Hepatocellular Carcinoma Xenografts Established From Needle Biopsies Preserve the Characteristics of the Originating Tumors.
ABSTRACT: Hepatocellular carcinoma (HCC) is the second leading cause of cancer-related deaths worldwide. Treatment options for patients with advanced-stage disease are limited. A major obstacle in drug development is the lack of an in vivo model that accurately reflects the broad spectrum of human HCC. Patient-derived xenograft (PDX) tumor mouse models could overcome the limitations of cancer cell lines. PDX tumors maintain the genetic and histologic heterogeneity of the originating tumors and are used for preclinical drug development in various cancers. Controversy exists about their genetic and molecular stability through serial passaging in mice. We aimed to establish PDX models from human HCC biopsies and to characterize their histologic and molecular stability during serial passaging. A total of 54 human HCC needle biopsies that were derived from patients with various underlying liver diseases and tumor stages were transplanted subcutaneously into immunodeficient, nonobese, diabetic/severe combined immunodeficiency gamma-c mice; 11 successfully engrafted. All successfully transplanted HCCs were Edmondson grade III or IV. HCC PDX tumors retained the histopathologic, transcriptomic, and genomic characteristics of the original HCC biopsies over 6 generations of retransplantation. These characteristics included Edmondson grade, expression of tumor markers, tumor gene signature, tumor-associated mutations, and copy number alterations. Conclusion: PDX mouse models can be established from undifferentiated HCCs, with an overall success rate of approximately 20%. The transplanted tumors represent the entire spectrum of the molecular landscape of HCCs and preserve the characteristics of the originating tumors through serial passaging. HCC PDX models are a promising tool for preclinical personalized drug development.
Project description:The lack of a general clinic-relevant model for human cancer is a major impediment to the acceleration of novel therapeutic approaches for clinical use. We propose to establish and characterize primary human hepatocellular carcinoma (HCC) xenografts that can be used to evaluate the cytotoxicity of adoptive chimeric antigen receptor (CAR) T cells and accelerate the clinical translation of CAR T cells used in HCC.Primary HCCs were used to establish the xenografts. The morphology, immunological markers, and gene expression characteristics of xenografts were detected and compared to those of the corresponding primary tumors. CAR T cells were adoptively transplanted into patient-derived xenograft (PDX) models of HCC. The cytotoxicity of CAR T cells in vivo was evaluated.PDX1, PDX2, and PDX3 were established using primary tumors from three individual HCC patients. All three PDXs maintained original tumor characteristics in their morphology, immunological markers, and gene expression. Tumors in PDX1 grew relatively slower than that in PDX2 and PDX3. Glypican 3 (GPC3)-CAR T cells efficiently suppressed tumor growth in PDX3 and impressively eradicated tumor cells from PDX1 and PDX2, in which GPC3 proteins were highly expressed.GPC3-CAR T cells were capable of effectively eliminating tumors in PDX model of HCC. Therefore, GPC3-CAR T cell therapy is a promising candidate for HCC treatment.
Project description:Background & Aims: Hepatocellular carcinoma (HCC) is the second leading cause of cancer-related deaths worldwide with limited treatment options for patients with advanced stage disease. A major obstacle in preclinical drug development is the lack of an in vivo model that accurately reflects the broad spectrum of human HCC. Patient derived xenograft (PDX) mouse models could overcome the limitations of cancer cell lines. PDX models have been established from surgically resected HCCs. We aimed to establish and characterize PDX models from human HCC biopsies in order to expand the spectrum of HCC xenografts to advanced HCCs not amenable to surgery.
Methods: Fifty-four human HCC needle biopsies were transplanted subcutaneously into immunodeficient NOD-SCID gamma-c (NSG) mice. Tumor growth rates, histopathological characteristics, RNA sequencing and whole exome sequencing were used to characterize the newly established mouse models and to compare them to the originating HCCs.
Results: Eleven HCCs engrafted in NSG mice. They were derived from patients with various underlying liver diseases and tumor stages. All successfully transplanted HCCs were Edmondson grade III or IV. HCC PDX tumors retained the histopathological, transcriptomic and genomic characteristics of the original HCC biopsies over several generations of re-transplantation, including Edmondson grade, expression of tumor markers, tumor gene signature, tumor-associated mutations and copy number alterations.
