Differentiation of acute graft-versus-host disease from drug reaction in skin by a novel tissue-based biomarker assay

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Lehman, Surendra Dasari, Sindhuja Sominidi Damodaran, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2481845/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Acute graft-versus-host disease (GVHD), a serious complication of allogeneic hematopoietic cell transplantation (HCT), frequently involves the skin. Since clinicopathologic features of GVHD can mimic those of certain drug eruptions, accurate diagnosis can be challenging. We aimed to develop a tissue-based molecular assay to improve differentiation of GVHD from vacuolar interface drug eruption in the post-HCT period. After identifying 62 putative tissue-based biomarkers using unbiased molecular methods, we incorporated 26 into a quantitative RT-PCR assay. We tested skin biopsy specimens showing vacuolar interface changes attributable to either acute GVHD (n=67) or drug reaction (n=17), as classified by meticulous review of clinical features and course. Data were incorporated into clinical-molecular fusion diagnostic models. Significant differences in the expression of several genes, including MX1, MNDA, OAS, LIMA1, GSTM5 , and SPP1 , were detected between the two diagnostic groups (p<0.05). The optimal fusion model, which incorporated quantitative expression of MX1, MNDA, OAS, and GSTM5 , as well as the clinical feature of diarrhea, imparted high diagnostic accuracy (receiver operating characteristic area-under-curve: 0.89). This novel tissue-based diagnostic molecular assay and fusion model demonstrates potential to improve distinction of GVHD from drug eruptions in the post-HCT setting. Multicenter validation studies are required. Graft-versus-host disease hematopoietic cell transplantation rash molecular interferon precision medicine Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Hematopoietic cell transplantation (HCT) may be curative for patients with certain hematologic conditions [ 2 ]. However, the post-HCT course may be complicated by the development of acute graft-versus-host disease (GVHD), a potentially life-threatening attack on host tissue cells by engrafted immune cells. The earliest and most frequently involved organ in GVHD is the skin [ 4 , 22 ]. The clinical morphology and anatomic distribution of cutaneous GVHD can be non-specific, mimicking features of skin eruptions attributable to other causes, such as morbilliform drug eruption [ 1 , 5 ]. Adding to challenges associated with accurate diagnosis is the fact that microscopic features of GVHD, namely vacuolar interface dermatitis with keratinocyte apoptosis, are also not specific and may be seen in other entities such as certain drug eruptions [ 7 , 10 , 31 ]. Despite the lack of specificity of clinical and microscopic features of GVHD of the skin, early, accurate diagnosis is critical for initiation of appropriate therapy or avoidance of unnecessary escalation of immunosuppressive dose. Although progress is being made to identify prognostic biomarkers in the blood in patients with GVHD [ 14 , 15 , 21 , 23 ], there have been no significant advances in the diagnosis of cutaneous GVHD in decades. In particular, no ancillary tests to improve diagnostic accuracy beyond the current and imperfect gold standard of clinicopathologic correlation have been developed. We reported the proteomic profiles of archived skin biopsy specimens showing GVHD. Proteins found to be differentially expressed had roles in type I interferon signaling and purine ribonucleotide binding [ 11 ]. Here, we aimed to follow-up on these preliminary data to create a novel tissue-based diagnostic molecular biomarker assay with a clinical-molecular fusion model to help differentiate GVHD from drug eruptions with vacuolar interface changes in the post-HCT setting. Materials And Methods The study was approved by the Institutional Review Board and was conducted in accordance with the Declaration of Helsinki. For case selection, we queried our institution’s HCT database (transplantation dates between 1/1/2010 and 12/28/2016) and manually searched the electronic medical records of 629 patients for skin biopsy data. Patients with skin biopsies derived after HCT and with biopsy reports indicating the presence of vacuolar interface inflammation were included in the eligibility screen. Specimens from patients with ambiguous final diagnoses were not included. Slides representative of each skin biopsy specimen were reviewed by a dermatopathologist, and the cases showing mild to moderate vacuolar interface changes (equivalent to GVHD grade 1–3) [ 13 ] were included. Demographic and clinical data were collected, including immunosuppressive medications being taken at the time of skin biopsy. Discovery of candidate tissue-based biomarkers For RNAseq, we compared expression profiles of 8 cases of GVHD to 8 cases of vacuolar interface drug eruption. RNA was isolated using the RNeasy Plus Mini kit (74134, Qiagen) and sequenced, as previously described [ 16 ]. RNA-derived cDNA libraries were prepared using the TruSeq RNA Library Prep Kit v2 (Illumina). Concentration and size distribution of the resulting libraries were determined on an Agilent Bioanalyzer DNA 1,000 chip and confirmed by Qubit fluorometry (Life Technologies). Unique indexes were incorporated at the adaptor ligation phase for three-plex sample loading. Libraries were loaded onto paired end flow cells to generate cluster densities of 700,000 per mm 2 , following Illumina’s standard protocol. The flow cells were sequenced as 51-paired end-reads on an Illumina HiSeq 2000. The samples were processed through the Mayo RNA-Seq analysis pipeline, MAP-RSeq [ 6 ]. Raw and normalized (read per kilobase of gene per million mapped reads) gene expression read counts were obtained per sample. Differential gene expression analysis was carried out using the freely available edgeR bioconductor software package ( http://bioconductor.org ) [ 25 , 26 ]. Scaling by total lane counts can bias estimates of differential expression; therefore, edgeR uses trimmed mean normalization on raw read counts to determine whether genes are differentially expressed using the negative binomial method. The Benjamini and Hochberg correction was used to control for multiple testing to obtain a false discovery rate. We considered genes to be differentially expressed if they had absolute value fold changes greater or equal to 0.5 and a false discovery rate < 0.1. Diagnostic tissue-based biomarker assay development The named biomarkers identified via a proteomics approach (n = 26) and a RNA sequencing approach (n = 36) were researched using online resources [Human Gene Database, www.genecards.org ; NCBI Gene, https://ghr.nlm.nih.gov/gene , and literature searches (Pubmed.gov, with all combinations of the following search terms: “graft-versus-host disease”, “skin”, and each gene name]. Any gene known to have relevance in GVHD was included, as were others selected to represent a range of biologic functions and/or cytokine influences. We then designed a reverse-transcriptase polymerase chain reaction (RT-PCR) assay to assess expression of these 23 selected genes. We also included 3 additional serendipitous genes which our laboratory had studied previously in another context and that are known to be involved in cell-matrix interactions or chemotaxis (i.e. SPP1, CXCL1 , and IL8 ; Meves et al., 2015). Proof-of-concept pilot studies demonstrating the ability of this assay to differentiate between cutaneous GVHD and lupus erythematosus, the latter being another condition characterized by vacuolar interface changes that can be indistinguishable from those of GVHD, were successful and have been published previously [ 12 ]. For this assay, we identified additional archived skin biopsy specimens showing either GVHD or drug reaction with vacuolar interface changes on skin biopsy (n = 92). Cases showing complete epidermal-dermal separation (grade 4) were excluded (n = 3) to avoid confounding molecular results with non-specific markers of severe tissue destruction. Five cases were subsequently excluded due to extraction of insufficient RNA volumes. We also collected additional clinical features, including demographic information, clinical outcomes, and other clinical variables present at the time of the biopsied skin eruption that have previously been demonstrated to be helpful in differentiating GVHD from drug hypersensitivity reaction [ 1 ]. Specifically, we assessed for the presence or absence of facial involvement, palmar/plantar involvement, diarrhea, or hyperbilirubinemia. Of patients with GVHD, we sub-classified them as having fatal or non-fatal GVHD to determine whether gene expression profiles correlated with the clinical outcome of death associated with GVHD. For the assay, we started with RNA purification from FFPE skin biopsy specimens (Qiagen), as previously described [ 9 , 20 ]. Quantitative RT-PCR was done using the BioMark HD System and dynamic array integrated fluid circuits (Fluidigm). Forty-three specific targets in 32 genes (26 experimental and 6 control genes) were amplified per cDNA (standards, controls and experimental samples). The following cDNA were run per array: standards in triplicates; control cDNA (internal standard); experimental cDNA; the latter two were in duplicates. All cDNA