Scientists from Baylor College of Medicine and the Georgia Institute of Technology have won $900,000 from the Ovarian Cancer Research Fund to investigate the early detection of ovarian cancer.

The research, which comprises three separate projects, includes work with a new mouse model of ovarian cancer to identify early detection biomarkers; an effort to characterize proteins and protein variants secreted from ovarian tumors that could serve as serum biomarkers; and work to identify metabolic changes that could help diagnose the disease.

"This grant is a program project development grant, and the idea is to bring together a number of individuals around a common theme," Martin Matzuk, a BCM professor of pathology and immunology and one of the leaders of the project, told ProteoMonitor. "We were previously funded by OCRF along with a number of investigators to focus on the role of microRNAs in ovarian cancer. That work has gone very well, so we put together another proposal in which we decided to focus on biomarkers, whether they're protein or small molecule."

Matzuk is collaborating on the work with his BCM colleague Laising Yen as well as John McDonald, a professor, the associate dean for biology program development in the school of Biology at Georgia Tech, and a chief research scientist at Atlanta's Ovarian Cancer Institute.
McDonald, who will head up the search for metabolomic biomarkers, leads a research team that published a paper in August 2010 detailing a metabolomic ovarian cancer diagnostic that identified women with ovarian cancer with 100 percent accuracy in a 94-subject trial (PM 8/20/2010).

That test used direct-analysis-in-real-time mass spectrometry to measure thousands of metabolites in subjects' blood samples, classifying them with a functional support vector machine-based machine-learning algorithm. McDonald's team is still validating their findings, McDonald told ProteoMonitor this week, but thus far "everything is looking good," and, he said, the researchers hope to finish validating the results sometime within the year.

Under the OCRF grant, the Georgia Tech team plans to use LC-MS/MS to identify specific metabolites detected by their DART-MS work in hopes of combining them with protein biomarkers identified by Matzuk's lab to build an early detection panel for ovarian cancer.
The DART analysis "gives us thousands of features, and for most of them we don't know what they are," McDonald said. "From a diagnostic point of view we don't really care as long as it's a reliable diagnostic. But at the same time we're now running LC-MS/MS to try to whittle it down to identify … the specific metabolites involved."

"The idea is that we'll put it together [with Matzuk's markers] to see what an optimal diagnostic might consist of," he said.

Matzuk and the BCM researchers will be looking for protein biomarkers using a recently developed mouse model of high-grade serous ovarian cancer in which the cancer actually begins in the fallopian tube as opposed to the ovary itself. The model reflects an alternate view of ovarian cancer development "that is gaining a lot of support," Matzuk said.

Because ovarian cancer is difficult to detect early, often by the time patient samples are collected it's "too late to be trying to figure out what are the changes with regard to proteins or metabolic changes," he said. "The nice thing about having a mouse model is that these animals get cancers universally, and so you can open the animals up at a certain period and say, 'OK, at this time point what are the expression changes in these cancers? What are the earliest time points [they are visible]?"

"The goal of all three projects is to [identify] the various transcripts that are out there in these cancers," Matzuk said. "The idea is, once we catalog all of them, to go back in and then screen or develop antibodies to new variants of proteins or new secreted proteins and see whether or not those could be better markers."

The ultimate goal of the work, he said, "is to generate enough data so that we could actually go into the National Institutes of Health for a bigger project that we could start not only between our groups, but also with other groups and centers to look at various biomarkers."

Price will be a major consideration for any early detection test, Matzuk said, noting that he thinks even existing triage tests like Vermillion's OVA1 don't offer enough to justify their cost. Given the low prevalence of ovarian cancer in the general population, he said, any broad screening test for the disease would need to cost under $50 for it to be covered widely by insurers.

"I run a clinical chemistry laboratory in the county hospital, and for us to be doing this kind of screening of healthy women you need to have the cost low," he said.
However, Matzuk suggested, declining instrumentation prices could help bring costs down in the future – particularly in the case of mass spec-based tests, where multiplexing could significantly lower the price of multi-analyte assays.

"Maybe everyone will have [mass spec] analysis of their serum at some point," he said. "I think right now the instrumentation is too expensive and the testing is too expensive to go ahead and say this is for general [screening] tests, but if it turns out that these tests are extremely valuable, people are going to find a way to make them cheaper."