Exosomes are lipid-bilayer-enclosed extracellular vesicles that contain proteins and nucleic acids. They are secreted by all cells and circulate in the blood. Specific detection and isolation of cancer-cell-derived exosomes in the circulation is currently lacking. Using mass spectrometry analyses, we identify a cell surface proteoglycan, glypican-1 (GPC1), specifically enriched on cancer-cell-derived exosomes. GPC1+ circulating exosomes (crExos) were monitored and isolated using flow cytometry from the serum of patients and mice with cancer. GPC1+ crExos were detected in the serum of patients with pancreatic cancer with absolute specificity and sensitivity, distinguishing healthy subjects and patients with a benign pancreatic disease from patients with early- and late-stage pancreatic cancer. Levels of GPC1+ crExos correlate with tumour burden and the survival of pre- and post-surgical patients. GPC1+ crExos from patients and from mice with spontaneous pancreatic tumours carry specific KRAS mutations, and reliably detect pancreatic intraepithelial lesions in mice despite negative signals by magnetic resonance imaging. GPC1+ crExos may serve as a potential non-invasive diagnostic and screening tool to detect early stages of pancreatic cancer to facilitate possible curative surgical therapy.
This is a preview of subscription content, access via your institution
Open Access articles citing this article.
Cancer-derived small extracellular vesicles: emerging biomarkers and therapies for pancreatic ductal adenocarcinoma diagnosis/prognosis and treatment
Journal of Nanobiotechnology Open Access 14 October 2022
The roles of small extracellular vesicles in cancer and immune regulation and translational potential in cancer therapy
Journal of Experimental & Clinical Cancer Research Open Access 27 September 2022
Cancer Cell International Open Access 28 June 2022
Subscribe to Journal
Get full journal access for 1 year
only $3.90 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Get time limited or full article access on ReadCube.
All prices are NET prices.
Pan, B. T., Teng, K., Wu, C., Adam, M. & Johnstone, R. M. Electron microscopic evidence for externalization of the transferrin receptor in vesicular form in sheep reticulocytes. J. Cell Biol. 101, 942–948 (1985)
Trajkovic, K. et al. Ceramide triggers budding of exosome vesicles into multivesicular endosomes. Science 319, 1244–1247 (2008)
Skog, J. et al. Glioblastoma microvesicles transport RNA and proteins that promote tumour growth and provide diagnostic biomarkers. Nature Cell Biol. 10, 1470–1476 (2008)
Al-Nedawi, K. et al. Intercellular transfer of the oncogenic receptor EGFRvIII by microvesicles derived from tumour cells. Nature Cell Biol. 10, 619–624 (2008)
Demory Beckler, M. et al. Proteomic analysis of exosomes from mutant KRAS colon cancer cells identifies intercellular transfer of mutant KRAS. Mol. Cell Proteomics 12, 343–355 (2013)
Kahlert, C. et al. Identification of double-stranded genomic DNA spanning all chromosomes with mutated KRAS and p53 DNA in the serum exosomes of patients with pancreatic cancer. J. Biol. Chem. 289, 3869–3875 (2014)
Ostrowski, M. et al. Rab27a and Rab27b control different steps of the exosome secretion pathway. Nature Cell Biol. 12, 19–30 (2010)
Théry, C., Ostrowski, M. & Segura, E. Membrane vesicles as conveyors of immune responses. Nature Rev. Immunol. 9, 581–593 (2009)
Janowska-Wieczorek, A. et al. Microvesicles derived from activated platelets induce metastasis and angiogenesis in lung cancer. Int. J. Cancer 113, 752–760 (2005)
Hergenreider, E. et al. Atheroprotective communication between endothelial cells and smooth muscle cells through miRNAs. Nature Cell Biol. 14, 249–256 (2012)
Lotvall, J. et al. Minimal experimental requirements for definition of extracellular vesicles and their functions: a position statement from the International Society for Extracellular Vesicles. J. Extracell. Vesicles 3, 26913 (2014)
Taylor, D. D. & Gercel-Taylor, C. Exosomes/microvesicles: mediators of cancer-associated immunosuppressive microenvironments. Semin. Immunopathol. 33, 441–454 (2011)
Luga, V. et al. Exosomes mediate stromal mobilization of autocrine Wnt-PCP signaling in breast cancer cell migration. Cell 151, 1542–1556 (2012)
