Abstract 3058: Colocalization of cancer-associated biomarkers on single extracellular vesicles for early-cancer detection
Authors: Salem Daniel P. , Bortolin Laura T. , Couvillon Anthony D. , Gusenleitner Dan , Grosha Jonian , Banerjee Sanchari , Biette Kelly M. , Zabroski Ibukunoluwapo O. , Sedlak Christopher R. , Byrne Delaney M. , Duff Peter A. , Hamzeh Bilal F. , King MacKenzie S. , Cuoco Lauren T. , Winn-Deen Emily S. , Huang Eric K. , Sedlak Joseph C. 
Published: April 2023

Introduction: Extracellular vesicles (EVs) are abundant in biological fluids and increasingly recognized for their diagnostic and therapeutic potential. Unlike circulating tumor DNA which scales in abundance with tumor size and is a product of tumor cell death; tumor EVs are secreted continuously, are remarkably stable, and used by cancer cells as messengers to benefit tumor growth and metastasis, suggesting they may be a better indicator of early-stage cancer. Tumor-derived EVs reflect their cell of origin, making them an ideal analyte for liquid biopsy diagnostic assays. We describe a platform technology, Mercy Halo™, which interrogates millions of individual EVs within a plasma or serum sample to capture and detect cancer cell derived EVs. Using panels of antibodies targeting up to three cancer-associated biomarkers simultaneously, we demonstrate Mercy Halo enables sensitive and specific detection of early-stage high-grade serous ovarian carcinoma (HGSOC) with excellent reproducibility in a proof-of-concept (PoC) study.

Methods: Following purification of a sample by size-exclusion chromatography, EVs were captured using antibody-functionalized beads and interrogated using proximity ligation qPCR. EVs were characterized and biomarker expression confirmed by traditional immunoassay and Mercy Halo. The colocalization of specific biomarkers on single EVs was measured using a panel of antibodies against biomarkers upregulated in HGSOC and human cancer cell lines. Optimized antibody panels were then used in a feasibility study using EVs purified from plasma samples from early- and late-stage HGSOC, benign ovarian masses, and healthy controls.

Results: Mercy Halo detects colocalized biomarkers specifically and sensitively on the surface of tumor derived EVs. Surface biomarkers expressed by cancer cells were detected in our assay and signal strength correlated with protein expression, demonstrating that EVs can be used as a surrogate to indicate the presence or absence of cancer. We demonstrate that biomarkers present on cancer cells are colocalized on EVs derived from a tumor and are readily detectable using the Mercy Halo platform. A positive signal was dependent on the presence of all biomarkers on the same EV. Importantly, there was significant improvement in both the specificity and sensitivity of the assay as the number of biomarker antibodies used to interrogate the EVs increases. Finally, in a small clinical cohort PoC study, we demonstrate improved detection of early stage HGSOC relative to CA125 ELISA.

Conclusions: Tumor-derived EVs are a robust analyte for liquid-biopsy assays which can readily be detected using our platform technology. This PoC study suggests Mercy Halo can discriminate all stages of HGSOC from benign and healthy samples in human plasma with high sensitivity and specificity and is being investigated for extensibility in other cancers and diseases.

Citation Format: Daniel P. Salem, Laura T. Bortolin, Anthony D. Couvillon, Dan Gusenleitner, Jonian Grosha, Sanchari Banerjee, Kelly M. Biette, Ibukunoluwapo O. Zabroski, Christopher R. Sedlak, Delaney M. Byrne, Peter A. Duff, Bilal F. Hamzeh, MacKenzie S. King, Lauren T. Cuoco, Emily S. Winn-Deen, Eric K. Huang, Joseph C. Sedlak. Colocalization of cancer-associated biomarkers on single extracellular vesicles for early-cancer detection [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3058.