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• W2983248093 abstract "Diagnosing lung transplant rejection currently depends on histologic assessment of transbronchial biopsies (TBB) with limited reproducibility and considerable risk of complications. Mucosal biopsies are safer but not histologically interpretable. Microarray-based diagnostic systems for TBBs and other transplants suggest such systems could assess mucosal biopsies as well. We studied 243 mucosal biopsies from the third bronchial bifurcation (3BMBs) collected from seven centers and classified them using unsupervised machine learning algorithms. Using the expression of a set of rejection-associated transcripts annotated in kidneys and validated in hearts and lung transplant TBBs, the algorithms identified and scored major rejection and injury-related phenotypes in 3BMBs without need for labeled training data. No rejection or injury, rejection, late inflammation, and recent injury phenotypes were thus scored in new 3BMBs. The rejection phenotype correlated with IFNG-inducible transcripts, the hallmarks of rejection. Progressive atrophy-related changes reflected by the late inflammation phenotype in 3BMBs suggest widespread time-dependent airway deterioration, which was especially pronounced after two years posttransplant. Thus molecular assessment of 3BMBs can detect rejection in a previously unusable biopsy format with potential utility in patients with severe lung dysfunction where TBB is not possible and provide unique insights into airway deterioration. ClinicalTrials.gov NCT02812290. Diagnosing lung transplant rejection currently depends on histologic assessment of transbronchial biopsies (TBB) with limited reproducibility and considerable risk of complications. Mucosal biopsies are safer but not histologically interpretable. Microarray-based diagnostic systems for TBBs and other transplants suggest such systems could assess mucosal biopsies as well. We studied 243 mucosal biopsies from the third bronchial bifurcation (3BMBs) collected from seven centers and classified them using unsupervised machine learning algorithms. Using the expression of a set of rejection-associated transcripts annotated in kidneys and validated in hearts and lung transplant TBBs, the algorithms identified and scored major rejection and injury-related phenotypes in 3BMBs without need for labeled training data. No rejection or injury, rejection, late inflammation, and recent injury phenotypes were thus scored in new 3BMBs. The rejection phenotype correlated with IFNG-inducible transcripts, the hallmarks of rejection. Progressive atrophy-related changes reflected by the late inflammation phenotype in 3BMBs suggest widespread time-dependent airway deterioration, which was especially pronounced after two years posttransplant. Thus molecular assessment of 3BMBs can detect rejection in a previously unusable biopsy format with potential utility in patients with severe lung dysfunction where TBB is not possible and provide unique insights into airway deterioration. ClinicalTrials.gov NCT02812290. Detecting rejection in lung transplants is difficult: transbronchial biopsy (TBB) diagnosis of T cell–mediated rejection (TCMR, also called acute cellular rejection or ACR) has low reproducibility1Arcasoy SM Berry G Marboe CC et al.Pathologic interpretation of transbronchial biopsy for acute rejection of lung allograft is highly variable.Am J Transplant. 2011; 11: 320-328Crossref PubMed Scopus (76) Google Scholar and carries risks that limit its use in patients with respiratory compromise. Histologic diagnosis of antibody-mediated rejection (ABMR) in TBBs is even more problematic.2Westall GP Snell GI. Antibody-mediated rejection in lung transplantation: fable, spin, or fact?.Transplantation. 2014; 98: 927-930Crossref PubMed Scopus (21) Google Scholar Mucosal biopsies are safer to collect than TBBs3Salva PS Theroux C Schwartz D. Safety of endobronchial biopsy in 170 children with chronic respiratory symptoms.Thorax. 2003; 58: 1058-1060Crossref PubMed Scopus (33) Google Scholar,4Hernandez Blasco L Sanchez Hernandez IM Villena Garrido V de Miguel PE Nunez Delgado M Alfaro AJ. Safety of the transbronchial biopsy in outpatients.Chest. 