Long read nanopore DNA sequencing with adaptive sampling to identify tyrosine kinase fusion genes
Bibliographic record
Abstract
Diverse haematological neoplasms are driven by tyrosine kinase (TK) fusion genes formed by recurrent or non-recurrent genomic rearrangements. The resulting chimeric proteins often present excellent targets for treatment with kinase inhibitors, and the fusion transcripts or genomic junctions can be used as specific targets for molecular monitoring. Whilst the TK genes involved are generally well characterised (e.g. ABL1, PDGFRA, FGFR1), the fusion partners are very diverse, presenting a challenge for detection and characterisation of these structural variants (SV) using current diagnostic methods. We assessed the ability of targeted nanopore sequencing using adaptive sampling to detect fusion genes in myeloid neoplasms. We sequenced genomic DNA from patients (n = 20) with a known or suspected TK gene fusion and identified rearrangements in 18 cases, including all cases with a known TK fusion, typical and atypical BCR::ABL1 rearrangements, an 843Kb deletion causing a FIP1L1::PDGFRA fusion, novel AGAP2::PDGFRB and NFIA::PDGFRB fusions, and a complex CCDC88C::PDGFRB rearrangement with multiple translocation events. The approach was fast (<72 h/sample from DNA to result), flexible with minimal hands-on laboratory time, and provided accurate, patient-specific characterisation of genomic breakpoints.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".