Pilot multi-omic analysis of human bile from benign and malignant biliary strictures: a machine-learning approach

Cholangiocarcinoma (CCA) and pancreatic adenocarcinoma (PDAC) may lead to the development of extrahepatic obstructive cholestasis. However, biliary stenoses can also be caused by benign conditions, and the identification of their etiology still remains a clinical challenge. We performed metabolomic and proteomic analyses of bile from patients with benign (n = 36) and malignant conditions, CCA (n = 36) or PDAC (n = 57), undergoing endoscopic retrograde cholangiopancreatography with the aim of characterizing bile composition in biliopancreatic disease and identifying biomarkers for the differential diagnosis of biliary strictures. Comprehensive analyses of lipids, bile acids and small molecules were carried out using mass spectrometry (MS) and nuclear magnetic resonance spectroscopy (1H-NMR) in all patients. MS analysis of bile proteome was performed in five patients per group. We implemented artificial intelligence tools for the selection of biomarkers and algorithms with predictive capacity. Our machine-learning pipeline included the generation of synthetic data with properties of real data, the selection of potential biomarkers (metabolites or proteins) and their analysis with neural networks (NN). Selected biomarkers were then validated with real data. We identified panels of lipids (n = 10) and proteins (n = 5) that when analyzed with NN algorithms discriminated between patients with and without cancer with an unprecedented accuracy.This research was funded by: Instituto de Salud Carlos III (ISCIII) co-financed by Fondo Europeo de Desarrollo Regional (FEDER) Una manera de hacer Europa, grant numbers: PI16/01126 (M.A.A.), PI19/00819 (M.J.M. and J.J.G.M.), PI15/01132, PI18/01075 and Miguel Servet Program CON14/00129 (J.M.B.); Fundación Científica de la Asociación Española Contra el Cáncer (AECC Scientific Foundation), grant name: Rare Cancers 2017 (J.M.U., M.L.M., J.M.B., M.J.M., R.I.R.M., M.G.F.-B., C.B., M.A.A.); Gobierno de Navarra Salud, grant number 58/17 (J.M.U., M.A.A.); La Caixa Foundation, grant name: HEPACARE (C.B., M.A.A.); AMMF The Cholangiocarcinoma Charity, UK, grant number: 2018/117 (F.J.C. and M.A.A.); PSC Partners US, PSC Supports UK, grant number 06119JB (J.M.B.); Horizon 2020 (H2020) ESCALON project, grant number H2020-SC1-BHC-2018–2020 (J.M.B.); BIOEF (Basque Foundation for Innovation and Health Research: EiTB Maratoia, grant numbers BIO15/CA/016/BD (J.M.B.) and BIO15/CA/011 (M.A.A.). Department of Health of the Basque Country, grant number 2017111010 (J.M.B.). La Caixa Foundation, grant number: LCF/PR/HP17/52190004 (M.L.M.), Mineco-Feder, grant number SAF2017-87301-R (M.L.M.), Fundación BBVA grant name: Ayudas a Equipos de Investigación Científica Umbrella 2018 (M.L.M.). MCIU, grant number: Severo Ochoa Excellence Accreditation SEV-2016-0644 (M.L.M.). Part of the equipment used in this work was co-funded by the Generalitat Valenciana and European Regional Development Fund (FEDER) funds (PO FEDER of Comunitat Valenciana 2014–2020). Gobierno de Navarra fellowship to L.C. (Leticia Colyn); AECC post-doctoral fellowship to M.A.; Ramón y Cajal Program contracts RYC-2014-15242 and RYC2018-024475-1 to F.J.C. and M.G.F.-B., respectively. The generous support from: Fundación Eugenio Rodríguez Pascual, Fundación Echébano, Fundación Mario Losantos, Fundación M Torres and Mr. Eduardo Avila are acknowledged. The CNB-CSIC Proteomics Unit belongs to ProteoRed, PRB3-ISCIII, supported by grant PT17/0019/0001 (F.J.C.). Comunidad de Madrid Grant B2017/BMD-3817 (F.J.C.).Peer reviewe

Pilot Multi-Omic Analysis of Human Bile from Benign and Malignant Biliary Strictures: A Machine-Learning Approach