1991; 99: 562-565Abstract Full Text Full Text PDF PubMed Scopus (73) Google Scholar and would provide insights into airway changes, but there is no standard histologic interpretation of mucosal biopsies despite promising preliminary studies.5Ward C Snell G Zheng L et al.Endobronchial biopsy and bronchoalveolar lavage in stable lung transplant recipients and chronic rejection.Am J Respir Crit Care Med. 1998; 158: 84-91Crossref PubMed Scopus (52) Google Scholar, 6Snell GI Ward C Wilson JW Orsida B Williams TJ Walters EH. Immunopathological changes in the airways of stable lung transplant recipients.Thorax. 1997; 52: 322-328Crossref PubMed Scopus (30) Google Scholar, 7Ward C Snell GI Orsida B Zheng L Williams TJ Walters EH. Airway versus transbronchial biopsy and BAL in lung transplant recipients: different but complementary.Eur Respir J. 1997; 10: 2876-2880Crossref PubMed Scopus (28) Google Scholar, 8Snell GI Levvey BJ Zheng L et al.Everolimus alters the bronchoalveolar lavage and endobronchial biopsy immunologic profile post-human lung transplantation.Am J Transplant. 2005; 5: 1446-1451Crossref PubMed Scopus (11) Google Scholar The inability to safely perform TBB in patients with respiratory compromise currently excludes these patients from studies and represents a critical source of selection bias. If mucosal tissue could be interpreted, this would operationalize a previously unusable biopsy, expand the pool of patients from which biopsies are obtained, and gain insight into molecular airway changes. Recent advances in molecular diagnosis of rejection and injury in kidney,9Halloran PF Venner JM Madill-Thomsen KS et al.Review: the transcripts associated with organ allograft rejection.Am J Transplant. 2018; 18: 785-795Abstract Full Text Full Text PDF PubMed Scopus (90) Google Scholar,10Reeve J Böhmig GA Eskandary F et al.Generating automated kidney transplant biopsy reports combining molecular measurements with ensembles of machine learning classifiers.Am J Transplant. 2019; 19: 2719-2731Abstract Full Text Full Text PDF PubMed Scopus (54) Google Scholar heart,9Halloran PF Venner JM Madill-Thomsen KS et al.Review: the transcripts associated with organ allograft rejection.Am J Transplant. 2018; 18: 785-795Abstract Full Text Full Text PDF PubMed Scopus (90) Google Scholar,11Halloran PF Reeve J Aliabadi AZ et al.Exploring the cardiac response-to-injury in heart transplant biopsies.JCI Insight. 2018; 3: e123674Crossref PubMed Scopus (32) Google Scholar and lung transplants (TBBs)12Halloran KM Parkes MD Chang J et al.Molecular assessment of rejection and injury in lung transplant biopsies.J Heart Lung Transplant. 2019; 38: 504-513Abstract Full Text Full Text PDF PubMed Scopus (34) Google Scholar raise the possibility that molecular approaches could make mucosal biopsies interpretable. The present study assessed whether molecular assessment of rejection is feasible in lung transplant mucosal biopsies. We used unsupervised machine learning based on expression of rejection-associated transcripts (RATs) derived in kidney,9Halloran PF Venner JM Madill-Thomsen KS et al.Review: the transcripts associated with organ allograft rejection.Am J Transplant. 2018; 18: 785-795Abstract Full Text Full Text PDF PubMed Scopus (90) Google Scholar,13Halloran PF Famulski KS Reeve J. Molecular assessment of disease states in kidney transplant biopsy samples.Nat Rev Nephrol. 2016; 12: 534-548Crossref PubMed Scopus (110) Google Scholar a strategy successful in heart transplants11Halloran PF Reeve J Aliabadi AZ et al.Exploring the cardiac response-to-injury in heart transplant biopsies.JCI Insight. 2018; 3: e123674Crossref PubMed Scopus (32) Google Scholar,14Loupy A Duong Van Huyen JP Hidalgo L et al.Expression profiling for the identification and classification of antibody-mediated heart rejection.Circulation. 2017; 135: 917-935Crossref PubMed Scopus (101) Google Scholar and TBBs12Halloran KM Parkes MD Chang J et al.Molecular assessment of rejection and injury in lung transplant biopsies.J Heart Lung Transplant. 2019; 38: 504-513Abstract Full Text Full Text PDF PubMed Scopus (34) Google Scholar because RATs are not organ specific. We selected the third bronchial bifurcation (3BMB) to avoid ischemic changes more proximal to the bronchial anastomosis. We hypothesized that this would reveal 3BMB phenotypes of rejection and injury analogous to the phenotypes documented in lung TBBs and kidney9Halloran PF Venner JM Madill-Thomsen KS et al.Review: the transcripts associated with organ allograft rejection.Am J Transplant. 