was pre-amplified (TaqMan Preamp Master Mix, Applied Biosystems). Array-based quantitative PCR was with the help of the TaqMan Gene Expression Master Mix (Applied Biosystems). After thermal cycling, raw Ct data for standards was checked for linear amplification. Data that did not pass both linear amplification and reproducibility checks were discarded. For comparisons of gene expression between GVHD and drug eruption, the quantitative expression of each gene was compared using Wilcoxon test, with p < 0.05 representing statistical significance. For the model development, all cases were included in the analysis, with all genes being subjected to variable selection for candidate gene identification. This process generated 1000 randomly seeded, logistic regression models. Each of these models was designed to use a three-fold cross-validation. All models with nonzero coefficients for variables were considered. For final model building, all possible combinations of final variables were tested in logistic regression models. We included models showing no interdependent variables and prioritized those with the highest receiver operating characteristic (ROC) area under the curve (AUC). This method of layered model development optimized identification of equally well-performing classifiers while minimizing the risk of overfitting the data. Results Our approach was comprised of three phases: (1) Biomarker discovery using molecular methods, (2) Development of diagnostic tissue-based biomarker assay, and (3) Data analysis and modeling (Fig. 1 ). Discovery of candidate tissue-based biomarkers RNAseq led to the identification of 36 named genes that were differentially expressed in GVHD vs. vacuolar interface drug reaction (Fig. 2 A). Trends amongst the biological processes represented by these genes included enrichment amongst those involved in collagen fibril (4; 11.1%) or extracellular matrix organization (n = 7; 19.4%). Each of these genes was distinct from the prior 26 genes identified by our group using proteomics methods (Fig. 2 B) [ 11 ]. Diagnostic tissue-based biomarker assay development Demographic information and transplantation features, skin biopsy changes, and clinical features of patients included in one of the two diagnostic groups (i.e. GVHD or non-GVHD skin eruption) are outlined in Table 1 . Examples of histopathologic features are depicted in Fig. 3 . Table 1 Clinical and histopathologic features of patients with skin biopsies showing acute graft-versus-host disease or vacuolar interface inflammation drug reaction. No differences were observed between the two groups (p > = 0.05, data not shown). n/a = not applicable. TBI = total body irradiation. Acute GVHD Drug eruption n (%) n (%) 67 17 Demographic features Female sex (%) 26 38.8 6 35.3 Age at time of biopsy (in years) 54.4 n/a 55.8 n/a Time of biopsy after HCT (in days) 92.6 n/a 89.9 n/a Hematologic diagnosis AML/ANLL 25 37.3 11 64.7 MDS 22 32.8 2 11.8 Other Leukemias 6 9.0 4 23.5 CML 4 6.0 0 0.0 NHL 4 6.0 0 0.0 ALL 4 6.0 0 0.0 Multiple myeloma 4 6.0 0 0.0 Transplantation features Transplantation type Allogeneic transplant (%) 67 100.0 17 100.0 Peripheral blood stem cells (%) 52 77.6 15 88.2 Double cord blood (%) 8 11.9 0 0.0 Bone marrow (%) 6 9.0 2 11.8 Relation of graft donor to recipient Unrelated 34 50.7 11 64.7 Full sibling (not identical twin) 28 41.8 5 29.4 Child 2 3.0 1 5.9 Other relative 2 3.0 0 0.0 Conditioning regimen Busulfan/cyclophosphamide 12 17.9 6 35.3 Busulfan/fludarabine 12 17.9 0 0.0 Melphalan/fludarabine 12 17.9 6 35.3 Cyclophosphamide/TBI 11 16.4 6 35.3 Fludarabine/Cyclophosphamide/TBI 9 13.4 6 35.3 Fludarabine/TBI 6 9.0 1 5.9 Fludarabine/Cyclophosphamide/ATG 2 3.0 6 35.3 Post-transplantation immunosuppression Tacrolimus 31 46.3 10 58.8 Cyclosporine 29 43.3 5 29.4 Methotrexate 17 25.4 7 41.2 Mycophenolate mofetil 10 14.9 1 5.9 Systemic corticosteroids 9 13.4 0 0.0 Unknown 3 4.5 0 0.0 None 2 3.0 1 5.9 Other 2 3.0 1 5.9 Key clinical features Presence of facial involvement (when known) 25 42.4 5 35.7 Presence of palm/sole involvement 10 24.4 2 13.3 Presence of diarrhea 26 40.6 5 29.4 Presence of hyperbilirubinemia 23 34.8 5 29.4 Histopathologic features from skin biopsy Vacuolar interface changes Grade 1 33 49.3 6 35.3 Grade 2 28 41.8 10 58.8 Grade 3 6 9.0 1 5.9 Grade 4 0 0.0 0 0.0 Degree of interface inflammation Mild 35 52.2 10 58.8 Moderate 28 41.8 7 41.2 Abundant 4 6.0 0 0.0 For development of the RT-PCR based assay, case selection and classification were performed using methods above and as outlined in Fig. 4 . We included 67 cases of GVHD in the development of the biomarker assay. Of these patients, GVHD was a contributing cause of death in 20 (29.8%). These cases were compared to 17 cases of drug eruption, which were attributed to antibiotics (n = 5 cases; 29.4%), chemotherapy (n = 5), granisetron (n = 1; 5.9%), omeprazole (n = 1), and unknown (n = 4; 23.5%). Vacuolar interface changes were present in all cases to various degrees, as outlined in Table 1 . Genes selected for inclusion in the diagnostic tissue-based biomarker assay, along with the ratio of gene expression levels, are listed in Table 2 . On replicate testing, gene expression showed a high level of correlation (average: 0.98; range: 0.90–0.99), supporting the reproducibility of laboratory methods. Multiple fusion models were generated based on clinical and molecular data, and we selected the one with the highest ROC AUC value (i.e. 0.89) without data over-fitting. This model incorporates expression of MX1, MNDA, OAS , and GSTM5 , as well as the clinical feature of the presence of diarrhea (Fig. 5 ). Comparison of gene expression profiles in patients with fatal vs. non-fatal GVHD, or in patients with GVHD clinical grade I vs. grades II-IV revealed no significant differences. Table 2 Genes tested (with primary biologic function of the corresponding protein), with expression ratio of acute graft-versus-host disease to vacuolar interface drug reaction. P-value < 0.05, marked with an asterisk, denotes statistical significance. Gene Ratio p-value Gene function MLANA 0.96 0.99 Melanocyte differentiation marker MITF 0.97 0.37 Melanocyte differentiation marker MX1 0.73 0.01* Interferon-inducible protein with antiviral properties MNDA 0.82 0.03* Granulocyte/monocyte cell- specific response to interferon IVL 0.7 0.19 Formation of cell envelope OAS/SMOC1 0.74 0.02* Regulation of osteoblast differentiation TAP1 0.95 0.28 Antigen processing CAPN6 0.98 0.69 Microtubule stabilization STAT3 0.9 0.2 Transcription factor GBP2 0.74 0.06 Interferon-inducible GTPase ITGB3 0.97 0.72 Integrin involved in cell-matrix interaction FN1 0.88 0.29 Cell-matrix interaction THBS2 0.93 0.3 Cell-matrix interaction LIMA1 0.87 0.02* Cytoskeletal signaling KRT6B 10.6 0.96 Cytokeratin GSTM5 0.74 < 0.001* Conjugation of reduced glutathione LAMA4 0.91 0.06 Mediation of cell attachment and migration CREB3L1 0.95 0.36 Transcription factor OGN 0.98 0.67 Osteoinductive factor KRT17 1.13 0.72 Cytokeratin SPP1 0.77 0.03* Cell-matrix interaction ITGA5 1.01 0.64 Cellular signaling ITGA3 0.94 0.17 Cellular signaling ITGA2 1 0.81 Cellular signaling MXRA5 1.01 0.49 Cellular adhesion CXCL1 0.97 0.66 Chemotactic cytokine for neutrophils IL8 0.96 0.64 Chemotactic cytokine for neutrophils, T-cells, and basophils FSTL5 0.88 0.07 Uncertain Discussion Differentiation of acute cutaneous GVHD and non-GVHD vacuolar interface skin eruptions represents a major challenge in the care of patients following HCT. In an attempt to improve diagnostic accuracy in this setting, we discovered potential tissue-based molecular biomarkers, incorporated a selection of these biomarkers into a quantitative RT-PCR assay, and created a clinical-molecular fusion model that confers a ROC AUC of 0.89. This novel diagnostic approach demonstrates the potential to differentiate GVHD from drug reactions with vacuolar interface changes on skin biopsy in the post-HCT period using a limited set of clinical and molecular data. Early in the history of HCT, it was recognized that GVHD is a syndrome and that the diagnosis of skin GVHD is complex, has no single clinical or histopathologic pathognomonic finding, and requires clinicopathologic correlation [ 27 ]. A prior study designed to distinguish GVHD from drug eruptions included a robust analysis of clinical features, with an emphasis on those dermatologic [ 1 ]. It was found that facial involvement was more frequently observed in GVHD (59%) than in drug hypersensitivity reaction (24%; p = 0.05), and the addition of palms/soles involvement increased specificity (36% of patients with GVHD; 0% in patients with drug hypersensitivity reaction; p = 0.006). While these clinical findings may be helpful when present, they lack diagnostic sensitivity. Little progress has been made in the histopathologic diagnosis of GVHD in the last 3 decades. An early attempt to differentiate GVHD from drug reactions on skin biopsy histopathology and immunohistochemistry found no definite differences and concluded, “neither morphological nor immunohistochemical analysis of skin biopsies is of much help in distinguishing [GVHD] reaction, grade I and II, from drug-induced skin reactions in patients developing skin lesions after bone-marrow transplantation” [ 3 ]. A widely cited analysis of histopathologic features of GVHD concluded that skin biopsy “is not a useful tool for differentiating