Combining Clinical, Pathology, and Gene Expression Data to Predict Recurrence of Hepatocellular Carcinoma
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ABSTRACT:
PROVIDER: PRJNA125555 | ENA |
REPOSITORIES: ENA
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Project description:Background: Hepatocellular carcinoma (HCC) is among the top-five cancer killers worldwide. Here, we describe our analysis of 20 gene signatures with reported prognostic value in a cohort of patients with early stage HCC treated with surgical resection. Methods: We performed gene-expression profiling of 164 formalin-fixed, paraffin-embedded HCC from three hepatology centers participating the HCC Genomic Consortium. We utilized IlluminaM-bM-^@M-^Ys whole-genome DASL assay measuring ~24,000 annotated human genes. We evaluated genomic concordance and prognostic performance of 20 already reported prognostic gene signatures. Additionally, since only the largest nodule was profiled, we also excluded patients with multiple tumors to avoid biases related to intra-individual genomic tumor heterogeneity. Results: Among the 20 signatures evaluated, 15 were able to confidently allocate patients (FDR<0.05) within their predicted poor-outcome subclass. Pairwise comparisons showed a significant overlap between almost all poor-outcome signatures derived from the tumor (P<0.001), except for the metastatic HCC signature. Interestingly, poor-outcome signatures from the tumor were not significantly associated with those obtained from non-tumor adjacent tissue in the same patient. The prognosis analysis for earliest stages (BCLC 0 or A) with single nodules revealed that the G3 signature reported by Boyault et al. Conclusions: We present a composite genomic model for prediction of tumor recurrence in early HCC. It will allow risk stratification, personalized surveillance, and eventually customized interventions in patients with early HCC candidates for resection. Samples with "used for prognostic prediction: yes" were included in the study. This SuperSeries is composed of the SubSeries listed below. Refer to individual Series
2011-02-17 | E-GEOD-20140 | biostudies-arrayexpress
Project description:Background: Hepatocellular carcinoma (HCC) is among the top-five cancer killers worldwide. Here, we describe our analysis of 20 gene signatures with reported prognostic value in a cohort of patients with early stage HCC treated with surgical resection. Methods: We performed gene-expression profiling of 164 formalin-fixed, paraffin-embedded HCC from three hepatology centers participating the HCC Genomic Consortium. We utilized Illumina’s whole-genome DASL assay measuring ~24,000 annotated human genes. We evaluated genomic concordance and prognostic performance of 20 already reported prognostic gene signatures. Additionally, since only the largest nodule was profiled, we also excluded patients with multiple tumors to avoid biases related to intra-individual genomic tumor heterogeneity. Results: Among the 20 signatures evaluated, 15 were able to confidently allocate patients (FDR<0.05) within their predicted poor-outcome subclass. Pairwise comparisons showed a significant overlap between almost all poor-outcome signatures derived from the tumor (P<0.001), except for the metastatic HCC signature. Interestingly, poor-outcome signatures from the tumor were not significantly associated with those obtained from non-tumor adjacent tissue in the same patient. The prognosis analysis for earliest stages (BCLC 0 or A) with single nodules revealed that the G3 signature reported by Boyault et al. Conclusions: We present a composite genomic model for prediction of tumor recurrence in early HCC. It will allow risk stratification, personalized surveillance, and eventually customized interventions in patients with early HCC candidates for resection. Samples with "used for prognostic prediction: yes" were included in the study. This SuperSeries is composed of the SubSeries listed below.
2011-02-17 | GSE20140 | GEO
Project description:In approximately 70% of patients with hepatocellular carcinoma (HCC) treated by resection or ablation, disease recurs within 5 years. Although gene expression signatures have been associated with outcome, there is no method to predict recurrence based on combined clinical, pathology, and genomic data (from tumor and cirrhotic tissue). We evaluated gene expression signatures associated with outcome in a large cohort of patients with early stage (Barcelona-Clinic Liver Cancer 0/A), single-nodule HCC and heterogeneity of signatures within tumor tissues.We assessed 287 HCC patients undergoing resection and tested genome-wide expression platforms using tumor (n = 287) and adjacent nontumor, cirrhotic tissue (n = 226). We evaluated gene expression signatures with reported prognostic ability generated from tumor or cirrhotic tissue in 18 and 4 reports, respectively. In 15 additional patients, we profiled samples from the center and periphery of the tumor, to determine stability of signatures. Data analysis included Cox modeling and random survival forests to identify independent predictors of tumor recurrence.Gene expression signatures that were associated with aggressive HCC were clustered, as well as those associated with tumors of progenitor cell origin and those from nontumor, adjacent, cirrhotic tissues. On multivariate analysis, the tumor-associated signature G3-proliferation (hazard ratio [HR], 1.75; P = .003) and an adjacent poor-survival signature (HR, 1.74; P = .004) were independent predictors of HCC recurrence, along with satellites (HR, 1.66; P = .04). Samples from different sites in the same tumor nodule were reproducibly classified.We developed a composite prognostic model for HCC recurrence, based on gene expression patterns in tumor and adjacent tissues. These signatures predict early and overall recurrence in patients with HCC, and complement findings from clinical and pathology analyses.
