Development of a NanoString assay to detect leukemogenic fusion transcripts in acute myeloid leukemia
Bibliographic record
Abstract
INTRODUCTION: Detection of leukemogenic fusion transcripts in acute myeloid leukemia (AML) is critical for AML diagnosis. NanoString nCounter system is a novel probe-based gene expression platform capable of measuring up to 800 targets with advantages of reproducibility, accuracy, and sample type flexibility. To study the potential application of NanoString in leukemia at clinic, we used this technology to detect AML leukemogenic fusion transcripts and compared the performances with clinical molecular assays. METHODS: We developed a NanoString assay to detect seven leukemogenic fusion transcripts, namely RUNX1-RUNX1T1 (e5e12), PML-RARA (bcr1, bcr2, and bcr3), and CBFB-MYH11 (e5e12, e5e8, and e5e7). We set up the cut-off value for each fusion transcript and tested 42 de novo AML samples. We compared the results with reverse transcriptase-polymerase chain reaction (RT-PCR) and TaqMan reverse quantitative-polymerase chain reaction (RQ-PCR), the molecular methods standardly used at clinic. RESULTS: We demonstrated that the NanoString and RT-PCR results correlate well (P < 0.0001) and are highly concordant (95.2%). Using TaqMan RQ-PCR as a validation method and gold standard, we demonstrated superior accuracy and sensitivity of NanoString compared to RT-PCR and comparable specificity. Furthermore, we showed that NanoString is not as sensitive as TaqMan RQ-PCR in detecting very low level of fusion transcripts. CONCLUSIONS: NanoString can serve as a reliable and alternative molecular method to multiplexed RT-PCR for diagnosis of de novo AML with the perspective of screening/quantitation of a large number of leukemogenic fusion transcripts and prognostic genes. However, NanoString may not be an alternative method for monitoring minimal residual disease in AML.
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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 | 0.002 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".