Biology of Blood and Marrow Transplantation
Volume 15, Issue 1, Supplement , Pages 33-38, January 2009

Plasma Biomarkers in Graft-versus-Host Disease: A New Era?

  • Sophie Paczesny

      Affiliations

    • Department of Pediatrics, University of Michigan, Ann Arbor, Michigan
    • Corresponding Author InformationCorrespondence and reprint requests: Sophie Paczesny, MD, PhD, University of Michigan Comprehensive Cancer Center 6420,1500 E Medical Center Dr. Ann Arbor, MI 48109-5942, Ph# 734-615-3342, Fax# 734-615-3947.
    • ,
  • John E. Levine

      Affiliations

    • Department of Pediatrics, University of Michigan, Ann Arbor, Michigan
    • Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan
    • ,
  • Thomas M. Braun

      Affiliations

    • Department of Biostatistics, University of Michigan, Ann Arbor, Michigan
    • ,
  • James L.M. Ferrara

      Affiliations

    • Department of Pediatrics, University of Michigan, Ann Arbor, Michigan
    • Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan

Article Outline

Acute graft versus host disease (GVHD) remains a major complication of allogeneic hematopoietic cell transplantation (HCT). The diagnosis of acute GVHD is based on strictly clinical criteria and its severity also determined by these criteria. Currently, there is no validated diagnostic blood test for acute GVHD. This review will summarize proteomics approaches to identify biomarkers for GVHD in the plasma with diagnostic, prognostic and predictive value. If successful, these studies could establish a novel biomarker panel that will contribute important information including long term survival, and that may eventually facilitate therapeutic decisions for allogeneic HCT patients.

Key Words: Graft versus host disease, proteomics, plasma biomarker, biomarkers panel

 

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Introduction 

The diagnosis of acute graft-versus-host disease (GVHD) is based on clinical criteria that may be confirmed by biopsy of 1 of the 3 target organs (skin, gastrointestinal tract, or liver). The severity of acute GVHD is graded clinically from I to IV using a standardized system that evaluates 3 principal target organs [1], with increased mortality rates associated with significant GVHD (grades II-IV) [2].

There is no validated diagnostic blood test for acute GVHD, although small studies have identified multiple blood proteins as potential biomarkers 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23. Differences in any single protein lack sufficient specificity and sensitivity to be of clinical use, however. Although recent mass spectrometry (MS) profiling of urine 24, 25 and serum [26] has found spectral patterns associated with GVHD, these approaches do not identify specific proteins. We previously reported a quantitative analysis of a number of potential biomarkers for GVHD in the plasma of a small number of patients [27]. To date, however, no study has developed a simple noninvasive test that indicates GVHD in a sufficient number of patient samples that would allow determination of its significance with respect to clinical outcomes.

The complex pathophysiology of GVHD [28] suggests that plasma proteins involved in multiple processes (eg, T cell alloreactivity, inflammation, tissue damage and repair) might be altered in patients with the disease. Furthermore, the dynamic nature of the circulatory system and the ease of blood sampling make blood a logical choice for biomarker applications. Blood components include various cellular elements, such as immunologic cells, leukemic cells, cell-free DNA and RNA, proteins, peptides, and metabolites. Proteins that are detectable in plasma or serum form the basis of common tests to screen and monitor several cancers, including prostate-specific antigen for prostate cancer and Ca125 for ovarian cancer. The goal of having such biomarker in the blood for the diagnostic and prognostic of acute GVHD has not yet been achieved.

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One Proteomics Discovery Approach 

Experimental design plays a crucial role in a successful biomarker search. The first step in this design is to choose the most informative specimens and achieve adequate matching between cases and controls to avoid bias. This goal is best achieved through a database containing high-quality samples linked to quality-controlled clinical information. We started a repository at the University of Michigan in 2000 that currently contains approximately 8000 samples from 850 individuals. Blood was drawn at approximately weekly intervals in the first 2 months after hematopoietic cell transplantation. We analyzed these samples using an antibody microarray containing arrayed antibodies to 120 human proteins that targeted diverse classes of proteins, including acute-phase reactants, cytokines, angiogenic factors, tumor markers, leukocyte adhesion molecules, and metalloproteinases and their inhibitors. We hypothesized that samples from patients with severe GVHD would be most likely to yield informative biomarkers.

We first performed a discovery study that compared samples from 21 patients with severe acute GVHD (GVHD+severe) with samples from 21 patients without GVHD who were similar in age, intensity of conditioning regimen (reduced vs full), donor source (related vs unrelated), and time of sample acquisition. Figure 1 shows the 35 biomarkers that exhibited the greatest differences between the 2 groups [29].

