New ASU research aims to find a way to stay a step ahead of common cancers

There have been more than 2 million new cancer diagnoses in the U.S. so far this year, according to the National Cancer Institute. And for a number of those patients, that cancer will either spread or stop responding to treatment.

But new research being done by scientists at Arizona State University, Mayo Clinic, MIT, Stanford and other institutions is hoping to prevent that from happening — and to stay a step ahead of those tumors.

Ken Buetow is a professor at ASU with the Center for Evolution and Medicine in the School of Complex Adaptive Systems; he’s also with the School of Life Sciences.

He says the research is starting with a focus on three metastatic cancers: breast, colon and lung, but is open to exploring all cancers. Buetow spoke more with The Show about this work. They started with how novel the approach is that he’s trying to take to help cancer patients — is this a new approach or more of a new technology?

Full conversation

KEN BUETOW: It's actually a new approach enabled by new technology. The technology is somewhat incremental from what we've had in the past. Over the last two decades, we've had the ability to increasingly do finer resolution characterization, at the molecular level of cancer, but what is new, there, there's something genuinely new here, and that's that what we're taking into account is not just what does the tumor look like today but actually, its capacity to evolve and adapt to the treatments that we are, that we're going to be administering.

MARK BRODIE: So are you trying to deal with the issue of the tumor changing based on the treatment that the patient is getting? Like if a patient is getting chemotherapy or radiation or something, that might cause the tumor to change, which might make it less that the treatment less effective.

BUETOW: Absolutely. And one of the things that we've come to understand, as we've done more and more genomic characterization of tumors, is that tumors are really complex and complicated. They are not static entities, they actually change dramatically over time and in response to the environment.

Part of that environment, as you might guess, would be when we treat cancer. As you're more than aware, we've made great strides, in novel, targeted therapeutics directed at cancer, but nevertheless, still all too commonly, cancer comes back, and oftentimes it comes back, you know, very, very aggressively. What we're attempting to do in this new program is to try to anticipate how cancer responds to those therapies and then direct our next iteration of therapy at how it evaded the particular therapy it received.

BRODIE: Would this have more utility during maybe that first round of treatment, or is it more targeted at, as you reference, when a tumor in many cases, unfortunately, inevitably comes back after having been shrunk or or entirely gotten rid of the first time?

BUETOW: Well, we're hoping to actually make a combination of both. Part of the, so for sure, it'll have application at the recurrence of cancer, and that's, in particular, the whole thrust of this research program is to look at how the cancer recurs, and then to target that mechanisms by how it was able to recur.

But part of what we're hoping is once we identify this portfolio resistance mechanisms, our initial treatments can explore and actually target those resistance pathways at the same time that we target the original targeted pathways that we are examining.

BRODIE: Is it possible that that kind of approach might preclude the tumor from coming back?

BUETOW: I mean, that certainly is the the great promissory note is that if we come up with more effective combinatorial therapy, recognizing that the dynamic nature of the tumor and trying to anticipate, to me, I'd like use this sort of metaphor here of, of, and I realize sports metaphors overused, but the, the, the great Gretzky always said that his success as a hockey player was not to skate to where the puck is, but where the puck is going to be to be.

So our idea would be is if we can see where the tumor is going to go, we not only treat what we see today, we not only go to where the puck is, but we actually start treating where the tumor might go.

BRODIE: How personalized does this need to be? I mean, for example, if you have two patients with the same kind of cancer and tumors with similar characteristics who are getting similar treatments, might this work for both of them, or is it so unique to each individual patient that everybody who has this would need their own specific sort of mechanism for getting it?

BUETOW: Well, what we're hoping is that we will identify some commonalities, some common pathways or processes that the cancer uses, but almost assuredly, we're gonna have to have precision personalized medicines. So this is going to require us to be looking at each individual's tumor to see what their unique characteristics are, because I, I don't think in advance we'll be able to identify what might be an efficacious treatment.

BRODIE: Well, it sounds like going back to your sports metaphor, it sounds like the real challenge is figuring out where the puck is going and trying to get there first.

BUETOW: Indeed, and, and I think that's what's new in this approach of this broader program and credit to the program's organizers to recognize that there is, we're now at an inflection point, this unique opportunity where we actually have the technology, the modeling approaches. We use artificial intelligence in order to interpret through these huge volumes of data to try to find out where the puck is going, and we're hoping that, you know, with these new approaches, we're going to actually save lives, really will be able to make a difference both in preventing the recurrence and when recurrence occurs having a much more efficacious outcome.

BRODIE: And is the expectation that that next iteration of treatment would look kind of like what the treatments we imagine today, just like maybe a different recipe for chemotherapy or a different dose of radiation? Or is there some other kind of treatment that might be on the offing?

BUETOW: So actually a combination of both. Part of what we're hoping to be able to do is more effectively target the existing therapies. So in other words, rather than just having to shoot into the dark and say, well, this could work in this patient, we'll say, well, based on their mechanisms here, this should work in this patient.

So rather than could — should. So we're hoping a more, you know, be effective at targeting the existing therapies, but what we really think is one of those sort of mind expanding opportunity here is as we identify these mechanisms of resistance, it may, it may open up all sorts of new doors for new therapies that don't exist today.

We say, oh well, the reason these folks aren't responding is because of this. If we had a therapy that could target that, then we may be able to have more progress. So I think it'll be a combination of more effective delivery of existing approved therapies, but actually giving us clues as to what could be the next set of therapies.

BRODIE: And are you imagining that that could include different modes of treatment, like it could be in pill form or an injection or something as opposed to, you know, having the way that cancer patients get their treatment now?

BUETOW: Oh yeah, I think the sky is the limit, and I don't, I think we've only just begun to scratch the surface of what might be the potential mechanisms that we could be utilizing.