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At its root, cancer is a disease of the DNA. But to cure it, we need to move beyond genetics and work together to uncover cancer’s deeper cellular chemistry, says Ronnie Andrews, President, Genetic and Medical Sciences at Life Technologies. Scientific American spoke with Andrews about this new approach to combat cancer in the 21st century.

How has our understanding of cancer changed in recent years?

We now consider cancer as a systems biology disease, not just a disease of genetics. Depending on what numbers you use, 12 to 18 percent of cancers are familial or hereditary—which means the rest of them are not. They are somatic, in that the mutations that cause the cancer arise during a person's lifetime. So there's something about people’s biology that either predisposes them to cancer or keeps them healthy. In the next four to five years we will be peeling back this onion, trying to figure out what it is about people that make them either more or less susceptible.  

What does this mean in terms of finding a cure? Are we still really far away?

I'm going to give you my Wall Street explanation of how cancer develops. Let's say the blueprint for a cancer is developed in a downtown Los Angeles architectural firm. But the cell doesn’t become cancerous unless the blueprint makes it to a manufacturing facility located in Newport Beach. So the architects give their blueprint to a courier who drives to Newport Beach and can get there one of many different ways—if a road is blocked off somewhere, he can take a different route. After the Human Genome Project, pharmaceutical companies realized that they may not be able to change the blueprint for cancer—as in, the genetic instructions for it—but they could potentially block the highway system so that the courier couldn’t get to the manufacturing center and deliver these instructions. By this I mean that it might be possible to keep the cancer genome from being transcribed—to prevent the "bad" proteins that create massive cell production from ever being made. 

Can you walk me through how an individual cancer patient might get treated using this approach?

Let's say you get a biopsy of a person’s tumor and identify a handful of cells of interest in that tumor. Then you do a whole genome sequence analysis of those cells and see all of the multiple potential mutational drivers of the cancer. At the same time, there are new proteomic tools coming out that will show us in real great detail and quantification not only what proteins are being produced, but where they’re being produced in the cell. 

Once we have all this information, what do we do? Let's go back to my analogy. What we want to do is make a Google map of the patient's cancer cells. Then a doctor could figure out where the courier is at any point in time and pick drugs that stop the courier today by disrupting a particular biochemical pathway. And, after identifying future off-ramps that the courier might take, doctors could also prescribe drugs that would thwart the cancer in the future. 

To find this perfect cocktail, you can imagine that a doctor would flip open an iPad, log on to a portal and pull down his patient’s information, which is password and HIPAA protected. Then he would relate the data from his patient to outcomes from similar patients from the past, whose details have been kept, anonymously, in a centralized database. He'll be able to query the database and say, "show me 50 patients around the world that look like my patient at the genome and protein level, and now show me the top protocols that have allowed for the best survival rates for that patient." This is the power of bioinformatics that we’re now all chasing. 

This kind of approach requires scientists and doctors to share their data. Isn't this unrealistic? I mean, scientists are notoriously protective of their data so they can publish it.

The reality is, in cancer, no single discovery is going to be a cure. So it's time for us to become a little more selfless and to think about contributing to the greater good. Memorial Sloan-Kettering Cancer Center, for instance, may see 6,000 to 8,000 breast cancers next year, and MD Anderson Cancer Center might see 10,000. But there are hundreds of subtypes of breast cancer at the molecular level, so if these institutions don’t share their data, they will never collect enough of one cancer type to create a statistically powered data set allowing them to predict the best course of therapy for a given patient. At Life Technologies, we are creating a multi-company-sponsored program involving 20 institutions globally to do just this. We need to aggregate our data so that we can find solutions today instead of in 20 years—because in doing so, we will save thousands of lives.