Diverse mutations and structural variations contribute to Notch signaling deregulation in paediatric T-cell lymphoblastic lymphoma
Bibliographic record
Abstract
Background T-cell lymphoblastic lymphoma (T-LBL) is an aggressive neoplasm closely related to T-cell acute lymphoblastic leukaemia (T-ALL). Despite their similarities, and contrary to T-ALL, studies on pediatric T-LBL are scarce and, therefore, its molecular landscape has not been fully elucidated yet. Procedure To characterize the genetic and molecular heterogeneity of paediatric T-LBL, 33 patients were analyzed using an integrated approach, including targeted next generation sequencing, RNA-sequencing transcriptome analysis and copy-number arrays. Results Copy number and mutational analyses allowed the detection of recurrent homozygous deletions of 9p/CDKN2A (78%), trisomy 20 (19%) and gains of 17q24-q25 (16%), as well as frequent mutations of NOTCH1 (62%), followed by BCL11B (23%), WT1 (19%) and FBXW7, PHF6 and RPL10 genes (15%, respectively). This genetic profile did not differ to that described in T-ALL in terms of mutation incidence and global genomic complexity level but unveiled virtually exclusive 17q25 gains and trisomy 20 in T-LBL. Additionally, we identified novel gene fusions in paediatric T-LBL including NOTCH1-IKZF2, RNGTT-SNAP91 and DDX3X-MLLT10, the latter being the only one previously described in T-ALL. Moreover, clinical correlations highlighted the presence of Notch pathway alterations as a factor related to favorable outcome. Conclusions In summary, the genomic landscape of paediatric T-LBL is similar to that observed in T-ALL and Notch signaling pathway deregulation remains the cornerstone in its pathogenesis, including not only mutations but fusion genes targeting NOTCH1.
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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 | 0.001 |
| 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".