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In a First for Biosimulation, GNS Partners with Crowley Center to Model Cancer Trial Data

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Gene Network Sciences said last week that it has entered into a partnership with the Mary Crowley Medical Research Center that will mark the first use of biosimulation technology in cancer clinical trials.

GNS will use its computational modeling technology to identify biomarkers associated with response in cancer clinical trials — an approach that the Crowley Center is betting will prove more powerful than traditional bioinformatics analysis of microarray and proteomic data.

John Nemunaitis, executive medical director at the Crowley Center, said that the clinic began recruiting patients for genomic and proteomic profiling several months ago, but "that technology has so many variables that it's very difficult to sort out or prioritize the information that you get." The typical output from such analyses — lists of differentially expressed genes or proteins — offer "too many possibilities to actually develop any kind of treatment plan," he added.

The Crowley Center decided to partner with GNS so that the biosimulation firm could "to receive all the possible combinations of how these genes and proteins could interact with each other in normal cells and in cancer cells and … sort out what's unique about the cancer cells."

Nemunaitis said the Crowley Center expects GNS to "identify the proteins that are most commonly used by the cancer — those proteins that are the most highly connected. We want to look for the major nodes of activity, and that's going to be a factor that we will use to base a therapeutic on."


"That's the whole idea —
to begin to develop what
I would call a more acceptable rationale for treating cancer, one that involves an assessment of that individual patient based on molecular information."


Nemunaitis said that the Crowley Center is developing small-molecule therapies to target specific pathways involved in cancer, as well as siRNA-based therapeutics. Between the two, he said, "We've developed therapies that can inhibit any gene from being made, and we've got to know which gene to target, so that's what this project is all about."

Colin Hill, CEO of GNS, said that the "end goal" of the project will be identifying biomarkers that separate responders from non-responders in a range of Phase I and Phase II clinical trials. Hill said that the partnership is not focused on any particular type of cancer or class of drug. The center will provide GNS with gene expression data from the Affymetrix GeneChip platform for around 30 patients per year — out of a total of 350 patients that it treats annually — and GNS will develop "causal models" of patient response for each trial.

GNS has access to data for four patients so far, Nemunaitis said. "They've developed the mechanisms that they feel will be appropriate for the analysis, and we're waiting for those results."

Ultimately, he said, the Crowley Center would like to use computational modeling to drive more personalized approaches to cancer treatment.

"That's the whole idea — to begin to develop what I would call a more acceptable rationale for treating cancer, one that involves an assessment of that individual patient based on molecular information," he said, "because every one of our cancers have a different molecular profile, and it stands to reason that [they] would also demand a different treatment for best effectiveness."

A New Market for Biosimulation?

Hill said the Crowley Center partnership will be the first "large-scale" project in which GNS will use gene expression data from patient tumor samples to reconstruct molecular pathways that influence cancer drug response.

To date, GNS has primarily used its modeling platform in preclinical research collaborations with pharmaceutical firms, and has relied on data from human cell lines and mouse tissue as input for its network inference technology. "We've modeled patient data before, but not in a big way," Hill said.

While the agreement nudges biosimulation into the realm of personalized medicine, Hill stressed that "the idea of an individual computational model for each patient would be extremely challenging," and perhaps not even feasible with current technology. Instead, he said, the company hopes to use its technology to break patient groups into subpopulations based on their response to certain compounds, and from there dig a bit deeper to identify specific mechanisms responsible for "linking a drug to a particular end point."

Financial details of the agreement were not provided, but GNS said that it will be compensated on a "per-patient basis."

Hill is confident that computational modeling will have clear advantages over bioinformatics analysis of microarray or proteomics data, because it will generate "more accurate biomarkers" for classifying patient populations.

The project is not without its risks, however. Hill said that it is still unclear whether the company will have access to enough data to generate statistically valid models. While GNS has a good handle on its data requirements for preclinical research, the clinical realm is new territory, he noted. Ideally, "the more data, the smaller error bars you have," he said, but as for whether 30 patients per year will be enough, "we'll find out." So far, he said, GNS has performed some "theoretical tests" on synthetic data, which indicate that it is on the right track, but that has yet to be proven on real patient data.

Another unknown, Hill said, is the effect of patient variability on network modeling. "Clearly this is going to evolve," he said. "We can't take what worked in preclinical and apply it directly here — it's not a straight shot."

Nevertheless, he stressed, "this is the first time that these kinds of advanced computational methods have been applied to clinical data." While acknowledging that some have "questioned how well it will work," he said, "I'm confident that it will be better than what they're using now." The company's ultimate goal in the project, he said, is to determine whether biosimulation "can impact patient care in real time."

Nemunaitis agreed that there are no guarantees that the computational modeling approach will pay off. "You never know," he said, "but we've never been able to look at cancer from the inside, and that's what this is doing. I would be hopeful that more of the world would be open to doing that — molecular profiling of cancer cells. Some are doing it, but very few are, and it's certainly not being done routinely." He added that the Crowley Center is "beginning to activate this as a program in all of the patients who are treated in our clinic."

Hill noted that despite the increased use of molecular profiling technology in cancer research, cancer treatment is still largely a "trial-and-error" process that has left "a huge gap between what can be done and what's done in practice." He said that recent trends may alter that situation, however, as physicians are under pressure from insurers to find patient populations that will respond to specific drugs.

"Insurers will stop [paying] if they're only getting 10-15 percent efficacy for a treatment that costs hundreds of thousands of dollars," he said.

Hill said that GNS is "in discussions" with other cancer centers about the possibility of using its modeling technology to classify patients in additional clinical trials, but again stressed that the collaboration with the Crowley Center is "still experimental."

The Crowley Center's mission is also a bit different from that of other cancer clinics. The center, based at Baylor University Medical Center in Dallas, is charged with "exploring investigational vaccine, gene, and cellular therapies with the goal of expanding treatment options for all cancer patients," according to its website. This focus on experimental treatments makes the Crowley Center a bit more adventurous when it comes to adopting unproven methods, Hill said.

Neither Hill nor Nemunaitis provided a timeline for when the project may begin to bear fruit, but Nemunaitis was confident that the Crowley Center should see some benefit from the approach. "We think that by combining the efforts of Gene Network Sciences and some of the other tools that we use to focus on the relevant proteins … we have a cutting-edge start over other programs on knowing how to target those proteins that are most relevant, and we think that can be used to take right into individualized care."

— Bernadette Toner ([email protected])

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