Colin Hill is gunning to turn raw data into key SNP discoveries.
June 10, 2008 | Personal and consumer genomics are boiling right now. Yet sifting through the data flood and connecting the dots in ways that accurately predict single nucleotide polymorphisms (SNPs)-to-outcomes remains a huge challenge. Gene Network Sciences CEO Colin Hill says flatly, “GNS is gunning to be the first group that really breaks this open, by having a scalable, supercomputer-driven automated platform that can turn that raw data into discoveries of the key SNPs driving the outcomes.”
To hit that target, GNS is pushing ahead (See “GNS Charts Unknown Biology,” Bio-IT World, October 2006). Hill recently hired Boston University biosimulation pioneer James Collins, as chief science officer. GNS has also expanded its reverse-engineering/forward simulation platform to accommodate DNA sequence data, in addition to traditional molecular data. Hill says the company doesn’t need more money, partners, or computational power simply to put the platform to work to prove its power.
Bio-IT World executive editor John Russell spoke with Hill about GNS’ progress and its ambitious plans.
Bio-IT World: How is GNS different today than it was a year and a half ago?
Hill: The technology’s become a lot more robust, more scalable. We’ve gone through rewrites of the code to make it more robust. It’s the same platform—reverse engineering, forward simulations is the core technology of the company—but it’s become faster, stronger. There are a greater number of interaction forms, which are really the building blocks that describe interactions between components, between drugs and genes, between genes and other genes, between proteins and outcomes, clinical variables.
Is GNS attacking different questions than before?
A key focus area for the company is going from “SNPs to outcomes.” We weren’t as focused on DNA sequence information [before]. We hired somebody who’s driving that effort. We’re also driving a lot of our own discovery and we’ve found some partnerships with academic groups such as the Moffitt Cancer Center in Florida and the Weill Cornell Medical School in New York. That’s enabling us to go after some of our own discoveries in addition to the big pharma and biotech collaborations.
But this SNP-to-outcome problem is a really big one. With all the progress that groups like Steve Turner’s company [Pacific Biosciences] is making and other groups like deCODE or 23andMe, the data [are] going to be there. We have a lot of information on the variations that make us all different and determine our disease progression and response to therapeutics. But we have a big problem determining which of the three million genetic variations are causative of the outcomes. That’s a very difficult computational problem that nobody has solved…
Can you build causal relationships from SNP data a priori without reference to the literature?
Yes—under certain conditions, depending on the information you have about inheritance and other information such as gene expression together with genomic variation and outcomes. Eric Schadt leads a group at Merck [Rosetta], and they’ve been gunning for this problem for some time and have certainly made some breakthroughs related to metabolic disease. GNS is gunning to be the first group that really breaks this open, by having a scalable, supercomputer-driven automated platform that can turn that raw data into discoveries of the key SNPs driving the outcomes.
Are you trying to fund platform development through R&D collaborations, while the real goal is to generate, capture, and commercialize biological IP?
You’re mainly right. We’re not planning to become a drug company. We understand where our expertise is. We think we’re the best in the world at data-driven computation in this sphere. We have no desire to try to bring on capabilities that are well outside of that, [such as] medicinal chemistry...
Everybody agrees the drug discovery industry has to change. Pharma’s in the toilet with Wall Street and everybody’s calling for gloom and doom and such. Everyone agrees there needs to be new tools to advance the state of the art. The pharma companies know this better than anybody. However, companies that have breakthrough technologies still have a hard time commercializing those technologies and capturing some of the upside from them. From an investor’s point of view a lot of these platform companies have not performed well…
Are systems biology companies naïve to think they are going to change the drug game in a significant way in a five year window?
No. I don’t think so. I honestly think it’s the technology. I think many of the approaches that dominated the early days of systems biology were off the mark. People will look back some years from now and say those approaches couldn’t have worked. There was too much unknown about biology, it was too complex, and there wasn’t enough data. I’m referring to approaches based on literature as the starting point, whether it’s assembling that information together into databases so you can visualize your molecular profiling data in this context or it’s doing the simulation models based on literature information. I think those approaches have inherent limitations.
