Fighting drug-resistant cancers with mathematical modeling

Scientists have made great strides in developing drugs to fight cancer. But some cancers have now become resistant to drug therapies. In this segment we meet researchers from the Moffitt Cancer Center in Tampa, Florida, who are using mathematical modeling to help fight drug-resistant cancers.

TRANSCRIPT

Scientists have made great strides in developing drugs to fight cancer, but some cancers have now become resistant to drug therapies.

In this segment, we meet researchers from the Moffitt Cancer Center in Tampa, Florida, who are using mathematical modeling to help fight drug-resistant cancers.

Dr. Alexander Anderson is a mathematical biologist who spent 12 years studying mathematical models in cancer at the University of Dundee in Scotland.

I moved my whole group, my family, to Moffitt because I really believed in the power of integration.

To be inside the cancer center meant I was going to be with the biologist and the clinicians that I wanted to collaborate with, and I thought IMO, then, as a truly integrated department, would drive the understanding of what mathematical models could do for cancer research.

Dr. Anderson's team of physicists, computer scientists, and mathematicians have worked with clinical and research oncologists at the Moffitt Cancer Center in Tampa for the past 8 years.

This kind of team science perspective, or integrative perspective, I believe, is really the future of science in general, but I think it's particularly the future of how we're going to manage and treat cancer.

Cancer's resistance to certain therapies have kept researchers and clinicians from being able to eradicate the disease.

My primary interest is in how cancers evolve, how you go in the transition from normal to cancer, and then how cancer cells evolve resistance when you give therapy to them.

For 50 years, oncologists have blasted tumors with as maximum a dose as possible.

What you then get is a phenomenon called competitive release, which means that you're killing all of the cells except for the ones that are resistant, and we're removing all of their competitors.

This left the resistant cells to grow unabated, as if it had magical powers.

It's just a population that evolves.

There's nothing evil about it, there is nothing about it that is magic.

It's simply obeying the rules of living systems that all living systems obey, which means we can understand it and we can manipulate it.

Through mathematical modeling, scientists can now develop ways to predict the growth of a tumor.

We have normal cells in the background there in gray, we have the tumor in the middle in green, and these are little, white blood vessels.

And what we're interested in understanding is how does that tumor grow and invade into this tissue, and evolve.

So I'm going to show you a little movie here that shows the evolution of the cancer, and what you see is initially it becomes a little bit more acidic as resistant, and you have this green rim of these metabolically normal cells, but inside, where it's starved of oxygen, there are these purplish cells.

And eventually some of these purple-pink cells breach that nice metabolic boundary and rapidly invade the surrounding tissue.

Anderson and Gatenby have worked with the oncologists at Moffitt to create new strategies for fighting cancer.

Instead of trying to kill all of the cancer cells when you know you can't do that, the goal is to manage it.

So, in the patients, what we try to do is to keep their cancer under control, not using the maximum dose possible, but the minimum dose necessary.

What we then do is we tend to use evolutionary dynamics to control the tumor rather than letting the tumor use evolution to beat our therapy.

Developing new research and treatment plans didn't come easy for the team.

It took a while for the mathematicians to become grounded in the reality of biology and oncology, and it took a long time for the biologists and oncologists to recognize that the mathematicians were offering them insights that they wouldn't get otherwise.

In the end, out of it comes what seems like a feasible trial that you could try.

And then it's very scary to start seeing patients being treated, not entirely knowing what's going to happen.

One of those patients for the clinical trial is Robert Butler, who was first diagnosed for prostate cancer in 2008.

I came along to the Moffitt, and I had 8 weeks of radiation treatment.

And in 2016, Robert became a candidate for the new trial on managing cancer in his body.

He has received three rounds of treatment over the past year.

So clearly, if you only show these cancer cells once in a while, this integer, instead of continuously, it seems to the layman that that seems a pretty good idea to at least delay the cancer cells from seeing that this integer is a pretty deadly thing now to combat it.

So I was very pleased to go on it.

There was never any doubt that I would do so.

Because of the complex nature of cancer, math has been shown to be a critical tool in managing its treatment.

One of the key features of mathematics is that with great simplicity, you can produce amazing complexity.

And so that ability of mathematics is something, I think, that is not well-understood, but is something that we should really cherish and be proud of.

I'm very sensitive to trying to do the best for patients, and I'm always amazed by how remarkable they are.

I'm happy to be able to make whatever contribution that I can.

And I see that, really, I believe, and one of the reasons why I came to Moffitt, is that I think that is the future of how we're going to treat cancer.

We're going to have a mathematical model for every patient's cancer, and that will be how we decide what to treat, when to treat, and how long the treatment should be.