HOXD8 hypermethylation as a fully sensitive and specific biomarker for biliary tract cancer detectable in tissue and bile samples.

BACKGROUND: Biliary tract cancers (BTC) are rare but highly aggressive tumours with poor prognosis, usually detected at advanced stages. Herein, we aimed at identifying BTC-specific DNA methylation alterations. METHODS: Study design included statistical power and sample size estimation. A genome-wide methylation study of an explorative cohort (50 BTC and ten matched non-tumoral tissue samples) has been performed. BTC-specific altered CpG islands were validated in over 180 samples (174 BTCs and 13 non-tumoral controls). The final biomarkers, selected by a machine-learning approach, were validated in independent tissue (18 BTCs, 14 matched non-tumoral samples) and bile (24 BTCs, five non-tumoral samples) replication series, using droplet digital PCR. RESULTS: We identified and successfully validated BTC-specific DNA methylation alterations in over 200 BTC samples. The two-biomarker panel, selected by an in-house algorithm, showed an AUC > 0.97. The best-performing biomarker (chr2:176993479-176995557), associated with HOXD8, a pivotal gene in cancer-related pathways, achieved 100% sensitivity and specificity in a new series of tissue and bile samples. CONCLUSIONS: We identified a novel fully efficient BTC biomarker, associated with HOXD8 gene, detectable both in tissue and bile by a standardised assay ready-to-use in clinical trials also including samples from non-invasive matrices.

Next-generation sequencing of bile cell-free DNA for the early detection of patients with malignant biliary strictures.

OBJECTIVE: Despite significant progresses in imaging and pathological evaluation, early differentiation between benign and malignant biliary strictures remains challenging. Endoscopic retrograde cholangiopancreatography (ERCP) is used to investigate biliary strictures, enabling the collection of bile. We tested the diagnostic potential of next-generation sequencing (NGS) mutational analysis of bile cell-free DNA (cfDNA). DESIGN: A prospective cohort of patients with suspicious biliary strictures (n=68) was studied. The performance of initial pathological diagnosis was compared with that of the mutational analysis of bile cfDNA collected at the time of first ERCP using an NGS panel open to clinical laboratory implementation, the Oncomine Pan-Cancer Cell-Free assay. RESULTS: An initial pathological diagnosis classified these strictures as of benign (n=26), indeterminate (n=9) or malignant (n=33) origin. Sensitivity and specificity of this diagnosis were 60% and 100%, respectively, as on follow-up 14 of the 26 and eight of the nine initially benign or indeterminate strictures resulted malignant. Sensitivity and specificity for malignancy of our NGS assay, herein named Bilemut, were 96.4% and 69.2%, respectively. Importantly, one of the four Bilemut false positives developed pancreatic cancer after extended follow-up. Remarkably, the sensitivity for malignancy of Bilemut was 100% in patients with an initial diagnosis of benign or indeterminate strictures. Analysis of 30 paired bile and tissue samples also demonstrated the superior performance of Bilemut. CONCLUSION: Implementation of Bilemut at the initial diagnostic stage for biliary strictures can significantly improve detection of malignancy, reduce delays in the clinical management of patients and assist in selecting patients for targeted therapies.

Bile Processing Protocol for Improved Proteomic Analysis.

One of the critical issues to warrant the success of a proteome-wide analysis is sample preparation. Efficient protein extraction in the absence of interferent material is mandatory to achieve an ample proteome coverage by mass spectrometry. The study of biological fluids is always challenging due to their specific biochemical composition. However, there is increasing interest in their characterization as it will provide proteins that may advice disease setting, state, and progression. In particular, bile is proximal to liver and pancreas, and its study is especially attractive since it might provide valuable information for the clinical management of severe diseases afflicting these organs, which are at an urgent need of new biomarkers. Though previous efforts have been made to optimize protocols to analyze bile proteome, only partial descriptions were achieved due to its complex composition, where proteins represent less than 5% of the organic components. Here we describe a new method that significantly increases the bile proteome coverage while reducing by a factor of six the amount of sample required for the proteomic analysis.

