br based on resulting amplifiable


based on resulting amplifiable copies per μl. FNA smear TNA isolations followed the targeted DNA-Seq protocol for sample inputs described in previous work [27] to attain a target of 400 amplifiable DNA template copies per reaction. RNA libraries were not prepared from FNA smears as only one sample yielded sufficient RNA for evaluation. For surgical resections and CNBs, a minimum of 200 copies per reaction was used as input to gene-specific PCR for both DNA and RNA libraries. RNA and DNA libraries were either prepared in a shared 96-well PCR-plate and co-sequenced on a shared MiSeq run or prepared and sequenced as separate batches. RNA libraries were prepared utilizing the QuantideX® NGS RNA Lung Cancer Kit (Asuragen) reagents according to the manufacturer's protocol. The Z-VAD-FMK (RT) product resulting from the RNA QC was transferred to a multiplexed PCR for target-specific enrichment. Preparation of the DNA libraries followed processes, buffers, and cycling conditions identical to the QuantideX® NGS RNA Lung Cancer Kit with the exclusion of the reverse transcription step and utilization of DNA pool gene-specific primers. Following multiplexed target-specific enrichment PCR, libraries were trans-ferred to a tagging PCR to simultaneously incorporate sample-specific index codes and sequencing adapters for MiSeq NGS compatibility. The resultant libraries were purified with Library Pure Prep Beads. Individual purified libraries were diluted in two serial 100-fold dilutions, and the library concentrations were measured using the included Library Quant qPCR assay (QuantideX® NGS RNA Lung Cancer Kit, Asuragen). The DNA and RNA purified libraries were normalized according to the relative coverage requirements of each library and pooled to 2.5 nM total concentration. The final library pool was denatured to allow for 15 pM loading concentration with the addition of PhiX (Illumina, San Diego, CA) at 1.2 pM. The denatured library pool and QuantideX® NGS custom sequencing primers were run at 2×201 cycles on the Illumina MiSeq with v3 MiSeq reagents (Illumina).
NGS Analysis
Postsequencing, data from DNA and RNA library pools were demultiplexed and analyzed through separately optimized informatics pipelines, and results were integrated for supplemental analyses. Outside of specifically referenced tools, all custom pipeline analysis code was developed in Python.
FASTQs generated from RNA libraries were adapter trimmed and filtered for dual index code purity. The I7 and I5 dual index code pairs were identified within the forward and reverse reads, and read pairs with unexpected code pairs were filtered. This index purity
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filtering step improves the specificity of the final calls and addresses a known limitation of Illumina's chemistry [18,33,34]. A local, gapped alignment to a custom reference transcriptome (inclusive of targeted breakpoint junctions) was performed using bowtie2 v2.0.5 (using the option: –sensitive-local) [35]. Alignments that did not match the expected amplicon boundaries or contain large gaps were filtered. Fusions and splice variant detection employed an upper-tailed Poisson test statistic, and 3′/5′ expression imbalances were assessed on normalized 3′ and 5′ expression data described in previously published work [28]. Gene expression was normalized by the geometric mean of endogenous control genes (TBP, RAB5C, and GGNBP2). Library QC was determined based on preanalytical QC and sequencing coverage measures [28].
DNA library analysis followed a similar workflow wherein read pairs were adapter-trimmed and associated with expected amplicons through local sequencing alignment using bfast. Unassociated amplicons were filtered prior to a second pass alignment against the GRCh37 reference using bwa-mem v0.6.1 (using the following options: -O 5,5 -E 1,1) [36]; local realignment and QScore recalibration were performed using GATK v1.3-21 [37]. INDEL calling was performed based on the % variant allele frequency (VAF) and coverage depth with empirically determined thresholds [27]. A decision tree algorithm trained on an independent cohort of 400 FFPE samples was used to call SNVs [27]. The SNV classifier incorporates multiple features including functional input copies, sequence quality, sample specific error rates, local sequence complexity, and coverage depth. Evidence of copy number amplifications was assessed after normalizing each amplicon's coverage against the mean across all amplicons within a given library. SNPs and synonymous variants were filtered prior to supplementary analyses.
Gene Fusion and MET Exon 14 Splice Variant Confirmation by Orthogonal Assays
RT was performed on the original TNA isolations to generate a replicate cDNA product. Primers were designed (or reused from the panel) for each confirmation target to produce an amplicon of known length within a singleplex reaction with common sequence tags for second-stage tagging PCR. Singleplex PCR enrichments were tagged with FAM-labeled primers. PCRs were evaluated via capillary electrophoresis (CE) on a 3500XL Genetic Analyzer (Thermo Fisher Scientific, Waltham, MA) to confirm expected size. MET exon 14 skipping was confirmed by a digital PCR assay using custom primers on the QX200™ Droplet Digital™ PCR System (Bio-Rad Laborato-