Continuing its plunge into genomics and personalized medicine, Stanford Medicine is teaming with Google to put genomic sequencing into the hands of clinicians to help diagnose disease.
Indeed, the new collaboration combines Stanford Medicine’s skills in healthcare research and clinical work with Google’s proficiency in cloud technology and data science. As such, the Google Cloud Platform will aim to help clinicians and scientists securely store and analyze massive genomic datasets with the ultimate goal of transforming patient care and medical research.
Stanford’s forthcoming Clinical Genomics service will be built using Google Genomics, a service that applies the same technologies that power Google Search and Maps to securely store, process, explore and share genomic data sets, according to a report in the Stanford Medicine News Center.
As an example, the Clinical Genomics Service will enable physicians at Stanford Health Care and Stanford Children’s Health, both part of Stanford Medicine, to order genome sequencing for patients who have distinctive or unusual symptoms that might be caused by a wayward gene. The genomic data would then go to the Google Cloud Platform to join masses of aggregated and anonymous data from other Stanford patients. “As the new service launches,” said Euan Ashley, a Stanford associate professor of medicine and of genetics, “we’ll be doing hundreds and then thousands of genome sequences.”
Pravene Nath, chief information officer for Stanford Health Care, added, “We are excited to support the creation of the Clinical Genomics service by connecting our clinical care technologies with Google’s extraordinary capabilities for cloud data storage, analysis and interpretation, enabling Stanford to lead in the field of precision health.”
The Clinical Genomics service aims to make genetic testing a normal part of healthcare for patients. “Genetic testing is built into the whole system,” said Ashley. A physician who thinks a genome-sequencing test could help a patient can simply request sequencing along with other blood tests, he said. “The DNA gets sequenced and a large amount of data comes back,” he said. At that point, Stanford can use Google Cloud to analyze the data to decide which gene variants might be responsible for the patient’s health condition. Then a data curation team will work with the physician to narrow the possibilities, he said.
Ashley noted that medicine mostly deals in small data, such as lab tests. But genomic studies, patient health records, medical images from MRI and CT scans, and wearable devices that monitor activity, gait or blood chemistry involve huge amounts of data that can allow doctors and researchers alike to analyze myriad aspects of patient health in ways that lead to improved medical decisions and products that are tailored to the patient—the essence of a precision health approach.
All of this work is consistent with Stanford Medicine’s focus on precision health. “You could imagine that, going forward, potentially every patient could be sequenced,” said Michael Halaas, chief information officer for the School of Medicine. “The technology challenge we need to solve is how to derive useful insights from data and apply it directly to the care of a patient in near real time and also make progress on research.”
Halaas said the Stanford-Google agreement does more than provide Stanford with server space. “It’s not just stacks of servers,” he said. “It includes layers and layers of innovative technology. This agreement allows us to do the analytics in a way that is fast and secure.”
The analytics applications and virtual supercomputers available through Google Genomics could pave the way for other kinds of projects, as well, according to officials. Working with Google’s engineers, Stanford researchers could make advances in visual learning that might, for example, enable computers to distinguish malignant tumors from benign ones in medical images. The Stanford-Google collaboration is a critical step on the path to precision health, said Lloyd Minor, M.D., dean of the School of Medicine. “This is the foundational work for bringing patient health information and other big data to the bedside,” he said.
