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New Analysis Platform Identifies VUS as Potential Biomarker for Endometriosis


NEW YORK – Researchers from molecular diagnostics firm Genzeva and their collaborators have developed an analysis engine for genetic variants that has shown promise for uncovering new potential biomarkers for endometriosis.

Described in a paper in the Journal of Molecular Diagnostics in late March, the platform, dubbed Biomimetic Digital Twin Ecosystem, was developed in partnership with researchers from Rylti BioPharma, LumaGene, Qiagen Digital Insights, and Brigham & Women’s Hospital.

In the study, the authors analyzed exome sequencing data for 12 endometriosis patients and matched controls. They incorporated DNA variants and phenotype-ranked variants generated with the Qiagen Clinical Insight (QCI) Interpret variant analysis software into a data pool, which they fed into an analysis engine. The engine is underpinned by so-called expert knowledge graphs that were constructed using previously reported variants linked to endometriosis, pathology reports on each endometriosis sample, and patients' medical records.

The analysis identified eight pathogenic mutations associated with endometriosis, endometrial cancer, or an endometrial form of ovarian cancer in the patient samples and none in the controls.

It also reported four variants of uncertain significance in patient samples that were absent in the controls, indicating possible association with endometriosis-related disorders. Notably, one VUS in the MUC20 gene was identified in all 12 patient samples, suggesting a possible biomarker for the disease.

The authors noted that further research is needed to confirm the role of the VUS in endometriosis.

"We do have to do additional studies for confirmation," said William Kearns, cofounder, CEO, and CSO of Genzeva and LumaGene and the corresponding author of the study.

Previously a professor at Johns Hopkins University School of Medicine, Kearns and his wife Laura Kearns, a coauthor of the study, are owners of Genzeva and LumaGene. They also own stocks in Rylti BioPharma’s parent company, Rylti.

Potential limitations of the study are the small sample size and that all patients were of European ancestry. Additionally, the tool requires expert knowledge graphs, which could also introduce unintentional bias, according to the authors.

Kearns said Genzeva, which is based in Rockville, Maryland, has filed a provisional patent application for the analysis workflow. Meanwhile, Rylti has filed a patent related to the Biomimetic Digital Twin Ecosystem technology, he noted.

Genzeva and Rylti, which started their collaboration roughly a year ago, have a bidirectional licensing agreement, enabling the former to sell and license the latter’s technology to potential customers. The companies also have a profit-sharing agreement in place, he said.

Kearns also noted that the collaborators are now applying the method to a larger cohort to help uncover molecular insights for "a very complex autoimmune disease." 

While the paper referenced a report by the National Academies of Sciences, Engineering, and Medicine (NAS) on digital twins, the platform, despite its name, does not appear to employ digital twin technology per the NAS standard.

The NAS report defined a digital twin as "a set of virtual information constructs that mimics the structure, context, and behavior of a natural, engineered, or social system (or system-of-systems), is dynamically updated with data from its physical twin, has a predictive capability, and informs decisions that realize value." It also noted that "the bidirectional interaction between the virtual and the physical is central to the digital twin."

According to Reinhard Laubenbacher, director of the Laboratory for Systems Medicine at the University of Florida, who was not involved in the Genzeva study, the analysis engine does not meet that definition. "The NAS report makes quite clear that the key feature of a digital twin is the bidirectional interplay between the physical twin (the patient or a patient population) and its digital counterpart (a model or algorithm calibrated to the specific physical twin)," he said. "This is the connection missing in the platform, as described."

Joseph Glick, cofounder and chief innovation officer of Rylti and a coauthor of the study, acknowledged that the technology his team developed was named independently from the NAS report, and the platform should not be confused with the concept of digital twins as described in that report.