Single Nucleotide Polymorphism (SNP) Approach of Multiple Candidate Pathways Predicting the Risk of Acute / Chronic Graft-Versus-Host Disease or Transplant Outcomes Following Allogeneic Hematopoietic Stem Cell Transplantation: Potential Involvement of Nuclear Factor Kappa-B (NFKB), Platelet-Derived Growth Factor (PDGF) and Transforming Growth Factor-Beta (TGF-β) Pathway with Chronic Graft-Versus-Host Disease Graft-Versus-Host Disease.
Bibliographic record
Abstract
Abstract Abstract 2221 Poster Board II-198 Background: Acute graft-versus-host disease (GVHD) was known to be involved in the Th1 cytokine activation and alloreactive T-cell cytotoxicity, while the pathogenesis of chronic GVHD is yet revealed fully although in which Th2 cytokine activation or transforming growth factor (TGF) mediated pathway was suggested to be involved. The current study is a hypothesis generating study in order to identify potential predictive surrogate associated with the risk of acute or chronic GVHD in addition with transplant outcomes after allogeneic hematopoietic stem cell transplantation (HSCT). Methods: The current study was performed to identify genetic surrogates predicting the risk of acute / chronic GVHD, relapse free survival, non-relapse mortality and overall survival in 394 pairs transplanted at the Princess Margaret Hospital, Toronto, ON, Canada. In addition, the predictive markers for organ specific incidence of acute / chronic GVHD were also evaluated (i.e. for skin/liver/gut acute GVHD or skin, eye, oral, lung or liver chronic GVHD). Total of 261 single nucleotide polymorphisms (SNPs) in 56 genes were determined for donor/recipients' genotypes using MALDI-TOF based platform, involving in the pathways of 1) cytokines (i.e. IL1A, IL1B and its receptor, IL1R1, IL2 & IL2RA, IL4 & IL4R, IL6 & IL6R, IL8, IL10 & IL10RA, RB, IL12A/BandIL12RB1, IFNG & IFNGR1/2, TNFTI/II/II), 2) NFKB (NFKB1/2/A, NFKBIA/B, IKB, IKK1, IKBKB, RelB), 3) apoptosis (FAS, TRAIL & TRAILR1), 4) endothelium nitric oxide regulation (EDN1, NOS1/2A/3), 5) PDGF (PDGFB/C/D & PDGFRA/B), 6) TGF-β (TGFB1/2 & TGFBR1/2/3, TGFRB1), 7) Toll-like receptor (TLR4/5), 8) NOD2/CARD15 and 9) prostaglandin-endoperoxide synthase (PTGS1/2). The candidate genotypes have been selected by choosing the SNPs in non-synonymous SNPs in exon region with minor allele frequency of > 0.05 to 0.1. Results: Followings are the lists of recipients' and donors' genotypes with p-value<0.05 thus associating with clinical outcomes following allogeneic HSCT: In summary, the risk of chronic GVHD was significantly associated with SNP of the genes involved in the pathway of NFKB, PDGF, TGF-β, and some of cytokines (esp. type II, IL6 & IL4), while that of acute GVHD associates with the genotypes in the pathway of TNF and apoptosis. In addition, survival after allogeneic transplantation was associated with the genotypes in NOS (nitric oxide synthase, endothelial nitric oxide synthesis pathway), IL-2 and TGF pathway. Conclusion: Because of complex nature of GVHD pathogenesis, multiple candidate pathway SNPs has been explored targeting SNPs in the pathway of cytokines, NFKB, apoptosis, endothelium nitric oxide regulation, NOD2/CARD15, PDGF, PTGS1/2, TGF-β and TLR. Different involvements were noted of TGF-β, PDGF or NFKB with chronic GVHD versus TNF and apoptosis-associated SNPs with acute GVHD. Further study will help us to reach more clear conclusion which genotype is the predictor of the risk of GVHD. 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".