2018; 18: 785-795Abstract Full Text Full Text PDF PubMed Scopus (90) Google Scholar,13Halloran PF Famulski KS Reeve J. Molecular assessment of disease states in kidney transplant biopsy samples.Nat Rev Nephrol. 2016; 12: 534-548Crossref PubMed Scopus (110) Google Scholar and heart transplant biopsies.15Halloran PF Potena L Duong Van Huyen JP et al.Building a tissue-based molecular diagnostic system in heart transplant rejection: the heart molecular microscope MMDx.J Heart Lung Transplant. 2017; 36: 1192-1200Abstract Full Text Full Text PDF PubMed Scopus (68) Google Scholar We also postulated that rejection and injury phenotypes in 3BMBs would correlate with those changes in TBBs taken at the same time. Given the known increase in cumulative incidence in chronic lung allograft dysfunction (CLAD) over time in lung transplant populations,16Khush KK Cherikh WS Chambers DC et al.The international thoracic organ transplant registry of the international society for heart and lung transplantation: thirty-fifth adult heart transplantation report-2018; focus theme: multiorgan transplantation.J Heart Lung Transplant. 2018; 37: 1155-1168Abstract Full Text Full Text PDF PubMed Scopus (296) Google Scholar we further hypothesized that 3BMBs would show time-dependent deterioration in the airway mucosa. Consenting adult lung transplant recipients from seven centers were prospectively enrolled (Table S1). Mucosal biopsies were performed in patients undergoing bronchoscopy with TBB for clinical concern and surveillance per local standard of care (SOC). SOC assessments included pulmonary function testing, bronchoalveolar lavage for microbiology, HLA antibody testing, and blood cytomegalovirus polymerase chain reaction. Histologic assessment was performed for matched TBBs but not 3BMBs. One- or two-piece 3BMBs were collected from an accessible airway, typically at the third airway bifurcation between the right middle and lower lobe (right lung) or left upper lobe and lingula (left lung). Samples were stabilized in RNAlater™. RNA extraction, labeling (3’ IVT labeling kit), and hybridization to PrimeView™ GeneChips® (Thermo Fisher Scientific, Santa Clara, CA) followed published protocols.17Inc. A. 3’ IVT Express Kit Insert. http://tools.thermofisher.com/content/sfs/manuals/3_ivt_express_kit_insert.pdf. 2017 [updated 2017].Google Scholar If two pieces were available, they were combined and processed on a single chip (21 chips) or processed separately (24 chips) to assess sampling variation. Data were preprocessed using robust multiarray averaging. The .CEL files are available at Gene Expression Omnibus (GEO) (GSE125004). We cultured primary human B cells, CD4 and CD8 effector T cells, NK cells, monocytes, macrophages (±IFNG treatment), immature dendritic cells, mature lipopolysaccharides-treated dendritic cells, human umbilical vein endothelial cells (HUVEC, ±IFNG treatment), and renal proximal tubule epithelial cells (RPTEC, ±IFNG treatment) as previously described.18Hidalgo LG Einecke G Allanach K Halloran PF. The transcriptome of human cytotoxic T cells: similarities and disparities among allostimulated CD4(+) CTL, CD8(+) CTL and NK cells.Am J Transplant. 2008; 8: 627-636Crossref PubMed Scopus (67) Google Scholar,19Hidalgo LG Sis B Sellares J et al.NK cell transcripts and NK cells in kidney biopsies from patients with donor-specific antibodies: evidence for NK cell involvement in antibody-mediated rejection.Am J Transplant. 2010; 10: 1812-1822Crossref PubMed Scopus (308) Google Scholar Unsupervised analyses used RATs as features.20Halloran PF Venner JM Famulski KS. Comprehensive analysis of transcript changes associated with allograft rejection: combining universal and selective features.Am J Transplant. 2017; 17: 1754-1769Abstract Full Text Full Text PDF PubMed Scopus (47) Google Scholar RATs are the nonoverlapping union of the top 200 transcripts associated with each of three classes of histologically diagnosed rejection in kidney transplants: all rejection (Rej-RATs) and TCMR (TCMR-RATs)21Reeve J Böhmig GA Eskandary F et al.Assessing rejection-related disease in kidney transplant biopsies based on archetypal analysis of molecular phenotypes.JCI Insight. 