Generalizable transcriptome-based tumor malignant level evaluation and molecular subtyping towards precision oncology.

In cancer treatment, therapeutic strategies that integrate tumor-specific characteristics (i.e., precision oncology) are widely implemented to provide clinical benefits for cancer patients. Here, through in-depth integration of tumor transcriptome and patients' prognoses across cancers, we investigated dysregulated and prognosis-associated genes and catalogued such important genes in a cancer type-dependent manner. Utilizing the expression matrices of these genes, we built models to quantitatively evaluate the malignant levels of tumors across cancers, which could add value to the clinical staging system for improved prediction of patients' survival. Furthermore, we performed a transcriptome-based molecular subtyping on hepatocellular carcinoma, which revealed three subtypes with significantly diversified clinical outcomes, mutation landscapes, immune microenvironment, and dysregulated pathways. As tumor transcriptome was commonly profiled in clinical practice with low experimental complexity and cost, this work proposed easy-to-perform approaches for practical clinical promotion towards better healthcare and precision oncology of cancer patients.

Machine learning and multi-omics data reveal driver gene-based molecular subtypes in hepatocellular carcinoma for precision treatment.

The heterogeneity of Hepatocellular Carcinoma (HCC) poses a barrier to effective treatment. Stratifying highly heterogeneous HCC into molecular subtypes with similar features is crucial for personalized anti-tumor therapies. Although driver genes play pivotal roles in cancer progression, their potential in HCC subtyping has been largely overlooked. This study aims to utilize driver genes to construct HCC subtype models and unravel their molecular mechanisms. Utilizing a novel computational framework, we expanded the initially identified 96 driver genes to 1192 based on mutational aspects and an additional 233 considering driver dysregulation. These genes were subsequently employed as stratification markers for further analyses. A novel multi-omics subtype classification algorithm was developed, leveraging mutation and expression data of the identified stratification genes. This algorithm successfully categorized HCC into two distinct subtypes, CLASS A and CLASS B, demonstrating significant differences in survival outcomes. Integrating multi-omics and single-cell data unveiled substantial distinctions between these subtypes regarding transcriptomics, mutations, copy number variations, and epigenomics. Moreover, our prognostic model exhibited excellent predictive performance in training and external validation cohorts. Finally, a 10-gene classification model for these subtypes identified TTK as a promising therapeutic target with robust classification capabilities. This comprehensive study provides a novel perspective on HCC stratification, offering crucial insights for a deeper understanding of its pathogenesis and the development of promising treatment strategies.

A novel liver zonation phenotype-associated molecular classification of hepatocellular carcinoma.

Background: Liver zonation is a unique phenomenon in which the liver exhibits distinct functions among hepatocytes along the radial axis of the lobule. This phenomenon can cause the sectionalized initiation of several liver diseases, including hepatocellular carcinoma (HCC). However, few studies have explored the zonation features of HCC. Methods: Four single-cell RNA sequencing datasets were used to identify hepatocyte-specific zonation markers. Integrative analysis was then performed with a training RNA-seq cohort (616 HCC samples) and an external validating microarray cohort (285 HCC samples) from the International Cancer Genome Consortium, The Cancer Genome Atlas, Gene Expression Omnibus, and EMBL's European Bioinformatics Institute for clustering using non-negative matrix factorization consensus clustering based on zonation genes. Afterward, we evaluated the prognostic value, clinical characteristics, transcriptome and mutation features, immune infiltration, and immunotherapy response of the HCC subclasses. Results: A total of 94 human hepatocyte-specific zonation markers (39 central markers and 55 portal markers) were identified for the first time. Subsequently, three subgroups of HCC, namely Cluster1, Cluster2, and Cluster3 were identified. Cluster1 exhibited a non-zonational-like signature with the worst prognosis. Cluster2 was intensively associated with a central-like signature and exhibited low immune infiltration and sensitivity toward immune blockade therapy. Cluster3 was intensively correlated with a portal-like signature with the best prognosis. Finally, we identified candidate therapeutic targets and agents for Cluster1 HCC samples. Conclusion: The current study established a novel HCC classification based on liver zonation signature. By classifying HCC into three clusters with non-zonational-like (Cluster1), central-like (Cluster2), and portal-like (Cluster3) features, this study provided new perspectives on the heterogeneity of HCC and shed new light on delivering precision medicine for HCC patients.