Cholangiocarcinoma (CCA) and pancreatic adenocarcinoma (PDAC) may lead to the development of extrahepatic obstructive cholestasis. However, biliary stenoses can also be caused by benign conditions, and the identification of their etiology still remains a clinical challenge. We performed metabolomic and proteomic analyses of bile from patients with benign (n = 36) and malignant conditions, CCA (n = 36) or PDAC (n = 57), undergoing endoscopic retrograde cholangiopancreatography with the aim of characterizing bile composition in biliopancreatic disease and identifying biomarkers for the differential diagnosis of biliary strictures. Comprehensive analyses of lipids, bile acids and small molecules were carried out using mass spectrometry (MS) and nuclear magnetic resonance spectroscopy (1H-NMR) in all patients. MS analysis of bile proteome was performed in five patients per group. We implemented artificial intelligence tools for the selection of biomarkers and algorithms with predictive capacity. Our machine-learning pipeline included the generation of synthetic data with properties of real data, the selection of potential biomarkers (metabolites or proteins) and their analysis with neural networks (NN). Selected biomarkers were then validated with real data. We identified panels of lipids (n = 10) and proteins (n = 5) that when analyzed with NN algorithms discriminated between patients with and without cancer with an unprecedented accuracy.Union Europea. Horizonte 2020Ministerio de Economía y Competitividad (MINECO)/FEDERMinisterio de Ciencia e Innovación (MICCIN)Comunidad de MadridInstituto de Salud Carlos III (ISCIII) / FEDERCentro de Excelencia Severo OchoaFundación Científica de la Asociación Española Contra el Cáncer (AECC Scientific Foundation)Gobierno de Navarra SaludFundación La CaixaDepto. de Inmunología, Oftalmología y ORLFac. de MedicinaTRUEpu

Successful Optimization of Adalimumab Therapy in Refractory Uveitis Due to Behçet's Disease.

To assess efficacy, safety, and cost-effectiveness of adalimumab (ADA) therapy optimization in a large series of patients with uveitis due to Behçet disease (BD) who achieved remission after the use of this biologic agent. Open-label multicenter study of ADA-treated patients with BD uveitis refractory to conventional immunosuppressants. Sixty-five of 74 patients with uveitis due to BD, who achieved remission after a median ADA duration of 6 (range, 3-12) months. ADA was optimized in 23 (35.4%) of them. This biologic agent was maintained at a dose of 40 mg/subcutaneously/2 weeks in the remaining 42 patients. After remission, based on a shared decision between the patient and the treating physician, ADA was optimized. When agreement between patient and physician was reached, optimization was performed by prolonging the ADA dosing interval progressively. Comparison between optimized and nonoptimized patients was performed. Efficacy, safety, and cost-effectiveness in optimized and nonoptimized groups. To determine efficacy, intraocular inflammation (anterior chamber cells, vitritis, and retinal vasculitis), macular thickness, visual acuity, and the sparing effect of glucocorticoids were assessed. No demographic or ocular differences were found at the time of ADA onset between the optimized and the nonoptimized groups. Most ocular outcomes were similar after a mean ± standard deviation follow-up of 34.7±13.3 and 26±21.3 months in the optimized and nonoptimized groups, respectively. However, relevant adverse effects were only seen in the nonoptimized group (lymphoma, pneumonia, severe local reaction at the injection site, and bacteremia by Escherichia coli, 1 each). Moreover, the mean ADA treatment costs were lower in the optimized group than in the nonoptimized group (6101.25 euros/patient/year vs. 12 339.48; P ADA optimization in BD uveitis refractory to conventional therapy is effective, safe, and cost-effective

Pilot multi-omic analysis of human bile from benign and malignant biliary strictures: A machine-learning approach

