Assessing rejection-related disease in kidney transplant biopsies based on archetypal analysis of molecular phenotypes

Reeve, J.; Böhmig, Georg A.; Eskandary, Farsad; Einecke, G.; Lefaucheur, Carmen; Loupy, Alexandre; Halloran, Philip F.

doi:10.1172/jci.insight.94197

Cited by 140 publications

(183 citation statements)

References 37 publications

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“…21 The top ranked rejection-associated transcripts in one study population will not necessarily predict the top-ranked transcripts in a new population because rank is influenced by random error ("noise") as well as the prevalence of disease states ("signal"). 21 The top ranked rejection-associated transcripts in one study population will not necessarily predict the top-ranked transcripts in a new population because rank is influenced by random error ("noise") as well as the prevalence of disease states ("signal").…”

Section: Analysis Strategies For Discovering Rejection-associated Tmentioning

confidence: 99%

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Review: The transcripts associated with organ allograft rejection

Halloran

Venner

Madill‐Thomsen

et al. 2018

American Journal of Transplantation

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149

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The molecular mechanisms operating in human organ transplant rejection are best inferred from the mRNAs expressed in biopsies because the corresponding proteins often have low expression and short half-lives, while small non-coding RNAs lack specificity. Associations should be characterized in a population that rigorously identifies T cell-mediated (TCMR) and antibody-mediated rejection (ABMR). This is best achieved in kidney transplant biopsies, but the results are generalizable to heart, lung, or liver transplants. Associations can be universal (all rejection), TCMR-selective, or ABMR-selective, with universal being strongest and ABMR-selective weakest. Top universal transcripts are IFNG-inducible (eg, CXCL11 IDO1, WARS) or shared by effector T cells (ETCs) and NK cells (eg, KLRD1, CCL4). TCMR-selective transcripts are expressed in activated ETCs (eg, CTLA4, IFNG), activated (eg, ADAMDEC1), or IFNG-induced macrophages (eg, ANKRD22). ABMR-selective transcripts are expressed in NK cells (eg, FGFBP2, GNLY) and endothelial cells (eg, ROBO4, DARC). Transcript associations are highly reproducible between biopsy sets when the same rejection definitions, case mix, algorithm, and technology are applied, but exact ranks will vary. Previously published rejection-associated transcripts resemble universal and TCMR-selective transcripts due to incomplete representation of ABMR. Rejection-associated transcripts are never completely rejection-specific because they are shared with the stereotyped response-to-injury and innate immunity.

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Section: Analysis Strategies For Discovering Rejection-associated Tmentioning

confidence: 99%

“…Machine learning can assign disease diagnoses using existing classifications but can also be used to discovery new classifications using clustering methods such as archetype analysis 21. Ranking of probe sets varies with the positive class and negative class, ie, case mix matters for label classes.…”

mentioning

confidence: 99%

Review: The transcripts associated with organ allograft rejection

Halloran

Venner

Madill‐Thomsen

et al. 2018

American Journal of Transplantation

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149

126

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“…The search for stable and predictive biomarkers for ABMR is currently ongoing, and progress has been made in the genomics and proteomics field . Some new ABMR classifiers in biopsy material have been introduced as potentially useful addition to histology and DSA identification . Additionally, the molecular mechanisms involved in ABMR have been further elucidated by associating the gene expression and regulation in graft material with this type of rejection .…”

Section: Discussionmentioning

confidence: 99%

“…19,20 Some new ABMR classifiers in biopsy material have been introduced as potentially useful addition to histology and DSA identification. 21,22 Additionally, the molecular mechanisms involved in ABMR have been further elucidated by associating the gene expression and regulation in graft material with this type of rejection. 20 The discovery and further analysis of robust markers regulated during ABMR after KTx cannot only lead to the development of new diagnostic or monitoring tools, but also to the essential extension of the insufficient understanding of the mechanisms and pathways involved in ABMR allowing the identification of therapeutic targets.…”

