Genomic Profiling of Combined Hepatocellular Cholangiocarcinoma Reveals Genomics Similar to Either Hepatocellular Carcinoma or Cholangiocarcinoma. (August 2021)

• Record Type: Journal Article
• Authors: Murugesan, Karthikeyan; Sharaf, Radwa; Montesion, Meagan; Moore, Jay A.; Pao, James; Pavlick, Dean C.; Frampton, Garrett M.; Upadhyay, Vivek A.; Alexander, Brian M.; Miller, Vincent A.; Javle, Milind M.; Bekaii Saab, Tanios S.; Albacker, Lee A.; Ross, Jeffrey S.; Ali, Siraj M.
• Is Part Of: JCO precision oncology. Volume 5(2021)
• Publication Date: 2021-08
• Subjects: Precision Medicine; Neoplasms; Pharmacogenetics; Molecular Targeted Therapy; Personalized medicine; Oncology; Pharmacogenomics; Periodical; Periodicals; 616.994
• DOI: 10.1200/PO.20.00397
• Languages: English
• ISSNs: 2473-4284

Abstract

Abstract : PURPOSE: Combined hepatocellular cholangiocarcinoma (cHCC-CCA) is a rare, aggressive primary liver carcinoma, with morphologic features of both hepatocellular carcinomas (HCC) and liver cholangiocarcinomas (CCA). METHODS: The genomic profiles of 4, 975 CCA, 1, 470 HCC, and 73 cHCC-CCA cases arising from comprehensive genomic profiling in the course of clinical care were reviewed for genomic alterations (GA), tumor mutational burden, microsatellite instability status, genomic loss of heterozygosity, chromosomal aneuploidy, genomic ancestry, and hepatitis B virus status. RESULTS: In cHCC-CCA, GA were most common in TP53 (65.8%), TERT (49.3%), and PTEN (9.6%), and 24.6% cHCC-CCA harbored potentially targetable GA. Other GA were predominantly associated with either HCC or CCA, including, but not limited to, TERT, FGFR2, IDH1, and presence of hepatitis B virus. On the basis of these features, a machine learning (ML) model was trained to classify a cHCC-CCA case as CCA-like or HCC-like. Of cHCC-CCA cases, 16% (12/73) were ML-classified as CCA-like and 58% (42/73) cHCC-CCA were ML-classified as HCC-like. The ML model classified more than 70% of cHCC-CCA as CCA-like or HCC-like on the basis of genomic profiles, without additional clinico-pathologic input. CONCLUSION: These findings demonstrate the use of ML for classification as based on a targeted exome panel used during routine clinical care. Classification of cHCC-CCA by genomic features alone creates insights into the biology of the disease and warrants further investigation for relevance to clinical care.