Molecular phenotyping of rejection in mucosal biopsies from lung transplant recipients
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ABSTRACT:
PROVIDER: PRJNA514864 | ENA |
REPOSITORIES: ENA
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Project description: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.
2019-11-19 | GSE125004 | GEO
Project description:We previously developed molecular assessment systems for lung transplant transbronchial biopsies (TBBs) with high surfactant and bronchial mucosal biopsies (3BMBs). Unsupervised machine learning identified T cell-mediated rejection (TCMR) based on expression of validated rejection-associated transcripts (RATs). The relationship to patient outcomes is not known. We aimed to establish whether molecular TCMR in TBBs and 3BMBs has implications for future graft loss. Molecular TCMR scores assigned in all TBBs or high surfactant TBBs agreed closely, indicating that variation in alveolar content in TBBs does not prevent detection of TCMR. Molecular assessments of 3BMB-3BMB pairs showed less variation than TBB-TBB pairs. TBB TCMR scores correlated with those in 3BMB. In both formats, molecular TCMR was associated with increased risk of graft loss whereas the presence of histologic rejection or donor-specific antibodies was not. Molecular TCMR can be detected in TBBs or 3BMBs and is associated with future risk of graft loss. TCMR emerges as a n important contributor to risk of failure. ClinicalTrials.gov NCT02812290.
2020-10-07 | GSE150156 | GEO
Project description:Molecular phenotyping of biopsies affords opportunities for increased precision and improved disease classification to address the limitations of conventional histologic diagnostic systems. We applied archetypal analysis, an unsupervised method similar to cluster analysis, to microarray data from 1208 prospectively collected kidney transplant biopsies from 13 centers. Seven machine learning-generated cross-validated classifier scores per biopsy were used as input for the archetypal analysis. Six archetypes representing extreme phenotypes were generated: no rejection; T cell-mediated rejection (TCMR); three phenotypes associated with antibody-mediated rejection (ABMR) - early-stage, fully-developed, and late-stage; and mixed rejection (TCMR plus early-stage ABMR). Each biopsy was assigned six scores, one for each archetype, that together represent a probabilistic assessment of that biopsy based on its rejection-related molecular properties. Viewed as clusters, the archetypes were similar to existing histologic Banff categories, but there was 32% disagreement, much of it probably reflecting the “noise” in the current histologic assessment system. Graft survival was worst for fully-developed and late-stage ABMR and was better predicted by molecular archetype scores than histologic diagnoses. The results provide a system for precision molecular assessment of biopsies and a new standard for recalibrating conventional diagnostic systems. (ClinicalTrials.gov NTC1299168) We applied archetypal analysis, an unsupervised method similar to cluster analysis, to microarray data from 1208 prospectively collected kidney transplant biopsies from 13 centers. This dataset is part of the TransQST collection.
2017-07-10 | E-GEOD-98320 | biostudies-arrayexpress
Project description:Complications due to long-term administration of immunosuppressive therapy increase the morbidity and mortality of liver transplant recipients. Discontinuation of immunosuppressive drugs in recipients spontaneously developing operational tolerance could substantially lessen this burden. However, this strategy results in the development of rejection in a high proportion of recipients who require lifelong immunosuppression. Thus, there is a need to identify predictive factors of successful drug withdrawal and to define the clinical and histological outcomes of operationally tolerant liver recipients. Methods. We enrolled 102 stable liver transplant recipients in an immunosuppression withdrawal trial in which drugs were gradually discontinued over a 6-9 month period. Patients with stable graft function and no signs of rejection in a liver biopsy conducted 12 months after cessation of immunosuppressive therapy were considered operationally tolerant. Results. Out of the 98 recipients who completed the study, immunosuppression discontinuation was successful in 41 recipients and rejection occurred in 57. Rejection episodes were mild and were resolved in all cases. Development of tolerance was independently associated with time elapsed since transplantation, recipient age, and male gender. No histological damage was apparent in protocol biopsies performed after successful drug withdrawal.
