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New biomarkers for the diagnosis and prognosis of GvHD

Aug 30, 2022
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Learning objective: After reading this article, learners will be able to cite new biomarkers used for monitoring patients with GvHD.

For many patients with hematologic malignancies, the only curative option is hematopoietic stem cell transplantation (HSCT). However, HSCT is associated with a high treatment-related mortality, infection, and, in particular, graft-versus-host disease (GvHD).1,2 Previous work on biomarkers for GvHD led to the development of a panel of acute GvHD (aGvHD) markers that included interleukin (IL)-2 receptor alpha chain, tumor necrosis factor 1, hepatocyte growth factor, and IL-8.3 The use of this panel achieved an 85% confirmed diagnosis rate in patients at symptom onset.3

Research is now being undertaken to investigate the role that biomarkers may have as predictors of subsequent aGvHD and chronic GvHD (cGvHD), severity of disease, treatment response, and clinical outcome.1,2 Here, we summarize recent studies in this area.

Pre-HSCT predictors of GvHD1

Despite the major advances in the understanding of genetic factors associated with acute myeloid leukemia (AML) in the past 30 years, 20–30% of patients fail to achieve remission with the current standard of care. In older patients with AML, outcomes are worse with an overall survival (OS) of <1 year in those aged >60 years. Due to the increasing incidence of AML with age, and the changing biology of AML associated with age, Siamakpour-Reihani, et al.1 hypothesized that metabolomic and inflammatory biomarkers may be predictive of HSCT outcome in patients with AML. They assessed plasma biomarkers in 34 patients and compared expression levels between shorter and longer OS groups, younger and older age groups, and several other clinical outcomes.

Metabolic biomarkers

Lower baseline levels of lactate were found to be associated with the development of aGvHD (odds ratio [OR], 0.24; 95% confidence interval [CI], 0.06–0.69; p = 0.019). It is thought that the dysregulation of lactate-pyruvate fuel metabolism is associated with poor clinical outcomes after HSCT. T cells rely on glycolysis and lactate for effector functions, with mouse models showing lactate plasma levels to be significantly elevated at Day 7 post-HSCT. The authors postulate that this timing of lactate elevation may explain the association they observed between lower baseline lactate levels and aGvHD.

Baseline acylcarnitines also showed an association with the development of aGvHD. Patients who developed aGvHD had lower baseline levels of C2-acylcarnitine, 3-hydroxy-tetradecanoyl carnitine, or dodecanedioyl carnitine (OR, 0.24; 95% CI, 0.05–0.75; p = 0.028) and lower levels of 3-hydroxy-tetradecenoyl carnitine (OR, 0.35; 95 % CI, 0.11–0.93; p = 0.049) compared with patients who did not have aGvHD. Long-chain acylcarnitines were also lower at baseline in patients who developed aGvHD.

Elevated levels of glutaryl carnitine were associated with cGvHD, with higher baseline levels being predictive of cGvHD (OR, 4.97; 95% CI, 1.49–21.99; p = 0.017). Acylcarnitines, such as glutaryl carnitine, are associated with omega oxidation and oxidative stress.

Blood-based biomarkers

One plasma marker was found to be significantly associated with cGvHD. Fms-like tyrosine kinase 1 (Flt‑1) was observed at higher baseline expression levels in patients who developed cGvHD post-HSCT than in those who did not (OR, 1.71; 95% CI, 1.13–2.78; p = 0.017).

Both IL-9 and eotaxin-3 were observed at lower baseline levels in patients who developed aGvHD following HSCT, as shown below.

  • IL-9 (OR, 0.37; 95% CI, 0.16–0.71; p = 0.009)
  • Eotaxin-3 (OR, 0.35; 95% CI, 0.11–0.84; p = 0.040)

Biomarkers to predict aGvHD severity2

Studies have identified several biomarkers associated with GvHD severity, with one panel of seven biomarkers (detailed below) having been extensively studied. Robin, et al.2 aimed to undertake an independent external validation of the biomarker panel to assess their prognostic value in terms of OS, non-relapse mortality (NRM), and corticosteroid response in GvHD.2 The following biomarkers were included in the panel:

  • Elafin
  • Hepatocyte growth factor
  • IL-2 receptor alpha
  • IL-8
  • Regenerating islet-derived 3-alpha
  • Tumor necrosis factor receptor 1 (TNFR1)
  • Suppression of tumorigenicity 2

In a single-center study, biomarker levels were assessed at GvHD onset prior to corticosteroid treatment and their ability to predict complete response to corticosteroid treatment was assessed at Day 28. OS and NRM were analyzed at Day 180. Analyses of nonresponse, survival, and NRM according to biomarker levels are detailed in Table 1. In the univariate analysis, all biomarkers were associated with the three outcomes. In the multivariate analysis, adjusted for age, the initial GvHD grade and initial liver involvement maintained a significant association between NRM and OS at Day 180.

