Dataset Information

β-Sheet Richness of the Circulating Tumor-Derived Extracellular Vesicles for Noninvasive Pancreatic Cancer Screening.

ABSTRACT: Tumor-derived extracellular vesicles (EVs) are under intensive study for their potential as noninvasive diagnosis biomarkers. Most EV-based cancer diagnostic assays trace supernumerary of a single cancer-associated marker or marker signatures. These types of biomarker assays are either subtype-specific or vulnerable to be masked by high background signals. In this study, we introduce using the β-sheet richness (BR) of the tumor-derived EVs as an effective way to discriminate EVs originating from malignant and nonmalignant cells, where EV contents are evaluated as a collective attribute rather than single factors. Circular dichroism, Fourier transform infrared spectroscopy, fluorescence staining assays, and a de novo workflow combining proteomics, bioinformatics, and protein folding simulations were employed to validate the collective attribute at both cellular and EV levels. Based on the BR of the tumorous EVs, we integrated immunoprecipitation and fluorescence labeling targeting the circulating tumor-derived EVs in serum and developed the process into a clinical assay, named EvIPThT. The assay can distinguish patients with and without malignant disease in a pilot cohort, with weak correlations to prognosis biomarkers, suggesting the potential for a cancer screening panel with existing prognostic biomarkers to improve overall performance.

SUBMITTER: Rasuleva K

PROVIDER: S-EPMC8715533 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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Publications

β-Sheet Richness of the Circulating Tumor-Derived Extracellular Vesicles for Noninvasive Pancreatic Cancer Screening.

Rasuleva Komila K Elamurugan Santhalingam S Bauer Aaron A Khan Mdrakibhasan M Wen Qian Q Li Zhaofan Z Steen Preston P Guo Ang A Xia Wenjie W Mathew Sijo S Jansen Rick R Sun Dali D

ACS sensors 20211130 12

Tumor-derived extracellular vesicles (EVs) are under intensive study for their potential as noninvasive diagnosis biomarkers. Most EV-based cancer diagnostic assays trace supernumerary of a single cancer-associated marker or marker signatures. These types of biomarker assays are either subtype-specific or vulnerable to be masked by high background signals. In this study, we introduce using the β-sheet richness (BR) of the tumor-derived EVs as an effective way to discriminate EVs originating from ...[more]

PMID: 34846848

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Project description:PurposeKnowledge about pancreatic cancer (PC) biology has been growing rapidly in recent decades. Nevertheless, the survival of PC patients has not greatly improved. The development of a novel methodology suitable for deep investigation of the nature of PC tumors is of great importance. Molecular imaging techniques, such as Fourier transform infrared (FTIR) spectroscopy and Raman hyperspectral mapping (RHM) combined with advanced multivariate data analysis, were useful in studying the biochemical composition of PC tissue.MethodsHere, we evaluated the potential of molecular imaging in differentiating three groups of PC tumors, which originate from different precursor lesions. Specifically, we comprehensively investigated adenocarcinomas (ACs): conventional ductal AC, intraductal papillary mucinous carcinoma, and ampulla of Vater AC. FTIR microspectroscopy and RHM maps of 24 PC tissue slides were obtained, and comprehensive advanced statistical analyses, such as hierarchical clustering and nonnegative matrix factorization, were performed on a total of 211,355 Raman spectra. Additionally, we employed deep learning technology for the same task of PC subtyping to enable automation. The so-called convolutional neural network (CNN) was trained to recognize spectra specific to each PC group and then employed to generate CNN-prediction-based tissue maps. To identify the DNA methylation spectral markers, we used differently methylated, isolated DNA and compared the observed spectral differences with the results obtained from cellular nuclei regions of PC tissues.ResultsThe results showed significant differences among cancer tissues of the studied PC groups. The main findings are the varying content of β-sheet-rich proteins within the PC cells and alterations in the relative DNA methylation level. Our CNN model efficiently differentiated PC groups with 94% accuracy. The usage of CNN in the classification task did not require Raman spectral data preprocessing and eliminated the need for extensive knowledge of statistical methodologies.ConclusionsMolecular spectroscopy combined with CNN technology is a powerful tool for PC detection and subtyping. The molecular fingerprint of DNA methylation and β-sheet cytoplasmic proteins established by our results is different for the main PC groups and allowed the subtyping of pancreatic tumors, which can improve patient management and increase their survival. Our observations are of key importance in understanding the variability of PC and allow translation of the methodology into clinical practice by utilizing liquid biopsy testing.

Project description:Most cancer-related deaths are caused by distant metastases, which are tumour cells that have escaped from a primary tumour and passed into the bloodstream to colonize a new organ. In this context, communication between tumour and stromal cells is essential. Indeed, tumor cells interact with cells in the tumor microenvironment and are able to modify them to their advantage. Both extracellular vesicles (EVs) and exosomes are heterogeneous populations of small vesicles present in the tumor microenvironment and in body fluids that have recently emerged as powerful mediators involved in this communication and their transport in fluids. Tumor cells release large quantities of exosomes containing tumor markers, which can then spread to distant locations. The exosomes are of endosomal origin. They are composed of proteins, lipids, RNA and DNA, and they circulate in the bloodstream. They can be internalized by specific distant cells and thus deliver a functional message. It has recently been shown that tumor exosomes containing pro-metastatic factors form pre-metastatic niches, before the tumor cells actually arrive, while determining the metastatic organotropism of tumors. These properties are now opening up new avenues of research in tumor biomarkers. In recent years, several studies have highlighted different markers contained specifically in exosomes derived from cancer cells. Consequently, exosomes are considered as potential reservoirs of tumor biomarkers that could be clinically useful for the non-invasive diagnosis of cancer, with the advantage of being performed by liquid biopsy. The study of microRNA (miRNA) is of particular interest. Indeed, miRNAs are small non-coding RNAs (between 21 and 25 nucleotides) involved in the regulation of gene expression and which are frequently deregulated in cancer. Several studies underline that the variation of free miRNAs in the blood is correlated with the progression of the disease, particularly in colon cancer. However, the stability of free miRNAs is controversial. Therefore, exosomes represent a very advantageous means of transporting miRNAs in the blood, as they are able to protect miRNAs from degradation by RNAase. The hypothesis of the project is that circulating exosomes derived from tumours contain markers including specific miRNAs that could be used as biomarkers of early prognosis (survival and progression), easily measured in blood samples from patients with colon cancer. But other molecules contained in exosomes could also be of interest.

Dataset Information

β-Sheet Richness of the Circulating Tumor-Derived Extracellular Vesicles for Noninvasive Pancreatic Cancer Screening.

Publications

β-Sheet Richness of the Circulating Tumor-Derived Extracellular Vesicles for Noninvasive Pancreatic Cancer Screening.

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