Clinical trial NCT06894524: Retrospective Clinical Validation of HepatoPredict
Official title: Retrospective Clinical Validation of HepatoPredict, a Prognostic Tool for Hepatic Surgery Based on a Molecular Signature
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Hepato Cellular Carcinoma (HCC)
Liver Resection
Liver Transplantation
Keywords: HepatoPredict
Liver Transplant
Liver Resection
Prognosis
Hepatocellular Carcinoma (HCC)
HCC Recurrence
Lead sponsor: Ophiomics
Sponsor class: INDUSTRY
Collaborators: name: University Hospital Birmingham NHS Foundation Trust
First posted: 2025-03-25
Enrollment: 200
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients with early to intermediate HCC that underwent liver transplantation or liver resection, with at least 5-year clinical follow-up, that have FFPE surgical fragments/explants still available.

Brief summary: HepatoPredict is an innovative prognostic tool to support hepatologists, hepatobiliary surgeons and multidisciplinary teams in deciding on the best therapeutical approach for a patient with Hepatocellular Carcinoma, the most common type of primary liver cancer. HepatoPredict is a laboratory test that analyses a molecular signature from a small tumour sample and combines this information with details from imaging tests, such as the number of nodules and their size. Using a computational model, HepatoPredict determines whether a patient is likely to remain disease-free after hepatic surgery (good prognosis) or if there is a higher chance of the tumour returning (bad prognosis).

Current selection criteria to assess the eligibility of patients with Hepatocellular Carcinoma for liver transplantation present several problems, including:

1. selection of patients that will not benefit from a transplant. This could be due to recurrence of cancer or early death from another cause.
2. exclusion of patients who could benefit from a liver transplant but are currently not eligible;
3. increased tumor recurrence rates. Thus, improved tools that predict the likelihood of cancer coming back are needed to better assess if a patient will benefit from hepatic surgery. This will allow better use of organs, waiting list times, and improve ways of identifying the most appropriate treatments for individual patients.

HepatoPredict accurately selects patients for hepatic surgery, outperforming conventional clinical criteria. In previous retrospective studies, HepatoPredict predicted successful surgery outcomes in patients who were not eligible by currently used criteria.

This study aims to retrospectively validate the prognostic tool HepatoPredict in assessing how well a patient with Hepatocellular Carcinoma performed considering recurrence-free survival (no cancer recurrence) and overall survival after 5 years follow-up after liver surgery or liver transplantation.

Detailed description: Liver cancer remains a global health challenge with growing incidence worldwide. Hepatocellular carcinoma (HCC) is the most common liver cancer, accounting for \~90% of the cases and for 75% of the deaths. HCC is the third cancer with the highest mortality rate worldwide. Surgery is the treatment of choice for HCC, leading to the best outcomes of any available treatment in well selected candidates, with 5-year survival rates of 60-80%. Liver resection (LR) and liver transplantation (LT) represent the first treatment option for patients with early to intermediate HCC.

Eligibility for LR comprises assessment of multiple factors including liver function, portal hypertension, extent of hepatectomy and surgical invasiveness. Moreover, LR is currently the treatment of choice for non-cirrhotic patients, allowing for larger and more complex resections. Nonetheless, a significant percentage of the patients undergoing resection recur within 5 years and although with 1 year morbidity and mortality lower than LT, recurrence free survival is higher in patients submitted to LR. When possible, LT is the best curative option for patients with HCC. However, due to the small number of organs available for LT concerning the actual need for patients on waiting lists, the selection criteria for candidates for transplantation are very strict and rigorous. Current selection criteria are mainly based on clinical variables and limit transplantation to HCC patients with oncologic disease within specific parameters (such as number and diameter of tumors). Although these parameters appear to be related to disease severity and the biology of the tumor, they do not unequivocally predict the favorable prognosis of a patient with HCC who is a candidate for transplantation. Therefore, not only do these selection criteria fail in more than 30% of transplanted patients who eventually relapse, but they also exclude from transplantation many patients outside criteria with favorable prognosis and who could potentially be cured with LT. Thus, it is of extreme importance to develop predictive tools that can provide solid support in the selection of patients with HCC for transplantation. In addition, different scores such as RETREAT, Moral and R3-AFP have been proposed to prognosticate HCC recurrence post-liver transplantation as discrepancies between pre-LT tumor assessment and explant have been frequently reported. These post-surgical scores are helpful to standardize post-LT patient surveillance schemes and select candidates for adjuvant therapies. However, just as the pre-LT criteria, post-LT criteria also have several pitfalls and more accurate models to predict chance of recurrence are needed.