2017; 2: e94197Crossref PubMed Google Scholar (ABMR-RATs were not used in this study because lung ABMR is not well characterized – see Discussion). The use of RATs to discover biopsy phenotypes has been successful in heart biopsies11Halloran PF Reeve J Aliabadi AZ et al.Exploring the cardiac response-to-injury in heart transplant biopsies.JCI Insight. 2018; 3: e123674Crossref PubMed Scopus (32) Google Scholar,14Loupy A Duong Van Huyen JP Hidalgo L et al.Expression profiling for the identification and classification of antibody-mediated heart rejection.Circulation. 2017; 135: 917-935Crossref PubMed Scopus (101) Google Scholar,15Halloran PF Potena L Duong Van Huyen JP et al.Building a tissue-based molecular diagnostic system in heart transplant rejection: the heart molecular microscope MMDx.J Heart Lung Transplant. 2017; 36: 1192-1200Abstract Full Text Full Text PDF PubMed Scopus (68) Google Scholar and lung TBBs.12Halloran KM Parkes MD Chang J et al.Molecular assessment of rejection and injury in lung transplant biopsies.J Heart Lung Transplant. 2019; 38: 504-513Abstract Full Text Full Text PDF PubMed Scopus (34) Google Scholar The TCMR-RATs and Rej-RATs were filtered on the interquartile range (IQR) of their expression; only those with an IQR > 0.5 across 243 3BMBs were used as features in the unsupervised analyses. The result was 315 IQR-filtered RATs. Pathogenesis-based transcript (PBT) sets reflect biological processes in rejection and injury. PBT sets were empirically derived from cell lines, mouse models, and human transplant biopsies. The PBT sets utilized in this study represent cytotoxic T cells (QCAT), macrophages (QCMAT) and alternatively activated macrophages (AMAT1), interferon-gamma (IFNG) inducible transcripts (GRIT3), endothelial transcripts (ENDAT), endothelial transcripts selective for donor-specific antibody (DSA) positivity (eDSAST), mast cell transcripts (MCAT), immunoglobulin transcripts (IGT), B cell transcripts (BAT), injury-related transcripts (IRRAT), and damage-associated molecular patterns (DAMP). PBT sets are documented at https://www.ualberta.ca/medicine/institutes-centres-groups/atagc/research/gene-lists. We generated two new PBT sets representing lung transplant (LT) parenchyma present in 3BMBs: LT3s, the top 1333 transcripts that anticorrelate with the IRRAT PBT score (discussed later) after excluding solute carrier transcripts; and LT4s, the 63 solute carrier transcripts excluded from the LT3s. Expression of the probe sets in each PBT list was summarized as a PBT score: the geometric mean fold difference in the probe sets’ expression in a given biopsy compared to their geometric mean expression across all 243 3BMBs. PBT lists were IQR filtered in the 243 (IQR > 0.5) before calculating PBT scores. Archetype, PCA, and PBT scores were calculated in 3BMBs following the published TBB strategy.12Halloran KM Parkes MD Chang J et al.Molecular assessment of rejection and injury in lung transplant biopsies.J Heart Lung Transplant. 2019; 38: 504-513Abstract Full Text Full Text PDF PubMed Scopus (34) Google Scholar PCA was performed with the R package “FactoMineR” and AA used the “archetypes” package. PCA and AA used the 315 RATs as features. A detailed description of AA is published.22Cutler A Breiman L. Archetypal analysis.Technometrics. 1994; 36: 10Crossref Scopus (368) Google Scholar AA is similar to cluster analysis, where observations (biopsies) are grouped based on their features (ie, expression of RATs). AA assigns a predesignated number of theoretical extreme cases (archetypes) that best explain the real biopsies in the data set, analogous to a clinician comparing patients to “pure” diagnoses they have previously seen to build a probabilistic differential diagnosis. All biopsies are compared to these archetypes and assigned probabilistic scores (R1-Rx) that describe their similarity to each. Biopsies are assigned to the groups (A1-Ax) for which they have the highest score (probability). PCA collapses high-dimensionality data (RAT expression) into a smaller set of hidden dimensions that describe most of the variation in the data, ranking them by the amount of variance they explain. The first three PCA scores (PC1, PC2, PC3) were examined in this study. All 243 3BMBs had paired TBBs collected for histology, but additional paired TBBs were only collected for the Molecular Microscope® Diagnostic System (MMDx) assessment when not precluded by risk. There were 118 paired samples among 230 TBBs and 243 3BMBs assessed by MMDx. TBBs were analyzed as previously described,12Halloran KM Parkes MD Chang J et al.Molecular assessment of rejection and injury in lung transplant biopsies.J Heart Lung Transplant. 