A multi-modal molecular characterization of the Philadelphia translocation featuring long read sequencing
Bibliographic record
Abstract
OBJECTIVE: Clinically significant structural variation (SV), notably chromosomal translocation, results in the formation of fusion genes that drive leukaemogenesis. Detection of SVs is vital in clinical diagnosis, prognosis and therapy of haematological malignancies. Current methods for SV identification are low in sensitivity for cryptic cases and time-consuming for complex cases. This study investigated the feasibility of long read sequencing as an approach for SV detection and precise breakpoint characterization. METHODS: Six archival samples, including 4 bone marrow blood samples (F/66 B-ALL, F/25 B-ALL, M/53 CML, F/34 B-ALL) and 1 cytogenetic cell pellet each in cell culture medium (M/52 CML) or Carnoy's fixative (M/44 CML) with known and previously characterized BCR::ABL1 fusion transcript were selected for study. The genomic DNA was extracted from each case for further breakpoint characterization by long read sequencing (MinION R9.4.1 flow cell, Oxford Nanopore Technologies, UK). RESULTS: All the genomic breakpoints were concordant with the RNA fusion transcript breakpoints. Three typical (e1a2, e13a2, and e14a2) and 3 variant (e23a2Ins52, e8a2, and e13a2ins74) BCR breakpoints were identified. CONCLUSION: Using the Ph translocation as an example, long read sequencing is a promising alternative method to detect SV, revolutionizing detection of chromosomal translocation to a higher precision. A more comprehensive spectrum of SV can be resolved along with cytogenetic results, enabling precise diagnosis and personalized monitoring of haematological malignancies.
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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 | 0.000 | 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) | 0.002 | 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".