Théry, C., Amigorena, S., Raposo, G. & Clayton, A. Isolation and characterization of exosomes from cell culture supernatants and biological fluids. Curr. Protoc. Cell Biol. Chapter 3, Unit–3.22 (2006)
Théry, C., Zitvogel, L. & Amigorena, S. Exosomes: composition, biogenesis and function. Nature Rev. Immunol. 2, 569–579 (2002)
Wilson, I. D. et al. High resolution “ultra performance” liquid chromatography coupled to oa-TOF mass spectrometry as a tool for differential metabolic pathway profiling in functional genomic studies. J. Proteome Res. 4, 591–598 (2005)
Matsuda, K. et al. Glypican-1 is overexpressed in human breast cancer and modulates the mitogenic effects of multiple heparin-binding growth factors in breast cancer cells. Cancer Res. 61, 5562–5569 (2001)
Kleeff, J. et al. The cell-surface heparan sulfate proteoglycan glypican-1 regulates growth factor action in pancreatic carcinoma cells and is overexpressed in human pancreatic cancer. J. Clin. Invest. 102, 1662–1673 (1998)
Su, G. et al. Glypican-1 is frequently overexpressed in human gliomas and enhances FGF-2 signaling in glioma cells. Am. J. Pathol. 168, 2014–2026 (2006)
Kahlert, C. & Kalluri, R. Exosomes in tumor microenvironment influence cancer progression and metastasis. J. Mol. Med. 91, 431–437 (2013)
Morris, J. P., t, Wang, S. C. & Hebrok, M. KRAS, Hedgehog, Wnt and the twisted developmental biology of pancreatic ductal adenocarcinoma. Nature Rev. Cancer 10, 683–695 (2010)
Chen, W. W. et al. BEAMing and droplet digital PCR analysis of mutant IDH1 mRNA in glioma patient serum and cerebrospinal fluid extracellular vesicles. Mol. Ther. Nucleic Acids 2, e109 (2013)
Del Villano, B. C. et al. Radioimmunometric assay for a monoclonal antibody-defined tumor marker, CA 19–9. Clin. Chem. 29, 549–552 (1983)
Özdemir, B. C. et al. Depletion of carcinoma-associated fibroblasts and fibrosis induces immunosuppression and accelerates pancreas cancer with reduced survival. Cancer Cell 25, 719–734 (2014)
Ijichi, H. et al. Aggressive pancreatic ductal adenocarcinoma in mice caused by pancreas-specific blockade of transforming growth factor-β signaling in cooperation with active Kras expression. Genes Dev. 20, 3147–3160 (2006)
Lee, E. S. & Lee, J. M. Imaging diagnosis of pancreatic cancer: A state-of-the-art review. World J. Gastroenterol. 20, 7864–7877 (2014)
Rickes, S., Unkrodt, K., Neye, H., Ocran, K. W. & Wermke, W. Differentiation of pancreatic tumours by conventional ultrasound, unenhanced and echo-enhanced power Doppler sonography. Scand. J. Gastroenterol. 37, 1313–1320 (2002)
Murphy, S. J. et al. Genetic alterations associated with progression from pancreatic intraepithelial neoplasia to invasive pancreatic tumor. Gastroenterol. 145, 1098–1109 (2013)
Bardeesy, N. & DePinho, R. A. Pancreatic cancer biology and genetics. Nature Rev. Cancer 2, 897–909 (2002)
Yachida, S. et al. Distant metastasis occurs late during the genetic evolution of pancreatic cancer. Nature 467, 1114–1117 (2010)
Hidalgo, M. Pancreatic cancer. N. Engl. J. Med. 362, 1605–1617 (2010)
Ballehaninna, U. K. & Chamberlain, R. S. Biomarkers for pancreatic cancer: promising new markers and options beyond CA 19–9. Tumour Biol. 34, 3279–3292 (2013)
Jazieh, K. A., Foote, M. B. & Diaz, L. A. Jr . The clinical utility of biomarkers in the management of pancreatic adenocarcinoma. Semin. Radiat. Oncol. 24, 67–76 (2014)
Locker, G. Y. et al. ASCO 2006 update of recommendations for the use of tumor markers in gastrointestinal cancer. J. Clinical Oncol. 24, 5313–5327 (2006)
Okano, K. & Suzuki, Y. Strategies for early detection of resectable pancreatic cancer. World J. Gastroenterol. 20, 11230–11240 (2014)
Conlon, K. C., Klimstra, D. S. & Brennan, M. F. Long-term survival after curative resection for pancreatic ductal adenocarcinoma. Clinicopathologic analysis of 5-year survivors. Ann. Surg. 223, 273–279 (1996)
Bilimoria, K. Y. et al. National failure to operate on early stage pancreatic cancer. Ann. Surg. 246, 173–180 (2007)
Murtaza, M. et al. Non-invasive analysis of acquired resistance to cancer therapy by sequencing of plasma DNA. Nature 497, 108–112 (2013)
Yong, E. Cancer biomarkers: Written in blood. Nature 511, 524–526 (2014)
David, G. et al. Molecular cloning of a phosphatidylinositol-anchored membrane heparan sulfate proteoglycan from human lung fibroblasts. J. Cell Biol. 111, 3165–3176 (1990)