Identification of hepatocellular carcinoma subtypes based on PcG-related genes and biological relevance with cancer cells.

BACKGROUND: Hepatocellular carcinoma (HCC) is an extensive heterogeneous disease where epigenetic factors contribute to its pathogenesis. Polycomb group (PcG) proteins are a group of subunits constituting various macro-molecular machines to regulate the epigenetic landscape, which contributes to cancer phenotype and has the potential to develop a molecular classification of HCC. RESULTS: Here, based on multi-omics data analysis of DNA methylation, mRNA expression, and copy number of PcG-related genes, we established an epigenetic classification system of HCC, which divides the HCC patients into two subgroups with significantly different outcomes. Comparing these two epigenetic subgroups, we identified different metabolic features, which were related to epigenetic regulation of polycomb-repressive complex 1/2 (PRC1/2). Furthermore, we experimentally proved that inhibition of PcG complexes enhanced the lipid metabolism and reduced the capacity of HCC cells against glucose shortage. In addition, we validated the low chemotherapy sensitivity of HCC in Group A and found inhibition of PRC1/2 promoted HCC cells' sensitivity to oxaliplatin in vitro and in vivo. Finally, we found that aberrant upregulation of CBX2 in Group A and upregulation of CBX2 were associated with poor prognosis in HCC patients. Furthermore, we found that manipulation of CBX2 affected the levels of H3K27me3 and H2AK119ub. CONTRIBUTIONS: Our study provided a novel molecular classification system based on PcG-related genes data and experimentally validated the biological features of HCC in two subgroups. Our founding supported the polycomb complex targeting strategy to inhibit HCC progression where CBX2 could be a feasible therapeutic target.

Classification of Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma Based on Radiomic Analysis.

INTRODUCTION: Considering the narrow window of surgery, early diagnosis of liver cancer is still a fundamental issue to explore. Hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICCA) are considered as two different types of liver cancer because of their distinct pathogenesis, pathological features, prognosis, and responses to adjuvant therapies. Qualitative analysis of image is not enough to make a discrimination of liver cancer, especially early-stage HCC or ICCA. METHODS: This retrospective study developed a radiomic-based model in a training cohort of 122 patients. Radiomic features were extracted from computed tomography (CT) scans. Feature selection was operated with the least absolute shrinkage and operator (LASSO) logistic method. The support vector machine (SVM) was selected to build a model. An internal validation was conducted in 89 patients. RESULTS: In the training set, the AUC of the evaluation of the radiomics was 0.855 higher than for radiologists at 0.689. In the valuation cohorts, the AUC of the evaluation was 0.847 and the validation was 0.659, which indicated that the established model has a significantly better performance in distinguishing the HCC from ICCA. CONCLUSION: We developed a radiomic diagnosis model based on CT image that can quickly distinguish HCC from ICCA, which may facilitate the differential diagnosis of HCC and ICCA in the future.

Molecular classification of hepatocellular carcinoma: prognostic importance and clinical applications.

Hepatocellular carcinoma (HCC) is a lethal human malignancy with a very low overall and long-term survival rate. Poor prognostic outcomes are predominantly associated with HCC due to a huge landscape of heterogeneity found in the deadliest disease. However, molecular subtyping of HCC has significantly improved the knowledge of the underlying mechanisms that contribute towards the heterogeneity and progression of the disease. In this review, we have extensively summarized the current information available about molecular classification of HCC. This review can be of great significance for providing the insight information needed for development of novel, efficient and personalized therapeutic options for the treatment of HCC patients globally.

BCLC strategy for prognosis prediction and treatment recommendation: The 2022 update.

