Screening rare genetic diagnoses for amenability to bespoke antisense oligonucleotide therapy development: a retrospective cohort study
Bibliographic record
Abstract
ABSTRACT Purpose To estimate the proportion of molecular genetic diagnoses in a real-world, phenotypically heterogeneous patient cohort that are amenable to antisense oligonucleotide (ASO) treatment. Methods We retrospectively applied the N=1 Collaborative’s VARIANT ( V ariant A ssessments towa r ds El i gibility for An tisense Oligonucleotide T reatment) guidelines to all diagnostic variants found by clinical genome-wide sequencing at a single pediatric hospital in 532 patients over a 6-year period. Variants were classified as either “eligible”, “likely eligible”, “unlikely eligible”, or “not eligible” in relation to the different ASO approaches, or “unable to assess”. Results In total, 25 unique variants across 26 patients (4.9% of 532 patients) were eligible or likely eligible for ASO treatment at a molecular genetic level, via canonical exon skipping (4), splice correction (3), or mRNA knockdown (18). Only eight of these molecular genetic diagnoses were made within a year of symptom onset. After considering disease and delivery related factors, 11 diagnoses were still considered candidates for bespoke ASO development. Conclusion A meaningful proportion of genetic diagnoses identified by genome-wide sequencing may be amenable to ASO treatment. These results underscore the importance of timely diagnosis, and the proactive identification and accelerated functional testing of genetic variants amenable to ASO treatments.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".