Each expert team needs a panel, but what makes a panel suited to each lab is unique. We took a look at a large universe of NGS panels to help identify what makes panels great.
Lab directors ask themselves: how do I choose or create the best NGS panel for my laboratory? At the same time, clinical teams ask themselves: what is the best NGS panel to understand biology in a difficult case? Each expert team needs a panel, but what makes a panel suited to each lab is unique.
We took a look at a large universe of panels to help identify what makes panels great. We learned:
Cases run: In order for a panel to be widely adopted, it needs to be fully functional at scale. In genomics, this means a panel has run over 100,000 samples. The top three panels based on estimated number of cases are Foundation Medicine’s FoundationOne CDx, Thermo Fisher’s Oncomine Comprehensive, and Illumina’s TruSight Oncology 500. All have FDA clearance currently or are pursuing clearance. These platforms are stable and have robust validation data.
Complete workflow: Academic medical centers are workflow integrators and include physician sign-off. The three major centers still providing independent NGS panel designs included MSKCC, University of Michigan and OHSU. It is our impression from reading company literature, Tempus XT is the only product with a physician signed-off report.
Highest analytical performance: To understand analytical performance, we combined information from 10 attributes into a qualitative performance score (see key terms below for definition). The panels with the best combined scores were Tempus XT, xGen Pan Cancer Panel and TruSight Oncology 500. Quickly on the heels of these developers are Oncomine Comprehensive, MI TurmorSeek and MLabs (University of Michigan). We look forward to the new comprehensive cancer panel from Agilent which should bring their aggregate score into the top cohort.
Technology developers take different approaches. Roche, Thermo and Illumina are the leading platforms with validated panels. IDT (Integrated DNA Technologies) pursues a differentiation approach by developing high sensitivity assays modules without investing in software tools. Lack of software tools does not hinder assay developers who often use IDT product modules when creating their assays. The developers then build software solutions by customizing open source tools or proprietary software. Agilent takes a third approach by developing a workflow alternative to Thermo and Illumina. It should be noted, Agilent also sells workflow modules separately and this offering has been popular with diagnostics companies such as Gaurdant and PGDx, as well as academic medical centers.
Fewer academic medical centers were in top cohort than expected. This may be due to the preference for running exomes at these centers instead of comprehensive panels or these centers may be running tumor specific panels. An emerging trend is for centers to partner with emerging diagnostic companies for part of their NGS workflow. In January 2020, the Mayo clinic announced they would be collaborating with PGDx. MD Anderson is also experimenting with the PGDx platform and others may follow.
To advance our understanding of this complicated field, we would love to hear your ideas. Do you agree? Did we miss a company or a panel that you think should be in the list? Do you have insights into platforms outside the US that we should include?
Please reach out to us on Facebook or LinkedIn, we are happy to update this study with your feedback.
Universe & Segmentation Strategy: We did market research to analyze a universe of over 3000 panels. Sources we used included the NCBI’s Genetic Test Registry, company websites, publications, webinars and company literature. From this universe we narrowed the analysis to comprehensive cancer panels for liquid and solid tumors; this segment is slightly under 300 panels. From this 300, about 30 panels had significant publicly available information. What is presented are 15 comprehensive panels that are not redundant by institution. This report is biased to US companies and institutions mainly due to the availability of competitive information.
KEY TERMS DEFINED
Cases: samples run on the platform for patient treatment. Total number of samples run on a platform is likely larger than patient cases.
NA: Information not available at time of writing
Performance Score: This is a qualitative ranking from ten scored attributes. Included in the attributes are detection and accuracy for the main classes of variants (SNV, CNV, Indel & Fusions), turn-around-time, completeness. Weakness in the accuracy of this score may be the result of missing information, or missing attributes in score such as uniformity. Furthermore, the scoring model rewards panels designed for high sensitivity applications such as liquid biopsy, panels which have more complete workflows and panels run on-premise and therefore have shorter turn-around-times.
Turn-around-time: Total number of days or weeks to run an assay on a platform or have a sample run in a lab as a service.
Workflow completeness: For this analysis we broke the workflow into four key sections: library prep (LIB), variant calling file (VCF), VCF interpretation (INT) and physician signed-off of report (DR-RPT). We realize that some may critique this segmentation as overly broad. One weakness in this segmentation is it does not distinguish between VCF interpretation and reporting strategies which can be very different; for example an N-of-One interpretation and report vs. a Pierian Dx interpretation and report.
The author is MD Napier, MS, MPhil, MBA. The author was a paid consultant to Larvol Inc. and has over 10 years’ experience in product marketing NGS panels, arrays and software at these major genomics companies: Agilent, Affymetrix and QIAGEN
The advent of precision oncology is marked by many new changes and improvements in diagnostic, prognostic and treatment methods for cancer patients. Researchers looking to bring new drugs to market are now also privy to a vast database of material from previously conducted studies thanks to a push forward in research collaborations and the creation of shared data hubs.