Omics

Chapter 20

Omics Sophie Paczesny1 and Leslie Kean2 1

Department of Pediatrics, Department of Microbiology Immunology, and Melvin and Bren Simon Cancer Center, Indiana University School of

Medicine, Indianapolis, IN, United States; 2Department of Pediatrics, University of Washington, Seattle Children’s Research Institute, and the Fred Hutchinson Cancer Research Center, Seattle, WA, United States

Chapter Outline Transcriptomics in Graft-Versus-Host Disease Proteomics in Graft-Versus-Host Disease and Graft-VersusLeukemia Definition of Biomarkers and Pending Issues for Posthematopoietic Stem Cell Transplantation Clinicians Types of Biomarkers Biological Fluids of Interest and Sample Collection PostHSCT Current Proteomics Technologies for Biomarker Discovery Antibody-Based Approaches MS-Based Approaches Mass Cytometry, or CyTOF Proteomics Approach for High-Throughput Validation of GVHD Biomarkers Major Phases of Biomarker Development Statistical Considerations Sample Sizes Receiver Operating Characteristics Curves Single-Versus-Multiple-Marker Panels in GVHD Evaluation

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Validation With Training and Validation Sets, Independent Sets, and Samples From Multicenter Prospective Studies Prognostic Markers and Risk Stratification aGVHD Biomarkers: From the Identification of Candidates to Their Validation Identification and Validation of Biomarkers of Other Early Complications Post-HSCT Identification and Validation of Chronic GVHD Biomarkers Identification of GVL and Minimal Residual Disease Biomarkers Genomics Studies and Next-Generation Sequencing to Tackle GVL and MRD Possible Proteomics Approaches to Distinguish GVL From GVHD Incorporating GVHD Biomarkers in Clinical Trials Future Research on Biomarkers: From Diagnosis to Therapy Conclusions References

388 389 389 389 390 390 391 391 392 393 394 394

Allogeneic hematopoietic stem cell transplantation (allo-HSCT) is the most effective form of tumor immunotherapy available to date. However, while allo-HSCT can induce beneficial graft-versus-leukemia (GVL) effects, the adverse effect of graft-versus-host disease (GVHD), which is closely linked to GVL, is the major source of morbidity and mortality following HSCT. Until recently, available diagnostic and staging tools frequently fail to identify those at higher risk of disease progression or death. Furthermore, there are shortcomings in the prediction of the need for therapeutic interventions or the response rates to different forms of therapy. The past decade has been characterized by an explosive evolution of ‘-omics’ technologies, largely due to important advances in chemistry, engineering, high-throughput technical devices, and bioinformatics. Building on these opportunities, blood biomarkers have been identified and validated both as promising diagnostic tools that predict future occurrence of GVHD and as prognostic tools for responsiveness to GVHD therapy and nonrelapse mortality. These biomarkers might facilitate timely and selective therapeutic intervention. However, such blood tests are not yet available for routine clinical care. This chapter identifies clinical questions in need of answers in the postHSCT setting, summarizes current information on transcriptomics as well as on clinical proteomics and the GVHD and GVL biomarkers available. Finally, it proposes future directions for the blinded evaluation of protein biomarkers in samples collected as part of a multicenter prospective study, as well as a new large-scale study of the T-cell transcriptome, to improve personalized diagnosis and prognostication for GVHD.

Immune Biology of Allogeneic Hematopoietic Stem Cell Transplantation. https://doi.org/10.1016/B978-0-12-812630-1.00020-7 Copyright © 2019 Elsevier Inc. All rights reserved.

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TRANSCRIPTOMICS IN GRAFT-VERSUS-HOST DISEASE The search for strategies to improve outcomes after allogeneic hematopoietic stem cell transplantation (allo-HSCT) can be divided into those that are focused on pretransplant risk mitigation and those that seek to have diagnostic and prognostic significance posttransplant. With regard to pretransplant risk mitigation, the field has greatly benefited from the detailed evaluation and risk stratification based on HLA genetics, as well as on both candidate-gene approaches and genome-wide association studies of polymorphisms that either increase transplant risk or are protective against complications [1e16]. These studies have been highly successful in creating risk-stratification paradigms for the degree of HLA-matching between donor and recipient and have begun to rigorously analyze the impact that single-nucleotide polymorphisms (SNPs) have on risk of complications of transplant. While these pretransplant genetic analyses can make a major impact on the posttransplant risks, even during allo-HSCT between HLA-matched siblings, which comprise the most favorable risk category based on donor/recipient genetics, there remains significant morbidity and mortality posttransplant; moreover, in the majority of patients for whom an HLA-matched sibling is not available, these risks are compounded. This chapter focuses on understanding these posttransplant complications and the ability of both transcriptomics and proteomics to identify the risks and, potentially, guide prevention and treatment strategies. While the field of proteomics (discussed in Section Proteomics in Graft-Versus-Host Disease and Graft-Versus-Leukemia, below) has advanced the farthest, the application of advanced transcriptomic methods to allo-HSCT is rapidly maturing and beginning to provide important insights into the molecular mechanisms underlying posttransplant complications. Here we will discuss the advances recently made in this field, and the path forward for studies of gene expression as a key component of precision medicine for allo-HSCT. While allo-HCT is the only chance for cure for patients with both malignant and nonmalignant hematologic diseases, the complications associated with this treatment significantly limit its effectiveness. Nonrelapse mortality (NRM), attributed to transplant-mediated toxicities prominently including graft-versus-host disease (GVHD) and infectious complications, predominate the cause-of-death for patients with both malignant and nonmalignant diseases. Historically, our tools for evaluating these complications, risk-stratifying patients, and identifying patient-specific treatment paradigms have been limited, especially for GVHD. Thus, the diagnosis of GVHD has rested on clinical criteria (although serum biomarkers are quickly becoming a part of standard-of-care, discussed in detail in Section Proteomics in Graft-Versus-Host Disease and Graft-Versus-Leukemia), without the contribution of patient-specific molecular markers of disease severity or prognosis. In recent years, however, the addition of gene expression signatures has become an important component of the immunologic examination of animal models of GVHD and, increasingly, of the clinical evaluation of this disease as well. As with genomic analysis, gene expression analysis of GVHD can be divided between candidate-gene studies and genome-wide studies. In this chapter, we will focus on genome-wide studies, which have the potential advantage of offering a more unbiased approach to identifying genes, pathways, and gene expression networks that are active in disease. In the past decade, the field of immunology has greatly expanded its application of genome-wide, “big data” and systems biology approaches, amidst a growing appreciation for the complexity of the networks controlling successful immune responses and the resultant limitations of candidate-gene and other more supervised approaches to dissect these processes. The greatest successes have been in the fields of infectious disease and vaccinology, where large transcriptomic initiatives have enabled major advances [17e24]. The field of solid organ transplant has also been very active in transcriptomic studies, with a series of studies designed to understand the immune networks active in those patients who demonstrated clinical tolerance to their allografts and were able to be weaned from immunosuppression without rejection [25e37]. The solid organ transplant studies have focused mostly on gene array technologies and have used peripheral blood mononuclear cells (PBMCs) or whole peripheral blood as the starting material for gene expression analysis. Studies on unfractionated blood have the advantage of being agnostic as to the cell source causing perturbations in circulating gene expression signatures. For example, the gene expression signature identified in operationally tolerant renal transplant patients was identified as a B-cell signature [31,32] rather than the expected T-cell signature, a discovery made possible because transcriptomics were performed on all PBMCs and not just T cells. However, in contrast to the situation in solid organ transplant, in allo-HSCT these whole blood signatures may have significant disadvantages, especially when studying events that occur early after transplant, such as acute GVHD (aGVHD), which occurs during a time period of rapid hematologic reconstitution. The rapid changes in absolute and proportional cell numbers during this early time period can significantly confound gene expression results performed on whole blood or PBMC by reflecting patient-specific rates of hematologic reconstitution rather than immune mechanisms of disease. This is in contrast to the study of chronic GVHD (cGVHD), which occurs later after transplant, outside the window or rapid hematologic reconstitution, and which has several mechanistic similarities to chronic allograft rejection. Thus, for cGVHD (and similar to solid organ transplantation), whole blood transcriptomic approaches may benefit from the ability to scan multiple cell types, given that the participation