between [GVHD] and other entities that lack specific histologic features such as drug eruptions and viral exanthems” [ 8 ]. A subsequent study of clinical outcomes based on skin biopsy results, which found no significant correlations, made the following recommendation: “We suggest that the routine practice of subjecting patients to such biopsies could be abandoned without compromising their care” [ 30 ]. More recently, a study found that the CD123 stain for plasmacytoid dendritic cells (a marker of type I interferon influence) may have utility in the diagnosis of GVHD of the colon [ 17 ]. Based on this finding, as well as the knowledge that plasmacytoid dendritic cells can be found in skin affected by GVHD [ 18 ], our group revisited the potential diagnostic utility of skin biopsy in differentiating cutaneous GVHD from drug reactions with vacuolar interface changes in a small study of 24 skin biopsies [ 10 ]. While no differences between CD123 patterns were observed, skin biopsies of GVHD did show significantly less spongiosis and fewer dermal eosinophils than drug reactions. Though dermal eosinophil density has been shown to be inconsistent in GVHD in other studies [ 19 , 28 ], spongiosis had not been well-documented in this setting previously. Though the absence of spongiosis may warrant further study, data are insufficient to regard it as an independently reliable histopathologic feature of GVHD. Important of molecular investigations of GVHD have focused on prognostic serum biomarkers for this disease [ 14 , 15 , 21 , 23 ]. Comparative molecular evaluations of skin biopsies of GVHD vs. drug eruption do not appear to have been attempted previously, however. Prior researchers have evaluated genetic expression profiles in the skin of murine allogeneic GVHD models and recognized that genes that are interferon-induced, are involved in antigen presentation and cellular adhesion, or are acute phase reactants are upregulated [ 29 ]. In addition, the importance of type I interferon and plasmacytoid dendritic cells in skin tissue affected by GVHD has been demonstrated [ 18 , 24 ]. Our group confirmed upregulation of interferon-mediated and other proteins in skin affected by GVHD [ 11 ] and in the present study, demonstrated that their quantitative expression can be used to differentiate GVHD from drug eruption with vacuolar interface changes. Results of the biomarker discovery phase of this study offer new insights on the biology of GVHD. Specifically, genes that were found to be enriched in GVHD included those that are involved in response to cellular stress, mediated by type I interferons, involved in purine ribonucleoside, anion, or carbohydrate derivative binding, or involved in collagen fibril and extracellular matrix organization ( www.string-db.org/ ). These genes and pathogenic mechanisms warrant further study as possible mediators of GVHD or potential therapeutic targets. Genes that were incorporated into the final model included MX1 (interferon-induced mediator of Th1 immunity), MNDA (mediator of granulocyte/monocyte cell-specific response to interferon), OAS (interferon-induced regulator of osteoblast differentiation), and GSTM5 (catalyst of reduced glutathione conjugation). The single clinical variable that contributed to the model was the presence of diarrhea. Though this feature is not specific to gastrointestinal GVHD, it has been shown previously to be a relatively specific finding for GVHD over drug eruption when coupled with a morbilliform skin eruption in the post-HCT period (73% vs. 12% in patients with GVHD or drug reaction, respectively; p = 0.000) [ 1 ]. Strengths of this study included ensuring that every sample representing drug eruption was derived from patients who had previously undergone HCT, thereby attempting to control for post-HCT-related factors. Each case showed vacuolar interface changes, thereby making sure that cases were comparable to GVHD. In addition, the identification of biomarkers using an unbiased approach allowed for recognition of genes of interest that have do not appear to have been studied in GVHD previously. The primary limitation to this study is the difficulty in assuring accuracy with case classification, due to the lack of certainty inherent in current diagnostic methods. We attempted to mitigate this limitation by carefully examining the electronic medical record for each patient not only for documentation about patient features but also for clinical course after skin biopsy. The study also is limited by the relatively small sample size, particularly with regards to drug reaction cases. The small number of patients in this group is attributable to the high level of stringency used during case selection. That is, only cases with a high degree of diagnostic certainty and vacuolar interface changes on skin biopsy were included. In addition, this study is limited by the single-center, retrospective design, as it is remains unknown whether this assay will be valuable applicable when used prospectively and/or for patients cared for at other institutions. We anticipate future larger multi-institution studies to validate the utility of this diagnostic tissue-based molecular assay and clinical-molecular fusion models. Abbreviations GVHD=acute graft-versus-host disease; HCT=hematopoietic cell transplantation; RT-PCR=reverse-transcriptase polymerase chain reaction; ROC=receiver operating characteristic; AUC=area under the curve; RNAseq=transcriptomic analysis with RNA sequencing. Declarations Funding sources: This work was supported by the small grants program from the Mayo Clinic Departments of Dermatology (Mayo CCaTS grant number UL1TR000135; J.S.L.). J.S.L. is the recipient of a Dermatology Foundation Dermatopathology Career Development Award. A.M. is the recipient of a National Institutes of Health K08 award and the Gerstner Family Career Development Award. The sponsors of this study are public or nonprofit organizations that support science in general. They had no role in gathering, analyzing, or interpreting the data. Acknowledgements: We thank Jayne Feind, Pathology Reporting Specialist, Mayo Clinic, for study coordination. 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Zhou Y, Barnett MJ, Rivers JK (2000) Clinical significance of skin biopsies in the diagnosis and management of graft-versus-host disease in early postallogeneic bone marrow transplantation. Arch Dermatol 136:717–721. Ziemer M, Haeusermann P, Janin A, Massi D, Ziepert M, Wolff D, et al. (2014) Histopathological diagnosis of graft-versus-host disease of the skin – an interobserver comparison. J Eur Acad Dermatol Venereol 28:915–924. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2481845","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":167952344,"identity":"fc17a9ea-77d1-4746-ab17-dbbc6b679565","order_by":0,"name":"Julia S. Lehman","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYBACPgYeMM3MwM7YwPABKiqBTwsbXAszYwPjDFK0gDQxMMPY+LWw9x7d8HMHA7vBYebGz7Y5don9DMwHb/Pg08JzLu1m7xkGZoPDjM3SuduSE2c2sCVb49UikWN2g7cNrKUBqIU5ccMBHjNpvFrk35jd/AvR0vzbcls9UAv/N/xaJHjMbkNtaZNm3HYYZAsbfi08OWa3ZdskmCWBWix7tx03ntnMZmw5B48WfvYzZjffttkk8x1vf3zj57Zq2X725oc33uDRAgUSyTCWYwMzYeVgYAdj2BOpYRSMglEwCkYQAADAg0VZeISBAAAAAABJRU5ErkJggg==","orcid":"","institution":"Mayo Clinic","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Julia","middleName":"S.","lastName":"Lehman","suffix":""},{"id":167952345,"identity":"f0bd8362-67e8-4b60-ba5d-c6084a75d439","order_by":1,"name":"Surendra Dasari","email":"","orcid":"","institution":"Mayo Clinic","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Surendra","middleName":"","lastName":"Dasari","suffix":""},{"id":167952347,"identity":"b7b619d1-327a-435d-897a-2ce82467b2da","order_by":2,"name":"Sindhuja Sominidi Damodaran","email":"","orcid":"","institution":"Mayo Clinic","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sindhuja","middleName":"Sominidi","lastName":"Damodaran","suffix":""},{"id":167952348,"identity":"88471971-c722-44ce-8da4-98eca115dcdf","order_by":3,"name":"Ming Li","email":"","orcid":"","institution":"Mayo Clinic","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ming","middleName":"","lastName":"Li","suffix":""},{"id":167952349,"identity":"0cc1bd65-7971-4875-a44c-6c9cd72b6c36","order_by":4,"name":"Shahrukh K. Hashmi","email":"","orcid":"","institution":"University Hospitals Case Medical Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shahrukh","middleName":"K.","lastName":"Hashmi","suffix":""},{"id":167952351,"identity":"c9d91c81-0d01-4471-beda-bc1e20d554e0","order_by":5,"name":"Rokea A. el-Azhary","email":"","orcid":"","institution":"Mayo Clinic","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Rokea","middleName":"A.","lastName":"el-Azhary","suffix":""},{"id":167952353,"identity":"45d616fe-2db8-4346-90c5-978078150562","order_by":6,"name":"Lawrence E. Gibson","email":"","orcid":"","institution":"Mayo Clinic","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lawrence","middleName":"E.","lastName":"Gibson","suffix":""},{"id":167952355,"identity":"2196b2b3-9b2c-4e3b-ab2f-ca9c9dfd4d71","order_by":7,"name":"Hillard M. Lazarus","email":"","orcid":"","institution":"University Hospitals Case Medical Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hillard","middleName":"M.","lastName":"Lazarus","suffix":""},{"id":167952356,"identity":"e4f97186-801c-40f5-b98e-8045a23ba3e4","order_by":8,"name":"William