Section: Discussionmentioning

confidence: 99%

The regulation of interferon type I pathway‐related genes RSAD2 and ETV7 specifically indicates antibody‐mediated rejection after kidney transplantation

Matz

Heinrich

Zhang

et al. 2018

Clinical Transplantation

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Context Antibody‐mediated rejection (ABMR) after kidney transplantation (KTx) remains the crucial obstacle to successful long‐term graft function. The identification of gene signatures involved in ABMR could grant the basis for better prevention and treatment strategies. Objective The identification of gene signatures in whole blood cells specific for ABMR after KTx. Materials and methods Total RNA from blood cells of 16 kidney‐transplanted patients with ABMR, stable graft function (SGF), and with T‐cell‐mediated rejection (TCMR) was isolated. Gene expression was determined by high‐throughput sequencing followed by validation and analyses of differentially expressed candidates on mRNA level and on protein level in a large patient cohort (n = 185) in patients with SGF, urinary tract infection (UTI), borderline rejection (BL), TCMR, ABMR, and interstitial fibrosis and tubular atrophy. Results From the 570 genes detected, 111 discriminated ABMR from SGF and TCMR. A distinct enrichment of interferon (IFN) type I and type II signature gene set was observed. The expression of candidate genes IFIT1, ETV7, and RSAD2 distinguished ABMR patients from patients with SGF and also TCMR, whereas ETV7 and RSAD2 differentiated ABMR also from BL. Conclusion The IFN‐inducible genes ETV7 and RSAD2 represent specific biomarkers for ABMR episodes after KTx.

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“…45 The same information cannot currently be inferred from an ah0 lesion score because there is simply too little granularity in the current ah0,1,2,3 grades, and they have limited reproducibility. 45 The same information cannot currently be inferred from an ah0 lesion score because there is simply too little granularity in the current ah0,1,2,3 grades, and they have limited reproducibility.…”

Section: Discussionmentioning

confidence: 99%

A molecular biopsy test based on arteriolar under-hyalinosis reflects increased probability of rejection related to under-immunosuppression

Einecke

Reeve

Halloran

2018

American Journal of Transplantation

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Calcineurin inhibitor immunosuppressive drugs induce changes such as arteriolar hyalinosis (ah) in kidney transplants, raising the possibility that molecular changes in biopsies related to histologic ah can provide information about drug exposure. We hypothesized that molecular changes associated with less-than-expected hyalinosis might highlight a subpopulation of patients with under-immunosuppression/nonadherence at intermediate times of biopsy posttransplant (TxBx). Using gene expression data from 562 indication biopsies, we developed a molecular classifier for predicting the expected ah lesions (M ) at a particular TxBx. M -scores increased linearly with log(TxBx), but some biopsies had lower scores than expected for TxBx. The deviation of individual M -scores below the predicted regression line of M -scores vs TxBx is defined as "low hyalinosis index." Low hyalinosis indices were frequent in biopsies between 3 months and 3 years posttransplant, particularly among biopsies lacking histologic hyalinosis (ah0), and were associated with T cell-mediated rejection and a subset of recent-onset antibody-mediated rejection without glomerular double contours. In patients with medical records available for review, low hyalinosis indices were frequently associated with physician-recorded concerns about nonadherence (suspected or proven). We conclude that the M classifier and hyalinosis index identify indication biopsies with rejection for which the possibility of patient nonadherence should be considered.

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Assessing rejection-related disease in kidney transplant biopsies based on archetypal analysis of molecular phenotypes

Cited by 140 publications

References 37 publications

Review: The transcripts associated with organ allograft rejection

Review: The transcripts associated with organ allograft rejection

The regulation of interferon type I pathway‐related genes RSAD2 and ETV7 specifically indicates antibody‐mediated rejection after kidney transplantation

A molecular biopsy test based on arteriolar under-hyalinosis reflects increased probability of rejection related to under-immunosuppression

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