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

The Search for Risk, Diagnostic and Prognostic Biomarkers of Cholangiocarcinoma and their Biological and Clinicopathological Significance

Cholangiocarcinomas (CCAs) are a heterogeneus group of malignant tumors that originate from the biliary tract. They are usually diagnosed in advanced stages, leading to a dismal prognosis for affected patients. As CCA often arises as a sporadic cancer in individuals lacking specific risk factors or with heterogeneous backgrounds, and there are no defined high-risk groups, the implementation of effective surveillance programs for CCA is problematic. The identification and validation of new biomarkers useful for risk stratification, diagnosis, prognosis and prediction of treatment response remains an unmet need for patients with CCA, even though numerous studies have been conducted lately to try to discover and validate CCA biomarkers. In this review, we overview the available information about the different types of biomarkers that have been investigated in recent years using minimally invasive biospecimens (blood, serum/plasma, bile, urine) and their potential usefulness in diagnosis, prognosis and risk stratification. It is widely accepted that early detection of CCA will impact patients' outcomes, by improving survival rates, quality of life, and the possibility of less invasive and/or curative treatments, however, challenges to its translation and clinical application for CCA patients need to be resolved.

Comparison of biliary protein spectrum in gallstone patients with obesity and those with normal body weight

BACKGROUND

Obesity is a common public health issue and is currently deemed a disease. Research has shown that the risk of gallstones in individuals with obesity is elevated. This study aimed to explore the bile proteomics differences between cholelithiasis patients with obesity and normal body weight.

METHODS

Bile samples from 20 patients (10 with obesity and 10 with normal body weight) who underwent laparoscopic cholecystectomy at our center were subjected to tandem mass tag labeling (TMT) and liquid chromatography-tandem mass spectrometry (LC-MS/MS), followed by further bioinformatic analysis.

RESULTS

Among the differentially expressed proteins, 23 were upregulated and 67 were downregulated. Bioinformatic analysis indicated that these differentially expressed proteins were mainly involved in cell development, inflammatory responses, glycerolipid metabolic processes, and protein activation cascades. In addition, the activity of the peroxisome proliferator-activated receptor (PPAR, a subfamily of nuclear receptors) signaling pathway was decreased in the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Two downregulated proteins in the PPAR signaling pathway, APO A-I and APO A-II, were confirmed using enzyme-linked immunosorbent assay.

CONCLUSIONS

The PPAR signaling pathway may play a crucial role in the development of cholelithiasis among patients with obesity. Furthermore, biliary proteomics profiling of gallstones patients with obesity is revealed, providing a reference for future research.

Refinement of paramagnetic bead-based digestion protocol for automatic sample preparation using an artificial neural network

Despite technological advances in the proteomics field, sample preparation still represents the main bottleneck in mass spectrometry (MS) analysis. Bead-based protein aggregation techniques have recently emerged as an efficient, reproducible, and high-throughput alternative for protein extraction and digestion. Here, a refined paramagnetic bead-based digestion protocol is described for Opentrons® OT-2 platform (OT-2) as a versatile, reproducible, and affordable alternative for the automatic sample preparation for MS analysis. For this purpose, an artificial neural network (ANN) was applied to maximize the number of peptides without missed cleavages identified in HeLa extract by combining factors such as the quantity (μg) of trypsin/Lys-C and beads (MagReSyn® Amine), % (w/v) SDS, % (v/v) acetonitrile, and time of digestion (h). ANN model predicted the optimal conditions for the digestion of 50 μg of HeLa extract, pointing to the use of 2.5% (w/v) SDS and 300 μg of beads for sample preparation and long-term digestion (16h) with 0.15 μg Lys-C and 2.5 μg trypsin (≈1:17 ratio). Based on the results of the ANN model, the manual protocol was automated in OT-2. The performance of the automatic protocol was evaluated with different sample types, including human plasma, Arabidopsis thaliana leaves, Escherichia coli cells, and mouse tissue cortex, showing great reproducibility and low sample-to-sample variability in all cases. In addition, we tested the performance of this method in the preparation of a challenging biological fluid such as rat bile, a proximal fluid that is rich in bile salts, bilirubin, cholesterol, and fatty acids, among other MS interferents. Compared to other protocols described in the literature for the extraction and digestion of bile proteins, the method described here allowed identify 385 unique proteins, thus contributing to improving the coverage of the bile proteome.

Bile metabolic fingerprints distinguish biliary tract cancer from benign biliary diseases

BACKGROUND AND AIMS

Biliary tract cancers are aggressive gastrointestinal malignancies characterized by a dismal 5-year overall survival rate <20%. Current diagnostic modalities suffer from limitations regarding sensitivity and specificity. This study aimed to develop a bile metabolite-based platform for precise discrimination between malignant and benign biliary diseases.

APPROACH AND RESULTS

Samples were collected from 336 patients with biliary tract cancer or benign biliary diseases across 3 independent cohorts. Untargeted metabolic fingerprinting was performed on 300 bile samples using novel nanoparticle-enhanced laser desorption/ionization mass spectrometry. Subsequently, a diagnostic assay was developed based on the exploratory cohort using a selected bile metabolic biomarker panel, with performance evaluated in the validation cohort. Further external validation of disease-specific metabolites from bile samples was conducted in a prospective cohort (n = 36) using quantitative analysis. As a result, we established a novel bile-based assay, BileMet, for the rapid and precise detection of malignancies in the biliary tract system with an AUC of 0.891. We identified 6-metabolite biomarker candidates and discovered the critical role of the chenodeoxycholic acid glycine conjugate as a protective metabolite associated with biliary tract cancer.

