Validation, Implementation, and Clinical Impact of the Oncomine Myeloid Targeted-Amplicon DNA and RNA Ion Semiconductor Sequencing Assay
Bibliographic record
Abstract
The identification of clinically significant genes recurrently mutated in myeloid malignancies necessitates expanding diagnostic testing with higher throughput, such as targeted next-generation sequencing. We present validation of the Thermo Fisher Oncomine Myeloid Next-Generation Sequencing Panel (OMP), targeting 40 genes and 29 fusion drivers recurrently mutated in myeloid malignancies. The study includes data from a sample exchange between two Canadian hospitals demonstrating high concordance for detection of DNA and RNA aberrations. Clinical validation demonstrates high accuracy, sensitivity, and specificity of the OMP, with a lower limit of detection of 5% for single-nucleotide variants and 10% for insertions/deletions. Prospective sequencing was performed for 187 samples from 168 unique patients presenting with suspected or confirmed myeloid malignancy and other hematological conditions to assess clinical impact of identifying variants. Of detected variants, 48% facilitated or clarified diagnoses, 29% affected prognoses, and 25% had the potential to influence clinical management. Of note, OMP was essential to identifying patients with premalignant clonal states likely contributing to cytopenias. We also found that the detection of even a single variant by the OMP assay, versus 0 variants, was predictive of overall survival, independent of age, sex, or diagnosis (P = 0.03). This study demonstrates that molecular profiling of myeloid malignancies with the OMP represents a promising strategy to advance molecular diagnostics.
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".