There have been major advances in the armamentarium for hepatocellular carcinoma (HCC) since the last official update of the Barcelona Clinic Liver Cancer prognosis and treatment strategy published in 2018. Whilst there have been advances in all areas, we will focus on those that have led to a change in strategy and we will discuss why, despite being encouraging, data for select interventions are still too immature for them to be incorporated into an evidence-based model for clinicians and researchers. Finally, we describe the critical insight and expert knowledge that are required to make clinical decisions for individual patients, considering all of the parameters that must be considered to deliver personalised clinical management.

Construction and Comprehensive Analysis of a Stratification System Based on AGTRAP in Patients with Hepatocellular Carcinoma.

BACKGROUND: With the development of sequencing technology, several signatures have been reported for the prediction of prognosis in patients with hepatocellular carcinoma (HCC). However, the above signatures are characterized by cumbersome application. Therefore, the study is aimed at screening out a robust stratification system based on only one gene to guide treatment. METHODS: Firstly, we used the limma package for performing differential expression analysis on 374 HCC samples, followed by Cox regression analysis on overall survival (OS) and disease-free interval (PFI). Subsequently, hub prognostic genes were found at the intersection of the above three groups. In addition, the topological degree inside the PPI network was used to screen for a unique hub gene. The rms package was used to construct two visual stratification systems for OS and PFI, and Kaplan-Meier analysis was utilized to investigate survival differences in clinical subgroups. The ssGSEA algorithm was then used to reveal the relationship between the hub gene and immune cells, immunological function, and checkpoints. In addition, we also used function annotation to explore into putative biological functions. Finally, for preliminary validation, the hub gene was knocked down in the HCC cell line. RESULTS: We discovered 6 prognostic genes (SKA1, CDC20, AGTRAP, BIRC5, NEIL3, and CDC25C) for constructing a PPI network after investigating survival and differential expression genes. According to the topological degree, AGTRAP was chosen as the basis for the stratification system, and it was revealed to be a risk factor with an independent prognostic value in Kaplan-Meier analysis and Cox regression analysis (P < 0.05). In addition, we constructed two visualized nomograms based on AGTRAP. The novel stratification system had a robust predictive value for PFI and OS in ROC analysis and calibration curve (P < 0.05). Meanwhile, AGTRAP upregulation was associated with T staging, N staging, M staging, pathological stage, grade, and vascular invasion (P < 0.05). Notably, AGTRAP was overexpressed in tumor tissues in all pancancers with paired samples (P < 0.05). Furthermore, AGTRAP was associated with immune response and may change immune microenvironment in HCC (P < 0.05). Next, gene enrichment analysis suggested that AGTRAP may be involved in the biological process, such as cotranslational protein targeting to the membrane. Finally, we identified the oncogenic effect of AGTRAP by qRT-PCR, colony formation, western blot, and CCK-8 assay (P < 0.05). CONCLUSION: We provided robust evidences that a stratification system based on AGTRAP can guide survival prediction for HCC patients.

Rare variants of primary liver cancer: Fibrolamellar, combined, and sarcomatoid hepatocellular carcinomas.

Hepatocellular carcinoma (HCC) constitutes 80% of all primary liver cancers. Based on key developments in the understanding of its carcinogenesis and the advancement of treatment options, detailed algorithms and practice guidelines have been published to guide the clinical management of HCC. Furthermore, several subclasses of HCC have been described based on molecular profiles and linked to pathological characteristics, clinical features, and disease aggressiveness. Most recently, the combination of the checkpoint inhibitor atezolizumab plus bevacizumab has significantly increased treatment response in the first line systemic treatment of HCC. Unfortunately, rare HCC variants, in particular fibrolamellar liver cancer (FLC), combined hepatocellular carcinoma and cholangiocarcinoma (cHCC-CCA), and sarcomatoid hepatocellular carcinoma (sHCC), were excluded from phase III studies. Therefore, data for decision-making and treatment allocation for these distinct entities, representing 1-5% of all primary liver cancers, is scarce. Moreover, most of the knowledge available for these rare HCC variants is based on registry data and retrospective studies. In this position paper, we briefly summarize the current clinical knowledge regarding FLC, cHCC-CCA, and sHCC. Based on our summary, we propose future clinical research activities within the framework of the European Reference Network on Hepatological Diseases (ERN RARE-LIVER).

