Plasma-based protein biomarkers can predict the risk of acute graft-versus-host disease and non-relapse mortality in patients undergoing allogeneic hematopoietic stem cell transplantation

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Abstract

Predictive biomarkers for acute graft-versus-host disease (aGVHD) is currently lacking. In this study, we employed an unbiased proteome profiling method to prospectively collected plasma samples from allogeneic hematopoietic stem cell transplantation (alloHSCT) recipients to identify protein biomarkers that predict the risk of aGVHD and non-relapse mortality (NRM). In the discovery set, including five aGVHD patients and five controls, we identified seven candidate proteins. Patients with high levels of these proteins tended to exhibit a higher risk of aGVHD and NRM compared to patients with low levels in post-engraftment plasma samples from an independent validation set (n = 89). Tissue inhibitor of metalloproteinase 1, plastin-2, and regenerating islet-derived protein 3-α were selected as the most-predictive biomarkers via an exhaustive variable screening algorithm and were collectively used to develop a biomarker panel score ranging from 0 to 3. The biomarker panel score correlated significantly with aGVHD and NRM risk in univariable and multivariable Cox models. Furthermore, using the biomarker panel score in conjunction with clinical predictors significantly improved the discriminatory performance of the Cox model in predicting aGVHD and NRM risk. Our findings suggest that plasma-derived protein biomarkers can be used to predict aGVHD and NRM before the onset of clinical manifestations.

Introduction

Allogeneic hematopoietic stem cell transplantation (alloHSCT) is a cornerstone treatment for many malignant and non-malignant hematologic disorders, often providing the only chance of cure. In malignant diseases such as acute leukemia and lymphoma, alloHSCT provides an additional therapeutic benefit via the graft-versus-tumor effect [1,2]. However, despite routine prophylaxis with immunosuppressive agents, in approximately half of alloHSCT recipients, the allogeneic immune system recognizes and attacks normal host tissue, a condition known as acute graft-versus-host disease (aGVHD) [3]. aGVHD represents a critical barrier to widespread clinical application of alloHSCT as an upfront therapeutic option and is a major cause of non-relapse mortality (NRM) in alloHSCT patients [1].

The current approach to aGVHD prophylaxis is poorly standardized across countries and centers [4]. Therefore, an emphasis on establishing a consensus guideline regarding optimal prevention strategies has emerged [5]. However, most current recommendations depend solely on clinical factors that are insufficient for precise risk stratification, such as conditioning regimen, donor-recipient relationship, and HLA match status [5]. Development of a finely tailored prophylactic therapy based on an individual's risk profile will thus require a more comprehensive model that enables accurate prediction of aGVHD risk before clinical manifestation of symptoms.

Over the last decade, extensive research has been devoted to identifying GVHD-specific biomarkers with diagnostic or prognostic value. The greatest success achieved to date involves plasma-derived protein markers associated with aGVHD diagnosis [[6], [7], [8], [9]], treatment response [7,[9], [10], [11], [12]], and survival [[6], [7], [8], [9], [10], [11], [12]]. Studies have suggested that the proteome patterns of patients who develop aGVHD are distinct from patients who do not develop the disease, even before the onset of clinical symptoms, suggesting that risk-adaptive prophylactic therapies are possible [[13], [14], [15], [16]]. However, although both diagnostic and prognostic biomarkers have been extensively investigated, few studies have explored the value of these biomarkers for predicting future occurrence of aGVHD. A support vector machine-derived classifier based on urine proteome profiles of aGVHD patients was capable of predicting aGVHD occurrence prior to clinical diagnosis with promising sensitivity and specificity [13,14]. However, the extensive filtration, reabsorption, and metabolism proteins undergo during renal excretion suggest that the plasma proteome might better reflect the pathophysiology of aGVHD [17]. Other researchers identified plasma biomarkers that could diagnose aGVHD in the preclinical stage, but they focused only on candidate biomarkers preselected based on biological relevance [15,16]. In this study, we employed an unbiased proteomics approach using a liquid chromatography (LC)-tandem mass spectrometry (MS/MS)-based method to identify protein biomarkers that can predict the risk of aGVHD and NRM in prospectively collected plasma samples from alloHSCT patients. We found that a biomarker panel score involving three proteins significantly correlated with aGVHD and NRM risk and improved the discriminatory performance of the model when added to clinical characteristics in terms of predicting aGVHD and NRM risk before the onset of clinical manifestations.

Section snippets

Study design

Eligible patients were retrospectively identified from a registry of patients with benign or malignant hematologic disorders who underwent first alloHSCT between 2005 and 2011 at Seoul National University Hospital (SNUH) and had provided written informed consent for plasma sample collection. All patients in the registry provided plasma samples at the beginning of conditioning chemotherapy, on the day of stem cell infusion, and weekly thereafter until discharge or death. Clinical diagnosis of

Patient characteristics

Patient characteristics are summarized separately for the discovery and validation sets in Table 1. The discovery set included two acute myeloid leukemia (AML) pairs, one acute lymphoblastic leukemia (ALL) pair, one acute biphenotypic leukemia-ALL pair, and one myelodysplastic syndrome (MDS) pair of patients. All patients were below the age of 60 years at the time of transplantation. Eight patients received myeloablative conditioning with busulfan and cyclophosphamide (BuCy), whereas one AML

Discussion

In the discovery set of the present study, we used an LC-MS/MS-based quantitative proteomics method to comprehensively profile the plasma proteomes of aGVHD and control patients who underwent alloHSCT. A total of seven candidate biomarkers were identified, and their predictive value was validated in terms of aGVHD and NRM risk using post-engraftment plasma samples from an independent patient cohort. Three of the candidate biomarkers (TIMP-1, plastin-2, and REG3α) were used to develop a simple

Conflict of interest

The authors declare that they have no conflict of interest.

Authors' contributions

JS, KD, JWK, SHS, and IK conceptualized and designed the study. SSY, SP, and IK provided patient information and materials. KD and JWK selected patients and samples used in the discovery phase. JS and KD selected patients and samples used in the validation phase. JS, KD, and JWK reviewed medical records to retrieve clinical data. KD performed the laboratory work. JS performed statistical analyses. JS and KD wrote the manuscript. DH, JWK, KKK, SHS, and IK provided critical input on data

Acknowledgments

This work was supported by grant no. 0320150150 from the Seoul National University Hospital Research Fund and in part by grant no. 0420160240 from the Seoul National University Hospital Research Fund. This manuscript was edited by a native English-speaking expert associated with BioScience Writers LLC, Houston, TX, USA.

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