CONCLUSIONS

Our findings confirmed the improved diagnostic capabilities of BileMet assay in a clinical setting. If applied, the BileMet assay enables intraoperative testing and fast medical decision-making for cases with suspected malignancy where brush cytology detection fails to support malignancy, ultimately reducing the economic burden by over 90%.

Immunotherapy and targeted therapy for cholangiocarcinoma: Artificial intelligence research in imaging

Cholangiocarcinoma (CCA) is a highly aggressive hepatobiliary malignancy, second only to hepatocellular carcinoma in prevalence. Despite surgical treatment being the recommended method to achieve a cure, it is not viable for patients with advanced CCA. Gene sequencing and artificial intelligence (AI) have recently opened up new possibilities in CCA diagnosis, treatment, and prognosis assessment. Basic research has furthered our understanding of the tumor-immunity microenvironment and revealed targeted molecular mechanisms, resulting in immunotherapy and targeted therapy being increasingly employed in the clinic. Yet, the application of these remedies in CCA is a challenging endeavor due to the varying pathological mechanisms of different CCA types and the lack of expressed immune proteins and molecular targets in some patients. AI in medical imaging has emerged as a powerful tool in this situation, as machine learning and deep learning are able to extract intricate data from CCA lesion images while assisting clinical decision making, and ultimately improving patient prognosis. This review summarized and discussed the current immunotherapy and targeted therapy related to CCA, and the research progress of AI in this field.

Altered bile metabolome and its diagnostic potential for biliopancreatic malignancies

BACKGROUND

Due to the difficulty of pathological sampling, the clinical differentiation between benign and malignant biliopancreatic diseases remains challenging. Endoscopic retrograde cholangiopancreatography (ERCP) is used to investigate biliary diseases, enabling the collection of bile. This study assessed potential metabolic alterations in biliopancreatic malignancies by exploring changes in the bile metabolome and the diagnostic potential of bile metabolome analysis.

METHODS

A total of 264 bile samples were collected from patients who were divided into a discovery cohort (n = 85) and a validation cohort (n = 179). Untargeted metabolomic analysis was used in the discovery cohort, while targeted metabolomic analysis was used in the validation cohort for further investigation of the differentially abundant metabolites.

RESULTS

The untargeted metabolomic analysis revealed that the metabolic changes associated with biliopancreatic malignancies occurred mainly in lipid metabolites, among which fatty acid metabolism was most significantly altered, and differentially abundant metabolites identified in the discovery cohort were mainly enriched in unsaturated fatty acid synthesis and linolenic acid synthesis pathways. Analysis of free fatty acid (FFA) metabolism in the validation cohort revealed that the FFA levels and related indicators verified the abnormal fatty acid metabolism associated with biliopancreatic malignancies. The combined model for biliopancreatic malignancies based on the fatty acid indexes and clinical test results improved the diagnostic performance of current clinical level. Then, we used machine learning to define three different FFA metabolic clusters of biliopancreatic malignancies, and survival analysis showed significant differences in prognostic outcomes among the three clusters.

CONCLUSIONS

This study found metabolic alterations in biliopancreatic malignancies based on bile samples, which may provide new insights for the clinical diagnosis and prognostic assessment of biliopancreatic malignancies.

Application of AI on cholangiocarcinoma

Cholangiocarcinoma, classified as intrahepatic, perihilar, and extrahepatic, is considered a deadly malignancy of the hepatobiliary system. Most cases of cholangiocarcinoma are asymptomatic. Therefore, early detection of cholangiocarcinoma is significant but still challenging. The routine screening of a tumor lacks specificity and accuracy. With the application of AI, high-risk patients can be easily found by analyzing their clinical characteristics, serum biomarkers, and medical images. Moreover, AI can be used to predict the prognosis including recurrence risk and metastasis. Although they have some limitations, AI algorithms will still significantly improve many aspects of cholangiocarcinoma in the medical field with the development of computing power and technology.

Comparative analysis of bile metabolic profile in patients with biliary obstruction complicated by Clonorchis sinensis infection

Background

Clonorchiasis is an important foodborne parasitic disease. However, eggs of Clonorchis sinensis (C. sinensis) cannot be detected in feces during biliary obstruction. Moreover, many diseases can cause biliary obstruction, such as gallstones, adenocarcinoma, cholangiocarcinoma and Ascaris lumbricoides infection. Therefore, it is of great significance to distinguish between patients of biliary obstruction and biliary obstruction with C. sinensis infection.

Methods

A total of 48 biliary obstruction patients were enrolled, including 23 infected with C. sinensis (C. sinensis) (OB+C.s) and 25 non-infected subjects (OB). The bile samples were collected by endoscopic retrograde cholangiopancreatography and analyzed using ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-QTOF MS). Additionally, multivariate statistical analysis methods were employed to identify differential metabolites. Next, bile amino acid levels were determined by targeted metabolomics analysis.