Liver stiffness measured by magnetic resonance elastography in early recurrence of hepatocellular carcinoma after treatment: A protocol for systematic review and meta analysis.

BACKGROUND: With high diagnostic accuracy, magnetic resonance elastography (MRE) is a noninvasive tool and can be adopted to measure liver stiffness (LS). In this study, meta-analysis was carried out to further evaluate whether LS measured by MRE can predict early recurrence in patients with hepatocellular carcinoma (HCC). METHODS: PUBMED, EMBASE, Web of Science, China National Knowledge Infrastructure, and Cochrane Library database were searched for studies related to LS measured by MRE in the prediction of recurrence in patients with HCC. Survival outcome was estimated by hazard ratios and 95% confidence intervals. Meta-analysis was conducted with the Stata 16.0. RESULTS: The results of this meta-analysis will be submitted to a peer-reviewed journal for publication. CONCLUSION: This study will provide evidence support for LS measured by MRE in predicting the recurrence of HCC. ETHICS AND DISSEMINATION: The private information from individuals will not be published. This systematic review also should not damage participants' rights. Ethical approval is not available. The results may be published in a peer-reviewed journal or disseminated in relevant conferences. OSF REGISTRATION NUMBER: DOI 10.17605/ OSF.IO / SURH3.

A farewell to Barcelona Clinic Liver Cancer (BCLC) classification for hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the most common primary liver cancer with expected increasing frequency in the next few decades. At early stages, HCC is curable, with most common therapeutic modalities to include surgical resection and liver transplantation. The Barcelona Clinic Liver Cancer (BCLC) Staging System is widely adopted tool to guide the therapeutic algorithms of patients with HCC. This classification is guiding the clinical practice for the last 2 decades. However, there are emerging data demonstrating that patients beyond the traditional criteria of operability, resectability or transplantability actually can benefit from surgical treatment, emphasizing the need of refinement or even change of current BCLC criteria.

An update on subtypes of hepatocellular carcinoma: From morphology to molecular.

The morphologic spectrum of hepatocellular carcinoma (HCC) is quite broad. While in about one-third of cases, the neoplasms can be categorized into meaningful subtypes based on morphology, a vast majority of these neoplasms are morphologically heterogeneous. With extensive tumor profiling, data has begun to emerge which can correlate specific morphologic features with underlying molecular signatures. A true morphologic subtype not only has reproducible H & E features, further supported by specific immunohistochemical or molecular signatures, but also has specific clinical implications and prognostic associations. Eight such morphologic subtypes are recognized by the 2019 WHO classification of tumors with a few more additional subtypes described in the literature. The goal of this review is to familiarize the reader with the morphologic subtypes and elaborate on the clinical and molecular associations of these neoplasms.

Feature Selection and Classification of Clinical Datasets Using Bioinspired Algorithms and Super Learner.

A computer-aided diagnosis (CAD) system that employs a super learner to diagnose the presence or absence of a disease has been developed. Each clinical dataset is preprocessed and split into training set (60%) and testing set (40%). A wrapper approach that uses three bioinspired algorithms, namely, cat swarm optimization (CSO), krill herd (KH) ,and bacterial foraging optimization (BFO) with the classification accuracy of support vector machine (SVM) as the fitness function has been used for feature selection. The selected features of each bioinspired algorithm are stored in three separate databases. The features selected by each bioinspired algorithm are used to train three back propagation neural networks (BPNN) independently using the conjugate gradient algorithm (CGA). Classifier testing is performed by using the testing set on each trained classifier, and the diagnostic results obtained are used to evaluate the performance of each classifier. The classification results obtained for each instance of the testing set of the three classifiers and the class label associated with each instance of the testing set will be the candidate instances for training and testing the super learner. The training set comprises of 80% of the instances, and the testing set comprises of 20% of the instances. Experimentation has been carried out using seven clinical datasets from the University of California Irvine (UCI) machine learning repository. The super learner has achieved a classification accuracy of 96.83% for Wisconsin diagnostic breast cancer dataset (WDBC), 86.36% for Statlog heart disease dataset (SHD), 94.74% for hepatocellular carcinoma dataset (HCC), 90.48% for hepatitis dataset (HD), 81.82% for vertebral column dataset (VCD), 84% for Cleveland heart disease dataset (CHD), and 70% for Indian liver patient dataset (ILP).

