Genomic characterization of lymphomas in patients with inborn errors of immunity
Bibliographic record
Abstract
Patients with inborn errors of immunity (IEI) have a higher risk of developing cancer, especially lymphoma. However, the molecular basis for IEI-related lymphoma is complex and remains elusive. Here, we perform an in-depth analysis of lymphoma genomes derived from 23 IEI patients. We identified and validated disease-causing or -associated germline mutations in 14 of 23 patients involving ATM, BACH2, BLM, CD70, G6PD, NBN, PIK3CD, PTEN, and TNFRSF13B. Furthermore, we profiled somatic mutations in the lymphoma genome and identified 8 genes that were mutated at a significantly higher level in IEI-associated diffuse large B-cell lymphomas (DLBCLs) than in non-IEI DLBCLs, such as BRCA2, NCOR1, KLF2, FAS, CCND3, and BRWD3. The latter, BRWD3, is furthermore preferentially mutated in tumors of a subgroup of activated phosphoinositide 3-kinase δ syndrome patients. We also identified 5 genomic mutational signatures, including 2 DNA repair deficiency-related signatures, in IEI-associated lymphomas and a strikingly high number of inter- and intrachromosomal structural variants in the tumor genome of a Bloom syndrome patient. In summary, our comprehensive genomic characterization of lymphomas derived from patients with rare genetic disorders expands our understanding of lymphomagenesis and provides new insights for targeted therapy.
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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 | 0.000 |
| 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) | 0.001 | 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".