NEW YORK (GenomeWeb) – Researchers from Moffitt Cancer Center presented data at a major medical conference this week demonstrating the ability of a multi-gene algorithm to predict whether or not a person will respond to radiation therapy, which approximately 60 percent of all cancer patients receive during the course of their illness.
At the American Society for Therapeutic Radiation and Oncology's annual meeting in San Francisco this week, Moffitt researchers discussed multiple studies on a 10-gene expression classifier, called InterveneXRT, that they believe can be used to personalize radiation therapy for patients with different tumor types. Based on the analysis of several thousand samples from different types of tumors— breast, lung, prostate, head and neck, rectal, esophageal, and glioblastoma – the researchers found a 20-fold difference in response between the most radiation-sensitive cancers and the most radiation-resistant ones.
"How you're going to use this [test] will be different based on the specific disease you're treating," Javier Torres-Roca, co-developer of the gene classifier and a member of the radiation oncology, chemical biology, and molecular medicine programs at Moffitt Cancer Center, told PGx Reporter in a telephone interview from the conference. "The use of radiation varies across disease sites."
Although radiation therapy is commonly used to treat cancer and patient responses vary widely, there are no molecular diagnostics for predicting which patients will derive benefit from treatment. It has taken Torres-Roca and Steven Eschrich, an associate member of Moffitt's biostatistics and bioinformatics department, 11 years to develop their radiation sensitivity algorithm with gauges the expression of 10 genes.
The test, according to Torres-Roca, can predict whether a cancer patient will be sensitive to radiation therapy or whether they will be resistant and may be better treated with surgery. Additionally, the researchers hope to develop the test as a tool to guide radiation dosing decisions in cancer patients. "Classically, we treat patients with uniform doses of radiation," Torres-Roca said. "But we know that uniform doses of radiation provide different tumor effects."
The 10-gene classifier was developed initially using 48 cancer cell lines from multiple tissue types. Using a systems biology approach, Torres-Roca's team narrowed the gene panel from a microarray chip containing 7,000 genes down to a set of 500. "We then used software to interconnect the genes," he explained. "We had this idea that important [functions] in biology are regulated by systems biology principles where everything is organized within networks. So, if there are genes that are good at predicting response to radiation, then they should be somehow related; they shouldn't be working alone."
Eventually, researchers further narrowed the 500-gene network down to a 10-gene hub network. "Those were the genes that were most biologically important," Torres-Roca said. "Then, we developed the algorithm based on the gene expression of those 10 hubs."
InterveneXRT has been developed with $2 million in funding, much of it from the National Cancer Institute. In April, CvergenX, Torres-Roca's startup, announced that the NCI selected InterveneXRT to be part of its clinical development assay program, a government-funded effort aiming to identify tests that advance personalized medicine approaches. Under this program, the test will undergo analytical and clinical validation specifically to predict non response to radiation therapy in rectal cancer patients.
In rectal cancer patients with stage 2 or 3 disease, radiation therapy is standard of care before surgery. However, 40 percent of patients don't benefit from treatment and end up with the same sized tumor or a larger tumor at the time of surgery. The aim of the NCI research is to gauge if the test can pick out which rectal cancer patients will not benefit from pre-surgery chemoradiation with a 90 percent negative predictive value.
The NCI will provide funding for this study and its statisticians will perform the analysis, which is slated for completion next year. Contract research organization MRIGlobal will perform the test in a CLIA certified lab on a few hundred samples provided by Moffitt and a Korean institute.
CvergenX holds the exclusive license to commercialize InterveneXRT based on the algorithm, and Torres-Roca hopes to begin marketing the test by late 2016 if the validation studies are completed on schedule. The plan, he said, is to sell InterveneXRT through a centralized lab, similar to firms like Genomics Health and Agendia that market complex, algorithm-based lab tests.
"As we develop the clinical utility of the test, we will be commercializing it disease site by disease site with a specific use," Torres-Roca said. "We're focusing on rectal cancer initially … so we can identify those patients that won't benefit from radiation therapy and they can get surgery immediately." Next, Torres-Roca and his team will research the use of the test in gastric cancer and possibly in prostate cancer.
Another market for InterveneXRT would be as a tool doctors can use to decide what dose of radiation to give a patient. "Rather than remove radiation for some patients … we think we can actually optimize dosing to reflect the tumor effect that dose will have," Torres-Roca said. "This is something that would be unique to the field of radiation."
Radiation therapy varies in cost depending on the technique being used. One study estimated that it costs $1,700 for a single treatment of conventional radiation therapy to as much as $16,000 for multiple treatments of more advanced types of the therapy. But when patients are unlikely to benefit for radiation therapy, giving them treatment exposes them to adverse events and wastes healthcare dollars.
Torres-Roca has performed analysis showing that using InterveneXRT in rectal cancer patients would save healthcare dollars. He expects to price InterveneXRT comparably with other algorithm-based molecular tests, which on average have reimbursement levels between $2,000 and $4,500. "We think delivering the wrong treatment is very expensive," he said. "That's what we're trying to solve in the world of radiation."