Cholangiocarcinoma (CCA) and pancreatic adenocarcinoma (PDAC) may lead to the development of extrahepatic obstructive cholestasis. However, biliary stenoses can also be caused by benign conditions, and the identification of their etiology still remains a clinical challenge. We performed metabolomic and proteomic analyses of bile from patients with benign (n = 36) and malignant conditions, CCA (n = 36) or PDAC (n = 57), undergoing endoscopic retrograde cholangiopancreatography with the aim of characterizing bile composition in biliopancreatic disease and identifying biomarkers for the differential diagnosis of biliary strictures. Comprehensive analyses of lipids, bile acids and small molecules were carried out using mass spectrometry (MS) and nuclear magnetic resonance spectroscopy (1H-NMR) in all patients. MS analysis of bile proteome was performed in five patients per group. We implemented artificial intelligence tools for the selection of biomarkers and algorithms with predictive capacity. Our machine-learning pipeline included the generation of synthetic data with properties of real data, the selection of potential biomarkers (metabolites or proteins) and their analysis with neural networks (NN). Selected biomarkers were then validated with real data. We identified panels of lipids (n = 10) and proteins (n = 5) that when analyzed with NN algorithms discriminated between patients with and without cancer with an unprecedented accurac

Pilot multi-omic analysis of human bile from benign and malignant biliary strictures: A machine-learning approach

Cholangiocarcinoma (CCA) and pancreatic adenocarcinoma (PDAC) may lead to the development of extrahepatic obstructive cholestasis. However, biliary stenoses can also be caused by benign conditions, and the identification of their etiology still remains a clinical challenge. We performed metabolomic and proteomic analyses of bile from patients with benign (n = 36) and malignant conditions, CCA (n = 36) or PDAC (n = 57), undergoing endoscopic retrograde cholangiopancreatography with the aim of characterizing bile composition in biliopancreatic disease and identifying biomarkers for the differential diagnosis of biliary strictures. Comprehensive analyses of lipids, bile acids and small molecules were carried out using mass spectrometry (MS) and nuclear magnetic resonance spectroscopy (1H-NMR) in all patients. MS analysis of bile proteome was performed in five patients per group. We implemented artificial intelligence tools for the selection of biomarkers and algorithms with predictive capacity. Our machine-learning pipeline included the generation of synthetic data with properties of real data, the selection of potential biomarkers (metabolites or proteins) and their analysis with neural networks (NN). Selected biomarkers were then validated with real data. We identified panels of lipids (n = 10) and proteins (n = 5) that when analyzed with NN algorithms discriminated between patients with and without cancer with an unprecedented accurac

Comparative Study of Infliximab Versus Adalimumab in Refractory Uveitis due to Behçet's Disease: National Multicenter Study of 177 Cases.

To compare the efficacy of infliximab (IFX) versus adalimumab (ADA) as a first-line biologic drug over 1 year of treatment in a large series of patients with refractory uveitis due to Behçet's disease (BD). We conducted an open-label multicenter study of IFX versus ADA for BD-related uveitis refractory to conventional nonbiologic treatment. IFX or ADA was chosen as the first-line biologic agent based on physician and patient agreement. Patients received 3-5 mg/kg intravenous IFX at 0, 2, and 6 weeks and every 4-8 weeks thereafter, or 40 mg subcutaneous ADA every other week without a loading dose. Ocular parameters were compared between the 2 groups. The study included 177 patients (316 affected eyes), of whom 103 received IFX and 74 received ADA. There were no significant baseline differences between treatment groups in main demographic features, previous therapy, or ocular sign severity. After 1 year of therapy, we observed an improvement in all ocular parameters in both groups. However, patients receiving ADA had significantly better outcomes in some parameters, including improvement in anterior chamber inflammation (92.31% versus 78.18% for IFX; P = 0.06), improvement in vitritis (93.33% versus 78.95% for IFX; P = 0.04), and best-corrected visual acuity (mean ± SD 0.81 ± 0.26 versus 0.67 ± 0.34 for IFX; P = 0.001). A nonsignificant difference was seen for macular thickness (mean ± SD 250.62 ± 36.85 for ADA versus 264.89 ± 59.74 for IFX; P = 0.15), and improvement in retinal vasculitis was similar between the 2 groups (95% for ADA versus 97% for IFX; P = 0.28). The drug retention rate was higher in the ADA group (95.24% versus 84.95% for IFX; P = 0.042). Although both IFX and ADA are efficacious in refractory BD-related uveitis, ADA appears to be associated with better outcomes than IFX after 1 year of follow-up