Table 1. Biomarkers as risk factors for outcomes*

Outcome/cytokine panel

Univariate analysis

Adjusted analysis

OR or HR (95% Cl)

p value

OR or HR (95% Cl)

p value

Day 28 nonresponse

 

 

 

 

              High ST2

2.63 (1.36–5.06)

0.004

1.37 (0.63–2.98)

0.43

              High Panel-2

1.84 (1.01–3.37)

0.048

1.07 (0.52–2.18)

0.86

              High Panel-3

2.94 (1.52–5.67)

0.001

1.28 (0.57–2.86)

0.55

              High Panel-4

3.68 (1.90–7.14)

<0.001

2.03 (0.94–4.35)

0.07

              High Panel-6

2.63 (1.44–4.80)

0.002

1.39 (0.69–2.81)

0.36

Day 180 survival

 

 

 

 

              High ST2

3.89 (2.14–7.08)

<0.001

2.12 (1.13–3.99)

0.019

              High Panel-2

4.00 (2.14–7.50)

<0.001

2.65 (1.39–5.06)

0.003

              High Panel-3

4.07 (2.23–7.40)

<0.001

1.92 (1.00–3.68)

0.05

              High Panel-4

4.02 (2.21–7.31)

<0.001

2.16 (1.14–4.07)

0.017

              High Panel-6

3.92 (2.04–7.52)

<0.001

2.30 (1.16–4.55)

0.017

Day 180 non-relapse mortality

 

 

 

 

              High ST2

5.16 (2.54–10.4)

<0.001

2.43 (1.17–5.09)

0.018

              High Panel-2

4.50 (2.13–9.51)

<0.001

2.68 (1.25–5.77)

0.011

              High Panel-3

6.07 (2.96–12.4)

<0.001

2.45 (1.14–5.28)

0.022

              High Panel-4

5.25 (2.59–10.6)

<0.001

2.53 (1.21–5.28)

0.014

              High Panel-6

6.92 (2.85–16.8)

<0.001

3.61 (1.44–9.03)

0.006

CI, confidence interval; GvHD, graft-versus-host-disease; HGF, hepatocyte growth factor; HR, hazard ratio; IL-2Rα, interleukin 2 receptor alpha; IL-8, interleukin 8; OR, odds ratio; REG3α, regenerating islet-derived 3-alpha; ST2, suppression of tumorigenicity 2; TNFR1, tumor necrosis factor receptor 1.
*Adapted from Robin, et al.2
Panel-2 (ST2 and REG3α); Panel-3 (ST2, REG3α, and TNFR1); Panel-4 (IL-2Rα, TNFR1, HGF, and IL-8); Panel-6 (elafin, IL-2Rα, TNFR1, HGF, IL-8, and REG3α).
Adjusted on a simple clinical model where high-risk patients are those with initial liver GvHD, or those aged >50 years with initial GvHD Grade 3.

Despite these biomarkers being able to predict outcomes following GvHD diagnosis, Robin, et al. concluded that adding these biomarkers to clinical parameters only resulted in modest improvements in predictivity.

Biomarkers to predict response to treatment3

Corticosteroids are the first-line treatment for patients with aGvHD; however, the response rate is only 4060% and non-responders have a mortality rate of up to 80%.3 Adverse events, such as immunosuppression, osteopenia, and hyperglycemia, are commonly associated with prolonged corticosteroid use. Treatment response has traditionally been measured by changes in clinical symptoms and reduction in the overall grade after 4 weeks of therapy, but this method is imprecise with a poor predictive value. More recently, efforts have been focused on new therapies to taper and discontinue steroid use. The use of biomarkers to target new treatments to patients with a certain level of GvHD risk has demonstrated the utility of these markers. Pratta, et al.3 identified seven biomarkers and assessed these alongside JAK/STAT-related biomarkers and other validated prognostic biomarkers in a larger validation assessment of itacitinib treatment response:

