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Plasma cells shape the mesenchymal identity of ovarian cancers through transfer of exosome-derived microRNAs

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Science Advances  24 Feb 2021:
Vol. 7, no. 9, eabb0737
DOI: 10.1126/sciadv.abb0737
  • Fig. 1 Plasma cells enrich in mesenchymal-subtype ovarian cancer compared with other subtypes.

    (A) Computational biology analysis revealed the total content of different immune cells in ovarian cancer (Bonome dataset, n = 182). Tregs, regulatory T cells; NK, natural killer. (B) Plasma cell abundance in four different molecular subtypes of ovarian cancer in Mateescu’s cohort and Tothill’s cohort, as calculated by the CIBERSORT algorithm (Mateescu dataset, n = 79; Tothill dataset, n = 260). P values were calculated by the Wilcoxon rank sum tests. (C) Boxplot showing the abundance of the 22 subsets of immune cells for each subtype of ovarian cancer (Bonome dataset). Data are presented as mean ± SEM. Kruskal-Wallis test, ***P < 0.001 and **P < 0.01. (D) Correlation analysis for ACTA2 with CD138 (left) and plasma cell abundance (right) in all patients and the mesenchymal-subtype patients, respectively. (E) Immunofluorescent staining of WT1, CD138, and α-SMA in 40 independent clinical ovarian cancer specimens. Nuclei were stained with 4′,6-diamidino-2-phenylindole (DAPI) (blue). Scale bar, 50 μm. Images were captured by confocal fluorescence microscopy, and the signal intensity as protein expression level was quantified by ImageJ software. Correlation analysis was performed for the expression of CD138 protein (or CD138+ cell number) and α-SMA protein in ovarian cancer specimens. Coefficient of determination (R2) and statistical significance levels were determined by linear regression with linear model method. Shaded area indicates 95% confidence interval. Data are shown as mean ± SEM. *P < 0.05; **P < 0.01; ***P < 0.001.

  • Fig. 2 Treatment of ovarian cancer cells with plasma cells induces a mesenchymal phenotype.

    (A) Plasma cells that are marked by CD138 antibody were isolated from ovarian cancer patient blood by magnetic bead sorting and quantitatively verified by flow cytometry. PE, phycoerythrin; FSC, forward scatter. (B) Phase-contrast micrographs of COV318, OVCAR-3, SKOV-3, and COV504 cells treated with control or cocultured with plasma cells (PC) for 48 hours. Scale bar, 100 μm. Phalloidin staining was shown to quantify the difference between control or cocultured groups. The quantifications include cell area, perimeter, circularity, and elongation index (mean ± SEM of no less than 40 cells per group). n.s., not significant. (C) Western blotting analysis of epithelial-mesenchymal transition (EMT) markers in COV318 and OVCAR-3 cells cocultured with plasma cells compared with control for 48 hours. ImageJ software was used to quantify protein expression levels (n = 3 for each group). (D) Phalloidin staining of COV318 and OVCAR-3 cells was shown to quantify the difference between control and plasma cell exosome–treated groups for 48 hours. Scale bar, 100 μm. The quantifications include cell area, perimeter, circularity, and elongation index (mean ± SEM of no less than 40 cells per group). (E) Western blotting analysis of EMT markers in COV318 and OVCAR-3 cells treated with plasma cell exosomes compared with control for 48 hours (n = 3 for each group). ImageJ software was used to quantify protein expression levels. GAPDH, glyceraldehyde phosphate dehydrogenase. (F) mRNA levels of EMT markers in COV318 and OVCAR-3 cells treated with plasma cell exosomes compared with control for 48 hours (n = 3 for each group). In (B) to (F), statistical significance was determined by a two-tailed, unpaired Student’s t test. Data are shown as mean ± SEM. *P < 0.05; **P < 0.01; ***P < 0.001.

  • Fig. 3 Plasma cell exosomal miR-330-3p could be transferred to ovarian cancer.

    (A) Schematic diagram of plasma cells and ovarian cancer cells cocultured in six-well plates. (B) Ovarian cancer cells were cocultured in the absence or presence of primary PKH67-labeled plasma cells (green). Nuclei were stained with DAPI (blue) (n = 3 for each group). Scale bar, 20 μm. (C) Western blot analysis for CD63, CD81, and β-actin in plasma cell exosomes (n = 3 for each group). (D) Electron micrograph of plasma cell exosomes shows the morphological size (50 to 200 nm). Scale bar, 100 nm. (E) Size distribution of exosomes was measured using NanoSight analysis. (F and G) Immunofluorescent images of PKH67 abrogation in ovarian cancer cells with respective treatment. Scale bar, 20 μm. Statistical chart was plotted (n = 3 for each group). (H) Scheme chart for small RNA sequencing in plasma cell exosomes in patients with ovarian cancer. tRNA, transfer RNA; rRNA, ribosomal RNA; snRNA, small nuclear RNA; snoRNA, small nucleolar RNA; piRNA, Piwi-interacting RNA.(I) Venn diagram for overlapped miRNAs identified in ovarian cancer plasma cell exosomes. (J) Heatmap for unsupervised hierarchical clustering of GSE73582 dataset using plasma cell exosome–specific miRNA panel as classifiers. (K) Cellular programs enriched by GSEA for plasma cell exosome–specific miRNAs represented using Enrichment Map. (L) The univariate regression analyses of the identified top miRNAs associated with patient survival, OC179 (GSE73581), n = 179. CI, confidence interval. (M and N) Fluorescence diagram shows subcellular localization of miR-330-3p (yellow arrowheads) (n = 3 to 4 for each group). Scale bar, 20 μm. In (G), data are shown as mean ± SEM. All statistical significance was determined by a two-tailed, unpaired Student’s t test. *P < 0.05; **P < 0.01; ***P < 0.001.

