Extracellular vesicles (EVs) represent a heterogeneous population of small vesicles, consisting of a phospholipidic bilayer surrounding a soluble interior cargo. These vesicles play an important role in cellular communication by virtue of their protein, RNA, and lipid content, which can be transferred among cells. Peripheral blood is a rich source of circulating EVs. An analysis of EVs in peripheral blood could provide access to unparalleled amounts of biomarkers of great diagnostic, prognostic as well as therapeutic value. In the current study, a plasma EV enrichment method based on pluronic co-polymer was first established and characterized. Plasma EVs from breast cancer patients were then enriched, profiled and compared to non-cancer controls. Proteins signatures that contributed to the prediction of cancer samples from non-cancer controls were created by a random-forest based cross-validation approach. We found that a large portion of these signatures were related to breast cancer aggression. To verify such findings, KIAA0100, one of the features identified, was chosen for in vitro molecular and cellular studies in the breast cancer cell line MDA-MB-231. We found that KIAA0100 regulates cancer cell aggression in MDA-MB-231 in an anchorage-independent manner and is particularly associated with anoikis resistance through its interaction with HSPA1A. Lastly, plasma EVs contain not only individual proteins, but also numerous molecular complexes. In order to measure millions of proteins, isoforms, and complexes simultaneously, Adaptive Dynamic Artificial Poly-ligand Targeting (ADAPT) platform was applied. ADAPT employs an enriched library of single-stranded oligodeoxynucleotides to profile complex biological samples, thus achieving a deep coverage of system-wide, native biomolecules. Profiling of EVs from breast cancer patients was able to obtain a prediction AUC performance of 0.73 when compared biopsy-positive cancer patient to healthy controls and 0.64 compared to biopsy-negative controls and such performance was not associated with the physical breast condition indicated by BIRAD scores. Taken together, current research demonstrated the potential of profiling plasma EVs in searching for therapeutic targets as well as diagnostic signatures.

Technologies capable of characterizing the full breadth of cellular systems need to be able to measure millions of proteins, isoforms, and complexes simultaneously. We describe an approach that fulfils this criterion: Adaptive Dynamic Artificial Poly-ligand Targeting (ADAPT). ADAPT employs an enriched library of single-stranded oligodeoxynucleotides (ssODNs) to profile complex biological samples, thus achieving an unprecedented coverage of system-wide, native biomolecules. We used ADAPT as a highly specific profiling tool that distinguishes women with or without breast cancer based on circulating exosomes in their blood. To develop ADAPT, we enriched a library of ~10[superscript 11] ssODNs for those associating with exosomes from breast cancer patients or controls. The resulting 10[superscript 6] enriched ssODNs were then profiled against plasma from independent groups of healthy and breast cancer-positive women. ssODN-mediated affinity purification and mass spectrometry identified low-abundance exosome-associated proteins and protein complexes, some with known significance in both normal homeostasis and disease. Sequencing of the recovered ssODNs provided quantitative measures that were used to build highly accurate multi-analyte signatures for patient classification. Probing plasma from 500 subjects with a smaller subset of 2000 resynthesized ssODNs stratified healthy, breast biopsy-negative, and -positive women. An AUC of 0.73 was obtained when comparing healthy donors with biopsy-positive patients.