Treatment of patients diagnosed with advanced pulmonary adenocarcinoma depends on the presence of genomic aberrations that are targetable for a specific tyrosine kinase inhibitor. Subsequent treatment lines depend on presence of mutations that are associated with emerging resistance. These aberrations include a variety of gene activating mutations, including single nucleotide variants, small insertion-deletions, exon skipping events, and gene fusions. At this moment different assays are used to detect these aberrations in the clinic. In this paper we introduce a novel method that can detect these genomic alterations in a single, RNA-based, assay. The design of the all-in-one assay is flexible allowing addition of new targets in subsequent designs. We show that this all-in-one assay has a high accuracy even on formalin-fixed-paraffin-embedded tissue samples, making it readily applicable in a clinical diagnostic setting.
The number of genomic aberrations known to be relevant in making therapeutic decisions for non-small cell lung cancer patients has increased in the past decade. Multiple molecular tests are required to reliably establish the presence of these aberrations, which is challenging because available tissue specimens are generally small. To optimize diagnostic testing, we developed a transcriptome-based next-generation sequencing (NGS) assay based on single primed enrichment technology. We interrogated 11 cell lines, two patient-derived frozen biopsies, nine pleural effusion, and 29 formalin-fixed paraffin-embedded (FFPE) samples. All clinical samples were selected based on previously identified mutations at the DNA level in EGFR, KRAS, ALK, PIK3CA, BRAF, AKT1, MET, NRAS, or ROS1 at the DNA level, or fusion genes at the chromosome level, or by aberrant protein expression of ALK, ROS1, RET, and NTRK1. A successful analysis is dependent on the number of unique reads and the RNA quality, as indicated by the DV200 value. In 27 out of 51 samples with >50 K unique reads and a DV200 >30, all 19 single nucleotide variants (SNVs)/small insertions and deletions (INDELs), three MET exon 14 skipping events, and 13 fusion gene transcripts were detected at the RNA level, giving a test accuracy of 100%. In summary, this lung-cancer-specific all-in-one transcriptome-based assay for the simultaneous detection of mutations and fusion genes is highly sensitive.