Gonçalves-Reis M, Proença D, Frazão LP, Neto JL, Silva S, Pinto-Marques H, Pereira-Leal JB, Cardoso J. Analytical validation and algorithm improvement of HepatoPredict kit to assess hepatocellular carcinoma prognosis before a liver transplantation.
Pract Lab Med 2024;
39:e00365. [PMID:
38371895 PMCID:
PMC10869278 DOI:
10.1016/j.plabm.2024.e00365]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 01/22/2024] [Accepted: 01/23/2024] [Indexed: 02/20/2024] Open
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.
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