Genetic predispositions in children with primary immunodeficiency and lymphoma: Experience from king hussein cancer center
Bibliographic record
Abstract
Abstract Introduction: Primary immunodeficiencies (inborn errors of immunity) confer susceptibility to recurrent infections and malignancies—particularly B-cell lymphomas—due to immune surveillance deficits and poor control of oncogenic viruses such as Epstein-Barr virus. Moreover, lymphoma arising in a PID patient complicates management, making treatment challenging and necessitating tailored chemotherapy and vigilant supportive care. Methods: In this retrospective single-center study, we identified 17 pediatric patients with lymphoma and concurrent primary immunodeficiency. All patients met ≥1 criterion of the McGill Interactive Pediatric OncoGenetic Guidelines (MIPOGG) for cancer predisposition and were referred to our Pediatric Cancer Predisposition Clinic. Each patient underwent targeted next-generation sequencing using a primary immunodeficiency gene panel. Lymphoma subtype, specific immunodeficiency diagnosis, and survival status were recorded alongside any treatment modifications (dose reductions, prophylaxis). Clinical data, genetic findings, and outcomes were analyzed descriptively. Results: Seventeen children (10 males, 7 females) with primary immunodeficiency and lymphoma were identified, including 7 Hodgkin lymphoma (HL) and 10 non-Hodgkin lymphoma (NHL) cases. The median age at lymphoma diagnosis was 8 years (range 2–17). Pathogenic or likely pathogenic homozygous variants were identified in four genes—ATM (n=2 patients), DCLRE1C (Artemis, n=3), RASGRP1 (n=1), and STK4 (n=1). The remaining 10 patients carried variants of uncertain significance in the following genes: ITK (n=3), RASGRP1 (n=1), TNFRSF9 (CD137, n=1), DOCK8 (n=1), FOXI3 (n=1), PLCG2 (n=1), IRF8 (n=1), and PIK3CD (n=1). These genetic findings corresponded to a range of primary immunodeficiency disorders, from severe combined immunodeficiencies (e.g., Artemis-SCID) and DNA repair disorders (Ataxia-Telangiectasia) to immune dysregulation syndromes (e.g., activated PI3K-delta syndrome and PLCG2-associated PLAID). Most patients received reduced-intensity chemotherapy protocols due to their immunodeficiency, along with supportive therapy such as monthly intravenous immunoglobulin (IVIG), antimicrobial prophylaxis, and rituximab for Epstein-Barr virus reactivation. Despite these measures, outcomes were poor: at last follow-up, only 7 of 17 patients (41%) were alive in remission (3 of 7 HL and 4 of 10 NHL), while 10 had died (mostly from infections). Conclusion: Survival for children with lymphoma and PID was worse than for immunocompetent patients. Our findings underscore the need to screen newly diagnosed pediatric lymphoma cases for underlying immunodeficiency (especially in consanguineous families or those with recurrent infections). Early identification enables tailored chemotherapy, infection prophylaxis, and timely transplantation to improve outcomes.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".