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Acute Graft-Versus-Host Disease Prognosis: Are Biomarkers Ready for Preemptive Clinical Trials?

Acute Graft-Versus-Host Disease Prognosis: Are Biomarkers Ready for Preemptive Clinical Trials? Although many factors are known to influence hematopoietic cell transplant (HCT)2 outcomes, including the age of the recipient, comorbidity, conditioning intensity, donor source, donor–recipient human leukocyte antigen (HLA) compatibility, conditioning regimen, and posttransplant graft-versus-host disease (GVHD) prophylaxis, they incompletely predict results. Biomarkers promise to further refine our ability to risk-stratify individual patients for the likelihood of a certain event's occurrence. It is important to define the clinical context of the use of biomarker(s) of interest and the outcome data that will be captured to assess a clinical endpoint, for example, grade II acute GVHD (aGVHD) or 6-month nonrelapse mortality (6m-NRM). Once the biomarkers are validated, risk stratification may allow prevention of complications via a biomarker-based personalized treatment approach. To develop the best biomarkers, the 2014 NIH Chronic GVHD Consensus Biomarker Working Group, which included world experts in the field and U.S. Food and Drug Administration advisors, defined the different types of biomarkers and summarized an ideal framework for biomarker development (1). The group defined 4 types of GVHD markers: (a) diagnostic biomarkers that are used to identify patients at the onset of clinical disease, (b) prognostic biomarkers that are used to categorize patients by degree of risk for disease occurrence, (c) predictive biomarkers that are used to categorize patients by their likelihood of response or outcome to a particular treatment when measured before the treatment, and (d) response biomarkers to treatment that are measured after initiation of therapy and serve as a substitute for a clinical efficacy endpoint. One important recommendation the group made was the verification of a potential GVHD marker in at least 2 independent cohorts (each having sufficient power for statistical significance), which would support its use in GVHD clinical trials and patient management. One promising marker is STimulation-2 (ST2), the interleukin (IL)-33 receptor. Of note, the literature has been misnaming ST2 as “suppressor of tumorigenicity 2,” when in fact the original name was “growth STimulation expressed gene 2.” It was recently renamed to “STimulation-2,” as it was first discovered to function as a mediator of type 2 inflammatory responses. IL-1 receptor-like 1 (IL1RL1) is located on chromosome 2q12.1 in humans, whereas the gene “suppressor of tumorigenicity 2,” also called ST2, is located on chromosome 11p14.3-p12 in humans. Here, I refer to ST2 as the IL-33 receptor. ST2 concentrations measured at day 14 after transplantation closely correlated with 6-mNRM in 2 independent cohorts (2). More importantly, the addition of ST2 to the clinical risk factors significantly reclassified the patients to a higher risk category. In a recent Journal of Clinical Investigation Insight issue, Ferrara et al. measured ST2 and regenerated islet-derived 3-α concentrations on day 7 after transplantation and generated an algorithm for determining high- and low-risk patients for 6m-NRM with a training set; applied it to an independent test set and validation set; and achieved consistent classification of patients (3). This study further validated, in a large multicenter cohort of 1287 patients, the need for measuring the GVHD biomarkers early posttransplant and supported the rational selection of high- and low-risk patients as defined by a biomarker panel for novel targeted therapies. A limitation of using the day 7 posttransplant time point is that there could be several confounding biological factors such as mucositis, sinusoidal obstruction syndrome, thrombotic microangiopathy, or idiopathic pneumonia syndrome that usually manifest before GVHD, as well as preengraftment cytokine release. Biomarkers such as ST2 may be increased during all these events, creating a risk of more false-positive tests. A disadvantage of using the day 14 posttransplant timepoint is that there is a small portion of patients (<5% with the current HCT platforms) who will have already developed hyperacute GVHD before day 14, and