IBM Watson Takes on the Genetics of Brain Cancer

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IBM has been promoting its AI as a killer app for health care, thanks to Watson's natural language processing skills and machine learning abilities.

Twenty patients with an aggressive form of brain cancer will have a new doctor on their medical team: the learned geneticist known as IBM Watson. In a collaboration announced today between IBM and the New York Genome Center, IBM's Jeopardy-beating AI will analyze the genomes of those 20 patients in hopes of providing insights for their oncologists.

IBM has been promoting its AI as a killer app for health care, thanks to Watson's natural language processing skills and machine learning abilities. Over the past two years Watson has been engaged in a separate project at New York's Memorial Sloan-Kettering Cancer Center, in which doctors are training the AI to understand the language of medicine. In that project, Watson is being taught to read patients' records and search the medical literature for relevant suggestions on treatment.

This new project will show that Watson can provide deeper analysis for such point-of-care applications, said IBM Research's Raminderpal Singh after a press conference in New York today. For these 20 cancer patients, Watson won't just scan the medical literature for information. The AI will also scan the genetic data from the patients' own healthy cells and cancer cells, and will then search for information that's relevant to the genetic mutations in the tumor. "As genome sequencing becomes more commonplace—and it will—we'll need a way to go from mutation information to clinically actionable information," said Singh.

Robert Darnell, president of the New York Genome Center, explained that the project will take the form of a research study spanning nine hospitals and institutions, and involving 20 patients with glioblastoma, a malignant brain cancer that kills more than 13,000 people in the United States each year. Glioblastoma also kills quickly, Darnell said, with a typical life expectancy after diagnosis of just 12 to 14 months.

Sequencing the DNA of a patient's tumor provides "a gusher of information," Darnell said, as there may be thousands of mutations that cause a glioblastoma tumor cell to differ from a healthy cell. What's more, each patient's tumor will have its own unique set of mutations. Oncologists are still learning how to analyze and make use of that data, Darnell said. "We need time to think about it—and time is not your friend when it comes to glioblastoma." A dedicated team of geneticists might take several weeks to come up with a treatment plan for a patient, he said, which is enormously commendable, but not very scalable.

Watson can take a patient's set of mutations and scan thousands of medical papers in seconds to find out how those mutations affect the cancer cell, and which treatments have been most effective on genetically similar tumors. The AI then presents the clinician with a summary of the patient's mutations and various treatment options. In the image below, a cancer cell's mutations are mapped on a cell protein pathway. 

The current research project is intended to evaluate Watson's usefulness as a tool for oncologists; the final treatment decisions still rest with the human doctor. Presumably that will be the case for the foreseeable future, as patients probably aren't ready to put themselves entirely in the hands of a robo-doc. What's more, it's fairly easy to envision scenarios in which human intelligence would trump the AI, which operates best when dealing with lots of data. For example, Watson might find hundreds of studies regarding an older drug for glioblastoma, and only a single study regarding a revolutionary new treatment that trumps the old drug. A human doctor who is up on the literature might recommend the new treatment right away, whereas Watson wouldn't be confident in doing so until a critical mass of follow-on papers had deemed it effective.

But if the project is successful and Watson continues to ingest data, the AI will only get smarter over time. IBM's Singh says the team is already building in a function to include knowledge of past outcomes in Watson's decision-making process. "Watson will know whether clinicians used its findings, and also the patients' outcomes," he says.

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