Cancer?

Every cell in your body is controlled by a network of molecules that coordinate all of

the cell's basic functions.

This network maintains a steady pattern of signals that can respond to changes in the

environment.

Cancer works by hijacking this network, forcing it to focus on one thing: growth.

A cell with cancer will keep dividing under any circumstances — well beyond the point

when a healthy cell would self-destruct.

That's what makes cancer so hard to treat: they are your own body's cells, but reprogrammed

to multiply without limits.

The first drugs that were developed to treat cancer were aimed at this growth behavior.

Cytotoxic chemotherapy drugs wipe out any cells that multiply too quickly, often by

interfering with the DNA-copying machinery of the cell.

That's also why traditional therapies come with so many negative side effects.

An alternate approach is to use targeted drug therapies.

Targeted therapies knock out individual proteins in the hijacked network that causes the uncontrolled

growth.

But, while shutting down a single protein in the signalling network might work for a

time, cancer cells can adapt, causing patients to relapse.

One way to defeat cancer's cleverness is to target several proteins at the same time

— that's where drug combinations, or “cocktails,” come in.

With two, three, or several drugs all targeting different parts of the network, the potential

for effective treatment is greater.

Even better, these treatments can be tailored to individual patients.

Because of the complexity of the network, two people with the same diagnosis can respond

differently to the same combination of drugs.

This is where personalized medicine can help.

But how do we find the most effective drug combinations for each person?

For each new drug developed, the number of possible combinations increases exponentially--resulting

in millions of possible treatments.

It would be practically impossible to experimentally find the right combination for each individual

patient.

Instead, our lab is trying to move that trial and error process from the lab to the computer.

By simulating the cell signalling network and the effects that the drugs have on cancer

cells, we can rewrite the network as a system of mathematical rules,

and use it to see what happens when multiple drugs interact.

This way, we can try out many, many drug combinations in a virtual environment, and test only the

most effective ones in the lab.

The idea is to move away from guesswork, and apply an engineering mindset to the problem.

It's the right combination to discover tomorrow's anti-cancer therapy.