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Body Composition Features Predict Overall Survival in Patients With Hepatocellular Carcinoma.

ABSTRACT: Existing prognostic models for patients with hepatocellular carcinoma (HCC) have limitations. Analytic morphomics, a novel process to measure body composition using computational image-processing algorithms, may offer further prognostic information. The aim of this study was to develop and validate a prognostic model for HCC patients using body composition features and objective clinical information.Using computed tomography scans from a cohort of HCC patients at the VA Ann Arbor Healthcare System between January 2006 and December 2013, we developed a prognostic model using analytic morphomics and routine clinical data based on multivariate Cox regression and regularization methods. We assessed model performance using C-statistics and validated predicted survival probabilities. We validated model performance in an external cohort of HCC patients from Parkland Hospital, a safety-net health system in Dallas County.The derivation cohort consisted of 204 HCC patients (20.1% Barcelona Clinic Liver Cancer classification (BCLC) 0/A), and the validation cohort had 225 patients (22.2% BCLC 0/A). The analytic morphomics model had good prognostic accuracy in the derivation cohort (C-statistic 0.80, 95% confidence interval (CI) 0.71-0.89) and external validation cohort (C-statistic 0.75, 95% CI 0.68-0.82). The accuracy of the analytic morphomics model was significantly higher than that of TNM and BCLC staging systems in derivation (P<0.001 for both) and validation (P<0.001 for both) cohorts. For calibration, mean absolute errors in predicted 1-year survival probabilities were 5.3% (90% quantile of 7.5%) and 7.6% (90% quantile of 12.5%) in the derivation and validation cohorts, respectively.Body composition features, combined with readily available clinical data, can provide valuable prognostic information for patients with newly diagnosed HCC.

SUBMITTER: Singal AG

PROVIDER: S-EPMC4893682 | biostudies-other | 2016

REPOSITORIES: biostudies-other

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Project description:Abstract Background Hepatocellular carcinoma is one of the most lethal cancers worldwide. Novel prognostic and/or predictive biomarkers are urgently needed to improve patient management. Alpha‐fetoprotein (AFP) is a well‐established and widely used biomarker for hepatocellular carcinoma. However, diagnostic accuracy of static AFP values is limited and the clinical potential is a matter of ongoing scientific discussion. Objective We here evaluated the prognostic impact of pretreatment static and dynamic AFP variables on overall survival of hepatocellular carcinoma patients in a Western cohort. Methods Patients with confirmed hepatocellular carcinoma (n = 809) treated at the Johannes Gutenberg University Mainz between 1998 and 2014 and two available pretreatment AFP‐values (AFP‐slope) were retrospectively analysed. Clinicopathological baseline parameters, pretreatment static values and AFP‐slope were assessed. Prognostic impact was determined by Kaplan–Meier analyses and Cox regression models. Results High static and dynamic AFP variables prior to therapy were associated with reduced survival rates of hepatocellular carcinoma patients. Several known clinical parameters such as Child–Pugh B (p < 0.01) and C stage (p < 0.001), portal vein thrombosis (p < 0.001) and extrahepatic spread (p < 0.001) were confirmed as independent predictors for overall survival. Addition of static and/or dynamic AFP variable resulted in higher time‐dependent area under the curves. Notably, in patients with more favourable prognosis, AFP‐slope prior to therapy was a slightly stronger predictor for overall survival compared with static AFP values. Conclusion Static and dynamic AFP variables prior to therapy are predictive for overall survival of hepatocellular carcinoma patients. Addition of AFP‐slope to established prognostic parameters might improve prognostic classification for a subgroup of hepatocellular carcinoma patients with preserved liver function and without portal vein tumour thrombosis. Key Summary Alpha‐fetoprotein (AFP) is the most commonly used biomarker for hepatocellular carcinomas (HCCs), but accuracy of static and dynamic AFP values is limited and the prognostic significance is under debate. High static and dynamic AFP variables prior to therapy are associated with reduced survival rates of HCC patients across different tumour stages and treatment modalities. In patients with more favourable prognosis, AFP‐slope prior to therapy was a better predictor for overall survival in comparison with static AFP values. Addition of AFP‐slope to established prognostic parameters might improve prognostic classification for a subgroup of HCC.

