• 2019-10
  • 2019-11
  • 2020-03
  • 2020-07
  • 2020-08
  • br b e subtypes consistent with previous reports


    b 2e-16) subtypes, consistent with previous reports [42]. Additional immune checkpoint related expression markers (CTLA4, IFNG, ISG15, PDCD1) also significantly correlated with PD-L1 and PD-L2 mRNA across both subtypes (data not shown).
    Integrated Copy Number Variation Analysis of DNA and RNA Targets
    842 An Integrated Next-Generation Sequencing System Haynes et al. Translational Oncology Vol. 12, No. 6, 2019
    Figure 5. Evidence of copy number variation by integrated DNA and RNA analysis. (A) Increased levels of DNA coverage in LUSC (orange) cases relative to LUAD (blue) cases for PIK3CA and FGFR1 in surgical specimens. (B) FGFR1 displays concomitantly elevated levels of normalized DNA coverage and RNA expression in surgical specimens specific to the LUSC subtype.
    Analysis of normalized DNA coverage revealed copy number variation specific to histopathological subtype and consistent with previous molecular characterization studies. FGFR1 and PIK3CA amplification was evident in LUSCs in GW311616 to LUADs for surgical specimens (Figure 5A). This observation was less pronounced for CNB specimens. Among the DNA loci that were targeted, FGFR1 and PIK3CA were the two most frequently amplified genes in LUSC as reported by TCGA with frequencies of 18% and 46%, respectively [31]. Evidence of FGFR1 amplification in the LUSC subtype was further supported by targeted RNA-Seq evidence of concomitant overexpression of FGFR1 (SCC: 0.56; P b 3.5e-6; Figure 5B). Putative MET amplifications were also identified in two LUADs with concomitant MET mRNA overexpression (data not shown).
    DNA Variant Analysis of the BATTLE-2 NSCLC Cohort
    We tested the technical limits of the targeted DNA/RNA NGS technology by assessing a subset of 50 FNA smears from the BATTLE-2 trial. Smears contained only ~100 to a few hundred cells. Assessment by the preanalytical QC assays determined that only a single specimen yielded sufficient RNA for analysis, whereas 24/50 had sufficient yield for DNA analysis. Targeted DNA-Seq analysis was performed, and results were compared against matched data from the FoundationOne NGS assay [43], generated using much higher inputs of less challenging FFPE tissue specimens. When assessing DNA regions that were commonly covered by both assays, FoundationOne NGS analysis of tissue specimens was in 97% agreement with targeted DNA-Seq results from FNA smears (Table S5). Only one discordant variant was identified, MET p.T1010I, detected at 59% VAF in our assay but not reported by FoundationOne. This variant is annotated as both a COSMIC variant and an SNP and likely to be germline in origin.
    NGS analysis of DNA and RNA has increasingly become the primary enabling technology of precision medicine. Despite the centrality of this approach, NGS analyses of DNA and RNA have largely remained separate workflows. In this work, we developed an NSCLC panel using an NGS workflow that joins analysis of DNA and RNA through a single source TNA input and harmonized PCR conditions. We applied this workflow to the analysis of a cohort of FFPE NSCLC specimens, demonstrating its utility to accurately detect SNVs, 
    INDELs, CNVs, gene fusions, and gene expression signatures consistent with previously published molecular analyses of NSCLC. We then applied the approach to a challenging cohort of FNA smears with 97% concordance with an orthogonal analysis of matched surgical resections.
    The need for dual testing of DNA and RNA is primarily driven by the increasingly multiomic composition of the guidelines for molecular testing such as NCCN and European Society for Medical Oncology (ESMO), which now recommend testing of SNVs, INDELs, CNVs, gene fusions, and protein biomarkers within a single indication. NSCLC is perhaps one of the best examples of where this confluence of molecular marker categories is coming to the fore. Combined testing of DNA and RNA enables broad coverage of these categories, and RNA expression profiling may be an analytical substitute for certain protein expression biomarkers such as PD-L1 [44]. For some categories of markers, detection is possible with either DNA or RNA molecules, yet each offers distinct advantages. For example, RNA is better suited for the detection of gene fusions because a multitude of DNA-level intron-intron breakpoints can give rise to a single RNA exon-exon gene fusion. The average intron size is approximately 3.4 kb [45], which means that the DNA coverage requirements to resolve gene fusion events are at least an order of magnitude greater than the RNA coverage requirements. In addition, RNA fusion expression is often appreciably higher than the corresponding DNA, improving the sensitivity of detection. In contrast, DNA offers distinct advantages when profiling variants that lie outside of transcribed regions, such as promotor mutations. There is currently far more evidence associating DNA variant allele frequencies (VAFs) to therapeutic outcome than RNA VAFs. As such, the molecular pathology community is more accustomed to interpreting SNVs and INDELs from DNA-based assays. The advent of integrative analysis of DNA and RNA presents an opportunity to assess the relative merits of each analyte.