We’re sharing everything these days. Rides. Spare rooms. Pictures of things wrapped in bacon. Our thoughts, 24 hours a day. Next up: Cancer data?
It can’t come soon enough for Charles Sawyers. Sawyers is one of the world’s most decorated cancer researchers, and his long resumé includes crucial work that helped make imatinib (Gleevec) one of the first drugs approved to go after a cancer—in Gleevec’s case, chronic myeloid leukemia—that was driven by a specific genetic mutation.
While much of the biotech world was running around Philadelphia at the giant BIO convention last week, Sawyers was in Salt Lake City helping run a much smaller conference sponsored by the American Association for Cancer Research.
In his wrap-up keynote, he told his cancer research peers that they needed to break down the walls of their institutions and share the ever-greater volume of data coming from research labs, cancer clinics, and medical centers.
Linking hospitals and research centers via data networks isn’t the stuff of screaming headlines, but people like Sawyers say breakthroughs in care with price tags society can afford won’t come regularly without those data connections. So when I heard about what Sawyers and others were saying in Salt Lake last week, I got them on the phone to hear how much of this advocacy was just talk, and how much was leading to action.
After explaining the need for “a new culture where data sharing across institutions happens much more quickly and easily,” Sawyers said he has convinced his colleagues and the leadership at Memorial Sloan-Kettering to get on board. A big plan for extramural sharing is underway, he said, but he couldn’t discuss details except to say there will be a formal announcement in the fall.
One of those groups sharing with MSK could be Intermountain Healthcare, an integrated health provider and insurer based in Salt Lake City. Its director of cancer genomics, Lincoln Nadauld, told me Intermountain was in discussions with “several academic institutions and integrated delivery” groups to share patient outcomes data. “We don’t want to be exclusive,” he said.
A talk Nadauld gave at the conference illustrated why his organization needs to share. He told the audience that an examination of 72 of Intermountain’s cancer patients with advanced disease hinted that those receiving targeted therapies—the vanguard of personalized medicine—saw their cancers halted (called progression free survival, or PFS) for about twice as long as those receiving standards of care like chemotherapy. What’s more, Intermountain calculated that the personalized medicine care, which included notoriously expensive drugs, didn’t cost any more than the standard care.
“We finally have some data that shows across all cancer types this approach does appear to provide some benefit regarding survival,” said Nadauld. “We also thought that more expensive drugs would make the ‘targeted’ cohort more expensive. But the punch line is that we were able to improve PFS without increasing costs. Neither of those things have come out before.”
But there are big caveats. Not only were the sample sizes small—36 patients in each group—the patients were plucked from records retrospectively, which can expose studies even by the most well-meaning researchers to biases. Nadauld also cautioned that the study hasn’t yet been peer-reviewed.
To know whether Intermountain’s findings are a slice of something more profound, at least two things need to happen: Much bigger patient groups have to be compared, and prospective studies that start from scratch and measure into the future, not back into the past, need to be conducted.
And for those to happen, Intermountain needs to share—first, to conduct stronger retrospective studies, but as time goes on and more patients receive targeted therapies, the sharing of outcomes going forward will be important, too.
Sawyers said small sample sizes will hamstring researchers who don’t share. For example, he cited the “long tail” problem of cancer mutations. When tumors are sequenced, a small number of genes tend to show up with frequent mutations. (P53 is one, for example.) But a chart of all mutations will show a scattering of genes with rare mutations—the “long tail” of the chart, that is. “It never hits zero. The more patients you sequence, the longer the tail gets, and you keep discovering very rare mutations,” said Sawyers.
Some of those rare mutations could be “oncogenes”—that is, the ones in the driver’s seat making the tumor grow—but without the pooled data it will be harder to know. It’s not just looking at more patients with the same type of cancer. Powerful analyses across all cancers might reveal that a mutation known in one type of cancer is showing up in patients across the continent with other types of cancer.
And thanks to new types of clinical trials, patients can be grouped and tested based on mutation profile, not on the tissue or organ where the cancer is growing. “If it’s a single case at one institution, no one will change clinical practice based on that case,” said Sawyers. “But if four patients at four institutions responded to a drug, perhaps you can announce it to the world.”
Lillian Siu thinks a lot about cancer trial design. She’s an oncologist at the Princess Margaret Cancer Centre in Toronto, and she told me
