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Malignancy Assessment Using Gene Identification in Captured Cells Algorithm for the Prediction of Malignancy in Women With a Pelvic Mass

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Multiplexed gene expression evaluation of cells captured from blood, with or without serum biomarker levels, accurately detects malignancy in patients with a pelvic mass. OBJECTIVE: To evaluate the detection of… Click to show full abstract

Multiplexed gene expression evaluation of cells captured from blood, with or without serum biomarker levels, accurately detects malignancy in patients with a pelvic mass. OBJECTIVE: To evaluate the detection of malignancy in women with a pelvic mass by using multiplexed gene expression analysis of cells captured from peripheral blood. METHODS: This was an IRB-approved, prospective clinical study. Eligible patients had a pelvic mass and were scheduled for surgery or biopsy. Rare cells were captured from peripheral blood obtained preoperatively by using a microfluidic cell capture device. Isolated mRNA from the captured cells was analyzed for expression of 72 different gene transcripts. Serum levels for several commonly assayed biomarkers were measured. All patients had a tissue diagnosis. Univariate and multivariate logistic regression analyses for the prediction of malignancy using gene expression and serum biomarker levels were performed, and receiver operating characteristic curves were constructed and compared. RESULTS: A total of 183 evaluable patients were enrolled (average age 56 years, range 19–91 years). There were 104 benign tumors, 17 low malignant potential tumors, and 62 malignant tumors. Comparison of the area under the receiver operating characteristic curve for individual genes and various combinations of genes with or without serum biomarkers to differentiate between benign conditions (excluding low malignant potential tumors) and malignant tumors showed that a multivariate model combining the expression levels of eight genes and four serum biomarkers achieved the highest area under the curve (AUC) (95.1%, 95% CI 92.0–98.2%). The MAGIC (Malignancy Assessment using Gene Identification in Captured Cells) algorithm significantly outperformed all individual genes (AUC 50.2–65.2%; all P<.001) and a multivariate model combining 14 different genes (AUC 88.0%, 95% CI 82.9–93.0%; P=.005). Further, the MAGIC algorithm achieved an AUC of 89.5% (95% CI 81.3–97.8%) for stage I–II and 98.9% (95% CI 96.7–100%) for stage III–IV patients with epithelial ovarian cancer. CONCLUSION: Multiplexed gene expression evaluation of cells captured from blood, with or without serum biomarker levels, accurately detects malignancy in women with a pelvic mass. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov, NCT02781272. FUNDING SOURCE: This study was funded by ANGLE Europe Limited (Surrey Research Park, Guildford, Surrey, United Kingdom).

Keywords: malignancy; expression; pelvic mass; malignancy women; gene

Journal Title: Obstetrics and Gynecology
Year Published: 2022

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