Retrospective Clinical Validation of HepatoPredict

Overview

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.

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.