Researchers at the New York Genome Center (NYGC), The Rockefeller University, and other NYGC member institutions recently demonstrated the potential of IBM Watson for Genomics to analyze complex genomic data from state-of-the-art DNA sequencing of whole genomes and provide a report of potential clinically actionable insights within 10 minutes.
The proof-of-concept study used a beta version of Watson for Genomics technology to help interpret whole-genome sequencing (WGS) data for one patient. For the research, Watson’s time to generate a report of 10 minutes was measured against human curation and analysis of the same patient data, which took 160 hours to reach the same conclusions.
The findings were published in the journal Neurology Genetics, and was a part of the NYGC’s ongoing efforts to advance the use of next-generation sequencing (NGS), particularly WGS, in precision medicine.
In the study, NYGC researchers and bioinformatics experts analyzed DNA and RNA from a glioblastoma tumor specimen and DNA from the patient’s normal blood, and compared potentially actionable insights to those derived from a commercial targeted panel that had previously been performed. The whole-genome and RNA sequencing data were analyzed by a team of bioinformaticians and oncologists at the NYGC as well as a beta version of IBM Watson for Genomics, an automated system for prioritizing somatic variants and identifying potential therapies.
Watson for Genomics processed both abstracts and full text articles from PubMed; and using this information, the NYGC and Watson collaborated to identify gene alterations of therapeutic significance.
“Our partnership has explored cutting-edge challenges and opportunities in harnessing genomics to help cancer patients,” said Robert Darnell, M.D., Ph.D., a professor and senior attending physician at The Rockefeller University and founding director of the NYGC. “We provide initial insights into two critical issues: What clinical value can be extracted from different commercial and academic cancer genomic platforms and how to think about scaling access to that value.”
In addition to the proof-of-concept for the Watson platform component of the study, it also showed that WGS identified more clinically actionable mutations than the current standard of examining a limited panel of genes. WGS currently requires significant manual analysis and interpretation, which is made more challenging by the volume of data generated by WGS and the constantly growing body of published knowledge on the molecular drivers of cancer and potential targeted therapies.
Watson for Genomics aims to leverage WGS data with AI to significantly reduce the time needed between the completion of genome sequencing and providing doctors with meaningful clinical-decision support.
“This study documents the strong potential of Watson for Genomics to help clinicians scale precision oncology more broadly,” said Vanessa Michelini, Watson for Genomics innovation leader. “Clinical and research leaders in cancer genomics are making tremendous progress toward bringing precision medicine to cancer patients, but genomic data interpretation is a significant obstacle, and that’s where Watson can help.”
The research was conducted in 2015–2016 using a beta version of Watson for Genomics. The platform is now commercially available for genomic data interpretation via IBM’s partnerships with Quest Diagnostics and Illumina, and as a cloud-based software for clinicians and researchers.