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of multiple cell populations (including T cells, B cells, NK cells, among others) has been demonstrated to cause pathology in this disease. To perform transcriptomic analysis on bulk circulating cells, multiple strategies can be employed. These include a totally unbiased approach which examines the whole blood in its entirety, and which takes advantage of almost instantaneous RNA stabilization after the blood draw using specialized collection tubes [38e43]. While these whole blood approaches can be informative, they remain highly affected by the granulocyte cell content, which predominates the peripheral blood. To mitigate this effect, many groups make a single purification step prior to RNA isolation, by first preparing PBMC. These PBMC-based approaches have been the most widely used for studies of cGVHD and are increasingly yielding interesting data. Thus, in 2015, Pidala et al. published the first tolerance-associated gene set in alloHSCT [44]. This study, despite the relatively small numbers of patients analyzed (15 identified as tolerant, 17 identified as nontolerant, and 10 healthy controls) has provided the first potential tolerance-associated transcript list in allo-HSCT. Pidala et al.’s analysis utilized gene array technology on purified PBMC and identified a 20 probe set classifier that was able to accurately distinguish tolerant and nontolerant subjects in the analysis cohort (Table 20.1). Although this work has not yet been independently verified with other patient cohorts, it provides an important first step in demonstrating that peripheral blood samples can be used to classify patients and points the field toward a more evidence-based approach for identifying patients that could potentially be stable after discontinuation of immunosuppression. The multicenter Chronic Disease Consortium published an additional gene expression study of cGVHD in 2016, which highlights the progress as well as ongoing challenges with the PBMC approach to transcriptional analysis [45]. The goal of this study was to identify biomarkers (including gene expression patterns as well as clinical parameters) that could be used to accurately identify cGVHD patients. The authors discovered a signature that included a combination of three RNA biomarkers (IRS2, PLEKHF1, and IL1R2) and two clinical variables (recipient cytomegalovirus (CMV) serostatus and conditioning regimen intensity) that could accurately segregate cGVHD cases from controls. Unfortunately, this optimal biomarker panel could not identify higher-risk versus lower-risk cGVHD status, which, if successful, would have been a significant step forward. While there are some important advantages to taking a “whole blood” or “whole PBMC” approach to transcriptomic analysis, given the ability to broadly scan all circulating cells, one of the disadvantages of this method is that the resulting transcriptome is dominated by the quantitatively largest cell population, thus potentially limiting insights gained into less populous, but potentially more pathogenic cell types. To overcome this drawback, our group and others have focused on an

TABLE 20.1 Top Probe Sets and Corresponding Genes Selected in Classifier Construction and Leave 10% of Cross Validation Number of Times Selected

Probe Set ID

Gene Symbol

10

235230_at

PLCXD2

Phosphatidylinositol-specific phospholipase C, X domain containing 2

10

231776_at

EOMES

Eomesodermin

10

226625_at

TGFBR3

Transforming growth factor, beta receptor III

10

219566_at

PLEKHF1

Pleckstrin homology domain-containing family F (with FYVE domain) member 1

10

214119_s_at

FKBP1A

FK506 binding protein 1A, 12 kDa

10

206974_at

CXCR6

Chemokine (C-X-C motif) receptor 6

10

206486_at

LAG3

Lymphocyte-activation gene 3

10

204787_at

VSIG4

V-set and immunoglobulin domain containing 4

10

204731_at

TGFBR3

Transforming growth factor, beta receptor III

10

204530_s_at

TOX

Thymocyte selection-associated high mobility group box

10

1557985_s_at

CEP78

Centrosomal protein 78 kDa

9

218832_x_at

ARRB1

Arrestin, beta 1

Gene Name

Pidala J Bloom GC, Eschrich S. Sarwal M. Enkemann S. et al. (2015) Tolerance Associated Gene Expression following Allogeneic Hematopoietic Cell Transplantation. PL OS ONE 10(3): e0117001. https://doi.org/10.1371/journal pone 0117001.

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‘index sorting’ approach to transcriptomic analysis, wherein a cell type of interest is first purified prior to RNA purification, such that the downstream analyses are more sharply focused on immune pathways present in the chosen cell population. In GVHD, and especially for aGVHD, the vast majority of this work has focused on sorting T cells, given their prominent role in disease pathogenesis. While there are many candidate-gene studies of T-cell pathogenesis during GVHD, the application of more unsupervised analyses is still in its infancy in this field. However, new studies in mice, nonhuman primates, and patients are increasingly demonstrating the power of these approaches. In mice, several laboratories have isolated both CD8þ and CD4þ T-cell populations and performed gene array, RNAseq, and micro-RNA (miRNA) analyses to identify potentially important pathways and molecules causing GVHD [46e59]. These studies have been used to identify a number of immunologic drivers of disease, including [1] the role of PDL-1 on donor T cells [2], the role of miRNAs, including miR146b, mIR-146a, mIR-142, mIR155, amongst others, which can strongly modulate GVHD, and [3] the role of proinflammatory Tc17 in inducing GVHD. Our group has focused on NHP and patient studies, using sorted T cells, and supervised as well as unsupervised gene expression analysis to identify pathways controlling disease as well as novel druggable targets. To do this, we have relied on an index sorting approach, purifying total CD3þ cells from NHP and CD4þ and CD8þ T cells for human studies. In NHP, we have utilized both longitudinal analysis of T cells isolated from the blood and terminal analysis of T cells isolated from multiple GVHD target organs (Fig. 20.1). In patients, our data have thus far focused on longitudinal analysis from the blood [58,60e62]. However, as will be discussed below, we are now performing a new study which will examine gastrointestinal (GI) samples as well as blood samples to discern the tissue-specific GVHD transcriptome in transplant patients. The majority of our work to date with both NHP and human transcriptional studies has focused on gene array analysis, given the historically superior reliability of the annotation of gene array (vs. RNASeq) for NHP samples, and our desire to perform cross-species comparisons. However, new alignments of the rhesus macaque genome are now significantly improving the ability to perform RNASeq on NHP samples, and we and others are now increasingly focusing on this platform.

FIGURE 20.1 The NHP GVHD Prevention model. Fig. 20.1 depicts the schema of longitudinal and terminal analyses performed in the NHP GVHD model. In this model, allo-HSCT is performed using transplant pairs of defined MHC disparity. Autologous transplant controls and untransplanted healthy controls are also analyzed. Donors are mobilized for 5 days with 50 mcg/kg of GCSF. Apheresis products are then collected using a Spectra Optia apheresis machine. Recipients undergo pretransplant conditioning using either chemotherapy and/or total body irradiation. Standard or experimental GVHD prophylaxis strategies are provided to the recipient. Longitudinal analyses include clinical scoring of GVHD using the NHP GVHD score [181], clinical chemistry, and CBC analysis. In addition, longitudinal flow cytometry of T-cell reconstitution, as well as monitoring for markers of T-cell proliferation and activation is performed. T cells are also sorted from the peripheral blood for transcriptome analysis. When longitudinal colon biopsies are performed, these biopsies undergo pathologic examination as well as flow cytometric and transcriptomic analysis. At terminal analysis, a full necropsy is performed after high-volume saline perfusion. Recipients undergo a complete clinical necropsy which is evaluated by a trained veterinary pathologist. Flow cytometry is performed on the peripheral blood as well as all GVHD target organs, and lymphocytes are also purified and cryopreserved from these organs. Finally, T cells are sorted from GVHD target organs for transcriptomic studies. Transcriptomics is performed via gene array, population-based RNASeq, and single-cell RNASeq techniques. allo-HSCT, Allogeneic hematopoietic stem cell transplantation; GVHD, graft-versus-host disease.

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A typical workflow pipeline for gene expression analysis on both NHP and patient samples is as follows [58,60e62]: After purifying T-cell populations, RNA is immediately stabilized in T-cell lysates with RLT buffer, and RNA subsequently purified using commercially available columns. The purified RNA is then analyzed for RNA quantity and quality followed by cDNA/cRNA synthesis, and, for gene array analysis, target hybridization to Rhesus Macaque or Humanspecific Genome Arrays. The resultant fluorescent signals are almost uniformly processed and normalized using the Robust Multichip Averaging (RMA) Method [63]. For microarrays performed in multiple batches (which is the norm for both NHP and patient studies), we implement the “ComBat” algorithm to adjust for batch effects [64]. Probe sets are then filtered to eliminate low signal-to-noise probes, to enhance statistical testing power [65]. One of the first analyses that our research group typically completes with the resultant normalized data is Principal Component Analysis (PCA), using the Bioconductor MADE4 package [66], which is applied to summarize modes of gene array variance between samples. For all gene expression studies, having a robust annotation file is critical for all downstream analyses. For our studies of NHP, we often optimize probe set annotation by combining sources, with our most recent annotation being a combination of 1) annotation files from Dr. Robert B. Norgren Jr., [67], 2) annotation files provided by the chip manufacturer (Affymetrix, release 33, 10-30-12), and 3) data provided by Ingenuity Systems (Ingenuity Systems, www.ingenuity.com). Analyses of gene differential expression is then performed using an empirical Bayes moderated t-statistic, with a cutoff of 0.05, corrected for multiple hypothesis testing using BenjaminieHochberg procedure and an absolute fold change cutoff > 1.4 with the Bioconductor limma package [68]. Further analysis of differentiating characteristics of T-cell transcriptional profiles often involves gene set enrichment analysis (GSEA) using gene sets from the Molecular Signatures Database both on aggregate sample data from each cohort as a whole [64,69,70] and by using single sample GSEA [71] with subsequent visualization using Constellation Plotting [24]. GSEA can identify whether the members of a gene set (a collection of which are housed in the Molecular Signatures Database (MSig-DB)dBroad Institute, Boston, MA) are enriched in an independent rank-ordered profile of genes that are differentially expressed between two experimental groups. In this manner, GSEA is able to provide definitions of overrepresented biological functions without implicit bias associated with cutoff-based analyses. Pathway analysis is also often performed on transcriptome data, using both IPA (Ingenuity Systems, www.ingenuity.com) and other commercially available systems, as well as DAVID [72] (encapsulating Biocarta, KEGG, and Reactome pathway analysis). Applying the correct statistical testing to these results is critical: the most common being a right-tailed Fisher’s exact test using the BenjaminieHochberg procedure (or other more conservative approaches) to account for multiple testing. When enough individual transcriptomes are available to compare (usually >8e10 per group), unsupervised network analysis can be performed, commonly using weighted gene coexpression network construction and analysis (WGCNA) [73]. To perform this analysis, a subset of the most variant genes in a multiplexed comparison is first chosen and then displayed using topologic overlap matrix construction. Subsequently, soft thresholding power is chosen using scale-free topology and then module size chosen for maximal sensitivity and specificity [61]. Merging of modules is then performed using a dynamic tree cut, and meta-modules identified by clustering consensus module eigengenes [74] with clinical characteristics. We and others typically perform visualization of WGCNA modules using Cytoscape software [75]. These transcriptomic analysis paradigms have begun to provide important insights into the mechanisms causing GVHD in patients and NHP, and to identify potentially druggable targets. They have thus far uncovered the following (1): That, in both NHP and humans, aGVHD is characterized by distinct “Hyperacute” and “Breakthrough” mechanisms: with Hyperacute aGVHD driven by Th/Tc1-mediated dysfunction, whereas Breakthrough aGVHD is driven by inflammatory IL17-dominated pathways [61] (Fig. 20.2 and 20.3). (2) That Hyperacute aGVHD can be controlled by standard immunoprophylaxis, whereas Breakthrough aGVHD occurs despite current prevention strategies ([61]). (3) That Aurora Kinase A (AURKA) is induced in both NHP and human allo-reactive T cells and is a novel mediator of aGVHD [60]. (4) That OX40:OX40L pathway activation occurs in both NHP and human GVHD, and that, in NHP, when OX40L blockade is combined with mTOR inhibition with sirolimus, long-term control of both Hyperacute and Breakthrough aGVHD can be induced [62]. These insights are now driving a new wave of investigation into novel mediators of disease as well as new pathways for disease prevention and treatment. While significant progress has now been made using transcriptomics approaches, it is important to note that we are still in the infancy of applying these techniques to understanding the drivers of human disease. The studies described above remain limited by the following: (1) They were almost exclusively derived from gene array experiments, rather than RNASeq, and as such they lack information on transcript splice variants, and only have limited information on miRNAs. (2) All of the studies on the transcriptomics of GVHD performed to date have focused on analysis of populations of cells rather than on single-cell RNASeq (scRNASeq) techniques. While population analysis has many advantages, in terms of its high-throughput capacity, and well-developed analysis and quality control pipelines, there are mechanistic questions that can only be answered by single-cell analysis, because this technique is uniquely capable of overcoming the issue of

