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Proteomics Profile Early Detection Ovarian Cancer

By Labmedica International staff writers
Posted on 26 Mar 2019
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Image: The Q Exactive hybrid mass spectrometer (Photo courtesy of Thermo Fisher Scientific).
Image: The Q Exactive hybrid mass spectrometer (Photo courtesy of Thermo Fisher Scientific).
High-grade ovarian cancer (HGOC) is the leading cause of mortality from gynecological malignancies, due to diagnosis at a metastatic stage. Current screening options fail to improve mortality due to the absence of early-stage-specific biomarkers.

Fewer than half of ovarian cancer patients survive until five years after diagnosis. According to the American Cancer Society, this is because only about one-fifth of ovarian cancer cases are detected early, when the chances of successful treatment and recovery are highest.

A team of Israeli scientists working with the Tel Aviv University (Tel Aviv-Yafo, Israel) used proteomics and compared thousands of proteins in uterine microvesicles from 12 healthy volunteers and 12 cancer patients. Then they used machine-learning algorithms to search for patterns that could distinguish between the samples.

They postulated that a liquid biopsy, such as utero-tubal lavage (UtL), may identify localized lesions better than systemic approaches of serum/plasma analysis. Liquid biopsies from 49 HGOC patients and 127 controls were divided into a discovery and validation sets. The team tested the set's accuracy in a cohort of 152 women, 37 of who were known to have ovarian cancer. Data-dependent analysis of the samples on the Q-Exactive mass spectrometer provided depth of 8,578 UtL proteins in total, and on average ~3,000 proteins per sample.

The test had 70% diagnostic sensitivity, meaning that it correctly detected cancer 25 of the 37 study participants who truly had cancer; and 76% specificity, meaning that it correctly identified about three out of every four healthy volunteers as healthy. It outperformed previous proteomics-based tests, which had less than 60% sensitivity. They used support vector machine algorithms for sample classification, and crossed three feature-selection algorithms, to construct and validate a 9-protein classifier to achieve the sensitivity and the specificity. The signature correctly identified all Stage I lesions. These results demonstrate the potential power of microvesicle-based proteomic biomarkers for early cancer diagnosis.

The authors propose that their test may be useful for young women whose risk of developing ovarian cancer is known to be high. They also believe that the method of isolating microvesicles from bodily fluids to detect fainter cancer signals shows promise for other difficult-to-detect types of cancer. Keren Levanon, MD, PhD, an Oncologist and a senior author of the study, said, “We developed a diagnostic set of nine proteins that distinguishes women with ovarian cancer from healthy women with greater sensitivity and specificity than reported before.” The study was published on February 13, 2019, in the journal Molecular & Cellular Proteomics.

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