RESUMO
OBJECTIVE: A phase II study was performed to evaluate the efficacy and tolerability of bevacizumab and erlotinib in advanced hepatocellular carcinoma (HCC) patients, and to investigate clinical and molecular predictors of outcome. METHODS: 59 patients with advanced HCC received 10 mg/kg i.v. of bevacizumab every 14 days and 150 mg p.o. of erlotinib daily. The primary endpoint was progression-free survival (PFS) at 16 weeks. Clinical characteristics and plasma biomarkers expression levels were analyzed. RESULTS: PFS at 16 weeks was 64% (95% CI 51-76): 14 patients achieved partial response (24%), 33 had stable disease (56%), 6 progressed (10%), and 6 were not evaluable (10%). Median overall survival was 13.7 months (95% CI 9.6-19.7), and median PFS was 7.2 months (95% CI 5.6-8.3). Grade 3-4 adverse events included fatigue (30%), diarrhea (17%), hypertension (14%), elevated transaminases (12%), and gastrointestinal hemorrhage (10%). High plasma angiopoietin-2, epidermal growth factor receptor, and endothelin-1, and lack of acneiform rash were associated with poor outcome. CONCLUSIONS: The combination of bevacizumab with erlotinib achieved encouraging results in patients with advanced HCC. Current correlatives may help to guide future HCC studies.
Assuntos
Protocolos de Quimioterapia Combinada Antineoplásica/uso terapêutico , Carcinoma Hepatocelular/tratamento farmacológico , Neoplasias Hepáticas/tratamento farmacológico , Adulto , Idoso , Idoso de 80 Anos ou mais , Angiopoietina-2/sangue , Anticorpos Monoclonais Humanizados/administração & dosagem , Anticorpos Monoclonais Humanizados/efeitos adversos , Bevacizumab , Carcinoma Hepatocelular/mortalidade , Intervalo Livre de Doença , Receptores ErbB/sangue , Cloridrato de Erlotinib , Feminino , Humanos , Neoplasias Hepáticas/mortalidade , Masculino , Pessoa de Meia-Idade , Modelos de Riscos Proporcionais , Quinazolinas/administração & dosagem , Quinazolinas/efeitos adversosRESUMO
In this paper we propose a novel Bayesian statistical methodology for spatial survival data. Our methodology broadens the definition of the survival, density and hazard functions by explicitly modeling the spatial dependency using direct derivations of these functions and their marginals and conditionals. We also derive spatially dependent likelihood functions. Finally we examine the applications of these derivations with geographically augmented survival distributions in the context of the Louisiana Surveillance, Epidemiology, and End Results (SEER) registry prostate cancer data.