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FIGURE 20.2 T-cell profiles from animals with hyperacute GVHD contain an abundance of transcripts associated with proliferation and exhibit Th1 skewing. (A) First and third principal component projections reveal clustering of transplanted animals by immunoprophylactic strategy. Each dot represents an array sample. The center of inertia ellipses corresponds to the mean projections of the group. P < .05. (B) The first principal component shows a significant correlation with survival in a linear regression model. (Adj. R2 ¼ 0.3299; P value < .004). GVHD, graft-versus-host disease.

‘confounding by regression to the mean’, by which low-frequency but highly dysfunctional cells cannot be identified in population studies [76e82]. scRNASeq will be particularly important for the study of low-cell input samples (such as GI endoscopy samples) for which index sorting is not feasible and for which there is a large number of resident cell types. These scRNASeq techniques are increasingly being applied to clinical samples, and our group has now developed a new clinical study that is designed to take advantage of this technique to study the transcriptomics of GVHD at a new level of accuracy. This study is called ‘Precision Diagnostics in Inflammatory Bowel Disease, Cellular Therapy and Transplantation’ (‘PREDICT’). PREDICT represents a landmark clinical study in which we will rigorously apply transcriptomics-based systems analysis in patients undergoing transplantation and those with autoimmune GI disease. This study will enroll 250 transplant patients, 100 IBD patients, and 100 control patients, with neither disease. These patients will be closely studied for their clinical and immunologic outcomes as well as for the transcriptomic signatures of their disease and disease course. This will occur through both population-based and single-cell RNASeq from both blood and GI samples, such that a detailed atlas of both tissues, tied to clinical outcomes, can be created. The power of PREDICT is expected to lie in its ability to pair immune analysis with both disease-specific and individual-patient clinical outcomes. The goal will be to lay the groundwork for a detailed understanding of what gene expression networks cause disease and the impact these networks make on disease course. The focus of PREDICT will be on determining the networks controlling patient-specific outcomes, as a first step to developing a precision medicine approach to diagnosis and treatment of GVHD.

PROTEOMICS IN GRAFT-VERSUS-HOST DISEASE AND GRAFT-VERSUS-LEUKEMIA Definition of Biomarkers and Pending Issues for Post-hematopoietic Stem Cell Transplantation Clinicians A biomarker, typically a protein, is defined as a characteristic that can be objectively measured and evaluated as an indicator of a normal biologic process, pathogenic process, or pharmacologic response to a therapeutic intervention [83]. The need for biomarkers post-HSCT is due to the limitations of current predictors. Known risk factors pre-HSCT are related to genetic factors, including HLA disparities between donor and recipient, age, unrelated transplant, conditioning regimen intensity, malignant disease status, and donor graft content. A diagnosis of aGVHD post-HSCT relies entirely on clinical signs in one of three major target organsdskin, liver, and/or GI tract [84]dand can be confirmed by biopsies of these organs in these fragile patients. In addition, histologic severity on biopsy has not been consistently correlated with clinical outcome [84], and there are no validated blood tests currently available for clinical use.

Types of Biomarkers Various types of biomarkers have been identified. The 2014 NIH Chronic GVHD Consensus Biomarker Working Group, which included world experts in the field and US Food and Drug Administration experts, defined the different types of biomarkers and summarized an ideal framework for biomarker development [85]. Diagnostic biomarkers identify the presence of a disease (e.g., aGVHD). Furthermore, markers can also identify disease in a target organ compared with other

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FIGURE 20.3 Weighted gene coexpression network analysis (WGCNA) reveals a Th/Tc17 transcriptional program mediating Breakthrough acute GVHD in NHP. (A) Topological overlap matrix plot with hierarchical clustering tree and the resulting gene modules from a weighted network of T-cell transcripts. (B) Eigengene adjacency heatmap showing module eigengene similarity to NHP clinical cohorts. (C) Visualization of gene coexpression network connections between the most connected genes in the Orange module using Cytoscape. Shown are nodes and network connections with topological overlap above a threshold of 0.05. Mean expression fold change values of Breakthrough Acute vs. Autologous cohorts for each gene is visualized using a false color scale. (D) Visualization of gene coexpression network connections between the most connected genes in the Black module using Cytoscape. Shown are nodes with network connections whose topological overlap is above a threshold of 0.05. Edges with network connections above the threshold of 0.07 are shown. Mean expression fold change values of Breakthrough Acute vs. Autologous cohorts for each gene is visualized using a false color scale. (E) Flow cytometric analysis of peripheral blood mononuclear cells at the time of terminal analysis stimulated with PMA/ Ionomycin and measured for the production of IL17a. * ¼ P < .05 using an unpaired T test. (F) Pathway enrichment for genes in the black WGCNA module performed using DAVID59. Shown are those terms with a P-value < .05 (corrected for multiple hypothesis testing using the Benjamini procedure). Significance values are displayed using a false color scale and are given in units of -log10 of the corrected P-value. GVHD, graft-versus-host disease

complications that present with the same symptoms in the target organ (e.g., GI GVHD vs. infectious enteritis). Other examples of diagnostic markers post-HSCT are biomarkers that differentiate GVHD from graft-versus-leukemia (GVL) and vice versa. An ideal diagnostic marker should fulfill several criteria: (1) high specificity for a given disease (i.e., few false positives); (2) high sensitivity (i.e., few false negatives); (3) ease of use; (4) standardization; and (5) clarity and readability of the results. All of these factors will affect biomarker performance in the clinical setting. Prognostic biomarkers identify a baseline patient or disease characteristic that categorizes patients by degree of risk for disease occurrence. A prognostic biomarker provides information about the natural history of the disorder in that particular patient, usually before the clinical signs appear. Predictive biomarkers are used to categorize patients by their likelihood of response or outcome to a particular treatment when measured before the treatment. Predictive biomarkers after HSCT will also indicate maximum GVHD severity, NRM, and overall survival. Predictive biomarkers allow for patient enrichment to

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maximize benefit from specific therapies. Response biomarkers differentiate between patient populations who have responded or not to a particular treatment (i.e., favorably or unfavorably), as opposed to patients who did not have that response. These biomarkers may identify subpopulations that can respond to a treatment in various ways. For example, they may correlate with an increased risk of drug toxicity or an increased chance of drug benefit. Such biomarkers serve as cornerstones in personalized medicine, enabling practitioners to select the most appropriate treatment for individual patients. Treatment efficacy biomarkers (i.e., pharmacogenomics biomarkers) are biomarkers found early after initiation of treatment that indicate that the particular patient has shown some biological response to the treatment and thereby has the potential to receive benefit compared with those who have not shown a biological response and therefore will not benefit. Toxicity biomarkers serve as early sensitive indicators of treatment-induced toxicity prior to significant harm. In the context of HSCT, two categories of markers should be prioritized: (1) prognostic biomarkers measured early in the course of transplantation that predict occurrence of GVHD prior to clinical signs will be the most beneficial, allowing for preemptive treatment and (2) predictive biomarkers measured before the treatment that will allow for intensification of treatment in the high-risk group and decreased immunosuppression in the low-risk group. Furthermore, in the post-alloHSCT setting, biomarkers able to distinguish GVHD from GVL would be beneficial.