Project description:BackgroundHepatocellular carcinoma (HCC) is a major cause of cancer mortality and an increasing incidence worldwide; however, there are very few effective diagnostic approaches and prognostic biomarkers.Materials and methodsOne hundred forty-nine pairs of HCC samples from Gene Expression Omnibus (GEO) were obtained to screen differentially expressed genes (DEGs) between HCC and normal samples. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, Gene ontology enrichment analyses, and protein-protein interaction network were used. Cox proportional hazards regression analysis was used to identify significant prognostic DEGs, with which a gene expression signature prognostic prediction model was identified in The Cancer Genome Atlas (TCGA) project discovery cohort. The robustness of this panel was assessed in the GSE14520 cohort. We verified details of the gene expression level of the key molecules through TCGA, GEO, and qPCR and used immunohistochemistry for substantiation in HCC tissues. The methylation states of these genes were also explored.ResultsNinety-eight genes, consisting of 13 upregulated and 85 downregulated genes, were screened out in three datasets. KEGG and Gene ontology analysis for the DEGs revealed important biological features of each subtype. Protein-protein interaction network analysis was constructed, consisting of 64 nodes and 115 edges. A subset of four genes (SPINK1, TXNRD1, LCAT, and PZP) that formed a prognostic gene expression signature was established from TCGA and validated in GSE14520. Next, the expression details of the four genes were validated with TCGA, GEO, and clinical samples. The expression panels of the four genes were closely related to methylation states.ConclusionThis study identified a novel four-gene signature biomarker for predicting the prognosis of HCC. The biomarkers may also reveal molecular mechanisms underlying development of the disease and provide new insights into interventional strategies.

Project description:ObjectivesGastric neuroendocrine carcinoma (GNEC) is a class of rare histological subtypes in gastric cancer (GC). This retrospective case-control study aimed to explore the clinicopathological features and overall survival (OS) of patients with GNEC.MethodsA large population of GNEC and intestinal-type GC (IGC) patients were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. The 1:1 propensity score matching (PSM) analysis was initiated to adjust the confounders between GNEC and IGC cohorts. Kaplan-Meier (KM) plots with log-rank tests were used to compare the survival differences in GNEC versus IGC. Additionally, Cox proportional hazard regression models were adopted to characterize the prognostic factors relevant to OS of the GNEC patients.ResultsAn entity of 4596 patients were collected, including 3943 (85.8%) IGC patients and 653 (14.2%) GNEC patients. The PSM analysis well-balanced all confounders in GNEC versus IGC (all P > .05). The KM plots showed that GNEC had significantly superior OS to IGC both before and after PSM analysis. Before PSM, the median OS was 52 (33.6-70.4) months in GNEC versus 32 (29.3-34.7) months in IGC (P = .0015). After PSM, the median OS was 26 (18.3-33.7) months in GNEC versus 21 (17.7-24.3) months in IGC (P = .0039). Stratified analysis indicated that GNEC had superior survivals to IGC in early stage patients and those who received surgery. In Cox regression analysis, age ≥ 60, tumor size > 50 mm, stage II-IV, T2, and N3 were independent risk factors for the GNEC patients (hazard ratio [HR]>1, P < .05). By contrast, year 2010 to 2015, female, and surgery were independent protective factors for these patients (HR < 1, P < .05).ConclusionsGNEC has unique clinicopathological features quite different from IGC and may have a superior survival to IGC in early stage patients. The prognostic factors identified here may assist the clinicians to more individually treat these patients.

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Body Composition Features Predict Overall Survival in Patients With Hepatocellular Carcinoma.

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