  • Fig. 4 miR-330-3p targets junctional adhesion molecule 2 for the maintenance of mesenchymal identity of ovarian cancer.

    (A) Venn diagram showing the most possible up-regulated genes targeted by plasma cell exosome–containing mir-330-3p. (B) Univariate regression analysis of the six overlapped target genes associated with ovarian cancer patient survival (Bonome dataset, n = 182). (C) mRNA level of JAM2 in COV318 and OVCAR-3 cells with respective treatment (n = 3 to 4 for each group). (D) Western blotting analysis of JAM2 protein levels in COV318 and OVCAR-3 cells with respective treatment (n = 3 for each group). (E) mRNA levels of JAM2 and EMT markers in miR-330-3p mimic–transfected or control COV318 and OVCAR-3 cells (n = 3 to 4 for each group). (F) Western blotting analysis of JAM2 and EMT markers in miR-330-3p mimic–transfected or control COV318 and OVCAR-3 cells (n = 3 for each group). (G) Wound healing analysis to assess the migration ability of COV318 and OVCAR-3 cells with respective treatment (n = 3 to 5 for each group). (H) Western blotting analysis of JAM2 and EMT markers in COV318 and OVCAR-3 cells with respective treatment (n = 3 for each group). si, small interfering. (I) mRNA levels of JAM2 in COV318 and OVCAR-3 cells with respective treatment (n = 3 to 4 for each group). (J) Transwell chamber analysis to assess the migration ability of COV318 and OVCAR-3 cells with respective treatment (n = 3 to 5 for each group). (K) Wound healing analysis to assess the migration ability of COV318 and OVCAR-3 cells with respective treatment (n = 3 to 5 for each group). Data are shown as mean ± SEM. All statistical significance was determined by a two-tailed, unpaired Student’s t test. *P < 0.05; **P < 0.01; ***P < 0.001.

  • Fig. 5 The importance of miR-330-3p/JAM2 axis for the mesenchymal identity of ovarian cancer in vivo.

    (A, D, G, J, and M) A total of 5 × 106 ID8 cells with respective treatment were subcutaneously injected into C57 mice together with plasma cell–derived exosomes and control (n = 6 for each group). Growth curve was plotted. (B, E, H, K, and N) Tumor weight and volume of ID8 cells in each group were measured at indicated time. (C, F, I, L, and O) In vivo bioluminescent imaging of tumor growth in each group was performed in mice 30 days after injection (n = 3 to 5 for each group). Data are shown as mean ± SEM. In (A), (D), (G), (J), and (M), P value out of two-way repeated measures analysis of variance (ANOVA). Otherwise, statistical significance was determined by a two-tailed, unpaired Student’s t test. *P < 0.05; **P < 0.01. ***P < 0.001. DMSO, dimethyl sulfoxide; sh, small hairpin.

  • Fig. 6 Clinical significance of miR-330-3p/JAM2 axis in the cross-talk between plasma cells and ovarian cancer cells.

    (A) Unsupervised classification of plasma gene signature shows the optimal classification using two clusters (left), as supported by the gap statistic (right). (B) Heatmap for unsupervised hierarchical clustering of the Tothill dataset using plasma cell gene signature as classifiers. (C) Survival difference between the plasma cell–high subgroup and the plasma cell–low subgroup in the Tothill dataset. OS, overall survival. (D) Survival difference between the plasma cell–high subgroup and the plasma cell–low subgroup in all patients and the mesenchymal-subtype patients in the Mateescu dataset. (E) Differential progression-free survival (PFS) between plasma cell exosome–specific miRNA signature–high group and plasma cell exosome–specific miRNA signature–low group of patients with ovarian cancer in OC133 and OC179 datasets. (F) Differential overall survival and progression survival between miR-330-3p–high group and miR-330-3p–low group of patients with ovarian cancer in the OC179 dataset. (G) Expression levels of miR-330-3p in patients with ovarian cancer with relapse and without relapse. (H) Expression levels of miR-330-3p in patients with ovarian cancer with distinct differentiation grades. (I) Differential overall survival between JAM2-high group and JAM2-low group of patients with ovarian cancer in the Tothill, Bonome, and TCGA datasets. Data are shown as mean ± SEM. In (C), (D), (E), (F), and (I), survival difference was calculated by log-rank test; in (G) and (H), P value was calculated using the two-tailed Student’s t test. **P < 0.01.

Supplementary Materials

  • Supplementary Materials

    Plasma cells shape the mesenchymal identity of ovarian cancers through transfer of exosome-derived microRNAs

    Zhengnan Yang, Wei Wang, Linjie Zhao, Xin Wang, Ryan C. Gimple, Lian Xu, Yuan Wang, Jeremy N. Rich, Shengtao Zhou

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