Factors associated with survival in dogs with a histopathological diagnosis of hepatocellular carcinoma: 94 cases (2007-2018).

Background: Hepatocellular carcinoma (HCC) is the most common form of primary liver cancer in dogs. Despite this, relatively few reports of this disease exist pertaining to prognostic factors and outcome. Aim: To evaluate factors associated with survival in dogs with all subtypes of HCC diagnosed on histopathology. Methods: A retrospective single institutional study was carried out on 94 client-owned dogs with a histopathologic diagnosis of HCC between 2007 and 2018 obtained by biopsy (21/94) or attempted definitive resection (73/94). Signalment, preoperative features, surgical findings, and postoperative outcomes were recorded. Associations between survival to discharge data were collected and univariable logistical regression was carried out. Kaplan-Meier survival analysis was carried out to identify negative risk factors for long-term prognosis. Results: The median survival time (MST) for all patients was 707 days (95% CI = 551-842). MST was not significantly different (p > 0.05) between patients who had suspected versus incidentally diagnosed HCC (695 vs. 775 days), between complete versus incomplete surgical margins (668 vs. 834 days), or between patients with massive subtype versus nodular/diffuse subtype (707 vs. 747 days). Logistical regression identified an association with the excision of the right medial lobe and risk of perioperative death (OR = 9.2, CI 1.5-55.9, p = 0.016). An American Society of Anesthesiologists score ≥4, disease present within the quadrate lobe, and elevated blood urea nitrogen, potassium or gamma-glutamyltransferase were identified as negative prognosticators during multivariable Cox regression. Preoperative imaging (ultrasound or CT) agreed with the surgical location in 91% of the cases. Preoperative cytology was consistent with a diagnosis of HCC in 15/32 (46.9%) cases. Conclusion: Type of diagnosis (incidental vs presumed), completeness of excision, and subtype were not associated with MST in this study. Preoperative identification of tumors within the central division may be related to a less favorable outcome. Results of preoperative cytology were not highly sensitive for identifying a malignancy.

Convolutional neural network for classifying primary liver cancer based on triple-phase CT and tumor marker information: a pilot study.

PURPOSE: To develop convolutional neural network (CNN) models for differentiating intrahepatic cholangiocarcinoma (ICC) from hepatocellular carcinoma (HCC) and predicting histopathological grade of HCC. MATERIALS AND METHODS: Preoperative computed tomography and tumor marker information of 617 primary liver cancer patients were retrospectively collected to develop CNN models categorizing tumors into three categories: moderately differentiated HCC (mHCC), poorly differentiated HCC (pHCC), and ICC, where the histopathological diagnoses were considered as ground truths. The models processed manually cropped tumor with and without tumor marker information (two-input and one-input models, respectively). Overall accuracy was assessed using a held-out dataset (10%). Area under the curve, sensitivity, and specificity for differentiating ICC from HCCs (mHCC + pHCC), and pHCC from mHCC were also evaluated. We assessed two radiologists' performance without tumor marker information as references (overall accuracy, sensitivity, and specificity). The two-input model was compared with the one-input model and radiologists using permutation tests. RESULTS: The overall accuracy was 0.61, 0.60, 0.55, 0.53 for the two-input model, one-input model, radiologist 1, and radiologist 2, respectively. For differentiating pHCC from mHCC, the two-input model showed significantly higher specificity than radiologist 1 (0.68 [95% confidence interval: 0.50-0.83] vs 0.45 [95% confidence interval: 0.27-0.63]; p = 0.04). CONCLUSION: Our CNN model with tumor marker information showed feasibility and potential for three-class classification within primary liver cancer.