J. Hogan","email":"","orcid":"","institution":"Mayo Clinic","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"William","middleName":"J.","lastName":"Hogan","suffix":""},{"id":167952357,"identity":"abff9a1d-76b9-4df4-b6ad-902ca45258bc","order_by":9,"name":"Saad S. Kenderian","email":"","orcid":"","institution":"Mayo Clinic","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Saad","middleName":"S.","lastName":"Kenderian","suffix":""},{"id":167952358,"identity":"90d7a98f-7c0a-40c1-ad41-1e4e13094313","order_by":10,"name":"Mrinal M. Patnaik","email":"","orcid":"","institution":"Mayo Clinic","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mrinal","middleName":"M.","lastName":"Patnaik","suffix":""},{"id":167952359,"identity":"4aaed24a-3600-4388-b451-fdbf24de5f93","order_by":11,"name":"Mark R. Litzow","email":"","orcid":"","institution":"Mayo Clinic","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mark","middleName":"R.","lastName":"Litzow","suffix":""},{"id":167952360,"identity":"098cfbb4-d92a-4366-bc09-034b0360b961","order_by":12,"name":"Alexander Meves","email":"","orcid":"","institution":"Mayo Clinic","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Alexander","middleName":"","lastName":"Meves","suffix":""}],"badges":[],"createdAt":"2023-01-16 00:59:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2481845/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2481845/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":31694586,"identity":"92ea93d7-b15e-48dc-bf8f-ebc94b201f09","added_by":"auto","created_at":"2023-01-17 16:06:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":131976,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSchematic representation of experimental design.\u003c/strong\u003eRNAseq=transcriptomic analysis with RNA sequencing. RT-PCR=reverse transcriptase polymerase chain reaction\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-2481845/v1/d106624a2dd52feb92f28dfb.png"},{"id":31694587,"identity":"46944f12-e5a9-4117-bf86-74e679a1f8c9","added_by":"auto","created_at":"2023-01-17 16:06:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":503407,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGenes differentially expressed in acute graft-versus-host disease. \u003c/strong\u003e(A) Genes differentially regulated in acute graft-versus-host disease compared with vacuolar drug reaction, as identified using transcriptomic analysis with RNA sequencing (RNAseq). Lines indicate biologic relationships. Figure courtesy of https://string-db.org/. (B) Genes differentially regulated in acute graft-versus-host disease compared with normal post-hematopoietic cell transplantation skin, as identified using laser capture microdissection-tandem mass spectrometry.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-2481845/v1/29336516c57d8a16fbf87a2b.png"},{"id":31694588,"identity":"878c1249-5383-4d4c-9b43-eeefabd9cc87","added_by":"auto","created_at":"2023-01-17 16:06:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":722599,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHistopathologic features of drug reaction or acute graft-versus-host disease.\u003c/strong\u003eVacuolar interface inflammation with apoptotic keratinocytes in the epidermis in skin biopsies from two patients with different confirmed clinical diagnoses. A. Morbilliform drug eruption with vacuolar interface changes. B. Acute graft-versus-host disease, histopathologic grade II. Hematoxylin-eosin stain; Olympus BX46 microscope; UPlanSApo objective; 20x original magnification; Acquisition software: cellSens Standard, Olympus.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-2481845/v1/a19dce054ddb9287d66072d5.png"},{"id":31693488,"identity":"d59eef22-ba83-4c2c-8c1f-2d6ab1d06a8d","added_by":"auto","created_at":"2023-01-17 15:58:49","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":291531,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePatient skin biopsy specimen classification.\u003c/strong\u003e DR=drug reaction. GVHD=acute graft-versus-host disease. HCT=hematopoietic cell transplantation.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-2481845/v1/603f44db59915081de24b7ac.png"},{"id":31693491,"identity":"ce01bc09-707b-4135-a737-fb8828d120e3","added_by":"auto","created_at":"2023-01-17 15:58:49","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":429333,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReceiver operating characteristic, parameter estimates, and effect likelihood ratio tests for a clinical-molecular fusion model for the differentiation of acute graft-versus-host disease from vacuolar interface drug reaction.\u003c/strong\u003eMolecular expression is determined by reverse-transcriptase-polymerase chain reaction methods on formalin-fixed, paraffin-embedded skin biopsy specimens. Area-under-curve=0.89.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-2481845/v1/a4fa2e42ca05e42d924e3480.png"},{"id":42110036,"identity":"3dae2553-3f0a-4b03-81b8-97dcc44b0537","added_by":"auto","created_at":"2023-08-25 01:52:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2495248,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2481845/v1/f538f47b-eb6f-4e62-8fe3-9fe10838d5cc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Differentiation of acute graft-versus-host disease from drug reaction in skin by a novel tissue-based biomarker assay","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHematopoietic cell transplantation (HCT) may be curative for patients with certain hematologic conditions [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, the post-HCT course may be complicated by the development of acute graft-versus-host disease (GVHD), a potentially life-threatening attack on host tissue cells by engrafted immune cells. The earliest and most frequently involved organ in GVHD is the skin [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The clinical morphology and anatomic distribution of cutaneous GVHD can be non-specific, mimicking features of skin eruptions attributable to other causes, such as morbilliform drug eruption [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Adding to challenges associated with accurate diagnosis is the fact that microscopic features of GVHD, namely vacuolar interface dermatitis with keratinocyte apoptosis, are also not specific and may be seen in other entities such as certain drug eruptions [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite the lack of specificity of clinical and microscopic features of GVHD of the skin, early, accurate diagnosis is critical for initiation of appropriate therapy or avoidance of unnecessary escalation of immunosuppressive dose. Although progress is being made to identify prognostic biomarkers in the blood in patients with GVHD [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], there have been no significant advances in the diagnosis of cutaneous GVHD in decades. In particular, no ancillary tests to improve diagnostic accuracy beyond the current and imperfect gold standard of clinicopathologic correlation have been developed.\u003c/p\u003e \u003cp\u003eWe reported the proteomic profiles of archived skin biopsy specimens showing GVHD. Proteins found to be differentially expressed had roles in type I interferon signaling and purine ribonucleotide binding [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Here, we aimed to follow-up on these preliminary data to create a novel tissue-based diagnostic molecular biomarker assay with a clinical-molecular fusion model to help differentiate GVHD from drug eruptions with vacuolar interface changes in the post-HCT setting.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003e The study was approved by the Institutional Review Board and was conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e \u003cp\u003eFor case selection, we queried our institution\u0026rsquo;s HCT database (transplantation dates between 1/1/2010 and 12/28/2016) and manually searched the electronic medical records of 629 patients for skin biopsy data. Patients with skin biopsies derived after HCT and with biopsy reports indicating the presence of vacuolar interface inflammation were included in the eligibility screen. Specimens from patients with ambiguous final diagnoses were not included. Slides representative of each skin biopsy specimen were reviewed by a dermatopathologist, and the cases showing mild to moderate vacuolar interface changes (equivalent to GVHD grade 1\u0026ndash;3) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] were included. Demographic and clinical data were collected, including immunosuppressive medications being taken at the time of skin biopsy.