Liver allocation for transplant candidates with hepatocellular carcinoma is limited by OPTN policy to patients whose tumors remain within size limits defined by Milan criteria. More accurate markers of HCC recurrence beyond size and number are needed. We retrospectively analyzed a series of HCC from transplant patients to seek molecular correlates of posttransplant tumor recurrence

Differential gene expression analysis was performed by RNA-Seq on FFPE HCC samples from 50 liver explants. 41 of these HCC also had matched normal tissue allowing assessment of somatic mutation and copy number variation (CNV) by whole exome sequencing. 814 genes were upregulated, and 351 downregulated, in tumors from patients with posttransplant recurrence (n=24) compared to those who were recurrence-free (n=26). Differential CNV in recurrent (n=17) vs. nonrecurrent (n=24) tumors was seen mainly in chromosomes 4, 6, 13 and 14. TP53 was commonly deleted (9/41) but showed no association with recurrence. Individual mutations correlated with outcomes; however, mutations aggregated within pathways and the latter were used in subsequent model assembly. Six of the top 20 enrichment pathways had direct relevance for inflammation/immune response.

Several prediction models were constructed based on individual variables (differential gene expression, CNV of individual genes, pathways related to mutation patterns) and combinations thereof using a K top scoring pairs approach. A model combining differential gene analysis and mutations gave the best discriminative performance for recurrence/non-recurrence status with sensitivity of 88% and specificity of 88%.

This preliminary study demonstrates that molecular features of HCC correlate with posttransplant tumor behavior. Validation studies are necessary and are currently underway at our institution. In addition to primary tumor characteristics, other factors such as peritumoral microenvironment and transplant-associated injury of the donor liver can also impact tumor behavior. Incorporation of these features into future models may further refine our ability to predict posttransplant tumor recurrence risk in individual patients.

CITATION INFORMATION: Humar A., Liu P., Luo J., Nalesnik M., Singhi A., Tseng G., Michalopoulos G. Prediction of Hepatocellular Carcinoma Recurrence Following Liver Transplantation Using Combined Differential Gene Expression, Copy Number Variation and Somatic Mutation Analysis Am J Transplant. 2017;17 (suppl 3).

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