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Validating Biomarkers: Early Detection Research Network Launches First Phase III Study

Tom Reynolds

The National Cancer Institute’s Early Detection Research Network (EDRN) will launch its first phase III study of a biomarker in the next few months. Designed to evaluate microsatellite instability as a marker for recurrent bladder cancer, the multicenter collaboration is expected to become the first of many that will emerge in the coming years from the network’s wide-ranging consortium of biomarker discovery and validation laboratories.

The EDRN, established 3 years ago, aims to exploit the powerful new tools of biotechnology to rapidly bring molecular markers to the clinic (see News, May 17, 2000). In contrast to clinical testing of new therapeutic agents, which has a well-defined development sequence, biomarker development lacked an agreed-upon structure to bring a marker from discovery to clinical application—until the establishment of the EDRN.

In 2001, the EDRN unveiled a "road map" outlining five phases of biomarker development (see News, July 18, 2001), which "is now widely accepted by investigators both within and outside the network," said EDRN program director Sudhir Srivastava, Ph.D., chief of NCI’s Cancer Biomarkers Research Group.



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Dr. Sudhir Srivastava

 
The work of the EDRN is concentrated on the first three phases of that road map—the discovery of markers, the validation of markers in distinguishing the presence or absence of cancer, and testing markers for the ability to detect preclinical and early-stage disease. Srivastava highlighted several promising research programs within the EDRN, representing genomics, proteomics, and epigenomics. A sample of these follows.

Microsatellite Instability

The microsatellite instability assay to be tested in the upcoming study was developed by David Sidransky, M.D., and Mark Schoenberg, M.D., of Johns Hopkins University in Baltimore, and Li Mao, M.D., now of M. D. Anderson Cancer Center in Houston, who first described the use of this test in bladder cancer in Science in 1996. It is based on measuring changes in about 20 microsatellite loci, repetitive stretches of short DNA sequences that show up in increased numbers in cancer cells. The test has several potential advantages, Schoenberg said, including ease of sample collection—using cells sloughed off the bladder and into the urine—and the relative stability of DNA in cells compared with the flux of protein expression.

"Microsatellite instability has been touted as a potential biomarker for bladder cancer for many years, but has never been validated in a large sample of patients," Srivastava said.

The study, expected to take 3 years to complete, will enroll 300 patients with bladder cancer to compare microsatellite analysis to cystoscopy (bladder endoscopy) and cytology, the current standards for monitoring these patients for recurrence. Major centers for the study include Johns Hopkins University, Memorial Sloan-Kettering Cancer Center in New York, and M. D. Anderson Cancer Center. Schoenberg said many other medical centers affiliated with EDRN are expected to participate, along with private practice physicians recruited through a clinical research organization. The wide range of settings for the collaboration should provide "a broad-spectrum view of the disease process and of how this technology might be used in the future in real medicine."

Although the microsatellite instability test, if validated, might someday be used to detect new bladder cancer cases as well as recurrences, Schoenberg cautioned that even with a sensitive and specific test, screening for bladder cancer is challenging because of its relatively low prevalence. But similar MSI tests might eventually find a role in screening for other cancers that shed cells into body fluids, such as tumors of the oral cavity and gastrointestinal tract, he said.

Genomics

Stephen Meltzer, M.D., who heads one of EDRN’s biomarkers development laboratories at the University of Maryland in Baltimore, is using bioinformatics to search for biomarkers in esophageal and gastric preneoplastic lesions.

"We have two things we want to gain from these bioinformatics studies," Meltzer said. "One is to find individual biomarkers: the ‘magic gene,’ or maybe it’s a few genes, that tell us who’s going to get cancer. But the other one, which is equally intriguing, is delineating patterns of dozens, hundreds, or thousands of genes, and using the patterns themselves as a biomarker."

Artificial neural networks are one of the tools Meltzer and colleagues use to harness the power of this vast genomic data. In a computer-training method called back-propagation, the computer is fed information on gene expression in known cases of varying conditions—for example, Barrett’s esophagus versus esophageal cancer. Starting out with random "guesses" as to which condition the case represents, the computer gradually learns to distinguish them as the researchers correct its mistakes. This process creates a complex algorithm with genes assigned unique numerical weights according to their predictive value.

"Then, to make sure we’ve succeeded, we take a test set of say, 10 to 20 new patients and ask the neural network to tell us what they are," Meltzer explained. "If it gets them all right, we know it’s working pretty well." In a proof-of-principle study, reported in the June 15, 2002, issue of Cancer Research, the neural network used 160 genes (from an original set of 8064) to correctly distinguish Barrett’s esophagus from cancer.

