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A New Urine Test May Be Able To Detect Ovarian Cancer Early

Researchers at Virginia Commonwealth University are working on developing a urine-based test to detect ovarian cancer at an earlier stage.

The research team investigated the possible use of nanotechnology in analyzing certain peptides that are found in the urine of people with ovarian cancer.

Although this discovery is promising, the diagnostic process technique is still in its early stages.

Scientists are working on a potential urine-based test to help detect ovarian cancer in its early stages.

The researchers from Virginia Commonwealth University published their study in the Journal of the American Chemical Society. They will also present their findings at the Biophysical Society Annual Meeting next week in Philadelphia.

The researchers' goal is for medical professionals to use this information, combined with CA-125 blood tests, transvaginal ultrasound, and family history, to provide early-stage detection, diagnosis, and treatment for ovarian cancer.

"There are no screening tests that are useful or available for ovarian cancer," said Dr. Deanna Gerber, a gynecological oncologist at NYU Langone's Perlmutter Cancer Center and an assistant professor of gynecology at NYU Langone Grossman School of Medicine-Long Island in New York who was not involved in the research.

"As such, the majority of ovarian cancers are diagnosed at stage three and four when they become symptomatic," Gerber told Medical News Today. "This technology is exciting because anything that may increase our chances of detecting cancer at an earlier stage will undoubtedly improve our chance of curing more ovarian cancers."

There are thousands of tiny particles, called peptides, in our urine and there are specific ones that signal ovarian cancer.

Currently, the techniques commonly used are not always straightforward or cost-effective to detect the molecules connected to ovarian cancer.

The researchers worked on a new approach they say could more efficiently and accurately detect these peptides by using nanopore sensing, which has the potential to detect multiple peptides.

Nanopore sensing involves passing molecules through a tiny pore (nanopore) and measuring the changes in electrical current or other properties as the molecules move through.

The researchers identified and analyzed 13 peptides, including those derived from leucine-rich a-2 glycoprotein (LRG-1), a known biomarker in the urine of people with ovarian cancer.

According to the researchers, they now know what the signatures of the peptides look like and how they can be used to detect ovarian cancer at earlier stages than current tests can.

"The science behind this is fascinating and seems very promising as a way to potentially detect ovarian cancer via urine," Gerber said. "I think this presents some hope for our patients and cancer care providers that the scientific community is continually looking to improve outcomes for gynecologic cancers. The ultimate goal will always be to prevent cancer before it starts, but if we cannot do that, catching it early will translate directly to improved outcomes and improved survival."

While the research has the potential to save lives, experts say there are still questions.

"Although the research is promising, it is far from prime time as a screening or diagnostic test for ovarian cancer," said Dr. Diana Pearre, a gynecologic oncologist at The Roy and Patricia Disney Family Cancer Center at Providence Saint Joseph Medical Center in California who was not involved in the study.

"I am optimistic about this technology eventually being able to aid us in helping detect ovarian cancer. Currently, the tests we use in our workup for ovarian cancer are pelvic ultrasounds and tumor markers (a blood test)," Pearre told Medical News Today. "There is currently no urine test to help in the workup for it."

"This is still a far away from reaching patients in clinic on a wide scale and will likely require a proof-of-concept trial to determine its sensitivity in detecting a rare disease," she added. "However, it still provides a promising new avenue to aid in our workup of ovarian cancer if and when it becomes available to patients."

"To my knowledge, nanopore technology is not being used for the detection or treatment of illness, but it is available in a very portable format for handheld genome sequencing," Pearre said.

Nanotechnology is not a product, but rather it is a process that uses the changes in the properties of a substance when examined at nanometer size, according to the International Institute for Nanotechnology.

It is a field not just about working with microscopic objects but about capitalizing on the unique and changing properties of nanoscale materials to create solutions to problems.

"Nanotechnology is the new frontier not only for diagnostic purposes but for therapeutic utilization as well," said Dr. Kecia Gaither, an OB/GYN and expert in maternal fetal medicine as well as the director of Perinatal Services/Maternal Fetal Medicine at NYC Health + Hospitals/Lincoln in the Bronx. "[It] has been utilized in the diagnosis of other types of cancers, infectious entities, and dermatological issues as examples."

