Transcriptome and Exome Analyses of Hepatocellular Carcinoma Reveal Patterns to Predict Cancer Recurrence in Liver Transplant Patients

• Published in: Hepatology communications Vol. 6; no. 4; pp. 710 - 727
• Main Authors: Liu, Silvia, Nalesnik, Michael A., Singhi, Aatur, Wood‐Trageser, Michelle A., Randhawa, Parmjeet, Ren, Bao‐Guo, Humar, Abhinav, Liu, Peng, Yu, Yan‐Ping, Tseng, George C., Michalopoulos, George, Luo, Jian‐Hua
• Format: Journal Article
• Language: English
• Published: United States Wolters Kluwer Health Medical Research, Lippincott Williams & Wilkins 01.04.2022; John Wiley and Sons Inc; Wolters Kluwer Health/LWW
• Subjects: Biomarkers; Carcinoma, Hepatocellular - diagnosis; Cell division; Discriminant analysis; Exome - genetics; Genomes; Hepatitis B; Hepatitis C; Humans; Hyperplasia; Liver cancer; Liver Neoplasms - diagnosis; Liver Transplantation; Liver transplants; Mutation; Neoplasm Recurrence, Local - diagnosis; Original; Retrospective Studies; Transcriptome - genetics; Tumors; Whole Exome Sequencing
• ISSN: 2471-254X; 2471-254X
• DOI: 10.1002/hep4.1846

Hepatocellular carcinoma (HCC) is one of the most lethal human cancers. Liver transplantation has been an effective approach to treat liver cancer. However, significant numbers of patients with HCC experience cancer recurrence, and the selection of suitable candidates for liver transplant remains a challenge. We developed a model to predict the likelihood of HCC recurrence after liver transplantation based on transcriptome and whole‐exome sequencing analyses. We used a training cohort and a subsequent testing cohort based on liver transplantation performed before or after the first half of 2012. We found that the combination of transcriptome and mutation pathway analyses using a random forest machine learning correctly predicted HCC recurrence in 86.8% of the training set. The same algorithm yielded a correct prediction of HCC recurrence of 76.9% in the testing set. When the cohorts were combined, the prediction rate reached 84.4% in the leave‐one‐out cross‐validation analysis. When the transcriptome analysis was combined with Milan criteria using the k‐top scoring pairs (k‐TSP) method, the testing cohort prediction rate improved to 80.8%, whereas the training cohort and the combined cohort prediction rates were 79% and 84.4%, respectively. Application of the transcriptome/mutation pathways RF model on eight tumor nodules from 3 patients with HCC yielded 8/8 consistency, suggesting a robust prediction despite the heterogeneity of HCC. Conclusion: The genome prediction model may hold promise as an alternative in selecting patients with HCC for liver transplant. We have determined algorithms which predict with high accuracy the possibility of a hepatocellular carcinoma (HCC) reappearing to a new transplanted liver, after the original HCC‐containing liver resection. The algorithm is based on genomic analyses of the HCC, predicated on transcriptome expression, and gene mutations in selected pathways.