2019; 38: 504-513Abstract Full Text Full Text PDF PubMed Scopus (34) Google Scholar IQR-filtering the PBTs first (IQR > 0.5) in 230 TBBs or 243 3BMBs. Analyses were performed with Bioconductor version 3.6 and R version 3.5.1. A detailed account of the statistics is provided in the Supplementary Methods. This study was approved by the University of Alberta Research Ethics Board (Pro00048176) and by the institutional review boards at participating centers. Written informed consent was received from patients prior to inclusion in this study. Between 2016 and 2019, 243 3BMBs were collected from 214 patients during diagnostic bronchoscopies at seven centers (Tables 1 and S1). 3BMBs showed negligible expression of surfactant as expected given they contain no alveoli.12Halloran KM Parkes MD Chang J et al.Molecular assessment of rejection and injury in lung transplant biopsies.J Heart Lung Transplant. 2019; 38: 504-513Abstract Full Text Full Text PDF PubMed Scopus (34) Google Scholar Primary diseases included chronic obstructive pulmonary disease (31%), idiopathic pulmonary fibrosis (24%), and interstitial lung disease (13%). The biopsies were relatively early (median 363 days posttransplant), 53% for clinical indications (ie, decline in spirometry or clinician concern) and 47% for surveillance. All biopsies taken >2.5 years posttransplant were for indications.TABLE 1INTERLUNG biopsy and patient characteristics at time of biopsyPatient characteristicsPatientsN = 214Recipient gender (% known)Female77 (36%)Male134 (64%)Not recorded (% total)3 (1%)Recipient age at transplant (median, range)59 (10-78)Primary disease (% known)aSome patients/biopsies fell under multiple categories.Chronic obstructive pulmonary disease53 (30%)Cystic fibrosis23 (13%)Idiopathic pulmonary fibrosis42 (24%)Interstitial lung disease23 (13%)Pulmonary arterial hypertension9 (6%)Previous failed lung transplant5 (3%)Other30 (17%)Not recorded (% total)34 (16%)Biopsy characteristicsBiopsiesN = 243Days (median, range) from transplant to biopsy (TxBx)363 (4-5125)Antirejection therapy prior to biopsy (% known)aSome patients/biopsies fell under multiple categories.Corticosteroids20 (8%)Antithymocyte globulin6 (2%)Intravenous immune globulin15 (6%)Rituximab4 (2%)Plasmapheresis2 (1%)Other8 (3%)None163 (84%)Not recorded (% total)48 (19%)Indication for biopsy (% known)Indication: clinician concerned about graft function128 (53%)Protocol/surveillance115 (47%)Not recorded (% total)0 (0%)Donor-specific antibody status at time of biopsyPositive51 (30%)Negative120 (70%)Not recorded (% total)72 (30%)Forced expiratory volume (FEV)FEV at biopsy (mean ± SD)2.3 ± 0.8FEV at biopsy, percentage predicted (mean ± SD)71% ± 21%Baseline FEV (mean ± SD)2.8 ± 0.9Baseline FEV, percentage predicted (mean ± SD)88% ± 22%Clinically relevant infection at biopsy (% known)aSome patients/biopsies fell under multiple categories.Viral31 (17%)Bacterial28 (16%)Fungal16 (10%)No infection82 (45%)Not recorded (% total)100 (47%)a Some patients/biopsies fell under multiple categories. Open table in a new tab No 3BMB pieces were assessed for histology because this is not SOC. However, TBBs taken at the same time were assessed histologically (Table 2). The case-mix was consistent with the Lung Allograft Rejection Gene Expression Observational (LARGO) study report (1): A grade lesions in 30 biopsies (37 not assessable), B grade lesions in seven biopsies (127 not assessable), and C grade lesions in five (144 not assessable, plus 33 biopsies from centers where C grade is not assessed as SOC).TABLE 2INTERLUNG histology in transbronchial biopsies taken at time of mucosal biopsyBiopsy characteristicsBiopsiesN = 243Local pathology diagnosis (% known)aDiagnoses described by the local pathologist. Some samples received multiple