High-Throughput Single-Cell Mass Spectrometry Reveals Abnormal Lipid Metabolism in Pancreatic Ductal Adenocarcinoma.
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ABSTRACT:
SUBMITTER: Liu Q
PROVIDER: S-EPMC8597026 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Despite numerous diagnostic and therapeutic advances, pancreatic ductal adenocarcinoma (PDAC) has a high mortality rate, and is the fourth leading cause of cancer death in developing countries. Besides its increasing prevalence, pancreatic malignancies are characterized by poor prognosis. Omics technologies have potential relevance for PDAC assessment but are time-intensive and relatively cost-intensive and limited by tissue heterogeneity. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) can obtain spatially distinct peptide-signatures and enables tumor classification within a feasible time with relatively low cost. While MALDI-MSI data sets are inherently large, machine learning methods have the potential to greatly decrease processing time. We present a pilot study investigating the potential of MALDI-MSI in combination with neural networks, for classification of pancreatic ductal adenocarcinoma. Neural-network models were trained to distinguish between pancreatic ductal adenocarcinoma and other pancreatic cancer types. The proposed methods are able to correctly classify the PDAC types with an accuracy of up to 86% and a sensitivity of 82%. This study demonstrates that machine learning tools are able to identify different pancreatic carcinoma from complex MALDI data, enabling fast prediction of large data sets. Our results encourage a more frequent use of MALDI-MSI and machine learning in histopathological studies in the future.
| S-EPMC9913229 | biostudies-literature
Project description:BackgroundPancreatic ductal adenocarcinoma (PDAC) remains one of the deadliest malignancies to date. The impressively developed stroma that surrounds and modulates the behavior of cancer cells is one of the main factors regulating the PDAC growth, metastasis and therapy resistance. Here, we postulate that stromal and cancer cell compartments differentiate in protein/lipid glycosylation patterns and analyze differences in glycan fragments in those compartments with clinicopathologic correlates.ResultsWe analyzed native glycan fragments in 109 human FFPE PDAC samples using high mass resolution matrix-assisted laser desorption/ionization Fourier-transform ion cyclotron resonance mass spectrometric imaging (MALDI-FT-ICR-MSI). Our method allows detection of native glycan fragments without previous digestion with PNGase or any other biochemical reaction. With this method, 8 and 18 native glycans were identified as uniquely expressed in only stromal or only cancer cell compartment, respectively. Kaplan-Meier survival model identified glycan fragments that are expressed in cancer cell or stromal compartment and significantly associated with patient outcome. Among cancer cell region-specific glycans, 10 predicted better and 6 worse patient survival. In the stroma, 1 glycan predicted good and 4 poor patient survival. Using factor analysis as a dimension reduction method, we were able to group the identified glycans in 2 factors. Multivariate analysis revealed that these factors can be used as independent survival prognostic elements with regard to the established Union for International Cancer Control (UICC) classification both in tumor and stroma regions.ConclusionOur method allows in situ detection of naturally occurring glycans in FFPE samples of human PDAC tissue and highlights the differences among glycans found in stromal and cancer cell compartment offering a basis for further exploration on the role of specific glycans in cancer-stroma communication.
| S-EPMC8636555 | biostudies-literature
Project description:We report an innovative multiplexed liquidchromatography-multiple reaction monitoring/mass spectrometry (LC-MRM/MS)-based assay for rapidly measuring a large number of disease specific protein biomarkers in human serum. Furthermore, this approach uses stable isotope dilution methodology to reliably quantify candidate protein biomarkers. Human serum was diluted using a stable isotope labeled proteome (SILAP) standard prepared from the secretome of pancreatic cell lines, subjected to immunoaffinity removal of the most highly abundant proteins, trypsin digested, and analyzed by LC-MRM/MS. The method was found to be precise, linear, and specific for the relative quantification of 72 proteins when analyte response was normalized to the relevant internal standard (IS) from the SILAP. The method made it possible to determine statistically different concentrations for three proteins (cystatin M, IGF binding protein 7, and villin 2) in control and pancreatic cancer patient samples. This method proves the feasibility of using a SILAP standard in combination with stable isotope dilution LC-MRM/MS analysis of tryptic peptides to compare changes in the concentration of candidate protein biomarkers in human serum.
