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

Determination of Expression Signature and Proportion of mtDNA in Plasma Fractions in Patients with Renal Cell Carcinoma

Renal Cell Carcinoma (RCC) is the third most common urologic malignancy, remaining one of the most lethal urological malignancies, preferably in developed countries. The incidence and mortality rates differ significantly according to sex, race, age and external factors such as smoking, obesity and hypertension increasing RCC risk. The use of novel predictive biomarkers is currently being increased as these improve the diagnosis, progression and prognosis of RCC. Since recent studies have demonstrated a promising association between mitochondrial DNA (mtDNA) copy number alteration in peripheral blood and the risk of developing RCC, we conducted a case-control study into a cohort of 15 controls and 13 patients to determine exosomes mtDNA content in plasma fractions as a potential novel non-invasive biomarker in liquid biopsy in order to monitor the RCC status in patients. In this way, plasma fractions highly purified in exosomes were obtained from blood samples from controls and RCC cases, and relative mtDNA content was measured by quantitative real-time polymerase chain reaction (qPCR). Our results show fragment size distribution profile and we observed that in phase F; with a higher content of exosomal mtDNA; p value shows statistically significant differences in mitochondrial genes HV long and CYB long

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

Mitochondrial Haplogroups and Polymorphisms Reveal No Association with Sporadic Prostate Cancer in a Southern European Population

[Background] It is known that mitochondria play an important role in certain cancers (prostate, renal, breast, or colorectal) and coronary disease. These organelles play an essential role in apoptosis and the production of reactive oxygen species; in addition, mtDNA also reveals the history of populations and ancient human migration. All these events and variations in the mitochondrial genome are thought to cause some cancers, including prostate cancer, and also help us to group individuals into common origin groups. The aim of the present study is to analyze the different haplogroups and variations in the sequence in the mitochondrial genome of a southern European population consisting of subjects affected (n = 239) and non-affected (n = 150) by sporadic prostate cancer. [Methodology and Principal Findings] Using primer extension analysis and DNA sequencing, we identified the nine major European haplogroups and CR polymorphisms. The frequencies of the haplogroups did not differ between patients and control cohorts, whereas the CR polymorphism T16356C was significantly higher in patients with PC compared to the controls (p = 0.029). PSA, staging, and Gleason score were associated with none of the nine major European haplogroups. The CR polymorphisms G16129A (p = 0.007) and T16224C (p = 0.022) were significantly associated with Gleason score, whereas T16311C (p = 0.046) was linked with T-stage. [Conclusions and Significance] Our results do not suggest that mtDNA haplogroups could be involved in sporadic prostate cancer etiology and pathogenesis as previous studies performed in middle Europe population. Although some significant associations have been obtained in studying CR polymorphisms, further studies should be performed to validate these results