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Cancer cells: Sensitive, resistant, competitive


In some ways, this feeling of sheer futility compounds the tragedy of losing people to cancer. Take Raj, for example: every time he went through some treatment or test, there was some other distressing development to tackle. Eventually, he himself understood the writing on the wall and chose home hospice care.

Cancer is like that. Many patients have noticed that chemotherapy seems to lose potency after the first time it is used. So you have to try another “cocktail” of drugs for the next round of chemotherapy, and that one loses potency too, and on we go on this carousel of therapies with hope only that the next round will produce the miracle you and your loved ones are yearning for.

Only, the miracle stays stubbornly out of reach. What’s going on here? Let’s say you have a brain tumour. You blast it with a dose of chemotherapy. You feel better, but a few months later, the tumour is back, and this time, the same chemotherapy is far less effective than before. Why?

One part of the answer goes like this. A given dose of chemotherapy kills only a certain percentage of the cancer cells in the tumour you’re attacking. So, each time you apply that dose, you are actually killing less and less cells. That is, you never actually eliminate all the dangerous cells. Result: the disease can return and metastasize.

Another part of the answer goes like this. The tumour is, of course, made up of cancerous cells. Some of those are “sensitive” to chemotherapy, meaning that the treatment will kill them. But some of them are “resistant”, in the sense that they will survive the chemotherapy assault. So, when the treatment is done, what’s left of the tumour? A bunch of cells, most of which are resistant. Result: next time you do chemotherapy, it has pretty much no effect on the tumour.

All this is, by the way, new to me. I learned about it because of some ongoing research by a team led by a professor at the University of Southern California — but not a team of microbiologists or medical specialists. What intrigued me about this effort is that these scientists are from fields like mathematics, physics and engineering, and they seek to apply techniques from those disciplines to the battle against cancer.

Part of the research is spelled out in a paper the professor, Paul Newton, and his colleagues Jeffrey West and Yongqian Ma wrote (“Capitalizing on competition: An evolutionary model of competitive release in metastatic castration resistant prostate cancer treatment”, Journal of Theoretical Biology, 23 July 2018). The idea of competition is important here, as this sentence from their introduction explains:

“When two (or more) sub-species compete for the same resources, with one species dominating the other, if the dominant species is removed, this can provide the needed release from competition that can allow the less dominant species to flourish.”

Now, this might remind you of what happens, for example, with predators and their prey. Let’s say a certain forest has a population of foxes and one of rabbits. The foxes eat the rabbits. Since that’s not good news for the rabbits, they might confine themselves to hard-to-reach burrows on the edge of the forest, say. But along come some fox-hunting men with their dogs, and they wipe out the foxes. Suddenly, there are rabbits hopping about with no need to hide, their population exploding because nobody’s eating them.

But wait: it’s not really that the foxes and rabbits are in “competition” for the same resources. Whereas competition is fundamental to the work this paper describes. Newton et al cite earlier research with two species of barnacles. No preying there, but one was just “more dominant” than the other in competing for resources. Remove that one, and the less dominant barnacle “happily” flourished. This is what’s known as “competitive release”.

What does this have to do with cancer cells? Remember, the tumour we want to treat is made up of sensitive and resistant cells. Both kinds are competing for resources from the body they occupy, but the resistant cells are “less fit” than sensitive ones. Why? Because there is a “cost of resistance” they must pay just for being resistant. They use “energy and resources in order to maintain their resistance”, Newton explained to me by email — energy and resources that their sensitive competitors are using instead to reproduce faster than they themselves can. So the sensitive cells tend to spread through the tumour, pushing out the weaker resistant cells.

Along comes a bout of chemotherapy, which wipes out some large fraction of the sensitive cells. It shrinks the tumour, but this competitive release means the shrunken tumour is dominated by resistant cells. As the paper notes, “the development of chemo-therapeutic resistance is now thought largely to be a consequence of the evolutionary mechanism of competitive release of pre-existing resistant cells”, as a result of chemotherapy.

But along come these researchers trained in mathematics and the like. They ask: can we turn that competition to our advantage? Is there a way to manage the competition so that healthy cells — the non-cancer ones — “win”, rather than either resistant or sensitive cancer cells? To answer that, they use game theory, in particular the classic “prisoners’ dilemma”. In its simplest form, authorities interrogate two prisoners, who cannot communicate with each other, about a crime. Given the incentives on offer—reduced prison terms, freedom, whatever—what are the individual choices they make? If they cooperate, they will most likely minimize their collective punishment. But their separate individual incentives persuade them to instead defect from this consensus, which maximizes their collective punishment. Any move one makes is dependent on her perception of what the other might make. This explains, for example, the tragedy of the commons. Cooperation will clearly allow a common resource to last and benefit all, but each individual is tempted to consume as much as she can as fast as she can, which finishes off the resource.

In Newton’s team’s models, healthy cells are the cooperators while cancer cells are defectors. There is a “decision matrix” that spells out how they react to each other. Without chemotherapy, the defectors will win. (Cancer does that—it tends to win). But what if we can tweak the incentives in the decision matrix to drive the dilemma towards a more desirable result? Chemotherapy is an incentive: by retarding the growth of sensitive cancer cells, the healthy cooperators can flourish. Each subsequent chemotherapy treatment is modelled by changing the terms in the decision matrix. Yet, the tweaking has to also ensure that we reduce the dominance of the sensitive cells over the resistant ones, so that we are not left with a tumour that is resistant to chemotherapy.

Thus the “adaptive therapy” that Newton, West and some other colleagues write about in a more recent paper (“Towards Multidrug Adaptive Therapy”, Cancer Research, 16 January 2020). Do a round of chemotherapy, monitor the results, tweak the decision matrix and thus “adapt” the therapy, do another round, and keep at it like this. Done right, this will allow “a significant population of treatment-sensitive cells to survive”, and this “suppress[es] proliferation of the less-fit resistant populations.” In effect, we want a tumour that remains vulnerable to repeated chemotherapy, but whose cells are unable to overwhelm the healthy cells in the patient’s body. This is what the team’s prisoners’ dilemma model seeks to achieve.

Of course, there are issues to tackle before this becomes a practical way to treat cancer. Regular monitoring is one: you can’t be extracting tissue samples from your patient for biopsies after every round of chemotherapy. There’s also the sobering realization that we are not really killing the disease, but instead managing its effect on a patient. That’s not necessarily a bad thing, but it may need a change in our mindset towards cancer and disease more generally. We may have eradicated smallpox; we may have to manage cancer.

Still, the potential payoff of this game-theoretic approach to cancer treatment is huge. Besides, I have to admit that the mental image of cancer cells in constant competition is a deeply satisfying one.

Once a computer scientist, Dilip D’Souza now lives in Mumbai and writes for his dinners. His Twitter handle is @DeathEndsFun

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