Breast cancer, one of the most common cancers in women, is classified by the expression of hormone receptors and the growth factor receptor HER2, which is important for personalised tumour treatment with HER2-targeted therapies. Tumour biopsies are required for histopathological diagnosis of HER2 expression by breast cancer cells but are subject to sampling error. In this study, we present a method for identifying and analysing cancer-derived EVs from plasma for the detection of HER2 expression in breast cancer without the need for additional processing steps. We detected nano-sized particles through an optimised flow cytometry approach that allows for the identification of HER2-expressing EVs and quantification of their HER2 expression levels. In a clinical study of 115 breast cancer patients, this optimised flow cytometric analysis detected a range of 1.3 to 50 × 10(3) HER2(+)EVs per µl of plasma. The number of HER2(+)EVs did not correlate directly with tumour size, grade, or metastasis. However, computational integration of data from the quantification of HER2(pos) EVs per µl/plasma and their HER2 expression levels on a single EV basis allowed for the reliable identification of HER2 expression levels in tumours. Our results reveal the potential for analysing cancer-derived EVs from plasma for the diagnosis and personalised therapy in breast cancer patients.