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GenomOncology Seeks to Advance Decision Support, Trial Matching With U of Nebraska Technology


CHICAGO (GenomeWeb) – Bioinformatics software developer GenomOncology this month announced a deal to license technology from the University of Nebraska Medical Center that automates the coding and mining of molecular pathology test results to help clinicians assess the efficacy of targeted cancer treatments.

In announcing the licensing, the Cleveland-based company compared older methods of manually searching genomic reports to "looking for a specific movie scene among a stack of old VHS tapes." According to the vendor, "That's great for 1995, but not in today's digital age that moves at warp speed."

Called the Nebraska Lexicon Synoptic Pathology Reporting Module, the licensed technology is an add-on to pathology reporting systems that codes free text genomics reports in SNOMED-CT, a widely adopted ontology for electronic health records. The module then sends these SNOMED codes to EHRs via Health Level Seven International (HL7) messages, which are readable by pretty much every modern EHR.

Thus, it allows doctors to make better use of genomic test results. According to Kelly Choi, chief commercial officer of GenomOncology, a recent analysis of the Flatiron Health database, published this month in the Journal of the American Medical Association, found that broad genomic sequencing did not lead to better outcomes for advanced non-small cell lung cancer.

"The main reason for that was because doctors did not act upon the results," Choi suggested.

"That's where we are trying to bridge the gap. How do we get doctors to actually use those results into either giving the patient the right targeted therapy or immunotherapy, or helping them to enroll into an available clinical trial?" wondered Choi, a physician who previously was chief operating officer at healthcare analytics technology company Cota. In other words, it enables exactly what was missing in the Flatiron analysis, according to Choi.

GenomOncology had been trying to accomplish this on its own before coming across the Nebraska technology. "It turns out that they've had an ongoing project there to take basically all of molecular pathology and create a SNOMED structure for it," said GenomOncology founder and CEO Manuel Glynias.

Working together, UNMC and GenomOncology came up with a way to turn HL7 OBX files, which represent clinical observations and results, into a structured message format that represents sequence data in pathology information systems in the same manner an HL7 ORU message — an unsolicited transmission of a result observation — explains blood chemistry, according to Glynias.

The HL7 messages also classify variants based on the Variant Normalization Toolkit and the Human Genome Variation Society nomenclatures.

HGVS answers questions posed by genetic reports, explained Scott Campbell, director of informatics for the public health and pathology laboratories at the UNMC Fred & Pamela Buffett Cancer Center. Together with biomedical informatician and practicing internist James Campbell — no relation — Campbell built a SNOMED CT authoring environment that standards body SNOMED CT has recognized.

Glynias wants to store detailed descriptions of results, including DNA variants and protein levels, in a way that a researcher or clinician 20 years in the future could easily find the results to use with some new technology. GenomOncology already used the HGVS standard, which includes not only the unique name of a DNA sample, but also the position of the variant within it, even specifying the version of the genetic test performed.

"We kill two birds with one stone. We give [customers] the kind of data that, in case they want to make a data warehouse someday and analyze all this stuff, they have a really hard science version of what the variant is, and in addition, they have the oncologist-friendly version, which they can put right in the flowsheet of the [electronic medical record]," Glynias said.

"We've created observable entity expressions and precoordinated concepts … that basically define a sequence variant in a named gene," Campbell added. "We typically throw in the pathogenicity flag in the HL7 lab message."

Originally, the Nebraska team wanted to create SNOMED CT content to support College of American Pathologists cancer worksheets and the parallel Royal College of Pathology "tissue pathways" that are used in the UK.

"There has never been enough content that's been descriptive enough and had enough definitional content to really make the data elements on the CAP cancer worksheet be useful outside of the context of the worksheet," Campbell explained. "We added all of the semantic definitions that were necessary to make those data elements fully computable apart from the original worksheet."

Choi said that the whole point of pushing pathology reports into EHRs in a structured format is clinical decision support.

"Oncologists are finding it harder and harder to interpret molecular data, whether it's NGS or FISH cytogenetics," she explained. "Oftentimes, for a given patient, you need to have multiple inputs of testing data to be able to make a decision. That's getting increasingly harder for a number of reasons, and that's exactly what this is enabling," Choi said.

The combined system seeks to aggregate and present actionable information to oncologists at the point of care to find appropriate treatments as well as potential clinical trials.

"The problem is not in the sequencing. The problem is in the fact that the doctors aren't using the results of the sequencing in a way that could benefit patients," Choi said.

Glynias said that breast cancer represented the "classic example" of what GenomOncology wants to accomplish.

"In breast cancer, if you sent a patient sample out to Foundation Medicine or any lab to get it sequenced, you will get back a very accurate, nice report of exactly what all the genomic alterations are on the tissue. But without knowing the status of ERPR and HER-2 and maybe AR and a couple of other things, you can't match the therapies and you can't match the trials. It's not possible for the lab to do it because they don't have that other data," he said.

"That's a place where our ability to use all those sources of data together, and then filter out all those thousands of possibilities and present the oncologist [with a list of reasons for the best options] for therapy or clinical trials based on all this information [comes into play]," Glynias continued. "They can zoom in on exactly why and we can show them exactly what the decision tree was."