Could next-generation computing lead to precise cancer treatments? We think so.

Let’s arm doctors and biologists with the power of parallel processing to attack the roots of cancer

Attacking a complex genetic problem

Cancer is a genetic problem, and a very complex one at that. The genetic abnormalities that cause tumors manifest themselves differently in different individuals. To make a hard problem harder, a healthy human body creates millions of these mutations and distributes the cell signaling equilibriums. The scientific challenge lies in determining, for each individual, which mutations are relevant in treating the patient’s disease.

Today we are able to gather billions of data points on how a patient’s cells are malfunctioning. By comparing those abnormalities to a normal human genome, we have the potential to narrow down and eventually go to the root cause of the patient’s disease.

Now let’s think bigger. By collecting and analyzing enough data on individual cases, we may be able to detect patterns in how the common circuitry in everyone’s cells can be manipulated to shut down the vast majority of cancers. The science is evolving, and the prognosis is getting better.

The computing challenge

So the goal is to identify and shut down the causes of many cancers. This achievement will require analysis of massive amounts of data that is available today and also that is being collected and accumulated at a frenetic pace because of advances in genome sequencing. What is currently lacking is the right combination of this science with next-generation computing power and capabilities.

With current computing limitations, it can take weeks of nonstop data processing to analyze the genetic data from a single person to identify cancer-causing abnormalities. The other key issue is when more data comes specifically from more patients with a particular type of cancer, doing the related data mining and observing different trends requires too much compute power.

This pushes us to come up with energy-efficient computing devices to address a larger population with far more compute/storage capacity and more power. To make this vision affordable, we need to look at the total cost of computation and energy and drive further gains in energy-efficient computing.

At Intel, we can envision a future—a not-too-distant future—in which a new generation of extreme-scale computing will allow us to compare billions of genetic aberrations in a sick patient with the billions of data points in a healthy human genome in a matter of hours. This comparison will single out the differences that are most relevant to the progression of the individual’s disease—information that can then guide narrowly targeted, personalized treatments.

What is the forecast? Cancer is pervasive. One in three women and one in two men would at some point in life be affected by cancer. It is accidental DNA cell copy flaws and, added to that, carcinogen-based mutations, that lead to cancer. Clearly, this is a tough problem to solve—but we have to believe that we can indeed solve it.

Attacking cancer with high-performance computing isn’t just a futuristic goal. Today, Intel and Oregon Health & Science University (OHSU) have formed a strategic collaboration to make this vision a reality. We are working together to use extreme scale computing to explore solutions for the most challenging problems in delivering personalized medicine to treat cancer and other complex diseases. The ultimate goal is to create a system of algorithms, computations, and models that can select a “high probability of success” treatment for an individual patient.

Ambitious design goals

To tackle this computational genomics problem, we need to optimize servers and software to create solutions that can quickly sort through enormous amounts of data to isolate the genetic variations that contribute to disease. And we need to do this in a cost-efficient manner, using general-purpose processors and energy-efficient configurations.

Here are some of the key design goals for these new solutions that will stem from our collaboration with OHSU:

  • Develop algorithms and software that parallelize everything to allow dozens of processor cores to work simultaneously on different aspect of a problem.
  • Develop a processor capable of doing all of the data crunching on a minimal amount of energy-efficient servers to avoid the high energy costs that come with running multiple servers in a supercomputing cluster.
  • Use accelerators to speed up computations.
  • Make the communication between the processor cores and servers highly efficient.
  • Process data in memory to avoid the I/O latency that comes with data going back and forth between memory and disk.

The Intel contribution

Intel is uniquely positioned to deliver the computing architecture that will solve today’s computational genomics problem. We know the hardware better than anyone, and we know how to optimize systems to make them run faster and be more energy efficient.

We now have the capability to deliver, say, 72 cores (Intel™ Xeon™ Sandybridge server with one Intel Xeon Phi) in a single server. This number will grow in the future, as will the processing power of the cores, as we design architectures to address a new generation of computing challenges.

Our collaboration with OHSU may even lead to advances in Intel Architecture, as we incorporate our learnings into the design of next-generation processors.

Ultimately, we believe that Intel technology has the potential to serve as the foundation for more advanced diagnostic tests that can zero in on all relevant abnormalities in a patient’s genes in a matter of hours.

Gans Srinivasa is a Senior Principal Engineer for Intel Corporation.