A Gene Expression Signature to Select Hepatocellular Carcinoma Patients for Liver Transplantation.

OBJECTIVE: To propose a new decision algorithm combining biomarkers measured in a tumor biopsy with clinical variables, to predict recurrence after liver transplantation (LT). BACKGROUND: Liver cancer is one of the most frequent causes of cancer-related mortality. LT is the best treatment for hepatocellular carcinoma (HCC) patients but the scarcity of organs makes patient selection a critical step. In addition, clinical criteria widely applied in patient eligibility decisions miss potentially curable patients while selecting patients that relapse after transplantation. METHODS: A literature systematic review singled out candidate biomarkers whose RNA levels were assessed by quantitative PCR in tumor tissue from 138 HCC patients submitted to LT (>5 years follow up, 32% beyond Milan criteria). The resulting 4 gene signature was combined with clinical variables to develop a decision algorithm using machine learning approaches. The method was named HepatoPredict. RESULTS: HepatoPredict identifies 99% disease-free patients (>5 year) from a retrospective cohort, including many outside clinical criteria (16%-24%), thus reducing the false negative rate. This increased sensitivity is accompanied by an increased positive predictive value (88.5%-94.4%) without any loss of long-term overall survival or recurrence rates for patients deemed eligible by HepatoPredict; those deemed ineligible display marked reduction of survival and increased recurrence in the short and long term. CONCLUSIONS: HepatoPredict outperforms conventional clinical-pathologic selection criteria (Milan, UCSF), providing superior prognostic information. Accurately identifying which patients most likely benefit from LT enables an objective stratification of waiting lists and information-based allocation of optimal versus suboptimal organs.

Centrosome amplification arises before neoplasia and increases upon p53 loss in tumorigenesis.

Centrosome abnormalities are a typical hallmark of human cancers. However, the origin and dynamics of such abnormalities in human cancer are not known. In this study, we examined centrosomes in Barrett's esophagus tumorigenesis, a well-characterized multistep pathway of progression, from the premalignant condition to the metastatic disease. This human cancer model allows the study of sequential steps of progression within the same patient and has representative cell lines from all stages of disease. Remarkably, centrosome amplification was detected as early as the premalignant condition and was significantly expanded in dysplasia. It was then present throughout malignant transformation both in adenocarcinoma and metastasis. The early expansion of centrosome amplification correlated with and was dependent on loss of function of the tumor suppressor p53 both through loss of wild-type expression and hotspot mutations. Our work shows that centrosome amplification in human tumorigenesis can occur before transformation, being repressed by p53. These findings suggest centrosome amplification in humans can contribute to tumor initiation and progression.