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

Authors :
Moore RG
Khazan N
Coulter MA
Singh R
Miller MC
Sivagnanalingam U
DuBeshter B
Angel C
Liu C
Seto K
Englert D
Meachem P
Kim KK
Source :
Obstetrics and gynecology [Obstet Gynecol] 2022 Oct 01; Vol. 140 (4), pp. 631-642. Date of Electronic Publication: 2022 Sep 07.
Publication Year :
2022

Abstract

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.<br />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.<br />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.<br />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.<br />Clinical Trial Registration: ClinicalTrials.gov, NCT02781272.<br />Funding Source: This study was funded by ANGLE Europe Limited (Surrey Research Park, Guildford, Surrey, United Kingdom).<br />Competing Interests: Financial Disclosure Richard G. Moore disclosed receiving consulting payments from Fujirebio Diagnostic Inc. and Abcodia Inc. He receives research funding from ANGLE Europe Limited. Michael C. Miller is a full-time employee of ANGLE North America, while Kelly Seto and David Englert are full-time employees of ANGLE Biosciences, Inc. Brent DuBeshter disclosed that money was paid to his institution from Angle PLC. The other authors did not report any potential conflicts of interest.<br /> (Copyright © 2022 The Author(s). Published by Wolters Kluwer Health, Inc.)

Details

Language :
English
ISSN :
1873-233X
Volume :
140
Issue :
4
Database :
MEDLINE
Journal :
Obstetrics and gynecology
Publication Type :
Academic Journal
Accession number :
36075062
Full Text :
https://doi.org/10.1097/AOG.0000000000004927