Biological Fluids of Interest and Sample Collection Post-HSCT Ideal clinical tests are based on noninvasive collection, which allows for repetitive collection of samples from the same patient in a short amount of time. GVHD biomarkers may be produced by several sources, such as donor cells, the local or systemic cytokine milieu, or recipient target tissues during disease development. These proteins may then be released into a variety of body fluids. For noninvasive tests used for diagnostics or screening, biofluids, such as plasma, sera, or urine, are the preferred samples. Collection of samples is the initial step of the analytical procedure and affects the chances of obtaining relevant data in the search for specific biomarkers. The various steps from patient sampling to sample storage should be considered potential sources of artifacts in any experimental design. Therefore, an enormous effort has been made to develop standardized methods of clinical sample collection for proteomic studies [86,87]. Biospecimen reporting for publications should include appropriate informed consent, conditions of biospecimen collection, and sample processing. The sample processing should include tube types; additives such as anticoagulants, preservatives, and protease inhibitors, if used; quality control standard operating procedures; information management with inventory control and tracking; storage and distribution conditions, such as storage temperature and length of storage; number of freeze/thaw cycles; and variations in collection and processing across biospecimen sets. Blood is the most frequently analyzed bodily fluid, and the ease with which it can be sampled makes it a logical choice for biomarker applications. The levels of individual blood proteins represent a summation of multiple, disparate events that occur in every organ system. Blood contains proteins shed by the affected tissue as well as proteins that reflect secondary systemic changes. In addition, the blood proteome depends on many other factors governing the actual state of the whole organism that may not be related to the monitored disease, complicating the evaluation and pertinence of the data obtained. Another factor that complicates the analysis of plasma/sera is the wide range of protein amounts and isoforms. Plasma and sera are highly complex mixtures containing high amounts of many different proteins with a wide dynamic range, spanning 12 orders of magnitude from albumin to the lowest abundance, often most clinically relevant, proteins such as cytokines and their receptors [88,89]. The 22 most abundant proteins constitute approximately 99% of the plasma proteome, whereas the remaining 1% of the plasma proteins are medium and low abundance proteins [89]. Thus, both depletion of the predominant proteins and subsequent fractionation of the proteome are usually required to allow the detection of low abundance proteins. Unfortunately, the steps involved in sample preparation may result in the loss of proteins of interest during the depletion step [90]. Considering that most clinically relevant plasma biomarkers belong to the low abundance plasma protein fraction and have concentrations 10 [89e91] times lower than those of albumin [88], highly sensitive detection methods are required. Urine samples represent an alternative to plasma/sera samples for biomarker discovery. Urine has three main advantages compared to plasma/sera: (1) it can be obtained in large quantities; (2) the protein mixture is far less complex and the variation in protein abundance is low [91]; and (3) it is more stable than plasma [92]. However, a limitation is that urine yields better information about diseases in the organs directly involved in its production and excretion, such as the kidneys, as the proteins are produced mainly from kidney function (w70%) and partially by glomerular filtration of plasma proteins (w30%) [8]; thus, urine is less informative for other systemic diseases. An ideal schedule of sample collection post-HSCT will contain both calendar- and event-driven collection. Based on currently validated biomarkers, we propose a cost-effective collection for plasma/sera that contains calendar samples: preHSCT, day 14, day 21, day 28, and day 100, day 180, day 360 post-HSCT. Days 14 and 21 post-HSCT are to capture

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GVHD before clinical signs occur, day 28 is to capture samples of non-GVHD patients at matched time points to those of GVHD patients, and day 100 is to risk stratify cGVHD before it occurs, day 180 is the median day of onset of cGVHD, and day 360 when most patients who will develop GVHD have signs. Event-driven samples should include onset of complications (e.g., aGVHD) during the 48-h window of treatment initiation, onset of cGVHD, and onset of other complications that can either mimic GVHD or pose a difficult diagnosis such as idiopathic pneumonia syndrome (IPS), sinusoidal obstruction syndrome (SOS), thrombotic microangiopathy (TMA), and sepsis. Sample quality, acquisition, and storage should be followed as specified above.

Current Proteomics Technologies for Biomarker Discovery Clinical proteomics can be defined as the identification and validation of disease biomarkers with the objective to improve the current state of the art in clinical practice. The classical paradigm that DNA determines the fate of the cell is currently being questioned, as complex regulatory processes at the level of both transcription and translation are better appreciated. In addition, advances in engineering have allowed for increased data throughput, enabling the study of complete sets of molecules (“-omics”) with exponential speed, accuracy, and cost-effectiveness. Thus, the analysis of the entire spectrum of molecular and cellular organization is now possible, enabling researchers to gain insight into the mechanism of disease, with fewer a priori assumptions. However, from genes (w20,000) to proteins, there are two more levels of complexity: the transcriptome (w100,000 RNA transcripts) and the proteome (w1,000,000 proteins). Here, we focus on the use of proteomics for the molecular diagnosis of GVHD post-HSCT, because proteins are more proximal than other cellular metabolites to the ongoing pathophysiology of this disease. Indeed, studies using genomics, transcriptomics, and gene polymorphisms incompletely correlate with the expression of functionally active proteins, which more accurately reflect cellular cross talk, such that it is likely that proteins will provide the most ideal disease biomarkers [93]. Proteomics technologies are currently used in two related areas: biomarker discovery and the elucidation of pathologic processes to identify novel therapeutic targets. The first could lead to new proteins that provide new insights into the biology of GVHD. Both non-mass spectrometry (MS)- and MS-based proteomic approaches have been employed to search for potential GVHD biomarkers. Several studies have examined specific proteins, whereas other large-scale studies have investigated qualitative and quantitative differences in the complete protein profiles among samples from patients with and without GVHD.

Antibody-Based Approaches Immunoassays are sensitive, analytical tests that harness the unique properties of antibodies. They proved to be one of the most productive technological contributions to medicine and fundamental life science research in the 20th century. The unique characteristics of antibodies are derived from their three important properties: (1) their ability to bind to an extremely wide range of natural and man-made chemicals, biomolecules, and cells, as antibody-binding sites are derived from a huge number of potential combinations of amino acid sequences; (2) their exceptional binding specificity that enables the measurement of picomolar (1012) amounts of proteins in blood samples; and (3) the strength of binding between an antibody and its target that makes the test accurate and precise, even at low concentrations [94]. To screen for aGVHD biomarkers, antibody microarrays dotted with hundreds of antibodies have been employed, allowing hundreds of proteins in complex biological matrices to be measured [95]. In summary, the advantages of immunoassays are that they are (1) suited to the characterization of complex protein mixtures, such as human plasma; (2) quantitative; (3) highly sensitive for low abundance proteins such as cytokines; and (4) high throughput. The disadvantages are that there are only a restricted number of antibodies on the array (thus, introduction of bias) and high cross-reactivity between antibodies and nontarget proteins [96].

MS-Based Approaches The majority of nonantibody proteomics strategies are based on MS, which has become a powerful tool for both characterizing and assessing both qualitative and quantitative changes in complex protein mixtures [97]. Two types of MS techniques in clinical proteomics have been used: (1) pattern profiling and (2) detailed characterization of proteins. Pattern profiling compares polypeptide spectra obtained by matrix-assisted laser desorption/ionization time-of-flight (MALDITOF) MS, which is used to show which patients suffer from a particular disease without the identification of individual profile components. A variant of MALDI-TOF MS is surface-enhanced laser desorption/ionization (SELDI-TOF) MS, which combines MALDI-TOF with selective sample fractionation on modified surfaces placed directly on the sample