thus would not be captured by this predictive tool. One approach that would conciliate both timepoints is to begin measurement around engraftment (days 10–12 post transplantation with mobilized peripheral blood stem cells, the most frequent type of transplant) and, if increased, repeat measurement 48 h later. Then a decision can be made regarding intervention if 2 subsequent measurements indicate high risk. To translate a continuous variable into a clinical decision, it is necessary to determine a cutoff point and to stratify patients into 2 groups, each requiring a different type of treatment. Two approaches are possible, with both aiming to optimize the correlation of the dichotomization with respect to an outcome or survival variable. One is an algorithm using logistic regression analysis after log-transformation for normalization, as generated in the Ferrara et al. study (3), and another uses cutoff points for each individual marker on the basis of real biomarker concentrations, as reported in a New England Journal of Medicine manuscript and recently in a multicenter phase 3 trial with uniform transplant characteristics post allogeneic HCT (2, 4). Although earlier studies have shown that a single biomarker lacks the sensitivity and specificity required for many clinical outcomes and that the combination of several biomarkers may overcome this limitation, it has also been shown that some biomarkers, such as ST2, are as good as a panel of 12 biomarkers for predicting 6m-NRM (42% and 44%, respectively) (2). Furthermore, there are caveats for the use of algorithms vs cutoff points per biomarker including the impossibility for the clinicians to participate in the interpretation of the results and the reluctance of the U.S. Food and Drug Administration and industry partners to use algorithms. As biomarkers are developed for a specific outcome but then often used for others, they are key to defining the most clinical useful study endpoint. For instance, predicting all GVHD may not be equal. Grade II aGVHD has been shown in multiple studies of matched and cord blood transplantation to improve survival. Markers that predict for grades III–IV aGVHD may have the most benefit as being the most correlated to NRM. However, ST2 has been discovered as a marker for risk of therapy-resistant GVHD, and among 381 patients, 135 had clinical grades I–II at therapy initiation and high plasma ST2 concentration with 6m-NRM of 31% [95% confidence interval (CI), 22–36], whereas 160 patients with low ST2 and grades I–II aGVHD had 6m-NRM of 11% [95% (CI), 7–17] (2). Thus, patients with high ST2 values had higher 6m-NRM than patients with low ST2 values, regardless of the GVHD grade. Furthermore, 2 recent cohorts (4, 5) showed that ST2 was associated with NRM but not with GVHD. The most likely explanation, besides the fact that the studies were slightly underpowered to detect grades II–IV aGVHD, is that deaths due to post-HCT complications other than GVHD such as excess sinusoidal obstruction syndrome deaths were accounted for (4). Altogether, these data suggest that ST2 is a marker of cause of death beyond just GVHD-induced NRM and that the most validated endpoint is 6m-NRM that includes deaths from aGVHD grades III–IV, therapy-resistant GVHD that initially presents as grades I–II, and other severe complications favored by alloreactivity such as sinusoidal obstruction syndrome, thrombotic microangiopathy, or idiopathic pneumonia syndrome. In any case, validation of these biomarkers opens opportunities for biomarker-based clinical trials. The Blood and Marrow Transplant Clinical Trials Network is currently conducting a randomized phase II multicenter open-label study evaluating sirolimus and prednisone in patients with Minnesota standard-risk and low-risk biomarker-confirmed acute GVHD (protocol 1501). There are also trials under development for patients with newly diagnosed aGVHD with high-risk biomarkers using intensified treatment. Currently, there are no preemptive intervention clinical trials using biomarkers measured before the clinical signs of GVHD. A schema for a preemptive trial to decrease aGVHD incidence using biomarkers is shown in Fig. 1. Comparison of box A and box B shows whether a rapid taper of aGVHD prophylaxis lowers relapse and infection rates in low-risk patients identified by biomarkers. Comparison of box C