2012-01-09 | GSE28842 | GEO
Project description:Molecular phenotyping of biopsies affords opportunities for increased precision and improved disease classification to address the limitations of conventional histologic diagnostic systems. We applied archetypal analysis, an unsupervised method similar to cluster analysis, to microarray data from 1208 prospectively collected kidney transplant biopsies from 13 centers. Seven machine learning-generated cross-validated classifier scores per biopsy were used as input for the archetypal analysis. Six archetypes representing extreme phenotypes were generated: no rejection; T cell-mediated rejection (TCMR); three phenotypes associated with antibody-mediated rejection (ABMR) - early-stage, fully-developed, and late-stage; and mixed rejection (TCMR plus early-stage ABMR). Each biopsy was assigned six scores, one for each archetype, that together represent a probabilistic assessment of that biopsy based on its rejection-related molecular properties. Viewed as clusters, the archetypes were similar to existing histologic Banff categories, but there was 32% disagreement, much of it probably reflecting the “noise” in the current histologic assessment system. Graft survival was worst for fully-developed and late-stage ABMR and was better predicted by molecular archetype scores than histologic diagnoses. The results provide a system for precision molecular assessment of biopsies and a new standard for recalibrating conventional diagnostic systems. (ClinicalTrials.gov NTC1299168)
2017-07-10 | GSE98320 | GEO
Project description:Previous studies of rejection-associated transcript expression in heart transplant biopsies identified not only rejection but a group of early biopsies with injury but no rejection. The present analysis used an expanded population of biopsies to explore parenchymal injury in all biopsies, with or without rejection, and its relationship to function, and outcome. Archetypal analysis defined five injury clusters: no-injury (N=376); mild (N=526); moderate (N=110); severe (N=87); and late (N=221). The late group, 62% of which had no rejection, had molecular characteristics associated with atrophy-fibrosis, depressed LVEF, and increased graft loss independent of rejection status. In random forest analysis, low LVEF was more strongly associated with injury scores than with rejection scores. Three-year graft failure was best predicted using a combination of injury and rejection scores. In heart transplant biopsies, injury-related molecular scores correlate with dysfunction and risk of failure and identify an important new group of late heart transplants, many with no rejection, that have impaired function and a high risk of graft loss. (ClinicalTrials.gov #NCT02670408).
2021-12-28 | GSE150059 | GEO
Project description:MOLECULAR PROFILING IMPROVES DIAGNOSES OF REJECTION AND INFECTION IN TRANSPLANTED ORGANS. The monitoring of transplanted hearts is currently based on histological evaluation of endomyocardial biopsies, a method that is fairly insensitive and that does not always accurately discriminate between rejection and infection in the heart. Accurate diagnosis of rejection and infection is absolutely crucial, however, as the respective treatments are completely different. Using microarrays we analyzed gene expression in 76 cardiac biopsies from 40 heart recipients undergoing rejection, no rejection, or T. cruzi infection. We found a set of 98 genes whose expression patterns were typical of acute rejection, and 87 genes that discriminated between rejection and T. cruzi infection. These sets revealed acute rejection episodes up to two weeks earlier, and trypanosome infection up to two months earlier than did histological evaluation. When applied to raw data from other institutions, the two sets of predictive genes were also able to accurately pinpoint acute rejection of lung and kidney transplants, as well as bacterial infections in kidneys. In addition to their usefulness as diagnostic tools, the data suggest that there are similarities in the biology of the processes involved in rejection of different grafts and also in the tissue responses to pathogens as diverse as bacteria and protozoa. Keywords: disease state analysis
2006-03-15 | GSE4470 | GEO
Project description:Bronchoalveolar lavage samples collected from lung transplant recipients. Numeric portion of sample name is an arbitrary patient ID and AxBx number indicates the perivascular (A) and bronchiolar (B) scores from biopsies collected on the same day as the BAL fluid was collected. Several patients have more than one sample in this series and can be determined by patient number followed by a lower case letter. Acute rejection state is determined by the combined A and B score - specifically, a combined AB score of 2 or greater is considered an acute rejection.
