Project description:This SuperSeries is composed of the following subset Series: GSE9843: Gene expression profiling of 91 hepatocellular carcinomas with hepatitis C virus etiology, Samples with "vascular invasion: Yes/No" were included in the study. GSE20017: Gene Signature to Identify Vascular Invasion in Hepatocellular Carcinoma Refer to individual Series
Project description:Background: The presence of vascular invasion (VI) in pathology specimens has been widely described as closely linked to poor outcome in hepatocellular carcinoma (HCC) patients after tumor resection. Previous attempts have been conducted to achieve molecular markers or signatures to predict HCC recurrence in HCC. Here, we aim to develop a diagnostic model combining clinical and genomic variables able to detect the presence of VI prior to surgery and link it to survival estimation. Methods: Seventy-nine HCV related HCC samples from patients that underwent surgical resection as a treatment for HCC were subjected to Genome-wide gene expression profiling and a predictive model of vascular invasion was constructed. The model was tested in an independent-validation set of 153 fixed tissue samples of resected HCC. Quantitative RTPCR and inmunohistochemistry were performed in HCC samples to test a potential biomarker. Results: A 39-gene signature was able to accurately (72%) identify vascular invasion in HCC patients treated with resection. A model including tumor size and the signature is able to predict presence of VI with 85% accuracy in HCV-related HCC patients, and is able to exclude VI in up to 87% cases in HCC from all etiologies. Conclusions: Using the VI gene signature together with tumor size, VI can be successfully detected in HCC patients. The diagnostic model, integrated in a previously reported survival chart is able to provide an estimated survival for selected cases. Clinical implications of this fact are relevant to provide objective data to further apply expanded indication of curative treatments in HCC.
Project description:Background: The presence of vascular invasion (VI) in pathology specimens has been widely described as closely linked to poor outcome in hepatocellular carcinoma (HCC) patients after tumor resection. Previous attempts have been conducted to achieve molecular markers or signatures to predict HCC recurrence in HCC. Here, we aim to develop a diagnostic model combining clinical and genomic variables able to detect the presence of VI prior to surgery and link it to survival estimation. Methods: Seventy-nine HCV related HCC samples from patients that underwent surgical resection as a treatment for HCC were subjected to Genome-wide gene expression profiling and a predictive model of vascular invasion was constructed. The model was tested in an independent-validation set of 153 fixed tissue samples of resected HCC. Quantitative RTPCR and inmunohistochemistry were performed in HCC samples to test a potential biomarker. Results: A 39-gene signature was able to accurately (72%) identify vascular invasion in HCC patients treated with resection. A model including tumor size and the signature is able to predict presence of VI with 85% accuracy in HCV-related HCC patients, and is able to exclude VI in up to 87% cases in HCC from all etiologies. Conclusions: Using the VI gene signature together with tumor size, VI can be successfully detected in HCC patients. The diagnostic model, integrated in a previously reported survival chart is able to provide an estimated survival for selected cases. Clinical implications of this fact are relevant to provide objective data to further apply expanded indication of curative treatments in HCC. Gene-expression profiling was performed using formalin-fixed, paraffin-embedded hepatocellular carcinoma tissues obtained at the time of surgical resection.
Project description:Vascular invasion is a major predictor of tumor recurrence after surgical treatments for hepatocellular carcinoma (HCC). While macroscopic vascular invasion can be detected by radiological techniques, pre-operative detection of microscopic vascular invasion, which complicates 30-40% of patients with early tumors, remains elusive.A total of 214 patients with hepatocellular carcinoma who underwent resection were included in the study. By using genome-wide gene-expression profiling of 79 hepatitis C-related hepatocellular carcinoma samples (training set), a gene-expression signature associated with vascular invasion was defined. The signature was validated in formalin-fixed paraffin-embedded tissues obtained from an independent set of 135 patients with various etiologies.A 35-gene signature of vascular invasion was defined in the training set, predicting vascular invasion with an accuracy of 69%. The signature was independently associated with the presence of vascular invasion (OR 3.38, 95% CI 1.48-7.71, p=0.003) along with tumor size (diameter greater than 3 cm, OR 2.66, 95% CI 1.17-6.05, p=0.02). In the validation set, the signature discarded the presence of vascular invasion with a negative predictive value of 0.77, and significantly improved the diagnostic power of tumor size alone (p=0.045).The assessment of a gene-expression signature obtained from resected biopsied tumor specimens improved the diagnosis of vascular invasion beyond clinical variable-based prediction. The signature may aid in candidate selection for liver transplantation, and guide the design of clinical trials with experimental adjuvant therapies.