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  • Figure 1 

    Antibody array heatmap of discovery set samples. This heatmap depicts relative protein values obtained from antibody microarrays after the removal of batch effects due to 3 separate analyses. Samples from 21 GVHD– patients (A) and 21 GVHD+ patients (B) are represented. Only the antibodies giving the 35 smallest P values for differences between GVHD+ and GVHD– patient plasma are shown. The P values compare the GVHD+ and GVHD– samples. (Reproduced with permission [29].)

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Validation Strategies for Discovered Protein Biomarkers 

For any biomarker, the path from discovery to approval for clinical use is arduous. The biomarker validation process is long and involves several steps (although it is more direct than the discovery step). Validation studies have obstacles of their own. Most noteworthy of these is the paucity of affinity-capture agents, such as high-quality antibodies with the required affinity and specificity for the target. The number of samples required for validation also increases as the biomarker advances though the phases, hence the need for high-throughput assays. The most relied-on approach for validation remains the sandwich enzyme-linked immunosorbent assay (ELISA), which is highly specific, with a pair of antibodies used against the candidate protein. In our study, we used a sequential ELISA protocol to maximize the number of measured analytes per sample. This sequential protocol measures multiple analytes per plasma sample by reusing the same aliquot consecutively in individual ELISA plates.

Another level of validation involves using a statistical validation set, a portion of the data set used to assess the performance of classification or prediction models that have been fit on a separate portion of the same data set (the training set). Both the training and validation sets are randomly selected, with the validation set providing a more objective measure of the performance of various models that are fit to the training data.

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Rational Design of Biomarkers Panels for GVHD Diagnosis 

Single biomarkers clearly lack the required sensitivity and specificity for most clinical applications. Specificity and sensitivity are best represented by a receiver operating characteristic (ROC) curve, a plot of the false-positive rate on the x axis and the true-positive rate on the y axis for every possible level of a marker. A perfect test would have a ROC curve appearing as a right angle, demonstrating 100% of true positives and no false positives. In this case, the corresponding area under the curve (AUC) will equal 1. A random test will have an AUC of 0.5, meaning 1 false positive for every 1 true positive. A biomarker panel might include a candidate biomarker that otherwise may be dismissed if it provides only modest sensitivity in initial studies. This biomarker may be valuable if it is informative with respect to a particular subset of subjects. For example, in our study, logistic regression models determined that a linear combination of values for interleukin (IL)-2Rα, tumor necrosis factor receptor 1 (TNFR1), hepatocyte growth factor (HGF), and IL-8 produced the best model for predicting the occurrence of acute GVHD. Figure 2 shows the ROC curves of these 4 biomarkers and the composite biomarker panel, with an AUC for the composite biomarker panel of 0.91 (95% confidence interval [CI] = 0.87 to 0.94). Levels of IL-2Rα and TNFR1 contributed primarily to the model's accuracy (P < .001 and P = .003, respectively). When logistic regression models were used to determine whether the 4 biomarker panel provided prognostic information, HGF was the only marker that predicted maximum GVHD grade (P = .003), and both HGF and IL-2Rα were associated with specific target organs (P = .03 and .04, respectively).

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  • Figure 2 

    ROC curves of 4 individual discriminator proteins and the composite panel in the training set. Individual ROC curves for IL-2Rα, TNFR1, HGF, and IL-8 and the composite panel. (Reproduced with permission [29]).

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Clinical Applications 

Physicians are interested in low-risk and high-risk groups for predicting the development of GVHD and clinical outcomes. In the present study, we divided the patients in the training set into a high-risk group and a low-risk group based on their predicted probability for developing GVHD. We determined the threshold for high so that the false-positive rate did not exceed 5%. We then investigated whether the 4- biomarker panel described above provided prognostic information regarding the eventual maximum grade of GVHD and nonrelapse mortality (NRM) and overall survival (OS).

In the training set, the low-risk group consisted primarily of patients with grade 0 GVHD, as shown in Figure 3A; only a few patients had GVHD grade I-IV. In the high-risk group, < 10% of patients had GVHD grade 0, and most patients had GVHD grade I-IV. Importantly, the results were very similar in the validation set, where the model predicted the GVHD grades of the patients without knowledge of their clinical symptoms (Figure 3B).

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  • Figure 3 

    Low-risk and high-risk groups correlated with GVHD grade. The blue boxes represent the low-risk groups, and the red boxes represent the high-risk groups, in the training (A) and validation (B) sets. The solid blue represents GVHD grade 0; the solid red, GVHD grade I-IV.

Figure 4 shows the NRM and OS for the low-risk and high-risk groups. When adjusted for age, donor type, HLA match, and intensity of conditioning, the differences in NRM between the 2 groups were highly significant (P = .001; Figure 4A). When we applied the same definition to the validation set, the false-positive rate in the high-risk group was 6%. The NRM between groups was again significantly different when adjusted for all 4 variables (Figure 4B; P < .001). The 2 groups also experienced significantly different OS in both the training set (Figure 4A; P = .006, adjusted) and validation set (Figure 4B; P = .02, adjusted).

