Analytical validation and algorithm improvement of HepatoPredict kit to assess hepatocellular carcinoma prognosis before a liver transplantation

Maria Gonçalves-Reis; Daniela Proença; Laura P Frazão; João L Neto; Sílvia Silva; Hugo Pinto-Marques; José B Pereira-Leal; Joana Cardoso

doi:10.1016/j.plabm.2024.e00365

Analytical validation and algorithm improvement of HepatoPredict kit to assess hepatocellular carcinoma prognosis before a liver transplantation

Pract Lab Med. 2024 Feb 5:39:e00365. doi: 10.1016/j.plabm.2024.e00365. eCollection 2024 Mar.

Authors

Maria Gonçalves-Reis¹, Daniela Proença¹, Laura P Frazão¹, João L Neto¹, Sílvia Silva², Hugo Pinto-Marques^{2

3}, José B Pereira-Leal¹, Joana Cardoso¹

Affiliations

¹ Ophiomics - Precision Medicine, Lisbon, Portugal.
² Hepato-Biliary-Pancreatic and Transplantation Centre, Curry Cabral Hospital, Centro Hospitalar Universitário de Lisboa Central, Lisbon, Portugal.
³ Chronic Diseases Research Center (CEDOC), NOVA Medical School, Universidade NOVA de Lisboa (NMS/UNL), Lisbon, Portugal.

Abstract

Objectives: To verify the analytical performance of the HepatoPredict kit, a novel tool developed to stratify Hepatocellular Carcinoma (HCC) patients according to their risk of relapse after a Liver Transplantation (LT).

Methods: The HepatoPredict tool combines clinical variables and a gene expression signature in an ensemble of machine-learning algorithms to forecast the benefit of a LT in HCC patients. To ensure the accuracy and reliability of this method, extensive analytical validation was conducted to verify its specificity and robustness. The experiments were designed following the guidelines for multi-target genomic assays such as ISO201395-2019, MIQE, CLSI-MM16, CLSI-MM17, and CLSI-EP17-A. The validation process included reproducibility between operators and between RNA extractions and RT-qPCR runs, and interference of input RNA levels or varying reagent levels. A recently retrained version of the HepatoPredict algorithms was also tested.

Results: The validation process demonstrated that the HepatoPredict kit met the required standards for robustness (p > 0.05), analytical specificity (inclusivity of 95 %), and sensitivity (LoB, LoD, linear range, and amplification efficiency between 90 and 110 %). The operator, equipment, input RNA, and reagents used had no significant effect on the HepatoPredict results. Additionally, the testing of a recently retrained version of the HepatoPredict algorithm, showed that this new version further improved the accuracy of the kit and performed better than existing clinical criteria in accurately identifying HCC patients who are more likely to benefit LT.

Conclusions: Even with the introduced variations in molecular and clinical variables, the HepatoPredict kit's prognostic information remains consistent. It can accurately identify HCC patients who are more likely to benefit from a LT. Its robust performance also confirms that it can be easily integrated into standard diagnostic laboratories.

Keywords: Analytical validation; HepatoPredict; Hepatocellular carcinoma; Liver transplantation; Multi-target genomic assay; Prognostic.