Abstract 1764: Accurate and reproducible detection of fusions and exon skipping events in NSCLC-derived samples using a comprehensive, targeted RNA-Seq system across multiple laboratories
Bibliographic record
Abstract
Abstract Introduction: Reliable assessment of cancer-associated RNA markers in lung cancer produced by gene-fusions or exon skipping events by next-generation sequencing requires integrated reagents, protocols, and interpretive software that can harmonize procedures and ensure consistent results across laboratories. We evaluated a comprehensive system for targeted RNA-Seq that includes reagents for nucleic acid quantification, library prep, run controls, and companion bioinformatics software. The reproducibility of this system was evaluated in a multi-phase study design at 5 independent laboratories. Methods: Total nucleic acid (TNA) was isolated from formalin-fixed, paraffin-embedded (FFPE) residual non-small cell lung cancer (NSCLC) tumor biopsies and cell-lines (RT-112, H596, HCC78). These TNA isolates were used to prepare a set of 30 test samples, including a dilution series to assess assay sensitivity. A non-template control and kit positive control were also included. The sample set was evaluated using the QuantideX® NGS RNA Lung Cancer Kit RUO (Asuragen) and sequenced on the MiSeq® system (Illumina) at Asuragen and 4 independent laboratories. Analyses were conducted using QuantideX® NGS Reporter RUO (Asuragen), a software suite that includes a FASTQ processing pipeline and incorporates pre-analytical QC information into the fusion-caller algorithm and reporting tool. Results: Laboratories were trained on the assay workflow and companion bioinformatics software in less than two days followed by independent library preparation and sequencing workflows. A total of 266 sample libraries were evaluated from inputs down to <10 ng. A single library was excluded from sequencing due to a failed QC status. Specific targeted fusions (ALK, ROS1, FGFR3) and splice variants (MET exon 14 skipping) were detected in 132 libraries and were concordant at all sites. Designs to detect 3’/5’ expression imbalances reported the presence of gene fusions for ALK and ROS1 in a total of 96 libraries with one aberrant call and one missed call, which occurred in libraries flagged as “at risk” by the interpretive software. Conclusions: The accuracy of this novel targeted NGS assay for RNA fusions and splice variants in NSCLC was demonstrated in a multi-site laboratory evaluation using clinically-relevant specimens and low inputs of TNA. The ability of the panel to detect both common and rare gene fusion transcripts and exon skipping events within an integrated wet- and dry-bench workflow provides a foundation for the reliable detection of oncogenic RNA fusions and aberrant splicing events that can respond to current and emerging targeted therapies. This study highlighted the ease of implementation and consistent performance that can be achieved in different laboratories when the process from sample-to-report is highly integrated. Citation Format: Gary J. Latham, Richard Blidner, Brian C. Haynes, Shobha Gokul, Maria L. Aguirre, Stephen Hyter, Ziyan Y. Pessetto, Maria Curtis, Dan Su, Tom Halsey, Victor Weigman, Patrick Hurban, Andrew K. Godwin, Leon C. van Kempen. Accurate and reproducible detection of fusions and exon skipping events in NSCLC-derived samples using a comprehensive, targeted RNA-Seq system across multiple laboratories [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1764. doi:10.1158/1538-7445.AM2017-1764
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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 | 0.009 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".