| S-EPMC3081971 | biostudies-literature
Project description:The DNA methylation value in early-stage hepatocellular carcinoma was undetermined. The Illumina Infinium 450k Human DNA methylation Beadchip was used to identify recurrence-related abbrent CpG methylation. This study was performed in a total of 66 early-stage HCC samples, including 29 recurrence samples and 37 recurrence-free samples
2017-04-30 | E-GEOD-75041 | biostudies-arrayexpress
Project description:Integrative miRNA networks predict prostate cancer recurrence
| PRJNA559178 | ENA
Project description:The DNA methylation value in early-stage hepatocellular carcinoma was undetermined. The Illumina Infinium 450k Human DNA methylation Beadchip was used to identify recurrence-related abbrent CpG methylation.
2017-04-30 | GSE75041 | GEO
Project description:Endometrial cancer (EC) incidence and mortality continues to rise. Molecular profiling of EC promises improvement of risk assessment and treatment selection. Though, we still lack robust and accurate models to predict those at risk of failing treatment. The objective of this pilot study is to create models with clinical and genomic data that will discriminate patients with EC at risk of disease recurrence. We performed a pilot, retrospective, case-control study evaluating patients with EC, endometrioid type: 7 with recurrence of disease (cases), and 54 without (controls). RNA was extracted from frozen specimens and sequenced (RNAseq). Genomic features from RNAseq included transcriptome expression, genomic, and structural variation. Feature selection for variable reduction was performed with univariate ANOVA with cross-validation. Selected variables, informative for EC recurrence, were introduced in multivariate lasso regression models. Validation of models was performed in machine learning platforms (ML) and independent datasets (TCGA). The best performing prediction models (out of >170) contained the same lncRNA features (AUC of 0.9, and 95% CI: 0.75, 1.0). Models were validated with excellent performance in ML platforms and good performance in an independent dataset. Prediction models of EC recurrence containing lncRNA features have better performance than models with clinical data alone
2022-11-02 | GSE216872 | GEO
Project description:Integration of genomic and clinical data to predict endometrioid endometrial cancer recurrence
| PRJNA895900 | ENA
Project description:We compared the prediction powers for disease recurrence between gene set prognostic model and clinical prognostic model developed in a single large population to see whether genetic quantitative approach will have significant prognostic role in early cervical cancer patients who underwent radical hysterectomy with or without adjuvant therapies. Gene set model to predict disease free survival of early cervical cancer was developed using DASL assay dataset from the cohort of early cervical cancer patients who were treated with radical surgery with or without adjuvant therapies at the Samsung Medical Center of Sungkyunkwan University School of Medicine in Seoul, Korea, between January 2002 and September 2008. Clinical prediction model was also developed in the same cohort and the ability of predicting recurrence from each model was compared. Adequate DASL assay profiles were obtained in 300 patients and we selected 12 genes for the gene set model. When the proportions of patients were categorized as having a low or high risk by the prognostic scores using these genes from LOOCV procedure, the Kaplan-Meier curve showed significant different recurrence rate between two groups. Clinical model was developed using FIGO stage as well as post-surgical pathological findings.
2013-05-01 | E-GEOD-44001 | biostudies-arrayexpress
Project description:We compared the prediction powers for disease recurrence between gene set prognostic model and clinical prognostic model developed in a single large population to see whether genetic quantitative approach will have significant prognostic role in early cervical cancer patients who underwent radical hysterectomy with or without adjuvant therapies. Gene set model to predict disease free survival of early cervical cancer was developed using DASL assay dataset from the cohort of early cervical cancer patients who were treated with radical surgery with or without adjuvant therapies at the Samsung Medical Center of Sungkyunkwan University School of Medicine in Seoul, Korea, between January 2002 and September 2008. Clinical prediction model was also developed in the same cohort and the ability of predicting recurrence from each model was compared.
2013-05-01 | GSE44001 | GEO