and box D shows whether preemptive treatment lowers GVHD in high-risk patients identified by biomarkers. The expectation is that these markers will have a lower specificity and sensitivity than diagnostic markers. Biomarker-based GVHD preemptive trial design. Fig. 1. Open in new tabDownload slide Biomarkers are measured on days 7 and 14 and repeated 48 h later. In the low-risk group of patients based on biomarkers results, randomization will occur between no intervention and rapid tapering of immunosuppression. Comparison between box A and box B will show whether rapid tapering of immunosuppression of aGVHD prophylaxis lowers relapse and infection rates, as well as 6-month non-relapse mortality (6m-NRM), in low-risk patients identified by biomarkers. In the high-risk group of patients, randomization will occur between no-intervention and intervention groups with either the standard of care (prednisone 1 mg/kg) or a new targeted agent that needs to be determined. Comparison between box C and box D shows whether preemptive treatment lowers the incidence of GVHD I–IV, therapy-resistant GVHD, sinusoidal obstruction syndrome (SOS), thrombotic microangiopathy (TMA), idiopathic pneumonia syndrome (IPS), and 6m-NRM in high-risk patients identified by biomarkers. Fig. 1. Open in new tabDownload slide Biomarkers are measured on days 7 and 14 and repeated 48 h later. In the low-risk group of patients based on biomarkers results, randomization will occur between no intervention and rapid tapering of immunosuppression. Comparison between box A and box B will show whether rapid tapering of immunosuppression of aGVHD prophylaxis lowers relapse and infection rates, as well as 6-month non-relapse mortality (6m-NRM), in low-risk patients identified by biomarkers. In the high-risk group of patients, randomization will occur between no-intervention and intervention groups with either the standard of care (prednisone 1 mg/kg) or a new targeted agent that needs to be determined. Comparison between box C and box D shows whether preemptive treatment lowers the incidence of GVHD I–IV, therapy-resistant GVHD, sinusoidal obstruction syndrome (SOS), thrombotic microangiopathy (TMA), idiopathic pneumonia syndrome (IPS), and 6m-NRM in high-risk patients identified by biomarkers. Biomarkers that are mechanistic (rooted in the pathophysiology of the disease) and druggable targets are the most interesting and will provide the basis for a rational and specific preemptive intervention. Thus, ST2 is an ideal biomarker therapeutic target for GVHD, as it has been shown that intestinal stromal cells and alloreactive T cells, particularly CD4 and CD8 T cells co-producing interferon-γ and interleukin IL-17, are major sources of soluble (s)ST2 during GVHD. Furthermore, blockade of sST2 in the peritransplant period with a neutralizing monoclonal antibody has been shown to reduce GVHD severity and mortality by increasing the availability of IL-33 to T cells expressing membrane-bound ST2 [T helper 2 (TH2) cells and regulatory T cells] and decreasing production of sST2 by type 1 CD4 and CD8 T cells. In contrast, regenerating islet-derived 3-α, which functions as an antimicrobial protein that protects the gastrointestinal epithelium during inflammation, including colitis and GVHD, is difficult to target and serves as mostly marker of gastrointestinal epithelial damage. Together, recent research efforts have made major strides in GVHD biomarker discovery and validation. In view of the scarcity of treatment options aside from glucocorticoids that have been proposed in the past 30 years, the development of biomarker-based clinical trials is encouraging and suggests that a new era focusing on drug-targetable biomarkers for GVHD-specific immunosuppression has finally emerged. 