MAPK and JAK-STAT pathways dysregulation in plasmablastic lymphoma
Why this work is in the frame
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Bibliographic record
Abstract
Plasmablastic lymphoma (PBL) is an aggressive B-cell lymphoma with an immunoblastic/large-cell morphology and terminal B-cell differentiation. The differential diagnosis from Burkitt lymphoma, plasma cell myeloma and some variants of diffuse large B-cell lymphoma may be challenging because of the overlapping morphological, genetic and immunophenotypic features. Furthermore, the genomic landscape in PBL is not well known. To characterize the genetic and molecular heterogeneity of these tumors, we investigated 34 cases of PBL using an integrated approach, including fluorescence in situ hybridization, targeted sequencing of 94 B-cell lymphoma-related genes, and copy-number arrays. PBL were characterized by high genetic complexity including MYC translocations (87%), gains of 1q21.1-q44, trisomy 7, 8q23.2- q24.21, 11p13-p11.2, 11q14.2-q25, 12p and 19p13.3-p13.13, losses of 1p33, 1p31.1-p22.3, 13q and 17p13.3-p11.2, and recurrent mutations of STAT3 (37%), NRAS and TP53 (33%), MYC and EP300 (19%) and CARD11, SOCS1 and TET2 (11%). Pathway enrichment analysis suggested a cooperative action between MYC alterations and MAPK (49%) and JAK-STAT (40%) signaling pathways. Of note, Epstein-Barr virus (EBV)-negative PBL cases had higher mutational and copy-number load and more frequent TP53, CARD11 and MYC mutations, whereas EBV-positive PBL tended to have more mutations affecting the JAK-STAT pathway. In conclusion, these findings further unravel the distinctive molecular heterogeneity of PBL identifying novel molecular targets and the different genetic profile of these tumors in relation to EBV infection.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it