Conclusion: PDX mouse models can be established from undifferentiated HCCs with an overall success rate of about 20%. The transplanted tumors represent the entire spectrum of the molecular landscape of HCCs and preserve the characteristics of the originating tumors. HCC PDX models are a promising tool for preclinical personalized drug development.
Project description:While patient-derived xenograft (PDX) models of hepatocellular carcinoma (HCC) have been successfully generated from resected tissues, no reliable methods have been reported for the generation of PDXs from patients who are not candidates for resection and represent the vast majority of patients with HCC. Here we compare two methods for the creation of PDXs from HCC biopsies and find that implantation of whole biopsy samples without the addition of basement membrane matrix favors the formation of PDX tumors that resemble Epstein-Barr virus (EBV)-driven B-cell lymphomas rather than HCC tumors. In contrast, implantation with Matrigel supports growth of HCC cells and leads to a high rate of HCC tumor formation from these biopsies. We validate the resulting PDXs, confirm their fidelity to the patients' disease and conclude that minimally invasive percutaneous liver biopsies can be used with relatively high efficiency to generate PDXs of HCC.
Project description:<h4>Background</h4>HMGA1 is a non-histone nuclear protein that regulates cellular proliferation, invasion and apoptosis and is overexpressed in many carcinomas. In this study we sought to explore the expression of HMGA1 in HCCs and cirrhotic tissues, and its effect in in vitro models.<h4>Methods</h4>We evaluated HMGA1 expression using gene expression microarrays (59 HCCs, of which 37 were matched with their corresponding cirrhotic tissue and 5 normal liver donors) and tissue microarray (192 HCCs, 108 cirrhotic tissues and 79 normal liver samples). HMGA1 expression was correlated with clinicopathologic features and patient outcome. Four liver cancer cell lines with stable induced or knockdown expression of HMGA1 were characterized using in vitro assays, including proliferation, migration and anchorage-independent growth.<h4>Results</h4>HMGA1 expression increased monotonically from normal liver tissues to cirrhotic tissue to HCC (P<.01) and was associated with Edmondson grade (P<.01). Overall, 51% and 42% of HCCs and cirrhotic tissues expressed HMGA1, respectively. Patients with HMGA1-positive HCCs had earlier disease progression and worse overall survival. Forced expression of HMGA1 in liver cancer models resulted in increased cell growth and migration, and vice versa. Soft agar assay showed that forced expression of HMGA1 led to increased foci formation, suggesting an oncogenic role of HMGA1 in hepatocarcinogenesis.<h4>Conclusions</h4>HMGA1 is frequently expressed in cirrhotic tissues and HCCs and its expression is associated with high Edmondson grade and worse prognosis in HCC. Our results suggest that HMGA1 may act as oncogenic driver of progression, implicating it in tumor growth and migration potential in liver carcinogenesis.
Project description:BackgroundDue to heterogeneity of hepatocellular carcinoma (HCC), outcome assessment of HCC with transarterial chemoembolization (TACE) is challenging.MethodsWe built histologic-related scores to determine microvascular invasion (MVI) and Edmondson-Steiner grade by training CT radiomics features using machine learning classifiers in a cohort of 494 HCCs with hepatic resection. Meanwhile, we developed a deep learning (DL)-score for disease-specific survival by training CT imaging using DL networks in a cohort of 243 HCCs with TACE. Then, three newly built imaging hallmarks with clinicoradiologic factors were analyzed with a Cox-Proportional Hazard (Cox-PH) model.FindingsIn HCCs with hepatic resection, two imaging hallmarks resulted in areas under the curve (AUCs) of 0.79 (95% confidence interval [CI]: 0.71–0.85) and 0.72 (95% CI: 0.64–0.79) for predicting MVI and Edmondson-Steiner grade, respectively, using test data. In HCCs with TACE, higher DL-score (hazard ratio [HR]: 3.01; 95% CI: 2.02–4.50), American Joint Committee on Cancer (AJCC) stage III+IV (HR: 1.71; 95% CI: 1.12–2.61), Response Evaluation Criteria in Solid Tumors (RECIST) with stable disease?+?progressive disease (HR: 2.72; 95% CI: 1.84–4.01), and TACE-course > 3 (HR: 0.65; 95% CI: 0.45–0.76) were independent prognostic factors. Using these factors via a Cox-PH model resulted in a concordance index of 0.73 (95% CI: 0.71–0.76) for predicting overall survival and AUCs of 0.85 (95% CI: 0.81–0.89), 0.90 (95% CI: 0.86–0.94), and 0.89 (95% CI: 0.84–0.92), respectively, for predicting 3-year, 5-year, and 10-year survival.InterpretationOur study offers a DL-based, noninvasive imaging hallmark to predict outcome of HCCs with TACE.FundingThis work was supported by the key research and development program of Jiangsu Province (Grant number: BE2017756).
