treatments.
This study suggests fibrolamellar may be more common than had been thought. The authors analyzed a clinical database at the University of California, San Francisco, and found the incidence to be five to eight fold higher than previous estimates. Additionally, the authors analyzed the same data base and associated laboratory data to consider hyperammonemia, a complication of fibrolamellar cancer that can cause mental confusion and even death. The analysis of patients in the database with hyperammonemia emphasize that hyperammonemia may be insufficiently recognized in fibrolamellar. They showed that fibrolamellar patients have a pattern of laboratory findings consistent with a previous reported source of the problem in fibrolamellar (see paper from March 2017: A Proposed Physiopathological Pathway to Hyperammonemic Encephalopathy in a Non-Cirrhotic Patient with Fibrolamellar Hepatocellular Carcinoma without Ornithine Transcarbamylase (OTC) Mutation.)
Understanding a rare disease requires that it be correctly diagnosed. An accurate understanding of its prevalence will help avoid misdiagnosis and underdiagnosis.
Written by Dr. Phil Coffino
Author’s abstract:
The incidence and biochemical consequences of rare tumor subtypes are often hard to study. Fibrolamellar liver cancer (FLC) is a rare malignancy affecting adolescents and young adults. To better characterize the incidence and biochemical consequences of this disease, we combined a comprehensive analysis of the electronic medical record and national payer data and found that FLC incidence is likely five to eight times higher than previous estimates. By employing unsupervised learning on clinical laboratory data from patients with hyperammonemia, we find that FLC-associated hyperammonemia mirrors metabolic dysregulation in urea cycle disorders. Our findings demonstrate that advanced computational analysis of rich clinical datasets can provide key clinical and biochemical insights into rare cancers.
