Analytical validation of HepatoPredict kit to assess hepatocellular carcinoma prognosis prior to a liver transplantation

Background The best curative treatment for hepatocellular carcinoma (HCC) is liver transplant (LT), but the limited number of organs available for LT dictates strict eligibility criteria. Despite this patient selection stringency, current criteria often fail in pinpointing patients at risk of HCC relapse and in identifying good prognosis patients that could benefit from a LT. HepatoPredict kit was developed and clinically validated to forecast the benefit of LT in patients diagnosed with HCC. By combining clinical variables and a gene expression signature in an ensemble of machine learning algorithms, HepatoPredict stratifies HCC patients according to their risk of relapse after LT. Methods Aim: ing at the characterization of the analytical performance of HepatoPredict kit in terms of sensitivity, specificity and robustness, several variables were tested which included reproducibility between operators and between RNA extractions and RT-qPCR runs, interference of input RNA levels or varying reagent levels. The described methodologies, included in the HepatoPredict kit, were tested according to analytical validation criteria of multi-target genomic assays described in guidelines such as ISO201395-2019, MIQE, CLSI-MM16, CLSI-MM17, and CLSI-EP17-A. Furthermore, a new retrained version of the HepatoPredict algorithms is also presented and tested. Results The results of the analytical performance demonstrated that the HepatoPredict kit performed within the required levels of robustness ( p > 0.05), analytical specificity (inclusivity ≥ 95 %), and sensitivity (LoB, LoD, linear range, and amplification efficiency between 90 – 110 %). The introduced operator, equipment, input RNA and reagents into the assay had no significant impact on HepatoPredict classifier results. As demonstrated in a previous clinical validation, a new retrained version of the HepatoPredict algorithm still outperformed current clinical criteria, in the accurate identification of HCC patients that more likely will benefit from a LT. Conclusions Despite the variations in the molecular and clinical variables, the prognostic information obtained with HepatoPredict kit and does not change and can accurately identify HCC patients more likely to benefit from a LT. HepatoPredict performance robustness also validates its easy integration into standard diagnostic laboratories.