Expanding the Landscape of Inborn Errors of Immunity in Hematological Disorders: A Prospective Study on Hidden IEI and IEI Phenocopies
Bibliographic record
Abstract
Background Inborn errors of immunity (IEI) are increasingly associated not only with recurrent infections but also with hematological complications. The discovery of somatic mutations leading to “IEI phenocopies” has expanded the genetic understanding of these disorders. However, the clinical and genetic scope of IEI in hematological disorders remains underexplored in large-scale studies. Methods This study recruits patients under 25 years old with hematological abnormalities, categorized into four subgroups: autoimmune cytopenias (AICs), polyclonal lymphoproliferation (PL), monoclonal lymphoproliferation (ML), and bone marrow failure/myelodysplasia (BMF/MDS). Participants undergo immunological evaluations, including immunophenotyping, cytokine profiling, and autoantibody assays. Next-generation sequencing (NGS) is used to identify germline and somatic variants, with bulk RNA sequencing applied to validate variants and explore pathways in inconclusive cases. Additionally, this study aims to establish a dedicated consortium for the comprehensive study of IEI-related hematological disorders, bringing together multiple centers to collaborate on data collection and analysis. Patient advocacy organizations (PAOs) are involved to raise awareness and support participants. Results Retrospective data from Meyer Children’s Hospital IRCCS in Florence (2020–2024) showed feasibility, identifying 71 eligible patients: 38 with AICs, 15 with PL, 20 with lymphoma, and 12 with BMF/MDS. A similar number of patients are expected to enroll at this site over three years, with collaborating referral centers projected to recruit approximately 680 participants in total. Preliminary results from the initial 71 participants show a 35% detection rate of hidden IEI, supporting the study's premise. Conclusions This research is poised to enhance the understanding of IEI and IEI phenocopies in hematological disorders, revealing novel genetic contributors and biomarkers. The findings could lead to earlier diagnoses, personalized therapies, and more timely hematopoietic stem cell transplantation. Collaboration with PAOs will improve patient education, treatment adherence, and overall outcomes, while reducing healthcare burdens. This study represents a significant advancement in addressing the unmet needs of patients with hematological complications of IEI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".