Performance of an RNA-Based Next-Generation Sequencing Assay for Combined Detection of Clinically Actionable Fusions and Hotspot Mutations in NSCLC
Bibliographic record
Abstract
INTRODUCTION: With its expanding list of approved and emerging therapeutic indications, NSCLC is the exemplar tumor type requiring upfront assessment of several biomarkers to guide clinical management. Next-generation sequencing allows identification of different types of molecular alterations, each with specific analytical challenges. Library preparation using parallel DNA and RNA workflows can overcome most of them, but it increases complexity of laboratory operations, turnaround time, and costs. We describe the performance characteristics of a 15-gene RNA panel on the basis of anchored multiplex polymerase chain reaction for combined detection of clinically relevant oncogenic fusion transcripts and hotspot small variants. METHODS: Formalin-fixed, paraffin-embedded NSCLC clinical samples (N = 58) were used along cell lines and commercial controls to validate the assay's analytical performance, followed by an exploratory prospective cohort (N = 87). RESULTS: The raw assay sensitivity for hotspot mutations and fusions was 83% and 93%, respectively, reaching 100% after filtering for key assay metrics. Those include quantity and quality of input of nucleic acid and sequencing metric from primers on housekeeping genes included in the assay. In the prospective cohort, driver alterations were identified in most cases (≥58%). CONCLUSIONS: This ultrafocused RNA-next-generation sequencing assay offers an advantageous option with single unified workflow for simultaneous detection of clinically relevant hotspot mutations and fusions in NSCLC, focusing on actionable gene targets.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".