Result

A total of 146 and 132 significant metabolites were identified in electrospray ionization (ESI)+ and ESI- modes, respectively. The levels of amino acids (asparagine, glutamate, ornithine) and polyamines (spermidine and spermine) were significantly changed. Targeted analysis showed that the levels of amino acids (such as L-arginine, L-glutamine, L-lysine, L-propionic, and L-tyrosine) were lower in OB+C.s patients compared to those in OB patients. Marked metabolic pathways were involved in "Glutathione metabolism", "Caffeine metabolism", "Alanine, aspartate and glutamate metabolism", "Arginine and proline metabolism", "Purine metabolism", "Beta-Alanine metabolism", and "D-glutamine and D-glutamate metabolism".

Conclusion

These results show that there were significant differences between OB+C.s and OB patients, especially in amino acids. The metabolic signature and perturbations in metabolic pathways may help to better distinguish OB+C.s and OB patients.

Metabolomics of Duodenal Juice for Biliary Tract Cancer Diagnosis

The poor prognosis of malignant biliary diseases is partially caused by their difficult early diagnosis. Therefore, many patients are only diagnosed at advanced stages. This study aimed to improve diagnosis by clarifying the differences in the duodenal juice metabolomes of benign and malignant biliary diseases. From October 2021 to January 2023, duodenal juice was obtained from 67 patients with suspected biliary diseases who required endoscopic ultrasonography and endoscopic retrograde cholangiography for diagnosis/treatment. The samples metabolomes were analyzed via nuclear magnet resonance spectroscopy using an 800-MHz spectrometer. Metabolomes of malignant and benign diseases were then compared, and multivariate analysis was performed to determine the relevant factors for malignancy/benignancy. For benignancy, no significant predictors were observed. For malignancy, acetone was a significant predictor, with higher concentrations in the malignant group than in the benign group. Regarding the receiver operating characteristic curve analysis for biliary tract carcinoma diagnosis, the predictive value of acetone in duodenal juice was comparable with serum CA19-9 levels (area under the curve: 0.7330 vs. 0.691, p = 0.697). In conclusion, duodenal juice metabolomics is a feasible method that is available for differential diagnosis in the biliary disease field.

Promising Highly Targeted Therapies for Cholangiocarcinoma: A Review and Future Perspectives

To overcome the poor prognosis of cholangiocarcinoma (CCA), highly targeted therapies, such as antibody-drug conjugates (ADCs), photodynamic therapy (PDT) with/without systemic chemotherapy, and experimental photoimmunotherapy (PIT), have been developed. Three preclinical trials have investigated the use of ADCs targeting specific antigens, namely HER2, MUC1, and glypican-1 (GPC1), for CCA. Trastuzumab emtansine demonstrated higher antiproliferative activity in CCA cells expressing higher levels of HER2. Similarly, "staphylococcal enterotoxin A-MUC1 antibody" and "anti-GPC1 antibody-monomethyl auristatin F" conjugates showed anticancer activity. PDT is effective in areas where appropriate photosensitizers and light coexist. Its mechanism involves photosensitizer excitation and subsequent reactive oxygen species production in cancer cells upon irradiation. Hematoporphyrin derivatives, temoporfin, phthalocyanine-4, talaporfin, and chlorine e6 derivatives have mainly been used clinically and preclinically in bile duct cancer. Currently, new forms of photosensitizers with nanotechnology and novel irradiation catheters are being developed. PIT is the most novel anti-cancer therapy developed in 2011 that selectively kills targeted cancer cells using a unique photosensitizer called "IR700" conjugated with an antibody specific for cancer cells. PIT is currently in the early stages of development for identifying appropriate CCA cell targets and irradiation devices. Future human and artificial intelligence collaboration has potential for overcoming challenges related to identifying universal CCA cell targets. This could pave the way for highly targeted therapies for CCA, such as ADC, PDT, and PIT.

Detection of Gallbladder Disease Types Using Deep Learning: An Informative Medical Method

Nowadays, despite all the conducted research and the provided efforts in advancing the healthcare sector, there is a strong need to rapidly and efficiently diagnose various diseases. The complexity of some disease mechanisms on one side and the dramatic life-saving potential on the other side raise big challenges for the development of tools for the early detection and diagnosis of diseases. Deep learning (DL), an area of artificial intelligence (AI), can be an informative medical tomography method that can aid in the early diagnosis of gallbladder (GB) disease based on ultrasound images (UI). Many researchers considered the classification of only one disease of the GB. In this work, we successfully managed to apply a deep neural network (DNN)-based classification model to a rich built database in order to detect nine diseases at once and to determine the type of disease using UI. In the first step, we built a balanced database composed of 10,692 UI of the GB organ from 1782 patients. These images were carefully collected from three hospitals over roughly three years and then classified by professionals. In the second step, we preprocessed and enhanced the dataset images in order to achieve the segmentation step. Finally, we applied and then compared four DNN models to analyze and classify these images in order to detect nine GB disease types. All the models produced good results in detecting GB diseases; the best was the MobileNet model, with an accuracy of 98.35%.

Phosphatidylcholine in bile-derived small extracellular vesicles as a novel biomarker of cholangiocarcinoma

BACKGROUND

Owing to the lack of definite diagnostic modalities, it is challenging to distinguish malignant cases of cholangiocarcinoma (CCA), which often causes biliary tract obstruction, from benign ones. Here, we investigated a novel lipid biomarker of CCA in bile-derived small extracellular vesicles (sEVs) and developed a simple detection method for clinical application.

