Long a perennial demand, the call for improved data sharing at last week's Bio-IT World Expo in Boston was especially strong -- and emotional.
Heidi L. Rehm, Chief Laboratory Director for the Laboratory for Molecular Medicine, warned in her opening keynote that the consequences of failing to share data can lead to devastating results for patients. Rehm related the tragic tale of a doctor sending a fetal blood sample to her lab that showed a genetic mutation identified by researchers as likely to cause debilitating defects. Rehm's subsequent report to the doctor led to the mother terminating the pregnancy. Later research, however, demonstrated that this mutation was benign (and, in fact, common among certain ethnicities). This life-or-death discovery, said Rehm, could have been achieved sooner with better data sharing and consensus.
Now, Rehm devotes much of her work to getting genomics researchers to collaborate, and come to a consensus based upon their collective data.
The call for consistency
Rehm discovered that lack of consensus among genomicists does not always indicate true disagreement. Frequently, the problems are merely taxonomical."We've actually followed up with certain laboratories," said Rehm. "It turns out [one] lab doesn't use the 'likely benign' category."
This discovery and others like it, related Rehm, have led to greater resolution as she and her colleagues have begun to understand how inconsistent language and categorization have led to discordant scientific conclusions.
The call for standards
Standards -- or the lack of good, clear, consistently applied ones -- have also been problematic. "It would be great if we had better standards so we could all interpret in the same way," Rehm told her audience. "It is no easy task to try to professionally agree[.]"
Rehm described an effort she worked on standardizing terms related to how genomicists define the ability of an organism to cause modern diseases."Now the moment we published this guideline…we decided to do a bakeoff," said Rehm. "[W]e compared between labs using their old rules using the [new] rules." The initial results were "surprising;" the pre-standard and post-standard results between labs were mostly the same, demonstrating only 34-percent concordance.
The call for collaboration
The real problem? Human error. Rehm and her colleagues were only able to make this big-data discovery with small-data technology, picking up the phone. "[W]e walked through each of those pieces of evidence [the labs used]," Rehm said. "[W]e were able to agree as a group as to which rules actually did apply and which rules had been misapplied."
Improving the concordance of their data, Rehm explained, took more than the latest analytics technology. Rather, it took sharing both data and data methods, efforts requiring education on both the new standards and old ways of working.
"Some of them hadn't been able to work with these rules; some hadn't even read their own guideline," said Rehm. "So it became a really great framework for us to discuss evidence… Through this effort, we've worked to resolve either by phone or by email...exact interpretations by 70 percent, with a confidence level of 80 percent.
The moral here: Talk to colleagues to understand their thinking and get them to understand yours.
Or, as per Rehm, "if you're going to apply a ruleset, do it correctly."
Though conceding that individual professional judgment bars total consensus, Rehm insisted that the more researchers share with each other, the better work they will produce. "It's going to take…massive efforts in data sharing…to improve our knowledge of genome variation [and] improve our consistency," said Rehm. "The world is watching and patients' lives are at stake."