I’ve said to many people that for a number of years GNS was misguided. [Our] approach of trying to model all of the known pathways involving cancer cell biology had its merits, certainly as an academic effort, and had some use in the commercial setting, but I think it was limited.
We first need to discover what are the key molecules driving disease progression. We have to discover what the key molecules are driving drug response, both from efficacy and the safety perspective. There’s been some recent papers from the Cancer Genome Anatomy Project and [Bert] Vogelstein’s group at Johns Hopkins showing a huge amount of heterogeneity in human tumors: lung cancer, breast cancer. Assuming we believe those results, this is telling us that something is misguided about the view that there is this canonical uber-model that controls disease progression and is going to be common to everybody.
If that’s true, what does it mean for the GNS value proposition?
… The value proposition for our partners, whether pharma partners or academic clinics, is we now have the tool. It scales with the power of IBM’s largest supercomputers that allows us to take in data from a variety of sources, heterogeneous data, and actually discover the causal regulatory models connecting either genetic perturbation or drug perturbation to the molecular entities, be they genes or proteins or metabolites, and the clinical outcomes that they’re driving.
Is most of GNS’s current work in discovery or the comparison of compounds?
That’s a very good question. I want to say it’s about half and half; there is a good mix at this point and across a variety of data types. Like I said, we’re doing our first set of projects in genomics being sequence versus molecular profiling. The team is now operating at a different level of test in terms of the number of projects they can execute on simultaneously. It’s putting the platform we’ve been investing in to the test.
This is what we were practicing for and developing and investing in for all these years and we’re starting to see it really pay off. I mean the scalability of this approach goes well beyond whatever you can do manually. Part of the beauty of this approach is it is automated. You have to do some statistical analysis of the data ahead of time. You have to understand the experimental design. Often we work with a collaborator to design the experiments in the first place. But once the data is in the right form, the process of reverse engineering the models and then doing the simulations to discover the key molecules driving outcomes, that part’s pretty fast.
Is biopharma more or less enthusiastic about this approach today?
More enthusiastic, but it’s a sober enthusiasm. It’s more specific about solving problems. For example, combo therapies in cancer. This is something that a lot of companies need to solve. They can’t run enough clinical trials to explore all the combinations with standard of care therapies with their new targeted cancer drug. So here’s an area where this kind of approach has a clear win, from single drugs applied at multiple doses in your biological system. We have a platform that can combine those drugs in two-way combinations or three-way combinations and determine the most synergistic combinations and the dose ratios needed to get to those results.
Who are some big commercial collaborators?
I can cite Pfizer and CombinatoRx. The academic partners are also important and are becoming more commercially focused these days, that’s clear. You see more and more partnerships between big pharma and these groups... The big focuses, in terms of our internal discovery, are oncology, naturally, metabolic disease, meaning diabetes, Types I and II, and Alzheimer’s.
Milestones for the next 12 to 18 months?
SNPs-to-outcomes across a few different therapeutic areas—that’s what we’re gunning for, really being able to relate SNPs to change and changes to outcomes. Essentially be able to do in an automated fashion in weeks or months what Eric Schadt at the Rosetta/Merck group did over the course of a couple years. Number two is combo therapies and oncology, to be the first to take a single drug, multiple dose, data sets and explore very quickly billions and trillions of drug and dose combinations of cancer drugs and discover the most efficacious combinations and the corresponding markers that indicate the patients that will have the strongest response.
That’s all I care about. We are doubled down on our investments in technology. We are out there buying up data, partnering to get data, and the things that were clear bottlenecks to GNS a year and a half ago, two years ago, they’re not there anymore. Could we do more with more money? Absolutely. We’d love to blow this out in a bigger way and I think the issue I’ll be dealing with over the next year, 18 months, will be when is the time to possibly pull the trigger and accelerate. It’s a bet. If we’re right—and this is the way forward—this will yield discoveries at a pace and a scale that have never been seen before.
This article appeared in Bio-IT World Magazine.
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