KIYATEC’s Matthew Gevaert talks about the Greenville company’s ability to predict chemotherapy success
By Chris Haire
Cancer is a cruel and savage disease, one that can destroy a seemingly healthy person in months or put another on an uncertain path of treatment, remission and fear. And as a result of the radiation and chemotherapy, treatment itself can be its own form of torture.
While the scientific community has recently made significant advances in combating cancer—especially in the area of immuno-oncology—the days of a cure are still a ways off. Regardless, doctors have plenty of reason to be hopeful.
Consider the case of Greenville-based KIYATEC, Inc., a Clemson University biotech spinoff founded in 2005 by Matthew Gevaert and David Orr.
Currently, Gevaert and the KIYATEC team are developing techniques that will help healthcare providers determine which chemotherapy drugs will be the most effective on individual ovarian cancer patients. The tool at their disposal: KIYATEC’s Ex Vivo 3D Cell Culture technology (EV3D).
EV3D is an innovative new approach that allows researchers to take a patient’s cancer cells and place them in a three-dimensional cell culture, an environment which is more like the human body than what is found in a flat petri dish. Scientists then use these cell cultures to determine which chemotherapy drugs are the most effective, on a person-by-person basis.
And if an August study in Scientific Reports is any indication, EV3D appears to be a cancer-treatment game-changer: the EV3D test correctly predicted which drugs would work, and on whom, with 89% accuracy.
Greenville Business Magazine recently had the opportunity to talk with Gevaert about EV3D, what the latest study suggests about the effectiveness of this new tool, working out of the company’s home base at the Prisma Health Upstate main campus, and reasons for optimism in the fight against this dreaded disease.
The following is a distillation of what was discussed; it has been edited for clarity and length.
The Problem with Chemo
If you have experience with anybody who has cancer, you probably know this yourself; these are sick people. They get drugs and the drugs are typically toxic, so it makes them even sicker. And then sometimes, unfortunately too often, down the road they find out the drug doesn’t even work right—so they took a drug that made them sicker—and then Plan B is going to dig them out of a deeper hole to restore their health, and by the way, Plan B is even less likely to work than Plan A was.
So you see where that quickly gets us in a situation that we all would really like to avoid, and that's really the big problem that we're solving, which is, today, there's no way of knowing in advance whether the drugs will work or not unless we use it in the patient and see.
Visualizing a Three-Dimensional Cell Culture
My mom, when she would make Jell-O, she put fruit in it. So a little piece of fruit in Jell-O.
You have a cube of that.
Make a little piece of orange into a cell in your mind and then make the gelatin, which is the stuff that is making the cube, make that from collagen. Now you have a three-dimensional cell culture. It's a living cell in there, and it's growing in a protein, frankly, that's a constituent of your tissue.
That’s a three-dimensional cell culture. That’s how we grow cells.
The Problem With Petri Dishes
Take your clenched fist and then put it on the desk in front of you and then spread it out. Make it like a pancake and have it stick to that desk. That desk in my model is the bottom of a petri dish, a flat surface, and that cell is now a pancaked cell that is stuck to the bottom. That's a two-dimensional cell culture; that's the status quo that's been going on since the 1950s.
So the bottom line is: data from cells grown in 2D is not accurate to the cells in your body when it comes to questions about cancer drugs. Data from our 3D [culture] means that the cells in our 3D very much appear like they're tracking parallel to the live cells in the patient's body. And we can say that confidently with newly diagnosed ovarian cancer.
What we're really doing is on a patient-specific basis—so line up 44 women who all who have ovarian cancer, and then for each one sequentially we're gonna make the call [will this drug work or not]. Yes, she will respond. Yes, she will respond. No, she won't respond. Yes, she will respond.
We're not predicting across 44 women … we are literally predicting each woman independently.
The first 44 times we made yes-no predictions on will it work, which is predicting an event that'll happen up to one year in the future, we were right 39 out of 44 times, which is a really amazing percentage: 89 percent.
Think about your own life; think about a complex problem that you could predict 44 times that you'll get the result in one year from today. How many of those problems can you predict with about 90 percent accuracy?
Even More Promising
This is a remarkable achievement to be able to predict the future, a future complex outcome with that kind of accuracy. So that’s really exciting. That gives us a strong signal that, hey, you’ve got something here that is working really well.
Even in a way that is as exciting, within a subset of patients...within a subset of 29 of them, we were perfect. So 29 for 29, 100% accurate in that subset. That’s what we found and that’s why it’s exciting.
Being Based at Prisma’s Main Upstate Campus
We are in the trenches with the doctors and the patients that are receiving cancer therapies in this location.
They've done something very different in this hospital than they have about anywhere else we know, which is to say we're going to welcome some private-sector companies to do research, to problem solve in a way that that nobody else can do, because of the partnership.
They are doing something really unique.
Just by the nature of what they do, doctors are really busy, and they're highly scheduled, and so it's one thing to schedule a piece of their time, which is often difficult to do, but with the convenience of hallway conversations, we can be much more effective in kind of treating those ideas and working together.
The Future of Cancer Treatment
In decades prior they were looking for simpler solutions and trying those first. So the magic bullet, or whatever I call it, it's just not there for cancer. And so these complex solutions, in my mind, they have a lot greater potential for real meaningful impact and to make this progress.
Another example of these complex solutions that are really making a difference today is immuno-oncology. So it's a sort of a new wave in drugs. And the purpose of these drugs, unlike other ones, where you took a drug and you wanted to kill your cancer cell directly, the purpose of these drugs is basically to teach the patient's immune system a trick and then let the immune cells go kill the [cancer].
So that's a complex approach, and frankly when you are doing anything with some of the immune system, it’s difficult to do, but now we sort of embrace that despite the complexity there. The potential is huge to make an impact that's not been made before.