diagnoses.Acute cellular rejection (T cell–mediated rejection)23 (10%)Antibody-mediated rejection2 (<1%)Rejection not otherwise specified1 (<1%)Bronchiolitis obliterans5 (1%)Lymphocytic bronchiolitis2 (<1%)Other28 (12%)No findings132 (61%)IndeterminatebSamples were of insufficient quality to make a diagnosis based on histologic assessment.31 (14%)Not recorded (% total)27 (11%)ISHLT A grade (% known)0169 (71%)122 (9%)25 (2%)31 (<1%)42 (1%)x37 (15%)Not recorded (% total)5 (2%)ISHLT B grade (% known)099 (42%)1R6 (3%)2R1 (<1%)x127 (53%)Not done (% total)4 (2%)Not recorded (% total)6 (2%)ISHLT C grade (% known)052 (22%)15 (2%)x144 (61%)Not done (% total)33 (14%)Not recorded (% total)9 (4%)ISHLT, International Society for Heart and Lung Transplantation.a Diagnoses described by the local pathologist. Some samples received multiple diagnoses.b Samples were of insufficient quality to make a diagnosis based on histologic assessment. Open table in a new tab ISHLT, International Society for Heart and Lung Transplantation. Following strategies previously published for TBBs,12Halloran KM Parkes MD Chang J et al.Molecular assessment of rejection and injury in lung transplant biopsies.J Heart Lung Transplant. 2019; 38: 504-513Abstract Full Text Full Text PDF PubMed Scopus (34) Google Scholar we analyzed the microarray results using PCA and AA based on the expression of 315 previously characterized RATs (see Methods) (Figure 1). We studied the first three principal component scores from PCA (PC1, PC2, and PC3). We used four archetypes for AA because additional archetypes did not appreciably improve the fit of the model (measured by the residual sum of squares, Figure 1A). Each 3BMB was assigned four archetype scores, R1-4, and was assigned to an archetype group (A1-4) based on its highest score (Figure 1B,C): no rejection or injury (A1normal, N = 167), rejection (A2rejection, N = 24), late inflammation-atrophy changes (A3late, N = 36), and recent injury (A4injury, N = 16), named for their molecular phenotypes as described next. PBT sets are lists of transcripts that reflect different biological aspects of rejection and injury in organ transplants and are useful for identifying processes associated with different groups of biopsies. Table 3 characterizes the mean expression of PBTs in each AA group. A1normal biopsies had low expression of all rejection- and injury-related PBTs. A2rejection biopsies expressed PBTs representing effector T cell infiltration (QCATs), IFNG effects (GRIT3s), and RATs, which are all associated with rejection; and macrophage transcripts (QCMATs, AMAT1s), which are associated with inflammation in general. IRRATs and DAMPs, signifying the universal cellular response to recent injury, were also increased in A2rejection biopsies. A2rejection biopsies demonstrated reduced expression of transcripts expressed in relatively healthy 3BMB parenchyma (LT3, LT4), compatible with dedifferentiation. The A3late biopsies showed high expression of PBTs associated with atrophy-fibrosis in late deteriorating organ transplants 23Einecke G Reeve J Mengel M et al.Expression of B cell and immunoglobulin transcripts is a feature of inflammation in late allografts.Am J Transplant. 2008; 8: 1434-1443Crossref PubMed Scopus (74) Google Scholar,24Mengel M Reeve J Bunnag S et al.Molecular correlates of scarring in kidney transplants: the emergence of mast cell transcripts.Am J Transplant. 2009; 9: 169-178Crossref PubMed Scopus (89) Google Scholar: transcripts associated with B cells (BATs), plasma cells (IGTs), and mast cells (MCATs). A3late biopsies also expressed QCATs but not GRIT3s, which is also part of the pattern of late inflammation previously documented in kidney transplants with active atrophy-fibrosis.25Venner JM Famulski KS Reeve J Chang J Halloran PF. Relationships among injury, fibrosis, and time in human kidney transplants.J Clin Invest Insight. 