Personal Genome Diagnostics Inc (PGDx), Baltimore, Md, a leader in cancer genomics, has announced that its machine learning based technology, Cerebro, outperformed existing methods to identify tumor-specific or somatic mutations in a study using data from 1,368 samples, enabling more accurate next-generation sequencing (NGS) clinical test results.
Sam Angiuoli, PhD, PGDx.
“Increasingly, NGS diagnostic tests are used to identify genetic alterations that help oncologists make decisions with their patients about the potential effectiveness of therapies. However, different NGS approaches have varying results, calling into question the ability of NGS to detect real mutations,” says Sam Angiuoli, PhD, chief information officer at PGDx. “We know it is absolutely critical to get the right answer for patients, so we pioneered the development of automated NGS software that incorporates machine learning strategies to improve the accuracy of somatic mutation detection. As this study reveals, our approach yields better results compared to alternatives, and highlights the importance of combining state-of-the-art software and data science in genomic testing.”
Cerebro’s machine learning approach analyzes a wide variety of characteristics to assess whether any given identified mutation is real. The Cerebro study used simulated and experimentally validated whole-exome and targeted gene analyses of cancer specimens to compare the accuracy of the technology with existing methods for somatic mutation identification. Cerebro was able to detect tumor alterations with higher sensitivity and positive predictive value compared with other methods.
The Cerebro study also evaluated the importance of improved somatic mutation detection in clinical NGS assays. PGDx performed head-to-head comparisons of clinical sequencing with or without the Cerebro machine learning approach, including a comparison of predicted outcomes for patients treated with immune checkpoint blockade.
“Highly accurate mutation detection may be particularly important for biomarkers like tumor mutation burden (TMB) which correlates with response to immunotherapies,” says Angiuoli. “Our data showed improved classification of patients when using Cerebro, and highlights the importance of accurate mutation detection on treatment decisions.”
This study brings to light the potential for variability in detecting tumor-specific alterations across different NGS tests, underscores the importance of standardization as NGS tests move into more routine clinical use, and demonstrates the higher accuracy of PGDx’s solution when compared against other commercially available methods.
John Simmons, PhD, PGDx.
“Developing a highly accurate bioinformatics software solution that automates mutation detection is integral to PGDx’s approach to create a complete solution from DNA sample to high-quality patient result,” says John Simmons, PhD, director of translational science and diagnostics at PGDx. “Our decentralized product approach includes optimized assay chemistry combined with a fully automated bioinformatics software platform, which reduces subjective analyses and increases reproducible results. This comprehensive solution will enable local testing in molecular laboratories worldwide, with a faster turnaround of reliable results to support critical clinical decisions.”
PGDx empowers the fight against cancer by unlocking actionable information from the genome. The company was established by researchers from Johns Hopkins University who are pioneers in cancer genome sequencing and liquid biopsy technologies. PGDx is committed to developing a portfolio of regulated tissue-based and liquid biopsy genomic products for laboratories worldwide.
To learn more, visit PGDx.
