Big Data shows big promise in medicine
In handling some life-or-death medical judgements, computers have already surpassed the abilities of doctors. We’re looking at the promise of self-driving cars, according to Zak Kohane, a doctor and researcher at Harvard Medical School. On the roads, replacing drivers with computers could save lives that would otherwise be lost to human error. In medicine, replacing intuition with machine intelligence might save patients from drug side effects or otherwise incurable cancers.
Consider precision medicine, which involves tailoring drugs to individual patients. And to understand its promise, look to Shirley Pepke, a physicist who migrated into computational biology. When she developed a deadly cancer, she responded like a scientist and fought it using Big Data. And she is winning. She shared her story at a recent conference organized by Kohane.
In 2013, Pepke was diagnosed with advanced ovarian cancer. She was 46, and her children were nine and three years old. It was just two months after her annual gynaecological exam. She had symptoms, which the doctors brushed off, until her bloating got so bad she insisted on an ultrasound. She was carrying six litres of fluid caused by the cancer, which had metastasized.
She did what most people do in her position. She agreed to a course of chemotherapy. She also did something most people wouldn’t know how to do—she started looking for useful data. After all, tumours are full of data. They carry DNA with various abnormalities, some of which make them malignant or resistant to certain drugs. Armed with that information, doctors design more effective, individualized treatments. Already, breast cancers are treated differently depending on whether they have a mutation in a gene called HER2. So far, scientists have found no such genetic divisions for ovarian cancers.
But there was some data. Years earlier, scientists had started a data bank called the Cancer Genome Atlas. There were genetic sequences on about 400 ovarian tumours. To help her extract information, she turned to Greg ver Steeg, a professor at the University of Southern California, who was working on an automated pattern-recognition technique called correlation explanation (CorEx). It had not been used to evaluate cancer, but she and Ver Steeg thought it might work. She also got genetic sequencing done on her tumour.
In the meantime, she found out she was not one of the lucky patients cured by chemotherapy. The cancer came back.
But CorEx had turned up a clue. Her tumour had something common with those of the luckier women who responded to the chemotherapy—an off-the-charts signal for an immune system product called cytokines. She reasoned that in those luckier patients, the immune system was helping kill the cancer, but in her case, there was something blocking it.
Eventually she concluded that her one shot at survival would be to take a drug called a checkpoint inhibitor, which is geared to break down cancer cells’ defences against the immune system. At the same time, she went in for another round of chemotherapy.
The checkpoint inhibitor destroyed her thyroid gland, she said, and the chemotherapy was damaging her kidneys. She stopped, not knowing whether her cancer was still there or not. To the surprise of her doctors, she started to get better. Her cancer became undetectable. Still healthy today, she works on ways to allow other cancer patients to benefit from Big Data the way she did.
Kohane, the Harvard Medical School researcher, said similar data-driven efforts might help find side effects of approved drugs. Clinical trials are often not big enough or long-running enough to pick up even deadly side effects that show up when a drug is released to millions of people. Thousands died from heart attacks associated with the painkiller Vioxx before it was taken off the market.
Last month, an analysis by another health site suggested a connection between the rheumatoid arthritis drug Actemra and heart attack deaths, though the drug had been sold to doctors and their patients without warning of any added risk of death. Kohane suspects there could be many other unnecessary deaths from drugs whose side effects didn’t show up in testing.
So what’s holding this technology back? Others are putting big money into Big Data with the aim of selling things and influencing votes. Why not use it to save lives?
First there’s the barrier of tradition, said Kohane, whose academic specialty is bioinformatics, a combination of math, medicine and computer science. “Medicine does not understand itself as an information-processing discipline,” he said. “It still sees itself as a combination of intuitive leaps and hard science.” And doctors aren’t collecting the right kinds of data. “We’re investing in information technology that’s not optimized to do anything medically interesting,” he said. “It’s there to maximize income but not to make us better doctors.”
Physicians aren’t likely to be replaced by algorithms, at least not right away, but their skill sets might have to change. Already, machines have proven themselves better than humans in the ability to read scans and evaluate skin lesions. Pepke ended her talk by saying that in the future, doctors may have to think less statistically and more scientifically. Her doctors made decisions based on rote statistical information about what would benefit the average patient—but Pepke was not the average patient. The status quo is an advance over guessing or tradition, but medicine has the potential to do so much better. Bloomberg View
Faye Flam is a Bloomberg View columnist.
Comments are welcome at email@example.com