Early Prediction of Moderate-Severe Chronic GvHD By Immunity Related Transcriptome
Bibliographic record
Abstract
Abstract Introduction:Graft versus host disease (GvHD) is the most common and debilitating complication of allogeneic hematopoietic cell transplantation (HCT). Among the two clinically significant forms, moderate-severe chronic GvHD (cGvHD) is more difficult to treat than grade 2-4 acute GvHD (aGvHD). Chronic GvHD rarely shows a sustained complete response to immunosuppressive drugs and non-responders either die or suffer long-term. However, if an early and accurate prediction of ensuing cGvHD is posiible, then the high risk patients can be identified and treated preemptively. In the present investigation, we retrospectively analyzed the transcriptome of immunity related genes at one month post-transplant with the aim to identify a potential early and accurate predictor of clinically significant cGvHD. Methods:A cohort of 73 HLA matched first allogeneic HCT recipients with moderate-severe cGvHD (n = 31) and no chronic GvHD (n = 42) were included in the study. All patients received similar myeloablative conditioning regimens along with 4.5 mg/kg antithymocyte globulin as GvHD prophylaxis. The diagnosis of cGvHD was based on National Institutes of Health consensus criteria and median day of diagnosis in our cohort was 126 days. Total RNA was extracted from cryopreserved peripheral blood mononuclear cells (PBMNCs) at one month post-transplant. Gene expression CodeSet profiling of 594 immunity related genes including 15 internal reference genes was performed using Nano string Technology Immunology panel. Differential gene expression and receiver operating characteristic (ROC) analyses was performed using R statistical packages and GraphPad PRISM software. Benjamini Hochberg procedure was used to calculate the P value and the false discovery rate (FDR). Differentially expressed genes with a Benjamini Hochberg P value (BHP) of <0.05 and FDR of <0.01 were considered significant. Cumulative incidence of moderate-severe chronic GvHD was calculated using Fine-Gray competing risk analysis. Results:A gene panel consisting of immunity related 12 genes was identified at one month post-transplant whose transcriptome profile was significantly different between patients that developed moderate-severe cGvHD compared to the patients who did not develop cGvHD. The identified gene panel included highly upregulated T cell activation and proliferation markers like CD3D (BHP=0.005), CD3E (BHP=0.01), CCR7 (BHP=0.005), CD5 (BHP=0.006); TNF receptor superfamily markers like B- and T-lymphocyte attenuator-BTLA (BHP=0.004), CD27 (BHP=0.004), CD40LG (BHP=0.005); and Costimulatory signaling molecules like Inducible T-cell Co-stimulator ICOS (BHP=0.001), CD28 (BHP=0.006) in patients with cGVHD. A gene score of >7 in this panel of 12 genes was able to differentiate patients with high risk of developing mod-severe cGvHD with a sensitivity of 79% and specificity of 88% (AUC = 0.88, P <0.0001) (Figure 1). Conclusions:The cGvHD gene panel consisting of 12 genes from multiple immune regulatory pathways identified in the present study provided an early prediction of moderate-severe cGvHD (one month) with high sensitivity and specificity. The robust and rapid technique of nanostring based transcriptome profiling has the potential to be implemented in the clinic. Differentiating patients at high risk of mod-severe cGvHD can help develop new strategies for preemptive therapy and pave the way towards precision medicine leading to improved outcomes of HCT. 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 | 0.000 | 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) | 0.001 | 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".