Dataset Information

Novel biomarker-based model for the prediction of sorafenib response and overall survival in advanced hepatocellular carcinoma: a prospective cohort study.

ABSTRACT:

Background

Prediction of the outcome of sorafenib therapy using biomarkers is an unmet clinical need in patients with advanced hepatocellular carcinoma (HCC). The aim was to develop and validate a biomarker-based model for predicting sorafenib response and overall survival (OS).

Methods

This prospective cohort study included 124 consecutive HCC patients (44 with disease control, 80 with progression) with Child-Pugh class A liver function, who received sorafenib. Potential serum biomarkers (namely, hepatocyte growth factor [HGF], fibroblast growth factor [FGF], vascular endothelial growth factor receptor-1, CD117, and angiopoietin-2) were tested. After identifying independent predictors of tumor response, a risk scoring system for predicting OS was developed and 3-fold internal validation was conducted.

Results

A risk scoring system was developed with six covariates: etiology, platelet count, Barcelona Clinic Liver Cancer stage, protein induced by vitamin K absence-II, HGF, and FGF. When patients were stratified into low-risk (score ≤ 5), intermediate-risk (score 6), and high-risk (score ≥ 7) groups, the model provided good discriminant functions on tumor response (concordance [c]-index, 0.884) and 12-month survival (area under the curve [AUC], 0.825). The median OS was 19.0, 11.2, and 6.1 months in the low-, intermediate-, and high-risk group, respectively (P < 0.001). In internal validation, the model maintained good discriminant functions on tumor response (c-index, 0.825) and 12-month survival (AUC, 0.803), and good calibration functions (all P > 0.05 between expected and observed values).

Conclusions

This new model including serum FGF and HGF showed good performance in predicting the response to sorafenib and survival in patients with advanced HCC.

SUBMITTER: Kim HY

PROVIDER: S-EPMC5859435 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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Publications

Novel biomarker-based model for the prediction of sorafenib response and overall survival in advanced hepatocellular carcinoma: a prospective cohort study.

Kim Hwi Young HY Lee Dong Hyeon DH Lee Jeong-Hoon JH Cho Young Youn YY Cho Eun Ju EJ Yu Su Jong SJ Kim Yoon Jun YJ Yoon Jung-Hwan JH

BMC cancer 20180320 1

<h4>Background</h4>Prediction of the outcome of sorafenib therapy using biomarkers is an unmet clinical need in patients with advanced hepatocellular carcinoma (HCC). The aim was to develop and validate a biomarker-based model for predicting sorafenib response and overall survival (OS).<h4>Methods</h4>This prospective cohort study included 124 consecutive HCC patients (44 with disease control, 80 with progression) with Child-Pugh class A liver function, who received sorafenib. Potential serum bi ...[more]

PMID: 29558905

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Project description:BackgroundPhase III trials show sorafenib improves survival in advanced hepatocellular carcinoma (HCC). Because of narrow trial eligibility, results may not be generalizable to a broader HCC population. We sought to evaluate the effectiveness of initial sorafenib versus no treatment among Medicare beneficiaries with advanced HCC.Materials and methodsPatients with advanced HCC diagnosed from 2008 to 2011 were identified from the Surveillance, Epidemiology, and End Results-Medicare database. Eligible patients received initial sorafenib or no therapy and were covered by Medicare parts A, B, and D. Sorafenib use and outcomes were described in this population. Using a propensity score (PS)-matched sample, we compared the effectiveness of sorafenib versus no treatment by Cox proportional hazards and binomial regression, using a landmark requiring all patients to survive ≥60 days after diagnosis.ResultsOf 1,532 patients, 27% received initial sorafenib. Median duration of sorafenib use was 60 days (interquartile range [IQR], 30-107 days), and median survival from first prescription was 3 months (IQR, 1-8 months). In the PS-matched cohort, median survival was 3 months from the 60-day landmark in sorafenib-treated (n = 223) and 2 months in untreated (n = 223) patients (adjusted hazard ratio, 0.95 [95% confidence interval (CI), 0.78-1.16]). Sorafenib was associated with a nonsignificant reduction in mortality at 3 months (44% versus 51%; adjusted risk ratio, 0.88 [95% CI, 0.72-1.07]), but no reduction thereafter.ConclusionSurvival after sorafenib initiation in newly diagnosed Medicare beneficiaries with HCC is exceptionally short, suggesting trial results are not generalizable to all HCC patients. The downsides of sorafenib use-high drug-related symptom burden and high drug cost-must be considered in light of this minimal benefit.Implications for practiceThe findings of a median survival of only 3 months in Medicare beneficiaries with HCC prescribed sorafenib as first-line therapy highlight the questionable value of sorafenib in this population. Patients should be cautioned that outside of the narrow confines of randomized trials, their life expectancy may be very short, and any benefit of sorafenib is likely to be quite small. Given that sorafenib causes considerable adverse effects and offers no symptom palliation, supportive care should be discussed as a reasonable alternative to sorafenib, particularly for patients who have a poor performance status or advanced cirrhosis.