METHODS

Bile samples from seven patients with malignant diseases (hilar CCA = 4, distal CCA = 3) and eight patients with benign diseases (gallstones = 6, primary sclerosing cholangitis = 1, autoimmune pancreatitis = 1) were collected through a nasal biliary drainage tube. sEVs were isolated via serial ultracentrifugation and characterized using nanoparticle tracking analysis, transmission electron microscopy, and immunoblotting (with CD9, CD63, CD81, and TSG101). Comprehensive lipidomic analysis was performed using liquid chromatography-tandem mass spectrometry. Using a measurement kit, we further confirmed whether lipid concentrations could be used as a potential CCA marker.

RESULTS

Lipidomic analysis of bile sEVs in the two groups identified 209 significantly increased lipid species in the malignant group. When focusing on lipid class, phosphatidylcholine (PC) level was 4.98-fold higher in the malignant group than in the benign group (P = 0.037). The receiver operating characteristic (ROC) curve showed a sensitivity of 71.4%, a specificity of 100%, and an area under the curve (AUC) of 0.857 (95% confidence interval [CI]:0.643-1.000). Using a PC assay kit, the ROC curve showed a cutoff value of 16.1 μg/mL, a sensitivity of 71.4%, a specificity of 100%, and an AUC of 0.839 (95% CI: 0.620-1.000).

CONCLUSION

PC level in sEVs from human bile is a potential diagnostic marker for CCA and can be assessed by a commercially available assay kit.

Revision of potential prognostic markers of cholangiocarcinoma for clinical practice

INTRODUCTION

Cholangiocarcinoma (CCA) is an aggressive cancer arising from any part of the biliary system. Effective treatment of CCA remains limited, resulting in the poor overall prognosis of patients. The effective prognostic biomarkers for CCA remain lacking, and most are at the research level.

AREAS COVERED

The incidences of CCAs, classification, genetic and molecular characteristics, and distinct clinical outcomes in each subtype are introduced. The prognostic markers currently used in clinical practice are reviewed. Studies of biomarkers in defining the aggressiveness of CCA, identifying patients with a potential tumor recurrence, and predicting the survival time, are reviewed. Emerging biomarkers discovered from advanced high throughput technology over the past five years are updated and summarized. Finally, in-depth and critical revision on the prognostic biomarkers for CCA reported from various sources of specimens, e.g. tissues, blood, bile, etc. are discussed.

CONCLUSION

Many prognostic biomarkers for CCA have been proposed and hold promising clinical value. However, these markers are rarely used in the real clinical world due to several factors. Understanding the roles and importance of these prognostic markers may fundamentally impact the therapeutic management of CCA, and hopefully, improve the development of custom and patient-directed therapies for CCA.

Diagnosing malignant distal bile duct obstruction using artificial intelligence based on clinical biomarkers

Diagnosing distal bile duct obstruction remains challenging. This study aimed to examine the diagnostic ability of artificial intelligence (AI) based on clinical biomarkers in diagnosing malignant distal bile duct obstruction. A total of 206 patients with distal bile duct obstruction were included in this study. Clinical laboratory parameters were collected from the patients and evaluated using AI. All clinical parameters were input into the AI algorithm, and the AI value for malignant distal bile duct obstruction was calculated. The benign and malignant diagnostic capabilities of AI and other factors (alkaline phosphatase [ALP], intrahepatic bile duct [IHBD] diameters, and total bile duct [CBD] diameters) were compared. Benign and malignant bile duct obstruction were diagnosed in 142 and 64 patients, respectively. The median AI value of malignant distal bile duct obstruction was significantly greater than that of benign distal bile duct obstruction (0.991 vs. 0.002, p < 0.001). The area under the receiver operating characteristic curve of AI, ALP, IHBD diameter, and CBD diameter were 0.908, 0.795, 0.794, and 0.775, respectively. AI showed a sensitivity, specificity, and accuracy of 83.1%, 87.2%, and 85.9%. AI-based on clinical biomarkers could serve as an auxiliary for diagnosing malignant bile duct obstruction.

Biliary Metabolome Profiling for Evaluation of Liver Metabolism and Biliary Tract Function Related to Organ Preservation Method and Degree of Ischemia in a Porcine Model

The development of surgical techniques, immunosuppressive strategies and new organ preservation methods have meant that transplant centers have to face the problem of an insufficient number of organs for transplantation concerning the constantly growing demand. Therefore, using organs from expanded criteria donors and developing new analytical solutions to find parameters or compounds that would allow a more efficient assessment of organ quality before transplantation are options for meeting this challenge. This study proposed bile metabolomic analysis to evaluate liver metabolism and biliary tract function depending on the organ preservation method and degree of warm ischemia time. The analyses were performed on solid-phase microextraction-prepared bile samples from porcine model donors with mild (heart beating donor [HBD]) and moderate warm ischemia (donation after circulatory death [DCD]) grafts subjected to static cold storage (SCS) or normothermic ex vivo liver perfusion (NEVLP) before transplantation. Bile produced in the SCS-preserved livers was characterized by increased levels of metabolites such as chenodeoxycholic acid, arachidonic acid and 5S-hydroxyeicosatetraeonic acid, as well as saturated and monounsaturated lysophosphatidylcholines (LPC). Such changes may be associated with differences in the bile acid synthesis pathways and organ inflammation. Moreover, it has been shown that NEVLP reduced the negative effect of ischemia on organ function. A linear relationship was observed between levels of lipids from the LPC group and the time of organ ischemia. This study identified metabolites worth considering as potential markers of changes occurring in preserved grafts.

