R N Smith1. 1. Department of Pathology, Massachusetts General Hospital, 501 Warren Bldg, 55 Fruit Street, Boston, MA, 02114, USA. rnsmith@mgh.harvard.edu.
Abstract
BACKGROUND: RNA gene expression of renal transplantation biopsies is commonly used to identify the immunological patterns of graft rejection. Mostly done with microarrays, seminal findings defined the patterns of gene sets associated with rejection and non-rejection kidney allograft diagnoses. To make gene expression more accessible, the Molecular Diagnostics Working Group of the Banff Foundation for Allograft Pathology and NanoString Technologies partnered to create the Banff Human Organ Transplant Panel (BHOT), a gene panel set of 770 genes as a surrogate for microarrays (~ 50,000 genes). The advantage of this platform is that gene expressions are quantifiable on formalin fixed and paraffin embedded archival tissue samples, making gene expression analyses more accessible. The purpose of this report is to test in silico the utility of the BHOT panel as a surrogate for microarrays on archival microarray data and test the performance of the modelled BHOT data. METHODS: BHOT genes as a subset of genes from downloaded archival public microarray data on human renal allograft gene expression were analyzed and modelled by a variety of statistical methods. RESULTS: Three methods of parsing genes verify that the BHOT panel readily identifies renal rejection and non-rejection diagnoses using in silico statistical analyses of seminal archival databases. Multiple modelling algorithms show a highly variable pattern of misclassifications per sample, either between differently constructed principal components or between modelling algorithms. The misclassifications are related to the gene expression heterogeneity within a given diagnosis because clustering the data into 9 groups modelled with fewer misclassifications. CONCLUSION: This report supports using the Banff Human Organ Transplant Panel for gene expression of human renal allografts as a surrogate for microarrays on archival tissue. The data modelled satisfactorily with aggregate diagnoses although with limited per sample accuracy and, thereby, reflects and confirms the modelling complexity and the challenges of modelling gene expression as previously reported.
BACKGROUND: RNA gene expression of renal transplantation biopsies is commonly used to identify the immunological patterns of graft rejection. Mostly done with microarrays, seminal findings defined the patterns of gene sets associated with rejection and non-rejection kidney allograft diagnoses. To make gene expression more accessible, the Molecular Diagnostics Working Group of the Banff Foundation for Allograft Pathology and NanoString Technologies partnered to create the Banff Human Organ Transplant Panel (BHOT), a gene panel set of 770 genes as a surrogate for microarrays (~ 50,000 genes). The advantage of this platform is that gene expressions are quantifiable on formalin fixed and paraffin embedded archival tissue samples, making gene expression analyses more accessible. The purpose of this report is to test in silico the utility of the BHOT panel as a surrogate for microarrays on archival microarray data and test the performance of the modelled BHOT data. METHODS: BHOT genes as a subset of genes from downloaded archival public microarray data on human renal allograft gene expression were analyzed and modelled by a variety of statistical methods. RESULTS: Three methods of parsing genes verify that the BHOT panel readily identifies renal rejection and non-rejection diagnoses using in silico statistical analyses of seminal archival databases. Multiple modelling algorithms show a highly variable pattern of misclassifications per sample, either between differently constructed principal components or between modelling algorithms. The misclassifications are related to the gene expression heterogeneity within a given diagnosis because clustering the data into 9 groups modelled with fewer misclassifications. CONCLUSION: This report supports using the Banff Human Organ Transplant Panel for gene expression of human renal allografts as a surrogate for microarrays on archival tissue. The data modelled satisfactorily with aggregate diagnoses although with limited per sample accuracy and, thereby, reflects and confirms the modelling complexity and the challenges of modelling gene expression as previously reported.
Authors: K Solez; R B Colvin; L C Racusen; M Haas; B Sis; M Mengel; P F Halloran; W Baldwin; G Banfi; A B Collins; F Cosio; D S R David; C Drachenberg; G Einecke; A B Fogo; I W Gibson; D Glotz; S S Iskandar; E Kraus; E Lerut; R B Mannon; M Mihatsch; B J Nankivell; V Nickeleit; J C Papadimitriou; P Randhawa; H Regele; K Renaudin; I Roberts; D Seron; R N Smith; M Valente Journal: Am J Transplant Date: 2008-02-19 Impact factor: 8.086
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Authors: R N Smith; B A Adam; I A Rosales; M Matsunami; T Oura; A B Cosimi; T Kawai; M Mengel; R B Colvin Journal: Am J Transplant Date: 2018-02-02 Impact factor: 8.086
Authors: R N Smith; M Matsunami; B A Adam; I A Rosales; T Oura; A B Cosimi; T Kawai; M Mengel; R B Colvin Journal: Am J Transplant Date: 2018-02-17 Impact factor: 8.086
Authors: Aaron M Newman; Chloé B Steen; Chih Long Liu; Andrew J Gentles; Aadel A Chaudhuri; Florian Scherer; Michael S Khodadoust; Mohammad S Esfahani; Bogdan A Luca; David Steiner; Maximilian Diehn; Ash A Alizadeh Journal: Nat Biotechnol Date: 2019-05-06 Impact factor: 54.908
Authors: Myrthe van Baardwijk; Iacopo Cristoferi; Jie Ju; Hilal Varol; Robert C Minnee; Marlies E J Reinders; Yunlei Li; Andrew P Stubbs; Marian C Clahsen-van Groningen Journal: Front Immunol Date: 2022-05-10 Impact factor: 8.786