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDiscovery of candidate tissue-based biomarkers\u003c/h2\u003e \u003cp\u003eFor RNAseq, we compared expression profiles of 8 cases of GVHD to 8 cases of vacuolar interface drug eruption. RNA was isolated using the RNeasy Plus Mini kit (74134, Qiagen) and sequenced, as previously described [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. RNA-derived cDNA libraries were prepared using the TruSeq RNA Library Prep Kit v2 (Illumina). Concentration and size distribution of the resulting libraries were determined on an Agilent Bioanalyzer DNA 1,000 chip and confirmed by Qubit fluorometry (Life Technologies). Unique indexes were incorporated at the adaptor ligation phase for three-plex sample loading. Libraries were loaded onto paired end flow cells to generate cluster densities of 700,000 per mm\u003csup\u003e2\u003c/sup\u003e, following Illumina\u0026rsquo;s standard protocol. The flow cells were sequenced as 51-paired end-reads on an Illumina HiSeq 2000. The samples were processed through the Mayo RNA-Seq analysis pipeline, MAP-RSeq [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Raw and normalized (read per kilobase of gene per million mapped reads) gene expression read counts were obtained per sample. Differential gene expression analysis was carried out using the freely available edgeR bioconductor software package (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bioconductor.org\u003c/span\u003e\u003cspan address=\"http://bioconductor.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Scaling by total lane counts can bias estimates of differential expression; therefore, edgeR uses trimmed mean normalization on raw read counts to determine whether genes are differentially expressed using the negative binomial method. The Benjamini and Hochberg correction was used to control for multiple testing to obtain a false discovery rate. We considered genes to be differentially expressed if they had absolute value fold changes greater or equal to 0.5 and a false discovery rate\u0026thinsp;\u0026lt;\u0026thinsp;0.1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDiagnostic tissue-based biomarker assay development\u003c/h2\u003e \u003cp\u003eThe named biomarkers identified via a proteomics approach (n\u0026thinsp;=\u0026thinsp;26) and a RNA sequencing approach (n\u0026thinsp;=\u0026thinsp;36) were researched using online resources [Human Gene Database, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://bioconductor.org\" target=\"_blank\"\u003ewww.genecards.org\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.genecards.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; NCBI Gene, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ghr.nlm.nih.gov/gene\u003c/span\u003e\u003cspan address=\"https://ghr.nlm.nih.gov/gene\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, and literature searches (Pubmed.gov, with all combinations of the following search terms: \u0026ldquo;graft-versus-host disease\u0026rdquo;, \u0026ldquo;skin\u0026rdquo;, and each gene name]. Any gene known to have relevance in GVHD was included, as were others selected to represent a range of biologic functions and/or cytokine influences. We then designed a reverse-transcriptase polymerase chain reaction (RT-PCR) assay to assess expression of these 23 selected genes. We also included 3 additional serendipitous genes which our laboratory had studied previously in another context and that are known to be involved in cell-matrix interactions or chemotaxis (i.e. \u003cem\u003eSPP1, CXCL1\u003c/em\u003e, and \u003cem\u003eIL8\u003c/em\u003e; Meves et al., 2015). Proof-of-concept pilot studies demonstrating the ability of this assay to differentiate between cutaneous GVHD and lupus erythematosus, the latter being another condition characterized by vacuolar interface changes that can be indistinguishable from those of GVHD, were successful and have been published previously [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor this assay, we identified additional archived skin biopsy specimens showing either GVHD or drug reaction with vacuolar interface changes on skin biopsy (n\u0026thinsp;=\u0026thinsp;92). Cases showing complete epidermal-dermal separation (grade 4) were excluded (n\u0026thinsp;=\u0026thinsp;3) to avoid confounding molecular results with non-specific markers of severe tissue destruction. Five cases were subsequently excluded due to extraction of insufficient RNA volumes.\u003c/p\u003e \u003cp\u003eWe also collected additional clinical features, including demographic information, clinical outcomes, and other clinical variables present at the time of the biopsied skin eruption that have previously been demonstrated to be helpful in differentiating GVHD from drug hypersensitivity reaction [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Specifically, we assessed for the presence or absence of facial involvement, palmar/plantar involvement, diarrhea, or hyperbilirubinemia. Of patients with GVHD, we sub-classified them as having fatal or non-fatal GVHD to determine whether gene expression profiles correlated with the clinical outcome of death associated with GVHD.\u003c/p\u003e \u003cp\u003eFor the assay, we started with RNA purification from FFPE skin biopsy specimens (Qiagen), as previously described [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Quantitative RT-PCR was done using the BioMark HD System and dynamic array integrated fluid circuits (Fluidigm). Forty-three specific targets in 32 genes (26 experimental and 6 control genes) were amplified per cDNA (standards, controls and experimental samples). The following cDNA were run per array: standards in triplicates; control cDNA (internal standard); experimental cDNA; the latter two were in duplicates. All cDNA was pre-amplified (TaqMan Preamp Master Mix, Applied Biosystems). Array-based quantitative PCR was with the help of the TaqMan Gene Expression Master Mix (Applied Biosystems). After thermal cycling, raw Ct data for standards was checked for linear amplification. Data that did not pass both linear amplification and reproducibility checks were discarded.\u003c/p\u003e \u003cp\u003eFor comparisons of gene expression between GVHD and drug eruption, the quantitative expression of each gene was compared using Wilcoxon test, with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 representing statistical significance. For the model development, all cases were included in the analysis, with all genes being subjected to variable selection for candidate gene identification. This process generated 1000 randomly seeded, logistic regression models. Each of these models was designed to use a three-fold cross-validation. All models with nonzero coefficients for variables were considered. For final model building, all possible combinations of final variables were tested in logistic regression models. We included models showing no interdependent variables and prioritized those with the highest receiver operating characteristic (ROC) area under the curve (AUC). This method of layered model development optimized identification of equally well-performing classifiers while minimizing the risk of overfitting the data.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eOur approach was comprised of three phases: (1) Biomarker discovery using molecular methods, (2) Development of diagnostic tissue-based biomarker assay, and (3) Data analysis and modeling (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eDiscovery of candidate tissue-based biomarkers\u003c/h2\u003e \u003cp\u003eRNAseq led to the identification of 36 named genes that were differentially expressed in GVHD vs. vacuolar interface drug reaction (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Trends amongst the biological processes represented by these genes included enrichment amongst those involved in collagen fibril (4; 11.1%) or extracellular matrix organization (n\u0026thinsp;=\u0026thinsp;7; 19.4%). Each of these genes was distinct from the prior 26 genes identified by our group using proteomics methods (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eDiagnostic tissue-based biomarker assay development\u003c/h2\u003e \u003cp\u003eDemographic information and transplantation features, skin biopsy changes, and clinical features of patients included in one of the two diagnostic groups (i.e. GVHD or non-GVHD skin eruption) are outlined in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Examples of histopathologic features are depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eClinical and histopathologic features of patients with skin biopsies showing acute graft-versus-host disease or vacuolar interface inflammation drug reaction.\u003c/b\u003e No differences were observed between the two groups (p\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;0.05, data not shown). n/a\u0026thinsp;=\u0026thinsp;not applicable. TBI\u0026thinsp;=\u0026thinsp;total body irradiation.