Hepatocellular carcinoma (HCC): Epidemiology, etiology and molecular classification.

Hepatocellular carcinoma (HCC), the primary malignancy of hepatocytes, is a diagnosis with bleak outcome. According to National Cancer Institute's SEER database, the average five-year survival rate of HCC patients in the US is 19.6% but can be as low as 2.5% for advanced, metastatic disease. When diagnosed at early stages, it is treatable with locoregional treatments including surgical resection, Radio-Frequency Ablation, Trans-Arterial Chemoembolization or liver transplantation. However, HCC is usually diagnosed at advanced stages when the tumor is unresectable, making these treatments ineffective. In such instances, systemic therapy with tyrosine kinase inhibitors (TKIs) becomes the only viable option, even though it benefits only 30% of patients, provides only a modest (~3months) increase in overall survival and causes drug resistance within 6months. HCC, like many other cancers, is highly heterogeneous making a one-size fits all option problematic. The selection of liver transplantation, locoregional treatment, TKIs or immune checkpoint inhibitors as a treatment strategy depends on the disease stage and underlying condition(s). Additionally, patients with similar disease phenotype can have different molecular etiology making treatment responses different. Stratification of patients at the molecular level would facilitate development of the most effective treatment option. With the increase in efficiency and affordability of "omics"-level analysis, considerable effort has been expended in classifying HCC at the molecular, metabolic and immunologic levels. This review examines the results of these efforts and the ways they can be leveraged to develop targeted treatment options for HCC.

Comparison of Prognostic Scores in Patients With Hepatocellular Carcinoma Treated With Sorafenib.

INTRODUCTION: Prognostic classifications for patients treated with sorafenib for hepatocellular carcinoma (HCC) facilitate stratification in trials and inform clinical decision making. Recently, 3 different prognostic models (hepatoma arterial-embolization prognosis [HAP] score, sorafenib advanced HCC prognosis [SAP] score, and Prediction Of Survival in Advanced Sorafenib-treated HCC [PROSASH]-II) have been proposed specifically for patients treated with sorafenib. This study aimed to compare the prognostic performance of different scores. METHODS: We analyzed a large prospective database gathering data of 552 patients treated with sorafenib from 7 Italian centers. The performance of the HAP, SAP, and PROSASH-II models were compared with those of generic HCC prognostic models (including the Barcelona Clinic for Liver Cancer and Italian Liver Cancer staging systems, albumin-bilirubin grade, and Child-Pugh score) to verify whether they could provide additional information. RESULTS: The PROSASH-II model improved discrimination (C-index 0.62) compared with existing prognostic scores (C-index ≤0.59). Its stratification significantly discriminated patients, with a median overall survival of 21.5, 15.3, 9.3, and 6.0 months for risk group 1, 2, 3, and 4, respectively. The HAP and SAP score were also validated but with a poorer performance compared with the PROSASH-II. DISCUSSION: Although suboptimal, PROSASH-II is the most effective prognostic classification model among other available scores in a large Italian population of patients treated with sorafenib.

HCC subtypes based on the activity changes of immunologic and hallmark gene sets in tumor and nontumor tissues.

The prognostic role of adjacent nontumor tissue in hepatocellular carcinoma (HCC) patients is still not clear. The activity changes of immunologic and hallmark gene sets in adjacent nontumor tissues may substantially impact on prognosis by affecting proliferation of liver cells and colonization of circulating tumor cells after HCC treatment measures such as hepatectomy. We aimed to identify HCC subtypes and prognostic gene sets based on the activity changes of gene sets in tumor and nontumor tissues, to improve patient outcomes. We comprehensively revealed the activity changes of immunologic and hallmark gene sets in HCC and nontumor samples by gene set variation analysis (GSVA), and identified three clinically relevant subtypes of HCC by nonnegative matrix factorization method (NMF). Patients with subtype 1 had good overall survival, whereas those with subtype 2 and subtype 3 had poor prognosis. Patients with subtype 1 in the validation group also tended to live longer. We also identified three prognostic gene sets in tumor and four prognostic gene sets in nontumor by least absolute shrinkage and selection operator method (LASSO). Interestingly, functional enrichment analysis revealed that in nontumor tissues, genes from four gene sets correlated with immune reaction, cell adhesion, whereas in tumor tissue, genes from three gene sets closely correlated with cell cycle. Our results offer new insights on accurately evaluating prognosis-the important role of gene sets in both tumor and adjacent nontumor tissues, suggesting that when selecting for HCC treatment modality, changes in tumor and nontumor tissues should also be considered, especially after hepatectomy.