| S-EPMC3292696 | biostudies-literature
Project description:Alteration of maternal lipid metabolism early in development has been shown to trigger obesity, insulin resistance, type 2 diabetes and cardiovascular diseases later in life in humans and animal models. Here, we set out to determine (i) lipid composition dynamics in single oocytes and preimplantation embryos by high mass resolution desorption electrospray ionization mass spectrometry (DESI-MS), using the bovine species as biological model, (ii) the metabolically most relevant lipid compounds by multivariate data analysis and (iii) lipid upstream metabolism by quantitative real-time PCR (qRT-PCR) analysis of several target genes (ACAT1, CPT 1b, FASN, SREBP1 and SCAP). Bovine oocytes and blastocysts were individually analyzed by DESI-MS in both positive and negative ion modes, without lipid extraction and under ambient conditions, and were profiled for free fatty acids (FFA), phospholipids (PL), cholesterol-related molecules, and triacylglycerols (TAG). Principal component analysis (PCA) and linear discriminant analysis (LDA), performed for the first time on DESI-MS fused data, allowed unequivocal discrimination between oocytes and blastocysts based on specific lipid profiles. This analytical approach resulted in broad and detailed lipid annotation of single oocytes and blastocysts. Results of DESI-MS and transcript regulation analysis demonstrate that blastocysts produced in vitro and their in vivo counterparts differed significantly in the homeostasis of cholesterol and FFA metabolism. These results should assist in the production of viable and healthy embryos by elucidating in vivo embryonic lipid metabolism.
| S-EPMC3779253 | biostudies-literature
Project description:PurposePancreatic ductal adenocarcinoma (PDAC) is an aggressive and lethal disease that develops relatively symptom-free and is therefore advanced at the time of diagnosis. The absence of early symptoms and effective treatments has created a critical need for identifying and developing new noninvasive biomarkers and therapeutic targets.Experimental designWe investigated the metabolism of a panel of PDAC cell lines in culture and noninvasively in vivo with (1)H magnetic resonance spectroscopic imaging (MRSI) to identify noninvasive biomarkers and uncover potential metabolic targets.ResultsWe observed elevated choline-containing compounds in the PDAC cell lines and tumors. These elevated choline-containing compounds were easily detected by increased total choline (tCho) in vivo, in spectroscopic images obtained from tumors. Principal component analysis of the spectral data identified additional differences in metabolites between immortalized human pancreatic cells and neoplastic PDAC cells. Molecular characterization revealed overexpression of choline kinase (Chk)-α, choline transporter 1 (CHT1), and choline transporter-like protein 1 (CTL1) in the PDAC cell lines and tumors.ConclusionsCollectively, these data identify new metabolic characteristics of PDAC and reveal potential metabolic targets. Total choline detected with (1)H MRSI may provide an intrinsic, imaging probe-independent biomarker to complement existing techniques in detecting PDAC. The expression of Chk-α, CHT1, and CTL1 may provide additional molecular markers in aspirated cytological samples.
| S-EPMC4297549 | biostudies-literature
Project description:AbstractAs pancreatic cancer (PC) is highly malignant, its patients tend to develop metastasis at an early stage and show a poor response to conventional chemotherapies. First-line chemotherapies for PC, according to current guidelines, include fluoropyrimidine- and gemcitabine-based regimens. Accumulating research on drug resistance has shown that biochemical metabolic aberrations in PC, especially those involving glycolysis and glutamine metabolism, are highly associated with chemoresistance. Additionally, lipid metabolism is a major factor in chemoresistance. However, emerging compounds that target these key metabolic pathways have the potential to overcome chemoresistance. This review summarizes how PC develops chemoresistance through aberrations in biochemical metabolism and discusses novel critical targets and pathways within cancer metabolism for new drug research.
