Name: Computational analysis of cholangiocarcinoma phosphoproteomes identifies patient-specific drug targets
Keywords: Cholangiocarcinoma

Project description:Background & Aims: Cholangiocarcinomas (CCAs), heterogeneous biliary tumors with dismal prognosis, lack accurate early-diagnostic methods, especially important for individuals at high-risk (i.e., primary sclerosing cholangitis (PSC)). Here, we searched for protein biomarkers in serum extracellular vesicles (EVs). Methods: EVs from patients with isolated PSC (n=45), concomitant PSC-CCA (n=42), PSC who developed CCA during follow-up (PSC to CCA; n=25), CCAs from non-PSC etiology (n=56), hepatocellular carcinoma (n=34) and healthy individuals (n=55) were characterized by mass-spectrometry. Diagnostic biomarkers of PSC-CCA, non-PSC CCA or CCAs regardless etiology (pan-CCAs) were defined, and their expression was evaluated in human organs/tissues and within CCA tumors at single-cell level. Prognostic EV-biomarkers for CCA were investigated. Results: High-throughput proteomics identified candidate diagnostic biomarkers for PSC-CCA, non-PSC CCA or pan-CCA, as well as and for differential diagnosis of intrahepatic CCA and HCC, that were cross-validated by ELISA using total serum. Machine learning logit modelling disclosed CRP/FRIL/Fibrinogen algorithm with diagnostic value for early-stage PSC-CCA vs isolated PSC (AUC=0.944; OR=82.0), overpowering CA19-9 (AUC=0.735; OR=9.3). An algorithm combining CRP/VWF/PIGR/ /Fibrinogen allowed the diagnosis of early-stage non-PSC CCAs compared to healthy individuals (AUC=0.999; OR=1115). Noteworthy, levels of Fibrinogen/CRP/PIGR/FRIL showed predictive capacity for CCA development in patients with PSC before clinical evidences of malignancy. Multi-organ transcriptomic analysis revealed that serum EV-biomarkers were mostly expressed in hepatobiliary tissues, and scRNA-seq and immunofluorescence analysis of CCA tumors showed their presence mainly in malignant cholangiocytes. Multivariable analysis unveiled EVprognostic biomarkers independent to clinical features, with COMP/GNAI2/CFAI and ACTN1/MYCT1/PF4V associated negatively or positively to patients’ survival, respectively. Conclusions: Serum EVs contain protein biomarkers for the prediction, early diagnosis and prognosis estimation of CCA, representing a novel tumor cell-derived liquid biopsy for personalized medicine.

Project description:In gas chromatography-mass spectrometry-based untargeted metabolomics, metabolites are identified by comparing mass spectra and chromatographic retention time with reference databases or standard materials. In that sense, machine learning has been used to predict the retention time of metabolites lacking reference data. However, the retention time prediction of trimethylsilyl derivatives of metabolites, typically analyzed in untargeted metabolomics using gas chromatography, has been poorly explored. Here, we provide a rationalized framework for machine learning-based retention time prediction of trimethylsilyl derivatives of metabolites in gas chromatography. We compared different machine learning paradigms, in addition to exploring the influence of the computational molecular structure representation to train the prediction models: fingerprint class and fingerprint calculation software. Our study challenged predicted retention time when using chemical ionization and electron impact ionization sources in simulated and real cases, demonstrating a good correct identity ranking capability by machine learning, despite observing a limited false identity filtering power in cases where a spectrum or a monoisotopic mass match to multiple candidates. Specifically, machine learning prediction yielded median absolute and relative retention index (relative retention time) errors of 37.1 retention index units and 2%, respectively. In addition, fingerprint class and fingerprint calculation software, as well as the molecular structural similarity between the training and test or real case sets, showed to be critical modulators of the prediction performance. Finally, we leveraged the structural similarity between the training and test or real case set to determine the probability that the prediction error is below a specific threshold. Overall, our study demonstrates that predicted retention time can provide insights into the true structure of unknown metabolites by ranking from the most to the least plausible molecular identity, and sets the guidelines to assess the confidence in metabolite identification using predicted retention time data.

Project description:Cholangiocarcinoma (CCA) is a hepatobiliary malignancy with dismal prognosis. Currently available models fail to recapitulate the full complexity of CCA, particularly the desmoplastic environment and the interplay between cancer cells and the extracellular matrix (ECM). We aimed to study the role of epithelial tumor cells in ECM deposition and desmoplasia by designing a model encompassing primary epithelial CCA organoids (CCAOs) and native liver and tumor scaffolds obtained by decellularization Background & Aims: Cholangiocarcinoma (CCA) is a hepatobiliary malignancy with dismal prognosis. Currently available models fail to recapitulate the full complexity of CCA, particularly the desmoplastic environment and the interplay between cancer cells and the extracellular matrix (ECM). We aimed to study the role of epithelial tumor cells in ECM deposition and desmoplasia by designing a model encompassing primary epithelial CCA organoids (CCAOs) and native liver and tumor scaffolds obtained by decellularization. Results: Decellularization resulted in effective removal of cells while preserving ECM structure and retaining important characteristics of the tissue origin. When culturing CCAOs in CCA-M, compared to TFL-M and BME, the genome-wide gene expression profile much more resembled the transcriptome of primary CCA tumor tissue in vivo, with an accompanying increase in chemoresistance. CCAOs in decellularized matrix, both CCA-M and TFL-M, exhibited the formation of complex morphological structures, and revealed environment-dependent proliferation and migration dynamics, driven by the occurrence of epithelial-mesenchymal transition (EMT). CCA-M induced specific extracellular matrix protein production in CCAOs, such as fibronectin 1 (FN1), which is related to desmoplasia and patient survival. In TFL-M, lacking the desmoplastic environment, CCAOs were able to initiate a desmoplastic reaction directly through increased production of multiple collagen types. Conclusions: This improved model of cholangiocarcinoma, combining organoids and native extracellular matrix, recapitulates key components of CCA tumor biology, including transcriptome profiles, migration patterns, EMT, and ECM protein production. The increased production of extracellular matrix proteins suggests that epithelial tumor cells can contribute to their own desmoplastic environment.

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Computational analysis of cholangiocarcinoma phosphoproteomes identifies patient-specific drug targets

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Computational Analysis of Cholangiocarcinoma Phosphoproteomes Identifies Patient-Specific Drug Targets.

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