Conversation

How is this useful? Well, take an ecological model of pests and pesticides. Let’s say you’re a farmer. You hate moths. You dump a huge amount of the best pesticides on your field, killing as many moths as possible. Congratulations: you’ve just selected for resistance.

Do this a couple hundred times, with a dozen different pesticides, and you get the diamondback moth, which is basically resistant to everything. So: not a good idea.

What you do instead is you try and spray pesticides over 3/4 of your fields, and you keep 1/4 of your field free of pesticides. So you cut down on the large bulk of your pests, but you leave enough of the non-resistant moths to let them compete against the resistant moths.

The intuition here is that resistance incurs a cost, so if you let enough non-resistant moths survive, they should outcompete the resistant moths when you have a non-pesticide-filled field.

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So what does this have to do with cancer? Well, imagine that the cancer cells are the moths and the drugs you use in chemotherapy are your pesticides. What if you used the same idea? That is, you try not to pump the body full of chemo until you get resistance, but taper it?

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“And what we found was … if you had 3 cycles (of chemotherapy), at that sweet spot, the resistant cells progressively went to extinction.” Of course, the next logical idea Dr Gatenby had was, hmm, what if you went and looked at extinction models and tried to replicate that?

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So of course that’s what he did. I mean, I think the really interesting thing here is just the modelling approach. Steal techniques from ecological modelling and see what it can tell you about possible treatment approaches.

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