Using advanced liquid chromatography and mass spectrometry techniques coupled with machine learning computer algorithms, researchers have identified 16 metabolite compounds that provided unprecedented accuracy in distinguishing 46 women with early-stage ovarian cancer from a control group of 49 women who did not have the disease. Blood samples for the study were collected from a broad geographic area – Canada, Philadelphia and Atlanta.

While the set of biomarkers reported in this study are the most accurate reported thus far for early-stage ovarian cancer, more extensive testing across a larger population will be needed to determine if the high diagnostic accuracy will be maintained across a larger group of women representing a diversity of ethnic and racial groups.

The research was reported November 17 in the journal Scientific Reports, an open access journal from the publishers of Nature.

“This work provides a proof of concept that using an integrated approach combining analytical chemistry and learning algorithms may be a way to identify optimal diagnostic features,” said John McDonald, a professor in the School of Biology at the Georgia Institute of Technology and director of its Integrated Cancer Research Center. “We think our results show great promise and we plan to further validate our findings across much larger samples.”

Ovarian cancer has been difficult to treat because it typically is not diagnosed until after it has metastasized to other areas of the body. Researchers have been seeking a routine screening test that could diagnose the disease in stage one or stage two – when the cancer is confined to the ovaries.

Working with three cancer treatment centers in the U.S. and Canada, the Georgia Tech researchers obtained blood samples from women with stage one and stage two ovarian cancer. They separated out the serum, which contains proteins and metabolites – molecules produced by enzymatic reactions in the body.

The serum samples were analyzed by ultra-performance liquid chromatography-mass spectrometry (UPLC-MS), which is two instruments joined together to better separate samples into their individual components. Heavier molecules are separated from lighter molecules, and the molecular signatures are determined with enough accuracy to identify the specific compounds. The Georgia Tech researchers decided to look only at the metabolites in their research.

“People have been looking at proteins for diagnosis of ovarian cancer for a couple of decades, and the results have not been very impressive,” said Facundo Fernández, a professor in Georgia Tech’s School of Chemistry and Biochemistry who led the analytical chemistry part of the research. “We decided to look in a different place for molecules that could potentially provide diagnostic capabilities. It’s one of the places that people had really not studied before.”

Samples from each of the 46 cancer patients were divided so they could be analyzed in duplicate. The researchers also looked at serum samples from 49 women who did not have cancer. The work required eliminating unrelated compounds such as caffeine, and molecules that were not present in all the cancer patients.

“We used really high resolution equipment and instrumentation to be able to separate most of the components of the samples,” Fernández explained. “Otherwise, detection of early-stage ovarian cancer is very difficult because you have a lot of confounding factors.”

The chemical work identified about a thousand candidate compounds. That number was reduced to about 255 through the work of research scientist David Gaul, who removed duplicates and unrelated molecules from the collection.

These 255 compounds were then analyzed by a learning algorithm which evaluated the predictive value of each one. Molecules that did not contribute to the predictive accuracy of the screening were eliminated. Ultimately, the algorithm produced a list of 16 molecules that together differentiated cancer patients with extremely high accuracy – greater than 90 percent.

“The algorithm looks at the metabolic features and correlates them with whether the samples were from cancer or control patients,” McDonald explained. “The algorithm has no idea what these compounds are. It is simply looking for the combination of molecules that provides the optimal predictive accuracy. What is encouraging is that many of the diagnostic features identified are metabolites that have been previously implicated in ovarian cancer.”

As a next step, McDonald and Fernández would like to study samples from a larger population that includes significant numbers of different ethnic and racial groups. Those individuals may have different metabolites that could serve as biomarkers for ovarian cancer.

Though sophisticated laboratory equipment was required to identify the 16 molecules, a screening test would not require the same level of sophistication, Fernández said.

“Once you know what these molecules are, the next step would be to set up a clinical assay,” he said. “Mass spectrometry is a common tool in this field. We could use a clinical mass spectrometer to look at only the molecules we are interested in. Moving this to a clinical assay would take work, but I don’t see any technical barriers to doing it.”

The Fernández and McDonald groups have used a similar approach with prostate cancer and plan to explore its utility for detecting other types of cancer.

The research was supported by grants from The Laura Crandall Brown Ovarian Cancer Foundation, The Ovarian Cancer Research Fund, The Ovarian Cancer Institute, Northside Hospital (Atlanta), The Robinson Family Fund, and the Deborah Nash Endowment Fund.