Whipple, C. A., Young, A. L. & Korc, M. A KrasG12D-driven genetic mouse model of pancreatic cancer requires glypican-1 for efficient proliferation and angiogenesis. Oncogene 31, 2535–2544 (2012)
Aikawa, T. et al. Glypican-1 modulates the angiogenic and metastatic potential of human and mouse cancer cells. J. Clin. Invest. 118, 89–99 (2008)
Brand, R. E. et al. Serum biomarker panels for the detection of pancreatic cancer. Clin. Cancer Res. 17, 805–816 (2011)
Hingorani, S. R. et al. Preinvasive and invasive ductal pancreatic cancer and its early detection in the mouse. Cancer Cell 4, 437–450 (2003)
Chen, H. & Boutros, P. C. VennDiagram: a package for the generation of highly-customizable Venn and Euler diagrams in R. BMC Bioinformatics 12, 35 (2011)
Rachagani, S. et al. Activated KrasG12D is associated with invasion and metastasis of pancreatic cancer cells through inhibition of E-cadherin. Br. J. Cancer 104, 1038–1048 (2011)
Rothstein, D. M. et al. Targeting signal 1 through CD45RB synergizes with CD40 ligand blockade and promotes long term engraftment and tolerance in stringent transplant models. J. Immunol. 166, 322–329 (2001)
Livak, K. J. & Schmittgen, T. D. Analysis of relative gene expression data using real-time quantitative PCR and the 2−ΔΔCT method. Methods 25, 402–408 (2001)
Schmid, A., Braumuller, H., Wehrl, H. F., Rocken, M. & Pichler, B. J. Non-invasive monitoring of pancreatic tumor progression in the RIP1-Tag2 mouse by magnetic resonance imaging. Mol. Imaging Biol. 15, 186–193 (2013)
DeLong, E. R., DeLong, D. M. & Clarke-Pearson, D. L. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44, 837–845 (1988)
Work supported by the Cancer Prevention and Research Institute of Texas and UT MD Anderson Cancer Center. R.K. is also supported by National Institutes of Health (NIH) grants CA-155370, CA-151925, DK 081576 and Metastasis Research Center at the MD Anderson Cancer Center (P30CA016672). V.S.L. is supported by the NIH/NCI under the award number P30CA016672 and the UT MDACC Khalifa Bin Zayed Al Nahya Foundation. D.P.-W. and S.T.G. are supported by the NIH P50-CA094056. Institutional Core Grant CA16672 for High Resolution Electron Microscopy Facility. The Flow Cytometry MDACC core facility is partially funded by NIH P30CA16672. The MDACC Small Animal Imaging Facility is partially funded by NIH grants P30-CA016672 and 5U24-CA126577. S.A.M. is a Human Frontiers Science Program Fellow. C.K. is funded by a Research Fellowship of the Deutsche Forschungsgemeinschaf (DFG). We thank P. A. Kurywchak and S. Kamerkar for the help with the sucrose gradients and K. Dunner Jr for the help with the transmission electron microscopy.
The authors declare no competing financial interests.
Extended data figures and tables
a, Exosome concentration and size distribution by NanoSight analysis of culture supernatant from NIH/3T3, MCF10A, HDF, MDA-MB-231 and E10 cells. Size mode: 105 nm (3 technical replicates). b, TEM micrograph of MDA-MB-231-derived exosomes. Top right image shows a digitally zoomed inset. c, TEM micrograph of MDA-MB-231-derived exosomes following immunogold labelling for CD9. Gold particles are depicted as black dots. Top right image shows a digitally zoomed inset. d, Immunoblot of flotillin1 and CD81 in exosomal proteins extracted from culture supernatant of E10, NIH/3T3, MDA-MB-231, MCF10A and HDF cells. e, qRT–PCR measurement of GPC1 mRNA levels in HMEL, HDF, HMLE, MCF7, MDA-MB-231, T3M4, Panc-1 and MIA Paca2 cells. Results are mean ± s.d.; n = 3, 3 biological replicates, with 3 technical replicates each. f, Immunoblot of GPC1 in HMEL, HDF, HMLE, MCF7, MDA-MB-231, T3M4, Panc-1 and MIA Paca2 cell lysates (top). β-actin was used as a loading control (bottom). g, Immunoblot of GPC1 in exosomal protein lysates derived from the culture supernatant of 3 non-tumorigenic cell lines (HDF, HMEL and HMLE) and 5 tumorigenic cell lines (MCF7, MDA-MB-231, T3M4, Panc-1 and MIA Paca2) (top). Flotillin1 was used as loading control (bottom). h, Immunoblot of flotillin1 in exosomal protein lysates from the culture supernatant of MDA-MB-231 and T3M4 following sucrose gradient purification. The protein content is assayed in each of the density layers listed.
