Outlier expression of isoforms by targeted RNA sequencing as clinical markers of genomic variants in B lymphoblastic leukemia and other tumor types
Bibliographic record
Abstract
Abstract Recognition of aberrant gene isoforms indicative of underlying DNA events can impact molecular classification and risk stratification of B lymphoblastic leukemia (B-ALL). Aberrant ERG isoforms have been proposed as markers of the favorable-risk DUX4 -rearranged ( DUX4 r) subtype while deletion-mediated IKZF1 isoforms are associated with adverse prognosis in non- DUX4 r B-ALL. The high-risk IKZF1 plus signature depends on gene deletions including PAX5 while intragenic PAX5 amplifications (PAX5amp) are recurrent in the provisional B-ALL with PAX5 -alteration subtype. In this study, we screened for outlier expression of isoforms within targeted RNA sequencing assays designed for fusions. Outlier analysis of known and novel IKZF1 , ERG , and PAX5 isoforms was 97.0% (32/33), 90% (9/10), and 100% (6/6) sensitive and 97.8% (226/231), 100% (35/35), and 88.5% (23/26) specific for IKZF1 intragenic or 3’ deletions, DUX4 r, and PAX5 intragenic deletions respectively, where false positives were favored to represent low-level deletions below the limit of DNA-based detection. Outlier analysis also identified putative PAX5amp cases and revealed partial tandem duplication (PTD) spanning IKZF1 N159Y in the B-ALL with mutated N159Y subtype. To demonstrate utility in other tumor types, outlier analysis was 100% (9/9) sensitive and 100% (255/255) specific for KMT2A -PTD in hematologic samples and 100% (7/7) sensitive and 100% (79/79) specific for FGFR1 tyrosine kinase domain duplication in brain tumors. These findings support the use of aberrant isoform analysis in targeted RNA sequencing data as a robust strategy for the detection of clinically significant DNA events.
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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 | 0.001 |
| 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".