Project description:BackgroundSorafenib is a multiple receptor tyrosine kinase inhibitor known to prolong overall survival in patients with advanced hepatocellular carcinoma (HCC). Predicting this drug's survival benefits is challenging because clinical responses are rarely measurable during treatment. In this study, we hypothesized that serum cytokines levels could predict the survival of advanced HCC patients, as sorafenib targets signaling pathways activated in the tumor stromal microenvironment and potentially affects serum cytokine profiles.MethodsOf 143 patients with advanced-stage HCC, 104 who were recruited between 2003 and 2007 received hepatic arterial infusion chemotherapy (HAIC) that mainly targets tumor epithelial cells at S-phase (cohort 1); additionally, 39 recruited between 2010 and 2012 received sorafenib, which primarily targets the stromal vascular endothelial cells. Serum samples were collected and aliquoted prior to the treatment. Serum EGF, bFGF, HGF, IFN-γ, IL-10, IL-12, IL-2, IL-4, IL-5, IL-6, IL-8, IP-10, MIG, PDGF-BB, SCF, SDF1, TGF-β, TGF-α, TNF-α, and VEGF-A were measured via enzyme-linked immunosorbent assays. The Modified Response Evaluation Criteria in Solid Tumors were used to assess tumor responses.ResultsThe median survival time of HCC patients in cohorts 1 (HAIC-treated) and 2 (sorafenib-treated) were 12.0 and 12.4 months, respectively. Kaplan-Meier analysis revealed no significant survival differences between the 2 groups. Patients who survived more than 2 years after sorafenib treatment exhibited higher serum levels of IL-10, IL-12, TNF-a, IL-8, SDF-1, EGF, PDGF-BB, SCF, and TGF-α. Furthermore, cohort 2 patients with higher serum IL-5 (>12 pg/mL), IL-8 (>10 pg/mL), PDGF-BB (>300 pg/mL), and VEGF-A (>50 pg/mL) levels achieved longer survival; cohort 1 patients did not. Hierarchical cluster analysis of 6 cytokines robustly enriched for comparison analysis between cohorts 1 and 2 (IL-5, IL-8, TGF-α, PDGF-BB, CXCL9, and VEGF-A) revealed that elevation of these cytokines correlated with better survival when treated with sorafenib but not with HAIC.ConclusionsPatients who exhibited survival benefits owing to sorafenib treatment tended to present higher serum cytokines levels, potentially reflecting the activation of stromal signaling in the tumor microenvironment. Our study thus introduces novel biomarkers that may identify advanced HCC patients who may experience survival benefits with sorafenib treatment.

Project description:ObjectivesTo develop a deep learning model combining CT scans and clinical information to predict overall survival in advanced hepatocellular carcinoma (HCC).MethodsThis retrospective study included immunotherapy-treated advanced HCC patients from 52 multi-national in-house centers between 2018 and 2022. A multi-modal prognostic model using baseline and the first follow-up CT images and 7 clinical variables was proposed. A convolutional-recurrent neural network (CRNN) was developed to extract spatial-temporal information from automatically selected representative 2D CT slices to provide a radiological score, then fused with a Cox-based clinical score to provide the survival risk. The model's effectiveness was assessed using a time-dependent area under the receiver operating curve (AUC), and risk group stratification using the log-rank test. Prognostic performances of multi-modal inputs were compared to models of missing modality, and the size-based RECIST criteria.ResultsTwo-hundred seven patients (mean age, 61 years ± 12 [SD], 180 men) were included. The multi-modal CRNN model reached the AUC of 0.777 and 0.704 of 1-year overall survival predictions in the validation and test sets. The model achieved significant risk stratification in validation (hazard ratio [HR] = 3.330, p = 0.008), and test sets (HR = 2.024, p = 0.047) based on the median risk score of the training set. Models with missing modalities (the single-modal imaging-based model and the model incorporating only baseline scans) can still achieve favorable risk stratification performance (all p < 0.05, except for one, p = 0.053). Moreover, results proved the superiority of the deep learning-based model to the RECIST criteria.ConclusionDeep learning analysis of CT scans and clinical data can offer significant prognostic insights for patients with advanced HCC.Critical relevance statementThe established model can help monitor patients' disease statuses and identify those with poor prognosis at the time of first follow-up, helping clinicians make informed treatment decisions, as well as early and timely interventions.Key pointsAn AI-based prognostic model was developed for advanced HCC using multi-national patients. The model extracts spatial-temporal information from CT scans and integrates it with clinical variables to prognosticate. The model demonstrated superior prognostic ability compared to the conventional size-based RECIST method.

Dataset Information

Novel biomarker-based model for the prediction of sorafenib response and overall survival in advanced hepatocellular carcinoma: a prospective cohort study.

Background

Methods

Results

Conclusions

Publications

Novel biomarker-based model for the prediction of sorafenib response and overall survival in advanced hepatocellular carcinoma: a prospective cohort study.

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