Big Hopes for Big Data: Digital Information Focus Underpins Cancer Moonshot Goals

Andrew D. Smith
Published: Friday, Jan 13, 2017
Daniel F. Hayes, MD

Daniel F. Hayes, MD

The passage of the 21st Century Cures Act1 sparked unusually robust holiday celebrations throughout the cancer research community, which harbors high hopes for both the newly funded initiatives and future budget increases.

The act will provide the National Cancer Institute (NCI) an extra $1.8 billion to fund the Cancer Moonshot, an effort to make 10 years of research gains in just 5 years by directing new money to 10 potentially transformative research areas (Chart, Table). It also gives the FDA the ability to consider real-world clinical data in determining drug indications.

The Moonshot money is the first major boost in federal spending on cancer research in many years, but researchers see reason to hope that it won’t be the last. The surge in new cancer drugs2, the improvement of Big Data analysis3, and the often incredible results from immunotherapy trials seem to have increased enthusiasm for research spending among politicians and the public.

“As with everything in cancer research, there are some caveats. The vote in December only allocated the first year of funding, and the extra money could go away next year. All that duly noted, the 21st Century Cures Act is very exciting,” said Daniel F. Hayes, MD, president of the American Society of Clinical Oncology (ASCO). “It should provide both an increase in funding for the National Institutes of Health, and therefore the NCI, as well as additional funding for [former] Vice President Biden’s Cancer Moonshot initiative.

“NCI funding has been flat for a decade now, and inflation has decimated real spending to the point that only a tenth of research proposals get funded. If this new initiative is a harbinger of steady increases, then it’s very good news for cancer researchers trying to get support and, more importantly, for our patients who will benefit from it,” said Hayes, who is also co-director of the Breast Oncology Program at the Michigan Comprehensive Cancer Center. “Even if it turns out to be a onetime deal, at least it directs a significant amount of money to many areas that promise big returns.”

How Big Data Links Plans

Asked to name the most promising of the Moonshot’s 10 research foci, Hayes replied that doing so would be as hard as naming the most promising of his children. Asked what tied the various aspects of the Moonshot together, he answered with a single word: data. Most of the specific research recommendations envision the aggregation of huge amounts of data and the extraction of important discoveries from those collection points.

More than 50 years have elapsed since data analysis produced one of the biggest ever breakthroughs in the war against cancer: indisputable proof that smoking dramatically increases cancer risk.4 Observers have been predicting for decades now that increasing computer power and digital storage would enable many more analytical triumphs. Although those predictions have yet to come true, many see more promise than ever in Big Data.

The importance of information analysis to the Moonshot becomes apparent in the very first focus area: offering comprehensive tumor profiling to a large number patients who agree to participate in a national network that will track what strategy works on what tumor and provides researchers an easy way to instantly find people who qualify for particular clinical trials.

Other goals that explicitly seek to use Big Data analysis or to encourage such studies include the creation of a national data ecosystem that allows sharing between many existing and all future datasets, the retrospective analysis of biospecimens from patients treated with existing standards of care, the generation of 3-D tumor atlases that map the development of every tumor, and the creation of a bank of tumors categorized by responses generated with various forms of immunotherapy.

The raw technology for most of these initiatives has been available for years now, but a lack of digital data and the inability—or unwillingness—to share what data existed have always limited the impact of data mining on health research.

Many industries had switched entirely to electronic records by the mid-1990s, but less than 30% of all office-based physicians were using electronic health record (EHR) systems just a decade ago.5 The percentage of US physicians using some sort of EHR didn’t break 80% until 2014. Even then, most doctors used only basic systems. Nearly 40% of doctors had yet to start storing and viewing imaging results electronically.6

That information that was stored electronically, moreover, has been mostly unavailable to researchers hoping to mine it for hidden treasure. Privacy laws, turf wars, and the technical incompatibility of different collection and storage systems have made it nearly impossible to aggregate truly huge amounts of information in ways that would maximize its value for would-be analysts.7

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