New molecular mechanisms in cholangiocarcinoma: signals triggering interleukin-6 production in tumor cells and KRAS co-opted epigenetic mediators driving metabolic reprogramming

Background

Cholangiocarcinoma (CCA) is still a deadly tumour. Histological and molecular aspects of thioacetamide (TAA)-induced intrahepatic CCA (iCCA) in rats mimic those of human iCCA. Carcinogenic changes and therapeutic vulnerabilities in CCA may be captured by molecular investigations in bile, where we performed bile proteomic and metabolomic analyses that help discovery yet unknown pathways relevant to human iCCA.

Methods

Cholangiocarcinogenesis was induced in rats (TAA) and mice (Jnk^Δhepa + CCl₄ + DEN model). We performed proteomic and metabolomic analyses in bile from control and CCA-bearing rats. Differential expression was validated in rat and human CCAs. Mechanisms were addressed in human CCA cells, including Huh28-KRAS^G12D cells. Cell signaling, growth, gene regulation and [U-¹³C]-D-glucose-serine fluxomics analyses were performed. In vivo studies were performed in the clinically-relevant iCCA mouse model.

Results

Pathways related to inflammation, oxidative stress and glucose metabolism were identified by proteomic analysis. Oxidative stress and high amounts of the oncogenesis-supporting amino acids serine and glycine were discovered by metabolomic studies. Most relevant hits were confirmed in rat and human CCAs (TCGA). Activation of interleukin-6 (IL6) and epidermal growth factor receptor (EGFR) pathways, and key genes in cancer-related glucose metabolic reprogramming, were validated in TAA-CCAs. In TAA-CCAs, G9a, an epigenetic pro-tumorigenic writer, was also increased. We show that EGFR signaling and mutant KRAS^G12D can both activate IL6 production in CCA cells. Furthermore, phosphoglycerate dehydrogenase (PHGDH), the rate-limiting enzyme in serine-glycine pathway, was upregulated in human iCCA correlating with G9a expression. In a G9a activity-dependent manner, KRAS^G12D promoted PHGDH expression, glucose flow towards serine synthesis, and increased CCA cell viability. KRAS^G12D CAA cells were more sensitive to PHGDH and G9a inhibition than controls. In mouse iCCA, G9a pharmacological targeting reduced PHGDH expression.

Conclusions

In CCA, we identified new pro-tumorigenic mechanisms: Activation of EGFR signaling or KRAS mutation drives IL6 expression in tumour cells; Glucose metabolism reprogramming in iCCA includes activation of the serine-glycine pathway; Mutant KRAS drives PHGDH expression in a G9a-dependent manner; PHGDH and G9a emerge as therapeutic targets in iCCA.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13046-022-02386-2.

Cholangiocarcinoma progression depends on the uptake and metabolization of extracellular lipids

BACKGROUND AND AIMS

Cholangiocarcinoma (CCA) includes a heterogeneous group of biliary cancers with a dismal prognosis. We investigated if lipid metabolism is disrupted in CCA and its role in tumor proliferation.

APPROACH AND RESULTS

The in vitro and in vivo tumorigenic capacity of five human CCA cell lines was analyzed. Proteome, lipid content, and metabolic fluxes were evaluated in CCA cells and compared with normal human cholangiocytes (NHC). The Akt1/NOTCH1 intracellular cytoplasmic domain (Nicd1)-driven CCA mouse model was also evaluated. The proteome of CCA cells was enriched in pathways involved in lipid and lipoprotein metabolism. The EGI1 CCA cell line presented the highest tumorigenic capacity. Metabolic studies in high (EGI1) versus low (HUCCT1) proliferative CCA cells in vitro showed that both EGI1 and HUCCT1 incorporated more fatty acids (FA) than NHC, leading to increased triglyceride storage, also observed in Akt1/Nicd1-driven CCA mouse model. The highly proliferative EGI1 CCA cells showed greater uptake of very-low-density and HDLs than NHC and HUCCT1 CCA cells and increased cholesteryl ester content. The FA oxidation (FAO) and related proteome enrichment were specifically up-regulated in EGI1, and consequently, pharmacological blockade of FAO induced more pronounced inhibition of their tumorigenic capacity compared with HUCCT1. The expression of acyl-CoA dehydrogenase ACADM, the first enzyme involved in FAO, was increased in human CCA tissues and correlated with the proliferation marker PCNA.

CONCLUSIONS

Highly proliferative human CCA cells rely on lipid and lipoprotein uptake to fuel FA catabolism, suggesting that inhibition of FAO and/or lipid uptake could represent a therapeutic strategy for this CCA subclass.

Pathological and molecular diagnoses of early cancer with bile and pancreatic juice

The dismal prognosis of pancreaticobiliary malignancies is mainly attributed to the extremely difficult detection of early-stage lesions, including intraepithelial neoplasia. To improve prognosis, several studies on the early detection of cancer have been conducted using bile and pancreatic juices for pathological or molecular analyses. One approach is liquid biopsy that includes information about the tumor, such as circulating tumor cells, circulating tumor DNA, microRNAs, and exosomes released by the tumor. Another approach is proteomics/metabolomics that reflects specific conditions in the tumor. These two approaches lead to artificial intelligence-based multiomics analyses that comprises genomics, proteomics/metabolomics, and transcriptomics. Based on the findings of molecular analysis, pathological analysis using immunohistochemical staining/fluorescence in situ hybridization has also been developed. Moreover, there have been reports of new methods/ingenuities for obtaining appropriate samples for the diagnosis of early-stage cancer. Here we review the knowledge on cutting-edge pathological and molecular analyses of bile and pancreatic juices, introduce some ingenuities in sampling and sample processing to promote effective clinical practice, and provide a basis for future studies.