Project description:Hepatocellular carcinoma (HCC) is a heterogeneous disease, and despite considerable research efforts, no molecular classification of HCC has been introduced in clinical practice. The existing molecular classification systems were established using resected tumors, which introduces a selection bias towards patients without liver cirrhosis and with early stage HCCs. So far, these classification systems have not been validated in liver biopsy specimens from tumors diagnosed at intermediate and late stages. We generated and analyzed expression profiles from 60 HCC biopsies from an unselected patient population with all tumor stages. Unbiased clustering identified 3 HCC classes. Class membership correlated with survival, tumor size, and with Edmondson and BCLC stage. Most biopsy specimens could be assigned to the classes of published classification systems, demonstrating that gene expression profiles obtained from patients with early stage disease are preserved in all stages of HCC. When a reference set of healthy liver samples was integrated in the analysis, we observed that the differentially regulated genes up- or down-regulated in a given class relative to other classes were actually dysregulated in the same direction in all HCCs, with quantitative rather than qualitative differences between the molecular subclasses. With the exception of a subset of samples with a definitive β-catenin gene signature, biological pathway analysis could not identify class specific pathways reflecting the activation of distinct oncogenic programs. Our results suggest that gene expression profiling of HCC biopsies has limited potential to direct therapies that target specific driver pathways, but can identify subgroups of patients with different prognosis. Overall design: 60 biopsy pairs from hepatocellular carcinoma patients (1 biopsy of the tumor and 1 biopsy of the non-tumor liver from each patient), 5 normal liver biopsies
Project description:Hepatocellular carcinoma (HCC) is the most common primary liver cancer and the second most frequent cause of cancer-related mortality worldwide. The multikinase inhibitor sorafenib is the only treatment option for advanced HCC. Due to tumor heterogeneity, its efficacy greatly varies between patients and is limited due to adverse effects and drug resistance. Current in vitro models fail to recapitulate key features of HCCs. We report the generation of long-term organoid cultures from tumor needle biopsies of HCC patients with various etiologies and tumor stages. HCC organoids retain the morphology as well as the expression pattern of HCC tumor markers and preserve the genetic heterogeneity of the originating tumors. In a proof-of-principle study, we show that liver cancer organoids can be used to test sensitivity to sorafenib. In conclusion, organoid models can be derived from needle biopsies of liver cancers and provide a tool for developing tailored therapies.
Project description:BACKGROUND:The gene for chromodomain helicase/ATPase DNA binding protein 1-like (CHD1L) was recently identified as a target oncogene within the 1q21 amplicon, which occurs in 46% to 86% of primary hepatocellular carcinoma (HCC) cases. However, the prognostic significance of CHD1L in HCC remains uncertain. In this study, we investigated the roles of CHD1L in the prognosis of HCC. METHODS:We investigated the expressions of CHD1L in tumor tissue microarrays of 281 primary HCC patients who underwent surgical resection using immunohistochemistry. Prognostic factors of HCC were examined by univariate and multivariate analyses. The median follow-up period was 75.6 months. RESULTS:CHD1L expression was observed in 48 of the 281 HCCs (17.1%). CHD1L expression was associated with a younger age (p=0.033), higher Edmondson grade (p=0.019), microvascular invasion (p<0.001), major portal vein invasion (p=0.037), higher American Joint Committee on Cancer T stage (p=0.001), lower albumin level (p=0.047), and higher ?-fetoprotein level (p=0.002). Multivariate analyses revealed that CHD1L expression (p=0.027), Edmondson grade III (p=0.034), and higher Barcelona Clinic Liver Cancer stage (p<0.001) were independent predictors of shorter disease-free survival. CONCLUSIONS:CHD1L expression might be a prognostic marker of shorter disease-free survival in HCC patients after surgical resection.