2 Nonstandard abbreviations HCT hematopoietic cell transplantation IS immunosuppression GVHD graft-versus-host disease 6m-NRM 6-month nonrelapse mortality IL interleukin ST2 STimulation-2. " Author Contributions:All authors confirmed they have contributed to the intellectual content of this paper and have met the following 3 requirements: (a) significant contributions to the conception and design, acquisition of data, or analysis and interpretation of data; (b) drafting or revising the article for intellectual content; and (c) final approval of the published article. " Authors' Disclosures or Potential Conflicts of Interest:Upon manuscript submission, all authors completed the author disclosure form. Disclosures and/or potential conflicts of interest: " Employment or Leadership: None declared. " Consultant or Advisory Role: None declared. " Stock Ownership: None declared. " Honoraria: None declared. " Research Funding: None declared. " Expert Testimony: None declared. " Patents: S. Paczesny, 20130115232. References 1. Paczesny S , Hakim FT, Pidala J, Cooke K, Lathrop J, Griffith LM , et al. National Institutes of Health Consensus Development Project on criteria for clinical trials in chronic graft-versus-host disease: III. The 2014 Biomarker Working Group Report . Biol Blood Marrow Transplant 2015 ; 21 : 780 – 92 . Google Scholar Crossref Search ADS PubMed WorldCat 2. Vander Lugt MT , Braun TM, Hanash S, Ritz J, Ho VT, Antin JH , et al. St2 as a marker for risk of therapy-resistant graft-versus-host disease and death . N Engl J Med 2013 ; 369 : 529 – 39 . Google Scholar Crossref Search ADS PubMed WorldCat 3. Hartwell MJ , Ozbek U, Holler E, Renteria AS, Major-Monfried H, Reddy P , et al. An early-biomarker algorithm predicts lethal graft-versus-host disease and survival . JCI Insight 2017 ; 2 : e89798 Google Scholar Crossref Search ADS PubMed WorldCat 4. Abu Zaid M , Wu J, Wu C, Logan BR, Yu J, Cutler C , et al. Plasma biomarkers of risk for death in a multicenter phase 3 trial with uniform transplant characteristics post-allogeneic HCT . Blood 2017 ; 129 : 162 – 70 . Google Scholar Crossref Search ADS PubMed WorldCat 5. Kanakry CG , Bakoyannis G, Perkins SM, McCurdy SR, Vulic A, Warren EH , et al. Plasma-derived proteomic biomarkers in HLA-haploidentical or HLA-matched bone marrow transplantation using post-transplantation cyclophosphamide . Haematologica 2017 ; 102 : 932 – 40 . Google Scholar Crossref Search ADS PubMed WorldCat © 2017 The American Association for Clinical Chemistry This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Clinical Chemistry Oxford University Press

Acute Graft-Versus-Host Disease Prognosis: Are Biomarkers Ready for Preemptive Clinical Trials?

Paczesny,, Sophie
Clinical Chemistry , Volume 63 (10) – Oct 1, 2017
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Oxford University Press
Copyright
© 2017 The American Association for Clinical Chemistry
ISSN
0009-9147
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1530-8561
DOI
10.1373/clinchem.2017.272872
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Abstract

Although many factors are known to influence hematopoietic cell transplant (HCT)2 outcomes, including the age of the recipient, comorbidity, conditioning intensity, donor source, donor–recipient human leukocyte antigen (HLA) compatibility, conditioning regimen, and posttransplant graft-versus-host disease (GVHD) prophylaxis, they incompletely predict results. Biomarkers promise to further refine our ability to risk-stratify individual patients for the likelihood of a certain event's occurrence. It is important to define the clinical context of the use of biomarker(s) of interest and the outcome data that will be captured to assess a clinical endpoint, for example, grade II acute GVHD (aGVHD) or 6-month nonrelapse mortality (6m-NRM). Once the biomarkers are validated, risk stratification may allow prevention of complications via a biomarker-based personalized treatment approach. To develop the best biomarkers, the 2014 NIH Chronic GVHD Consensus Biomarker Working Group, which included world experts in the field and U.S. Food and Drug Administration advisors, defined the different types of biomarkers and summarized an ideal framework for biomarker development (1). The group defined 4 types of GVHD markers: (a) diagnostic biomarkers that are used to identify patients at the onset of clinical disease, (b) prognostic biomarkers that are used to categorize patients by degree of risk for disease occurrence, (c) predictive biomarkers that are used to categorize patients by their likelihood of response or outcome to a particular treatment when measured before the treatment, and (d) response biomarkers to treatment that are measured after initiation of therapy and serve as a substitute for a clinical efficacy endpoint. One important recommendation the group made was the verification of a potential GVHD marker in at least 2 independent cohorts (each having