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 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".