Evidence map›Paper›PMID 31871119›Full record

ArticleCancer immunology research2020

High-Throughput Prediction of MHC Class I and II Neoantigens with MHCnuggets.

Xiaoshan M Shao, Rohit Bhattacharya, Justin Huang, I K Ashok Sivakumar, Collin Tokheim, Lily Zheng, Dylan Hirsch, Benjamin Kaminow, Ashton Omdahl, Maria Bonsack and 5 more

Open access · bronzeAbstract read
In one paragraph

Article in Cancer immunology research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 110 papers.

0numbers the graph read from it
0cells of the map it votes in
110citing papers in PubMed
6.2field-weighted citation impact, top 3% 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

110 citing papers in PubMed, 189 citations in OpenAlex.

  1. Trial
  2. Trial
  3. Review
  4. Technical review of artificial intelligence in TCR-T therapy.Journal of the National Cancer Center · 2026
    Review
  5. Review
  6. Review
  7. Automating neoantigen selection for personalized cancer vaccine design.medRxiv : the preprint server for health sciences · 2026
    Article
  8. Article
  9. Article
  10. Article
  11. Review
  12. Review
  13. Article
  14. Review
  15. Article
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  17. Review
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  19. Article
  20. High-throughput screening for class I peptide MHC binding via yeast surface display.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article

50 more citing papers are in PubMed but not listed here.

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

15 authors at 2 institutions in 2 countries.

Xiaoshan M Shao *Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland.
Rohit Bhattacharya *Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland.ORCID 0000-0002-0555-9714
Justin Huang *Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland.
I K Ashok Sivakumar *Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland.ORCID 0000-0002-5645-9270
Collin TokheimInstitute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland.
Lily ZhengInstitute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland.ORCID 0000-0003-2722-3522
Dylan HirschInstitute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland.
Benjamin KaminowInstitute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland.
Ashton OmdahlInstitute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland.ORCID 0000-0001-5617-9589
Maria BonsackImmunotherapy and Immunoprevention, German Cancer Research Center (DKFZ), Heidelberg, Germany.ORCID 0000-0003-2087-7451
Angelika B RiemerImmunotherapy and Immunoprevention, German Cancer Research Center (DKFZ), Heidelberg, Germany.ORCID 0000-0002-5865-0714
Victor E VelculescuInstitute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland.ORCID 0000-0003-1195-438X
Valsamo AnagnostouThe Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland.
Kymberleigh A PagelInstitute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland.
Rachel KarchinInstitute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland. karchin@jhu.edu.
Johns Hopkins University · USGerman Cancer Research Center · DE

Funding

Translational Research Central ServicesP30CA006973 · NCI · JOHNS HOPKINS UNIVERSITY · PI WILLIAM George NELSON · 1985 to 2026
$208.6M
Tumor Antigens for Individual Signatures and TherapyP50CA062924 · NCI · JOHNS HOPKINS UNIVERSITY · PI JAFFEE, ELIZABETH M. · 1993 to 2022
$54.6M
BIOMEDICAL ENGINEERING TRAINING PROGRAMT32GM007057 · NIGMS · JOHNS HOPKINS UNIVERSITY · PI KANOLD, PATRICK O, KARCHIN, RACHEL · 1985 to 2023
$10.4M
Large-Scale Genetic Analyses of Human CancerR01CA121113 · NCI · JOHNS HOPKINS UNIVERSITY · PI ANAGNOSTOU, VALSAMO, VELCULESCU, VICTOR E. · 2006 to 2022
$4.3M
ECOG-ACRIN Thoracic Malignancies Integrated Translational Science CenterU10CA180950 · NCI · EMORY UNIVERSITY · PI RAMALINGAM, SURESH S · 2014 to 2018
$2.9M
Pre-Doctoral Training Program in Computational MedicineT32GM119998 · NIGMS · JOHNS HOPKINS UNIVERSITY · PI MAC GABHANN, FEILIM C · 2017 to 2021
$1.5M
NCI NIH HHS P30 CA006973NCI NIH HHS P50 CA062924NCI NIH HHS R01 CA121113NCI NIH HHS U10 CA180950NIGMS NIH HHS T32 GM007057NIGMS NIH HHS T32 GM119998
6 · The paper itself

Abstract

Computational prediction of binding between neoantigen peptides and major histocompatibility complex (MHC) proteins can be used to predict patient response to cancer immunotherapy. Current neoantigen predictors focus on

Indexed as

Neural Networks, ComputerAlgorithmsAntigens, NeoplasmArtificial IntelligenceCancer VaccinesCD8-Positive T-LymphocytesComputational BiologyData MiningHistocompatibility Antigens Class IHistocompatibility Antigens Class IIHumansMutation, MissenseNeoplasmsPredictive Value of TestsProtein BindingSoftwareAntigens, NeoplasmCancer VaccinesHistocompatibility Antigens Class IHistocompatibility Antigens Class II

Identifiers

PMID31871119
PMCPMC7056596
OpenAlexW2995494055

What OpenQuestion holds

Textmetadata
LicenceTDM
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.