An Extracellular Vesicle-Based Liquid Biopsy for Early Cancer Detection

Extracellular vesicles (EVs), which include exosomes, microvesicles, and apoptotic bodies, are cell-derived lipid-bilayer-enclosed structures, with sizes ranging from 30 to 5,000 nanometers. The vesicles, which contain RNA, proteins, lipids, and metabolites that are reflective of the cell type of origin, are either released from the cell when multivesicular bodies (MVBs) fuse with the plasma membrane, or they are released directly from the plasma membrane. In the past decade, EVs have emerged as important mediators of cell communication because they serve as vehicles for the intercellular transmission of biological signals (proteins or nucleic acids) capable of altering cell function and physiology. Some researchers have speculated that tumors may release EVs as a way to prepare other parts of the body to receive cancer cells when they spread.

To evaluate the potential for an EV-based liquid biopsy for early cancer detection, investigators at Memorial Sloan Kettering Cancer Center (New York, NY, USA) and Weill Cornell Medicine (New York, NY, USA) established the proteomic profile of extracellular vesicles and particles (EVPs) in 426 human samples from tissue explants (TEs), plasma, and other bodily fluids. In addition to blood and tissue samples from patients with one of 18 different cancers, including breast, colon, and lung, samples from cell lines and mouse models were analyzed. The research included samples which came primarily from MSK. A control group of samples was obtained from individuals who did not have cancer.

Results revealed that among the proteins identified by the assay system, traditional exosome markers included CD9, HSPA8, ALIX, and HSP90AB1, which represented pan-EVP markers. Other proteins, including ACTB, MSN, and RAP1B, were novel pan-EVP markers.

To confirm that EVPs were realistic diagnostic tools, the investigators analyzed proteomes of TE- (n = 151) and plasma-derived (n = 120) EVPs. Comparison of TE EVPs identified proteins (VCAN, TNC, and THBS2) that distinguished tumors from normal tissues with 90% sensitivity and 94% specificity. Machine-learning classification of plasma-derived EVP cargo, including immunoglobulins, revealed 95% sensitivity and 90% specificity in detecting cancer.

"One of the holy grails in cancer medicine is to diagnose an early cancer in a patient based on a blood test," said contributing author Dr. William Jarnagin, chief of the hepatopancreatobiliary service at Memorial Sloan Kettering Cancer Center. "This research is a proof-of-principle study; much more work is needed before it can be used as a screening tool. But ultimately, it would be fantastic if we could use this approach to find cancer in someone before they had symptoms. Even if this test became standard, we still would have to do CT and MRI scans to confirm where the tumor was located. But if you use a blood test to find who might be at risk of having a certain type of cancer, it would be a huge advance because we could target investigations to these high-risk patients."

The liquid biopsy study was published in the August 13, 2020, online edition of the journal Cell.

Related Links:
Memorial Sloan Kettering Cancer Center
Weill Cornell Medicine