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Scientific Method / Science & Exploration

Genetic computing used to kill specific cancer cells

Researchers have crafted a genetic circuit that can pick out one specific type …

Synthetic biological circuitry has been in the news a lot lately. These circuits can be quite complex and sophisticated, but compared to their silicon counterparts, they are quite slow. So why are people working on them? As we've noted in our past coverage, they have one key advantage: they can integrate with the processes that go on inside of cells. Today's issue of Science contains a dramatic demonstration of just how powerful this can be, as researchers have produced a construct that selectively kills a specific type of cancer cell.

Before the potential hype of that last sentence overwhelms you, let's make a few things clear. The authors were only testing their constructs in different types of cancer cells—they never checked whether it would kill cancer cells but leave normal ones untouched. It is also selective, but not exclusive; some of the targeted cells survived, and a few of the ones that weren't targeted ended up dead. To add to the list of issues, the killing mechanism didn't even work in most of the cells they looked at, and there's no obvious way of safely getting any of the DNA involved in this system into the cells of a human body. So, this isn't the cure for cancer.

Disclaimers out of the way, what is it? A rather clever proof of principle.

The fundamental idea behind the work is that different cell types are different because they express distinct combinations of genes and regulatory RNA. Thus, a cancer cell is different from a normal one, and cancer cells of different origins are all distinct as well. If you look at every RNA inside each of these cells, you can notice consistent patterns that will allow you to distinguish them. You can think of this as each cell having its own RNA fingerprint.

The authors took a collection of six cancer cell lines and looked at a set of regulatory RNAs, which control the levels and expression of other RNAs through processes like RNA interference. Based on the differences between the expression of regulatory RNAs, they identified a half-dozen regulatory RNAs that created a distinct fingerprint in one specific cancer cell line called HeLa. Some of these were present at high levels in HeLa cells, others largely absent.

This creates a bit of a challenge, since regulatory RNAs are generally best at shutting genes off. So, the authors set up a bit of DNA circuitry that handled both positive and negative inputs and fed them into a single on/off output. This is easiest to understand by working backwards from the output.

The authors used a single messenger RNA as the output. For test experiments, the messenger RNA encoded a fluorescent protein to make the output easy to read. In the final experiments, the output was a protein that can induce the cancer cells to commit a sort of orderly suicide called apoptosis. (The protein didn't work in all of the cells they tested, which is one of the reasons that this isn't anywhere close to being a general cancer therapy.) The goal was to keep the output off in most cells, but on in HeLa cells.

Regulatory RNAs act by base pairing to binding sites on messenger RNAs and shutting them off. So, it's easy to take advantage of this to register inputs from the regulatory RNAs that aren't present in HeLa cells—you just stick the binding sites on the messenger RNA. Since they're not present in HeLa cells, the messenger RNA should be made into protein there. Other cells are likely to have one of these RNAs around, which will help to shut the messenger off.

Handling the opposite situation, where the regulatory RNA is present at high levels in HeLa cells, is a bit more complicated. From the perspective of designing circuity, it's pretty easy: just flip the bit with the equivalent of a NOT circuit. Doing that with DNA is a bit harder, but the authors did this with a double negative: any regulatory RNAs that were high in HeLa cells would shut off a repressor. That repressor, in turn, blocks production of the output. The net result of the double negative is that, when these RNAs are high, the output gene gets made into a messenger RNA.

In testing various pieces of the circuitry, the authors found that some of them were a bit leaky, in that they weren't sufficiently sensitive to the differences between cell types. The authors responded by tweaking the system a bit, but they kept its general structure intact. As they started combining individual RNA sensors, however, they found that the discriminatory power went up: "The combined effect of all three HeLa-low sensors is generally stronger than a simple prediction according to individually measured miRNA activities."

With the entire system in place, using a fluorescent output protein showed that the sensor was good at picking out HeLa cells. It wasn't perfect; some of the cancer cell lines expressed the output as well, although much less frequently, and a few of the HeLa cells didn't. The same pattern was repeated when the output gene was swapped from one that would cause the cells to commit suicide—it mostly killed HeLa cells, but didn't kill them all, and took out a few innocent bystanders. Of course, the same thing can be said about most traditional chemotherapies.

Not a cure, yet

Which gets us back to curing cancer. Right now, this won't do it. It requires five separate DNA constructs; even if a few of those could be combined, that's still more DNA than we can package into a construct that we could easily put inside cells. And we'd be faced with the fact that we don't have any method that consistently gets DNA inside all of the target cells in the human body, which would drop its efficiency further. So, it's not even clear that this general approach could be turned into a therapy.

There are also questions about what the experimental setup tells us about identifying cancer cells. Discriminating between cancerous and normal cells might be easier than discriminating among cancer cells, as the research done here indicates. But, at the same time, the cells used in these experiments have been in culture for decades, and probably don't look much like the ones that originally came out of a patient's body.

The setup, however, does tell us a lot about synthetic biological circuitry. Just as its proponents have promised, it is possible to take the biological equivalent of binary circuitry—like the bit flip described above—and integrate it into the normal cellular machinery, tuning an output to the biochemical state of the cell. And there are all sorts of potential uses for that.

Science, 2011. DOI: 10.1126/science.1205527  (About DOIs).

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