Plasma Exosome Profiling of Cancer Patients by a Next Generation Systems Biology Approach.
Publication/Presentation Date
2-20-2017
Abstract
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(11) ssODNs for those associating with exosomes from breast cancer patients or controls. The resulting 10(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.
Volume
7
First Page
42741
Last Page
42741
ISSN
2045-2322
Published In/Presented At
Domenyuk, V., Zhong, Z., Stark, A., Xiao, N., O’Neill, H. A., Wei, X., … Spetzler, D. (2017). Plasma Exosome Profiling of Cancer Patients by a Next Generation Systems Biology Approach. Scientific Reports, 7, 42741. http://doi.org/10.1038/srep42741
Disciplines
Medical Sciences | Medicine and Health Sciences
PubMedID
28218293
Department(s)
Department of Medicine
Document Type
Article