Integration of Technical, Bioinformatic, and Variant Assessment Approaches in the Validation of a Targeted Next-Generation Sequencing Panel for Myeloid Malignancies
Bibliographic record
Abstract
CONTEXT: - Detection of variants in hematologic malignancies is increasingly important because of a growing number of variants impacting diagnosis, prognosis, and treatment response, and as potential therapeutic targets. The use of next-generation sequencing technologies to detect variants in hematologic malignancies in a clinical diagnostic laboratory setting allows for efficient identification of routinely tested markers in multiple genes simultaneously, as well as the identification of novel and rare variants in other clinically relevant genes. OBJECTIVE: - To apply a systematic approach to evaluate and validate a commercially available next-generation sequencing panel (TruSight Myeloid Sequencing Panel, Illumina, San Diego, California) targeting 54 genes. In this manuscript, we focused on the parameters that were used to evaluate assay performance characteristics. DATA SOURCES: - Analytical validation was performed using samples containing known variants that had been identified previously. Cases were selected from different disease types, with variants in a range of genes. Panel performance characteristics were assessed and genomic regions requiring additional analysis or wet-bench approaches identified. CONCLUSIONS: - We validated the performance characteristics of a myeloid next-generation sequencing panel for detection of variants. The TruSight Myeloid Sequencing Panel covers more than 95% of target regions with depth greater than 500×. However, because of unique variant types such as large insertions or deletions or genomic regions of high GC content, variants in CEBPA, FLT3, and CALR required supplementation with non-next-generation sequencing assays or with informatics approaches to address deficiencies in performance. The use of multiple bioinformatics approaches (2 variant callers and informatics scripts) allows for maximizing calling of true positives, while identifying limitations in using either method alone.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".