Cancer Centers Join Intel’s Collaborative Cancer Cloud

Two more cancer centers have joined Intel's Collaborative Cancer Cloud data sharing and research platform, the chip maker and the Oregon Health & Science University (OHSU) have announced.

Dana-Farber Cancer Institute and the Ontario Institute for Cancer Research have joined OHSU to use the platform to share patients’ genomic, imaging, and clinical data for research collaborations. The platform was designed to offer a secure cloud-computing platform.

The platform aims to make it “easier, faster and more affordable for developers, researchers and clinicians to determine how hundreds, even thousands of genes interact to drive disease in individual patients,” according to an OHSU news release. The platform will coordinate the storage, networking, data security, and advanced analytics of participating cancer centers’ pooled data.

The effort will allow individual cancer centers to expand on their own genomics “big data” efforts by pooling data with those of other large institutions.

Researchers increasingly require access to “rich molecular and clinical data sets,” said Lincoln Stein, MD, PhD, director of the Informatics and Bio-computing program at the Ontario Institute for Cancer Research, and professor, department of molecular genetics at the University of Toronto, in the news release. “However, the information is often siloed and unmanageably large, rendering it effectively inaccessible. Projects like the Collaborative Cancer Cloud overcome the barriers to working with these data sets by allowing multiple institutions to pool their data and to provide researchers with the computer power needed to work on the data remotely.”

“The entire research community can benefit from insights revealed in large data cohorts,” said Brian Druker, MD, director of the OHSU Knight Cancer Institute. “By securely sharing clinical and research data among institutions, while maintaining patient privacy, our goal is to turn a process that’s agonizing and uncertain for countless patients into a highly tailored, one-day diagnosis and treatment recommendation.”

The collaboration will develop genomic pilot projects to identify “novel analytics approaches using machine learning techniques against a collective set of molecular and imaging data in order to support big data analytics,” the press release states.

Within 5 years, participants hope to see personalized, genomics-based patient diagnoses using the collaboration’s accumulated data, with which targeted treatment can be quickly planned. “By 2020, we envision this happening in 24 hours,” said Intel’s Eric Dishman.