Novel biomarkers from the identification step:

  • Monocyte-chemotactic protein 3
  • Tumor necrosis factor receptor superfamily member 6B
  • Stem cell factor
  • C-X-C motif chemokine ligand 10
  • Pro-calcitonin-related polypeptide alpha/calcitonin-related polypeptide alpha (proCALCA/CALCA)
  • Paraoxonase 3
  • Chemokine ligand 19

Additional biomarkers added to the validation assessment:

  • IL-6
  • IL-8
  • Suppressor of tumorigenicity 2
  • Regenerating islet-derived protein 3A
  • TNFR1
  • IL-2 receptor alpha chain

The baseline levels of these markers stratified by response are shown in Table 2.

Table 2. Baseline serum levels of biomarkers from the validation cohort stratified by response*

Protein

Itacitinib/corticosteroids

Placebo/corticosteroids

CR
(n = 52)

PD
(n = 18)

p value

CR
(n = 44)

PD
(n = 25)

p value

Novel biomarkers from the identification cohort

              MCP3

0.26 ± 0.09

0.68 ± 0.16

0.0265

0.40 ± 0.09

0.49 ± 0.26

NS

              TNFRSF6B

6.00 ± 0.11

6.09 ± 0.15

NS

5.83 ± 0.09

6.19 ± 0.14

0.0278

              SCF

6.13 ± 0.07

5.82 ± 0.14

0.0316

6.23 ± 0.07

5.99 ± 0.08

0.0355

              CXCL10

6.63 ± 0.15

6.93 ± 0.26

NS

6.43 ± 0.15

6.61 ± 0.21

NS

              ProCALCA/CALCA

7.49 ± 0.07

7.80 ± 0.14

0.0396

7.48 ± 0.07

7.60 ± 0.11

NS

              PON3

13.04 ± 0.06

12.76 ± 0.14

0.0390

13.10 ± 0.07

12.81 ± 0.08

0.0119

              CCL19

7.00 ± 0.14

7.02 ± 0.24

NS

6.92 ± 0.14

7.04 ± 0.22

NS

Validated prognostic biomarkers

              REG3A

8.62 ± 0.14

9.25±0.30

0.0352

9.13 ± 0.16

9.27 ± 0.26

NS

              ST2

9.96 ± 0.11

10.65 ± 0.24

0.0031

10.15 ± 0.11

10.69 ± 0.15

0.0063

              TNFR1

9.01 ± 0.05

9.10 ± 0.07

NS

9.09 ± 0.05

9.12 ± 0.07

NS

              IL2RA

6.51 ± 0.09

6.55 ± 0.16

NS

6.53 ± 0.10

6.58 ± 0.13

NS

JAK/STAT-related biomarkers

              IL-6

−0.01 ± 0.17

0.49 ± 0.35

NS

−0.18 ± 0.14

0.33 ± 0.22

0.0455

              IL-8

1.62 ± 0.12

2.32 ± 0.32

0.0112

1.82 ± 0.09

2.84 ± 0.31

0.0003

CCL19, chemokine ligand 19; CR, complete response; CXCL10, C-X-C motif chemokine ligand 10; IL2RA, interleukin 2 receptor alpha; IL-6, interleukin 6; IL-8, interleukin 8; JAK, Janus kinase; MCP3, monocyte-chemotactic protein 3; NS, not significant; PD, progressive disease/death; PON3, paraoxonase 3; proCALCA/CALCA, pro-calcitonin-related polypeptide alpha/calcitonin-related polypeptide alpha; REG3A, regenerating islet-derived protein; SCF, stem cell factor; STAT, signal transducer and activator of transcription; ST2, suppression of tumorigenicity 2; TNFR1, tumor necrosis factor receptor 1; TNFRSF6B, tumor necrosis factor receptor superfamily member 6B.
*Adapted from Pratta, et al.3

 Biomarker levels were then compared between patients who experienced a complete response versus progressive disease/death. Monocyte-chemotactic protein 3, proCALCA/CALCA, and regenerating islet-derived protein 3A divided complete responders from non-responders in the itacitinib arm, and therefore, present potential predictive biomarkers for patients with aGvHD and itacitinib treatment. The biomarkers proCALCA/CALCA, suppressor of tumorigenicity 2, and TNFR1 were significantly reduced over time by itacitinib in responders, potentially representing response-to-treatment biomarkers.