a, Longitudinal blood collection; nude mice with orthotopic MDA-MB-231 tumours (n = 4 mice). b, Percentage of beads with GPC1+ crExos plotted against average tumour volume (n = 4 mice, each sample analysed in technical triplicates for GPC1). ANOVA, post-hoc Tamhane T2, **P < 0.01, ***P < 0.001. Data are mean ± s.d. c, Correlation between tumour volume and the percentage of beads with GPC1+ crExos (Pearson correlation test). d, NanoSight of exosomes from MDA-MB-231-CD63–GFP cells. Black: all exosomes; green: CD63–GFP+ exosomes (n = 3 technical replicates). e, NanoSight of crExos from mice with a MDA-MB-231-CD63–GFP orthotopic tumour. Black: all exosomes; green: CD63–GFP+ exosomes (n = 3 technical replicates). f, FACS analysis of beads with exosomes from cultured MDA-MB-231 (top left) and MDA-MB-231-CD63–GFP (top middle) cells, and crExos of mice with MDA-MB-231-CD63–GFP orthotopic tumours (bottom left). Staining of CD63–GFP+ (cancer-cell-derived) and CD63–GFP− (host-derived) crExos for GPC1 (allophycocyanin (APC)+ bottom right; n = 3 biological replicates and 3 technical replicates). The percentage of positive beads is listed. Negative control: secondary antibody alone (top right).
a, TEM micrograph of serum-derived exosomes from a cancer patient. Top right image shows a digitally zoomed inset. b, TEM micrograph of serum-derived exosomes from a cancer patient after immunogold labelling for CD9. Gold particles are depicted as black dots. Top right image shows a digitally zoomed inset. c, Immunoblot of flotillin1 of exosomal protein lysates from serum of cancer patient following exosome purification by a sucrose gradient. The protein content is assayed in each of the density layers listed. d, Exosome concentration by NanoSight analysis showing the number of exosomes per millilitre of serum derived from healthy donors (n = 100), breast cancer patients (n = 32) and patients with PDAC (n = 190) (ANOVA, post-hoc Tamhane T2, *P < 0.05, ****P < 0.0001; 3 technical replicates). e, Exosomes size distribution by NanoSight analysis showing the mode size of exosomes in 1 ml of serum derived from healthy donors (n = 100), breast cancer patients (n = 32) and patients with PDAC (n = 190) (ANOVA, post-hoc Tukey–Kramer test, ***P < 0.001; 3 technical replicates). f, Scatter dot plots depicting the percentage of beads with GPC1+-bound exosomes purified from the serum of breast cancer patients. The patients are subdivided into three subtypes: luminal A, luminal B and triple-negative breast cancer. g, Scatter plots depicting the serum CA19-9 concentration (U ml−1), evaluated by ELISA, in healthy donors (n = 100), patients with BPD (n = 26), PCPL (n = 5) and PDAC (n = 190). Discovery cohort, ANOVA, post-hoc Tamhane T2, *P < 0.05; ****P < 0.0001; 3 technical replicates. Data are mean ± s.d.
a, Table associated with ROC curve analysis depicted in Fig. 1f. b–f, ROC curve analysis for the percentage of GPC1+ crExos (red line), CA19-9 serum levels (blue scattered line), exosome concentration (black line) and exosome size (scattered black line) in patients with carcinoma in situ (CIS) or stage I pancreatic cancer (n = 5) (a), stage IIa pancreatic cancer (n = 18) (b), stage IIb pancreatic cancer (n = 117) (c), stage III pancreatic cancer (n = 11) (d), and stage IV pancreatic cancer (n = 41) (e), compared to healthy donors (n = 100) and patients with a benign pancreatic disease (n = 26), total n = 126. g, Table associated with ROC curve analysis depicted in Fig. 1h. CI, confidence interval.