Project description:Molecular classification of hepatocellular carcinomas (HCC) could guide patient stratification for personalized therapies targeting subclass-specific cancer 'driver pathways'. Currently, there are several transcriptome-based molecular classifications of HCC with different subclass numbers, ranging from two to six. They were established using resected tumours that introduce a selection bias towards patients without liver cirrhosis and with early stage HCCs. We generated and analyzed gene expression data from paired HCC and non-cancerous liver tissue biopsies from 60 patients as well as five normal liver samples. Unbiased consensus clustering of HCC biopsy profiles identified 3 robust classes. Class membership correlated with survival, tumour size and with Edmondson and Barcelona Clinical Liver Cancer (BCLC) stage. When focusing only on the gene expression of the HCC biopsies, we could validate previously reported classifications of HCC based on expression patterns of signature genes. However, the subclass-specific gene expression patterns were no longer preserved when the fold-change relative to the normal tissue was used. The majority of genes believed to be subclass-specific turned out to be cancer-related genes differentially regulated in all HCC patients, with quantitative rather than qualitative differences between the molecular subclasses. With the exception of a subset of samples with a definitive ?-catenin gene signature, biological pathway analysis could not identify class-specific pathways reflecting the activation of distinct oncogenic programs. In conclusion, we have found that gene expression profiling of HCC biopsies has limited potential to direct therapies that target specific driver pathways, but can identify subgroups of patients with different prognosis.
Project description:BACKGROUND: Engraftment of primary pancreas ductal adenocarcinomas (PDAC) in mice to generate patient-derived xenograft (PDX) models is a promising platform for biological and therapeutic studies in this disease. However, these models are still incompletely characterized. Here, we measured the impact of the murine tumor environment on the gene expression of the engrafted human tumoral cells. METHODS: We have analyzed gene expression profiles from 35 new PDX models and compared them with previously published microarray data of 18 PDX models, 53 primary tumors and 41 cell lines from PDAC. The results obtained in the PDAC system were further compared with public available microarray data from 42 PDX models, 108 primary tumors and 32 cell lines from hepatocellular carcinoma (HCC). We developed a robust analysis protocol to explore the gene expression space. In addition, we completed the analysis with a functional characterization of PDX models, including if changes were caused by murine environment or by serial passing. RESULTS: Our results showed that PDX models derived from PDAC, or HCC, were clearly different to the cell lines derived from the same cancer tissues. Indeed, PDAC- and HCC-derived cell lines are indistinguishable from each other based on their gene expression profiles. In contrast, the transcriptomes of PDAC and HCC PDX models can be separated into two different groups that share some partial similarity with their corresponding original primary tumors. Our results point to the lack of human stromal involvement in PDXs as a major factor contributing to their differences from the original primary tumors. The main functional differences between pancreatic PDX models and human PDAC are the lower expression of genes involved in pathways related to extracellular matrix and hemostasis and the up- regulation of cell cycle genes. Importantly, most of these differences are detected in the first passages after the tumor engraftment. CONCLUSIONS: Our results suggest that PDX models of PDAC and HCC retain, to some extent, a gene expression memory of the original primary tumors, while this pattern is not detected in conventional cancer cell lines. Expression changes in PDXs are mainly related to pathways reflecting the lack of human infiltrating cells and the adaptation to a new environment. We also provide evidence of the stability of gene expression patterns over subsequent passages, indicating early phases of the adaptation process.