A liquid biopsy test has detected changes in the BRCA1 or BRCA2 genes associated with ovarian and breast cancer that signal when the disease stops responding to treatment through platinum chemotherapy or poly ADP-ribose polymerase (PARP) inhibitors. [Source: © freshidea/Fotolia]
Credit: freshidea/Fotolia

A new variation of liquid biopsy technology claims to successfully boost ovarian cancer prediction in women with a cyst or other pelvic mass, according to a new study from the Wilmot Cancer Institute at the University of Rochester Medical Center published in Obstetrics & Gynecology “Green” journal.

The approach improves the capture of stray ovarian cancer cells, or circulating tumor cells, through use of the Parsortix system, a machine that harvests circulating tumor cells. “Once we capture those cells then we analyze the gene expression of RNA produced by those cells,” says senior author Richard Moore, MD of the Wilmot Cancer Institute’s Gynecologic Oncology program. The research team, which includes scientists from UK-based ANGLE PLC, developed an algorithm from nine gene transcripts and four biomarkers that were used for detecting ovarian cancers. Known as MAGIC (Malignancy Assessment using Gene Identification in Captured Cells), the algorithm achieved a sensitivity for detecting epithelial ovarian cancer of 95% and an accuracy of 83% for detecting the disease.

The path to developing MAGIC started with evaluating 72 different gene transcripts and seven blood biomarkers related to ovarian cancer. From this collection, the study ultimately focused on nine gene transcripts (PPIATBPTPT1 [transcripts 2 and 3], WFDC-2 (HE4), INHAVEGFACCR2, and SEPT2) and four serum biomarkers ( β-2-microglobulin, transferrin, CA 125, and HE4) to create the MAGIC algorithm.

In this clinical study of 183 participants with a pelvic mass, 104 (56.8%) were diagnosed with benign disease, 17 (9.3%) were diagnosed with low malignant potential tumors, 42 (23.0%) were diagnosed with ovarian cancer, 14 (7.6%) were diagnosed with non-ovarian gynecologic cancer, and six (3.3%) were diagnosed with non-gynecologic metastatic cancers. For the 37 (20.1%) patients diagnosed with invasive epithelial ovarian cancer, 12 (32.4%) had a stage I disease, four (10.8%) had stage II, nine (24.4%) had stage III, and 12 (32.4%) had stage IV.

“We learned that if one of the nine genes are expressed, or over-expressed, then the chances of a malignancy are higher,” Moore explained. “Adding the common serum biomarkers complements that.”  The MAGIC algorithm also detected other types of cancer that had spread to the pelvic region or originated there.

“In the past, liquid biopsy for ovarian cancer has been limited by the capture of the tumor cells themselves,” said Moore. They usually rely on a tumor cell’s epitope that is recognized and captured by an antibody. But, unlike breast and colon cancers, ovarian cancers lack the necessary epitope. The system Moore’s team uses captures the cells based on their size and their lack of passing through a filter gradient. “Cancer cells and larger rare cells do not make it through that process,” he adds. “Once they are separated out, they can be analyzed for RNA expression and that tells us much more about the types of cells we’ve captured.”

In this study, the genes selected are unique to ovarian cancer cells. The approach has been validated in earlier ovarian cancer studies in spiked serum samples as well as two previous clinical trials for predicting ovarian cancer in pelvic mass. The team has also used the approach for a clinical trial in breast cancer. The FDA recently approved it for monitoring advanced metastatic breast cancer.

“This is an important step forward for the detection of ovarian cancer in patients with a pelvic mass,” Moore said. “The fact that we can capture circulating tumor cells and analyze them from a simple blood draw gives us a lot of information.” The cells can be studied under a microscope, stained, and studied to predict what kind of cancer it is. “It gives us so much more than just what a serum biomarker does.”

Moore believes the technology will not be useful for screening as the studies have not been performed in that setting yet. “The studies we have done are in women with ovarian cysts or pelvic mass and this allows us to predict pretty accurately whether it is a cancer or not,” he said, adding it helps manage patients more efficiently. Those with a suspected ovarian cancer can be referred to a gynecological oncologist immediately, speeding up the triage process, and providing needed care more quickly.

“If the patient is at high risk for an ovarian cancer, being operated on by a surgeon who has great expertise with it or at an institution with a familiar with ovarian cancer, the studies show they have improved survival rates,” Moore said. He added that in the US, only about 50% of women with ovarian cancer are treated at institutions or by oncologists who are experienced with the disease. “This should really help improve that because the triage will be better.”

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