Abstract
BackgroundLate-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.MethodsHuman 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.ConclusionThis 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.
Highlights
Ovarian cancer, is the fifth most deadly cancer in females due to its diagnosis at advanced stages of the disease [1, 2]
The correlation of the expression of the 10 genes in the panel to disease-free survival was assessed in multiple patient datasets (Table S2) using OvMark, a bioinformatics tool developed by Molecular Therapeutics for Cancer, Ireland (MTCI) [36, 37]
Eight genes (THBS1, tissue inhibitor of metalloproteinase 3 (TIMP3), lysyl oxidase (LOX), ACTB, collagen type V alpha chain (COL5A1), AE Binding Protein (AEBP1), collagen type XI alpha chain (COL11A1), and POSTN) from the selected 10-gene panel showed that high expression in patients had a significantly greater correlation to survival outcomes than low expression levels, showing decreased progression free survival (Figure 1 and Table S3) based on OvMark analysis derived hazard ratio
Summary
Ovarian cancer, is the fifth most deadly cancer in females due to its diagnosis at advanced stages of the disease [1, 2]. Almost 80% of ovarian cancer diagnoses occur at advanced stages due to its non-specific symptoms [2, 3] and lack of tumor-specific screening tools [4] Current screening tools such as transvaginal ultrasound can assess volume- and morphologybased changes but are non-specific, leading to false-positive outcomes [4,5,6]. Brodsky et al identified a 6-gene signature that differentiates metastatic and primary ovarian lesions [12] While these show potential for gene signatures to predict disease progression, staging and treatment outcomes, most of these are done using samples collected from invasive biopsy-derived specimens [13,14,15,16,17]. 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. Our findings in this study show the promise of a sEV-derived signature as a non-invasive longitudinal screening tool in ovarian cancer
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