The next step is to examine gene expression in preserved tissues from patients whose subsequent history—whether they went on to develop cancer or not—is known. This data will be used to create neural networks that may be able to predict early on whether someone is at risk. Then, patients at high risk could be closely monitored through endoscopy—perhaps someday, even treated prophylactically—whereas those at low risk can be spared unnecessary invasive procedures.

Jeffrey Marks, Ph.D., leads another biomarkers development laboratory that is taking a genomic approach. His group at Duke University Medical Center in Durham, N.C., is collaborating with Abbott Laboratories, Abbott Park, Ill., to bring breast cancer biomarkers to the clinic. Mining a gene expression database created by Incyte Pharmaceuticals, Palo Alto, Calif., Marks has concentrated on finding ways to track down metastatic breast cancer cells in the blood before the primary tumor is detectable.

"We don’t necessarily need a cancer marker to pick up those cells," Marks said. "But we need something that is breast-specific. So if we look in the blood and we see expression of a breast cell marker, that’s a good indication that there is a breast cell where it shouldn’t be, and a tipoff that there might be a cancer." The team has found two markers whose expression is "highly restricted to breast epithelial cells," he said. One is a member of the uteroglobin family, and the other is a previously undescribed mucin gene.

"Using a very sensitive and specific polymerase chain reaction-based assay, we’re comparing the ability of these markers, plus a couple of established markers, to distinguish blood samples from breast cancer patients versus those with benign breast disease or no breast disease," he said.

Epigenomics

Epigenetic modulations of gene expression, such as acetylation and methylation, act not by deleting or mutating the gene but by adding a chemical group onto DNA.

"Hypermethylation has been identified as a way a number of important tumor suppressor genes are silenced," said Melvyn Tockman, M.D., Ph.D., who chairs the EDRN’s lung cancer collaborative group and heads the biomarkers development laboratory at the University of South Florida’s H. Lee Moffitt Cancer Center in Tampa. "So we’ve been looking at ways of using this as a potential marker."



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Dr. Melvyn Tockman

 
EDRN laboratories, including Tockman’s, Sidransky’s at Hopkins, and three others, have formed a working group focused on hypermethylation as a marker for lung cancer. The group’s first task has been to determine which of several ways to evaluate methylation status works best, so that all researchers would be using the same standard. In the course of this investigation, however, the group found that the methylation assays were more sensitive than the methods for sequencing the genes themselves. In the absence of a sensitive standard for detecting cytosine methylation, it is uncertain whether a positive assay is truly or falsely positive. This has temporarily sidetracked the development of hypermethylation as a lung cancer biomarker, but Tockman said the fact that a consortium of scientists is tackling the problem jointly will smooth the way for the future.

"When we find something that’s potentially very exciting, like the hypermethylation assay, it’s critical to understand how to validate it, to assure ourselves that what we are finding is clinically relevant," he said.

Proteomics

One of the principal tools of proteomics is surface-enhanced laser desorption/ionization (SELDI). Investigators using SELDI generate fingerprint maps of proteins expressed in normal tissue, premalignant conditions, or cancer. The resulting patterns of protein expression combined with bioinformatics tools are being applied for early detection. This method is also used to detect and analyze individual proteins. Daniel Cramer, M.D., heads an EDRN clinical and epidemiological center at Brigham and Women’s Hospital in Boston, which is focused on ovarian cancer detection.

"Our main interest is in using SELDI as a discovery tool ... to see whether some of the protein peaks clearly distinguish cases from controls, and then identify what those markers really are" through immune-based assays. One potential ovarian cancer biomarker that Cramer and co-workers have identified this way is alpha-haptoglobin, for which they have developed a serum ELISA assay that can sort cases from controls.

"When we pulled out this protein and sequenced it, we discovered there had been several things written about it for ovarian cancer," Cramer said, but this line of research apparently had been dropped. "It shows that you may end up rediscovering something whose value had not been appreciated from the previous work."

Srivastava concluded that "in a very short time, just about 3 years, EDRN has come up with a lot of good biomarkers. Hopefully, if they stand up to our rigorous validation testing, they will be very useful both for clinical trials and for early diagnosis."

The EDRN’s second report (available at http://www3.cancer.gov/prevention/cbrg/edrn/), released in October, discusses the network’s progress in detail.



             
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