Evidence map›Paper›PMID 27078882›Full record

ArticlePloS one2016

Immune Profiles to Predict Response to Desensitization Therapy in Highly HLA-Sensitized Kidney Transplant Candidates.

Julie M Yabu, Janet C Siebert, Holden T Maecker

Abstract read
In one paragraph

Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Article
  4. Full spectrum flow cytometry and mass cytometry: A 32-marker panel comparison.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2022
    Article
  5. Review
  6. Review
  7. Article
  8. Regulatory T cells in solid organ transplantation.Clinical & translational immunology · 2020
    Review
  9. Review
  10. Article
  11. Article
  12. Article
  13. Applying Mass Cytometry to the Analysis of Lymphoid Populations in Transplantation.American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons · 2017
    Review
  14. Immunotherapy biomarkers 2016: overcoming the barriers.Journal for immunotherapy of cancer · 2017
    Article
  15. Current status of alloimmunity.Current opinion in nephrology and hypertension · 2016
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Julie M YabuDepartment of Medicine, Stanford University School of Medicine, Palo Alto, CA, United States of America.
Janet C SiebertCytoAnalytics, Denver, CO, United States of America.
Holden T MaeckerInstitute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, Palo Alto, CA, United States of America.

Funding

Improving Kidney Transplant Outcomes Using Novel B-cell Immune ProfilingK23AI104401 · NIAID · STANFORD UNIVERSITY · PI YABU, JULIE M · 2012 to 2015
$528k
NIAID NIH HHS K23 AI104401NIAID NIH HHS K23AI104401
6 · The paper itself

Abstract

backgroundKidney transplantation is the most effective treatment for end-stage kidney disease. Sensitization, the formation of human leukocyte antigen (HLA) antibodies, remains a major barrier to successful kidney transplantation. Despite the implementation of desensitization strategies, many candidates fail to respond. Current progress is hindered by the lack of biomarkers to predict response and to guide therapy. Our objective was to determine whether differences in immune and gene profiles may help identify which candidates will respond to desensitization therapy. METHODS AND

findingsSingle-cell mass cytometry by time-of-flight (CyTOF) phenotyping, gene arrays, and phosphoepitope flow cytometry were performed in a study of 20 highly sensitized kidney transplant candidates undergoing desensitization therapy. Responders to desensitization therapy were defined as 5% or greater decrease in cumulative calculated panel reactive antibody (cPRA) levels, and non-responders had 0% decrease in cPRA. Using a decision tree analysis, we found that a combination of transitional B cell and regulatory T cell (Treg) frequencies at baseline before initiation of desensitization therapy could distinguish responders from non-responders. Using a support vector machine (SVM) and longitudinal data, TRAF3IP3 transcripts and HLA-DR-CD38+CD4+ T cells could also distinguish responders from non-responders. Combining all assays in a multivariate analysis and elastic net regression model with 72 analytes, we identified seven that were highly interrelated and eleven that predicted response to desensitization therapy.

conclusionsMeasuring baseline and longitudinal immune and gene profiles could provide a useful strategy to distinguish responders from non-responders to desensitization therapy. This study presents the integration of novel translational studies including CyTOF immunophenotyping in a multivariate analysis model that has potential applications to predict response to desensitization, select candidates, and personalize medicine to ultimately improve overall outcomes in highly sensitized kidney transplant candidates.

Indexed as

Desensitization, ImmunologicADP-ribosyl Cyclase 1AdultAgedAutoantibodiesB-LymphocytesBortezomibCD4-Positive T-LymphocytesFemaleHistocompatibility TestingHLA-DR AntigensHumansImmunoglobulins, IntravenousKidney Failure, ChronicKidney TransplantationLongitudinal StudiesADP-ribosyl Cyclase 1AutoantibodiesBortezomibHLA-DR AntigensImmunoglobulins, IntravenousMicrotubule-Associated ProteinsRituximabTNF receptor-associated factor 3 interacting protein 3, human

Identifiers

PMID27078882
PMCPMC4831845

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.