Systematic Review of NMR-Based Metabolomics Practices in Human Disease Research

Nuclear magnetic resonance (NMR) spectroscopy is one of the principal analytical techniques for metabolomics. It has the advantages of minimal sample preparation and high reproducibility, making it an ideal technique for generating large amounts of metabolomics data for biobanks and large-scale studies. Metabolomics is a popular “omics” technology and has established itself as a comprehensive exploratory biomarker tool; however, it has yet to reach its collaborative potential in data collation due to the lack of standardisation of the metabolomics workflow seen across small-scale studies. This systematic review compiles the different NMR metabolomics methods used for serum, plasma, and urine studies, from sample collection to data analysis, that were most popularly employed over a two-year period in 2019 and 2020. It also outlines how these methods influence the raw data and the downstream interpretations, and the importance of reporting for reproducibility and result validation. This review can act as a valuable summary of NMR metabolomic workflows that are actively used in human biofluid research and will help guide the workflow choice for future research.

Molecular diagnostics and biomarkers in cholangiocarcinoma

Regardless of anatomic origin, cholangiocarcinoma is generally an aggressive malignancy with a relatively high case fatality. Surgical resection with curative intent remains the best opportunity to achieve meaningful long-term survival. Most patients present, however, with advanced disease and less than 20% of patients are candidates for surgical resection. Unfortunately, even patients who undergo resection have a 5-year survival that ranges from 20 to 40%. Biomarkers are indicators of normal, pathologic, or biologic responses to an intervention and can range from a characteristic (i.e., blood pressure reading which can detect hypertension) to specific genetic mutations or proteins (i.e., carcinoembryonic antigen level). Novel biomarkers and improved molecular diagnostics represent an attractive opportunity to improve detection as well as to identify novel therapeutic targets for patients with cholangiocarcinoma. We herein review the latest advances in molecular diagnostics and biomarkers related to the early detection and treatment of patients with cholangiocarcinoma.

Artificial intelligence and cholangiocarcinoma: Updates and prospects

Artificial intelligence (AI) is the timeliest field of computer science and attempts to mimic cognitive function of humans to solve problems. In the era of “Big data”, there is an ever-increasing need for AI in all aspects of medicine. Cholangiocarcinoma (CCA) is the second most common primary malignancy of liver that has shown an increase in incidence in the last years. CCA has high mortality as it is diagnosed in later stages that decreases effect of surgery, chemotherapy, and other modalities. With technological advancement there is an immense amount of clinicopathologic, genetic, serologic, histologic, and radiologic data that can be assimilated together by modern AI tools for diagnosis, treatment, and prognosis of CCA. The literature shows that in almost all cases AI models have the capacity to increase accuracy in diagnosis, treatment, and prognosis of CCA. Most studies however are retrospective, and one study failed to show AI benefit in practice. There is immense potential for AI in diagnosis, treatment, and prognosis of CCA however limitations such as relative lack of studies in use by human operators in improvement of survival remains to be seen.

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.

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.

Recent advances in artificial intelligence for pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) remains the most lethal type of cancer. The 5-year survival rate for patients with early-stage diagnosis can be as high as 20%, suggesting that early diagnosis plays a pivotal role in the prognostic improvement of PDAC cases. In the medical field, the broad availability of biomedical data has led to the advent of the "big data" era. To overcome this deadly disease, how to fully exploit big data is a new challenge in the era of precision medicine. Artificial intelligence (AI) is the ability of a machine to learn and display intelligence to solve problems. AI can help to transform big data into clinically actionable insights more efficiently, reduce inevitable errors to improve diagnostic accuracy, and make real-time predictions. AI-based omics analyses will become the next alterative approach to overcome this poor-prognostic disease by discovering biomarkers for early detection, providing molecular/genomic subtyping, offering treatment guidance, and predicting recurrence and survival. Advances in AI may therefore improve PDAC survival outcomes in the near future. The present review mainly focuses on recent advances of AI in PDAC for clinicians. We believe that breakthroughs will soon emerge to fight this deadly disease using AI-navigated precision medicine.

Application of Mass Spectrometry in Pancreatic Cancer Translational Research

Pancreatic cancer (PC) is one of the most common malignant tumors in the digestive tract worldwide, with increased morbidity and mortality. In recent years, with the development of surgery, chemotherapy, radiotherapy, targeted therapy, and immunotherapy, and the change of the medical thinking model, remarkable progress has been made in researching comprehensive diagnosis and treatment of PC. However, the present situation of diagnostic and treatment of PC is still unsatisfactory. There is an urgent need for academia to fully integrate the basic research and clinical data from PC to form a research model conducive to clinical translation and promote the proper treatment of PC. This paper summarized the translation progress of mass spectrometry (MS) in the pathogenesis, diagnosis, prognosis, and PC treatment to promote the basic research results of PC into clinical diagnosis and treatment.