Digging deeper into bile proteome.

The analysis of biological fluids to identify proteins that may indicate a disease setting, state and progression, is an increasingly explored field. Despite the expectatives created, there are several hurdles that must be solved to reach an extensive proteome coverage using mass spectrometry, mainly due to the complex composition of the matrices. In this regard, bile is specially challenging and yet, very attractive, as a proximal fluid that might provide valuable information for the management of liver and pancreas associated diseases. Proteins account for less than 5% of bile organic components and, although optimized protocols for protein extraction have been developed, only partial descriptions of bile proteome have been achieved. In this manuscript a new procedure is described that significantly improves protein recovery from rat bile, which reduces by a factor of six the sample amount required for a typical proteomics analysis. Moreover, the number of proteins reliably identified in a single nanoLC-MS/MS run from 1 µg protein was increased by three-fold. This procedure provides a valuable resource to dig deeper into the molecular composition of bile and open new avenues to identify new hallmarks of disease such as cholangiocarcinoma, hepatocellular carcinoma and pancreatic cancer for their better clinical management.

Dual Pharmacological Targeting of HDACs and PDE5 Inhibits Liver Disease Progression in a Mouse Model of Biliary Inflammation and Fibrosis.

Liver fibrosis, a common hallmark of chronic liver disease (CLD), is characterized by the accumulation of extracellular matrix secreted by activated hepatic fibroblasts and stellate cells (HSC). Fibrogenesis involves multiple cellular and molecular processes and is intimately linked to chronic hepatic inflammation. Importantly, it has been shown to promote the loss of liver function and liver carcinogenesis. No effective therapies for liver fibrosis are currently available. We examined the anti-fibrogenic potential of a new drug (CM414) that simultaneously inhibits histone deacetylases (HDACs), more precisely HDAC1, 2, and 3 (Class I) and HDAC6 (Class II) and stimulates the cyclic guanosine monophosphate (cGMP)-protein kinase G (PKG) pathway activity through phosphodiesterase 5 (PDE5) inhibition, two mechanisms independently involved in liver fibrosis. To this end, we treated Mdr2-KO mice, a clinically relevant model of liver inflammation and fibrosis, with our dual HDAC/PDE5 inhibitor CM414. We observed a decrease in the expression of fibrogenic markers and collagen deposition, together with a marked reduction in inflammation. No signs of hepatic or systemic toxicity were recorded. Mechanistic studies in cultured human HSC and cholangiocytes (LX2 and H69 cell lines, respectively) demonstrated that CM414 inhibited pro-fibrogenic and inflammatory responses, including those triggered by transforming growth factor ß (TGFß). Our study supports the notion that simultaneous targeting of pro-inflammatory and fibrogenic mechanisms controlled by HDACs and PDE5 with a single molecule, such as CM414, can be a new disease-modifying strategy.

Epigenetics in Liver Fibrosis: Could HDACs be a Therapeutic Target?

Chronic liver diseases (CLD) represent a worldwide health problem. While CLDs may have diverse etiologies, a common pathogenic denominator is the presence of liver fibrosis. Cirrhosis, the end-stage of CLD, is characterized by extensive fibrosis and is markedly associated with the development of hepatocellular carcinoma. The most important event in hepatic fibrogenesis is the activation of hepatic stellate cells (HSC) following liver injury. Activated HSCs acquire a myofibroblast-like phenotype becoming proliferative, fibrogenic, and contractile cells. While transient activation of HSCs is part of the physiological mechanisms of tissue repair, protracted activation of a wound healing reaction leads to organ fibrosis. The phenotypic changes of activated HSCs involve epigenetic mechanisms mediated by non-coding RNAs (ncRNA) as well as by changes in DNA methylation and histone modifications. During CLD these epigenetic mechanisms become deregulated, with alterations in the expression and activity of epigenetic modulators. Here we provide an overview of the epigenetic alterations involved in fibrogenic HSCs transdifferentiation with particular focus on histones acetylation changes. We also discuss recent studies supporting the promising therapeutic potential of histone deacetylase inhibitors in liver fibrosis.

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.