2007-08-29 | E-GEOD-2018 | biostudies-arrayexpress
Project description:MOLECULAR PROFILING IMPROVES DIAGNOSES OF REJECTION AND INFECTION IN TRANSPLANTED ORGANS. The monitoring of transplanted hearts is currently based on histological evaluation of endomyocardial biopsies, a method that is fairly insensitive and that does not always accurately discriminate between rejection and infection in the heart. Accurate diagnosis of rejection and infection is absolutely crucial, however, as the respective treatments are completely different. Using microarrays we analyzed gene expression in 76 cardiac biopsies from 40 heart recipients undergoing rejection, no rejection, or T. cruzi infection. We found a set of 98 genes whose expression patterns were typical of acute rejection, and 87 genes that discriminated between rejection and T. cruzi infection. These sets revealed acute rejection episodes up to two weeks earlier, and trypanosome infection up to two months earlier than did histological evaluation. When applied to raw data from other institutions, the two sets of predictive genes were also able to accurately pinpoint acute rejection of lung and kidney transplants, as well as bacterial infections in kidneys. In addition to their usefulness as diagnostic tools, the data suggest that there are similarities in the biology of the processes involved in rejection of different grafts and also in the tissue responses to pathogens as diverse as bacteria and protozoa. pre-rejection: GSM47949, GSM48048, GSM49625 non-rejection: GSM47957, GSM48045, GSM48049, GSM48057, GSM48072, GSM48089, GSM48092, GSM49624, GSM49654, GSM49656, GSM49657, GSM49658, GSM49661, GSM49666, GSM49748, GSM49750, GSM49751, GSM49752, GSM49754, GSM49756, GSM49757, GSM49758, GSM49762, GSM49765, GSM49766, GSM49767, GSM49769, GSM49771, GSM49773, GSM49774, GSM49781, GSM49782, GSM49783, GSM49784 acute rejection: GSM48040, GSM48043, GSM48046, GSM48047, GSM48050, GSM48053, GSM48056, GSM49655, GSM49747, GSM49749, GSM49755, GSM49759, GSM49761, GSM49763, GSM49764, GSM49780 pre-Chagas disease reactivation: GSM48089, GSM49756, GSM49757, GSM49758, GSM49767, GSM49769, GSM49771 Chagas disease reactivation: GSM49753, GSM49760, GSM49768, GSM49772, GSM49775, GSM49776, GSM49777, GSM49778 Toxoplasma myocarditis: GSM48058 first subset ("summer"): GSM47949, GSM47957, GSM48040, GSM48043, GSM48045, GSM48046, GSM48047, GSM48048, GSM48049, GSM48050, GSM48053, GSM48056, GSM48057, GSM48058, GSM48072, GSM48089, GSM48092, GSM49624, GSM49625, GSM49654, GSM49655, GSM49656, GSM49657, GSM49658, GSM49661, GSM49666, GSM49747, GSM49748, GSM49749, GSM49750, GSM49751, GSM49752 second subset ("winter"): GSM49753, GSM49754, GSM49755, GSM49756, GSM49757, GSM49758, GSM49759, GSM49760, GSM49761, GSM49762, GSM49763, GSM49764, GSM49765, GSM49766, GSM49767, GSM49768, GSM49769, GSM49770, GSM49771, GSM49772, GSM49773, GSM49774, GSM49775, GSM49776, GSM49777, GSM49778, GSM49780, GSM49781, GSM49782, GSM49783, GSM49784 Keywords = cardiac transplant Keywords = acute rejection Keywords = infection Keywords: repeat sample
2005-10-24 | GSE2596 | GEO
Project description:Improved understanding of lung transplant disease states is essential because failure rates are high, often due to chronic lung allograft dysfunction. However, histologic assessment of lung transplant transbronchial biopsies (TBBs) is difficult and often uninterpretable even with 10 pieces. All 242 single-piece TBBs produced reliable transcript measurements. Paired TBB pieces available from 12 patients showed significant similarity but also showed some sampling variance. Alveolar content, as estimated by surfactant transcript expression, was a source of sampling variance. To offset sampling variation, for analysis we selected 152 single-piece TBBs with high surfactant transcripts. Unsupervised archetypal analysis identified four idealized phenotypes (archetypes) and scored biopsies for their similarity to each: normal, T cell-mediated rejection (TCMR; T cell transcripts), antibody-mediated rejection (ABMR)-like (endothelial transcripts), and injury (macrophage transcripts). Molecular TCMR correlated with histologic TCMR. The relationship of molecular scores to histologic ABMR could not be assessed because of the paucity of ABMR in this population. Molecular assessment of single-piece TBBs can be used to classify lung transplant biopsies and correlated with rejection histology. Two or three pieces for each TBB will probably be needed to offset sampling variance.
2019-08-16 | GSE125478 | GEO