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  • Figure 4 

    NRM and OS stratified by the biomarker panel in the training set (A) and validation set (B). In (A), the cumulative incidence of NRM and OS (determined by Kaplan-Meier) are plotted according to the predicted probability of acute GVHD: low (—; n = 193) and high (---; n = 89) (P = .001 and .006, adjusted for age, donor type, HLA match, and intensity of conditioning, for differences in NRM and OS, respectively). NRM at 3.5 years is 15% (95% CI = 9% to 21%) for the low-risk group and 36% (95% CI = 24% to 48%) for the high-risk group. OS at 3.5 years is 53% (95% CI = 45% to 63%) for the low-risk group and 33% (95% CI = 22% to 48%) for the high-risk group. In (B), the cumulative incidence of NRM and OS of the 2 groups are plotted for the validation set: low (—; n = 93) and high (---; n = 49) (P < .001 and .02, adjusted as before, for differences in NRM and OS, respectively). NRM at 3.5 years is 11% (95% CI = 4% to 19%) for the low-risk group and 38% (95% CI = 23% to 53%) for the high-risk group. OS at 3.5 years is 59% (95% CI = 49% to 72%) for the low-risk group and 44% (95% CI = 31% to 63%) for the high-risk group. (Reproduced with permission [29].)

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A Second Proteomics Approach for Target Organ–Specific Biomarkers 

Studies of the use of various mass spectrometry (MS)-based proteomic approaches to diagnose GVHD have produced promising results 24, 25, 26. An advantage of these approaches is that identification of proteins does not depend on the availability of antibodies, as is the case when using microarrays. These MS-based techniques do have some inherent disadvantages, including inability to identify specific proteins, labor-intensity, lack of speed, and limited sensitivity for proteins present at low levels. Newer technologies, such as tandem MS (MS/MS), make these approaches more attractive. Given the low abundance of known individual GVHD markers in serum and plasma, the issue is whether current proteomic technologies provide sufficient depth of analysis for novel biomarker discovery. Three studies using current MS/MS technologies have identified proteins in low concentrations in plasma 30, 31, 32.

No plasma biomarkers are specific to any of the 3 target organs of acute GVHD: skin, gastrointestinal (GI) tract, or liver. We sought to identify a biomarker that is specific for GVHD of the skin (sGVHD) in an initial discovery step using an intact proteomic analysis system (Figure 5). We compared plasma pooled from 10 patients with sGVHD only, plasma pooled from 10 patients with no GVHD, and plasma pooled from 10 patients with GVHD of the GI tract only. Of the 4 candidate proteins that were both significantly elevated only in the plasma of the sGVHD patients and could be measured by ELISA, we selected elafin, an epidermal proteinase inhibitor that is induced by TNF-α and found in inflamed epidermis in such diseases as psoriasis. We measured levels of elafin in individual samples of the discovery set and found that they were significantly higher in the samples from the patients with sGVHD compared with the samples from the patients with GI GVHD and without GVHD. We also analyzed a validation set of > 400 plasma samples from patients who had undergone allogeneic bone marrow transplantation at the University of Michigan. Elafin levels in the plasma from the patients with sGVHD were double those in the plasma from the patients without GVHD.

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  • Figure 5 

    Intact protein analysis system workflow and in-depth analysis of plasma proteins. Plasma pooled from 10 patients with GVHD was labeled with the heavy isotope and compared with plasma pooled from 10 patients with no GVHD labeled with the light isotope. The specimens were then subjected to extensive fractionation (by ion-exchange chromatography and reverse-phase chromatography) before individual fractions were analyzed. This decreased the complexity of individual fractions subjected to analysis by liquid chromatography-MS/MS. (Adapted from [30].)

We next explored whether elafin level can provide prognostic information regarding the eventual maximum stage of sGVHD, transplantation-related mortality (TRM), and OS. For this purpose, we divided the patients into 2 groups, using a threshold level of elafin that provided 85% specificity. The group with the high elafin levels developed more severe sGVHD (maximum stage), higher TRM at 1 year, and lower OS at 1 year compared with the group with low elafin levels. Our findings suggest that a biomarker such as elafin that is specific for a target organ (skin) can be discovered and validated and can provide important diagnostic and prognostic information that eventually could be useful in modifying our therapeutic options.

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Acknowledgments 

Financial disclosure: The authors have nothing to disclose.

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 Financial disclosure: See Acknowledgments on page 37.

PII: S1083-8791(08)00475-8

doi:10.1016/j.bbmt.2008.10.027

Biology of Blood and Marrow Transplantation
Volume 15, Issue 1, Supplement , Pages 33-38, January 2009

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