sufficient power for statistical significance), which would support its use in GVHD clinical trials and patient management. One promising marker is STimulation-2 (ST2), the interleukin (IL)-33 receptor. Of note, the literature has been misnaming ST2 as “suppressor of tumorigenicity 2,” when in fact the original name was “growth STimulation expressed gene 2.” It was recently renamed to “STimulation-2,” as it was first discovered to function as a mediator of type 2 inflammatory responses. IL-1 receptor-like 1 (IL1RL1) is located on chromosome 2q12.1 in humans, whereas the gene “suppressor of tumorigenicity 2,” also called ST2, is located on chromosome 11p14.3-p12 in humans. Here, I refer to ST2 as the IL-33 receptor. ST2 concentrations measured at day 14 after transplantation closely correlated with 6-mNRM in 2 independent cohorts (2). More importantly, the addition of ST2 to the clinical risk factors significantly reclassified the patients to a higher risk category. In a recent Journal of Clinical Investigation Insight issue, Ferrara et al. measured ST2 and regenerated islet-derived 3-α concentrations on day 7 after transplantation and generated an algorithm for determining high- and low-risk patients for 6m-NRM with a training set; applied it to an independent test set and validation set; and achieved consistent classification of patients (3). This study further validated, in a large multicenter cohort of 1287 patients, the need for measuring the GVHD biomarkers early posttransplant and supported the rational selection of high- and low-risk patients as defined by a biomarker panel for novel targeted therapies. A limitation of using the day 7 posttransplant time point is that there could be several confounding biological factors such as mucositis, sinusoidal obstruction syndrome, thrombotic microangiopathy, or idiopathic pneumonia syndrome that usually manifest before GVHD, as well as preengraftment cytokine release. Biomarkers such as ST2 may be increased during all these events, creating a risk of more false-positive tests. A disadvantage of using the day 14 posttransplant timepoint is that there is a small portion of patients (<5% with the current HCT platforms) who will have already developed hyperacute GVHD before day 14, and thus would not be captured by this predictive tool. One approach that would conciliate both timepoints is to begin measurement around engraftment (days 10–12 post transplantation with mobilized peripheral blood stem cells, the most frequent type of transplant) and, if increased, repeat measurement 48 h later. Then a decision can be made regarding intervention if 2 subsequent measurements indicate high risk. To translate a continuous variable into a clinical decision, it is necessary to determine a cutoff point and to stratify patients into 2 groups, each requiring a different type of treatment. Two approaches are possible, with both aiming to optimize the correlation of the dichotomization with respect to an outcome or survival variable. One is an algorithm using logistic regression analysis after log-transformation for normalization, as generated in the Ferrara et al. study (3), and another uses cutoff points for each individual marker on the basis of real biomarker concentrations, as reported in a New England Journal of Medicine manuscript and recently in a multicenter phase 3 trial with uniform transplant characteristics post allogeneic HCT (2, 4). Although earlier studies have shown that a single biomarker lacks the sensitivity and specificity required for many clinical outcomes and that the combination of several biomarkers may overcome this limitation, it has also been shown that some biomarkers, such as ST2, are as good as a panel of 12 biomarkers for predicting 6m-NRM (42% and 44%, respectively) (2). Furthermore, there are caveats for the use of algorithms vs cutoff points per biomarker including the impossibility for the clinicians to participate in the interpretation of the results and the reluctance of the U.S. Food and Drug Administration and industry partners to use algorithms. As biomarkers are developed for a specific outcome but then often used for others, they are key to defining the most clinical useful study endpoint. For instance, predicting all GVHD may not be equal. Grade II aGVHD has been shown in multiple studies of matched and cord blood transplantation to