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target [98]. These MS profiling methods do not require an in-depth analysis and, thus, are relatively high throughput. They are consequently less suitable for in-depth discovery approaches. Furthermore, because the factors influencing the final oligopeptide profiles of body fluid samples are so complex, MS profiling has not yet met the standards required in clinical practice. This technique has been applied in aGVHD research to screen biomarker candidates in both serum [99] and saliva [100]. Other approaches rely on separation of protein samples followed by MS. The most frequently employed gel-based techniques for protein separation are two-dimensional (2-D) polyacrylamide gel electrophoresis [101] and 2-D differential gel electrophoresis [102]. Three-dimensional separation of proteins, differentially labeled with fluorescent dyes [Cy3 (green) and Cy5 (red)] according to their charge, hydrophobicity, and molecular mass, have been applied to aGVHD diagnoses [103] and heart ischemic insult [104]. Despite the utility of gel-based techniques, gel-free separation methods, such as liquid chromatography (LC) and capillary electrophoresis [105e107], have provided better separation because they overcome several limitations of gel separation, such as time consumption; poor separation of proteins with low molecular weight (MW), high MW, or an extreme isoelectric point; and difficult quantification of mixed spots. Gel-free techniques also offer the prospect of an easy workflow with a direct connection with the mass spectrometer. MS is the final step in the analytical procedure and enables both the reliable identification of proteins and the determination of their isoforms and posttranslational modifications. MS allows unambiguous quantification, particularly when tandem MS (i.e., MS/MS) is employed [108], and has been used most recently for quantification with either label-free methods or isotopically labeled tags [109e111]. In addition, new instrumentation, such as the ultra-high-resolution linear ion trap Orbitrap mass spectrometers, facilitates top-down LCe MS/MS and versatile peptide fragmentation modes [112], increasing the number of proteins identified. The mass spectra are then matched to a sequence database to identify proteins [113]. At present, these approaches are not suitable for validation purposes because of time consumption, but they remain the most efficient methods for biomarker discovery in clinical research. Detailed below is the Intact Protein Analysis System (IPAS) workflow that we utilized. Briefly, GVHD-negative and -positive pools of 10 patients matched for other clinical characteristics were individually immunodepleted of the six most abundant plasma proteins (i.e., albumin, IgG, IgA, transferrin, haptoglobin, and antitrypsin). Intact proteins were then labeled on cysteine residues with acrylamide-stable isotopes. The GVHD-negative pool was labeled with the light acrylamide isotope 12C, whereas the GVHD-positive pool received the heavy acrylamide isotope 13C. The two pools were combined, and specimens were subjected to a 2-D protein fractionation procedure that included anion-exchange chromatography followed by reversed-phase chromatography. The individual fractions were then digested and analyzed on a new-generation LCeMS/MS instrument. Because protein digestion was performed in a top-down fashion prior to MS, the term “intact” protein analysis is used [109,114]. The acquired spectra were automatically processed by the high-throughput Computational Proteomics Analysis System to identify proteins in the sample, with a false discovery rate of <5% [113]. This process resulted in both the reduced complexity of individual fractions subjected to analysis and the identification of proteins with a range of concentrations spanning seven logs [115]. This technique was therefore able to detect low abundance proteins and is quantitative, as each GVHD pool was labeled with both heavy and light stable isotopes. We sequentially prioritized the list of proteins identified by the MS/MS method described above based on the degree of dysregulation, as indicated by at least a twofold increase in expression and known pathway networks, as well as uniqueness to the target organ associated with a given GVHD type. In summary, the IPAS approach has the advantages of (1) discovering candidate biomarkers in an unbiased fashion; (2) being quantitative; (3) using a top-down approach that keeps the protein intact until the last step; and (4) being high throughput because of the use of liquid-phase fractionation. There are limitations however as (1) IPAS is available only in specialized laboratories; (2) it is sensitive for low abundance proteins, but less so than antibody-based methods; and (3) at least in practice, follow-up is often limited to those proteins for which an antibody is currently available. Fig. 20.4 summarizes the IPAS workflow. Due to the labor intensity of the IPAS workflow, we have also used another proteomics pipeline with iTRAQ labeling (isobaric tags for relative and absolute quantification) [116,117]. As compared to IPAS, the iTRAQ workflow has a higher technical limitation in the detection of low abundance proteins because trypsin digestion is applied at the beginning of the workflow (before fractionation). Tandem mass tag labeling can be used instead of iTRAQ labeling with a similar workflow. Fig. 20.5 summarizes the iTRAQ workflow for discovery of bronchiolitis obliterans syndrome (BOS).

Mass Cytometry, or CyTOF CyTOF is a TOF MS that is used like flow cytometry but in which the antibodies are labeled with heavy metal ion tags instead of fluorochromes. The main advantage as compared to flow cytometry is that it allows for the combination of more

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6 most abundant proteins Isotopic labeling Light

Acrylamide alkylaƟon

Isotopic labeling Heavy

SAMPLES MIXED

ANION-EXCHANGE CHROMATOGRAPHY (8 AX)

REVERSED-PHASE CHROMATOGRAPHY (~60 fracƟons)

▪ Tag has to be small (cannot interfere MS/MS) ▪ No big change in physical-chemical characterisƟcs of proteins ▪ Good yield, reproducibility and no byproducts ▪ CompaƟble with the enƟre workflow

LC-MS/MS (96 RP pools)

FIGURE 20.4 Overview of the Intact Protein Analysis System workflow for GVHD biomarkers discovery. The first step depletes the six most abundant proteins because the plasma has a 1012 dynamic range. A pool of 10 patients without GVHD (GVHD) and a pool of 10 patients with GVHD (GVHDþ) are compared. Intact proteins are labeled with stable isotopes. The GVHD pool receives the light isotope and the GVHDþ pool receives the heavy isotope. The cysteine residues are labeled with the light [12C]acrylamide isotope and the heavy [13C]acrylamide isotope through a thiol alkylation shown here. The difference (D) of 3 between the isotopes allows for quantification. The choice of acrylamide was made because the tag has to be small to avoid interference with MS/MS. There is no big change in physicalechemical characteristics of the proteins. It allows a good yield and reproducibility and avoids by-products. It is compatible with the entire workflow. Then, the pools are mixed together for further analysis. A two-dimensional protein fractionation is performed in which anion-exchange chromatography represents the first dimension of the protein separation and reversed-phase chromatography the second dimension of separation. After digestion with trypsin, individual fractions are analyzed by a mass spectrometer. The acquired spectra (LCeMS/MS) are automatically processed by the Computational Proteomics Analysis System for the identification of proteins, with a false discovery rate of less than 5%. GVHD, graft-versus-host disease; LC, liquid chromatography; MS, mass spectrometry

antibody specificities in single samples (classically 30 to 40 antibodies but theoretically up to 100), without significant spillover between channels. This technology and its software tools permit discovery studies.

Proteomics Approach for High-Throughput Validation of GVHD Biomarkers Although proteomics holds great promise for biomarker development, gaps still remain between biomarker discovery and biomarker validation. Indeed, validation of biomarkers has obstacles of its own. Most noteworthy is the paucity of affinitycapture reagents, such as high-quality antibodies with the required affinities and specificities for the target, leading to a bias in the prioritization of candidate markers. Furthermore, the number of samples required for validation increases as the biomarker advances through each test phase, augmenting the need for high-throughput assays. The most applicable approach for the quantitation of individual proteins for validation remains the sandwich enzyme-linked immunosorbent assay (ELISA), which is highly specific and employs two antibodies specific for the candidate protein. The procedure is also relatively simple and highly reproducible from performer to performer and from laboratory to laboratory, limiting both inter- and intra-assay variability. Finally, because of the urgent need for GVHD blood tests, we primarily tested proteins with available antibodies. The main disadvantage of validation by ELISA is the large volume of patient plasma required. Thus, we used a sequential ELISA protocol to minimize freeze/thaw cycles and the use of precious plasma samples [118]. In addition, we chose sequential over other available multiplex platforms for two reasons: (1) most antibody pairs for novel proteins cannot easily be conjugated on beads or other materials and are both time-consuming and expensive and (2)

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BOS

Samples

Pulmonary Infecon

cGVHD no lung

No complicaon

Depleon Enzymac Digeson/ Protein Extracon

iTRAQ label

iTRAQ 114

iTRAQ 115

iTRAQ 116

iTRAQ 117

Combine labeled digests

Analysis by LC-MS/MS (Orbitrap Fusion Mass Spectrometer)

FIGURE 20.5 Overview of the isobaric tags for relative and absolute quantification (iTRAQ) workflow for BOS biomarkers discovery. Four pools of plasma were compared in the same proteomic experiment. Pool 1 contained plasma from patients with BOS available at the onset of clinical symptoms, pool 2 contained plasma from patients with pulmonary infection (at the onset of clinical symptoms and collected at similar time points as cGVHD samples), pool 3 contained plasma from patients with cGVHD with no lung involvement, and pool 4 contained plasma from patients without cGVHD (collected at similar time points as cGVHD samples). The four pooled plasmas were then individually immunodepleted of the common hyperabundant proteins followed by enzymatic digestion with trypsin and protein extraction. Each pool was then labeled for quantification by iTRAQ (isobaric tags for relative and absolute quantification), BOS with iTRAQ 114, pulmonary infection with iTRAQ 115, cGVHD with no lung with iTRAQ 116, and control with iTRAQ 117. All the pools were then combined for analysis by LC-MS/MS. BOS, bronchiolitis obliterans syndrome; cGVHD, chronic graft-versushost disease; LC, liquid chromatography; MS, mass spectrometry.

individual ELISAs are more precise than multiplex microarray or beads, secondary to the absence of cross-reactivity [96]. Ideally, we would like to develop a multiplex platform using a limited amount of plasma (<10 mL for 4e10 analytes) with no cross talk. Multiple-reaction monitoring has emerged as a potentially useful technique for clinical diagnostics [119]. This rapid tandem mass spectrometric technique enables the targeted monitoring and quantification of candidate molecules in complex samples.

Major Phases of Biomarker Development The development of biomarkers entails a number of phases, from the identification of promising molecular targets to longitudinal clinical trials in association with a specific treatment. Three major steps are required to develop a clinical test for screening. First, the discovery or pilot phase compares 20 to 40 cases and controls using antibody-based arrays or MSbased approaches or mass cytometry as discussed previously. It is recommended that the term “candidate biomarker” or “potential biomarker” be used to refer to findings of early phase studies when additional validation is needed. Next, the validation phase is usually performed with immunoassays rather than MS, and the sample set is created from a retrospective longitudinal caseecontrol repository. This process should be done on a training set followed by an independent validation set; validation using sets from multiple institutions is ideal. The final step focuses on a few biomarkers and requires a prospective multicenter validation, typically on thousands of samples before the release of the clinical test. For high-throughput purposes and standardization between laboratories, only immunoassays are used at this step. If the blood test detects disease early before clinical signs become apparent, both a screen-positive rule and a false-screen rate are defined. It is hoped that this step will lead to a clinical test that will be approved by the US Food and Drug Administration. Then, the impact of the screen on the reduction of disease burden on the population of interest is quantified [120]. This framework has also been established by the 2014 NIH Chronic GVHD Consensus Biomarker Working Group [85]. Fig. 20.6 shows the workflow of biomarker development post-HSCT.

