Evidence map›Paper›PMID 32546429›Full record

ArticleHuman immunology2020

Concordance between predicted HLA type using next generation sequencing data generated for non-HLA purposes and clinical HLA type.

Ann M Moyer, Brian Dukek, Patti Duellman, Brittany Schneider, Laurie Wakefield, Jennifer M Skierka, Rajeswari Avula, Aditya V Bhagwate, Krishna R Kalari, Justin D Kreuter and 4 more

Open access · greenAbstract read
In one paragraph

Article in Human immunology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.3field-weighted citation impact, top 44% 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.

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

4 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Human Leukocyte Antigen (HLA) Testing in Pharmacogenomics.Methods in molecular biology (Clifton, N.J.) · 2022
    Article
  3. Article
  4. 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

14 authors at 3 institutions in 1 country.

Ann M MoyerDepartment of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, United States.
Brian DukekDepartment of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, United States.
Patti DuellmanDepartment of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, United States.
Brittany SchneiderDepartment of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, United States.
Laurie WakefieldDepartment of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, United States.
Jennifer M SkierkaDepartment of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, United States.
Rajeswari AvulaDepartment of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, United States.
Aditya V BhagwateDepartment of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, United States.
Krishna R KalariDepartment of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, United States.
Justin D KreuterDepartment of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, United States.
Matthew P GoetzDepartment of Medical Oncology, Mayo Clinic, Rochester, MN, United States.
Judy C BougheyDepartment of Surgery, Mayo Clinic, Rochester, MN, United States.
John L BlackDepartment of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, United States.
Manish J GandhiDepartment of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, United States. Electronic address: Gandhi.Manish@Mayo.edu.
Mayo Clinic in Arizona · USMayo Clinic in Florida · USMayo Clinic · US

Funding

The Role of CHFR in Tumorigenesis and Paclitaxel-Sensitivity in Breast CancerP50CA116201 · NCI · MAYO CLINIC ROCHESTER · PI PETER C LUCAS · 2005 to 2026
$49.9M
NCI NIH HHS P50 CA116201
6 · The paper itself

Abstract

We explored the feasibility of obtaining accurate HLA type using pre-existing NGS data not generated for HLA purposes. 83 exomes and 500 targeted NGS pharmacogenomic panels were analyzed using Omixon HLA Explore, OptiType, and/or HLA-Genotyper software. Results were compared against clinical HLA genotyping. 765 (94.2%) Omixon and 769 (94.7%) HLA-Genotyper of 812 germline allele calls across class I/II loci and 402 (99.5%) of 404 OptiType class I calls were concordant to the second field (i.e. HLA-A*02:01). An additional 19 (2.3%) Omixon, 39 (4.8%) HLA-Genotyper, and 2 (0.5%) OptiType allele calls were first field concordant (i.e. HLA-A*02). Using Omixon, four alleles (0.4%) were discordant and 24 (3.0%) failed to call, while 4 alleles (0.4%) were discordant using HLA-Genotyper. Tumor exomes were also evaluated and were 85.4%, 91.6%, and 100% concordant (Omixon and HLA-Genotyper with 96 alleles tested, and Optitype with 48 class I alleles, respectively). The 15 exomes and 500 pharmacogenomic panels were 100% concordant for each pharmacogenomic allele tested. This work has broad implications spanning future clinical care (pharmacogenomics, tumor response to immunotherapy, autoimmunity, etc.) and research applications.

Indexed as

AllelesExomeGenotypeGenotyping TechniquesHigh-Throughput Nucleotide SequencingHistocompatibility TestingHLA AntigensHumansSequence Analysis, DNASoftwareHLA AntigensExomeHLAHuman leukocyte antigenMHCNext generation sequencingPharmacogeneticsPharmacogenomics

Identifiers

PMID32546429
PMCPMC7721171
OpenAlexW3034675803

What OpenQuestion holds

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Read underepoch 390

Registered trials

None linked

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.