CITATION: David A. Gaul, et al., “Highly-accurate metabolomics detection of early-stage ovarian cancer,” (Scientific Reports, 2015). http://www.dx.doi.org/10.1038/srep16351

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The current consensus among the vast majority of researchers is that most if not all cancers are caused by a change in or damage to genes, collectively called “mutations.” But research from two Georgia Institute of Technology cancer geneticists may alter the prevailing view.

“With the exception of the few things that we know are related to predisposition, the consensus view now is that cancer is due to ‘de novo’ mutations,” says John McDonald, professor of Biology and director of the Integrated Cancer Research Center (ICRC) at the Petit Institute for Bioengineering and Bioscience. 

The term “mutation” is typically used to encompass a broad spectrum on genomic level lesions ranging from small changes in the single letter DNA code (point mutations) to large chromosomal deletions and rearrangements (structural mutations) that can adversely affect the architecture and function of cells. 

The explosion in the number of genome sequencing studies carried out over the past several years comparing normal and cancer tissues has generated an abundance of genome databases ripe for computational analyses of the spectrum of mutations associated with cancer.  

In a study just reported in the online journal BMC Medical Genomics  McDonald and Vinay Mittal (former graduate student in McDonald’s lab, now a bioinformatics scientist with Thermo Fisher Scientific in Michigan) report the results of a detailed analysis of structural mutations associated with ovarian cancer – the deadliest of all gynecological cancers. 

Unlike most computational analyses of cancer mutations, the authors not only analyzed the structural variants found in cancer tissues but also those naturally occurring mutations present in the normal tissues of the same patients. 

“The results were remarkable in the shear number of structural mutations identified,” says McDonald. They found 4,516 structural mutations in the cancer tissues and 5,518 in the normal tissues. 

“Most of the structural variants identified are probably of little functional significance,” says Mittal. “But around 10 percent of the variants identified are ‘gene-fusions’ with the potential to generate hybrid proteins that may contribute significantly to cancer onset and progression.”

Disease of Misinformation

In addition to identifying and categorizing all of the structural variants associated with the ovarian cancer patients, Mittal and McDonald went on to determine the extent to which these variants were actually being expressed in the normal and cancer tissues.

While our DNA harbors all of the genetic information in our cells, like a blueprint, not all of this information is transmitted or “expressed” at any given time.  Information in DNA must be transmitted to an intermediary RNA molecule before it can result in the manufacture of a functional protein. For example, many of the genes necessary for liver cell function are not expressed in the brain, and vice versa.

The detailed pattern of regulatory controls typical in normal cells is often disrupted in cancer cells. Thus, cancer cells not only contain DNA lesions or mutations not detected in normal cells, they may also display abnormal expression patterns, “genetic information that is silenced in normal cells but abnormally expressed in cancer cells,” says McDonald, who has called cancer, “a disease of misinformation.”

Cells get wrong information, such as being told to rapidly divide when they should be inactive. It could be an error in the cell’s blueprint (a mutation). Or, it could be an error in the flow of information (abnormal expression), a regulation problem, which gets to what may be the most remarkable and unexpected finding of the study, according to McDonald. 

“At least some of the functionally significant abnormal gene-fusions expressed in the cancer tissues are also present in normal tissues,” he says. “But they are not being expressed.” 

The results of the study underscore the importance of gene regulation in cancer. It raises questions about how and when cancer is due to a de novo mutational event or a mistake in information flow.

“The accumulation of structural variants and other mutations in our cells with the potential to cause cancer may be inevitable as we age, but our cells may naturally strive to suppress the expression of some of this misinformation,” says McDonald. “This suggests that while mutations may be necessary for the onset of most cancers, they my not be sufficient. We need to better understand the regulatory mechanisms that can suppress cancer causing mutations in some individuals and how these mechanisms break down in cancer patients.”

Studies are currently on-going to see if the suppression of cancer causing genetic lesions extends to other classes of mutation and to understand the molecular processes that may underlie the suppression mechanism. The hope is that this work will lead to novel and more effective therapies and treatments for cancer.

The Parker H. Petit Institute for Bioengineering and Bioscience, an internationally recognized hub of multidisciplinary research at the Georgia Institute of Technology, brings engineers, scientists, and clinicians together to solve some of the world’s most complex health challenges. With 17 research centers, more than 170 faculty members, and $24 million in state-of-the-art facilities, the Petit Institute is translating scientific discoveries into game-changing solutions to solve real-world problems.

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