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BIOSPECIMENS REPOSITORY (Pre, day 14, 28, 100, 180, 360 post-HCT, and onset of complications)

GVHD -

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Antibody Arrays or Tandem mass spectrometry following multi-dimentional fractionation

Computer Analysis

VALIDATION High throughput immunoassays

CLINICAL TESTS: Improved diagnosis, prognosis, therapy responsiveness, and prediction. Potential for targeted therapy

Statistical Analysis

FIGURE 20.6 Clinical proteomics: from the bedside to the bench and back to the bedside for personalized therapy of GVHD post-HSCT. Biospecimens (body fluids such as plasma, serum, or urine) are collected on a calendar- and event-driven schedule. Their proteomes are analyzed in detail with technologies that permit in-depth identification and quantification of candidate proteins that are significantly altered in GVHD. This step is called the discovery phase. On validation with high-throughput immunoassays in a large series of patients and independent sets of patients, these can be considered biomarkers that could move into clinics. The clinical tests could help clinicians to better monitor their patients, thanks to blood tests that improve diagnosis, prognosis, responsiveness to therapy, and prediction of occurrence of the disease. In addition to adapting the current therapy with risk stratification, some of these biomarkers are drug-targetable and could help develop GVHD-specific drugs. GVHD, graft-versus-host disease; HSCT, hematopoietic stem cell transplantation.

Statistical Considerations At the discovery-phase level, recommendations for biomarker identification and qualification in clinical proteomics have been proposed to avoid overinterpretation of results, the use of inappropriate technologies or statistics, and inefficiency in the construction of a multimarker panel [85,121]. For reporting on observational study design and diagnostic accuracy, two guidelines have been established by the STROBE and STARD initiatives [122,123].

Sample Sizes The number of specimens that should be tested depends on the objective of the study and the extent of biomarker variability in the study. When the objective is to select a subset of biomarkers from a pool, the following factors contribute to variability: the prevalence of the disease and the subtypes of disease (i.e., skin aGVHD, GI aGVHD, lichenoid (erythematous), or sclerosis skin cGVHD) among the study samples, the capacity of the biomarkers to discriminate among the various disease subtypes, the number of biomarkers under study, the number of case and control subjects, and the statistical algorithm used to select promising biomarkers. Thus, as suggested by Pepe et al. [120], there are no simple methods for recommending sample sizes. In particular, traditional sample size calculations that are based on statistical tests of hypotheses are not relevant. Pepe et al. [120] propose that computer simulations guide the choice of sample sizes,

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FIGURE 20.7 Receiver operating characteristics (ROC) curves for diagnosis of acute graft-versus-host disease. An ROC curve is a plot of the truepositive rate on the y axis (sensitivity) versus the false-positive rate (1  specificity) on the x axis of a given biomarker. (A) A perfect test would result in an ROC curve appearing as a right angle, indicating that 100% of the samples are true as opposed to false positives. (B) In the perfect case, the corresponding area under the curve (AUC) will equal 1. (C) A random test will have an AUC of 0.5, meaning that there is one false-positive for every truepositive sample.

meaning that simulations should be performed with the guidance of investigators on biologically plausible models to generate data. By varying the numbers of cases and controls, one can assess at what sample size a reasonable proportion of promising biomarkers is likely to be selected for further study [120].

Receiver Operating Characteristics Curves Several statistical methods can be used to estimate the diagnostic likelihood ratio of a continuous biomarker, but the receiver operating characteristics (ROC) curve is primarily used because the diagnostic likelihood ratio is mathematically related to the slope of the ROC curve [124]. The ROC curve is a plot of the true-positive rate (sensitivity ¼ 1  falsenegative error rate) versus the false-positive rate (1  specificity), which is associated with rules that classify an individual as “positive” if the marker value is above a threshold C for all possible thresholds [125]. In addition, combinations of multiple markers are often required; combining the ROC curves of all biomarkers is an optimal way of estimating the risk score, defined as the probability of disease given data on multiple markers, as the ROC curve is maximized at every cut point [126]. Fig. 20.7 shows the principles of ROC curves.

Single-Versus-Multiple-Marker Panels in GVHD Evaluation The simultaneous use of several markers may increase specificity or diagnostic performance, as was the case in our first biomarker panel [95]. To create a comprehensive GVHD biomarker panel, we used a proportional odds logistic regression model to determine a composite panel that will generate an ROC curve with an area under the curve (AUC) > 0.8. However, if a biomarker is not highly correlated with either other biomarkers or clinical predictors, one or two biomarkers could be sufficient. Indeed, the soluble form of STimulation-2 (ST2), the IL33 receptor was as good a predictor for responsiveness to GVHD treatment and subsequent 6-month mortality without relapse as a panel of 12 biomarkers [127]. To best evaluate the number of biomarkers that will give the most information, optimized classification models that simultaneously minimize the misclassification error rate and maximize the AUC are used [128].

Validation With Training and Validation Sets, Independent Sets, and Samples From Multicenter Prospective Studies The most accepted statistical approach for the validation of biomarkers is to have training and validation sets. The statistical model is developed with the training set and subsequently tested in the validation set, representing therefore a blinded measure of biomarker performance. This statistical approach was performed in our first biomarker panel [95]. Because of potential center effects, the biomarkers must also be tested in independent sets from other centers, ideally in multicenter prospective samples [127,129e133].

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Prognostic Markers and Risk Stratification Prognostic markers are used to predict an individual’s risk for a future event, such as occurrence of aGVHD or other complications post-HCT [i.e., SOS, TMA, or IPS] or cGVHD. In this case, we would expect that the biomarkers will correlate with subclinical disease. In this context, the key issue is to identify subjects at high and low risk for the event and to quantify the information for the biomarker that is pertinent to such a prediction. Several statistical models for prognostic markers have been proposed for complications post-HCT. A common objective in such an observational study in which longitudinal biomarkers have been measured and may be highly associated with a time to event, such as aGVHD occurrence, is to characterize the relationship between the longitudinal marker and the time to event. Biomarkers will be systematically measured at the following time points: day 7, 14, and day 21 post-HSCT (7e21 days prior to the onset of aGVHD), and a statistical model using a Cox proportional hazard analysis with time-dependent covariates will be used. Time to aGVHD development is a time-to-event outcome; protein biomarkers measured on days 7, 14, and 21 post-HSCT are also time-dependent covariates; and other clinical and demographic variables are time-independent covariates. Two strategies can be used to model time-dependent biomarkers. One strategy is a simple linear interpolation and extrapolation of biomarker measurement to days other than days 7, 14, and 21. The second strategy is to model biomarkers using either a linear or a quadratic trend. Although these two strategies pose different Cox regression analyses with time-dependent covariates, they can be implemented into the likelihood model framework proposed by Wulfsohn and Tsiatis [134]. Several recent studies have used this approach [130,131,133]. Considerable interest has recently focused on so-called joint models, in which models for the eventetime distribution and longitudinal data depend on a common set of latent random effects [134]; this has still to be implemented for complications post-HSCT. A third statistical approach can be proposed using Bayesian model as it has been done for prognostic markers of SOS [135].

aGVHD Biomarkers: From the Identification of Candidates to Their Validation The paucity of validated biomarkers for GVHD is partly due to the complex pathology of GVHD, which can be considered in the framework of distinct sequential phases of immune system cellular activation and cytokine production, which are expected to influence specific cellular and protein levels in the blood of GVHD patients [136e138]. GVHD is not only a systemic immunological disorder but also affects specific organ systems, including the skin, GI tract, and liver. At the time of the first edition, 5 years ago, only a few aGVHD biomarker studies have attempted to identify candidate biomarkers using proteomics discovery with only some studies that had also validated these markers in one or more independent sets. Since that time, considerable strides have been made and several reviews and editorials, including a recent systematic review, have summarized the most useful biomarkers [139e141]. Details about important findings are described in the following paragraph. Several plasma biomarkers that correlate with clinical outcomes after allogeneic HSCT have been identified: a four-protein biomarker panel [interleukin (IL)-2 receptor a chain (sIL-2Ra/sCD25), tumor necrosis factor receptor-1 (TNFR1), IL-8, and hepatocyte growth factor (HGF)] panel for aGVHD diagnosis [95]; the soluble form of STimulation-2 (ST2), the interleukin (IL)-33 receptor with therapy-resistant aGVHD, and NRM [127,130,142e144]; regenerating islet-derived 3-alpha (Reg3a) and T cell immunoglobulin mucin-3 (TIM3) with GI aGVHD [129,143,145e147]; elafin with skin GVHD [114,148,149]; and HGF and cytokeratin-18 fragments (KRT18) with liver GVHD [146,150]. It is important to note that the literature has been misnaming ST2 as “suppressor of tumorigenicity 2”, when in fact the original name was “growth STimulation expressed gene 2” and was recently renamed to “STimulation-2” since it was first discovered to function as a mediator of type 2 inflammatory responses [137]. Clearly ST2 has emerged as the most validated biomarker for aGVHD either measured alone [127,131,142] or in combination with other markers [130,143,144]. Furthermore, ST2 has now been tested and validated on several platforms such as nonmyeloablative conditioning [151], in cord blood transplantation [142], in HLA-haploidentical or HLA-matched transplantation with use of cyclophosphamide posttransplant [131]. ST2 has also been validated as a prognostic marker for aGVHD and NRM in large cohorts when measure as early as day 7 [133] or day 14 [127,143] post-HSCT.