HepatoPredict is an innovative medical decision-making prognostic tool aimed at helping physicians and surgeons to select patients with HCC for effective hepatic surgery. This kit does not rely solely on clinical variables but also on tumor expression of a selected set of genes. This in combination with a proprietary algorithm, results in a more accurate prediction of patient outcome. While the current criteria used for selecting patients for LT, such as Milan and San Francisco criteria, only offer up to 70% success rate, HepatoPredict achieved up to 94% success.

This study aims to retrospectively validate HepatoPredict results in an independent cohort of patients with early to intermediate HCC in the context of:

1. Liver transplantation for HCC (1.1) Pre-surgery: selection of patients and (1.2) Post-surgery: assess risk of recurrence
2. Liver resection for HCC (2.1) Post-surgery: assess risk of recurrence. Archived tumour tissue from HCC patients who underwent surgery, either LT or LR, will be analyzed retrospectively using a gene expression signature. Specifically, it will use one slide H\&E-stained with the tumoral area clearly labeled by a pathologist (at least 30% of tumor cells in the marked area), and two unstained slides with 5 micrometers thickness of the same area.

RNA will be extracted from the two slides with 5 micrometers thickness and gene expression signature will be evaluated by RT-qPCR. Prognosis prediction, based on proprietary algorithm, will then be compared to the real outcome to calculate accuracy measures.

Eligibility criteria: Inclusion Criteria:

* Age ≥ 18 years old.
* Patients with HCC that underwent transplantation, with at least 5 years follow-up.
* Patients with HCC that underwent surgical resection, with at least 5 years follow-up.
* Completed informed consent process (for patient candidates that are still alive).

Exclusion Criteria:

* Age \< 18 years old.
* Other non-HCC liver cancers patients.
* Patients with tumor extra-hepatic spread at diagnosis.

Primary outcomes:
measure: Recurrence-Free Survival (RFS)
timeFrame: Prior to 2019, until the begining of the study
Secondary outcomes:
measure: Overall Survival (OS)
timeFrame: Prior to 2019, until the begining of the study
Other outcomes:
Locations:
Study officials:
References:
citation: Andrade R, Perez-Rojas J, da Silva SG, Miskinyte M, Quaresma MC, Frazao LP, Peixoto C, Cubells A, Montalva EM, Figueiredo A, Cipriano A, Goncalves-Reis M, Proenca D, Folgado A, Pereira-Leal JB, Oliveira RC, Pinto-Marques H, Tralhao JG, Berenguer M, Cardoso J. HepatoPredict Accurately Selects Hepatocellular Carcinoma Patients for Liver Transplantation Regardless of Tumor Heterogeneity. Cancers (Basel). 2025 Feb 2;17(3):500. doi: 10.3390/cancers17030500.
citation: Goncalves-Reis M, Proenca D, Frazao 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 Feb 5;39:e00365. doi: 10.1016/j.plabm.2024.e00365. eCollection 2024 Mar.
citation: Pinto-Marques H, Cardoso J, Silva S, Neto JL, Goncalves-Reis M, Proenca D, Mesquita M, Manso A, Carapeta S, Sobral M, Figueiredo A, Rodrigues C, Milheiro A, Carvalho A, Perdigoto R, Barroso E, Pereira-Leal JB. A Gene Expression Signature to Select Hepatocellular Carcinoma Patients for Liver Transplantation. Ann Surg. 2022 Nov 1;276(5):868-874. doi: 10.1097/SLA.0000000000005637. Epub 2022 Aug 1.