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Big Data: Sharing Information to Improve Care

Big data, using high-powered computers to gather and analyze massive amounts of patient information, promises to revolutionize cancer care. But experts say patient concerns about privacy and other issues must be addressed. By Stephen Ornes
<div style="margin:0in 0in 0pt;line-height:120%"><span style="font-size:10pt;font-family:&quot;helvetica&quot;, &quot;sans-serif&quot;;letter-spacing:-0.05pt;line-height:120%">PHOTO © iStock/MmeEmil</span></div>
PHOTO © iStock/MmeEmil

Patient advocate Linda House is excited, but anxious, about big data and cancer care. House, president of the Washington, D.C.-based nonprofit Cancer Support Community, has made it her mission to protect the interests of patients. Like many advocates, she welcomes the emerging projects that aim to make cancer care more precise by pooling patient information in giant electronic databases. But she is concerned about how patients will be informed about the process and how big data might play a role in treatment decisions.

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Learn some ways big data could make a difference. 
“How do we make sure we don’t lose the patient in the conversation?” she said at a town hall meeting about big data and cancer care attended by patients, providers, researchers and advocates in June 2015. The gathering, held in Alexandria, Virginia, was hosted by C-Change, an organization that brings together leaders from the cancer community. House moderated a panel on big data and patients.

“We can have all this data and pinpoint what might be the best treatment, but at the end, a patient’s beliefs and personal needs will shape whether they move forward with treatment,” House says. “We owe it to patients to not let them get lost in the groundswell.”

Big-data projects offer a tantalizing, if unrealized and untested, view of the future of cancer care. Traditionally, oncologists recommend treatments that have been evaluated and approved based on clinical trials. But clinical trial findings are distilled from the experiences of a limited number of selected patients during a particular time period. Small trials may involve fewer than 100 patients. According to the National Cancer Institute, only a tiny slice of the U.S. adult patient population—a mere 3 percent—even enrolls in clinical trials for new and emerging therapies. But what if treatment decisions could be based on the experiences of millions of patients? That is the question at the heart of big data.

“It’s a missed opportunity if we don’t leverage all this data,” says medical oncologist and cancer researcher Charles Sawyers of Memorial Sloan Kettering Cancer Center in New York City, a past president of the American Association for Cancer Research (AACR).

The overarching idea is that data on large numbers of cancer patients and their tumors—recorded during the course of treatment—could be archived and analyzed all together. This approach could widen treatment options for an individual with a rare type of cancer or a cancer with a rare mutation. It might also point to better treatment options for an entire population of patients who share similar disease characteristics, like genetic mutations. And while Sawyers cautions that researchers are probably still at least a decade away from a better understanding of how to pull meaning from millions of patient records, a slew of big-data projects are underway at leading cancer research organizations and institutions. Researchers are building databases, designing algorithms to organize the data and search for patterns, and even enlisting IBM’s Watson, the computer that achieved fame by competing and winning $1 million on the game show Jeopardy!
The Power of Big Numbers
More than 14 million people in the U.S. currently have or had cancer at one time. In the next four years or so, that number is predicted to climb to 18 million. They all have or will have medical records, blood samples and tumor tissue samples. Every patient lives a unique cancer story that includes symptoms, diagnosis, treatments, genes, family history, tumor characteristics, outcomes and other factors. Information collected from millions of patients adds up to a lot of data. Big-data projects are investigating ways to collect and analyze these large amounts of information, with the goal of identifying previously unobserved connections that could improve cancer treatment.

Imagine, for example, a patient diagnosed with a rare cancer at a rural hospital—matched with an oncologist who, having never treated this type of cancer, consults an online tool that identifies a treatment that helped similar patients with the same rare cancer. Or consider a breast cancer patient whose tumor carries a genetic mutation more typically found in lung cancer, leading an oncologist to recommend a drug, perhaps off-label, that helped a subset of lung cancer patients with the same mutation.

These connections are rare. On the other hand, given the large number of people diagnosed with cancer, “there are a lot of patients who are in that scenario for common tumor types,” says Sawyers, who is currently heading AACR Project GENIE, the data-sharing project of the American Association for Cancer Research that links patients’ tumor genome data to their clinical outcomes. GENIE stands for Genomics, Evidence, Neoplasia, Information, Exchange.

Cancer is a disease that arises from accumulated genetic mutations. AACR Project GENIE was designed to ultimately allow clinicians who are deciding on a patient’s treatment regimen to look at past patient experiences and see how people with particular molecular tumor characteristics—including mutations—responded to certain treatments. In other words, the profile of a person’s tumor may help show if they’re likely to respond to treatment, develop resistance or have some other response.

At the project’s launch in November 2015, AACR Project GENIE contained more than 17,000 patient records; by 2020, Sawyers says, the AACR hopes to have 100,000 records in the registry. Seven large cancer institutions have signed on to AACR Project GENIE’s first phase, which consists of using the data to answer a research question, for example, whether a drug for a particular genetic variant in a certain cancer would also be effective for the same mutation in another kind of cancer.

Oncologist Mia Levy leads the AACR Project GENIE arm at Vanderbilt-Ingram Cancer Center in Nashville, Tennessee. She says AACR Project GENIE offers the promise of using new findings from the laboratory to improve patient care. “I have this database full of patient data and a new patient sitting in front of me today,” she says, referring to a typical example. “Is there a way to use that information to treat this patient I’m seeing right now? That’s the broad vision, and the holy grail of treatment.”


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