Targeting cell-bound MUC1 on myelomonocytic, monocytic leukemias and phenotypically defined leukemic stem cells with anti-SEA module antibodies

Guillaume, Thierry; Dehame, Virginie; Chevallier, Patrice; Péterlin, Pierre; Garnier, Alice; Grégoire, Marc; Pichinuk, Edward; Rubinstein, D. B.; Wreschner, Daniel H.

doi:10.1016/j.exphem.2018.12.002

Cited by 3 publications

(1 citation statement)

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“…To verify this hypothesis, the EV from B‐ALL and T‐ALL patient's plasma were isolated, and the EpCAM and MUC1 expression of each sample were analyzed using Western Blot (Figure 5k). Although the overexpression of EpCAM and MUC1 in leukaemia cells has been reported in many studies (Guillaume et al., 2019, Stroopinsky et al., 2013, Yin et al., 2010, Zheng et al., 2017), their abundant expression on EV from patients with B‐ALL and T‐ALL was proposed for the first time. The utility of EpCAM + /MUC1 + circulating EV as potential biomarker for hematologic malignancies diagnosis will be further investigated and validated in a larger cohort in future.…”

Section: Resultsmentioning

confidence: 99%

Localized fluorescent imaging of multiple proteins on individual extracellular vesicles using rolling circle amplification for cancer diagnosis

Zhang

Shi

Zhang

et al. 2020

J of Extracellular Vesicle

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Extracellular vesicles (EV) have attracted increasing attention as tumour biomarkers due to their unique biological property. However, conventional methods for EV analysis are mainly based on bulk measurements, which masks the EV‐to‐EV heterogeneity in tumour diagnosis and classification. Herein, a localized fluorescent imaging method (termed Digital Profiling of Proteins on Individual EV, DPPIE) was developed for analysis of multiple proteins on individual EV. In this assay, an anti‐CD9 antibody engineered biochip was used to capture EV from clinical plasma sample. Then the captured EV was specifically recognized by multiple DNA aptamers (CD63/EpCAM/MUC1), followed by rolling circle amplification to generate localized fluorescent signals. By‐analyzing the heterogeneity of individual EV, we found that the high‐dimensional data collected from each individual EV would provide more precise information than bulk measurement (ELISA) and the percent of CD63/EpCAM/MUC1‐triple‐positive EV in breast cancer patients was significantly higher than that of healthy donors, and this method can achieve an overall accuracy of 91%. Moreover, using DPPIE, we are able to distinguish the EV between lung adenocarcinoma and lung squamous carcinoma patients. This individual EV heterogeneity analysis strategy provides a new way for digging more information on EV to achieve multi‐cancer diagnosis and classification.

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