The biomarker levels of patients who achieved a complete response were then compared to those with progressive disease/death. The serum levels of the following biomarkers were significantly reduced over time by itacitinib (from baseline to Day 28) in patients who responded:

  • ProCALCA/CALCA (7.50 to 7.12 in the treatment arm and 7.52 to 7.39 in the placebo arm)
  • Suppressor of tumorigenicity 2 (9.97 to 9.88 in the treatment arm and 10.09 to 10.08 in the placebo arm)
  • TNFR1 (9.04 to 8.77 in the treatment arm and 9.04 to 8.90 in the placebo arm)

Cell-free DNA as a biomarker for aGvHD4

Cell-free DNA and nucleosomes are often released upon cell damage. Increased levels of circulating nucleosomes have been found to correlate with the severity of various types of systemic inflammation. For example, high levels of nucleosomes have been observed in sepsis, with highest levels observed in septic shock, and they can predict mortality in meningococcal sepsis. Nucleosome involvement in GvHD is unknown.

Kroeze, et al.4 studied cell-free DNA and nucleosome levels in plasma from 37 patients who had received a HSCT and in a xenotransplantation mouse model. The authors found that cell-free DNA levels and nucleosome levels correlated strongly, so they used nucleosome levels as a surrogate measure for total cell-free DNA levels. Prior to HSCT, nucleosome levels were similar across all patients, but 1 month after the HSCT procedure, they were significantly higher. Patients who developed aGvHD had significantly higher nucleosome levels than those who did not have aGvHD after diagnosis: 177 AU/ml (range, 14.6–729.0) vs 17.1 AU/ml (range, 5.7–554; p = 0.0006). Work with xenotransplantation into a mouse model found that while cell-free DNA mainly originates from donor hematopoietic cells, damaged host cells are also a source, though to a lesser extent.

Kroeze, et al. cited a previous study of cell-free DNA in pediatric patients, which supports their findings and found that increased cell-free DNA levels at Days 30, 60, and 100 after HSCT were associated with aGvHD development. The authors suggest that cell-free DNA could function as a general damage marker due to being released during cell activation and/or death.

Conclusion

There are several potential biomarkers currently under investigation for the diagnosis and prognosis of GvHD. Siamakpour-Reihani, et al.1 identified metabolic markers, lactate and acylcarnitines, to be potential indicators of poor clinical outcome prior to HSCT. In a validation study of a panel of biomarkers to predict clinical outcomes following GvHD diagnosis, Robin, et al.2 found that although there was an association between biomarker expression and response, survival, and NRM, their addition to clinical parameters only resulted in modest improvements in predictivity. In terms of biomarkers of treatment response, Pratta, et al.3 found that the novel markers proCALCA/CALCA, suppression of tumorigenicity 2, and TNFR1 were significantly reduced over time by itacitinib in responders. Lastly, Kroeze, et al.4 found, despite study limitations such as the small patient cohort, a potential role for cell-free DNA as an addition to aGvHD biomarker panels for patients receiving HSCT, highlighting that donor-derived hematopoietic cells may contribute more to circulating cell-free DNA than previously thought.

Each of these biomarkers are in various stages of risk stratification for patients diagnosed with GvHD, with the primary goal of guiding interventions post-HSCT, as well as the prediction of treatment response and clinical outcomes. However, further studies are required to confirm the potential of these markers.

  1. Siamakpour-Reihani S, Cao F, Lyu J, et al. Evaluating immune response and metabolic related biomarkers pre-allogenic hematopoietic stem cell transplant in acute myeloid leukemia. PLoS One. 2022;17(6):e0268963. DOI: 10.1371/journal.pone.0268963
  2. Robin M, Porcher R, Michonneau D, et al. Prospective external validation of biomarkers to predict acute graft-versus-host disease severity. Blood Adv. 6(16):4763-4772. DOI: 1182/bloodadvances.2022007477
  3. Pratta M, Paczesny S, Socie G, et al. A biomarker signature to predict complete response to itacitinib and corticosteroids in acute graft‐versus‐host disease. Br J Haematol. 2022;198(4):729-739. DOI: 1111/bjh.18300
  4. Kroeze A, Cornelissen AS, Pascutti MF, et al. Cell‐free DNA levels are increased in acute graft‐versus‐host disease. Eur J Haematol. 2022;109(3):271-281. DOI: 1111/ejh.13806