Liver transplantation and BCLC classification: Limitations impede optimum treatment.

BACKGROUND: The Barcelona Clinic Liver Cancer (BCLC) system has been endorsed by international guidelines as a staging algorithm of hepatocellular carcinoma. This analysis was performed to assess the outcome of liver transplantation in patients treated against the BCLC recommendations. METHODS: The data of 198 patients who underwent liver transplantation for hepatocellular carcinoma were extracted from a prospectively maintained database to classify the patients according to the BCLC system. RESULTS: BCLC staging was as follows: 0, n = 5; A, n = 77; B, n = 41; C, n = 53; and D, n = 22. Accordingly, liver transplantation was performed in the majority of patients against BCLC recommendations. Surgery (n = 16), radiofrequency ablation (n = 15) and transarterial chemoembolization (n = 151) preceded liver transplantation in 182 patients. Sixteen patients were transplanted without pretreatment. The1-, 5- and 10-year survival rates were 83.8%, 62.4% and 45.9%, and 1-, 5-, and 10-year recurrence rates were 7.7%, 22.7% and 26.7%. The BCLC classification did neither impact survival (P = 0.796) nor recurrence (P = 0.693). In the Cox analysis, RECIST tumor progression and initial alpha fetoprotein were independent predictors of outcome. CONCLUSIONS: Neither the oncological nor the functional stratification imposed by the BCLC system was of importance for outcome. Lack of flexibility and disregard of biological parameters hamper its clinical applicability in liver transplantation.

Hepatocellular Carcinoma Score and Subclassification Into Aggressive Subtypes Using Immunohistochemical Expression of p53, ß-Catenin, CD133, and Ki-67.

Hepatocellular carcinoma (HCC) is the most common primary hepatic malignancy in adults. Several studies have classified HCC into molecular subtypes aiming at detecting aggressive subtypes. The aim of the present study was to investigate the role of p53, ß-catenin, CD133, and Ki-67 in subclassification of HCC into different aggressive subtypes and the correlation between those markers and the clinicopathologic characteristics of HCC patients. This retrospective study was conducted on paraffin-embedded blocks of 114 HCC specimens. Tissue microarray was constructed and immunostaining for p53, ß-catenin, CD133, and Ki-67 was performed and HCC score was formulated. P53 expression was associated with old age (P=0.028), large tumor size (P=0.019), poorly differentiated HCC (P=0.012), hepatitis B virus (HBV) positivity (P=0.032), and hepatitis C virus (HCV) negativity (P =0.046). ß-catenin expression was associated with small sized tumors (P=0.005), HBV negativity (P=0.027), early-staged tumors (P=0.029), and prolonged recurrence-free survival (P=0.045). High percentage of CD133 expression was associated with old patients (P=0.035) and HBV positivity (P= 0.045). Ki-67 expression was associated with large tumor size (P= 0.049), vascular invasion (P= 0.05), old age (P=0.035), and previous treatment of HCV by direct acting antiviral agents (P=0.005). Cases with high HCC score showed significant association with old patients (P=0.002), previous treatment of HCV by direct acting antiviral agents (P<0.001), large tumor size (P<0.001), and poorly differentiated tumors (P= 0.009). The proposed HCC score can divide HCC patients into subtypes necessitating tailoring of treatment strategy according to this proposed score to target and optimally treat the aggressive subtypes. This score needs to be further validated on large number of patients with longer follow-up period.