| Literature DB >> 35844527 |
Matthew J Bottomley1,2, Paul N Harden1, Kathryn J Wood2, Joanna Hester2, Fadi Issa2.
Abstract
Background: Malignancy is a major cause of morbidity and mortality in transplant recipients. Identification of those at highest risk could facilitate pre-emptive intervention such as reduction of immunosuppression. Reduced circulating monocytic HLA-DR density is a marker of immune depression in the general population and associates with poorer outcome in critical illness. It has recently been used as a safety marker in adoptive cell therapy trials in renal transplantation. Despite its potential as a marker of dampened immune responses, factors that impact upon monocytic HLA-DR density and the long-term clinical sequelae of this have not been assessed in transplant recipients.Entities:
Keywords: HLA-DR; cytokines; gene expression; immunosuppression; kidney transplantation; long term; malignancy; monocyte
Mesh:
Substances:
Year: 2022 PMID: 35844527 PMCID: PMC9283730 DOI: 10.3389/fimmu.2022.901273
Source DB: PubMed Journal: Front Immunol ISSN: 1664-3224 Impact factor: 8.786
Cohort characteristics at recruitment.
| Characteristic | Transplant Recipients (n = 135) | Controls (n=29) |
|---|---|---|
| Male gender | 92 (68.1) | 16 (55.2) |
| Caucasian ethnicity | 131 (97.0) | 29 (100) |
| Age (years) | 62.6 ± 10.8 | 74.9 ± 8.9 |
| >1 renal transplant | 25 (18.5) | NA |
| Body mass index (kg/m2) | 25.8 ± 4.7 | NA |
| Previous or current smoker | 52 (38.5) | NA |
| Cytomegalovirus (CMV) seropositive | 86 (63.7) | NA |
| Total HLA mismatch | 2.6 ± 1.5 | NA |
| Total duration of immunosuppression (months) | 244 ± 102 | NA |
| Time since most recent transplant (months) | 223 ± 99 | NA |
| eGFR at recruitment (mL/min/1.73m2) | 48.8 ± 19.1 | 70.0 ± 19.1 |
| eGFR at study end (mL/min/1.73m2) | 48.3 ± 20.8 | N/A |
| Induction therapy at most recent transplant | 99 (73.3) | NA |
| Immunosuppression use at recruitment | 107 (79.3) | NA |
| Number of immunosuppressive agents at recruitment | 17 (12.4) | NA |
| History of previous non-keratinocyte malignancy | 16 (11.9) | NA |
| History of previous SCC | 59 (43.7) | 20 (68.9) |
Categorical values are provided as n (%) whilst continuous variables are reported as mean ± SD. ‘NA’ not assessed/applicable. Data was available for all participants.
Figure 1Distribution of HLA-DR binding density in transplant recipients, stratified by gender. The broken line indicates the cut-off for diagnosis of immunodepression as described in (25).
Figure 2Forest plot of discrete variables at recruitment and their univariable influence upon mHLA-DRd in renal transplant recipients. Densities are recorded as mean and 95% confidence interval, with p values assessed by two-tailed independent t-test or analysis of variance with post-hoc Tukey testing. ‘ns’ not significant; * p<0.05; **p<0.01; ***p<0.001 across all groups (ANOVA or t-test, indicated on right, with post-hoc testing, where appropriate, indicated on left with the vertical line indicating comparison groups). #p<0.10.
Univariate analysis of continuous variables and their association with mHLA-DRd in renal transplant recipients.
| Factor | Univariate Correlation |
|
|---|---|---|
| Age | -0.02 | 0.80 |
| Body Mass Index | -0.02 | 0.85 |
| Total Immunosuppression Duration | -0.08 | 0.39 |
| Time since last transplant | -0.10 | 0.26 |
| Total HLA mismatch | -0.08 | 0.38 |
| eGFR at recruitment | 0.07 | 0.42 |
| % eGFR change from recruitment to study end | 0.08 | 0.39 |
| Serum C-reactive protein within 14 days of recruitment | -0.21 | 0.14 |
Spearman’s correlation is reported (r) and significance of correlation.
Figure 3Monocytic HLA-DR density shows stability over time. mHLA-DRd was calculated at enrolment and repeated a mean of 238 (A) and 385 (B) days later. Spearman’s test was used to assess goodness-of-fit (r2) and significance.