"[I am] quite optimistic about its utilization in the diagnosis of ovarian cancer by what is a noninvasive simple procedure as opposed to the operative invasive methodologies commonly used now in the diagnostic cascade for ovarian cancer," Gaither, who was not involved in the research, told Medical News Today. "I foresee there is likely to be an explosion of the use of nanotechnology for the diagnosis and treatment of other illnesses in the near future."

Ovarian Cancer Could Soon Be Detected Early Thanks To Simple Urine Test

The potentially deadly condition of ovarian cancer affects women over the age of 50, but the symptoms - such as bloating - are not always obvious.

Now, new research by Professor Joseph Reiner and his colleagues at Virginia Commonwealth University in the United States has shown promise for a urine-based test for ovarian cancer.

Previous studies have shown that there are thousands of small molecules, called peptides, in the urine of people with ovarian cancer.

While it is possible to detect those molecules using certain well-established techniques, those techniques aren't straightforward or cost effective.

Prof Reiner and his team sought a new approach to more easily detect those peptides. He turned to nanopore sensing, which has the potential to simultaneously detect multiple peptides.

The basic idea of nanopore sensing involves passing molecules through a tiny pore, or nanopore, and measuring the changes in electrical current or other properties as the molecules pass through. To harness the nanopore technology to detect various peptides, Prof Reiner used gold nanoparticles that can partially block the pore.

He explained that peptides - like those in the urine of people with ovarian cancer - will then "stick to the gold particle and basically dance around and show us a unique current signature."

He says the method is capable of simultaneously identifying multiple peptides, and in the study the team identified and analysed 13 peptides, including those derived from LRG-1, a biomarker found in the urine of ovarian cancer patients.

Of those 13 peptides, Prof Reiner said: "We now know what those signatures look like, and how they might be able to be used for this detection scheme. It's like a fingerprint that basically tells us what the peptide is."

He added: "Clinical data shows a 50 to 75 per cent improvement in five-year survival when cancers are detected at their earliest stages. This is true across numerous cancer types."

The team's ultimate goal is to develop a test that, combined with other information - such as blood tests, ultrasound scans, and family history - could improve early-stage ovarian cancer detection accuracy in the future. Prof Reiner presented the findings at the Biophysical Society Annual Meeting in Philadelphia, Pennsylvania.

Diagnostic Test Detects Ovarian Cancer With 93% Accuracy

Ovarian cancer – a "silent killer"

Ovarian cancer is often referred to as a "silent killer" due to the unfortunate fact that symptoms often arise once the disease has reached an advanced stage. By this point, effective treatment strategies can be limited. According to the Ovarian Cancer Research Alliance, the 5-year survival rate for patients diagnosed with stage I ovarian cancer is 89%; for stage IV, it's 20%.

"Clearly, there is a tremendous need for an accurate early diagnostic test for this insidious disease," Dr. John McDonald, professor emeritus in the school of biological sciences at the Georgia Tech Integrated Cancer Research Center (ICRC), said. McDonald is also the founding director of the ICRC.

Over the last three decades, there have been numerous efforts to create a highly accurate early-detection test for ovarian cancer, with limited success. That's largely because cancer development is a highly heterogeneous process. While two patients might ultimately be diagnosed with the same type of cancer, their cells and tissues might have undergone very different molecular journeys to reach that point of diagnosis.

"Because of this high-level molecular heterogeneity among patients, the identification of a single universal diagnostic biomarker of ovarian cancer has not been possible," McDonald said.

At the ICRC, McDonald and colleagues sought to identify and develop a machine learning-based classifier, which utilizes metabolic profiles of serum samples, to accurately identify people with ovarian cancer. The team's research is published in Gynecologic Oncology.

Continue reading below...

Metabolic profiles in cancer

In metabolomics studies, mass spectrometry (MS) can help to identify what metabolites are present in a sample – such as blood – by detecting their mass and charge signatures.