2016; 1 (https://doi.org/10.1172/jci.insight): e85323Google Scholar LT3s and LT4s were low in the A3late group. Transcripts associated with acute cellular response to stress (DAMPs) were markedly increased in the A4injury biopsies.TABLE 3Mean pathogenesis-based transcript (PBT) set scores in biopsies grouped according to their highest archetype score and correlations between PBT set scores and archetypes scoresA1normal(n = 167)A2rejection(n = 24)A3late(n = 36)A4injury(n = 16)Spearman correlation of time of biopsy posttransplant with archetype scoresR1normalR2rejectionR3lateR4injuryMedian time of biopsy posttransplant (d)578 (327)629 (465)1244 (772)4541-0.320.170.250.10PBTMean PBT score in each archetype groupaPBT scores are the geometric mean fold difference between mean PBT set expression in each group and all 243 third bronchial bifurcation mucosal biopsies (3BMBs) as a control. The highest and lowest mean scores in each row are in bold. The highest positive and lowest negative spearman correlations in each row are also in bold. For the purpose of PBT score calculation the PBT lists were interquartile range filtered to include transcripts whose logarithmic expression had an interquartile range >0.5 across all 243 3BMBs.Spearman correlation of PBT scores with archetype scoresA1normal(n = 167)A2rejection(n = 24)A3late(n = 36)A4injury(n = 16)R1normalR2rejectionR3lateR4injuryRejection-related transcriptsRejection-RAT – Rejection-associated transcripts0.822.251.421.14−0.900.830.42−0.08TCMR-RAT – T cell–mediated rejection-associated RATs0.832.081.421.08−0.930.750.51−0.06QCAT – Cytotoxic T cell–associated transcripts0.831.941.491.02−0.830.610.53−0.12GRIT3 – Interferon gamma-inducible transcripts0.881.831.191.00−0.730.880.28−0.20Endothelial transcriptsENDAT – Endothelial cell-associated transcripts0.951.171.111.11−0.340.260.140.09eDSAST – Endothelium-expressed donor-specific antibody-selective transcripts0.961.151.080.99−0.240.190.18−0.06Parenchymal injury-related transcriptsIRRAT – Injury/repair associated transcripts (human kidney)0.891.521.101.44−0.430.38−0.030.27DAMP – Damage-associated molecular pattern transcripts0.851.691.042.20−0.460.30−0.070.44Late-scarring transcriptsIGT – Immunoglobulin transcripts0.741.813.340.63−0.510.270.61−0.10BAT – B cell–associated transcripts0.941.121.241.00−0.700.330.640.04MCAT – Mast cell–associated transcripts0.890.871.701.24−0.38−0.050.480.19Macrophage transcriptsQCMAT – Constitutive macrophage-associated transcripts0.842.041.161.48−0.840.760.180.20AMAT1 – Alternatively activated macrophage transcripts0.812.231.281.64−0.810.710.170.23OtherLT3 – Lung parenchymal transcripts1.120.620.860.920.39−0.35−0.170.09LT4 – Lung parenchymal solute carrier transcripts1.100.650.890.910.42−0.44−0.100.05a PBT scores are the geometric mean fold difference between mean PBT set expression in each group and all 243 third bronchial bifurcation mucosal biopsies (3BMBs) as a control. The highest and lowest mean scores in each row are in bold. The highest positive and lowest negative spearman correlations in each row are also in bold. For the purpose of PBT score calculation the PBT lists were interquartile range filtered to include transcripts whose logarithmic expression had an interquartile range >0.5 across all 243 3BMBs. Open table in a new tab Correlations between AA and PBT scores confirmed these patterns (Table 3). R1normal anticorrelated with rejection- and injury-related PBTs and R2rejection correlated with rejection- and injury-related PBTs. R3late scores correlated with the late scarring-related PBTs but also with rejection-related PBTs. R4injury correlated with DAMPs. PCA scores and PBT scores showed similar relationships (Table 4). PC1 correlated most with rejection-related and macrophage PBTs, but also with injury PBTs and late scarring-related PBTs. PC2 correlated with late scarring PBTs. PC3 correlated with injury transcripts.TABLE 4Correlations between pathogenesis-based transcript (PBT) set scores and PCA scoresPBTaPBT scores are the geometric mean fold difference between mean PBT set expression in each group and all 243 third bronchial bifurcation mucosal biopsies (3BMBs) as a control. The highest positive and lowest negative Spearman correlations in each row are in bold. For the purpose of PBT score calculation the PBT lists were filtered to include transcripts whose logarithmic expression had an interquartile range >0.5 across all 243 3BMBs.Spearman corre" @default.
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• W2983248093 title "Molecular phenotyping of rejection-related changes in mucosal biopsies from lung transplants" @default.
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