Figure 4mHLA-DRd correlates inversely with CD19-CD3-CD56-CD14+/intCD11b+CD33+HLA-DRlo monocyte myeloid-derived suppressor-like cell accumulation. (A) Example of gating strategy to delineate monocyte subpopulations. The plots on the far left were derived from live (7AAD-negative), singlet cells. MDS like-cells were derived from a Boolean gate, and were HLA-DRlo, CD11b+CD33+ and of classical, non-classical or intermediate monocyte morphology. Correlation between mHLA-DRd and (B) proportion of mMDSC-like cells within the monocyte population and (C) absolute number of mMDSC-like cells. Spearman’s test was used to assess goodness-of-fit (r2) and significance.
Figure 5Monocytes from RTR with low mHLA-DRd exhibit suppression of gene sets relating to inflammatory response and chemotaxis. (A) Pathways with significantly altered enrichment in RTR exhibiting low mHLA-DRd, using the Gene Ontology knowledge base. Pathways are listed in order of descending GeneRatio. (B) Differential expression of core genes in ‘Chemokine receptor binding’ and ‘Inflammatory response’ Gene Ontology sets.
Figure 6Decreased mHLA-DRd is associated with subsequent malignancy development. (A) Receiver-operator characteristic (ROC) curve for prediction of malignancy in the year following mHLA-DRd quantification. Predictive performance is given [area under curve (AUC) and 95% confidence interval] as well as the optimal cut-off and corresponding sensitivity and specificity. (B) Kaplan-Meier curve demonstrating cumulative incidence of malignancy stratified by mHLA-DRd. (unadjusted and adjusted hazard ratios and P values are given in ; log-rank test for difference between curves is provided in-plot).
Hazard ratios for malignancy development within one year of mHLA-DRd quantification.
| 1 year cancer risk | Disease specific (CPH) | Competing Risk | |||
|---|---|---|---|---|---|
| Dependent: | All | HR (Univariate) | HR (Multivariate) | HR (Fine + Gray Multivariate) | |
| Age at enrolment (years) | Mean (SD) | 62.6 (10.8) | 1.08 (1.04-1.12, p<0.001) | 1.07 (1.03-1.12, p<0.001) | 1.07 (1.04-1.11, p<0.001) |
| Cumulative duration of immunosuppression (months) | Mean (SD) | 244.0 (103.2) | 1.00 (1.00-1.00, p=0.918) | 1.00 (0.99-1.00, p=0.434) | 1.00 (0.99-1.00, p=0.400) |
| Previous malignancy at enrolment | No | 119 (88.1) | |||
| Yes | 16 (11.9) | 0.55 (0.13-2.31, p=0.414) | 1.31 (0.29-5.88, p=0.721) | 1.33 (0.39-4.51, p=0.650) | |
| Taking prednisolone at recruitment | No | 89 (65.9) | |||
| Yes | 46 (34.1) | 0.73 (0.32-1.65, p=0.445) | 0.37 (0.15-0.89, p=0.027) | 0.37 (0.16-0.84, p=0.018) | |
| Chronic UV exposure at enrolment | No | 54 (40.0) | |||
| Yes | 81 (60.0) | 2.57 (1.04-6.35, p=0.040) | 2.19 (0.88-5.47, p=0.092) | 2.27 (0.86-5.94, p=0.096) | |
| Previous SCC | No | 77 (57.0) | |||
| Yes | 58 (43.0) | 4.80 (2.04-11.31, p<0.001) | 4.86 (1.95-12.07, p=0.001) | 4.84 (1.70-13.81, p=0.003) | |
| Enrolment mHLA-DRd | Low | 65 (48.1) | |||
| High | 70 (51.9) | 0.41 (0.18-0.90, p=0.027) | 0.29 (0.12-0.69, p=0.005) | 0.29 (0.12-0.70, p=0.006) | |
All factors analysed for univariate significant were included in multivariate models as a pre-specified analysis. Age at recruitment and age at last transplant demonstrated strong correlation, as did number of immunosuppressive agents and the use of prednisolone at recruitment, and so only age and use of prednisolone at recruitment were used in the multivariate models. Two models were generated: one using Cox Proportionate Hazards modelling and a second using competing-risk modelling, using graft loss and death as competing risks.