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAcute GVHD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eDrug eruption\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eDemographic features\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale sex (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at time of biopsy (in years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003en/a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003en/a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime of biopsy after HCT (in days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003en/a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003en/a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eHematologic diagnosis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAML/ANLL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Leukemias\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNHL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultiple myeloma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eTransplantation features\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eTransplantation type\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAllogeneic transplant (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeripheral blood stem cells (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDouble cord blood (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBone marrow (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eRelation of graft donor to recipient\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnrelated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFull sibling (not identical twin)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChild\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther relative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eConditioning regimen\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBusulfan/cyclophosphamide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBusulfan/fludarabine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMelphalan/fludarabine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCyclophosphamide/TBI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFludarabine/Cyclophosphamide/TBI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFludarabine/TBI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFludarabine/Cyclophosphamide/ATG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003ePost-transplantation immunosuppression\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTacrolimus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCyclosporine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMethotrexate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMycophenolate mofetil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystemic corticosteroids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eKey clinical features\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresence of facial involvement (when known)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresence of palm/sole involvement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresence of diarrhea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresence of hyperbilirubinemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eHistopathologic features from skin biopsy\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eVacuolar interface changes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrade 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrade 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrade 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrade 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eDegree of interface inflammation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMild\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbundant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor development of the RT-PCR based assay, case selection and classification were performed using methods above and as outlined in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. We included 67 cases of GVHD in the development of the biomarker assay. Of these patients, GVHD was a contributing cause of death in 20 (29.8%). These cases were compared to 17 cases of drug eruption, which were attributed to antibiotics (n\u0026thinsp;=\u0026thinsp;5 cases; 29.4%), chemotherapy (n\u0026thinsp;=\u0026thinsp;5), granisetron (n\u0026thinsp;=\u0026thinsp;1; 5.9%), omeprazole (n\u0026thinsp;=\u0026thinsp;1), and unknown (n\u0026thinsp;=\u0026thinsp;4; 23.5%). Vacuolar interface changes were present in all cases to various degrees, as outlined in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Genes selected for inclusion in the diagnostic tissue-based biomarker assay, along with the ratio of gene expression levels, are listed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. On replicate testing, gene expression showed a high level of correlation (average: 0.98; range: 0.90\u0026ndash;0.99), supporting the reproducibility of laboratory methods. Multiple fusion models were generated based on clinical and molecular data, and we selected the one with the highest ROC AUC value (i.e. 0.89) without data over-fitting. This model incorporates expression of \u003cem\u003eMX1, MNDA, OAS\u003c/em\u003e, and \u003cem\u003eGSTM5\u003c/em\u003e, as well as the clinical feature of the presence of diarrhea (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Comparison of gene expression profiles in patients with fatal vs. non-fatal GVHD, or in patients with GVHD clinical grade I vs. grades II-IV revealed no significant differences.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eGenes tested (with primary biologic function of the corresponding protein), with expression ratio of acute graft-versus-host disease to vacuolar interface drug reaction.\u003c/b\u003e P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, marked with an asterisk, denotes statistical significance.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRatio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGene function\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMLANA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMelanocyte differentiation marker\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMITF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMelanocyte differentiation marker\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMX1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInterferon-inducible protein with antiviral properties\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMNDA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.03*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGranulocyte/monocyte cell- specific response to interferon\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIVL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFormation of cell envelope\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOAS/SMOC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRegulation of osteoblast differentiation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTAP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAntigen processing\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAPN6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMicrotubule stabilization\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSTAT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTranscription factor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGBP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInterferon-inducible GTPase\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eITGB3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIntegrin involved in cell-matrix interaction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCell-matrix interaction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTHBS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCell-matrix interaction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLIMA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCytoskeletal signaling\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKRT6B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCytokeratin\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSTM5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConjugation of reduced glutathione\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAMA4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMediation of cell attachment and migration\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCREB3L1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTranscription factor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOGN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOsteoinductive factor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKRT17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCytokeratin\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSPP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.03*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCell-matrix interaction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eITGA5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCellular signaling\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eITGA3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCellular signaling\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eITGA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCellular signaling\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMXRA5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCellular adhesion\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCXCL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChemotactic cytokine for neutrophils\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChemotactic cytokine for neutrophils, T-cells, and basophils\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFSTL5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUncertain\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eDifferentiation of acute cutaneous GVHD and non-GVHD vacuolar interface skin eruptions represents a major challenge in the care of patients following HCT. In an attempt to improve diagnostic accuracy in this setting, we discovered potential tissue-based molecular biomarkers, incorporated a selection of these biomarkers into a quantitative RT-PCR assay, and created a clinical-molecular fusion model that confers a ROC AUC of 0.89. This novel diagnostic approach demonstrates the potential to differentiate GVHD from drug reactions with vacuolar interface changes on skin biopsy in the post-HCT period using a limited set of clinical and molecular data.