Analytical Validation of a Next-Generation Sequencing Assay to Monitor Immune Responses in Solid Tumors. - PubMed - NCBI
J Mol Diagn. 2017 Oct 20. pii: S1525-1578(17)30363-X. doi: 10.1016/j.jmoldx.2017.10.001. [Epub ahead of print]
Analytical Validation of a Next-Generation Sequencing Assay to Monitor Immune Responses in Solid Tumors.
Conroy JM1,
Pabla S2,
Glenn ST3,
Burgher B2,
Nesline M2,
Papanicolau-Sengos A2,
Andreas J2,
Giamo V2,
Lenzo FL2,
Hyland FCL4,
Omilian A5,
Bshara W5,
Qin M2,
He J2,
Puzanov I6,
Ernstoff MS6,
Gardner M2,
Galluzzi L7,
Morrison C8.
Abstract
We have developed a next-generation sequencing assay to quantify biomarkers of the host immune response in formalin-fixed, paraffin-embedded (FFPE) tumor specimens. This assay aims to provide clinicians with a comprehensive characterization of the immunological tumor microenvironment as a guide for therapeutic decisions on patients with solid tumors. The assay relies on RNA-seq to semi-quantitatively measure the levels of 43 transcripts related to anticancer immune responses and 11 transcripts reflecting the relative abundance of tumor-infiltrating lymphocytes, as well as on DNA-seq to estimate mutational burden. The assay has a clinically relevant five-day turnaround time and can be conducted on as little as 2.5 ng RNA and 1.8 ng genomic DNA extracted from three to five standard FFPE sections. The standardized next-generation sequencing workflow produced sequencing reads adequate for clinical testing of matched RNA and DNA from several samples in a single run. Assay performance with respect to gene-specific sensitivity, linearity, dynamic range, as well as detection threshold was estimated across a wide range of actual and artificial FFPE samples selected or generated to address i) pre-analytical variability linked to specimen features (eg, tumor-infiltrating lymphocyte abundance, percent of necrosis), and ii) analytical variability linked to assay features (eg, batch size, run, day, operator). Analytical precision studies demonstrated that the assay is highly reproducible and accurate as compared to established orthogonal approaches. Copyright © 2017. Published by Elsevier Inc.
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