Identification and Validation of Biomarkers of Other Early Complications Post-HSCT Hepatic SOS, previously known as veno-occlusive disease, is one of the major complications during the early period after HSCT. The disease is caused by both toxic injury of conditioning therapy to sinusoidal endothelial cells and inflammation, initiating the clinical symptoms of hyperbilirubinemia, tender hepatomegaly, ascites, and weight gain. The incidence and severity of SOS have decreased significantly in recent years, but fatal outcomes of SOS are still observed in clinical practice. Biomarkers for the diagnosis of SOS [ST2, angiopoietin-2 (ANG2), L-Ficolin, hyaluronic acid (HA),

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and vascular cell adhesion molecule-1 (VCAM1)] and prognosis of SOS [L-Ficolin, HA, and VCAM1] have identified through a proteomics study and validated in several cohorts [130,135]. ST2 has also been shown to be a prognostic biomarker of endothelial injury that link aGVHD and TMA [152]. A recent study showed that ST2 and IL-6 are diagnostic and prognostic biomarkers of IPS compared to unaffected controls, and TNFR1 as a diagnostic marker when compared to viral pneumonia. ST2 had the highest positive predictive value (PPV) with 50% PPV at onset and 25% at day 7 post-HSCT for IPS occurrence [153]. New-onset posttransplantation diabetes mellitus (PTDM) occurs commonly after HSCT and is associated with inferior survival. A recent study showed that high ST2 at engraftment predicted increased PTDM and NRM risk, independent of conditioning and grades 3 to 4 aGVHD [154].

Identification and Validation of Chronic GVHD Biomarkers cGVHD is the most common long-term complication of allo-HSCT and is a leading cause of mortality in patients that survive 2 years without relapse [155], limiting the wider application of this therapeutic approach to patients with hematologic malignancies and bone marrow failures. In contrast to aGVHD, cGVHD often presents with clinical manifestations that resemble those of autoimmune diseases, such as scleroderma, Sjogren’s syndrome, and systemic lupus erythematosus. Traditionally, a cutoff of 100 days post-HSCT was used to differentiate between acute (before) and chronic (after) GVHD. However, it is now recognized that earlier chronic disease onset could occur, as well as clinical syndromes with features of aGVHD, beyond 100 days post-HSCT, particularly after reduced-intensity conditioning regimens [156]. The median onset of cGVHD is between 4 and 5 months post-HSCT. There are many risk factors for the development of cGVHD, including age at transplantation, donor source and HLA disparity, peripheral blood grafts, and a history of prior aGVHD [157]. Depending on the presence or absence of these risk factors, the rates of cGVHD can be as high as 40%e70% [158]. Despite the high incidence of this complication, the pathophysiology of this disorder remains poorly understood. Its diagnosis is based on clinical symptoms (i.e., inflammatory and fibrotic components) of several target organs (e.g., skin, nails, mouth, eyes, genitalia, musculoskeletal, GI tract, liver, and lung) that can be confirmed by biopsies. At present, no simple diagnostic or prognostic test for cGVHD exists. However, in 2014, the National Institutes of Health cGVHD Biomarker Consensus Group and the Biology group summarized the state of the art for cGVHD biology and biomarkers [85,159]. Blood biomarkers (both cellular and protein) have been evaluated. Earlier studies have shown low platelet counts (<100,000/mm3) to be a negative survival predictor in cGVHD [160]. High eosinophil counts (>500/mm3) have been correlated with the presence or development of cGVHD [161]. However, these two markers have not been investigated in clinical trials. Furthermore, eosinophil counts did not significantly differ between patients with and without cGVHD in the Children’s Oncology Group study [162]. Biomarkers have been identified that are associated with active cGVHD, such as high levels of soluble B-cell activating factor (sBAFF) [163,164] and the balance of B-cell subsets during B-cell reconstitution (reviewed in [165,166]). The prolonged imbalance of CD4þCD25þFOXP3þ regulatory T cells versus conventional CD4þ T cells following HSCT has been associated with a loss of tolerance and significant cGVHD manifestations [167,168]. Some noteworthy novel biomarkers publications since the 2014 NIH consensus biomarker and biology papers are listed below. Using a quantitative proteomics approach, a biomarker panel of four proteins [ST2, CXCL9, matrix metalloproteinase 3 (MMP-3), and osteopontin] had significant correlation with cGVHD diagnosis. Moreover, when measured at day þ100 after HSCT it allowed patient stratification according to risk of cGVHD [132]. MMP-3 also correlated to BOS diagnosis [117]. In a recent study, both CXCL9 and CXCL10 were significantly correlated in multivariate analysis with cGVHD diagnosis in the first replication cohort, but only CXCL10 in the second [169]. In another recent study, gene expression profiling of circulating monocytes from cGVHD patients found significant upregulation of IFN-inducible (including CXCL10) and damage-response genes in cGVHD patients as compared to controls. These pathways were further confirmed in plasma ELISA assays showing elevated levels of CXCL9 and CXCL10 [170]. Altogether, the IFN-inducible chemokines CXCL9 and CXCL10, responsible for CXCR3 expressing Th1/NK lymphocyte recruitment into tissues, are upregulated at diagnosis [170e172] and are worth being pursued and tested in prospectively collected samples. An activated Th17-prone T-cell subset expressing both CD146 and CCR5 is involved in cGVHD and is sensitive to pharmacological inhibition [173]. Circulating T follicular helper cells have also been shown to correlate with cGVHD and to exhibit a Th17 profile [56]. Plasma CD163 concentration has been associated with de novo-onset cGVHD [174].

Identification of GVL and Minimal Residual Disease Biomarkers Reciprocal immune reactions between donor and recipient are a key feature of allo-HSCT. In the majority of cases, donor T lymphocytes react against both the patient’s normal host cells, causing GVHD, and the patient’s tumor cells, leading to the

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GVL effect. Although the GVL effect is the most important in the subset of allo-HSCT recipients with GVHD, the risk of disease relapse is also reduced in patients without GVHD [175]. Thus, the fundamental question in the field is whether the mechanisms or effectors of GVL differ from those of GVHD or whether GVL represents a subset of GVHD reactions. The potency of the GVL effect is illustrated by the use of donorelymphocyte infusion (DLI) to treat patients with tumors such as leukemia, lymphoma, and myeloma [176]. The recognition of the GVL effect is driving the evolution of allo-HSCT toward an immunotherapeutic approach that does not require toxic chemoradiotherapy for tumor eradication. Indeed, both experimental models and human studies have shown that nonmyeloablative conditioning can sufficiently suppress recipient immunity to allow allogeneic stem cell and immune cell reconstitution [177]. However, the immunological mechanisms and target molecules that are required for the elimination of malignant cells are only partly understood. Immune cells that are implicated include CD8þ and CD4þ T cells, natural killer cells, and dendritic cells. Target molecules of classical T cells include the minor histocompatibility antigens (mHAs) and tumor-associated proteins overexpressed by tumors. These targets have been identified using labor-intensive methods, including high-performance LCeMS, cDNA expression cloning, genetic linkage analysis, and polymorphic-peptide screening [178]. mHAs have either broad tissue expression (e.g., UGT2B17, HY, PANE1) or expression restricted to hematopoietic cells (e.g., HA-1, HA-2, HB-1, CD19), which is optimal for a GVL response without GVHD. This exclusive expression on leukemic cells and not on epithelial cells has been used by researchers to augment the GVL effect and to distinguish GVL from GVHD. Another strategy is to use adoptive transfer of T cells that are specific for proteins that are overexpressed by leukemic cells. Nonpolymorphic proteins such as Wilms tumor 1, proteinase 3, survivin, telomerase reverse transcriptase, CYPB1, and laminin have been investigated as targets for T cells, and several trials are currently ongoing [178].

Genomics Studies and Next-Generation Sequencing to Tackle GVL and MRD The most notable advances in the identification of new GVL targets and the separation of GVL from GVHD have occurred, thanks to the advent of next-generation sequencing (NGS). Since 2005, there has been an explosion of published work in hematologic diseases, particularly the discovery of novel genetic alterations that can encode peptides with restricted tumor expression; hence, these peptides can serve as potential target antigens of GVL responses. The NGS technology allows the detection of somatic mutations that are unique to the tumor, necessitating the sequencing of both the tumor and the normal tissue from the same patient to see the differences. This approach differs from genome-wide association studies that detect SNPs that represent variations in the DNA sequence that are unique to the individual. Researchers distinguish between the few driver mutations that allow for growth and survival advantages to the tumor from the majority of mutations that occur randomly and become fixed within the tumor clones (i.e., passenger mutations). With the cost of this technology falling, we may see the replacement of minimal residual disease (MRD) measurements by RT-PCR with NGS, potentially helping to discover targets of GVL to distinguish it from GVHD. Tumor neoantigens have previously been proposed as tumorspecific antigens that are suitable for distinguishing GVL from GVHD. However, their identification has been difficult because of technical limitations that can now be overcome with NGS. Similar to mHAs, tumor neoantigens arise from genetic changes, specifically tumor-driven mutations such as missense mutations, frameshift insertions or deletions, gene fusions, and alternative splicing, rather than from polymorphisms. The vast majority of these genetic changes appear to be unique to an individual tumor. These “passenger” mutations that are not interesting from the standpoint of oncogenesis do have the potential to be recognized and destroyed by the immune system without inducing associated GVHD. Thus, it is now possible to capitalize on these tumor-specific neoantigens to develop focused and potent tumor vaccines post-HSCT [179].