\u003c/p\u003e \u003cp\u003eEarly in the history of HCT, it was recognized that GVHD is a syndrome and that the diagnosis of skin GVHD is complex, has no single clinical or histopathologic pathognomonic finding, and requires clinicopathologic correlation [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. A prior study designed to distinguish GVHD from drug eruptions included a robust analysis of clinical features, with an emphasis on those dermatologic [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It was found that facial involvement was more frequently observed in GVHD (59%) than in drug hypersensitivity reaction (24%; p\u0026thinsp;=\u0026thinsp;0.05), and the addition of palms/soles involvement increased specificity (36% of patients with GVHD; 0% in patients with drug hypersensitivity reaction; p\u0026thinsp;=\u0026thinsp;0.006). While these clinical findings may be helpful when present, they lack diagnostic sensitivity.\u003c/p\u003e \u003cp\u003eLittle progress has been made in the histopathologic diagnosis of GVHD in the last 3 decades. An early attempt to differentiate GVHD from drug reactions on skin biopsy histopathology and immunohistochemistry found no definite differences and concluded, \u0026ldquo;neither morphological nor immunohistochemical analysis of skin biopsies is of much help in distinguishing [GVHD] reaction, grade I and II, from drug-induced skin reactions in patients developing skin lesions after bone-marrow transplantation\u0026rdquo; [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. A widely cited analysis of histopathologic features of GVHD concluded that skin biopsy \u0026ldquo;is not a useful tool for differentiating between [GVHD] and other entities that lack specific histologic features such as drug eruptions and viral exanthems\u0026rdquo; [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. A subsequent study of clinical outcomes based on skin biopsy results, which found no significant correlations, made the following recommendation: \u0026ldquo;We suggest that the routine practice of subjecting patients to such biopsies could be abandoned without compromising their care\u0026rdquo; [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. More recently, a study found that the CD123 stain for plasmacytoid dendritic cells (a marker of type I interferon influence) may have utility in the diagnosis of GVHD of the colon [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Based on this finding, as well as the knowledge that plasmacytoid dendritic cells can be found in skin affected by GVHD [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], our group revisited the potential diagnostic utility of skin biopsy in differentiating cutaneous GVHD from drug reactions with vacuolar interface changes in a small study of 24 skin biopsies [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. While no differences between CD123 patterns were observed, skin biopsies of GVHD did show significantly less spongiosis and fewer dermal eosinophils than drug reactions. Though dermal eosinophil density has been shown to be inconsistent in GVHD in other studies [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], spongiosis had not been well-documented in this setting previously. Though the absence of spongiosis may warrant further study, data are insufficient to regard it as an independently reliable histopathologic feature of GVHD.\u003c/p\u003e \u003cp\u003eImportant of molecular investigations of GVHD have focused on prognostic serum biomarkers for this disease [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Comparative molecular evaluations of skin biopsies of GVHD vs. drug eruption do not appear to have been attempted previously, however. Prior researchers have evaluated genetic expression profiles in the skin of murine allogeneic GVHD models and recognized that genes that are interferon-induced, are involved in antigen presentation and cellular adhesion, or are acute phase reactants are upregulated [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In addition, the importance of type I interferon and plasmacytoid dendritic cells in skin tissue affected by GVHD has been demonstrated [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Our group confirmed upregulation of interferon-mediated and other proteins in skin affected by GVHD [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] and in the present study, demonstrated that their quantitative expression can be used to differentiate GVHD from drug eruption with vacuolar interface changes.\u003c/p\u003e \u003cp\u003eResults of the biomarker discovery phase of this study offer new insights on the biology of GVHD. Specifically, genes that were found to be enriched in GVHD included those that are involved in response to cellular stress, mediated by type I interferons, involved in purine ribonucleoside, anion, or carbohydrate derivative binding, or involved in collagen fibril and extracellular matrix organization (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://bioconductor.org\" target=\"_blank\"\u003ewww.string-db.org/\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). These genes and pathogenic mechanisms warrant further study as possible mediators of GVHD or potential therapeutic targets.\u003c/p\u003e \u003cp\u003eGenes that were incorporated into the final model included \u003cem\u003eMX1\u003c/em\u003e (interferon-induced mediator of Th1 immunity), \u003cem\u003eMNDA\u003c/em\u003e (mediator of granulocyte/monocyte cell-specific response to interferon), \u003cem\u003eOAS\u003c/em\u003e (interferon-induced regulator of osteoblast differentiation), and \u003cem\u003eGSTM5\u003c/em\u003e (catalyst of reduced glutathione conjugation). The single clinical variable that contributed to the model was the presence of diarrhea. Though this feature is not specific to gastrointestinal GVHD, it has been shown previously to be a relatively specific finding for GVHD over drug eruption when coupled with a morbilliform skin eruption in the post-HCT period (73% vs. 12% in patients with GVHD or drug reaction, respectively; p\u0026thinsp;=\u0026thinsp;0.000) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eStrengths of this study included ensuring that every sample representing drug eruption was derived from patients who had previously undergone HCT, thereby attempting to control for post-HCT-related factors. Each case showed vacuolar interface changes, thereby making sure that cases were comparable to GVHD. In addition, the identification of biomarkers using an unbiased approach allowed for recognition of genes of interest that have do not appear to have been studied in GVHD previously.\u003c/p\u003e \u003cp\u003eThe primary limitation to this study is the difficulty in assuring accuracy with case classification, due to the lack of certainty inherent in current diagnostic methods. We attempted to mitigate this limitation by carefully examining the electronic medical record for each patient not only for documentation about patient features but also for clinical course after skin biopsy. The study also is limited by the relatively small sample size, particularly with regards to drug reaction cases. The small number of patients in this group is attributable to the high level of stringency used during case selection. That is, only cases with a high degree of diagnostic certainty and vacuolar interface changes on skin biopsy were included. In addition, this study is limited by the single-center, retrospective design, as it is remains unknown whether this assay will be valuable applicable when used prospectively and/or for patients cared for at other institutions.\u003c/p\u003e \u003cp\u003eWe anticipate future larger multi-institution studies to validate the utility of this diagnostic tissue-based molecular assay and clinical-molecular fusion models.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eGVHD=acute graft-versus-host disease; HCT=hematopoietic cell transplantation; RT-PCR=reverse-transcriptase polymerase chain reaction; ROC=receiver operating characteristic; AUC=area under the curve; RNAseq=transcriptomic analysis with RNA sequencing.\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding sources:\u0026nbsp;\u003c/strong\u003eThis work was supported by the small grants program from the Mayo Clinic Departments of Dermatology (Mayo CCaTS grant number UL1TR000135; J.S.L.). J.S.L. is the recipient of a Dermatology Foundation Dermatopathology Career Development Award. A.M. is the recipient of a National Institutes of Health K08 award and the Gerstner Family Career Development Award. The sponsors of this study are public or nonprofit organizations that support science in general. \u0026nbsp;They had no role in gathering, analyzing, or interpreting the data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eWe thank Jayne Feind, Pathology Reporting Specialist, Mayo Clinic, for study coordination.