A Universal Platform for Identification of Novel Lung Cancer Biomarkers Based on Exosomes
Abstract
Lung cancer is the predominant form of cancer in the United States due to its high incidence, often escaping diagnoses at an early stage. Over 158,000 Americans are estimated to die from this disease in 2015. Identifying reliable biomarkers for the early detection of lung cancer could thus significantly improve the survival rate of patients. Blood biomarkers for lung cancer have raised great expectations in their clinical applications addressing early diagnosis, prognosis, and therapeutic responses. However, low abundance and poor specificity of conventional serum markers have hampered their implementation for the early detection of lung cancer. Exosomes are small lipid bilayer extracellular vesicles (30-150nm) that are secreted by most cell types, including cancer cells. Exosomes circulating in the blood of patients with lung cancer have recently received substantial attention, as they may contain specific biomarkers that indicate disease state and progression. Various tumor types, including lung, release exosomes into the blood that are enriched with tumor-specific markers of the originating cancer cells. Thus, investigation of exosome-derived biomarkers will provide a promising avenue for early lung cancer diagnoses and improved prognosis. However, the lack of standardized methods to reliably isolate high purity exosomes poses a tremendous roadblock for their implementation as a reliable source for diagnosis. Here, we propose to develop an innovative exosome total isolation chip (ExoTIC) to non-invasively identify biomarkers of disease onset and progression. Uniquely, this cost-effective, single-step, size-based filtration device will allow rapid, high yield and high purity isolation of exosomes. It can be applied universally to study a wide range of body fluids, including blood. The implementation of the proposed ExoTIC device has the potential to transform the field of clinical biomarker discovery and the diagnosis, prognosis, and therapeutic strategies targeting multiple cancers including lung cancer.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jan 31, 2017
- Source ID
- W81XWH1610200
Entities
People
- Utkan Demirci
Organizations
- Stanford University
- United States Army