| S-EPMC10876258 | biostudies-literature
Project description:Most cancers harbor a diverse collection of cell types including a typically heterogeneous cancer cell fraction. To reconstruct cell-intrinsic and heterotypic interactions driving tumor progression, we combine the XDec deconvolution method with cell-type-specific gene expression correlation analysis into the XDec-CHI method. XDec-CHI identifies intra- and inter-cellular pathways using correlation and places them in the context of specific tumor subtypes, as defined by the state of constituent cancer cells. We make the method web-accessible for analysis of publicly accessible pancreatic ductal adenocarcinoma, breast, head and neck, glioblastoma, and glioma tumors. We apply the method to TCGA and ICGC datasets to identify immune-suppressive interactions within PDAC tumors that are relevant for immunotherapies targeting PD-L1. Subtype-specific interactions derived from correlative analyses validated in co-culture experiments suggest PDAC subtypes have distinct therapeutic weaknesses, with Basal-like and MSLN-high Classical B tumors most likely to respond to therapies targeting PD-L1.
| S-EPMC9579012 | biostudies-literature
Project description:Pancreatic ductal adenocarcinoma
| PRJNA432344 | ENA
Project description:Pancreatic cancer is characterized by nearly universal activating mutations in KRAS. Among other somatic mutations, TP53 is mutated in more than 75% of human pancreatic tumors. Genetically engineered mice have proven instrumental in studies of the contribution of individual genes to carcinogenesis. Oncogenic Kras mutations occur early during pancreatic carcinogenesis and are considered an initiating event. In contrast, mutations in p53 occur later during tumor progression. In our model, we recapitulated the order of mutations of the human disease, with p53 mutation following expression of oncogenic Kras. Further, using an inducible and reversible expression allele for mutant p53, we inactivated its expression at different stages of carcinogenesis. Notably, the function of mutant p53 changes at different stages of carcinogenesis. Our work establishes a requirement for mutant p53 for the formation and maintenance of pancreatic cancer precursor lesions. In tumors, mutant p53 becomes dispensable for growth. However, it maintains the altered metabolism that characterizes pancreatic cancer and mediates its malignant potential. Further, mutant p53 promotes epithelial-mesenchymal transition (EMT) and cancer cell invasion. This work generates new mouse models that mimic human pancreatic cancer and expands our understanding of the role of p53 mutation, common in the majority of human malignancies.
| S-EPMC5821189 | biostudies-literature
Project description:BackgroundPancreatic ductal adenocarcinoma (PDAC) is the fourth leading cause of cancer related death in the world with a five-year survival rate of less than 5%. Not all PDAC are the same, because there exist intra-tumoral heterogeneity between PDAC, which poses a great challenge to personalized treatments for PDAC.MethodsTo dissect the molecular heterogeneity of PDAC, we performed a retrospective meta-analysis on whole transcriptome data from more than 1200 PDAC patients. Subtypes were identified based on non-negative matrix factorization (NMF) biclustering method. We used the gene set enrichment analysis (GSEA) and survival analysis to conduct the molecular and clinical characterization of the identified subtypes, respectively.ResultsSix molecular and clinical distinct subtypes of PDAC: L1-L6, are identified and grouped into tumor-specific (L1, L2 and L6) and stroma-specific subtypes (L3, L4 and L5). For tumor-specific subtypes, L1 (~?22%) has enriched carbohydrate metabolism-related gene sets and has intermediate survival. L2 (~?22%) has the worst clinical outcomes, and is enriched for cell proliferation-related gene sets. About 23% patients can be classified into L6, which leads to intermediate survival and is enriched for lipid and protein metabolism-related gene sets. Stroma-specific subtypes may contain high non-epithelial contents such as collagen, immune and islet cells, respectively. For instance, L3 (~?12%) has poor survival and is enriched for collagen-associated gene sets. L4 (~?14%) is enriched for various immune-related gene sets and has relatively good survival. And L5 (~?7%) has good clinical outcomes and is enriched for neurotransmitter and insulin secretion related gene sets. In the meantime, we identified 160 subtype-specific markers and built a deep learning-based classifier for PDAC. We also applied our classification system on validation datasets and observed much similar molecular and clinical characteristics between subtypes.ConclusionsOur study is the largest cohort of PDAC gene expression profiles investigated so far, which greatly increased the statistical power and provided more robust results. We identified six molecular and clinical distinct subtypes to describe a more complete picture of the PDAC heterogeneity. The 160 subtype-specific markers and a deep learning based classification system may be used to better stratify PDAC patients for personalized treatments.
| S-EPMC5975421 | biostudies-literature