Extracellular Vesicle Molecular Signatures Characterize Metastatic Dynamicity in Ovarian Cancer

Amber Gonda; Nanxia Zhao; Jay V Shah; Jake N Siebert; Srujanesh Gunda; Berk Inan; Mijung Kwon; Steven K Libutti; Prabhas V Moghe; Nicola L Francis; Vidya Ganapathy

doi:10.3389/fonc.2021.718408

Extracellular Vesicle Molecular Signatures Characterize Metastatic Dynamicity in Ovarian Cancer

Front Oncol. 2021 Nov 18:11:718408. doi: 10.3389/fonc.2021.718408. eCollection 2021.

Authors

Amber Gonda¹, Nanxia Zhao², Jay V Shah¹, Jake N Siebert^{1

3}, Srujanesh Gunda⁴, Berk Inan⁴, Mijung Kwon⁵, Steven K Libutti⁵, Prabhas V Moghe^{1

2}, Nicola L Francis¹, Vidya Ganapathy¹

Affiliations

¹ Department of Biomedical Engineering, Rutgers University, Piscataway, NJ, United States.
² Department of Chemical and Biochemical Engineering, Rutgers University, Piscataway, NJ, United States.
³ Rutgers-Robert Wood Johnson Medical School, Rutgers University, New Brunswick, NJ, United States.
⁴ School of Environmental and Biological Sciences, Rutgers University, New Brunswick, NJ, United States.
⁵ Rutgers Cancer Institute of New Jersey, Rutgers University, New Brunswick, NJ, United States.

Abstract

Background: Late-stage diagnosis of ovarian cancer, a disease that originates in the ovaries and spreads to the peritoneal cavity, lowers 5-year survival rate from 90% to 30%. Early screening tools that can: i) detect with high specificity and sensitivity before conventional tools such as transvaginal ultrasound and CA-125, ii) use non-invasive sampling methods and iii) longitudinally significantly increase survival rates in ovarian cancer are needed. Studies that employ blood-based screening tools using circulating tumor-cells, -DNA, and most recently tumor-derived small extracellular vesicles (sEVs) have shown promise in non-invasive detection of cancer before standard of care. Our findings in this study show the promise of a sEV-derived signature as a non-invasive longitudinal screening tool in ovarian cancer.

Methods: Human serum samples as well as plasma and ascites from a mouse model of ovarian cancer were collected at various disease stages. Small extracellular vesicles (sEVs) were extracted using a commercially available kit. RNA was isolated from lysed sEVs, and quantitative RT-PCR was performed to identify specific metastatic gene expression.

Conclusion: This paper highlights the potential of sEVs in monitoring ovarian cancer progression and metastatic development. We identified a 7-gene panel in sEVs derived from plasma, serum, and ascites that overlapped with an established metastatic ovarian carcinoma signature. We found the 7-gene panel to be differentially expressed with tumor development and metastatic spread in a mouse model of ovarian cancer. The most notable finding was a significant change in the ascites-derived sEV gene signature that overlapped with that of the plasma-derived sEV signature at varying stages of disease progression. While there were quantifiable changes in genes from the 7-gene panel in serum-derived sEVs from ovarian cancer patients, we were unable to establish a definitive signature due to low sample number. Taken together our findings show that differential expression of metastatic genes derived from circulating sEVs present a minimally invasive screening tool for ovarian cancer detection and longitudinal monitoring of molecular changes associated with progression and metastatic spread.

Keywords: exosome; extracellular vesicle; gene signatures; metastasis; ovarian cancer 2.

Abstract

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