Progress Identifying and Analyzing the Human Proteome: 2021 Metrics from the HUPO Human Proteome Project

The 2021 Metrics of the HUPO Human Proteome Project (HPP) show that protein expression has now been credibly detected (neXtProt PE1 level) for 18 357 (92.8%) of the 19 778 predicted proteins coded in the human genome, a gain of 483 since 2020 from reports throughout the world reanalyzed by the HPP. Conversely, the number of neXtProt PE2, PE3, and PE4 missing proteins has been reduced by 478 to 1421. This represents remarkable progress on the proteome parts list. The utilization of proteomics in a broad array of biological and clinical studies likewise continues to expand with many important findings and effective integration with other omics platforms. We present highlights from the Immunopeptidomics, Glycoproteomics, Infectious Disease, Cardiovascular, Musculo-Skeletal, Liver, and Cancers B/D-HPP teams and from the Knowledgebase, Mass Spectrometry, Antibody Profiling, and Pathology resource pillars, as well as ethical considerations important to the clinical utilization of proteomics and protein biomarkers.

Biliary Strictures and Cholangiocarcinoma - Untangling a Diagnostic Conundrum

Cholangiocarcinoma is an uncommon and highly aggressive biliary tract malignancy with few manifestations until late disease stages. Diagnosis is currently achieved through a combination of clinical, biochemical, radiological and histological techniques. A number of reported cancer biomarkers have the potential to be incorporated into diagnostic pathways, but all lack sufficient sensitivity and specificity limiting their possible use in screening and early diagnosis. The limitations of standard serum markers such as CA19-9, CA125 and CEA have driven researchers to identify multiple novel biomarkers, yet their clinical translation has been slow with a general requirement for further validation in larger patient cohorts. We review recent advances in the diagnostic pathway for suspected CCA as well as emerging diagnostic biomarkers for early detection, with a particular focus on non-invasive approaches.

Mass Spectrometry-Based Proteomics Reveal Alcohol Dehydrogenase 1B as a Blood Biomarker Candidate to Monitor Acetaminophen-Induced Liver Injury

Acute liver injury (ALI) is a severe disorder resulting from excessive hepatocyte cell death, and frequently caused by acetaminophen intoxication. Clinical management of ALI progression is hampered by the dearth of blood biomarkers available. In this study, a bioinformatics workflow was developed to screen omics databases and identify potential biomarkers for hepatocyte cell death. Then, discovery proteomics was harnessed to select from among these candidates those that were specifically detected in the blood of acetaminophen-induced ALI patients. Among these candidates, the isoenzyme alcohol dehydrogenase 1B (ADH1B) was massively leaked into the blood. To evaluate ADH1B, we developed a targeted proteomics assay and quantified ADH1B in serum samples collected at different times from 17 patients admitted for acetaminophen-induced ALI. Serum ADH1B concentrations increased markedly during the acute phase of the disease, and dropped to undetectable levels during recovery. In contrast to alanine aminotransferase activity, the rapid drop in circulating ADH1B concentrations was followed by an improvement in the international normalized ratio (INR) within 10–48 h, and was associated with favorable outcomes. In conclusion, the combination of omics data exploration and proteomics revealed ADH1B as a new blood biomarker candidate that could be useful for the monitoring of acetaminophen-induced ALI.

Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology

Artificial intelligence (AI) is an umbrella term used to describe a cluster of interrelated fields. Machine learning (ML) refers to a model that learns from past data to predict future data. Medicine and particularly gastroenterology and hepatology, are data-rich fields with extensive data repositories, and therefore fruitful ground for AI/ML-based software applications. In this study, we comprehensively review the current applications of AI/ML-based models in these fields and the opportunities that arise from their application. Specifically, we refer to the applications of AI/ML-based models in prevention, diagnosis, management, and prognosis of gastrointestinal bleeding, inflammatory bowel diseases, gastrointestinal premalignant and malignant lesions, other nonmalignant gastrointestinal lesions and diseases, hepatitis B and C infection, chronic liver diseases, hepatocellular carcinoma, cholangiocarcinoma, and primary sclerosing cholangitis. At the same time, we identify the major challenges that restrain the widespread use of these models in healthcare in an effort to explore ways to overcome them. Notably, we elaborate on the concerns regarding intrinsic biases, data protection, cybersecurity, intellectual property, liability, ethical challenges, and transparency. Even at a slower pace than anticipated, AI is infiltrating the healthcare industry. AI in healthcare will become a reality, and every physician will have to engage with it by necessity.

Gallstone Formation Follows a Different Trajectory in Bariatric Patients Compared to Nonbariatric Patients

Since obese patients form cholesterol gallstones very rapidly after bariatric surgery, in patients who did not form gallstones during preceding years, we hypothesized that gallstone formation follows a different trajectory in bariatric patients compared to nonbariatric patients. We therefore analyzed the lipid composition of gallbladder bile derived from 18 bariatric gallstone patients and 17 nonbariatric gallstone patients (median (IQR) age, 46.0 (28.0-54.0) years; 33 (94%) female) during laparoscopic cholecystectomy using an enzymatic and lipidomics approach. We observed a higher concentration of total lipids (9.9 vs. 5.8 g/dL), bile acids (157.7 vs. 81.5 mM), cholesterol (10.6 vs. 5.4 mM), and phospholipids (30.4 vs. 21.8 mM) in bariatric gallstone patients compared to nonbariatric gallstone patients. The cholesterol saturation index did not significantly differ between the two groups. Lipidomics analysis revealed an interesting pattern. Enhanced amounts of a number of lipid species were found in the gallbladder bile of nonbariatric gallstone patients. Most striking was a fivefold higher amount of triglyceride. A concomitant ninefold increase of apolipoprotein B was found, suggesting secretion of triglyceride-rich lipoproteins (TRLs) at the canalicular pole of the hepatocyte in livers from nonbariatric gallstone patients. These findings suggest that gallstone formation follows a different trajectory in bariatric patients compared to nonbariatric patients. Impaired gallbladder emptying might explain the rapid gallstone formation after bariatric surgery, while biliary TRL secretion might contribute to gallstone formation in nonbariatric patients.