Possible Proteomics Approaches to Distinguish GVL From GVHD There have been no published studies on the use of high-throughput proteomics approaches to identify and validate GVL targets. Below are suggestions for possible approaches aimed at developing a simple, quantifiable, blood-based test to predict GVL or to monitor MRD post-HSCT. Here, we need to note that plasma proteome analysis will not reach the depth of DNA deep sequencing to find neoantigens. Some neoantigens might be found in the plasma by virtue of immune cells and tumor shedding, but this analysis will not be comprehensive. The major focus of plasma proteome analysis will be to discover proteins associated with the “milieu” (cytokines or others) that favors GVL responses without concomitant GVHD. One possible experiment is to use IPAS, which enables the assessment of more than 1000 plasma proteins with a wide range of protein abundance to identify candidate biomarkers that are differentially expressed at 30, 60, and 100 days post-DLI in patients exhibiting long-term remission without GVHD. These GVL candidate biomarkers would then be distinguished from those predicting GVHD post-DLI. The ability to identify patients who will not develop GVL early

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posttransplantation using a simple proteomics test has important therapeutic consequences, including more stringent monitoring of MRD and/or preventative care with DLI or tumor-specific vaccines. Determining which patients will develop GVL with GVHD and those at high risk for subsequent GVHD morbidity could lead to tailored treatment plans, including specific immunosuppressive treatments that do not influence GVL response. Equally important is the identification of patients who will develop GVL without GVHD, potentially enabling more rapid tapering of immunosuppressive regimens and thereby reducing long-term toxicity in these patients. The cost-effective monitoring of MRD posttransplantation via the detection of plasma proteins rather than through invasive methods such as bone marrow aspiration, which is both painful and not amenable to the frequent monitoring of patient status, will facilitate the development of preemptive DLI and vaccines in patients with positive MRD.

INCORPORATING GVHD BIOMARKERS IN CLINICAL TRIALS Given the progress being made in GVHD biomarker identification and validation, it is not surprising that clinical trial design has already begun incorporating biomarkers for some outcomes. Target-specific diagnostic biomarkers that can differentiate skin GVHD from other rashes and GI GVHD from other forms of enteritis will allow replacement of invasive biopsies. First, a simple observational trial during which samples and biopsies will be taken at onset of GVHD should be performed. During this trial, physicians will treat according to symptoms and perform biopsies as usual. A retrospective analysis of the samples with different thresholds of biomarkers will determine whether the biomarkers can replace the invasive biopsies as well as the best threshold. If this study concludes that the biomarker does as well as the biopsies, the next trial would be a randomized interventional trial; one-half of the patients will be treated according to the biopsy results, and one-half will be treated according to the biomarker results; the development of GVHD and other outcomes will then be evaluated. Another potential clinical application of GVHD biomarkers is to use them to stratify patients based on risk at the time of GVHD onset. GI GVHD is considered a high-risk feature for mortality, but given the absence of further risk stratification, the standard of care for all patients with GI GVHD is the prompt initiation of systemic steroid treatment, with the addition of second-line agents reserved for patients who fail initial therapy. Unfortunately, most patients who require second-line therapy die, highlighting the need for refinement of risk beyond what the current grading system provides. Early identification of patients at high risk for steroid unresponsiveness may permit alternative testing or additional therapies before the development of refractory disease. Equally important is the identification of low-risk patients who will respond well to treatment. These patients may tolerate a more rapid tapering of steroid regimens to reduce long-term toxicity, infections, and a loss of the GVL effect. The Blood and Marrow Transplant Clinical Trials Network (BMT CTN) 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 aGVHD (algorithm combining ST2 and REG3a) https://web.emmes.com/study/bmt2/protocol/1501_protocol/1501_protocol.html. There are also trials under development for patients with newly diagnosed aGVHD with high-risk biomarkers using intensified treatment. A schema of the scenario for treatment of steroid-resistant GVHD using biomarkers is shown in Fig. 20.8. The ability to identify patients at high risk for GVHD early after their transplantation and treatment course has important therapeutic consequences, including more stringent monitoring and/or preemptive interventions. As with any screening test, improvements in sensitivity come at the expense of specificity and vice versa; which aspect to emphasize is a matter of clinical judgment. The experience with post-HSCT CMV disease offers an instructive example in how the transplant community approaches this sort of problem. Prior to the development of CMV-predictive tests, the incidence of CMV disease was approximately 35%, with high mortality rates. The introduction of CMV-preemptive strategies guided by polymerase chain reaction or antigenemia studies reduced CMV disease to approximately 5%e15% [180]. Extrapolating from published data on the number of positive CMV screening tests compared to the expected number of cases of CMV disease, it appears that around 50% of positive CMV screens, if untreated, would not result in CMV disease. The sensitivity of CMV screening tests is very high, in the range of 90%, meaning that relatively few cases of CMV disease develop in the absence of a positive screening test. Thus, it has become common practice to administer preemptive therapy to patients who are not likely to develop CMV disease to effectively prevent cases of CMV disease. If we apply a similar standard to GVHD-preemptive therapy (1:1 true positive to false positive), the sensitivity of the aGVHD biomarkers measured at day 7 or day 17 post-HSCT are approximately 70% [127,133]. While not yet as accurate as the gold standard, CMV screening, we believe that these results are sufficient to design a clinical trial to test whether a preemptive strategy would prevent aGVHD. The toxicity of the intervention is an important consideration in trial design, as excess toxicity from preemption will dampen acceptance of the strategy. A short course of corticosteroid therapy at the time during which markers of alloreactivity are increasing may be a reasonable therapy to test. The success of preemption must include a

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Low (< T)

Test ?

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Treat ?

Test

Treat 2

Outcomes

Test

Treat STD

Outcomes

Test

Treat STD

Outcomes

Test

Treat Siro

Outcomes

Narrowlydefined scenario High (> T)

Low (< T)

FIGURE 20.8 Decision tree for modeling clinical utility of a biomarker. Each scenario in a decision tree should be defined narrowly, in such a way that a single treatment strategy would be clinically reasonable for acute graft-versus-host disease in the absence of the biomarker result. Making the scenario narrow allows the resulting estimate to represent a relatively homogeneous effect that is easy to translate into practice. The square node represents the decision node; in this example, two different strategies are evaluated (S1 and S2). Round nodes are probability nodes. In this example, the round nodes indicate a split of patients into subgroups defined by the underlying distribution of the biomarker; probabilities of having a high or low biomarker result (defined by the test threshold). STD, steroids, Siro, sirolimus.

reduction not only in the incidence of aGVHD but also in infectious complications and relapse. Ultimately, a randomized trial will be needed to assess the effectiveness of aGVHD preemption. Preemptive strategies for cGVHD, similar to those discussed for aGVHD, are also being designed.

Future Research on Biomarkers: From Diagnosis to Therapy Future directions include a blinded evaluation of novel-identified biomarkers with samples collected in a multicenter prospective study. Ideally, this requires a multicenter cohort, indispensable to the reduction of center effects and to the successful design of subsequent trials, which is ideally performed through a consortium such as the BMT CTN to both establish a unique resource for bone marrow transplantation clinicians and, further, a national resource for investigators to explore these biomarkers. Such a multicenter prospective trial validation is important because the algorithm should take into account the variability between centers (center effect) and the individual risks related to known risk factors, such as age, HLA match, donor source (particularly cord blood), and conditioning regimen, including T-cell depletion (in vivo or in vitro). This endeavor has happened through the BMT CTN protocol 1202: prospective multicenter cohort for the evaluation of biomarkers predicting risk of complications and mortality following allogeneic HSCT. However, studies using these samples just started and have not yet been published. The expected outcome of these studies will be to develop a single national BMT CTN biomarkers threshold and/or algorithm to predict a patient’s risk for aGVHD, allowing for innovative, cutting-edge personalized medicine. The ideal formula will be as simple as possible. In the best-case scenario, a single marker at a single time point and few transplantation risk factors (e.g., conditioning intensity, cord blood source, and use of T-cell depletion) would be investigated. Next, a trial of preemptive therapy for aGVHD using the formula would be initiated. Therapeutic approaches for aGVHD have largely been limited to the nonspecific targeting of effector cells. As a result, steroids remain the first-line treatment for patients presenting with aGVHD symptoms. Biomarkers represent promising targets for new therapeutics. In addition, we propose that the discovery of aGVHD-specific drugs based on biomarkers will target the appropriate effector T cells to both increase efficacy and lower toxicity. This approach represents the first step in a continuum of research that is expected to lead to the development of pharmacologic strategies to specifically treat GVHD. One direct outcome of this proposal will be the establishment of clinical trials using both biomarkers for risk stratification and new drugs for treatment in high-risk populations. A similar approach has still to be proposed for cGVHD.

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CONCLUSIONS Omics is a revolutionary field that includes detection technologies for RNA and proteins, and molecules that are the most proximal to the real-time pathophysiology of alloreactivity as compared to genes. In a short time, the use of transcriptomics and proteomics has led to the identification of novel mechanisms of allogeneic HSCT, which are unlikely to have been discovered by traditional hypothesis-driven research. A promising proteomics approach is to use protein biomarkers in risk stratification to better employ current disease treatment modalities. Furthermore, the biomarker findings presented in this chapter offer the potential for exploring targeted therapeutics.

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