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest:\u003c/strong\u003e The authors declare no competing financial interests. J.L. and A.M. have hold a patent (US 10,775,390) relating to the findings of this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cspan\u003eByun HJ, Yang JI, Kim BK, Cho KH (2011) Clinical differentiation of acute cutaneous GVHD from drug hypersensitivity reactions. J Am Acad Dermatol 65:726\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eCopelan EA. (2006) Hematopoietic stem-cell transplantation. N Engl J Med 354:1813\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eDrijkoningen M, De Wolf-Peeters C, Tricot G, Degreef H, Desmet V (1988) Drug-induced skin reactions and acute cutaneous graft-versus-host reaction: a comparative immunohistochemical study. Blut 56:69\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eFerrara JL, Cooke KR, Teshima T (2003) The pathophysiology of acute graft-versus-host disease. 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Am J Surg Pathol 21:988\u0026ndash;996.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eKolbert CP, Feddersen RM, Rakhshan F, Grill DR, Simon G, Middha S, et al. (2013) Multi-platform analysis of microRNA expression measurements in RNA from fresh frozen and FFPE tissues. PLoS One 8:e52517.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eLehman JS, Gibson, LE, el-Azhary RA (2015) Acute cutaneous graft-versus-host disease compared to drug hypersensitivity reaction with vacuolar interface changes: a blinded study of microscopic and immunohistochemical features. J Cutan Pathol 42(1):39\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eLehman JS, Hashmi SK, Lazarus HM, el-Azhary RA, Gibson LE, Hogan WJ, et al. (2017) Immunophenotypic and molecular comparison between allogeneic and autologous graft-versus-host disease of the skin: a retrospective study using immunohistochemical and proteomics methods. J Cutan Pathol 44:1087\u0026ndash;1091.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eLehman JS, Dasari S, Damodaran SS, el-Azhary RA, Gibson LE, Hashmi SK, et al. (2019) Differential expression of interferon-induced genes and other tissue-based biomarkers in acute graft-versus-host disease vs. lupus erythematosus in skin. Clin Exp Dermatol 44:e81-88.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eLerner KG, Kao GF, Storb R, Buckner CD, Clift RA, Thomas ED (1974) Histopathology of graft vs. host reaction in human recipients of marrow from HLA matched sibling donors. Transplant Proc 6(4):367\u0026ndash;371.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eLevine JE, Hogan WJ, Harris AC, Litzow MR, Efebera YA, Devine SM, et al. (2014) Improved accuracy of acute graft-versus-host disease staging among multiple centers. Best Pract Res Clin Haematol 27(3\u0026ndash;4):283\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eLevine JE, Braun TM, Harris AC, Holler E, Taylor A, Miller H, et al. (2015) A prognostic score for acute graft-versus-host disease based on biomarkers: a multicenter study. Lancet Haematol 2:e21-e29.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eLi M, Gray W, Zhang H, Chung CH, Billheimer D, Yarbrough WG, et al. (2010) Comparative shotgun proteomics using spectral count data and quasi-likelihood modeling. J Proteome Res 9:4295\u0026ndash;305.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eLin J, Chen S, Zhao Z, Cummings OW, Fan R (2013) CD123 is a useful immunohistochemical marker to facilitate diagnosis of acute graft-versus-host disease in colon. Hum Pathol 44:2075\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eMalard F, Bossard C, Brissot E, Chevalier P, Guillaume T, Delaunay J, et al. (2013) Increased plasmacytoid dendritic cells and RORgammat-expressing immune effectors in cutaneous acute graft-versus-host disease. J Leucoc Biol. 94:1337\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eMarra DE, Mckee PH, Nghiem P (2004) Tissue eosinophils and the perils of using skin biopsy specimens to distinguish between drug hypersensitivity and cutaneous graft-versus-host disease. J Am Acad Dermatol 51:543\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eMeves A, Nikolova E, Heim JB, Squirewell EJ, Cappel MA, Pittelkow MR, et al. (2015) Tumor Cell Adhesion As a Risk Factor for Sentinel Lymph Node Metastasis in Primary Cutaneous Melanoma. J Clin Oncol 10;33:2509\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003ePaczesny S, Braun TM, Levine JE, Hogan J, Crawford J, Coffing B, et al. (2010) Elafin is a biomarker of graft-versus-host disease of the skin. Sci Transl Med 2:13.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eRatanatharathorn V, Nash RA, Przepiorka D, Devine SM, Klein JL, Weisdorf D, et al. (1998) Phase III study comparing methotrexate and tacrolimus (prograf, FK506) with methotrexate and cyclosporine for graft-versus-host disease prophylaxis after HLA-identical sibling bone marrow transplantation. Blood 92:2303.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eRenteria AS, Levine JE, Ferrara JL (2016) Development of a biomarker scoring system for use in graft-versus-host disease. Biomark Med 10:793\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eReyes VE, Klimpel GR (1987) Interferon alpha/beta synthesis during acute graft-versus-host disease. Transplant 43:412\u0026ndash;416.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eRobinson MD, McCarthy DJ, Smyth GK (2010a) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics (Oxford, England) 26:139\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eRobinson MD, Oshlack A (2010b) A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol 11:R25.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eSale GE, Lerner KG, Barker EA, Shulman HM, Thomas ED (1977) Skin biopsy in the diagnosis of acute graft-versus-host disease in man. Am J Pathol 89:621\u0026ndash;636.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eSharon VR, Konia TH, Barr KL, Fung MA (2012) Assessment of the \u0026lsquo;no eosinophils\u0026rsquo; rule: are eosinophils truly absent in pityriasis lichenoides, connective tissue disease, and graft-versus-host disease? J Cutan Pathol 39:413\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eSugerman PB, Faber SB, Willis LM, Petrovic A, Murphy GF, Pappo J, et al. (2004) Kinetics of gene expression in murine cutaneous graft-versus-host disease. Am J Pathol 164:2189\u0026ndash;202.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eZhou Y, Barnett MJ, Rivers JK (2000) Clinical significance of skin biopsies in the diagnosis and management of graft-versus-host disease in early postallogeneic bone marrow transplantation. Arch Dermatol 136:717\u0026ndash;721.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eZiemer M, Haeusermann P, Janin A, Massi D, Ziepert M, Wolff D, et al. (2014) Histopathological diagnosis of graft-versus-host disease of the skin \u0026ndash; an interobserver comparison. J Eur Acad Dermatol Venereol 28:915\u0026ndash;924.\u003c/span\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Graft-versus-host disease, hematopoietic cell transplantation, rash, molecular, interferon, precision medicine","lastPublishedDoi":"10.21203/rs.3.rs-2481845/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2481845/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAcute graft-versus-host disease (GVHD), a serious complication of allogeneic hematopoietic cell transplantation (HCT), frequently involves the skin. Since clinicopathologic features of GVHD can mimic those of certain drug eruptions, accurate diagnosis can be challenging. \u0026nbsp;We aimed to develop a tissue-based molecular assay to improve differentiation of GVHD from vacuolar interface drug eruption in the post-HCT period. \u0026nbsp;After identifying 62 putative tissue-based biomarkers using unbiased molecular methods, we incorporated 26 into a quantitative RT-PCR assay. We tested skin biopsy specimens showing vacuolar interface changes attributable to either acute GVHD (n=67) or drug reaction (n=17), as classified by meticulous review of clinical features and course. Data were incorporated into clinical-molecular fusion diagnostic models. Significant differences in the expression of several genes, including \u003cem\u003eMX1, MNDA, OAS, LIMA1, GSTM5\u003c/em\u003e, and \u003cem\u003eSPP1\u003c/em\u003e, were detected between the two diagnostic groups (p\u0026lt;0.05). The optimal fusion model, which incorporated quantitative expression of \u003cem\u003eMX1, MNDA, OAS,\u003c/em\u003e and \u003cem\u003eGSTM5\u003c/em\u003e, as well as the clinical feature of diarrhea, imparted high diagnostic accuracy (receiver operating characteristic area-under-curve: 0.89). This novel tissue-based diagnostic molecular assay and fusion model demonstrates potential to improve distinction of GVHD from drug eruptions in the post-HCT setting. Multicenter validation studies are required.\u003c/p\u003e","manuscriptTitle":"Differentiation of acute graft-versus-host disease from drug reaction in skin by a novel tissue-based biomarker assay","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-01-17 15:58:44","doi":"10.21203/rs.3.rs-2481845/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"08f2e888-56cc-4e07-b952-96b3ffa529b7","owner":[],"postedDate":"January 17th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-08-25T01:44:13+00:00","versionOfRecord":[],"versionCreatedAt":"2023-01-17 15:58:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2481845","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2481845","identity":"rs-2481845","version":["v1"]},"buildId":"-HB7Z8yhvgn0wM9Nzuekk","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}