Next-Generation Biomarkers for Cholangiocarcinoma

Simple Summary

Early and non-invasive diagnosis of cholangiocarcinoma (CCA) is still challenging, thus largely contributing to the increased mortality rates observed worldwide. Consequently, several efforts have been made in order to report novel biomarkers for CCA, that would aid on diagnosis and also to predict prognosis and therapy response. We herein aim to provide an in-depth and critical revision on the next-generation biomarkers for CCA that have been recently proposed.

Abstract

The increasing mortality rates of cholangiocarcinoma (CCA) registered during the last decades are, at least in part, a result of the lack of accurate non-invasive biomarkers for early disease diagnosis, making the identification of patients who might benefit from potentially curative approaches (i.e., surgery) extremely challenging. The obscure CCA pathogenesis and associated etiological factors, as well as the lack of symptoms in patients with early tumor stages, highly compromises CCA identification and to predict tumor development in at-risk populations. Currently, CCA diagnosis is accomplished by the combination of clinical/biochemical features, radiological imaging and non-specific serum tumor biomarkers, although a tumor biopsy is still needed to confirm disease diagnosis. Furthermore, prognostic and predictive biomarkers are still lacking and urgently needed. During the recent years, high-throughput omics-based approaches have identified novel circulating biomarkers (diagnostic and prognostic) that might be included in large, international validation studies in the near future. In this review, we summarize and discuss the most recent advances in the field of biomarker discovery in CCA, providing new insights and future research directions.

Artificial intelligence in gastrointestinal diseases

Artificial intelligence (AI) applications are growing in medicine. It is important to understand the current state of the AI applications prior to utilizing in disease research and treatment. In this review, AI application in the diagnosis and treatment of gastrointestinal diseases are studied and summarized. In most cases, AI studies had large amounts of data, including images, to learn to distinguish disease characteristics according to a human’s perspectives. The detailed pros and cons of utilizing AI approaches should be investigated in advance to ensure the safe application of AI in medicine. Evidence suggests that the collaborative usage of AI in both diagnosis and treatment of diseases will increase the precision and effectiveness of medicine. Recent progress in genome technology such as genome editing provides a specific example where AI has revealed the diagnostic and therapeutic possibilities of RNA detection and targeting.

Personalized Endoscopy in Complex Malignant Hilar Biliary Strictures

Malignant hilar biliary obstruction (HBO) represents a complex clinical condition in terms of diagnosis, surgical and medical treatment, endoscopic approach, and palliation. The main etiology of malignant HBO is hilar cholangiocarcinoma that is considered an aggressive biliary tract's cancer and has still today a poor prognosis. Endoscopy plays a crucial role in malignant HBO from the diagnosis to the palliation. This technique allows the collection of cytological or histological samples, direct visualization of the suspect malignant tissue, and an echoendoscopic evaluation of the primary tumor and its locoregional staging. Because obstructive jaundice is the most common clinical presentation of malignant HBO, endoscopic biliary drainage, when indicated, is the preferred treatment over the percutaneous approach. Several endoscopic techniques are today available for both the diagnosis and the treatment of biliary obstruction. The choice among them can differ for each clinical scenario. In fact, a personalized endoscopic approach is mandatory in order to perform the proper procedure in the singular patient.

Omics-Based Platforms: Current Status and Potential Use for Cholangiocarcinoma

Cholangiocarcinoma (CCA) has been identified as a highly malignant cancer that can be transformed from epithelial cells of the bile duct, including intrahepatic, perihilar and extrahepatic. High-resolution imaging tools (abdominal ultrasound, computed tomography and percutaneous transhepatic cholangial drainage) are recruited for diagnosis. However, the lack of early diagnostic biomarkers and treatment evaluation can lead to serious outcomes and poor prognosis (i.e., CA19-9, MUC5AC). In recent years, scientists have established a large number of omics profiles to reveal underlying mechanisms and networks (i.e., IL-6/STAT3, NOTCH). With these results, we achieved several genomic alteration events (i.e., TP53_mut, KRAS_mut) and epigenetic modifications (i.e., DNA methylation, histone modification) in CCA cells and clinical patients. Moreover, we reviewed candidate gene (such as NF-kB, YAP1) that drive gene transcription factors and canonical pathways through transcriptomics profiles (including microarrays and next-generation sequencing). In addition, the proteomics database also indicates which molecules and their directly binding status could trigger dysfunction signatures in tumorigenesis (carbohydrate antigen 19-9, mucins). Most importantly, we collected metabolomics datasets and pivotal metabolites. These results reflect the pharmacotherapeutic options and evaluate pharmacokinetic/pharmacodynamics in vitro and in vivo. We reversed the panels and selected many potentially small compounds from the connectivity map and L1000CDS² system. In this paper, we summarize the prognostic value of each candidate gene and correlate this information with clinical events in CCA. This review can serve as a reference for further research to clearly investigate the complex characteristics of CCA, which may lead to better prognosis, drug repurposing and treatment strategies.