improve survival. Markers that predict for grades III–IV aGVHD may have the most benefit as being the most correlated to NRM. However, ST2 has been discovered as a marker for risk of therapy-resistant GVHD, and among 381 patients, 135 had clinical grades I–II at therapy initiation and high plasma ST2 concentration with 6m-NRM of 31% [95% confidence interval (CI), 22–36], whereas 160 patients with low ST2 and grades I–II aGVHD had 6m-NRM of 11% [95% (CI), 7–17] (2). Thus, patients with high ST2 values had higher 6m-NRM than patients with low ST2 values, regardless of the GVHD grade. Furthermore, 2 recent cohorts (4, 5) showed that ST2 was associated with NRM but not with GVHD. The most likely explanation, besides the fact that the studies were slightly underpowered to detect grades II–IV aGVHD, is that deaths due to post-HCT complications other than GVHD such as excess sinusoidal obstruction syndrome deaths were accounted for (4). Altogether, these data suggest that ST2 is a marker of cause of death beyond just GVHD-induced NRM and that the most validated endpoint is 6m-NRM that includes deaths from aGVHD grades III–IV, therapy-resistant GVHD that initially presents as grades I–II, and other severe complications favored by alloreactivity such as sinusoidal obstruction syndrome, thrombotic microangiopathy, or idiopathic pneumonia syndrome. In any case, validation of these biomarkers opens opportunities for biomarker-based clinical trials. The Blood and Marrow Transplant Clinical Trials Network is currently conducting a randomized phase II multicenter open-label study evaluating sirolimus and prednisone in patients with Minnesota standard-risk and low-risk biomarker-confirmed acute GVHD (protocol 1501). There are also trials under development for patients with newly diagnosed aGVHD with high-risk biomarkers using intensified treatment. Currently, there are no preemptive intervention clinical trials using biomarkers measured before the clinical signs of GVHD. A schema for a preemptive trial to decrease aGVHD incidence using biomarkers is shown in Fig. 1. Comparison of box A and box B shows whether a rapid taper of aGVHD prophylaxis lowers relapse and infection rates in low-risk patients identified by biomarkers. Comparison of box C and box D shows whether preemptive treatment lowers GVHD in high-risk patients identified by biomarkers. The expectation is that these markers will have a lower specificity and sensitivity than diagnostic markers. Biomarker-based GVHD preemptive trial design. Fig. 1. Open in new tabDownload slide Biomarkers are measured on days 7 and 14 and repeated 48 h later. In the low-risk group of patients based on biomarkers results, randomization will occur between no intervention and rapid tapering of immunosuppression. Comparison between box A and box B will show whether rapid tapering of immunosuppression of aGVHD prophylaxis lowers relapse and infection rates, as well as 6-month non-relapse mortality (6m-NRM), in low-risk patients identified by biomarkers. In the high-risk group of patients, randomization will occur between no-intervention and intervention groups with either the standard of care (prednisone 1 mg/kg) or a new targeted agent that needs to be determined. Comparison between box C and box D shows whether preemptive treatment lowers the incidence of GVHD I–IV, therapy-resistant GVHD, sinusoidal obstruction syndrome (SOS), thrombotic microangiopathy (TMA), idiopathic pneumonia syndrome (IPS), and 6m-NRM in high-risk patients identified by biomarkers. Fig. 1. Open in new tabDownload slide Biomarkers are measured on days 7 and 14 and repeated 48 h later. In the low-risk group of patients based on biomarkers results, randomization will occur between no intervention and rapid tapering of immunosuppression. Comparison between box A and box B will show whether rapid tapering of immunosuppression of aGVHD prophylaxis lowers relapse and infection rates, as well as 6-month non-relapse mortality (6m-NRM), in low-risk patients identified by biomarkers. In the high-risk group of patients, randomization will occur between no-intervention and intervention groups with either the standard of care (prednisone 1 mg/kg) or a new targeted agent that needs to be determined. Comparison between box C and box D shows whether preemptive treatment lowers the incidence of GVHD I–IV, therapy-resistant