What are metabolic profiles?

Metabolic profiles are a large set of biochemical markers and measurements that provide insight into an individual's metabolic state. They might include information on the levels of circulating lipids, proteins, carbohydrates and other metabolites that can be harnessed to create a picture of an individual's health.

MS only gets you so far, though. Identifying the exact chemical makeup of individual metabolites requires more extensive characterization, and only a small fraction of blood metabolites in the human body have been characterized. It's not possible, therefore, to accurately pinpoint the molecular processes that underpin an individual's metabolic profile – at least, not right now.

Even so, the presence of specific metabolites in the blood, as detected by MS, can be harnessed in the development of machine-learning based predictive models. "Because end-point changes on the metabolic level are known to be reflective of underlying changes operating collectively on multiple molecular levels, we chose metabolic profiles as the backbone of our analysis," said Dongjo Ban, a graduate research assistant in the McDonald lab, and first author of the study.

"The set of human metabolites is a collective measure of the health of cells," said co-author Professor Jeffrey Skolnick, "and by not arbitrary choosing any subset in advance, one lets the artificial intelligence figure out which are the key players for a given individual."

Continue reading below...

Utilizing artificial intelligence to develop an early diagnostic test for ovarian cancer

To obtain the data to train their model, McDonald and colleagues collected serum samples from 431 ovarian cancer patients and 133 healthy women across 4 locations: Northside Hospital, Atlanta (10 early- and 142 late-stage cancer samples), Fox Chase Cancer Center Biosample Repository Facility, Philadelphia (51 early- and 68 late-stage cancer samples, 133 control samples), University of North Carolina Medical School, Chapel Hill (17 early-stage cancer samples) and Alberta Health Services, Alberta (23 early- and 120 late-stage cancer samples).

"To help ensure the quality of our metabolic data, individual normal and ovarian cancer patient samples were collected from four geographically divergent locations and analyzed using ultra-performance liquid chromatography coupled with tandem mass spectrometry (UPLC-MS/MS-positive and negative modes and each sample independently pre-processed through two columns), generating four distinct datasets," the researchers described.

They then used recursive feature eliminiation (RFE) coupled with repeated cross-validation (CV) to identify the most reliable metabolites from the datasets.

What is recursive feature eliminaton and cross-validation in machine learning?

RFE is a method used in machine learning for feature selection, i.E., selecting a subset of the most important features from a dataset of features. In this study, "features" are the metabolites. In RFE, a model is trained on a dataset, where it ranks features based on specific criteria, and eliminates the least important features. This process is repeated several times.

CV is another technique that helps researchers evaluate the performance of machine learning models. By coupling RFE and CV, researchers can enhance the reliability of model evaluation and optimize feature selection.

McDonald and colleagues developed a consensus classifier – a final model – by aggregating the results of five independent machine learning algorithms. "The probabilities assigned to individuals by the consensus model were utilized to create a background distribution of probabilities that a given sample was cancer or normal," the researchers explained.

Model distinguishes cancer from controls with 93% accuracy

The consensus classification model was able to distinguish cancer from control samples with 93% accuracy, according to the researchers.

"This personalized, probabilistic approach to cancer diagnostics is more clinically informative and accurate than traditional binary (yes/no) tests," McDonald said. "It represents a promising new direction in the early detection of ovarian cancer, and perhaps other cancers as well."

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The model requires further refinements and analyses. Its accuracy in predicting women with ovarian cancer was "slightly greater" than its accuracy in predicting women without the disease, the researchers explained in the paper. Currently, they do not know why, though they suggested that it could be due to the model potentially detecting disease in women prior to clinical symptoms and diagnosis. "Time course studies are currently being instituted to test this hypothesis," they said.

Reference: Ban D, Housley SN, Matyunina LV, et al. A personalized probabilistic approach to ovarian cancer diagnostics. Gynecol Oncol. 2024;182:168-175. Doi: 10.1016/j.Ygyno.2023.12.030

This article is a rework of a press release issued by the Georgia Tech Integrated Cancer Research Center. Material has been edited for length and content.

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