The combination of hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA) is a rare primary liver malignancy known as combined HCC and CCA (cHCC-CCA). The morphologic features of cHCC-CCA pose challenges for evidence-guided management.
“The two most frequent forms of primary liver carcinomas (HCC and CCA) are ontologically, morphologically, and clinically distinct. However, as evoked by its name, cHCC-CCA is a poorly understood, aggressive rare primary liver cancer that exhibits morphologic characteristics of both HCC and CCA,” Karthikeyan Murugesan, MS, of Foundation Medicine, and colleagues noted in a recent study.1 “As such, cHCC-CCA is an extremely challenging disease regarding both diagnosis and management.”
For the study, Ms Murugesan and colleagues used comprehensive genomic profiling in a large series of patients with HCC, CCA, or cHCC-CCA as part of routine clinical care to identify genomic alterations enriched in HCC and CCA. Based on their findings, they built a machine-learning model to classify cHCC-CCA as HCC-like or CCA-like, by integrating genomic-derived data.
They analyzed the genomic profiles of 73 patients with cHCC-CCA, 4975 with CCA, and 1470 patients with HCC, all generated from a targeted exome next-generation sequencing assay used in the course of clinical care. The cases were reviewed for genomic alterations, tumor mutational burden, microsatellite instability status, genomic loss of heterozygosity, chromosomal aneuploidy, genomic ancestry, and hepatitis B virus (HBV) infection.
Among the cHCC-CCA cases, the researchers observed a median of 4 genomic alterations per tumor. Frequently altered genes in this cohort were TP53 (65.8%), TERT (49.3%), and PTEN (9.6%), and 24.6% of cHCC-CCA cases harbored potentially targetable genomic alterations. Other genomic alterations predominantly associated with CCA and with HCC include, but are not limited to, TERT, FGFR2, IDH1, and the presence of HBV infection.
Based on these features, a machine-learning model was trained to classify a cHCC-CCA case as CCA-like or as HCC-like. In total, 16.4% (12/73) of the cHCC-CCA cases were classified as CCA-like, 57.5% (42/73) cases as HCC-like, and the remaining 26.3% (19/73) of cases were classified as ambiguous. In addition, the model accurately classified more than 70% of cHCC-CCA cases as CCA-like or HCC-like using only genomic features.
“The concept of cHCC-CCA itself should continue to be critically evaluated as to what the implications are for etiology of CCA-like and HCC-like cHCC-CCA,” the investigators concluded. “The genomic testing platform combined with the ML [machine learning]-classifier developed here allow for easy classification of cHCC-CCA in the course of clinical care by use of a clinically validated test. However, further investigation is needed to identify the clinical impact of these findings.”
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