AI and ovarian cancer
Credit: Elena Merkulova/Getty Images

A nanopore-based urine test for ovarian cancer may be able to diagnose the disease much earlier, according to new research from Joseph Reiner and colleagues at Virginia Commonwealth University. The team has determined multiple peptide signatures that might herald the disease. Reiner presented their research at the 68th Biophysical Society Annual Meeting, February 10 – 14, 2024 in Philadelphia.

Ovarian cancer is one of the leading causes of cancer death among women, and it is not yet possible to screen for it as is done for breast or colon cancer. The current clinically used marker, CA-125, (Mucin-16 and a member of the mucin family glycoproteins) is not as accurate as doctors would like.

But people with ovarian cancer have many distinctive peptides in their urine. Because endogenous peptides are smaller and may enter the circulation more easily than proteins, Reiner thought a focus on the low-molecular-weight region might reveal novel biomarkers with enhanced sensitivity and specificity. 

It is possible to detect those molecules using mass spectrometry (MS) or ELISA but those techniques have key drawbacks, Reiner says. 

He tells Inside Precision Medicine, “MS will detect all peptides to varying degrees, which can make for complicated analysis. Additionally, mass spectroscopy requires considerable hardware to operate including chromatographic separation prior to analysis, which makes it not readily available outside of large hospitals and clinics.”

ELISA would be another approach, he points out, but it requires the production and isolation of specific antibodies that attach to individual peptides, which can difficult when searching for a large (~10-100) number of different peptides 

“In short,” Reiner adds, “There are no ‘handheld’ MS devices similar to the MinION system (Oxford Nanopore Technologies) that could be used in a larger number of clinical settings.”

Nanopore sensing can simultaneously detect multiple peptides. The process involves passing molecules through a tiny pore, or nanopore, and measuring the changes in electrical current or other properties as the molecules move through. This team used gold nanoparticles that can partially block the pore. The peptides, “stick to the gold particle and basically dance around and show us a unique current signature,” Reiner explained.

The team identified and analyzed 13 peptides, including those derived from LRG-1, a biomarker found in the urine of ovarian cancer patients. Of those 13 peptides, Reiner says, “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.”

The team selected the 13 peptides from a previous study that showed they were uniquely present in the urine of ovarian cancer patients when compared to normal or control patients. They started with peptides of lengths ranging from 8-23 amino acid residues long and subsequently selected those that contain a single cysteine residue in the sequence. This was done to minimize complications that might result from disulfide bond formation.  

Reiner’s and his team’s ultimate goal is to develop a test that, combined with other information like CA-125 blood tests, transvaginal ultrasound, and family history, could improve early-stage ovarian cancer detection accuracy in the future. “Clinical data shows a 50% to 75% improvement in five-year survival when cancers are detected at their earliest stages. This is true across numerous cancer types,” Reiner points out.

Reiner emphasizes that this was not a clinical study. But he adds, “Our research team is currently focusing on optimizing sample preparation methods. If this approach is going to have real world applications it will require the ability to prepare the gold nanoparticles with target peptides outside the nanopore environment, as opposed to delivering the peptides directly onto the nanopore as was done in this study.” 




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