Evidence map›Paper›PMID 28771616›Full record

ArticlePLoS computational biology2017

Quantification of transplant-derived circulating cell-free DNA in absence of a donor genotype.

Eilon Sharon, Hao Shi, Sandhya Kharbanda, Winston Koh, Lance R Martin, Kiran K Khush, Hannah Valantine, Jonathan K Pritchard, Iwijn De Vlaminck

2 registry-linked trialsOpen access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 45 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
45citing papers in PubMed, 1 pooled it
3.5field-weighted citation impact, top 7% of its field
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.

NCT04491552 unknown statusnot on this mapstarted 2020, after this paper: background citation

TruGraf® Long-term Clinical Outcomes Study

TypeobservationalSponsorTransplant Genomics, Inc.Ran2020 to 2023Enrolled2,000ConditionsKidney Transplant RejectionArmsPatients monitored with TruGraf and TRAC testing
NCT05335551 unknown statusnot on this mapstarted 2022, after this paper: background citation

A Clinical Pilot Study to Test the Acceptability and Feasibility of TruGraf Liver and TRAC Liver Testing as Part of Standard of Care: Optimizing Liver ImmunoSuppression (TRU-IMMUNO)

TypeobservationalSponsorTransplant Genomics, Inc.Ran2022 to 2023Enrolled130ConditionsLiver Transplant RejectionArmsPatients monitored with TruGraf and TRAC Liver testing
3 · Its place in the literature

Who cites it

45 citing papers in PubMed, 1 synthesis or guideline pooled it, 86 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Review
  4. Article
  5. Article
  6. Review
  7. Review
  8. Article
  9. Review
  10. Biomarkers of Rejection in Kidney Transplantation.American journal of kidney diseases : the official journal of the National Kidney Foundation · 2025
    Review
  11. Review
  12. Article
  13. Review
  14. Hypobaric hypoxia modulated structural characteristics of circulating cell-free DNA in high-altitude pulmonary edema.American journal of physiology. Lung cellular and molecular physiology · 2024
    Article
  15. Review
  16. Review
  17. Review
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  20. Assessment of Donor Derived Cell Free DNA (dd-cfDNA) at Surveillance and at Clinical Suspicion of Acute Rejection in Renal Transplantation.Transplant international : official journal of the European Society for Organ Transplantation · 2023
    Article
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

9 authors at 5 institutions in 1 country.

Eilon SharonDepartment of Genetics, Stanford University, Stanford, California, United States of America.ORCID http://orcid.org/0000-0002-1262-350X
Hao ShiMeinig School of Biomedical Engineering, Cornell University, Ithaca, New York, United States of America.ORCID http://orcid.org/0000-0002-9658-2847
Sandhya KharbandaPediatric Stem Cell Transplantation, Lucille Packard Children's Hospital, Stanford, California, United States of America.
Winston KohHoward Hughes Medical Institute, Stanford University, Stanford, California, United States of America.
Lance R MartinHoward Hughes Medical Institute, Stanford University, Stanford, California, United States of America.
Kiran K KhushDivision of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, California, United States of America.
Hannah ValantineNational Institutes of Health, Bethesda, Maryland, United States of America.
Jonathan K PritchardDepartment of Genetics, Stanford University, Stanford, California, United States of America.
Iwijn De VlaminckMeinig School of Biomedical Engineering, Cornell University, Ithaca, New York, United States of America.
Howard Hughes Medical Institute · USCornell University · USStanford University · USLucile Packard Children's Hospital · USNational Institutes of Health · US

Funding

Integration of genetic association mapping and functional data to elucidate genetic mechanisms of diseaseR01HG008140 · NHGRI · STANFORD UNIVERSITY · PI JONATHAN K PRITCHARD · 2016 to 2026
$7.3M
Genome Transplant Dynamics: non-invasive sequencing-based diagnosis of rejectionRC4AI092673 · NIAID · STANFORD UNIVERSITY · PI QUAKE, STEPHEN R, VALANTINE, HANNAH AUGUSTA · 2010 to 2010
$2.5M
Mitochondrial Cell-Free DNA as a Marker of Rejection and Damage-Associated Molecular Pattern in Organ TransplantationR21AI124237 · NIAID · CORNELL UNIVERSITY · PI DE VLAMINCK, IWIJN · 2016 to 2017
$427k
NHGRI NIH HHS R01 HG008140NIAID NIH HHS R21 AI124237NIAID NIH HHS RC4 AI092673
6 · The paper itself

Abstract

Quantification of cell-free DNA (cfDNA) in circulating blood derived from a transplanted organ is a powerful approach to monitoring post-transplant injury. Genome transplant dynamics (GTD) quantifies donor-derived cfDNA (dd-cfDNA) by taking advantage of single-nucleotide polymorphisms (SNPs) distributed across the genome to discriminate donor and recipient DNA molecules. In its current implementation, GTD requires genotyping of both the transplant recipient and donor. However, in practice, donor genotype information is often unavailable. Here, we address this issue by developing an algorithm that estimates dd-cfDNA levels in the absence of a donor genotype. Our algorithm predicts heart and lung allograft rejection with an accuracy that is similar to conventional GTD. We furthermore refined the algorithm to handle closely related recipients and donors, a scenario that is common in bone marrow and kidney transplantation. We show that it is possible to estimate dd-cfDNA in bone marrow transplant patients that are unrelated or that are siblings of the donors, using a hidden Markov model (HMM) of identity-by-descent (IBD) states along the genome. Last, we demonstrate that comparing dd-cfDNA to the proportion of donor DNA in white blood cells can differentiate between relapse and the onset of graft-versus-host disease (GVHD). These methods alleviate some of the barriers to the implementation of GTD, which will further widen its clinical application.

Indexed as

TransplantationBone MarrowDNAFemaleGenotypeGenotyping TechniquesGraft RejectionHumansMaleModels, StatisticalSequence Analysis, DNATissue DonorsTransplantsDNA

Identifiers

PMID28771616
PMCPMC5542400
OpenAlexW4232958736

What OpenQuestion holds

Textmetadata
LicenceCC0
Read underepoch 390

Registered trials

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.