a, Scatter plots of the percentage of beads with GPC1+ crExos by flow cytometry in patients with pancreatic cancer. Patients are divided based on metastatic disease (non-metastatic lesions, lymph node metastases and distant metastases) (ANOVA, post-hoc Tukey–Kramer test, *P < 0.05; 3 technical replicates). b, Scatter plots depicting serum CA19-9 levels (U ml−1) in patients with BPD (n = 4), PCPL (n = 4) and PDAC (n = 29) on the pre-operative day and post-operative day 7 in patients (paired two-tailed Student’s t-test, **P < 0.01; 3 technical replicates). c, d, Multivariate analysis (Cox proportional hazards regression model) of prognostic parameters for overall (c) and disease-specific (d) survival of patients with pancreatic cancer in the longitudinal cohort (n = 29). e, Scatter plots depicting serum GPC1 (ng ml−1) levels by ELISA in patients with BPD (n = 6), PDAC (n = 56) and healthy controls (n = 20) (ANOVA, post-hoc Tukey–Kramer test, **** P < 0.0001; 3 technical replicates). f, ROC curve for circulating GPC1 protein (red line) in patients with pancreatic cancer (n = 56) versus healthy donors (n = 20) and patients with a benign pancreatic disease (n = 26), total n = 6.
a, Schematic diagram depicting the spontaneous development and progression of pancreatic cancer in PKT mice, and haematoxylin and eosin of the pancreas at the indicated time points showing healthy pancreas, and PanIN and PDAC lesions. Scale bars, 100 μm. b, c, Exosome size (b) and concentration (c) assayed by NanoSight analysis from the serum of PKT mice (E: experimental, red) and control mice (C: control, blue) at 4, 5, 6, 7 and 8 weeks of age (ANOVA, post-hoc Tukey–Kramer test, *P < 0.05; 3 technical replicates). d, Graph depicting the time-wise progression of tumour volume measured by MRI and the percentage of GPC1+-bound crExo beads in individual PKT mice (blue: tumour volume, red: percentage of GPC1+ crExos). e, Percentage of GPC1+ crExo beads from control mice (n = 3) and mice with cerulein-induced acute pancreatitis (n = 4) (two-tailed Student’s t-test, ns: not significant; 3 technical replicates). f, Results from ROC curves for the percentage of GPC1+-bound crExo beads, exosome concentration and size in 4-, 5-, 6- and 7-week-old PKT mice (n = 7) versus control (including age-matched littermate healthy control (n = 6) and mice with induced acute pancreatitis (n = 4), n = 10)). Data are mean ± s.d.
a, Representative micrographs of haematoxylin-and-eosin-stained pancreas from 16-day-old control mice (left) and PKT mice presenting with (right, encircled) and without (middle) PanIN lesions. Scale bars, 100 μm. b, C t values following qPCR analyses for oncogenic KRAS G12D, wild-type KRAS and 18S internal control RNA from exosomes of 44–48-day-old PKT mice serum segregated using FACS for GPC1+-bead-bound exosomes (red) and GPC1−-bead-bound exosomes (blue). Data are mean ± s.d.
Extended Data Figure 8 Raw scatter dot plot depicting flow cytometry analyses of beads with GPC1+-bound exosomes
a, Scatter plots and histogram of flow cytometry analyses of serum exosomes on beads of a representative healthy control (left panels are secondary antibody only; right panels are GPC1 antibody and secondary antibody). b, Scatter plots and histogram of flow cytometry analysis of serum exosomes on beads of a representative pancreatic cancer sample (left panels are secondary antibody only; right panels are with GPC1 antibody and secondary antibody).
This file shows the uncropped blots for Extended Data Figures 1d, 1f,1g,1h and 2c. (PDF 77 kb)
This file contains the protein accession numbers of proteins found In exosomes by UPLC‐MS. (XLS 219 kb)
This table shows the chromatographic separation and mass spectrometric detection conditions. (PDF 236 kb)
About this article
Cite this article
Melo, S., Luecke, L., Kahlert, C. et al. Glypican-1 identifies cancer exosomes and detects early pancreatic cancer. Nature 523, 177–182 (2015). https://doi.org/10.1038/nature14581
This article is cited by
Cancer-derived small extracellular vesicles: emerging biomarkers and therapies for pancreatic ductal adenocarcinoma diagnosis/prognosis and treatment
Journal of Nanobiotechnology (2022)
Cancer Cell International (2022)
CD63-positive extracellular vesicles are potential diagnostic biomarkers of pancreatic ductal adenocarcinoma
BMC Gastroenterology (2022)
Extracellular vesicle-mediated crosstalk between pancreatic cancer and stromal cells in the tumor microenvironment
Journal of Nanobiotechnology (2022)
Small extracellular vesicles: from mediating cancer cell metastasis to therapeutic value in pancreatic cancer
Cell Communication and Signaling (2022)