GVHD, sinusoidal obstruction syndrome (SOS), thrombotic microangiopathy (TMA), idiopathic pneumonia syndrome (IPS), and 6m-NRM in high-risk patients identified by biomarkers. Biomarkers that are mechanistic (rooted in the pathophysiology of the disease) and druggable targets are the most interesting and will provide the basis for a rational and specific preemptive intervention. Thus, ST2 is an ideal biomarker therapeutic target for GVHD, as it has been shown that intestinal stromal cells and alloreactive T cells, particularly CD4 and CD8 T cells co-producing interferon-γ and interleukin IL-17, are major sources of soluble (s)ST2 during GVHD. Furthermore, blockade of sST2 in the peritransplant period with a neutralizing monoclonal antibody has been shown to reduce GVHD severity and mortality by increasing the availability of IL-33 to T cells expressing membrane-bound ST2 [T helper 2 (TH2) cells and regulatory T cells] and decreasing production of sST2 by type 1 CD4 and CD8 T cells. In contrast, regenerating islet-derived 3-α, which functions as an antimicrobial protein that protects the gastrointestinal epithelium during inflammation, including colitis and GVHD, is difficult to target and serves as mostly marker of gastrointestinal epithelial damage. Together, recent research efforts have made major strides in GVHD biomarker discovery and validation. In view of the scarcity of treatment options aside from glucocorticoids that have been proposed in the past 30 years, the development of biomarker-based clinical trials is encouraging and suggests that a new era focusing on drug-targetable biomarkers for GVHD-specific immunosuppression has finally emerged. 2 Nonstandard abbreviations HCT hematopoietic cell transplantation IS immunosuppression GVHD graft-versus-host disease 6m-NRM 6-month nonrelapse mortality IL interleukin ST2 STimulation-2. " Author Contributions:All authors confirmed they have contributed to the intellectual content of this paper and have met the following 3 requirements: (a) significant contributions to the conception and design, acquisition of data, or analysis and interpretation of data; (b) drafting or revising the article for intellectual content; and (c) final approval of the published article. " Authors' Disclosures or Potential Conflicts of Interest:Upon manuscript submission, all authors completed the author disclosure form. Disclosures and/or potential conflicts of interest: " Employment or Leadership: None declared. " Consultant or Advisory Role: None declared. " Stock Ownership: None declared. " Honoraria: None declared. " Research Funding: None declared. " Expert Testimony: None declared. " Patents: S. Paczesny, 20130115232. References 1. Paczesny S , Hakim FT, Pidala J, Cooke K, Lathrop J, Griffith LM , et al. National Institutes of Health Consensus Development Project on criteria for clinical trials in chronic graft-versus-host disease: III. The 2014 Biomarker Working Group Report . Biol Blood Marrow Transplant 2015 ; 21 : 780 – 92 . Google Scholar Crossref Search ADS PubMed WorldCat 2. Vander Lugt MT , Braun TM, Hanash S, Ritz J, Ho VT, Antin JH , et al. St2 as a marker for risk of therapy-resistant graft-versus-host disease and death . N Engl J Med 2013 ; 369 : 529 – 39 . Google Scholar Crossref Search ADS PubMed WorldCat 3. Hartwell MJ , Ozbek U, Holler E, Renteria AS, Major-Monfried H, Reddy P , et al. An early-biomarker algorithm predicts lethal graft-versus-host disease and survival . JCI Insight 2017 ; 2 : e89798 Google Scholar Crossref Search ADS PubMed WorldCat 4. Abu Zaid M , Wu J, Wu C, Logan BR, Yu J, Cutler C , et al. Plasma biomarkers of risk for death in a multicenter phase 3 trial with uniform transplant characteristics post-allogeneic HCT . Blood 2017 ; 129 : 162 – 70 . Google Scholar Crossref Search ADS PubMed WorldCat 5. Kanakry CG , Bakoyannis G, Perkins SM, McCurdy SR, Vulic A, Warren EH , et al. Plasma-derived proteomic biomarkers in HLA-haploidentical or HLA-matched bone marrow transplantation using post-transplantation cyclophosphamide . Haematologica 2017 ; 102 : 932 – 40 . Google Scholar Crossref Search ADS PubMed WorldCat © 2017 The American Association for Clinical Chemistry This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Journal

Clinical Chemistry – Oxford University Press

Published: Oct 1, 2017

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