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Brielle Parris
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P2.02 - Biology/Pathology (ID 616)
- Event: WCLC 2017
- Type: Poster Session with Presenters Present
- Track: Biology/Pathology
- Presentations: 1
- Moderators:
- Coordinates: 10/17/2017, 09:30 - 16:00, Exhibit Hall (Hall B + C)
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P2.02-002 - Digital Multiplexed Detection of Single Nucleotide Variants (SNV) in Non-Small Cell Lung Cancer Using NanoString Technology (ID 7881)
09:30 - 16:00 | Presenting Author(s): Brielle Parris
- Abstract
Background:
Non-small cell lung cancer (NSCLC) is a heterogeneous disease characterised by somatic mutations in many genes, some of which are actionable drivers. Modern management of NSCLC requires the identification such driver variants to predict sensitivity to targeted drug therapies. Current methods for mutation detection exhibit varying diagnostic accuracies and limitations. In this study, we determined the utility of a novel high-throughput assay by NanoString for mutation testing in comparison to an alternate platform. This is the first presentation of this combination of NanoString technologies in NSCLC.
Method:
Mutational status was evaluated using the nCounter SPRINT Profiler and the Vantage 3D DNA SNV Solid Tumour Panel in a cohort of 174 fresh-frozen NSCLC tumours, utilising digital counting of unique barcoded probes to detect 104 SNV, multi-nucleotide variants (MNVs) and InDels from 25 genes of clinical significance. 5ng of tumour-derived gDNA was subjected to multiplexed pre-amplification and hybridisation of variant-specific probes to unique fluorescent barcodes. Positive variant calls required raw digital count levels above 200, with a raw-count fold-change above the reference DNA sample greater than 2.0 and a statistical significance of p<0.01. SNV calls by the nCounter assay were made by comparison to a previous study using MALDI-TOF mass spectrometry. An agreement analysis was performed for variants common to both platforms to determine positive, negative and overall percentage agreement (PPA, NPA and OPA). A subset of discordant cases were validated using ddPCR.
Result:
The nCounter SNV assay detected at least one variant in 102/174 (58.6%) cases. Seven (4.0%) cases harboured two SNVs. KRAS variants were detected in 79 (45.4%) cases, EGFR in 6 (3.5%), PIK3CA in 3 (1.7%), TP53 in 3 (1.7%). Overall agreement analysis revealed PPA, NPA and OPA of 96.0%, 93.2% and 94.8% respectively. 5/9 discordant samples were available for validation using ddPCR. 4/5 validated cases favoured the nCounter assay, with one case harbouring a KRAS G12V variant confirmed at a fractional abundance of 1.15%.
Conclusion:
This study found the performance of the nCounter SNV assay and a contemporary platform to be highly concordant. The advantages of this technology include low DNA input, digital data output, reduced turn-around-time (<24hr) and customisability for inclusion of novel variants. The nCounter SNV assay is a robust and sensitive method for translational cancer research.
