Influence of Production Affecting Cytokine Genetic Variants on Allogeneic Hematopoietic Cell Transplantation Outcomes
Bibliographic record
Abstract
Abstract Introduction: High resolution human leukocyte antigen (HLA) matching is regarded as a prerequisite for the clinical success of allogeneic haematopoietic cell transplantation (HCT). However, studies have reported immunogenetic factors other than HLA also plays an important role in preventing and controlling post HCT complications, in particular graft-versus-host disease (GVHD) and infections. Cytokines and cytokine receptors, which are the key regulators of the immune surveillance against infections and/or allo-immune responses, are important candidates. The genetic control of cytokine production is evidenced by polymorphisms in cytokine gene regulatory regions resulting in low, moderate, or high cytokine production. Here, we evaluated the prognostic significance of cytokine genetic variants known to impact cytokine production on different outcomes of allogeneic HCT. Materials and Methods: A total of 793 subjects were accrued, including 360 adult allo-HCT donors (discovery cohort; n=240, and validation cohort; n=120), 382 HCT allo- recipients and 50 healthy individuals. All subjects of the discovery cohort and healthy individuals were analyzed for 22 single nucleotide variants located in the regulatory and/or exonic regions of 13 cytokine or cytokine receptor gene using sequence-specific primer based assay. All subjects of the validation cohort were genotyped SNPs located in the IL10 and IL1R gene promoter region by direct sequencing. . CMV specific immune response was assessed by stimulating PBMNCs from healthy individuals with CMV lysate and CMV peptide-pp65 followed by enumeration of IFN-g producing and CD107a expressing (degranulating) MNCs, T cells and their subsets. Results: Cytokine gene variants of donors and not that of recipients appear to influence post HCT complications. Two of these variant system- SNPs leading to low production of IL10 and high production of IL1R showed strong correlation with GVHD and posttransplant CMV infections respectively. Allo-HCT recipients who have received graft from donors carrying low IL-10 producing gene variants (AA) at IL-10 -1082G/A and (ATA/ATA at IL-10 -1082G/A, -819C/T and -592C/A) have high incidence of significant GVHD (defined as grade II-IV acute GVHD and/or chronic GVHD requiring systemic immunotherapy) (P=0.008, HR= 2.8, Figure 1; and P=0.03, HR= 1.8 respectively) compared to recipients who have received graft from donors carrying high producer IL-10 gene variants. Allo-HCT recipients who have received graft from donors carrying low IL-1R producing genotype (-1970 CC) showed high incidence of CMV reactivation (p=0.01; HR=2.1, Figure 2) in comparison to the HCT recipients receiving grafts from donors carrying high IL1-R producing genotype (-1970 GG). Further, the in-vitro culture analysis in healthy individuals showed that IL1R+ cells have 3-5 fold stronger anti-CMV immune responses (IFNg+ and/or CD107a+ cells) in comparison to IL1R- cells. Conclusions: Genetic predisposition to low IL-10 production is a strong predictor of GVHD while that tohigh production of IL-1R confers strong protection against CMV reactivation after allogeneic HCT. Thefindings implicate the importance of prospective assessment of cytokine gene variants to improve allogeneic HCT donor selection. Figure 1: Figure 1:. High incidence of significant GVHD in HCT recipients receiving grafts from donors carrying low IL-10 producing genotypes (-1082 AA) than donors carrying high IL-10 producing genotype (-1082 GG). Figure 2: Figure 2:. High incidence of CMV reactivation in HCT recipients receiving grafts from donors carrying low IL-1R producing genotype (-1970 CC) than donors carrying high IL-1R producing genotype Disclosures No relevant conflicts of interest to declare.
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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 | 0.000 |
| 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) | 0.002 | 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".