Design, Optimization, and Multisite Evaluation of a Targeted Next-Generation Sequencing Assay System for Chimeric RNAs from Gene Fusions and Exon-Skipping Events in Non–Small Cell Lung Cancer
Bibliographic record
Abstract
Lung cancer accounts for approximately 14% of all newly diagnosed cancers and is the leading cause of cancer-related deaths. Chimeric RNA resulting from gene fusions (RNA fusions) and other RNA splicing errors are driver events and clinically addressable targets for non-small cell lung cancer (NSCLC). The reliable assessment of these RNA markers by next-generation sequencing requires integrated reagents, protocols, and interpretive software that can harmonize procedures and ensure consistent results across laboratories. We describe the development and verification of a system for targeted RNA sequencing for the analysis of challenging, low-input solid tumor biopsies that includes reagents for nucleic acid quantification and library preparation, run controls, and companion bioinformatics software. Assay development reconciled sequence discrepancies in public databases, created predictive formalin-fixed, paraffin-embedded RNA qualification metrics, and eliminated read misidentification attributable to index hopping events on the next-generation sequencing flow cell. The optimized and standardized system was analytically verified internally and in a multiphase study conducted at five independent laboratories. The results show accurate, reproducible, and sensitive detection of RNA fusions, alternative splicing events, and other expression markers of NSCLC. This comprehensive approach, combining sample quantification, quality control, library preparation, and interpretive bioinformatics software, may accelerate the routine implementation of targeted RNA sequencing of formalin-fixed, paraffin-embedded samples relevant to NSCLC.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".