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A Cancer ‘Moonshot’ Needs Big Data

By Tom Coburn Jan. 14, 2016 6:15 p.m. ET

In his State of the Union address on Tuesday, President Obama called for America to become “the country that cures cancer once and for all.” As a three-time cancer survivor (metastatic colon, metastatic melanoma and metastatic prostate), I can tell you that this “moonshot,” as Vice President Joe Biden first called it, is a bold goal—but one within our grasp.

Last week’s report from the American Cancer Society shows that cancer mortality is down more than 20% over the past 20 years. Many patients are living longer thanks to better treatments and earlier detection. Science is tipping the odds of survival in favor of patients.

Ironically, we’re handicapping ourselves in the war on cancer, in part because of a web of privacy regulations like the Health Insurance Portability and Accountability Act. HIPAA makes it difficult for researchers to tap into large caches of clinical and genomic data shared across multiple institutions or firms, and then share their findings more broadly.

The law allows some research uses, but only if the uses (and informed patient consent) are specified in advance. As one analyst put it, “because obtaining [consent] from huge numbers of people or [institutional review board] waivers ranges from the impracticable to the impossible, important research has gone undone and important findings unshared.”

Harnessing that information—“big data”—would allow us to personalize prevention and treatment based on the genetic characteristics of a patient’s tumor, family history and personal preferences, while minimizing unwanted side effects. But today cancers are often fought “off the grid.” Patients whose cancers resist standard treatment, or whose tumors reappear years later, are medical puzzles. Their doctors cobble together treatments through intuition, experience and case studies scattered in the medical literature.

The clinical trials that pharmaceutical companies rely on for FDA approval and drug labeling capture too little of the information patients and physicians need. The trials only enroll 3% of cancer patients and can take years and tens of millions of dollars to finish. Many trials never enroll enough patients to get off the ground.

Big-data analysis could help tell us which cancer patients are most likely to be cured with standard approaches, and which need more aggressive treatment and monitoring. Harnessing the genetic and clinical data routinely generated by hospitals and physicians would also accelerate drug development, by rapidly matching targeted treatments sitting in companies’ research pipelines with the patients who are most likely to respond. This could save lives, streamline drug research and reduce ineffective care.

Insurance companies also could use big data to design contracts with drug companies that link payments to patient outcomes in specific groups of patients, such as fewer side effects or reduced hospital visits. This would ensure that drug prices reflect their real value to patients.

Better metrics for measuring patient outcomes are vital, but current debates on cancer drug pricing largely miss the mark. The total share of U.S. health spending on…

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